CN112904219B - Big data-based power battery health state prediction method - Google Patents
Big data-based power battery health state prediction method Download PDFInfo
- Publication number
- CN112904219B CN112904219B CN202110376068.XA CN202110376068A CN112904219B CN 112904219 B CN112904219 B CN 112904219B CN 202110376068 A CN202110376068 A CN 202110376068A CN 112904219 B CN112904219 B CN 112904219B
- Authority
- CN
- China
- Prior art keywords
- battery
- data
- value
- driving condition
- day
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/3644—Constructional arrangements
- G01R31/3648—Constructional arrangements comprising digital calculation means, e.g. for performing an algorithm
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Tests Of Electric Status Of Batteries (AREA)
- Secondary Cells (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention discloses a method for predicting the health state and the residual life of a power battery based on big data, which comprises the following steps: 1, acquiring a large amount of real-time running condition data from an electric vehicle, and cleaning and filling the data; 2, extracting four working condition characteristic values of temperature, speed, current and mileage value by processing discharge data to express increase of equivalent cycle times; 3, processing the charging data to obtain an accurate average capacity curve of the battery; 4, obtaining the relation between the SOH and the equivalent cycle times based on a battery charge-discharge characteristic experiment, and establishing a battery health state evaluation model based on the driving condition-charging calculation; and 5, constructing an integrated neural network machine learning model by taking the characteristic working condition as input and the difference of the equivalent cycle times as output, thereby realizing accurate estimation and prediction of the SOH of the battery.
Description
Technical Field
The invention is applied to the field of electric automobiles, in particular to a big data-based method for predicting the health state of a power battery, which is suitable for accurately estimating the health state of a battery automobile.
Background
In recent years, with the rapid development of lithium ion battery technology, the electric automobile industry gradually enters a new stage. State Of Health (SOH) estimation Of a Battery plays a crucial role in predicting the driving mileage and life Of an electric vehicle as one Of key technologies in a Battery Management System (BMS). However, since the battery is a highly non-linear electrochemical system, the identification and estimation of its internal state is still a huge challenge.
Since SOH cannot be directly measured, a large number of SOH estimation methods are proposed to accurately measure SOH of a battery. The most common are model-based estimation methods and big-data based estimation methods. The SOH estimation is carried out by establishing an equivalent circuit to simulate the internal working principle of the battery and combining corresponding algorithms such as particle filtering, kalman filtering, sliding-mode observers and the like based on the model estimation method, but the method excessively depends on the model precision and the algorithm design is complex.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a method for predicting the state of health of a power battery based on big data, so that the problems of modeling and parameter identification in a model-based estimation method can be avoided, and the accurate estimation and prediction of the SOH of the battery can be realized.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention relates to a big data-based method for predicting the health state of a power battery, which is characterized by comprising the following steps of:
the method comprises the following steps: gather the real-time operating mode data that traveles on the electric motor car, include: the method comprises the following steps of (1) carrying out vehicle speed, accumulated mileage, voltage data, current data, battery state of charge data and temperature data on the electric vehicle;
step two, data preprocessing;
step 2.1, respectively cleaning the real-time running condition data, and removing points with larger errors in the data to obtain an effective running condition data set;
2.2, establishing a temperature model based on the real-time driving condition data of the electric automobile, wherein the temperature model is used for filling the temperature data in the effective driving condition data set so as to obtain uniformly distributed temperature data;
step 2.3, fitting a change curve of the accumulated mileage in the effective driving condition data set;
step three, calculating the battery capacity in the charging process;
step 3.1, calculating to obtain average capacity data of the battery in a single charge state by using the current data and the charge state data of the battery after the initial data cleaning;
3.2, taking temperature data and charging current data as input, taking the average capacity of the battery in the single charge state as output, constructing a random forest regression model, and substituting standard temperature and standard current into the random forest regression model for calculation to obtain a standard value;
step 3.3, inputting the uniformly distributed temperature data and the current data in the effective driving condition data set into the random forest regression model for calculation to obtain an original battery average capacity value, making a difference with the standard value, and adding the obtained difference serving as a gain to the battery average capacity data to obtain regression-processed battery average capacity data;
step 3.4, drawing the average capacity data of the battery subjected to the regression processing of the random forest model into a scatter diagram;
step 3.5, performing cluster fitting on the scatter diagram to obtain a charging capacity curve;
step four, establishing a battery health state evaluation model based on driving condition-charging calculation;
step 4.1, calculating the battery state of health (SOH) of the battery on the t day by using the charging capacity curve and combining the formula (1) t ：
In the formula (1), cap t The battery capacity at day t, cap r Is the rated capacity of the battery;
step 4.2, the battery state of health SOH of the battery on the t day t Converting the equivalent cycle number value into an equivalent cycle number value of the battery, and calculating the difference value between the equivalent cycle number value and the t-1 day equivalent cycle number value to measure the service life standard of the battery consumed by the automobile in one day;
4.3, performing characteristic extraction on the discharge data in the effective running condition data set to obtain an average speed, an equivalent running mileage value, an average temperature and an average current value, and using the average speed, the equivalent running mileage value, the average temperature and the average current value as running condition characteristics of the electric automobile for describing the working condition of the discharge process;
establishing and training a machine learning model;
taking the driving condition characteristics as the input of the machine learning model, taking the difference value of equivalent cycle times as the output of the machine learning model, and taking the effective driving condition data set as a training set to be brought into the machine learning model, thereby obtaining the trained machine learning model;
step six, outputting a prediction result;
and performing characteristic random sampling processing on the effective driving condition data set to obtain sampled driving condition data, inputting the sampled driving condition data into a trained machine learning model for prediction calculation to obtain a prediction result of equivalent cycle times, and obtaining a final predicted battery health state according to a mapping relation between the equivalent cycle times and the battery health state.
The method for predicting the health state of the power battery based on the big data is also characterized in that: the conversion steps of equivalent cycle times in the step 4.2 are as follows:
step 4.2.1, obtaining a relation curve of the equivalent cycle number of the battery and the SOH of the battery based on a battery charging and discharging characteristic test, and establishing a relation shown as a formula (2) according to the relation curve:
in the formula (2), a 1 、b 1 Is a set attenuation factor of the electrode material, a 2 、b 2 A set attenuation factor for the electrolyte material; x represents the equivalent cycle number of the battery;
and 4.2.2, performing parameter identification on the four attenuation factors in the formula (1) by using a genetic algorithm to obtain values of the four attenuation factors, so as to obtain a final mapping relation between the equivalent cycle number and the SOH (state of health) of the battery.
The feature random sampling processing of the step six comprises the following steps:
6.1, respectively calculating the running condition characteristics of the electric vehicle in the effective running condition data set every day and the maximum value and the minimum value of each running condition characteristic in one day;
step 6.2, determining sampling time, and if at least one piece of time data corresponding to the sampling time exists in the effective running condition data set, randomly generating a number from the maximum value and the minimum value of the current day as a running condition characteristic value of the current day; if the effective running condition data set does not have data corresponding to the sampling time, taking the day closest to the sampling time to execute random number generation operation; if only one piece of time data corresponding to the sampling time exists in the effective driving condition data set, taking the corresponding data as a driving condition characteristic value;
and 6.3, repeatedly executing the step 6.2 to obtain the sampled running condition data.
Compared with the prior art, the invention has the beneficial effects that:
1. the method provided by the invention overcomes the problem that the battery state is difficult to estimate, establishes a battery health state evaluation model based on driving condition-charging calculation by using big data of real-time operation of the electric vehicle and selecting proper working condition characteristics, and digs out the change rule of the battery health state by combining a machine learning algorithm, so that the SOH value of the battery can be accurately estimated and predicted, and the method is high in precision, strong in robustness and easy to implement.
2. The relation between the equivalent cycle times and the SOH is established through a battery charging and discharging experiment, the SOH of the battery is indirectly estimated and predicted, and the method is visual, clear and good in accuracy.
3. The algorithm is simple in structure, extra equipment is not needed, only the BMS and the vehicle-mounted sensor are needed to collect real-time data, and the SOH of the battery can be accurately predicted through algorithm programming.
Drawings
FIG. 1 is an overall algorithm block diagram of a big data-based power battery state of health prediction method according to the present invention;
FIG. 2 is a graph of SOH versus equivalent cycle number established in the present invention;
FIG. 3 is a schematic diagram of an integrated neural network used in the present invention;
FIG. 4 is a flow chart of a genetic algorithm used in the present invention;
FIG. 5 is a scatter plot of battery capacity before and after a random forest normalization process in accordance with the present invention;
FIG. 6 is a graph of the change of battery capacity with the mileage of a vehicle obtained by a cluster fitting algorithm in the present invention;
fig. 7 is a battery SOH distribution histogram after one year predicted by the machine learning algorithm in the present invention.
Detailed Description
In the embodiment, a method for predicting the state of health of a power battery based on big data is characterized in that a running condition-charging calculation battery state of health evaluation model is established by utilizing the big data of real-time running of an electric vehicle, and the SOH value of the battery is accurately estimated and predicted by combining a machine learning algorithm, so that the problems of difficulty in estimating the SOH of the battery and low accuracy are solved; meanwhile, a relation between the battery SOH and the equivalent cycle times is established by utilizing a battery charging and discharging experiment, and the battery SOH is indirectly estimated through the equivalent cycle times, so that the prediction method is high in practicability, high in accuracy and visual and clear; specifically, as shown in fig. 1, the method is performed as follows:
the method comprises the following steps: in the embodiment, real-time running data on the electric vehicle, the speed, the accumulated mileage, voltage data, current data, the state of charge data of the battery and temperature data of the electric vehicle are collected through the vehicle-mounted sensor and the BMS;
step two, preprocessing the battery charging and discharging data;
step 2.1, cleaning the battery charging and discharging data respectively, and removing points with larger errors in the data to obtain an effective battery charging and discharging data set;
2.2, filling temperature data based on the data of the real-time running condition of the electric automobile; in the embodiment, the temperature is segmented by adopting an automatic segmentation fitting method, and the polynomial precision of each segment is controlled by adopting a root mean square error formula shown in formula (1), so that a temperature model is established, and the filling of temperature data in the data is realized;
in the formula (1), y i The approximate value of the temperature in the piecewise polynomial is shown, y represents the actual value of the temperature in the piecewise polynomial, and N represents the number of temperature sampling points on a certain day.
And 2.3, fitting a change curve of the accumulated mileage in the effective driving condition data set.
Step three, calculating the battery capacity in the charging process:
the capacity of the battery is mainly affected by SOH, charging current and temperature, and in order to obtain a more accurate change rule of the capacity along with SOH, the temperature and the current of all data need to be classified under the same condition, so as to ensure that the data do not interfere with the algorithm, so the embodiment performs data processing through the following steps.
Step 3.1, calculating to obtain average capacity data of the battery in a single charge state by using the current data and the charge state data of the battery after the initial data cleaning;
3.2, taking temperature data and charging current data as input, taking the average capacity of a battery in a single charge state as output, constructing a random forest regression model, and substituting the standard temperature and the standard current into the random forest regression model for calculation to obtain a standard value;
step 3.3, inputting the uniformly distributed temperature data and the current data concentrated by the effective driving condition data into a random forest regression model for calculation to obtain an original battery average capacity value, making a difference with a standard value, and adding the obtained difference serving as a gain to the battery average capacity data to obtain regression-processed battery average capacity data;
step 3.4, drawing the average capacity data of the battery subjected to the regression processing of the random forest model into a scatter diagram; the volume scatter plots before and after treatment are shown in FIG. 2;
and 3.5, performing cluster fitting on the scatter diagram to obtain a final curve of the charging capacity along with the change of the driving range, as shown in fig. 3.
In this embodiment, a charge-discharge characteristic experiment is performed on the battery, corresponding battery data is obtained, and a relation curve between the number x of charge-discharge cycles of the battery and the SOH is obtained, as shown in fig. 4, and is used for establishing a battery health state evaluation model based on driving condition-charge calculation. The method comprises the following specific steps:
step four, establishing a battery health state evaluation model based on driving condition-charging calculation;
step 4.1, obtaining the state of health (SOH) value of the battery on the t day by using a curve of the change of the charging capacity along with the driving range and combining the formula (2) t ；
In the formula (2), cap t The battery capacity at day t, cap r Is the rated capacity of the battery.
Step 4.2, converting the SOH of the battery into an equivalent cycle number value of the battery by the combination formula (3), and measuring the service life standard of the battery consumed by the automobile in one day by calculating the difference value between the SOH of the battery and the equivalent cycle number value of the t-1 day;
in the formula (3), a 1 、b 1 Is a set attenuation factor of the electrode material, a 2 、b 2 X represents the equivalent cycle number of the battery for the set attenuation factor of the electrolyte material;
and 4.3, performing parameter identification on the four attenuation factors in the formula (1) by using a genetic algorithm to obtain values of the four attenuation factors, so as to obtain a final mapping relation between the equivalent cycle number and the SOH (state of health) of the battery.
Genetic Algorithm (GA) is a heuristic Algorithm for simulating the natural evolution process of organisms to search for an optimal solution, and converts a problem to be solved into the processes of chromosome gene selection, crossing, mutation and recombination in the biological evolution so as to obtain the optimal solution of the problem. The method is used for solving the problem in the step 4.3, parameters to be identified can be obtained quickly, the precision is high, and a logic block diagram is shown in fig. 5.
4.4, performing characteristic extraction on the discharge data in the effective running condition data set to obtain an average speed, an equivalent running mileage value, an average temperature and an average current value, and using the average speed, the equivalent running mileage value, the average temperature and the average current value as running condition characteristics of the electric automobile for describing the working condition of the discharge process; wherein the equivalent mileage value is the single-day mileage divided by the SOH value.
Step five, establishing and training a machine learning model based on the integrated neural network
In order to make the model have good learning and generalization capabilities, the integrated neural network is used in this example to build the machine learning model. The artificial neural network has the advantages of high classification accuracy, strong learning ability, low sensitivity to noise interference data, better generalization and expansion ability and capability of approximating any nonlinear relation, and the structure of the artificial neural network is shown in fig. 6. The integrated artificial neural network is a process of combining a plurality of simple neural networks into a classifier on the basis of the artificial neural network, can overcome the problem that dead spots and divergence are easy to generate on a model due to the fact that a training data set is insufficient, and enhances the stability of the network by using an integrated learning method. The specific implementation steps are as follows:
and taking the driving condition characteristics as the input of the machine learning model, taking the difference value of the equivalent cycle times as the output of the machine learning model, and taking the effective driving condition data set as a training set to be brought into the machine learning model, thereby obtaining the trained machine learning model.
Step six, outputting the prediction result
The embodiment carries out characteristic random sampling treatment on the effective battery charging and discharging data set through the following steps:
6.1, respectively calculating the running condition characteristics of the electric vehicle in the effective running condition data set every day and the maximum value and the minimum value of each running condition characteristic in one day;
step 6.2, determining sampling time, and if at least one piece of time data corresponding to the sampling time exists in the effective running condition data set, randomly generating a number from the maximum value and the minimum value of the current day as a running condition characteristic value of the current day; if the effective running condition data set does not have data corresponding to the sampling time, taking the day closest to the sampling time to execute random number generation operation; if only one piece of time data corresponding to the sampling time exists in the effective driving condition data set, taking the corresponding data as a driving condition characteristic value;
and 6.3, repeatedly executing the step 6.2 to obtain a driving condition data set after the characteristic random sampling.
And inputting the data set subjected to the random feature sampling treatment into a trained machine learning model for prediction calculation to obtain a prediction result of equivalent cycle times, and obtaining a final predicted battery state of health (SOH) value according to an equation (3).
The SOH value predicted by the machine learning algorithm in this example after one year is shown by the SOH distribution histogram with the total frequency of 100, as shown in fig. 7.
Claims (3)
1. A big data-based power battery state of health prediction method is characterized by comprising the following steps:
the method comprises the following steps: gather the real-time operating mode data that traveles on the electric motor car, include: the speed, accumulated mileage, voltage data, current data, battery state of charge data and temperature data of the electric automobile;
step two, data preprocessing;
step 2.1, respectively cleaning the real-time running condition data, and removing points with larger errors in the data to obtain an effective running condition data set;
2.2, establishing a temperature model based on real-time driving condition data of the electric automobile, wherein the temperature model is used for filling temperature data in the effective driving condition data set so as to obtain uniformly distributed temperature data;
step 2.3, fitting a change curve of the accumulated mileage in the effective driving condition data set;
step three, calculating the battery capacity in the charging process;
step 3.1, calculating to obtain average capacity data of the battery in a single charge state by using the cleaned current data and the cleaned charge state data of the battery;
3.2, taking charging current data contained in the current data in the uniformly distributed temperature data and effective driving condition data set as input, taking the average capacity of the battery in the single charge state as output, constructing a random forest regression model, and substituting the standard temperature and the standard current into the random forest regression model for calculation to obtain a standard value;
step 3.3, charging current data contained in the uniformly distributed temperature data and the current data in the effective driving condition data set are input into the random forest regression model for calculation to obtain an original battery average capacity value, the original battery average capacity value is subjected to difference with the standard value, and the obtained difference is used as gain to be added with the battery average capacity data, so that the battery average capacity data after regression processing are obtained;
step 3.4, drawing the average capacity data of the battery subjected to the regression processing of the random forest model into a scatter diagram;
step 3.5, performing cluster fitting on the scatter diagram to obtain a charging capacity curve;
step four, establishing a battery health state evaluation model based on driving condition-charging calculation;
step 4.1, calculating the battery state of health (SOH) of the battery on the t day by using the charging capacity curve and combining the formula (1) t ：
In the formula (1), cap t The battery capacity at day t, cap r Is the rated capacity of the battery;
step 4.2, the SOH of the battery on the t day t Converting the equivalent cycle number value into an equivalent cycle number value of the battery, and calculating the difference value between the equivalent cycle number value and the t-1 day equivalent cycle number value to measure the service life standard of the battery consumed by the automobile in one day;
4.3, performing characteristic extraction on discharge data contained in the current data in the effective running condition data set to obtain an average speed, an equivalent running mileage value, an average temperature and an average current value, and using the average speed, the equivalent running mileage value, the average temperature and the average current value as running condition characteristics of the electric automobile for describing the working condition of the discharge process;
establishing and training a machine learning model;
taking the driving condition characteristics as the input of the machine learning model, taking the difference value of equivalent cycle times as the output of the machine learning model, and taking the effective driving condition data set as a training set to be brought into the machine learning model, thereby obtaining the trained machine learning model;
step six, outputting a prediction result;
and performing characteristic random sampling processing on the effective driving condition data set to obtain sampled driving condition data, inputting the sampled driving condition data into a trained machine learning model for prediction calculation to obtain a prediction result of equivalent cycle times, and obtaining a final predicted battery health state according to a mapping relation between the equivalent cycle times and the battery health state.
2. The big data based power battery state of health prediction method of claim 1, characterized in that: the conversion steps of equivalent cycle times in the step 4.2 are as follows:
step 4.2.1, obtaining a relation curve of the equivalent cycle number of the battery and the SOH of the battery based on a battery charging and discharging characteristic test, and establishing a relation shown as a formula (2) according to the relation curve:
in the formula (2), a 1 、b 1 Is a set attenuation factor of the electrode material, a 2 、b 2 A set attenuation factor for the electrolyte material; x represents the equivalent cycle number of the battery;
and 4.2.2, performing parameter identification on the four attenuation factors in the formula (2) by using a genetic algorithm to obtain values of the four attenuation factors, so as to obtain a final mapping relation formula of the equivalent cycle number and the SOH (state of health) of the battery.
3. The big data based power battery state of health prediction method of claim 1, characterized in that: the feature random sampling processing of the step six comprises the following steps:
6.1, respectively calculating the running condition characteristics of the electric vehicle in the effective running condition data set every day and the maximum value and the minimum value of each running condition characteristic in one day;
step 6.2, determining sampling time, and if at least one piece of time data corresponding to the sampling time exists in the effective running condition data set, randomly generating a number from the maximum value and the minimum value of the current day as a running condition characteristic value of the current day; if the effective running condition data set does not have data corresponding to the sampling time, taking the day closest to the sampling time to execute random number generation operation; if only one piece of time data corresponding to the sampling time exists in the effective driving condition data set, taking the corresponding data as a driving condition characteristic value;
and 6.3, repeatedly executing the step 6.2 to obtain the sampled running condition data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110376068.XA CN112904219B (en) | 2021-04-08 | 2021-04-08 | Big data-based power battery health state prediction method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110376068.XA CN112904219B (en) | 2021-04-08 | 2021-04-08 | Big data-based power battery health state prediction method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112904219A CN112904219A (en) | 2021-06-04 |
| CN112904219B true CN112904219B (en) | 2023-03-21 |
Family
ID=76110130
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110376068.XA Active CN112904219B (en) | 2021-04-08 | 2021-04-08 | Big data-based power battery health state prediction method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112904219B (en) |
Families Citing this family (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113721151B (en) * | 2021-11-03 | 2022-02-08 | 杭州宇谷科技有限公司 | Battery capacity estimation model and method based on double-tower deep learning network |
| CN114779109A (en) * | 2022-04-08 | 2022-07-22 | 湖北亿纬动力有限公司 | Method and device for determining battery health state, electronic equipment and storage medium |
| CN115296361B (en) * | 2022-07-15 | 2023-06-09 | 北京瑞祺皓迪技术股份有限公司 | Method, device, equipment and medium for dynamically adjusting electric quantity threshold of equipment |
| CN115877238B (en) * | 2022-12-06 | 2023-11-07 | 北汽福田汽车股份有限公司 | Method and device for detecting battery capacity, readable storage medium and electronic equipment |
| CN116413609B (en) * | 2023-06-08 | 2023-08-29 | 江苏正力新能电池技术有限公司 | Battery diving identification method and device, electronic equipment and storage medium |
| CN116908694B (en) * | 2023-07-13 | 2024-02-02 | 江苏果下科技有限公司 | SOH estimation method of household energy storage system |
| CN116679217B (en) * | 2023-07-17 | 2024-08-30 | 广东电网有限责任公司 | SOH detection method, device, equipment and medium based on second-order equivalent circuit |
| CN117150275B (en) * | 2023-11-01 | 2024-04-09 | 宁德时代新能源科技股份有限公司 | Machine learning model construction method, battery health degree prediction method and device |
| CN117841689A (en) * | 2024-01-09 | 2024-04-09 | 北京理工大学重庆创新中心 | Power battery remaining endurance prediction method based on time sequence large model algorithm |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110210147A (en) * | 2019-06-05 | 2019-09-06 | 杭州华塑加达网络科技有限公司 | The simulator and emulation mode of estimating state of health of battery |
| CN111323719A (en) * | 2020-03-18 | 2020-06-23 | 北京理工大学 | Method and system for online determination of health state of power battery pack of electric automobile |
| CN111381170A (en) * | 2020-05-15 | 2020-07-07 | 上海工程技术大学 | Electric vehicle battery pack health state prediction method and system based on big data |
| CN112180280A (en) * | 2020-09-27 | 2021-01-05 | 吉林大学 | Hybrid electric vehicle battery life optimization method considering battery health state |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB2537406B (en) * | 2015-04-16 | 2017-10-18 | Oxis Energy Ltd | Method and apparatus for determining the state of health and state of charge of lithium sulfur batteries |
| EP3721499B1 (en) * | 2017-12-07 | 2023-04-12 | Yazami Ip Pte. Ltd. | Method and system for online assessing state of health of a battery |
-
2021
- 2021-04-08 CN CN202110376068.XA patent/CN112904219B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110210147A (en) * | 2019-06-05 | 2019-09-06 | 杭州华塑加达网络科技有限公司 | The simulator and emulation mode of estimating state of health of battery |
| CN111323719A (en) * | 2020-03-18 | 2020-06-23 | 北京理工大学 | Method and system for online determination of health state of power battery pack of electric automobile |
| CN111381170A (en) * | 2020-05-15 | 2020-07-07 | 上海工程技术大学 | Electric vehicle battery pack health state prediction method and system based on big data |
| CN112180280A (en) * | 2020-09-27 | 2021-01-05 | 吉林大学 | Hybrid electric vehicle battery life optimization method considering battery health state |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112904219A (en) | 2021-06-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112904219B (en) | Big data-based power battery health state prediction method | |
| Li et al. | Data driven battery modeling and management method with aging phenomenon considered | |
| Wei et al. | Remaining useful life prediction and state of health diagnosis for lithium-ion batteries using particle filter and support vector regression | |
| CN109604186B (en) | Flexible evaluation and sorting method for performance of power battery | |
| CN111090047A (en) | Lithium battery health state estimation method based on multi-model fusion | |
| Li et al. | On-line estimation method of lithium-ion battery health status based on PSO-SVM | |
| CN111323719A (en) | Method and system for online determination of health state of power battery pack of electric automobile | |
| CN111220921A (en) | Lithium battery capacity estimation method based on improved convolution-long-and-short-term memory neural network | |
| CN111581904A (en) | Lithium battery SOC and SOH collaborative estimation method considering influence of cycle number | |
| CN106556802A (en) | A kind of accumulator battery exception cell recognition methodss and device | |
| CN113671394A (en) | Lithium ion battery expected life prediction method and system | |
| US20230305073A1 (en) | Method and apparatus for providing a predicted aging state of a device battery based on a predicted usage pattern | |
| CN116609676B (en) | Method and system for monitoring state of hybrid energy storage battery based on big data processing | |
| CN112505570A (en) | Method for estimating battery health state of electric automobile | |
| CN114462259B (en) | SOC-based battery remaining life prediction method and system, automobile and medium | |
| CN116609686B (en) | Battery cell consistency assessment method based on cloud platform big data | |
| CN114036647A (en) | Power battery safety risk assessment method based on real vehicle data | |
| CN113484774A (en) | Lithium battery pack capacity estimation method based on temperature calibration and neural network | |
| CN114167284A (en) | Lithium battery RUL prediction method and device based on BMS big data and ensemble learning | |
| CN113657030A (en) | Method for predicting remaining service life of lithium battery based on Gaussian process regression | |
| CN116224085A (en) | Lithium battery health state assessment method based on data driving | |
| Singh et al. | State of charge estimation techniques of Li-ion battery of electric vehicles | |
| CN115097312A (en) | Lithium ion battery fusion life prediction model combining data driving model and empirical model | |
| CN115684936A (en) | Power battery health failure probability calculation and health failure map establishment method based on machine learning | |
| CN114355218A (en) | Lithium ion battery charge state prediction method based on multi-feature quantity screening |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |