WO2023193139A1

WO2023193139A1 - Power control method and apparatus, and electric vehicle

Info

Publication number: WO2023193139A1
Application number: PCT/CN2022/085326
Authority: WO
Inventors: 胡玉莹; 吴自贤; 张明明
Original assignee: 华为技术有限公司
Priority date: 2022-04-06
Filing date: 2022-04-06
Publication date: 2023-10-12
Also published as: CN118591474A

Abstract

Provided are a power control method and apparatus, and an electric vehicle (100). The method comprises: acquiring a plurality of instantaneous driving powers of a motor of an electric vehicle (100) within a current operation period; determining a first probability distribution corresponding to the plurality of instantaneous driving powers according to a preset driving power interval; according to the first probability distribution and second probability distributions respectively corresponding to a plurality of preset motor driving working conditions, determining, from among the plurality of preset motor driving working conditions, a target motor driving working condition of the electric vehicle (100) within the current operation period; and controlling the power of an auxiliary electrical device (130) of the electric vehicle (100) according to the target motor driving working condition. The driving comfort of the electric vehicle (100) can be improved, and the energy efficiency of a power battery of the electric vehicle (100) can be enhanced.

Description

Power control method, device and electric vehicle

Technical field

The present application relates to the technical field of electric vehicles, and in particular to a power control method, device and electric vehicle.

Background technique

When an electric vehicle is running, the power battery provides it with full power output. On the one hand, the power battery provides driving power for the motor of electric vehicles and converts it into mechanical energy to ensure the vehicle's dynamic performance; on the other hand, it provides auxiliary electrical equipment for electric vehicles (such as positive temperature coefficient thermistors (positive temperature coefficient, PTC), air conditioners etc.) provide energy and improve driving comfort.

Motors are the main source of energy consumption in electric vehicles. In related technologies, electric vehicles are usually measured based on instantaneous status information such as the current driving power of the motor and the current driving power percentage of the motor (that is, the percentage of the current driving power of the motor and the maximum output power). Control the power of auxiliary electrical equipment.

However, instantaneous status information such as the current driving power of the motor and the percentage of the current driving power of the motor are affected by many factors, and the values change all the time and fluctuate greatly. It is too simple to directly control the power of the ancillary electrical equipment of the electric vehicle. It is easy to cause the working mode of auxiliary electrical equipment to switch frequently, which not only affects driving comfort, but also is not conducive to the improvement of the energy efficiency of the power battery.

Contents of the invention

In view of this, a power control method, device and electric vehicle are proposed.

In a first aspect, embodiments of the present application provide a power control method, applied to electric vehicles. The method includes: obtaining multiple instantaneous driving powers of the motor of the electric vehicle in the current operating cycle; according to a preset The driving power interval determines a first probability distribution corresponding to the plurality of instantaneous driving powers. The first probability distribution is used to represent the probability that the instantaneous driving power falls in each driving power interval; according to the first probability distribution and a second probability distribution respectively corresponding to a plurality of preset motor driving conditions. From the plurality of preset motor driving conditions, the target motor driving condition of the electric vehicle in the current operating cycle is determined. condition, wherein the motor driving working condition is related to the load level driven by the motor; and the power of the auxiliary electrical equipment of the electric vehicle is controlled according to the target motor driving working condition.

Embodiments of the present application can obtain multiple instantaneous drive powers of the electric vehicle's motor during the current operating cycle, determine the first probability distribution corresponding to the multiple instantaneous drive powers, and then determine the first probability distribution and multiple predetermined Assume the second probability distribution corresponding to the motor driving conditions, determine the target motor driving conditions of the electric vehicle in the current operating cycle from multiple preset motor driving conditions, and determine the electric vehicle's target motor driving conditions according to the target motor driving conditions. The power of the car's auxiliary electrical equipment is controlled, so that the target motor driving conditions can be determined based on the multiple instantaneous driving powers of the electric vehicle's motor within a certain period of time (within the current operating cycle), and based on the target motor driving conditions, Controlling the power of auxiliary electrical equipment of electric vehicles makes the control process of the power of auxiliary electrical equipment by electric vehicles smoother and reduces the switching of working modes during the use of auxiliary electrical equipment, thereby improving driving comfort. At the same time, it can improve the energy efficiency of electric vehicle power batteries.

According to the first aspect, in a first possible implementation of the power control method, the method further includes: based on the target motor driving conditions and a plurality of preset battery driving pressure levels and the plurality of preset battery driving pressure levels. Assuming a corresponding relationship between motor driving conditions, the target battery driving pressure level of the electric vehicle in the current operating cycle is determined from the plurality of preset battery driving pressure levels, wherein the battery driving pressure level is The pressure level is used to represent the impact of the motor driving of the electric vehicle on the discharge rate of the power battery; according to the target battery driving pressure level, the power of the auxiliary electrical equipment of the electric vehicle is controlled.

In the embodiments of the present application, the target battery driving pressure level of the electric vehicle during the current operating cycle can be determined, and the power of the auxiliary electrical equipment of the electric vehicle can be controlled according to the target battery driving pressure level, thereby realizing battery-based driving. Power control of auxiliary electrical equipment of pressure level.

According to a first possible implementation of the first aspect, in a second possible implementation of the power control method, the method further includes: displaying the target motor driving condition or the target battery driving pressure. grade.

In embodiments of the present application, the target motor driving conditions or the target battery driving pressure level can be displayed on the human-machine interface of the electric vehicle, so that the driver can know the current motor driving conditions or battery driving pressure level of the electric vehicle in a timely manner.

According to any one of the first aspect, the first possible implementation manner of the first aspect, and the second possible implementation manner of the first aspect, in the third possible implementation manner of the power control method, The method further includes prompting the driver to adjust driving behavior according to the target motor driving conditions or the target battery driving pressure level.

In the embodiment of the present application, the driver can be prompted to adjust the driving behavior in time, so that the energy efficiency of the power battery can be improved by adjusting the driver's driving behavior.

According to the first aspect, in a fourth possible implementation manner of the power control method, according to the first probability distribution and the second probability distribution respectively corresponding to a plurality of preset motor driving conditions, from the Determining the target motor driving conditions of the electric vehicle in the current operating cycle among the plurality of preset motor driving conditions includes: respectively determining the relationship between the first probability distribution and each second probability distribution. A first distance, the first distance being a bulldozer distance; determining the minimum value of the first distance as a second distance; from the second probability distribution corresponding to the plurality of preset motor driving conditions. , determine the target probability distribution corresponding to the second distance; determine the preset motor driving working condition corresponding to the target probability distribution as the target motor driving working condition of the electric vehicle in the current operating cycle .

In the embodiment of the present application, the first distance (being the bulldozer distance) between the first probability distribution and each second probability distribution can be calculated respectively, and the minimum value of the first distance is determined as the second distance, and then Determine a target probability distribution corresponding to the second distance from the second probability distribution corresponding to the plurality of preset motor driving conditions, and determine the preset motor driving condition corresponding to the target probability distribution as an electric vehicle The target motor driving conditions in the current operating cycle, so that the target motor driving conditions of the electric vehicle in the current operating cycle can be identified from multiple preset motor driving conditions based on the first distance, quickly and accurately. Can improve processing efficiency.

According to the first aspect or any one of the first possible implementation manner of the first aspect to the fourth possible implementation manner of the first aspect, in a fifth possible implementation manner of the power control method, The method also includes: determining a third probability distribution corresponding to the instantaneous driving power of each group of reference motors according to the preset driving power interval; and determining the third probability distribution according to the number of the preset motor driving conditions and the third probability distribution. The bulldozer distance between the clusters is used to cluster the third probability distribution to obtain multiple clusters and cluster centers of the multiple clusters, wherein the number of clusters is determined according to the number of preset motor driving conditions; According to the plurality of clusters, the plurality of preset motor driving conditions are determined; according to the cluster centers of the plurality of clusters, second probability distributions respectively corresponding to the plurality of preset motor driving conditions are determined.

In the embodiment of the present application, the third probability distribution corresponding to the instantaneous driving power of each group of reference motors can be determined according to the preset driving power interval, and based on the number of preset motor driving conditions and the third probability distribution distance between bulldozers, cluster the third probability distribution to obtain multiple clusters and cluster centers of multiple clusters, and then determine multiple preset motor driving conditions based on multiple clusters, and determine cluster centers based on multiple clusters , determine the second probability distribution corresponding to multiple preset motor driving conditions, so that the statistical characteristics of the instantaneous driving power of each group of reference motors (i.e., the third probability distribution) can be used to divide the motor driving conditions of electric vehicles , to obtain multiple preset motor driving conditions and second probability distributions respectively corresponding to the multiple preset motor driving conditions.

According to any one of the first possible implementation manner of the first aspect to the fifth possible implementation manner of the first aspect, in a sixth possible implementation manner of the power control method, the plurality of The preset motor driving conditions include low load conditions, normal load conditions, higher load conditions and high load conditions; the plurality of preset battery driving pressure levels include low pressure level, medium pressure level and higher pressure. grade and high pressure grade.

In the embodiment of the present application, the plurality of preset motor driving conditions include low load conditions, normal load conditions, higher load conditions and high load conditions, and the plurality of preset battery driving pressure levels include low pressure levels. , medium pressure level, higher pressure level and high pressure level, so that the motor driving working conditions of electric vehicles can be divided into 4 working conditions, and the battery driving pressure level of electric vehicles can be divided into 4 levels in order to correspond to the two. stand up.

In a second aspect, embodiments of the present application provide a power control device for use in electric vehicles. The device includes: a driving power acquisition module configured to acquire multiple instantaneous values of the motor of the electric vehicle during the current operating cycle. Driving power; a first probability distribution determination module, configured to determine a first probability distribution corresponding to the plurality of instantaneous driving powers according to a preset driving power interval, where the first probability distribution is used to represent the instantaneous driving power The probability of falling in each driving power interval; the first driving condition determination module is used to determine from the plurality of preset motor driving conditions according to the first probability distribution and the second probability distribution respectively corresponding to the plurality of preset motor driving conditions. Among the preset motor driving conditions, the target motor driving condition of the electric vehicle in the current operating cycle is determined, wherein the motor driving condition is related to the load level driven by the motor; the first control module , used to control the power of the auxiliary electrical equipment of the electric vehicle according to the target motor driving conditions.

According to the second aspect, in a first possible implementation manner of the power control device, the device further includes: a battery driving pressure level determination module configured to determine the battery driving pressure level according to the target motor driving conditions and a plurality of preset batteries. The corresponding relationship between the driving pressure level and the plurality of preset motor driving conditions, and the target battery drive of the electric vehicle in the current operating cycle is determined from the plurality of preset battery driving pressure levels. Pressure level, wherein the battery driving pressure level is used to represent the impact of the motor driving of the electric vehicle on the discharge rate of the power battery; a second control module is used to control the electric vehicle according to the target battery driving pressure level. Control the power of the car's auxiliary electrical equipment.

According to a first possible implementation manner of the second aspect, in a second possible implementation manner of the power control device, the device further includes: a display module for displaying the target motor driving condition or the Describe the target battery driving pressure level.

According to any one of the second aspect, the first possible implementation manner of the second aspect, and the second possible implementation manner of the second aspect, in a third possible implementation manner of the power control device, The device further includes: a prompting module for prompting the driver to adjust the driving behavior according to the target motor driving conditions or the target battery driving pressure level.

According to the second aspect, in a fourth possible implementation manner of the power control device, the first driving condition determination module includes: a first distance determination sub-module for determining the first probability distribution respectively The first distance between each second probability distribution, the first distance is the bulldozer distance; the second distance determination sub-module is used to determine the minimum value of the first distance as the second distance; the probability distribution a determination sub-module for determining a target probability distribution corresponding to the second distance from the second probability distribution corresponding to the plurality of preset motor driving conditions; a driving condition determination sub-module for The preset motor driving conditions corresponding to the target probability distribution are determined as the target motor driving conditions of the electric vehicle in the current operating cycle.

According to the second aspect or any one of the first possible implementation manner of the second aspect to the fourth possible implementation manner of the second aspect, in a fifth possible implementation manner of the power control device, The device also includes: a second probability distribution determination module, used to determine the third probability distribution corresponding to the instantaneous driving power of each group of reference motors according to the preset driving power interval; a clustering module, used to determine the third probability distribution corresponding to the instantaneous driving power of each group of reference motors according to the preset driving power interval. Assuming the number of motor driving conditions and the bulldozer distance between the third probability distributions, the third probability distributions are clustered to obtain multiple clusters and cluster centers of the multiple clusters, where, the clusters The number is determined according to the number of the preset motor driving conditions; the second driving condition determination module is used to determine the multiple preset motor driving conditions according to the multiple clusters; the third probability distribution determination module , used to determine second probability distributions respectively corresponding to the plurality of preset motor driving conditions according to the cluster centers of the plurality of clusters.

According to any one of the first possible implementation manner of the second aspect to the fifth possible implementation manner of the second aspect, in a sixth possible implementation manner of the power control device, the plurality of The preset motor driving conditions include low load conditions, normal load conditions, higher load conditions and high load conditions; the plurality of preset battery driving pressure levels include low pressure level, medium pressure level and higher pressure. grade and high pressure grade.

In a third aspect, embodiments of the present application provide a controller configured to implement one or more of the power control methods of the first aspect or multiple possible implementations of the first aspect. .

In a fourth aspect, embodiments of the present application provide an electric vehicle, including: a controller; a memory for storing instructions executable by the controller; wherein the controller is configured to implement the above when executing the instructions. One or more power control methods of the first aspect or multiple possible implementations of the first aspect.

In a fifth aspect, embodiments of the present application provide a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a controller, the above-mentioned first aspect or various possibilities of the first aspect are realized. One or several power control methods in the implementation.

In a sixth aspect, embodiments of the present application provide a computer program product, including computer instructions, which when executed by a controller implement the above-mentioned first aspect or one of multiple possible implementations of the first aspect. or several power control methods.

These and other aspects of the application will be better understood in the description of the embodiment(s) below.

Description of the drawings

The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate exemplary embodiments, features, and aspects of the application and together with the description, serve to explain the principles of the application.

Figure 1 shows a schematic diagram of an application scenario of a power control method according to an embodiment of the present application.

Figure 2a shows a schematic diagram of the speed-time curve of a reference electric vehicle according to an embodiment of the present application.

Figure 2b shows a schematic diagram of a third probability distribution according to an embodiment of the present application.

Figure 3 shows a flow chart of a power control method according to an embodiment of the present application.

FIG. 4 shows a schematic diagram of a processing process of a power control method according to an embodiment of the present application.

FIG. 5 shows a schematic diagram of a processing process of a power control method according to an embodiment of the present application.

FIG. 6 shows a block diagram of a power control device according to an embodiment of the present application.

Detailed ways

Various exemplary embodiments, features, and aspects of the present application will be described in detail below with reference to the accompanying drawings. The same reference numbers in the drawings identify functionally identical or similar elements. Although various aspects of the embodiments are illustrated in the drawings, the drawings are not necessarily drawn to scale unless otherwise indicated.

The word "exemplary" as used herein means "serving as an example, example, or illustrative." Any embodiment described herein as "exemplary" is not necessarily to be construed as superior or superior to other embodiments.

In addition, in order to better explain the present application, numerous specific details are given in the following detailed description. It will be understood by those skilled in the art that the present application may be practiced without certain specific details. In some instances, methods, means, components and circuits that are well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present application.

Currently, when an electric vehicle is running, the power battery provides all the power output. However, the power battery is a passive component, and high-current discharge will not only reduce the energy efficiency of the power battery, but also accelerate the aging of the power battery. The motor is the main energy consumption source in electric vehicles. In related technologies, in order to limit the output power of the power battery from the energy consumption source to reduce large current discharge, the current driving power of the motor, the current driving power percentage of the motor, etc. are usually used. Instantaneous status information is used to control the power of ancillary electrical equipment of electric vehicles.

For example, in some technical solutions, when the current driving power of the motor is high, some auxiliary electrical equipment is usually directly limited or shut down. This decision-making process is too simple and can easily lead to frequent switching of the working mode of the auxiliary electrical equipment (for example, when turning on , turning off frequent switching between the two working modes) not only affects driving comfort, but is also detrimental to the improvement of the energy efficiency of the power battery.

In other technical solutions, multi-level thresholds are set in advance for the state of charge (SoC) of the power battery and the vehicle insulation resistance, and the opening threshold is set for the accelerator pedal opening; when receiving the air conditioner (high-voltage accessory) (Electrical equipment) operation request, the operating status of the air conditioner is adjusted according to the vehicle insulation resistance, the SoC of the power battery and the accelerator pedal opening. In other words, this technical solution is to control the operating power of the air conditioner based on the instantaneous status information of the vehicle.

However, in different scenarios (such as low speed, high speed, climbing, etc.), with the same accelerator pedal opening, the demand for motor driving power may be very different. In this technical solution, only the accelerator pedal opening is used to determine the driving power. It means that the driving power of the motor is too simple, and because the instantaneous status information of the vehicle is affected by many factors, the value changes all the time and fluctuates greatly. Controlling the operating power of the air conditioner based on the instantaneous status information of the vehicle may cause the air conditioner to work at multiple times. Frequent switching between modes not only affects the temperature control effect and driving comfort, but is also detrimental to the improvement of the energy efficiency of the power battery.

In order to solve the above technical problems, the present application provides a power control method, which is applied to electric vehicles. The method includes: obtaining multiple instantaneous driving powers of the motor of the electric vehicle in the current operating cycle; according to the preset driving power interval, determine a first probability distribution corresponding to the plurality of instantaneous driving powers, the first probability distribution is used to represent the probability that the instantaneous driving power falls in each driving power interval; according to the first probability distribution and A second probability distribution corresponding to a plurality of preset motor driving conditions respectively, and determining the target motor driving condition of the electric vehicle in the current operating cycle from the plurality of preset motor driving conditions, Wherein, the motor driving working condition is related to the load level driven by the motor; according to the target motor driving working condition, the power of the auxiliary electrical equipment of the electric vehicle is controlled.

The power control method of the embodiment of the present application can obtain multiple instantaneous driving powers of the electric vehicle's motor during the current operating cycle, determine the first probability distribution corresponding to the multiple instantaneous driving powers, and then determine the first probability distribution and the The second probability distribution corresponding to the multiple preset motor driving conditions respectively determines the target motor driving conditions of the electric vehicle in the current operating cycle from the multiple preset motor driving conditions, and determines the target motor driving conditions according to the target motor driving conditions. , control the power of the auxiliary electrical equipment of electric vehicles, so that the target motor driving conditions can be determined based on the multiple instantaneous driving powers of the electric vehicle's motor within a certain period of time (within the current operating cycle), and the target motor driving conditions can be determined based on the target motor drive Working conditions, the power of the auxiliary electrical equipment of the electric vehicle is controlled, making the control process of the power of the auxiliary electrical equipment by the electric vehicle smoother, reducing the switching of working modes during the use of the auxiliary electrical equipment, and thus improving the efficiency of the electric vehicle. While improving driving comfort, it also improves the energy efficiency of electric vehicle power batteries.

The power control method in the embodiment of the present application can be applied to electric vehicles. In a possible implementation manner, the power control method of the embodiment of the present application can be loaded in the vehicle-mounted equipment or after-installed vehicle-mounted equipment of the electric vehicle in the form of software.

Among them, the vehicle-mounted equipment can be, for example, a vehicle-mounted controller (vehicle control unit, VCU, also called a vehicle controller) used to control the entire electric vehicle. The power control method of the embodiment of the present application can be in the form of software, loaded in the vehicle controller VCU in the early stage (for example, before the electric vehicle leaves the factory), or loaded in the vehicle controller VCU in the later stage (for example, during the use of the electric vehicle).

After-installed vehicle equipment refers to the vehicle equipment installed on the electric vehicle during the use of the electric vehicle. The power control method of the embodiment of the present application can be in the form of software, directly loaded into and sold as after-loaded vehicle equipment, or loaded into existing after-loaded vehicle equipment of electric vehicles. It should be noted that this application does not limit the specific types of vehicle-mounted equipment and after-installed vehicle-mounted equipment.

Figure 1 shows a schematic diagram of an application scenario of a power control method according to an embodiment of the present application. As shown in FIG. 1 , the power control method according to the embodiment of the present application is applied to an electric vehicle 100 . The electric vehicle 100 includes an information collection module 110 , an on-board controller 120 and ancillary electrical equipment 130 .

While the electric vehicle 100 is driving, the information collection module 110 can collect information related to the instantaneous drive power calculation of the motor, and send the collected information to the on-board controller 120 for processing.

The information collection module 110 may include a drive motor information collection unit or a vehicle motion state sensing unit. The drive motor information acquisition unit can be used to collect motor information related to the instantaneous drive power calculation of the motor, such as the DC bus voltage of the electric vehicle 100, motor controller current, motor torque, motor speed, etc. The vehicle motion state sensing unit can be used to collect speed, acceleration, slope and other information during the driving process of the electric vehicle 100 to support the on-board controller 120 in calculating the instantaneous driving power of the motor according to the motion state of the electric vehicle 100 .

In one example, when the motor information of the electric vehicle 100 can be directly obtained, the information collection module 110 may include a drive motor information collection unit; when the motor information of the electric vehicle 100 cannot be directly obtained, the information collection module 110 may Includes vehicle motion status sensing unit. In another example, the information collection module 110 may include a drive motor information collection unit and a vehicle motion state sensing unit at the same time. Those skilled in the art can determine the specific implementation of the information collection module 110 according to actual conditions, and this application does not limit this.

After receiving the information sent by the information collection module 110, the on-board controller 120 can obtain multiple instantaneous driving powers of the motor of the electric vehicle 100 in the current operating cycle based on the received information, and calculate the driving power according to the preset driving power interval. , determine the first probability distribution corresponding to the plurality of instantaneous driving powers, and then according to the first probability distribution and the second probability distribution respectively corresponding to the plurality of preset motor driving conditions, from the plurality of preset motor driving conditions, The target motor driving conditions of the electric vehicle 100 in the current operating cycle are determined, and the power of the accessory electrical equipment 130 of the electric vehicle 100 is controlled according to the target motor driving conditions.

In one example, when the on-board controller 120 controls the power of the auxiliary power consuming device 130 of the electric vehicle 100, it may send a control instruction to the auxiliary power consuming device 130, such as an enable command, a disable command, a power limit command, etc. ; When the accessory electrical equipment 130 receives the control instruction sent by the vehicle-mounted controller 120, it can adjust its own working mode according to the control instruction.

It should be noted that in actual electric vehicles, the above-mentioned driving motor information collection unit and vehicle motion state sensing unit may include multiple different or identical sensors, and this application is not limited to this. In an actual electric vehicle, the vehicle-mounted controller can be the main controller on the electric vehicle, and the vehicle-mounted controller can also include multiple controllers on the electric vehicle. For the sake of simplicity, this application will uniformly describe it as a vehicle-mounted controller. This application refers to the vehicle-mounted controller. The specific type of controller is not limited.

Before controlling the power of the auxiliary electrical equipment of an electric vehicle through the power control method described in the above embodiments of the present application, it is necessary to determine multiple preset motor driving conditions and their respective relationships with the multiple preset motor driving conditions. The corresponding second probability distribution.

In a possible implementation, multiple preset motor driving conditions and second probability distributions respectively corresponding to the multiple preset motor driving conditions can be determined based on multiple preset sets of reference motor instantaneous driving powers. Among them, each set of reference motor instantaneous drive power includes multiple reference motor instantaneous drive powers of the reference electric vehicle within a historical operating cycle. The reference electric vehicles corresponding to the instantaneous drive power of each group of reference motors may be the same or different, and this application does not limit this. The running cycle is a preset period of time. For example, the running cycle can be a minute-level time period, and its preset duration can be 2 minutes, 5 minutes, 10 minutes, etc. Those skilled in the art can set the specific duration of the running cycle according to the actual situation, and there is no restriction on this without application. .

In one possible implementation, multiple preset motor driving conditions can be determined through clustering. The third probability distribution corresponding to the instantaneous driving power of each group of reference motors may first be determined according to the preset driving power interval. For any set of reference motor instantaneous drive powers, the third probability distribution corresponding to the set of reference motor instantaneous drive powers can be used to represent the probability that the reference motor instantaneous drive power in the set of reference motor instantaneous drive powers falls in each drive power interval. .

There are multiple driving power intervals. The value range of the motor driving power of the electric vehicle can be divided into multiple driving power ranges according to a unified range division method. For example, assuming that the value range of the motor driving power of an electric vehicle is -100kw to 300kw, the value range of the motor driving power can be divided into 40 driving power intervals according to the division method of one interval every 10kw, which are [-100kw , -90kw), [-90kw, -80kw), ..., [-10kw, 0kw), [0kw, 10kw), [10kw, 20kw), ..., [280kw, 290kw), [290kw, 300kw].

For any set of reference motor instantaneous drive power, statistical calculations can be used to determine the probability that the reference motor instantaneous drive power in the set of reference motor instantaneous drive powers falls in each drive power range, and then obtain the instantaneous drive power of the set of reference motors. The corresponding third probability distribution.

For example, assuming that the number of driving power intervals is 40, and the driving power intervals are divided as described above, the total number of reference motor instantaneous drive powers in a certain group of reference motor instantaneous drive powers is 100, and these 100 can be determined separately. The driving power range corresponding to the instantaneous driving power of the reference motor. For example, assuming that the instantaneous driving power of the reference motor is -93kw, it can be determined that the driving power range corresponding to the instantaneous driving power of the reference motor is [-100kw, -90kw).

Then, based on the number of reference motor instantaneous drive powers corresponding to each drive power interval and the total number of reference motor instantaneous drive powers in the group of reference motor instantaneous drive powers, the probability that the reference motor instantaneous drive power falls within each drive power interval is determined. :

Assuming that the number of instantaneous driving powers of the reference motor corresponding to the first driving power interval [-100kw, -90kw) is 3, then 3/100=0.03 can be determined as the instantaneous driving power of the reference motor falling in the first driving power interval; assuming that the number of instantaneous drive powers of the reference motor corresponding to the second drive power interval [-90kw, -80kw) is 0, then 0/100=0 can be determined as the instantaneous drive power of the reference motor falling in the second drive power interval [-90kw, -80kw]. The probability of 2 drive power intervals; in a similar way, the probability that the instantaneous drive power of the reference motor falls in the other 38 drive power intervals can be determined, thereby obtaining the third probability distribution corresponding to the instantaneous drive power of the reference motor.

In a possible implementation, the third probability distribution P corresponding to any set of reference motor instantaneous drive power can be expressed by the following formula (1):

P={(p ₁ , ω ₁ ), (p ₂ , ω ₂ ),..., (p _n , ω _n )} (1)

In formula (1), p ₁ represents the center point of the first driving power interval; ω ₁ represents the probability that the instantaneous driving power of the reference motor in the group of reference motor instantaneous driving powers falls in the first driving power interval; p ₂ represents the center point of the second drive power interval; ω ₂ represents the probability that the instantaneous drive power of the reference motor in the group of reference motor instantaneous drive powers falls in the second drive power interval; p _n represents the nth drive power interval. Center point; ω _n represents the probability that the instantaneous driving power of the reference motor in the group of reference motor instantaneous driving powers falls in the nth driving power interval; n is a positive integer, indicating the number of driving power intervals.

Figure 2a shows a schematic diagram of the speed-time curve of a reference electric vehicle according to an embodiment of the present application. As shown in Figure 2a, the horizontal axis of the speed-time curve 210 of the reference electric vehicle represents time, in seconds (s), and the historical operating cycle is 800 seconds (ie, 13 minutes and 20 seconds); the vertical axis represents speed, in thousands. Meters/hour (km/h).

According to the speed-time curve 210 of the reference electric vehicle, the speed and acceleration of the reference electric vehicle at each moment in the historical operating cycle can be determined, and then combined with the slope (the default slope here is 0), the result of the reference electric vehicle can be calculated The speed-time curve 210 corresponds to a set of reference motor instantaneous driving powers. Then, through the statistical calculation method described in the above embodiment, a third probability distribution corresponding to the instantaneous driving power of the reference motor is obtained, as shown in Figure 2b.

Figure 2b shows a schematic diagram of a third probability distribution according to an embodiment of the present application. As shown in Figure 2b, the third probability distribution can be represented as a histogram 220. The horizontal axis of the histogram 220 represents the motor driving power in kilowatts (kw), and the vertical axis represents probability. Each histogram (ie, vertical bar) in the histogram 220 corresponds to a driving power interval and is used to represent the probability that the instantaneous driving power of the reference motor falls within the corresponding driving power interval.

In this way, the statistical features in the instantaneous driving power of each group of reference motors can be extracted to obtain the third probability distribution, which facilitates subsequent clustering.

In a possible implementation, after obtaining the third probability distribution corresponding to the instantaneous driving power of each group of reference motors, the third probability distribution can be calculated based on the number of preset motor driving conditions and the bulldozer distance between the third probability distributions. Probability distribution is clustered to obtain multiple clusters and cluster centers of multiple clusters. Among them, the number of preset motor driving conditions can be set according to the actual situation. For example, the number of preset motor driving conditions can be set to 4, or the number of preset motor driving conditions can be set to other values. This application There are no restrictions on this.

Different from using geometric distance (such as Euclidean distance, etc.) to measure the similarity between two objects in the traditional clustering process, the clustering object in the embodiment of the present application is the third probability distribution, and the similarity measure based on geometric distance cannot Capture differences between uncertain objects with different distributions. Therefore, in the embodiment of the present application, a similarity measure based on earth mover distance (EMD, also known as earth move distance) is used for clustering. The bulldozer distance EMD can be used to measure the similarity between two histograms. Since the third probability distribution can be expressed as a histogram, the two third probability distributions can be measured by the bulldozer distance between the two third probability distributions. Similarity between three probability distributions.

The calculation method of the bulldozer distance is exemplified below. Assume that the probability distribution represented by the first histogram is probability distribution R, and the probability distribution represented by the second histogram is probability distribution Q. Probability distribution R and probability distribution Q can be expressed as follows:

R={(r ₁ , ω _r1 ), (r ₂ , ω _r2 ),..., (r _m , ω _rm )}

Q={(q ₁ , ω _q1 ), (q ₂ , ω _q2 ),..., (q _m , ω _qm )}

Among them, r ₁ represents the center point of the first numerical interval of the probability distribution R (the position of the first histogram in the first histogram); ω _r1 represents that the value corresponding to the probability distribution R appears in the first numerical interval. The probability of (the height of the first histogram in the first histogram); r ₂ represents the center point of the second numerical interval of the probability distribution R (the position of the second histogram in the first histogram); ω _r2 represents The probability that the value corresponding to the probability distribution R appears in the second numerical interval (the height of the second histogram in the first histogram); r _m represents the center point of the mth numerical interval of the probability distribution R (the first The position of the m-th histogram in the first histogram); ω _rm represents the probability that the value corresponding to the probability distribution R appears in the m-th numerical interval (the height of the m-th histogram in the first histogram); m is positive Integer, representing the number of numerical intervals;

q ₁ represents the center point of the first numerical interval of the probability distribution Q (the position of the first histogram in the second histogram); ω _q1 represents the probability that the value corresponding to the probability distribution Q appears in the first numerical interval. (The height of the first histogram in the second histogram); q ₂ represents the center point of the second numerical interval of the probability distribution Q (the position of the second histogram in the second histogram); ω _q2 represents the probability The probability that the value corresponding to the distribution Q appears in the second numerical interval (the height of the second histogram in the second histogram); q _m represents the center point of the m-th numerical interval of the probability distribution Q (the second histogram) The position of the m-th histogram in the figure); ω _qm represents the probability that the value corresponding to the probability distribution Q appears in the m-th value interval (the height of the m-th histogram in the second histogram).

The bulldozer distance (EMD distance) d(R, Q) between the probability distribution R and the probability distribution Q can be calculated by the following formula (2):

In formula (2), EMD(R, Q) represents the bulldozer distance between R and Q; i is an integer and 1≤i≤m; j is an integer and 1≤j≤m; d _ij represents r _i and q _j The distance between; f _ij is obtained by solving the optimization problem.

For example, a constrained linear optimization problem can be established to solve the parameters f _ij in the above formula (2). The objective function F* of the optimization problem can be expressed as follows:

Constraint stf _ij ≥0, 1≤i≤m, 1≤j≤m,

Among them, J(R, Q, F) represents the cost function, F=(f ₁₁ , f ₁₂ ,..., f _1m , f ₂₁ ,..., f _mm ).

The above only uses the probability distribution R and the probability distribution Q as examples to illustrate the calculation process of the bulldozer distance. During the clustering process, the bulldozer distance between the two third probability distributions can be calculated through the above formula (2). distance.

In a possible implementation, the third probability distributions can be clustered according to the bulldozer distance between the third probability distributions through a K-medoids clustering algorithm. K is the number of preset motor driving conditions, that is to say, the number of clusters obtained by clustering is determined based on the number K of preset motor driving conditions. During the clustering process, the quality of the clustering results can be measured by the sum of absolute differences (SAD). Absolute difference and SAD can be used to represent the sum of squared errors between all objects in the input object set for clustering and their cluster centers. The SAD in the traditional K-medoids algorithm is calculated using the Euclidean distance, while the SAD in the K-medoids algorithm of the embodiment of the present application is calculated using the EMD distance.

In a possible implementation, in the process of clustering the third probability distribution, the absolute difference and SAD can be calculated through the following formula (3):

In formula (3), C _v represents the v-th cluster, U represents any third probability distribution in the v-th cluster C _v , O _v represents the cluster center of the v-th cluster C _v , dist(U, O _v ) represents the distance between U and O _v , EMD (U, O _v ) represents the bulldozer distance between U and O _v , v is an integer and 1≤v≤K.

Through the K-medoids clustering algorithm (K-medoids), the process of clustering the third probability distribution can be exemplified as follows:

Step 1: Use the third probability distribution corresponding to the instantaneous driving power of each group of reference motors as the input object of the K-center point clustering algorithm. That is, the input object set of the K-center point clustering algorithm includes multiple third probability distributions. ;

Step 2: Randomly select K third probability distributions from multiple third probability distributions as initial cluster centers. The cluster center of the v-th cluster C _v can be represented by O _v . The third probability distribution that is the center of the cluster can be regarded as a representative object, and other third probability distributions that are not the center of the cluster can be regarded as non-representative objects;

Step 3: Assign other third probability distributions to the cluster represented by the cluster center closest to (bulldozer distance), and calculate the first absolute difference and SAD1. The specific process is: for any other third probability distribution A third probability distribution, calculate the bulldozer distance between the third probability distribution and the center of each cluster, and divide the third probability distribution into clusters corresponding to the minimum bulldozer distance, so that the third probability distribution can be divided into In the cluster with the highest similarity; the first absolute difference and SAD1 can then be calculated through the above formula (3).

It can be seen from the above formula (3) that when the embodiment of the present application calculates the first absolute difference value and SAD1, the distance used is the bulldozer distance, which is different from the geometric distance in the prior art.

Step 4: Randomly select a third probability distribution as the non-representative object O′ _v from other third probability distributions that are not cluster centers;

Step 5: Calculate the cost of replacing the representative object O _v with the non-representative object O′ _v . This cost can be expressed by the second absolute difference and SAD2. The second absolute difference and SAD2 can be calculated by the above formula (3). When calculating, just replace O _v in the above formula (3) with O′ _v ;

Step 6: If the second absolute difference sum SAD2 is less than the first absolute difference sum SAD1, replace O _v with O′ _v , so that K new cluster centers can be obtained;

Step 7: Loop through steps 3 to 6 until the preset clustering end conditions are reached (for example, the number of iterations reaches the preset threshold, the cluster center no longer changes, etc.), then the clustering is ended and K clusters and K clusters are obtained. The cluster center of each cluster.

It should be noted that the above only uses the K-medoids clustering algorithm (K-medoids) as an example to illustrate the clustering process of the third probability distribution. Those skilled in the art will understand that other methods can also be used. The clustering algorithm clusters the third probability distribution, and this application does not limit this.

In one possible implementation, after the clustering is completed, multiple clusters and the cluster center of each cluster are obtained. Then multiple preset motor driving conditions can be determined based on the multiple clusters, and based on the clusters of the multiple clusters, center, determining second probability distributions respectively corresponding to multiple preset motor driving conditions. Wherein, the motor driving working condition is related to the load level driven by the motor. For example, different preset motor driving working conditions can be distinguished according to the load level driven by the motor. Each cluster corresponds to a preset motor driving condition, and each preset motor driving condition also corresponds to a cluster, that is, there is a one-to-one correspondence between clusters and preset motor driving conditions.

In a possible implementation, when multiple preset motor driving conditions are determined based on multiple clusters, multiple preset motor driving conditions may be determined based on the third probability distribution in each cluster. For example, assuming that the number of preset motor driving conditions K=4, after clustering the third probability distribution through the above method, four clusters and the cluster center of each cluster are obtained, and then the cluster center of each cluster can be obtained according to the third probability distribution in each cluster. Three probability distributions are used to determine multiple preset motor driving conditions. The specific process can be exemplified as follows:

The power distribution characteristics of the first cluster can be determined according to the third probability distribution in the first cluster. The power distribution characteristics can be, for example, the power concentration interval (the probability value in the third probability distribution can be greater than or equal to the preset probability threshold). method), assuming that the power concentration interval of the first cluster is (30kw, 50kw); according to the third probability distribution in the second cluster, determine the power concentration interval of the second cluster (i.e., power distribution characteristics), assuming The power concentration interval of the second cluster is (00kw, 20kw); according to the third probability distribution in the third cluster, determine the power concentration interval (i.e., power distribution characteristics) of the third cluster. Assume that the power of the third cluster The concentration interval is (50kw, 80kw); according to the third probability distribution in the 4th cluster, determine the power concentration interval (i.e., power distribution characteristics) of the 4th cluster. Assume that the power concentration interval of the 4th cluster is (100kw, 120kw);

Afterwards, multiple preset motor driving conditions can be determined based on each power concentration range and combined with the actual operating conditions of the electric vehicle. In one example, the lower the power value that can be set to the power concentration interval, the lower the load level corresponding to the preset motor driving condition. For example: when the power concentration interval is (00kw, 20kw), the motor of the electric vehicle The driving load is small, and the motor driving condition represented by the second cluster can be determined as a low load condition; when the power concentration range is (30kw, 50kw), the motor driving load of the electric vehicle is normal, and the motor driving condition represented by the second cluster can be determined as a low load condition. The motor driving conditions represented by 1 cluster are determined as general load conditions; when the power concentration range is (50kw, 80kw), the motor driving load of electric vehicles is relatively high, and the motor driving conditions represented by the third cluster can be The working condition is determined as a higher load condition; when the power concentration range is (100kw, 120kw), the motor drive load of the electric vehicle is very high, and the motor drive condition represented by the fourth cluster can be determined as a high load condition. load conditions;

Then low load conditions, general load conditions, higher load conditions and high load conditions are determined as 4 preset motor driving conditions. That is to say, in the above embodiment, the plurality of preset motor driving conditions include low load conditions, normal load conditions, higher load conditions and high load conditions.

After multiple preset motor driving conditions are determined, second probability distributions corresponding to the multiple preset motor driving conditions can be determined based on the cluster centers of the multiple clusters. For any preset motor driving condition, the cluster center of the cluster corresponding to the preset motor driving condition can be determined as the second probability distribution corresponding to the preset motor driving condition.

It should be noted that the above only takes four preset motor driving conditions as an example, and the determination process of multiple preset motor driving conditions and the second probability distribution corresponding to the multiple preset motor driving conditions respectively is carried out. By way of example, in practical applications, the number of preset motor driving conditions can also be other values, and the determination process is similar to the above embodiment, and will not be described again here. Those skilled in the art can set the specific number of preset motor driving conditions according to actual conditions, and this application does not limit this.

In embodiments of the present application, the third probability distribution corresponding to the instantaneous driving power of each group of reference motors can be determined respectively according to the preset driving power interval, and according to the number of preset motor driving conditions and the third probability distribution, bulldozer distance, cluster the third probability distribution to obtain multiple clusters and cluster centers of multiple clusters, and then determine multiple preset motor driving conditions based on multiple clusters, and determine cluster centers based on multiple clusters, Determine the second probability distribution corresponding to multiple preset motor driving conditions, so that the statistical characteristics of the instantaneous driving power of each group of reference motors (i.e., the third probability distribution) can be used to divide the motor driving conditions of the electric vehicle, A plurality of preset motor driving conditions and second probability distributions respectively corresponding to the plurality of preset motor driving conditions are obtained.

In a possible implementation manner, after obtaining multiple preset motor driving conditions, a corresponding preset battery driving pressure level can be defined for each preset motor driving condition. The battery driving pressure level may be used to represent the impact of the motor driving of the electric vehicle on the discharge rate of the power battery. Each preset motor driving condition corresponds to a preset battery driving pressure level.

Since the motor driving conditions of electric vehicles directly affect the discharge rate of the power battery of electric vehicles, and according to Peukert's law, at a higher discharge rate, the single available capacity of the power battery (i.e., the single discharge efficiency ) will decrease, and according to the dynamic battery capacity attenuation model, high-rate discharge will directly accelerate the aging process of the power battery (that is, the cycle life of the power battery is lost). Therefore, when defining the corresponding preset battery for each preset motor driving condition When driving the pressure level, the historical data related to the power battery under each preset motor driving condition can be obtained first, such as the SoC and discharge current of the power battery at each historical moment, and then according to Peukert's law, the dynamic battery The capacity attenuation model and historical data related to the power battery under each preset motor driving condition are used to determine the single discharge efficiency and cycle life loss of the power battery under each preset motor driving condition, and based on each preset motor driving condition Under the single discharge efficiency and cycle life loss of the power battery, the corresponding preset battery drive pressure level is defined for each preset motor drive working condition.

Among them, the discharge rate of the power battery is the ratio of the discharge current of the power battery to the rated capacity of the power battery. It is a measure of the discharge speed, that is: the discharge rate of the power battery = the discharge current of the power battery/the rated capacity of the power battery.

Peukert’s law can be expressed by the following formula (4), which is used to describe the single discharge efficiency of the power battery:

Among them, t represents a time period; SoC ⁰ represents the state of charge of the power battery at the first moment, and the first moment is any historical moment; SoC ^t represents the state of charge of the power battery at the second moment, and the second moment is from The moment after the t time period begins at the first moment; C represents the rated capacity of the power battery;

pc represents Peukert’s constant, and I′ represents the rated current of the power battery.

The dynamic battery capacity fading model is a relevant existing technology and can be used to describe the cycle life loss of power batteries.

Through the above method, the corresponding preset battery drive pressure level can be defined for each preset motor drive working condition. The lower the load level related to the preset motor drive industrial control, the lower the battery drive pressure level. Correspondences between multiple preset motor driving conditions and multiple preset battery drive pressure levels can then be established.

For example, assuming that multiple preset motor driving conditions include low load conditions, normal load conditions, higher load conditions, and high load conditions, a preset motor driving condition can be defined for each preset motor driving condition in the above manner. Set the battery drive pressure level: the preset battery drive pressure level under low load conditions is low pressure level, the preset battery drive pressure level under normal load conditions is medium pressure level, and the preset battery drive pressure level under higher load conditions is The driving pressure level is a higher pressure level, and the preset battery driving pressure level under high load conditions is a high pressure level. Then, multiple preset battery driving pressure levels can be determined including low pressure level, medium pressure level, higher pressure level and high pressure level, and multiple preset motor driving conditions and multiple preset battery driving pressure levels can be established. The corresponding relationship between: low load conditions correspond to low pressure levels, general load conditions correspond to medium pressure levels, higher load conditions correspond to higher pressure levels, and high load conditions correspond to high pressure levels. .

In a possible implementation, the number of preset battery driving pressure levels can be the same as the number of preset motor driving conditions, and the preset battery driving pressure levels correspond to the preset motor driving conditions one-to-one; the preset battery driving conditions The number of pressure levels may also be smaller than the number of preset motor driving conditions, and one preset battery driving pressure level corresponds to one or more preset motor driving conditions. Those skilled in the art can set the specific number of preset battery driving pressure levels according to actual conditions, and this application does not limit this.

The above-mentioned determination process of multiple preset motor driving conditions and multiple preset battery drive pressure levels can be regarded as an offline clustering process. After multiple preset motor driving conditions or multiple preset battery driving pressure levels are determined through the above method, the target motor driving conditions or target battery driving pressure levels of the electric vehicle can be identified online while the electric vehicle is driving. , so that the power of the electric vehicle's auxiliary electrical equipment can be controlled according to the target motor driving condition or the target battery driving pressure level of the electric vehicle.

Figure 3 shows a flow chart of a power control method according to an embodiment of the present application. As shown in Figure 3, the power control method includes:

Step S310: Obtain multiple instantaneous driving powers of the motor of the electric vehicle in the current operating cycle.

During the driving of the electric vehicle, information can be collected through an information collection module including at least one sensor. The collected information can include motor information related to the calculation of the instantaneous drive power of the motor (such as the DC bus voltage of the electric vehicle, the motor controller current, Motor torque, motor speed, etc.), information related to the vehicle's operating status (such as speed, acceleration, slope, etc.), etc., and then based on the collected information, multiple instantaneous driving powers of the electric vehicle's motor in the current operating cycle can be determined . Among them, the length of the current running cycle is the same as the length of the above historical running cycle.

In a possible implementation, when the collected information includes the DC bus voltage of the electric vehicle and the motor controller current, the instantaneous driving power P of the motor of the electric vehicle at the current moment can be calculated through the following formula (5) _d :

Pd＝U _DC ×I _DC (5)

In formula (5), U _DC represents the DC bus voltage of the electric vehicle at the current moment, and I _DC represents the motor controller current of the electric vehicle at the current moment.

It should be noted that for the convenience of expression here, the input current during driving and the output current during braking recovery are uniformly expressed by I _DC . The input current during driving is a positive value, and the output current during braking recovery is a negative value. Therefore, _Pd ＞0 during driving and _Pd ＜0 during braking recovery.

In a possible implementation, when the collected information includes the motor torque and motor speed of the electric vehicle, the instantaneous motor driving power P _d of the electric vehicle at the current moment can be calculated through the following formula (6):

P _d =T _q M _rpm /9550 (6)

In formula (6), T _q represents the motor torque of the electric vehicle at the current moment, and M _rpm represents the motor speed of the electric vehicle at the current moment.

In a possible implementation, when the information collected by the sensor includes speed, acceleration, slope and other information during the driving of the electric vehicle, the following formula (7) can be used to calculate the instantaneous motor of the electric vehicle at the current moment. Driving power _Pd :

In formula (7), F _d represents the motor driving force of the electric vehicle at the current moment, v represents the speed of the electric vehicle at the current moment, a represents the acceleration of the electric vehicle at the current moment, θ represents the slope at the current moment, and ρ _air represents the air. Density, C _d represents the air resistance coefficient, A represents the windward area of the electric vehicle, m represents the mass of the electric vehicle, and g represents the acceleration of gravity.

It should be noted that other methods can also be used to calculate the instantaneous driving power of the motor of the electric vehicle at the current moment, and this application does not limit this.

While the electric vehicle is driving, the instantaneous driving power of the electric vehicle's motor at the current moment can be calculated through the above method. As time goes by, multiple instantaneous driving powers of the electric vehicle's motor during the current operating cycle can be obtained.

It should be noted that the above calculation method for the instantaneous driving power of the motor of an electric vehicle at the current moment is applicable to human driving mode and automatic driving mode. In addition, for electric vehicles in autonomous driving mode, if the vehicle speed planning information is available, the motor drive power requirements can be calculated in advance through the above formula (7), thereby providing a priori information for the vehicle power control of the electric vehicle.

Step S320: Determine a first probability distribution corresponding to the plurality of instantaneous driving powers according to a preset driving power interval.

The first probability distribution may be used to represent the probability that the instantaneous driving power of the motor of the electric vehicle falls within each driving power range during the current operating cycle.

In a possible implementation, the first probability distribution corresponding to the multiple instantaneous driving powers can be determined according to the preset driving power interval in a manner similar to the above-described third probability distribution, which will not be described again here. The driving power interval used when determining the first probability distribution is the same as the driving power interval used when determining the third probability distribution described above. The first probability distribution can also be expressed by the above formula (1).

Step S330: According to the first probability distribution and the second probability distribution respectively corresponding to the plurality of preset motor driving conditions, it is determined from the plurality of preset motor driving conditions that the electric vehicle is in the Target motor drive conditions during the current operating cycle.

In a possible implementation, when determining the target motor driving conditions of the electric vehicle in the current operating cycle, the first distance between the first probability distribution and each second probability distribution may be first determined, where the first distance is the bulldozer distance. For example, assuming that the number of preset motor driving conditions is 4, and each preset motor driving condition corresponds to a second probability distribution, then the first probability distribution and each second probability distribution can be calculated respectively through the above formula (2). Bulldozer distance between probability distributions, resulting in 4 first distances.

Then, the minimum value of the first distance is determined as the second distance, and the target probability distribution corresponding to the second distance is determined from the second probability distribution corresponding to the multiple preset motor driving conditions, and then the target probability distribution corresponding to the second distance is determined. The preset motor driving conditions corresponding to the target probability distribution are determined as the target motor driving conditions of the electric vehicle in the current operating cycle.

For example, assuming that the multiple preset motor driving conditions are low load conditions, normal load conditions, higher load conditions and high load conditions, after calculating the four first distances through the above formula (2), you can Select the minimum value from the four first distances, determine the minimum value as the second distance, and then determine the second distance corresponding to the second probability distribution corresponding to the four preset motor driving conditions. The target probability distribution of ), determined as the target motor driving condition of the electric vehicle in the current operating cycle.

In this way, the first distance (the bulldozer distance) between the first probability distribution and each second probability distribution can be calculated respectively, and the minimum value of the first distance is determined as the second distance, and then from From the second probability distributions respectively corresponding to the preset motor driving conditions, a target probability distribution corresponding to the second distance is determined, and the preset motor driving conditions corresponding to the target probability distribution are determined as the current operating conditions of the electric vehicle. The target motor driving conditions within the cycle, so that the target motor driving conditions of the electric vehicle in the current operating cycle can be identified from multiple preset motor driving conditions based on the first distance, which is fast and accurate, and can improve processing efficiency.

Step S340: Control the power of the auxiliary electrical equipment of the electric vehicle according to the target motor driving conditions.

After determining the target motor driving conditions of the electric vehicle in the current operating cycle, control instructions (such as enabling instructions, non-enabling instructions, and power limits) for the electric vehicle's auxiliary electrical equipment can be generated based on the target motor driving conditions. instructions, etc.), and sends the control instructions to the auxiliary electrical equipment, so that the auxiliary electrical equipment adjusts its working mode according to the control instructions, thereby achieving control of the power of the auxiliary electrical equipment of the electric vehicle.

Taking the air conditioner in the accessory electrical equipment as an example, the power control process based on the target motor driving conditions is illustratively explained below.

When the target motor driving condition of the electric vehicle in the current operating cycle is a low load condition, if an air-conditioning cooling or heating request is received, the air-conditioning controller enable command and the first power limit command can be generated. The Directive allows air conditioners to use the maximum permissible power required for cooling or heating;

When the target motor driving condition of the electric vehicle in the current operating cycle is the general load condition, if an air-conditioning cooling or heating request is received, the air-conditioning controller enable command and the second power limit command can be generated. The second power limit Instructs to limit the maximum allowable power of the air conditioner for cooling or heating at a first preset limit rate;

When the target motor driving condition of the electric vehicle in the current operating cycle is a higher load condition or a high load condition, if an air conditioning cooling or heating request is received, it can be combined with the state of charge of the power battery at the current moment to generate Air conditioning controller enabling command or air conditioning controller disabling command. For example, when the state of charge of the power battery at the current moment is greater than the first threshold, an air conditioning controller enable instruction and a third power limit instruction are generated. The third power limit instruction limits air conditioning cooling or heating at a second preset limit rate. the maximum allowable power; when the state of charge of the power battery at the current moment is greater than the second threshold, an air conditioning controller enable instruction and a fourth power limit instruction are generated, and the fourth power limit instruction limits the air conditioner at a third preset limit magnification The maximum allowable power for cooling or heating; when the state of charge of the power battery at the current moment is less than the second threshold, a non-enabling instruction for the air conditioning controller is generated to limit the use of the air conditioner.

Among them, the first preset limit magnification > the second preset limit magnification > the third preset limit magnification, the first threshold > the second threshold, and the first preset limit magnification, the second preset limit magnification, the third preset limit magnification The limit magnification, the first threshold, and the second threshold can all be expressed in the form of percentages. Those skilled in the art can determine the specific values of the first preset limit magnification, the second preset limit magnification, the third preset limit magnification, the first threshold and the second threshold according to the actual situation, which is not limited in this application.

It should be noted that the above only uses the air conditioner as an example to illustrate the power control process based on the target motor driving conditions. The power control process of other auxiliary electrical equipment is similar to this and will not be described again here.

In a possible implementation manner, after determining the target motor driving conditions of the electric vehicle in the current operating cycle, the target motor driving conditions and multiple preset battery driving pressure levels can also be compared with the multiple preset motor driving conditions. According to the corresponding relationship between the conditions, the target battery driving pressure level of the electric vehicle in the current operating cycle is determined from multiple preset battery driving pressure levels.

For example, assuming that the target motor driving condition of the electric vehicle in the current operating cycle is a low load condition, and the low load condition corresponds to a low pressure level among multiple preset battery driving pressure levels, then the electric vehicle can be The target battery driving pressure level within the current operating cycle is determined to be a low pressure level.

After the target battery driving pressure level of the electric vehicle in the current operating cycle is determined, the power of the electric vehicle's auxiliary electrical equipment can be controlled according to the target battery driving pressure level. Specifically, according to the target battery driving pressure level, control instructions for the auxiliary electrical equipment of the electric vehicle (such as enable instructions, disable instructions, power limit instructions, etc.) can be generated, and the control instructions can be sent to the auxiliary electrical equipment. , so that the auxiliary electrical equipment can adjust its working mode according to the control instructions.

The power control process based on the target battery driving pressure level is exemplarily explained below, taking the air conditioner in the accessory electrical equipment as an example.

When the target battery driving pressure level of the electric vehicle in the current operating cycle is a low pressure level, if an air-conditioning cooling or heating request is received, the air-conditioning controller enable command and the first power limit command can be generated. The first power limit command The maximum allowable power that allows the air conditioner to use cooling or heating demand power;

When the target battery driving pressure level of the electric vehicle in the current operating cycle is the medium pressure level, if an air-conditioning cooling or heating request is received, the air-conditioning controller enable command and the second power limit command can be generated. The second power limit command Limit the maximum allowable power of the air conditioner for cooling or heating at the first preset limit rate;

When the target battery driving pressure level of the electric vehicle in the current operating cycle is a higher pressure level or a high pressure level, if an air conditioning cooling or heating request is received, the air conditioning control can be generated based on the state of charge of the power battery at the current moment. The air conditioner enable command or the air conditioning controller disable command.

In this way, the target battery driving pressure level of the electric vehicle during the current operating cycle can be determined, and the power of the ancillary electrical equipment of the electric vehicle can be controlled according to the target battery driving pressure level, thereby achieving a battery driving pressure level based on the target battery driving pressure level. Power control of accessory electrical equipment.

In one possible implementation, after determining the target motor driving conditions or target battery driving pressure level of the electric vehicle in the current operating cycle, the human-machine interface of the electric vehicle ( The human machine interface (HMI) displays the target motor driving conditions or target battery driving pressure levels, allowing the driver to understand the current motor driving conditions or battery driving pressure levels of the electric vehicle in a timely manner.

In one possible implementation, after determining the target motor driving conditions or the target battery driving pressure level of the electric vehicle in the current operating cycle, the driver can also be prompted according to the target motor driving conditions or the target battery driving pressure level. Adjust driving behavior. For example, when it is recognized that the target battery driving pressure level of the electric vehicle in the current operating cycle is a higher pressure level and the current state of charge of the power battery is low, the driver can be prompted to maintain a smooth and economical driving style as much as possible. The driver may also be advised to reduce the operating power of non-essential auxiliary electrical equipment, or the driver may be advised to turn off non-essential auxiliary electrical equipment.

In this way, the driver can be prompted to adjust the driving behavior in time, thereby improving the energy efficiency of the power battery through the adjustment of the driver's driving behavior.

FIG. 4 shows a schematic diagram of a processing process of a power control method according to an embodiment of the present application. As shown in Figure 4, the power control method of this embodiment is based on the target motor driving conditions, and the processing process is as follows:

Step S401, determine the third probability distribution corresponding to the instantaneous driving power of each group of reference motors according to the preset driving power interval;

Step S402: Cluster the third probability distribution according to the number of preset motor driving conditions and the bulldozer distance between the third probability distributions to obtain multiple clusters and cluster centers of the multiple clusters;

Step S403, determine multiple preset motor driving conditions based on multiple clusters;

Step S404, determine second probability distributions respectively corresponding to the plurality of preset motor driving conditions according to the cluster centers of the plurality of clusters;

Step S405, obtain multiple instantaneous driving powers of the electric vehicle's motor during the current operating cycle;

Step S406, determine a first probability distribution corresponding to multiple instantaneous driving powers according to the preset driving power interval;

Step S407: Determine the target motor drive of the electric vehicle in the current operating cycle from the multiple preset motor drive conditions based on the first probability distribution and the second probability distribution respectively corresponding to the multiple preset motor drive conditions. Working conditions;

Step S408: Control the power of the auxiliary electrical equipment of the electric vehicle according to the target motor driving conditions of the electric vehicle in the current operating cycle.

Among them, steps S401 to S404 are offline clustering processes, and steps S405 to S408 are online identification processes.

FIG. 5 shows a schematic diagram of a processing process of a power control method according to an embodiment of the present application. As shown in Figure 5, the power control method of this embodiment is based on the target battery driving pressure level, and the processing process is as follows:

Step S501, determine the third probability distribution corresponding to the instantaneous driving power of each group of reference motors according to the preset driving power interval;

Step S502, cluster the third probability distribution according to the number of preset motor driving conditions and the bulldozer distance between the third probability distributions, and obtain multiple clusters and cluster centers of the multiple clusters;

Step S503, determine multiple preset motor driving conditions based on multiple clusters;

Step S504, determine second probability distributions respectively corresponding to the plurality of preset motor driving conditions according to the cluster centers of the plurality of clusters;

Step S505, define corresponding preset battery driving pressure levels for each preset motor driving working condition, and establish corresponding relationships between multiple preset motor driving working conditions and multiple preset battery driving pressure levels;

Step S506, obtain multiple instantaneous driving powers of the electric vehicle's motor in the current operating cycle;

Step S507, determine a first probability distribution corresponding to multiple instantaneous driving powers according to the preset driving power interval;

Step S508: Determine the target motor drive of the electric vehicle in the current operating cycle from the plurality of preset motor drive conditions according to the first probability distribution and the second probability distribution respectively corresponding to the multiple preset motor drive conditions. Working conditions;

Step S509: According to the target motor driving conditions and the correspondence between the multiple preset battery driving pressure levels and the multiple preset motor driving conditions, determine the current operating conditions of the electric vehicle from the multiple preset battery driving pressure levels. Target battery drive stress levels during the operating cycle;

Step S510: Control the power of the auxiliary electrical equipment of the electric vehicle according to the target battery driving pressure level of the electric vehicle in the current operating cycle.

Among them, steps S501 to S505 are offline clustering processes, and steps S506 to S510 are online identification processes.

The power control method of the embodiment of the present application can actively control the auxiliary electrical equipment of the electric vehicle according to the target motor driving conditions of the electric vehicle in the current operating cycle, or according to the target battery driving pressure level of the electric vehicle in the current operating cycle. By controlling the power of the electric vehicle, it can not only improve driving comfort but also improve the energy efficiency of the electric vehicle's power battery, and also balance the power, safety, ancillary electrical equipment protection and comfort of the entire electric vehicle.

The power control method of the embodiment of the present application can also provide the driver with rich information by displaying the target motor driving conditions of the electric vehicle in the current operating cycle, or displaying the target battery driving pressure level of the electric vehicle in the current operating cycle. Provide feedback and promptly remind drivers to make reasonable adjustments to their driving behavior and make reasonable use of auxiliary power equipment on electric vehicles, such as limiting or turning off non-essential auxiliary power equipment.

In one possible implementation, the target motor driving conditions or the target battery driving pressure level of the electric vehicle during the current operating cycle can be used as a priori information for the power management of the entire electric vehicle, and the auxiliary design is related to power management. Other functions, such as power control optimization methods, etc.

FIG. 6 shows a block diagram of a power control device according to an embodiment of the present application. This power control device is used in electric vehicles, as shown in Figure 6. The power control device includes:

The driving power acquisition module 61 is used to acquire multiple instantaneous driving powers of the motor of the electric vehicle in the current operating cycle;

The first probability distribution determining module 62 is configured to determine a first probability distribution corresponding to the plurality of instantaneous driving powers according to a preset driving power interval, where the first probability distribution is used to represent that the instantaneous driving power falls within Probability in each driving power range;

The first driving condition determination module 63 is configured to determine from the plurality of preset motor driving conditions according to the first probability distribution and the second probability distribution respectively corresponding to the plurality of preset motor driving conditions. Obtain the target motor driving conditions of the electric vehicle in the current operating cycle, wherein the motor driving conditions are related to the load level driven by the motor;

The first control module 64 is used to control the power of auxiliary electrical equipment of the electric vehicle according to the target motor driving conditions.

In a possible implementation, the device further includes: a battery driving pressure level determination module, configured to determine the battery driving pressure level according to the target motor driving working conditions and multiple preset battery driving pressure levels and the multiple preset motor driving conditions. According to the corresponding relationship between the working conditions, the target battery driving pressure level of the electric vehicle in the current operating cycle is determined from the plurality of preset battery driving pressure levels, wherein the battery driving pressure level is It represents the influence of the motor driving of the electric vehicle on the discharge rate of the power battery; the second control module is used to control the power of the accessory electrical equipment of the electric vehicle according to the target battery driving pressure level.

In a possible implementation, the device further includes: a display module configured to display the target motor driving condition or the target battery driving pressure level.

In a possible implementation, the device further includes: a prompting module configured to prompt the driver to adjust the driving behavior according to the target motor driving condition or the target battery driving pressure level.

In a possible implementation, the first driving condition determination module 63 includes: a first distance determination sub-module, configured to respectively determine the first distance between the first probability distribution and each second probability distribution. distance, the first distance is the bulldozer distance; the second distance determination sub-module is used to determine the minimum value of the first distance as the second distance; the probability distribution determination sub-module is used to determine the minimum value from the plurality of Determine the target probability distribution corresponding to the second distance from the second probability distributions respectively corresponding to the preset motor driving conditions; the driving condition determination sub-module is used to determine the preset probability distribution corresponding to the target probability distribution. The motor driving condition is determined as the target motor driving condition of the electric vehicle in the current operating cycle.

In a possible implementation, the device further includes: a second probability distribution determination module, configured to respectively determine the third probability distribution corresponding to the instantaneous driving power of each group of reference motors according to the preset driving power interval; A class module configured to cluster the third probability distribution according to the number of preset motor driving conditions and the bulldozer distance between the third probability distributions to obtain multiple clusters and the multiple clusters cluster center, wherein the number of clusters is determined according to the number of preset motor driving conditions; a second driving condition determination module is used to determine the plurality of preset motor driving conditions according to the plurality of clusters. Working conditions; a third probability distribution determination module, configured to determine second probability distributions respectively corresponding to the plurality of preset motor driving working conditions according to cluster centers of the plurality of clusters.

In a possible implementation, the plurality of preset motor driving conditions include low load conditions, general load conditions, higher load conditions and high load conditions; the multiple preset battery driving pressures Levels include low pressure level, medium pressure level, higher pressure level and high pressure level.

Embodiments of the present application provide a controller configured to implement the above method.

An embodiment of the present application provides an electric vehicle, including: a controller and a memory for storing instructions executable by the controller; wherein the controller is configured to implement the above method when executing the instructions.

Embodiments of the present application provide a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a controller, the above method is implemented.

Embodiments of the present application provide a computer program product, including computer instructions, which implement the above method when executed by a controller.

Computer-readable storage media may be tangible devices that can retain and store instructions for use by an instruction execution device. The computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: portable computer disks, hard drives, random access memory (RAM), read only memory (ROM), erasable memory Electrically Programmable Read-Only-Memory (EPROM or Flash Memory), Static Random-Access Memory (SRAM), Portable Compact Disc Read-Only Memory (CD) -ROM), Digital Video Disc (DVD), memory stick, floppy disk, mechanical encoding device, such as a punched card or a raised structure in a groove with instructions stored thereon, and any suitable combination of the above .

Computer-readable program instructions or code described herein may be downloaded from a computer-readable storage medium to various computing/processing devices, or to an external computer or external storage device over a network, such as the Internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage on a computer-readable storage medium in the respective computing/processing device .

The computer program instructions used to perform the operations of this application can be assembly instructions, instruction set architecture (Instruction Set Architecture, ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, status setting data, or one or more Source code or object code written in any combination of programming languages, including object-oriented programming languages—such as Smalltalk, C++, etc., and conventional procedural programming languages—such as the “C” language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server implement. In situations involving remote computers, the remote computer can be connected to the user's computer through any kind of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or it can be connected to an external computer (e.g. Use an Internet service provider to connect via the Internet). In some embodiments, electronic circuits are customized by utilizing state information of computer-readable program instructions, such as programmable logic circuits, field-programmable gate arrays (Field-Programmable Gate Arrays, FPGAs) or programmable logic arrays (Programmable Logic Array (PLA), the electronic circuit can execute computer-readable program instructions to implement various aspects of the present application.

Various aspects of the present application are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, thereby producing a machine that, when executed by the processor of the computer or other programmable data processing apparatus, , resulting in an apparatus that implements the functions/actions specified in one or more blocks in the flowchart and/or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium. These instructions cause the computer, programmable data processing device and/or other equipment to work in a specific manner. Therefore, the computer-readable medium storing the instructions includes An article of manufacture that includes instructions that implement aspects of the functions/acts specified in one or more blocks of the flowcharts and/or block diagrams.

Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other equipment, causing a series of operating steps to be performed on the computer, other programmable data processing apparatus, or other equipment to produce a computer-implemented process , thereby causing instructions executed on a computer, other programmable data processing apparatus, or other equipment to implement the functions/actions specified in one or more blocks in the flowcharts and/or block diagrams.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality and operations of possible implementations of apparatuses, systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions that embody one or more elements for implementing the specified logical function(s). Executable instructions. In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two consecutive blocks may actually execute substantially in parallel, or they may sometimes execute in the reverse order, depending on the functionality involved.

It will also be noted that each block of the block diagram and/or flowchart illustration, and combinations of blocks in the block diagram and/or flowchart illustration, can be implemented by hardware (such as circuits or ASICs) that perform the corresponding function or action. Specific Integrated Circuit), or can be implemented with a combination of hardware and software, such as firmware.

Although the present invention has been described herein in conjunction with various embodiments, those skilled in the art, in practicing the claimed invention, will understand and understand by reviewing the drawings, the disclosure, and the appended claims. Other variations of the disclosed embodiments are implemented. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality. A single processor or other unit may perform several of the functions recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not mean that a combination of these measures cannot be combined to advantageous effects.

The embodiments of the present application have been described above. The above description is illustrative, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope of the illustrated embodiments. The terminology used herein is chosen to best explain the principles, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

A power control method, characterized in that it is applied to electric vehicles, and the method includes:

Obtain multiple instantaneous driving powers of the motor of the electric vehicle during the current operating cycle;

Determine a first probability distribution corresponding to the plurality of instantaneous drive powers according to a preset drive power interval, where the first probability distribution is used to represent the probability that the instantaneous drive power falls in each drive power interval;

According to the first probability distribution and the second probability distribution respectively corresponding to the plurality of preset motor driving conditions, it is determined from the plurality of preset motor driving conditions that the electric vehicle is in the current operating cycle. The target motor driving operating conditions within the motor driving operating conditions, wherein the motor driving operating conditions are related to the load level driven by the motor;

According to the target motor driving conditions, the power of the auxiliary electrical equipment of the electric vehicle is controlled.
The method of claim 1, further comprising:

According to the corresponding relationship between the target motor driving working conditions and the plurality of preset battery driving pressure levels and the plurality of preset motor driving working conditions, the selected battery driving pressure levels are determined from the plurality of preset battery driving pressure levels. The target battery driving pressure level of the electric vehicle in the current operating cycle, wherein the battery driving pressure level is used to represent the impact of the motor driving of the electric vehicle on the discharge rate of the power battery;

The power of the auxiliary electrical equipment of the electric vehicle is controlled according to the target battery driving pressure level.
The method of claim 2, further comprising:

Display the target motor driving condition or the target battery driving pressure level.
The method according to any one of claims 1-3, characterized in that the method further includes:

According to the target motor driving conditions or the target battery driving pressure level, the driver is prompted to adjust the driving behavior.
The method according to claim 1, characterized in that, according to the first probability distribution and the second probability distribution respectively corresponding to the plurality of preset motor driving conditions, the plurality of preset motor driving conditions are selected from the In this situation, the target motor driving conditions of the electric vehicle in the current operating cycle are determined, including:

Determine a first distance between the first probability distribution and each second probability distribution respectively, where the first distance is the bulldozer distance;

Determine the minimum value among the first distances as the second distance;

Determine a target probability distribution corresponding to the second distance from the second probability distribution corresponding to the plurality of preset motor driving conditions;

The preset motor driving conditions corresponding to the target probability distribution are determined as the target motor driving conditions of the electric vehicle in the current operating cycle.
The method according to any one of claims 1-5, characterized in that the method further includes:

According to the preset driving power interval, determine the third probability distribution corresponding to the instantaneous driving power of each group of reference motors;

According to the number of preset motor driving conditions and the bulldozer distance between the third probability distributions, the third probability distributions are clustered to obtain multiple clusters and cluster centers of the multiple clusters, where , the number of clusters is determined according to the number of preset motor driving conditions;

Determine the plurality of preset motor driving conditions according to the plurality of clusters;

According to the cluster centers of the plurality of clusters, second probability distributions respectively corresponding to the plurality of preset motor driving conditions are determined.
The method according to any one of claims 2 to 6, characterized in that the plurality of preset motor driving conditions include low load conditions, general load conditions, higher load conditions and high load conditions. ; The plurality of preset battery driving pressure levels include low pressure level, medium pressure level, higher pressure level and high pressure level.
A power control device, characterized in that it is applied to electric vehicles, and the device includes:

A driving power acquisition module, used to acquire multiple instantaneous driving powers of the motor of the electric vehicle during the current operating cycle;

A first probability distribution determination module, configured to determine a first probability distribution corresponding to the plurality of instantaneous driving powers according to a preset driving power interval, where the first probability distribution is used to represent that the instantaneous driving power falls within each Probability in the driving power range;

A first driving condition determination module is configured to determine from the plurality of preset motor driving conditions according to the first probability distribution and the second probability distribution respectively corresponding to the plurality of preset motor driving conditions. The target motor driving conditions of the electric vehicle during the current operating cycle, wherein the motor driving conditions are related to the load level driven by the motor;

The first control module is used to control the power of auxiliary electrical equipment of the electric vehicle according to the target motor driving conditions.
The device according to claim 8, characterized in that the device further includes:

A battery driving pressure level determination module, configured to determine the target motor driving working conditions and the corresponding relationship between the plurality of preset battery driving pressure levels and the plurality of preset motor driving working conditions. In the battery driving pressure level, the target battery driving pressure level of the electric vehicle in the current operating cycle is determined, wherein the battery driving pressure level is used to represent the discharge rate of the power battery by the motor driving of the electric vehicle. Impact;

The second control module is used to control the power of auxiliary electrical equipment of the electric vehicle according to the target battery driving pressure level.
The device of claim 9, further comprising:

A display module is used to display the target motor driving condition or the target battery driving pressure level.
The device according to any one of claims 8-10, characterized in that the device further includes:

A prompt module is used to prompt the driver to adjust the driving behavior according to the target motor driving conditions or the target battery driving pressure level.
The device according to claim 8, characterized in that the first driving condition determination module includes:

The first distance determination sub-module is used to determine the first distance between the first probability distribution and each second probability distribution respectively, where the first distance is the bulldozer distance;

The second distance determination submodule is used to determine the minimum value of the first distance as the second distance;

A probability distribution determination submodule, configured to determine a target probability distribution corresponding to the second distance from the second probability distribution corresponding to the plurality of preset motor driving conditions;

The driving condition determination sub-module is used to determine the preset motor driving condition corresponding to the target probability distribution as the target motor driving condition of the electric vehicle in the current operating cycle.
The device according to any one of claims 8-12, characterized in that the device further includes:

The second probability distribution determination module is used to determine the third probability distribution corresponding to the instantaneous driving power of each group of reference motors according to the preset driving power interval;

A clustering module, configured to cluster the third probability distribution according to the number of preset motor driving conditions and the bulldozer distance between the third probability distributions to obtain multiple clusters and the plurality of The cluster center of the cluster, wherein the number of clusters is determined according to the number of preset motor driving conditions;

a second driving condition determination module, configured to determine the plurality of preset motor driving conditions according to the plurality of clusters;

A third probability distribution determination module is configured to determine second probability distributions respectively corresponding to the plurality of preset motor driving conditions according to the cluster centers of the plurality of clusters.
The device according to any one of claims 9-13, characterized in that the plurality of preset motor driving conditions include low load conditions, general load conditions, higher load conditions and high load conditions. ; The plurality of preset battery driving pressure levels include low pressure level, medium pressure level, higher pressure level and high pressure level.
A controller, characterized in that the controller is configured to implement the method according to any one of claims 1-7.
An electric vehicle is characterized by including:

controller;

memory for storing instructions executable by the controller;

Wherein, the controller is configured to implement the method according to any one of claims 1-7 when executing the instructions.
A computer-readable storage medium on which computer program instructions are stored, characterized in that when the computer program instructions are executed by a controller, the method described in any one of claims 1-7 is implemented.
A computer program product includes computer instructions, characterized in that when the computer instructions are executed by a controller, the method according to any one of claims 1-7 is implemented.