CN114802189A - Energy consumption control method and device for vehicle, vehicle and storage medium - Google Patents
Energy consumption control method and device for vehicle, vehicle and storage medium Download PDFInfo
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- CN114802189A CN114802189A CN202210246069.7A CN202210246069A CN114802189A CN 114802189 A CN114802189 A CN 114802189A CN 202210246069 A CN202210246069 A CN 202210246069A CN 114802189 A CN114802189 A CN 114802189A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W20/00—Control systems specially adapted for hybrid vehicles
- B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
- B60W20/15—Control strategies specially adapted for achieving a particular effect
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W20/00—Control systems specially adapted for hybrid vehicles
- B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
- B60W20/11—Controlling the power contribution of each of the prime movers to meet required power demand using model predictive control [MPC] strategies, i.e. control methods based on models predicting performance
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W20/00—Control systems specially adapted for hybrid vehicles
- B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
- B60W20/12—Controlling the power contribution of each of the prime movers to meet required power demand using control strategies taking into account route information
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Abstract
The application relates to the technical field of vehicles, in particular to a method and a device for controlling energy consumption of a vehicle, the vehicle and a storage medium, wherein the method comprises the following steps: detecting the actual road condition of the vehicle, and acquiring the current environment data of the environment where the vehicle is located and the current vehicle state; calculating the actual energy consumption of the vehicle according to the actual road condition, the current environmental data, the current vehicle state and the historical energy consumption; and predicting a plurality of future energy consumptions planned to be driven by the vehicle along the current path according to the actual energy consumptions, and generating an optimal driving strategy corresponding to the minimum future energy consumptions. Therefore, the problems that road planning information and driving information of the hybrid electric vehicle cannot be predicted, the switching of working modes cannot be reasonably arranged, the engine does not need to be frequently started and stopped, and the fuel-saving potential cannot be fully exerted in the related technology are solved, the SOC charging and discharging interval of the battery system and the energy-potential energy conversion of the whole vehicle are fully utilized, the whole vehicle fuel-saving rate of the hybrid electric system is improved, and the newly-added cost recovery period of the hybrid electric system is shortened.
Description
Technical Field
The present disclosure relates to the field of vehicle technologies, and in particular, to a method and an apparatus for controlling energy consumption of a vehicle, and a storage medium.
Background
In the related technology, the whole vehicle control strategy of the plug-in hybrid power product is mainly developed based on logic gates, and the whole vehicle controller switches and controls the control mode according to the current state of the assemblies such as an engine, a motor, a battery and a pedal, so that the system robustness is good.
However, because comprehensive judgment cannot be performed according to the characteristics of the road ahead, the current State of the whole vehicle, the environmental State of the whole vehicle and other factors, the road planning information and the driving information of the hybrid electric vehicle cannot be predicted, the switching of the working modes cannot be reasonably arranged, the engine needs to be started and stopped frequently without need, a certain margin is reserved for the power battery at any time to meet the braking requirement and the accelerating requirement, the window of the power battery SOC (State of Charge) is small, the oil saving potential cannot be fully exerted, and the problem needs to be solved urgently.
Disclosure of Invention
The application provides an energy consumption control method and device for a vehicle, the vehicle and a storage medium, and aims to solve the problems that road planning information and driving information of a hybrid electric vehicle cannot be predicted, the switching of working modes cannot be reasonably arranged, an engine has unnecessary frequent starting and stopping, and the fuel-saving potential cannot be fully exerted in the related technology, so that the SOC charging and discharging interval of a battery system and the kinetic energy and potential energy conversion of the whole vehicle are fully utilized, the whole vehicle fuel-saving rate of a hybrid electric system is improved, and the newly-increased cost recovery period of the hybrid electric system is shortened.
An embodiment of a first aspect of the present application provides an energy consumption control method for a vehicle, including the following steps:
detecting the actual road condition of a vehicle, and acquiring the current environment data of the environment where the vehicle is located and the current vehicle state;
calculating the actual energy consumption of the vehicle according to the actual road condition, the current environmental data, the current vehicle state and the historical energy consumption; and
and predicting a plurality of future energy consumptions planned to be driven by the vehicle along the current path according to the actual energy consumption, and generating an optimal driving strategy corresponding to the minimum future energy consumption.
Optionally, the generating an optimal driving strategy corresponding to the minimum future energy consumption includes:
judging whether the energy consumption of the engine is in an optimal efficiency range or not;
if the energy consumption of the engine is in the optimal efficiency range, controlling the speed of the vehicle to be adjusted to a first preset speed after controlling a driving motor of the vehicle to work based on the battery charge state of the vehicle when the vehicle is in an uphill working condition; when the vehicle is in a downhill working condition, controlling the vehicle speed of the vehicle to be adjusted to a second preset vehicle speed, and controlling a driving motor of the vehicle to work based on the battery charge state of the vehicle; when the vehicle is in other working conditions except the uphill working condition and the downhill working condition, controlling a driving motor of the vehicle to work based on the battery charge state of the vehicle, and controlling the vehicle speed of the vehicle to be adjusted to a third preset vehicle speed;
if the energy consumption of the engine is not in the optimal efficiency range, when the vehicle is in an uphill working condition, controlling the rotating speed of the driving motor based on a downshift point, calculating a first average vehicle speed value of each track point in the current motion track of the vehicle by adopting an equivalent consumption method and a reverse algorithm, and controlling the vehicle according to the first average vehicle speed value; when the vehicle is in a downhill working condition, controlling the rotating speed of the driving motor based on the gear-up point, calculating a second average vehicle speed value of each track point in the current motion track of the vehicle by adopting an equivalent consumption method and a reverse algorithm, and controlling the vehicle according to the second average vehicle speed value.
Optionally, the actual road condition includes one or more of navigation electronic map basic data, path planning data, path information data, path altitude data, road pavement data and road flatness, the current environment data includes one or more of weather, temperature, altitude, wind speed and wind direction, and the current vehicle state includes one or more of vehicle driving habit, vehicle load, current tire pressure, vehicle basic state resistance and vehicle driving form resistance.
Optionally, the method further comprises:
recording the actual energy consumption of the current path planning driving;
and updating the energy consumption estimation model by utilizing the actual energy consumption, and estimating a plurality of next future energy consumptions by utilizing the energy consumption estimation model.
Optionally, before detecting an actual road condition of the vehicle and acquiring current environment data of an environment where the vehicle is located and a current vehicle state, the method further includes:
detecting the time or the running distance of the vehicle;
and when the current time meets the updating duration or the driving distance reaches the updating distance, judging that the vehicle meets the energy consumption prediction condition.
Optionally, the method further comprises:
determining whether the remaining energy consumption of the vehicle is greater than the minimum future energy consumption;
and if the residual energy consumption is less than the minimum future energy consumption, generating an energy supplement strategy according to the difference value of the residual energy consumption and the minimum future energy consumption, and controlling the engine and/or the driving motor of the vehicle to work according to the energy supplement strategy.
An embodiment of a second aspect of the present application provides an energy consumption control apparatus for a vehicle, including:
the system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is used for detecting the actual road condition of a vehicle and acquiring the current environment data of the environment where the vehicle is located and the current vehicle state;
the calculation module is used for calculating the actual energy consumption of the vehicle according to the actual road condition, the current environment data, the current vehicle state and the historical energy consumption; and
and the control module is used for predicting a plurality of future energy consumptions planned to be driven by the vehicle along the current path according to the actual energy consumption and generating an optimal driving strategy corresponding to the minimum future energy consumption.
Optionally, the control module is specifically configured to:
judging whether the energy consumption of the engine is in an optimal efficiency range or not;
if the energy consumption of the engine is in the optimal efficiency range, controlling the speed of the vehicle to be adjusted to a first preset speed after controlling a driving motor of the vehicle to work based on the battery charge state of the vehicle when the vehicle is in an uphill working condition; when the vehicle is in a downhill working condition, controlling the vehicle speed of the vehicle to be adjusted to a second preset vehicle speed, and controlling a driving motor of the vehicle to work based on the battery charge state of the vehicle; when the vehicle is in other working conditions except the uphill working condition and the downhill working condition, controlling a driving motor of the vehicle to work based on the battery charge state of the vehicle, and controlling the vehicle speed of the vehicle to be adjusted to a third preset vehicle speed;
if the energy consumption of the engine is not in the optimal efficiency range, when the vehicle is in an uphill working condition, controlling the rotating speed of the driving motor based on a downshift point, calculating a first average vehicle speed value of each track point in the current motion track of the vehicle by adopting an equivalent consumption method and a reverse algorithm, and controlling the vehicle according to the first average vehicle speed value; when the vehicle is in a downhill working condition, controlling the rotating speed of the driving motor based on the gear-up point, calculating a second average vehicle speed value of each track point in the current motion track of the vehicle by adopting an equivalent consumption method and a reverse algorithm, and controlling the vehicle according to the second average vehicle speed value.
Optionally, the actual road condition includes one or more of navigation electronic map basic data, path planning data, path information data, path altitude data, road pavement data and road flatness, the current environment data includes one or more of weather, temperature, altitude, wind speed and wind direction, and the current vehicle state includes one or more of vehicle driving habit, vehicle load, current tire pressure, vehicle basic state resistance and vehicle driving form resistance.
Optionally, the method further comprises:
the recording module is used for recording the actual energy consumption of the current path planning driving;
and the estimation module is used for updating the energy consumption estimation model by utilizing the actual energy consumption and estimating a plurality of next future energy consumptions by utilizing the energy consumption estimation model.
Optionally, before detecting an actual road condition of the vehicle and acquiring current environment data of an environment where the vehicle is located and a current vehicle state, the acquiring module further includes:
a detection unit for detecting a time or a travel distance of a vehicle;
and the judging unit is used for judging that the vehicle meets the energy consumption prediction condition when the time meets the updating duration or the running distance reaches the updating distance.
Optionally, the method further comprises:
a determination module configured to determine whether a remaining energy consumption of the vehicle is greater than the minimum future energy consumption;
and the generating module is used for generating an energy supplement strategy according to the difference value of the residual energy consumption and the minimum future energy consumption and controlling the work of an engine and/or a driving motor of the vehicle according to the energy supplement strategy if the residual energy consumption is less than the minimum future energy consumption.
An embodiment of a third aspect of the present application provides a vehicle, comprising: the energy consumption control system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the energy consumption control method of the vehicle according to the embodiment.
A fourth aspect of the present application provides a computer-readable storage medium, on which a computer program is stored, the program being executed by a processor for implementing the method for controlling energy consumption of a vehicle as described in the above embodiments.
Therefore, the actual energy consumption of the vehicle can be calculated according to the detected actual road condition of the vehicle, the current environmental data of the environment where the vehicle is located, the current vehicle state and the historical energy consumption, a plurality of future energy consumptions of the vehicle which is planned to run along the current path can be predicted according to the actual energy consumption, and the optimal running strategy corresponding to the minimum future energy consumption is generated. Therefore, the problems that road planning information and driving information of the hybrid electric vehicle cannot be predicted, the switching of working modes cannot be reasonably arranged, the engine has unnecessary frequent starting and stopping, and the fuel-saving potential cannot be fully exerted in the related technology are solved, the SOC charging and discharging interval of the battery system and the kinetic energy and potential energy conversion of the whole vehicle are fully utilized, the whole vehicle fuel-saving rate of the hybrid electric vehicle is improved, and the newly-increased cost recovery period of the hybrid electric vehicle is shortened.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow chart of a method for controlling energy consumption of a vehicle according to an embodiment of the present application;
FIG. 2 is a diagram of an exemplary control strategy development architecture for a method of controlling energy consumption of a vehicle according to one embodiment of the present application;
FIG. 3 is a schematic diagram of dynamic programming optimization according to one embodiment of the present application;
FIG. 4 is a flow chart of a vehicle control strategy according to an embodiment of the present application;
FIG. 5 is a block schematic diagram of an energy consumption control apparatus of a vehicle according to an embodiment of the present application;
FIG. 6 is a schematic structural diagram of a vehicle according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
An energy consumption control method and apparatus for a vehicle, and a storage medium according to embodiments of the present application are described below with reference to the accompanying drawings. In order to solve the problems that road planning information and driving information of a hybrid electric vehicle cannot be predicted, switching of working modes cannot be reasonably arranged, frequent starting and stopping of an engine are not needed, and the potential of oil saving cannot be fully exerted, the energy consumption control method of the vehicle is provided. Therefore, the problems that road planning information and driving information of the hybrid electric vehicle cannot be predicted, the switching of working modes cannot be reasonably arranged, the engine does not need to be frequently started and stopped, and the fuel-saving potential cannot be fully exerted in the related technology are solved, the SOC charging and discharging interval of the battery system and the energy-potential energy conversion of the whole vehicle are fully utilized, the whole vehicle fuel-saving rate of the hybrid electric system is improved, and the newly-added cost recovery period of the hybrid electric system is shortened.
Specifically, fig. 1 is a schematic flowchart of an energy consumption control method of a vehicle according to an embodiment of the present application.
As shown in fig. 1, the energy consumption control method of the vehicle includes the steps of:
in step S101, an actual road condition of the vehicle is detected, and current environmental data of an environment where the vehicle is located and a current vehicle state are acquired.
Optionally, in some embodiments, the actual road condition comprises one or more of navigation electronic map base data, path planning data, path information data, path altitude data, road surface data and road flatness, the current environmental data comprises one or more of weather, temperature, altitude, wind speed and wind direction, and the current full vehicle state comprises one or more of vehicle driving habits, vehicle load, current tire air pressure, vehicle base state resistance and vehicle driving pattern resistance.
Specifically, as shown in fig. 2, the embodiment of the present application may detect an actual road condition of a vehicle, so as to record and analyze current navigation electronic map basic data, path planning data, path information data, path altitude data, road surface condition, road flatness condition, and the like; the current state of the environmental conditions can be recorded and analyzed by acquiring current environmental data of the environment in which the vehicle is located, for example: weather (rain, snow, sunny day, etc.), temperature, altitude, wind speed, wind direction, etc.; the current vehicle state can be recorded and analyzed by acquiring the current vehicle state, for example: vehicle driving habits, vehicle load, current tire pressure, vehicle base state resistance, vehicle driving pattern resistance, and the like.
Optionally, in some embodiments, before detecting an actual road condition of the vehicle and acquiring current environment data of an environment where the vehicle is located and a current vehicle state, the method further includes: detecting the time or the running distance of the vehicle; and when the current time meets the updating duration or the driving distance reaches the updating distance, judging that the vehicle meets the energy consumption prediction condition.
The updating duration can be a duration preset by a user, can be a duration obtained through limited experiments, and can also be a duration obtained through limited computer simulation; the update distance may be a distance preset by a user, may be a distance obtained through a limited number of experiments, or may be a distance obtained through a limited number of computer simulations, which is not specifically limited herein.
Specifically, the update duration may be 10 minutes, and the update distance may be 5km, that is, if the time at which the vehicle is located satisfies 10 minutes, or the vehicle travel distance reaches 5km, it is determined that the vehicle satisfies the energy consumption prediction condition, and the vehicle energy consumption may be predicted.
In step S102, the actual energy consumption of the vehicle is calculated according to the actual road condition, the current environmental data, the current vehicle state and the historical energy consumption.
In step S103, a plurality of future energy consumptions for the vehicle to plan to travel along the current route are predicted according to the actual energy consumption, and an optimal travel strategy corresponding to the minimum future energy consumption is generated.
Specifically, as shown in fig. 2, in the embodiment of the application, real-time oil consumption analysis can be performed by combining actual road conditions, current environmental data, and current vehicle state with historical energy consumption, the oil consumption predictions after analysis are combined with a path planning function provided by a high-precision map, and the oil consumption predictions and the path planning information of the two are combined, so that the vehicle controller can give oil consumption predictions of different routes according to the information, and execute drive mode selection, torque distribution and vehicle speed planning after selecting a corresponding route. Therefore, in the embodiment of the application, the navigation electronic map basic data, the path planning, the map matching and other modules of the high-precision map of the GIS (Geographic Information System) are fused through the GPS (Global Positioning System) System Positioning and the GIS (Geographic Information System), and the Information of the current vehicle front map basic data, the path planning data, the path Information data, the path altitude data, the road pavement condition, the road flatness and the like is extracted; recording and analyzing the current environmental condition state through real-time environmental analysis; and recording and analyzing the current vehicle state through the vehicle controller. Providing data for real-time oil consumption analysis of road conditions, environmental conditions and vehicle conditions in combination with past oil consumption records to a vehicle controller, and then carrying out reasonable path planning based on the information; and by adopting a dynamic programming algorithm, the calculated amount of the dynamic programming algorithm is reduced by reducing the strategies of path track aggregation classification, variable sampling point step length, reachable area of the power assembly and the like, the actual vehicle application calculation time is less than 0.2 second, and the actual engineering application requirements are met.
Optionally, in some embodiments, generating an optimal driving strategy for minimal future energy consumption comprises: judging whether the energy consumption of the engine is in an optimal efficiency range or not; if the energy consumption of the engine is in the optimal efficiency range, controlling the speed of the vehicle to be adjusted to a first preset speed after controlling a driving motor of the vehicle to work based on the battery charge state of the vehicle when the vehicle is in an uphill working condition; when the vehicle is in a downhill working condition, controlling the vehicle speed of the vehicle to be adjusted to a second preset vehicle speed, and controlling a driving motor of the vehicle to work based on the battery charge state of the vehicle; when the vehicle is in other working conditions except an uphill working condition and a downhill working condition, controlling the vehicle speed to be adjusted to a third preset vehicle speed after controlling a driving motor of the vehicle to work based on the battery charge state of the vehicle; if the energy consumption of the engine is not in the optimal efficiency range, when the vehicle is in an uphill working condition, controlling the rotating speed of the driving motor based on the downshift point, calculating a first average vehicle speed value of each track point in the current motion track of the vehicle by adopting an equivalent consumption method and a reverse algorithm, and controlling the vehicle according to the first average vehicle speed value; when the vehicle is in a downhill working condition, the rotating speed of the driving motor is controlled based on the gear-up point, an equivalent consumption method and a reverse algorithm are adopted to calculate a second average vehicle speed value of each track point in the current motion track of the vehicle, and the vehicle is controlled according to the second average vehicle speed value.
The first preset vehicle speed, the second preset vehicle speed and the third preset vehicle speed may be vehicle speeds preset by a user, vehicle speeds obtained through limited experiments, vehicle speeds obtained through limited computer simulation, and the vehicle speeds are not specifically limited herein; the method for calculating the average vehicle speed value of each track point in the current motion track of the vehicle through the equivalent fuel consumption method and the inverse algorithm can adopt the method in the related technology, and is not described in detail herein in order to avoid redundancy.
Specifically, the motion trajectory can be divided based on the dividing lines, the trajectory between the current sampling point and the first positive and negative alpha change point is processed, and the updating is performed continuously in a rolling manner; if alpha is more than 1%, the road is a long uphill road; if alpha is less than or equal to 1 percent and more than or equal to-1 percent, the road is a horizontal road; if the alpha is less than or equal to-1%, the road is a long downhill road; and (3) different control strategies are executed by combining different slopes and the requirements that the vehicle speed change can or cannot cover the relative elevation change, wherein the specific control strategies are shown in the table 1.
TABLE 1
Wherein, the coverable relative elevation change can be understood as that the current energy consumption of the engine is in an optimal efficiency interval.
Optionally, in some embodiments, the above method for controlling energy consumption of a vehicle further includes: recording the actual energy consumption of the current path planning driving; and updating the energy consumption estimation model by utilizing the actual energy consumption, and estimating a plurality of next future energy consumptions by utilizing the energy consumption estimation model.
Specifically, the embodiment of the application can give the fuel consumption prediction of each driving state through information fusion, driving state prediction and current driving habit prediction of a driver on a future road section, and meanwhile, the current driving data can be recorded as a sample of fuel consumption prediction of the next stage, so that the accuracy of fuel consumption prediction and the rolling update of information are realized.
Specifically, in order to improve the fuel saving capability of the hybrid system of the vehicle, the system state variables are defined as the current vehicle speed, the gear and the battery SOC, the system control variables are defined as the target vehicle speed and the target gear from the current sampling point to the next sampling point, and in order to reduce the calculation amount, the vehicle speed change is simplified into a finite grid, and the speed interval Δ v is 0.3km/h, as shown in fig. 3.
According to the embodiment of the application, the reachable region can be compressed according to the set cruising speed and the state of the hybrid power assembly, the calculation amount of the DP algorithm can be effectively reduced, and the calculation time is compressed. Under the condition constraint, calculating i to 1 section, namely starting from the k point to the 1 st point, calculating the optimal solution of each sampling point, and continuously rolling and updating to obtain the global reference gear and the vehicle speed in the foreseeable running mode, thereby realizing the real-time predictive control of the vehicle.
Therefore, the fuel consumption condition under the condition of passing road conditions can be recorded and analyzed, and the fuel consumption of the vehicle is analyzed by combining the information. Meanwhile, aiming at the driving oil consumption logic of the vehicle, the optimal oil consumption planning and control are carried out on the future road section by combining the currently obtained path planning information, and meanwhile, the basic past oil consumption information can be updated in real time along with the advancing of the vehicle, so that the accuracy and the timeliness of data are ensured.
Moreover, the characteristics of the engine or the motor can be fully utilized to improve the efficiency by predicting the road information in front of the vehicle; by predicting the environmental information of the road in front of the vehicle, the influence of external environmental conditions on the driving of the whole vehicle can be fully evaluated, and the accuracy of prediction is improved; by predicting the vehicle information, the influence of the current state of the whole vehicle on the driving can be fully evaluated.
Optionally, in some embodiments, the above method for controlling energy consumption of a vehicle further includes: judging whether the residual energy consumption of the vehicle is greater than the minimum future energy consumption; and if the residual energy consumption is less than the minimum future energy consumption, generating an energy supplement strategy according to the difference value of the residual energy consumption and the minimum future energy consumption, and controlling the engine and/or the driving motor of the vehicle to work according to the energy supplement strategy.
Specifically, the road information can be classified according to the traffic conditions, wherein the green road sections represent unobstructed roads, the yellow road sections represent slow running, the red road sections represent road congestion, and the deep red road sections represent extreme congestion. The main working main bodies of the green road section are engines, the main working main bodies of the yellow road section are engines and motors, and the main working main bodies of the red road section and the dark red road section are motors.
According to the embodiment of the application, the mode switching sequence based on the passing road section can be drawn up according to the passing road section condition, the driving distance, the future energy consumption, the time and other parameters, whether the preset mode meets the requirements or not is estimated, if the preset mode does not meet the requirements, the energy supplement strategy based on the system mode at that time is carried out according to the capacity of the whole vehicle, and the energy supplement strategy is shown in a table 2.
TABLE 2
Therefore, the energy management control strategy based on the actual road condition, the current environmental data, the current vehicle state and the historical energy consumption predicts the road and environmental information ahead in advance, actively charges and discharges the power battery through the planning of the route and the prediction of the form strategy, charges in advance at a low speed or a congested road section, forbids or delays the intervention of an engine in the congested road section or in front of a traffic light, and discharges in front of the chargeable road section; in the running process, the control strategy is updated in real time according to real-time road condition information, environmental information and the current measured and calculated oil consumption, the condition that the electricity cannot be charged when the electricity is full or the electricity is not needed is reduced, and a margin is reserved for fully and reasonably switching modes and reducing the oil consumption.
In order to enable those skilled in the art to further understand the energy consumption control method for a vehicle according to the embodiment of the present application, a detailed description is provided below for a vehicle control strategy flow according to a specific embodiment.
Specifically, the embodiment of the application can enter a foreseeable driving mode by triggering the intelligent switch and the navigation planning, after the real-time information is imported, a motion track is generated according to the current position and the destination information, and the environmental information on the motion track is: the method comprises the steps of carrying out information fusion on road environment (road surface information, gradient information and the like), weather environment (temperature, wind speed, wind direction, humidity and the like) and the like to obtain a basic data model, then collecting vehicle information (a model under the vehicle loading, axle load distribution and vehicle basic driving state) through a vehicle controller to analyze to obtain a vehicle information model, and obtaining estimated driving models of different paths after fusing navigation information, environment information and vehicle information of a whole road section. Namely, the navigation driving model is provided by navigation software: and the estimated distance, the vehicle speed, the passing time, the signal lamp and other high-precision map data and the integrated driving data are formed.
The method comprises the steps that a historical targeting method is adopted for predicting fuel consumption in a self-adaptive equivalent fuel consumption minimum strategy, namely a vehicle controller fuses different road information, environment information and vehicle information and then generates a predicted fuel consumption analysis model by combining different vehicle driving states and driving habits of a driver, the prediction of the fuel consumption of each driving state is given through information fusion, driving state prediction and current driving habit prediction of the driver on a future road section, meanwhile, the current driving data can be recorded as a sample of the fuel consumption prediction of the next stage, and the steps are repeated so as to achieve accuracy of the fuel consumption prediction and rolling updating of the information.
And finally, estimating the mode oil consumption by adopting a self-adaptive equivalent fuel consumption minimum strategy according to a driving mode suggestion given by the estimated driving state, determining the next working mode and the working time length through a dynamic programming algorithm and continuously updating in a rolling manner because the current vehicle state and the road condition information state are known, wherein the flow is as follows:
a. and (3) mode activation: the instrument is provided with an energy management enhancement switch, route planning and driving mode setting can be carried out through vehicle-mounted equipment or external equipment, and the mode selection is usually avoided from being dispersed as much as possible by combining the battery electric quantity.
b. And (3) classifying motion tracks: after initialization activation is completed, mode planning is developed based on real-time road conditions and real-time oil consumption analysis, the assembly capacity and the calculation period requirements are considered, the driving mode is updated in real time according to real-time road condition feedback, the oil consumption analysis interval is usually 5km or 10min, latest oil consumption analysis is provided according to the last analysis interval, and part of useless analysis data, which are in a static state immediately after the automobile is started and is started, is discarded.
The method comprises the steps that the software measurement accuracy and the road condition complexity degree are considered, when the expected driving condition is inconsistent with the actual driving condition, the path and the oil consumption plan are updated in a rolling mode, when the path is inconsistent for a long time or the path cannot be inquired or other uncontrollable conditions occur, the current working mode is cancelled immediately, and the basic logic gate working mode is changed.
c. Control strategy implementation, and
d. real-time road condition updating and real-time oil consumption updating.
The vehicle control strategy is shown in fig. 4, and the vehicle control strategy comprises the following steps:
and S401, triggering a foreseeable running function switch.
S402, the controller initializes that the curvature is smaller than a certain threshold value.
And S403, aggregating the 60 sampling points according to the positive and negative change conditions of alpha, changing for i times, and aggregating for i +1 times.
The motion trajectory can be divided based on the dividing lines, the trajectory between the current sampling point and the first positive and negative alpha change point is processed, and the updating is continued in a rolling mode.
S404, if the alpha is more than 1%, determining that the road is a long uphill road; if alpha is less than or equal to 1 percent and more than or equal to-1 percent, the road is a horizontal road; if alpha is less than or equal to-1%, the road is a long downhill road.
And S405, different control strategies are executed by combining different slopes and the requirements that the vehicle speed change can or cannot cover the relative elevation change.
According to the energy consumption control method for the vehicle, the actual energy consumption of the vehicle can be calculated according to the detected actual road condition of the vehicle, the current environment data of the environment where the vehicle is located, the current vehicle state and the historical energy consumption, a plurality of future energy consumptions of the vehicle planned to run along the current path can be predicted according to the actual energy consumption, and the optimal running strategy corresponding to the minimum future energy consumption is generated. Therefore, the problems that road planning information and driving information of the hybrid electric vehicle cannot be predicted, the switching of working modes cannot be reasonably arranged, the engine does not need to be frequently started and stopped, and the fuel-saving potential cannot be fully exerted in the related technology are solved, the SOC charging and discharging interval of the battery system and the energy-potential energy conversion of the whole vehicle are fully utilized, the whole vehicle fuel-saving rate of the hybrid electric system is improved, and the newly-added cost recovery period of the hybrid electric system is shortened.
Next, an energy consumption control apparatus of a vehicle according to an embodiment of the present application will be described with reference to the drawings.
Fig. 5 is a block diagram schematically illustrating an energy consumption control apparatus of a vehicle according to an embodiment of the present application.
As shown in fig. 5, the energy consumption control device 10 of the vehicle includes: an acquisition module 100, a calculation module 200 and a control module 300.
The acquisition module 100 is configured to detect an actual road condition of a vehicle, and acquire current environment data of an environment where the vehicle is located and a current vehicle state;
the calculation module 200 is used for calculating the actual energy consumption of the vehicle according to the actual road condition, the current environmental data, the current vehicle state and the historical energy consumption; and
the control module 300 is configured to predict a plurality of future energy consumptions for the vehicle to travel along the current route according to the actual energy consumption, and generate an optimal travel strategy corresponding to the minimum future energy consumption.
Optionally, the control module 300 is specifically configured to:
judging whether the energy consumption of the engine is in an optimal efficiency range or not;
if the energy consumption of the engine is in the optimal efficiency range, controlling the speed of the vehicle to be adjusted to a first preset speed after controlling a driving motor of the vehicle to work based on the battery charge state of the vehicle when the vehicle is in an uphill working condition; when the vehicle is in a downhill working condition, controlling the vehicle speed of the vehicle to be adjusted to a second preset vehicle speed, and controlling a driving motor of the vehicle to work based on the battery charge state of the vehicle; when the vehicle is in other working conditions except an uphill working condition and a downhill working condition, controlling the vehicle speed to be adjusted to a third preset vehicle speed after controlling a driving motor of the vehicle to work based on the battery charge state of the vehicle;
if the energy consumption of the engine is not in the optimal efficiency range, when the vehicle is in an uphill working condition, controlling the rotating speed of the driving motor based on the downshift point, calculating a first average vehicle speed value of each track point in the current motion track of the vehicle by adopting an equivalent consumption method and a reverse algorithm, and controlling the vehicle according to the first average vehicle speed value; when the vehicle is in a downhill working condition, the rotating speed of the driving motor is controlled based on the gear-up point, an equivalent consumption method and a reverse algorithm are adopted to calculate a second average vehicle speed value of each track point in the current motion track of the vehicle, and the vehicle is controlled according to the second average vehicle speed value.
Optionally, in some embodiments, the actual road condition includes one or more of navigation electronic map basic data, path planning data, path information data, path altitude data, road pavement data and road flatness, the current environmental data includes one or more of weather, temperature, altitude, wind speed and wind direction, and the current vehicle state includes one or more of vehicle driving habits, vehicle load, current tire pressure, vehicle basic state resistance and vehicle driving shape resistance.
Optionally, in some embodiments, the energy consumption control device 10 of the vehicle further includes:
the recording module is used for recording the actual energy consumption of the current path planning driving;
and the estimation module is used for updating the energy consumption estimation model by utilizing the actual energy consumption and estimating a plurality of next future energy consumptions by utilizing the energy consumption estimation model.
Optionally, in some embodiments, before detecting an actual road condition of the vehicle and acquiring current environment data of an environment where the vehicle is located and a current vehicle state, the acquiring module 100 further includes:
a detection unit for detecting a time or a travel distance of a vehicle;
and the judging unit is used for judging that the vehicle meets the energy consumption prediction condition when the current time meets the updating time length or the driving distance reaches the updating distance.
Optionally, in some embodiments, the energy consumption control device 10 of the vehicle further includes:
the judging module is used for judging whether the residual energy consumption of the vehicle is larger than the minimum future energy consumption or not;
and the generating module is used for generating an energy supplement strategy according to the difference value of the residual energy consumption and the minimum future energy consumption and controlling the engine and/or the driving motor of the vehicle to work according to the energy supplement strategy if the residual energy consumption is less than the minimum future energy consumption.
It should be noted that the foregoing explanation of the embodiment of the energy consumption control method for a vehicle also applies to the energy consumption control device for a vehicle in this embodiment, and details thereof are omitted here.
According to the energy consumption control device for the vehicle, which is provided by the embodiment of the application, the actual energy consumption of the vehicle can be calculated according to the actual road condition of the detected vehicle, the current environmental data of the environment where the vehicle is located, the current vehicle state and the historical energy consumption, a plurality of future energy consumptions of the vehicle planned to run along the current path can be predicted according to the actual energy consumption, and the optimal running strategy corresponding to the minimum future energy consumption is generated. Therefore, the problems that road planning information and driving information of the hybrid electric vehicle cannot be predicted, the switching of working modes cannot be reasonably arranged, the engine does not need to be frequently started and stopped, and the fuel-saving potential cannot be fully exerted in the related technology are solved, the SOC charging and discharging interval of the battery system and the energy-potential energy conversion of the whole vehicle are fully utilized, the whole vehicle fuel-saving rate of the hybrid electric system is improved, and the newly-added cost recovery period of the hybrid electric system is shortened.
Fig. 6 is a schematic structural diagram of a vehicle according to an embodiment of the present application. The electronic device may include:
a memory 601, a processor 602, and a computer program stored on the memory 601 and executable on the processor 602.
The processor 602, when executing the program, implements the energy consumption control method of the vehicle provided in the above-described embodiment.
Further, the vehicle further includes:
a communication interface 603 for communication between the memory 601 and the processor 602.
The memory 601 is used for storing computer programs that can be run on the processor 602.
If the memory 601, the processor 602 and the communication interface 603 are implemented independently, the communication interface 603, the memory 601 and the processor 602 may be connected to each other through a bus and perform communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 6, but this is not intended to represent only one bus or type of bus.
Optionally, in a specific implementation, if the memory 601, the processor 602, and the communication interface 603 are integrated on a chip, the memory 601, the processor 602, and the communication interface 603 may complete mutual communication through an internal interface.
The processor 602 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement embodiments of the present Application.
The present embodiment also provides a computer-readable storage medium having stored thereon a computer program characterized in that the program, when executed by a processor, implements the energy consumption control method of a vehicle as above.
In the description of the present specification, reference to the description of "one embodiment," "some embodiments," "an example," "a specific example," or "some examples" or the like means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "N" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more N executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of implementing the embodiments of the present application.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
Claims (10)
1. A method of controlling energy consumption of a vehicle, characterized by comprising the steps of:
detecting the actual road condition of a vehicle, and acquiring the current environment data of the environment where the vehicle is located and the current vehicle state;
calculating the actual energy consumption of the vehicle according to the actual road condition, the current environmental data, the current vehicle state and the historical energy consumption; and
and predicting a plurality of future energy consumptions planned to be driven by the vehicle along the current path according to the actual energy consumption, and generating an optimal driving strategy corresponding to the minimum future energy consumption.
2. The method of claim 1, wherein generating the optimal driving strategy for the minimum future energy consumption comprises:
judging whether the energy consumption of the engine is in an optimal efficiency range or not;
if the energy consumption of the engine is in the optimal efficiency range, controlling the speed of the vehicle to be adjusted to a first preset speed after controlling a driving motor of the vehicle to work based on the battery charge state of the vehicle when the vehicle is in an uphill working condition; when the vehicle is in a downhill working condition, controlling the vehicle speed of the vehicle to be adjusted to a second preset vehicle speed, and controlling a driving motor of the vehicle to work based on the battery charge state of the vehicle; when the vehicle is in other working conditions except the uphill working condition and the downhill working condition, controlling a driving motor of the vehicle to work based on the battery charge state of the vehicle, and controlling the vehicle speed of the vehicle to be adjusted to a third preset vehicle speed;
if the energy consumption of the engine is not in the optimal efficiency range, when the vehicle is in an uphill working condition, controlling the rotating speed of the driving motor based on a downshift point, calculating a first average vehicle speed value of each track point in the current motion track of the vehicle by adopting an equivalent consumption method and a reverse algorithm, and controlling the vehicle according to the first average vehicle speed value; when the vehicle is in a downhill working condition, controlling the rotating speed of the driving motor based on the gear-up point, calculating a second average vehicle speed value of each track point in the current motion track of the vehicle by adopting an equivalent consumption method and a reverse algorithm, and controlling the vehicle according to the second average vehicle speed value.
3. The method of claim 1, wherein the actual road conditions comprise one or more of navigation electronic map base data, path planning data, path information data, path elevation data, road pavement data, and road flatness, the current environmental data comprises one or more of weather, temperature, elevation, wind speed, and wind direction, and the current overall vehicle state comprises one or more of vehicle driving habits, vehicle load, current tire air pressure, vehicle base state resistance, and vehicle driving profile resistance.
4. The method of claim 1, further comprising:
recording the actual energy consumption of the current path planning driving;
and updating the energy consumption estimation model by utilizing the actual energy consumption, and estimating a plurality of next future energy consumptions by utilizing the energy consumption estimation model.
5. The method of claim 1, before detecting the actual road condition of the vehicle and obtaining the current environmental data of the environment where the vehicle is located and the current vehicle state, further comprising:
detecting the time or the running distance of the vehicle;
and when the current time meets the updating duration or the driving distance reaches the updating distance, judging that the vehicle meets the energy consumption prediction condition.
6. The method of any one of claims 1-5, further comprising:
determining whether the remaining energy consumption of the vehicle is greater than the minimum future energy consumption;
and if the residual energy consumption is less than the minimum future energy consumption, generating an energy supplement strategy according to the difference value between the residual energy consumption and the minimum future energy consumption, and controlling the engine and/or the driving motor of the vehicle to work according to the energy supplement strategy, wherein the energy supplement strategy comprises a mapping relation between the actual road condition and the engine and/or the driving motor of the vehicle.
7. An energy consumption control apparatus of a vehicle, characterized by comprising:
the system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is used for detecting the actual road condition of a vehicle and acquiring the current environment data of the environment where the vehicle is located and the current vehicle state;
the calculation module is used for calculating the actual energy consumption of the vehicle according to the actual road condition, the current environment data, the current vehicle state and the historical energy consumption; and
and the control module is used for predicting a plurality of future energy consumptions planned to be driven by the vehicle along the current path according to the actual energy consumption and generating an optimal driving strategy corresponding to the minimum future energy consumption.
8. The apparatus of claim 7, wherein the actual road conditions comprise one or more of navigation electronic map base data, path planning data, path information data, path elevation data, road pavement data, and road flatness, the current environmental data comprise one or more of weather, temperature, elevation, wind speed, and wind direction, and the current overall vehicle state comprises one or more of vehicle driving habits, vehicle load, current tire air pressure, vehicle base state resistance, and vehicle driving morphology resistance.
9. A vehicle, characterized by comprising: memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the energy consumption control method of a vehicle according to any one of claims 1 to 5.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the program is executed by a processor for implementing the method for energy consumption control of a vehicle according to any of claims 1-5.
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| CN202210246069.7A CN114802189A (en) | 2022-03-14 | 2022-03-14 | Energy consumption control method and device for vehicle, vehicle and storage medium |
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| CN202210246069.7A CN114802189A (en) | 2022-03-14 | 2022-03-14 | Energy consumption control method and device for vehicle, vehicle and storage medium |
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| CN116512936A (en) * | 2023-06-14 | 2023-08-01 | 深圳深蕾科技股份有限公司 | New energy automobile driver-based running power control method |
| CN116729356A (en) * | 2023-06-02 | 2023-09-12 | 深圳市哲思特科技有限公司 | New energy automobile control system and method based on Internet of things technology |
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| CN116737845A (en) * | 2023-05-24 | 2023-09-12 | 瑞修得信息科技(无锡)有限公司 | Economic vehicle speed analysis method and system |
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| CN116946143A (en) * | 2023-07-12 | 2023-10-27 | 广州汽车集团股份有限公司 | Energy consumption calculation method applied to hybrid electric vehicle |
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| CN118659641A (en) * | 2024-08-19 | 2024-09-17 | 新誉集团有限公司 | Energy-saving train auxiliary converter control method, device and storage medium |
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