Equalization charging intelligent adjusting method for new energy automobile power battery

Abstract

The invention relates to an equalizing charge intelligent adjusting method for a power battery of a new energy automobile, which comprises the following steps of: acquiring a real-time state data set of each single battery from a power battery pack and carrying out filtering processing to acquire a state parameter sequence after filtering, the real-time state data set comprising voltage, temperature and charge state parameters; calculating a deviation value between each single battery and the group average value for the filtered state parameter sequence, and obtaining a deviation distribution matrix; if the deviation value of any single battery in the deviation distribution matrix exceeds a preset threshold value, the power battery pack is in an unbalanced state; performing battery state classification on the power battery pack in the unbalanced state, and obtaining an equalization strategy according to a classification result; and according to the equalization strategy control charging system and a preset equalization adjustment priority rule, carrying out equalization charging adjustment operation on the target single battery. According to the invention, the consistency of the battery pack can be improved, the service life is prolonged, the safety risk is reduced, and efficient and intelligent battery management is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery charging management, in particular to an intelligent equalization charging adjustment method for new energy vehicle power batteries. BACKGROUND

[0002] The performance and safety of new energy vehicle power battery packs are directly related to the vehicle's endurance, running stability and service life, and are the core technical field of promoting green travel and energy transformation. The power battery pack is composed of multiple single batteries, and its performance depends on the coordinated consistency of the state of each single battery. However, in actual operation, the voltage, temperature and state of charge of single batteries often deviate due to manufacturing differences, use environment or aging degree, and this inconsistency may lead to a decline in the overall performance of the battery pack, and even cause safety hazards. Therefore, how to effectively evaluate and control the balance of the battery pack has become a key problem that the industry needs to overcome.

[0003] The existing method often faces the problems of dynamicity and accuracy when monitoring and regulating the balance of the battery pack. Traditional technologies mostly rely on static parameter collection, which is difficult to capture the dynamic changes of single batteries in complex working conditions in real time, such as state fluctuations under fast charging or high temperature environment. In addition, the regulation strategy is usually based on fixed threshold or simple average value calculation, lacking precise response to the differentiated needs of each single battery. This extensive management method is difficult to effectively deal with the dynamic imbalance problem inside the battery pack in the face of complex use scenarios. The core technical difficulty lies in how to realize real-time and accurate monitoring and dynamic regulation of the state of single batteries. First, the battery pack is affected by temperature, current and use time during operation, and the state parameters of each single battery change at different speeds and amplitudes, making it difficult to accurately judge the overall balance through single-time data. For example, some single batteries may have uneven heat dissipation and temperature rise, leading to rapid deviation of voltage and state of charge from the average value, while existing technologies are difficult to capture such dynamic changes in real time. Secondly, the regulation of unbalanced state needs to flexibly develop differentiated charging strategies according to the deviation degree of each single battery. However, existing methods often cannot balance efficiency and accuracy when determining regulation targets and parameters, resulting in a long regulation process or failure to eliminate imbalance. Therefore, how to collect and analyze the state parameters of each single battery in real time under complex dynamic conditions, and accurately generate differentiated equalization charging control strategies based on this, has become a key problem to ensure the performance and safety of power battery packs. SUMMARY

[0004] The purpose of the present application is to provide an intelligent equalization charging adjustment method for new energy vehicle power batteries, which can improve the consistency of the battery pack, prolong the service life and reduce the safety risk, and realize efficient and intelligent battery management.

[0005] To achieve the above object, the present application provides the following scheme:

[0006] An equalization charging intelligent adjustment method for new energy vehicle power batteries, comprising:

[0007] Obtaining real-time state data sets of each single battery from a power battery pack and performing filtering processing to obtain a filtered state parameter sequence, wherein the real-time state data sets include voltage, temperature and state of charge parameters;

[0008] Calculating deviation values of each single battery and group average value for the filtered state parameter sequence to obtain a deviation distribution matrix;

[0009] If the deviation value of any single battery in the deviation distribution matrix exceeds a preset threshold value, the power battery pack is in an unbalanced state;

[0010] Performing battery state classification on the power battery pack in the unbalanced state, and obtaining an equalization strategy according to the classification result;

[0011] According to the equalization strategy and a preset equalization adjustment priority rule, performing equalization charging adjustment operation on a target single battery.

[0012] Optionally, the filtering processing on the real-time state data sets to obtain the filtered state parameter sequence comprises:

[0013] Performing format standardization on the real-time state data sets to obtain a first state sequence;

[0014] If the time stamp of the first state sequence is complete, performing state estimation on the first state sequence by using a Kalman filtering algorithm to obtain a second state sequence;

[0015] According to the second state sequence, calculating a dynamic update increment of the state parameter to determine a state change trend;

[0016] If the state change trend exceeds a preset threshold value, performing smoothing processing on the second state sequence to obtain a third state sequence;

[0017] Through time series analysis, extracting periodic characteristics of the third state sequence to generate a final parameter sequence;

[0018] According to the final parameter sequence, filling missing data by using an interpolation method to obtain the filtered state parameter sequence.

[0019] Optionally, the calculation of the deviation values of each single battery and the group average value for the filtered state parameter sequence comprises:

[0020] According to the filtered state parameter sequence, a group average value of parameters of each single battery in the power battery is calculated, and the group average value is obtained;

[0021] According to the group average value, a parameter deviation is calculated by using a multi-dimensional weighted calculation method, and a deviation sequence is obtained;

[0022] According to the deviation sequence, a deviation distribution matrix is constructed by using a matrix generation method, and the deviation distribution matrix is obtained.

[0023] Optionally, the multi-dimensional weighted calculation method for calculating the parameter deviation E includes:

[0024] E = a x (1-(max(V i )-min(V i )) / V avg )+b x (1-(max(T i )-min(T i )) / T avg )+g x (1-(max(SOC i )-min(SOC i )) / SOC avg );

[0025] Wherein, V i is the voltage of the i-th single battery, V avg is the average voltage of all single batteries; T i is the temperature of the i-th single battery, T avg is the average temperature of all single batteries; SOC i is the state of charge of the i-th single battery, SOC avg is the average state of charge of all single batteries; a, b, g are weight coefficients of voltage, temperature and state of charge respectively, and a+b+g=1.

[0026] Optionally, the battery state classification of the power battery group in the unbalanced state includes:

[0027] The filtered state parameter sequence is input into a support vector machine to obtain a classification result label, wherein the support vector machine is obtained by training historical state data with normal or abnormal labels;

[0028] According to the classification result label, the state parameter sequence with an abnormal label is extracted for feature extraction, and a change trend feature is input into a decision tree model to predict the state of the single battery, and a battery state label is obtained.

[0029] Optionally, the feature extraction of the state parameter sequence with an abnormal label includes: extracting features by time series analysis to obtain the change trend feature containing change rate and duration.

[0030] Optionally, the equalization strategy is obtained according to the classification result, and the equalization strategy is matched from a pre-established regulation library according to the classification result.

[0031] Optionally, the preset equalization regulation priority rule comprises:

[0032] The single battery with a state of charge deviation value greater than a preset state of charge deviation value is preferentially regulated, in the case that the state of charge deviation meets the requirement, the single battery with a voltage deviation value greater than a preset voltage deviation value is preferentially regulated, in the case that the voltage deviation meets the requirement, the single battery with a temperature deviation value greater than a preset temperature deviation value is preferentially regulated, and for a plurality of single batteries of the same type of deviation, the regulation priority is determined in the order of deviation values from large to small.

[0033] The application has the beneficial effects that: the application aims at the performance decline and safety hazard problems caused by the inconsistent states of single batteries in the power battery pack, real-time voltage, temperature and state of charge data are collected, the Kalman filtering algorithm is used to eliminate noise interference, high-precision state parameter sequences are generated, and the deviation distribution matrix of the single battery and the group average value is calculated to accurately identify abnormal batteries. For the battery with a deviation exceeding a threshold value, the dynamic change type is classified by using a support vector machine, and a differentiated equalization strategy in a preset regulation library is matched.

[0034] The application can significantly improve the consistency of the battery pack, prolong the service life and reduce the safety risk, and realizes efficient and intelligent battery management. BRIEF DESCRIPTION OF DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

[0036] Figure 1 A flowchart of an equalization charging intelligent regulation method for a new energy automobile power battery according to an embodiment of the present application. DETAILED DESCRIPTION

[0037] The technical solutions in the embodiments of the present application will be described clearly and completely in the embodiments of the present application in combination with the drawings. Obviously, the described embodiments only constitute some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0038] In order to make the above objectives, characteristics and advantages of the present application more apparent, further specific embodiments will be described in detail with reference to the accompanying drawings and specific embodiments.

[0039] As Figure 1 shown, the embodiment proposes an equalization charging intelligent adjustment method for new energy vehicle power battery, including:

[0040] Obtain real-time state data set of each single battery from power battery pack and perform filtering processing to obtain filtered state parameter sequence, wherein the real-time state data set includes voltage, temperature and state of charge parameter;

[0041] Calculate deviation value of each single battery and group average value for the filtered state parameter sequence to obtain deviation distribution matrix;

[0042] If the deviation value of any single battery in the deviation distribution matrix exceeds the preset threshold value, the power battery pack is in an unbalanced state;

[0043] Classify the battery state of the power battery pack in the unbalanced state, and obtain an equalization strategy according to the classification result;

[0044] According to the equalization strategy and the preset equalization adjustment priority rule, perform equalization charging adjustment operation on the target single battery.

[0045] Further, the filtering processing of the real-time state data set to obtain the filtered state parameter sequence includes:

[0046] Format standardization is performed on the real-time state data set to obtain a first state sequence;

[0047] If the time stamp of the first state sequence is complete, a Kalman filtering algorithm is used to perform state estimation on the first state sequence to obtain a second state sequence;

[0048] According to the second state sequence, calculate the dynamic update increment of the state parameter to determine the state change trend;

[0049] If the state change trend exceeds the preset threshold value, smooth processing is performed on the second state sequence to obtain a third state sequence;

[0050] Through time series analysis, the periodic characteristics of the third state sequence are extracted to generate a final parameter sequence;

[0051] According to the final parameter sequence, an interpolation method is used to fill in the missing data to obtain the filtered state parameter sequence.

[0052] Specifically, the present embodiment can collect the voltage, temperature and state of charge parameters of the single batteries from the power battery pack through the sensor network, and can construct a real-time state data set. Assuming that a battery pack contains 100 single batteries, data is collected once every second, the voltage range is 3.0-4.2V, the temperature range is 20-50°C, and the state of charge is 0-100%. When collecting the state parameters of each single battery in real time, a distributed collection architecture is adopted, and an independent collection module is arranged on each single battery of the power battery pack. The sampling frequency of the collection module is not less than 1Hz, and the error rate of the collected data is controlled within ±0.5%. At the same time, the collection module also has a data preprocessing function, which can filter, denoise and eliminate outliers for the collected raw data.

[0053] The timestamps in this embodiment can be recorded in milliseconds, and some data may be misaligned due to transmission delays. After standardization, the data sequence is arranged at uniform time intervals, such as one data point per second, forming a first state sequence. It should be noted that if the timestamps are incomplete, they may need to be completed through linear interpolation, but this example assumes that the timestamps are complete. Specifically, the Kalman filter algorithm is used to estimate the state of the first state sequence to generate a second state sequence. For example, noise in voltage data can be smoothed by filtering. Assuming a battery voltage sequence is [3.6V, 3.8V, 3.7V], the filtered result may be more stable [3.7V, 3.71V, 3.7V]. This method reduces the impact of noise and improves data reliability by combining historical data and current measurements through prediction and update steps. For example, based on the second state sequence, the dynamic update increment of the state parameter is calculated to analyze the voltage trend. Assuming that the voltage of a certain battery slowly decreases from 3.7V to 3.65V in 10 minutes, the increment is -0.005V / min, which exceeds the preset threshold -0.003V / min, indicating that there may be an abnormal discharge trend. It should be noted that the threshold is usually determined based on historical operation data of the battery pack or design specifications. In one possible implementation, if the trend is abnormal, the second state sequence is smoothed to generate a third state sequence. For example, using the moving average method to smooth the voltage sequence can reduce the impact of short-term fluctuations and obtain a more stable voltage trend curve, such as [3.7V, 3.69V, 3.68V]. This smoothing process helps to highlight long-term trends for subsequent analysis. Specifically, time series analysis can extract periodic features from the third state sequence. For example, by analyzing the periodicity of voltage data through Fourier transform, it may be found that the voltage of a certain battery fluctuates once an hour, which is related to the charging period. This feature can be used to identify battery operation patterns to generate a final parameter sequence. For example, for missing data in the final parameter sequence, an interpolation method is used to complete it. For example, if a certain battery temperature data sequence is missing at a certain time, it can be estimated to be 40.5°C according to the previous and subsequent data points, such as 40°C and 41°C. The continuous filtered state parameter sequence can provide reliable data support for subsequent battery management. In one possible implementation, the above method can be integrated into a battery management system to monitor the battery state in real time. For example, a certain electric vehicle battery pack generates a continuous state parameter sequence through the above process, which is used to optimize the charging strategy or warn of potential failures. This method ensures data accuracy and improves system stability.

[0054] Further, calculating the deviation of each single battery from the group average value of the filtered state parameter sequence includes:

[0055] According to the filtered state parameter sequence, the group average value of the parameters of each single battery in the power battery is calculated to obtain the group average value.

[0056] According to the group average value, the parameter deviation is calculated by using a multi-dimensional weighted calculation method to obtain a deviation sequence;

[0057] According to the deviation sequence, a deviation distribution matrix is constructed by using a matrix generation method to obtain the deviation distribution matrix.

[0058] Further, the parameter deviation E is calculated by using a multi-dimensional weighted calculation method, which includes:

[0059] E = a x (1-(max(V i )-min(V i )) / V avg )+b x (1-(max(T i )-min(T i )) / T avg )+g x (1-(max(SOC i )-min(SOC i )) / SOC avg );

[0060] Wherein, V i is the voltage of the i-th single battery, V avg is the average voltage of all single batteries; T i is the temperature of the i-th single battery, T avg is the average temperature of all single batteries; SOC i is the state of charge of the i-th single battery, SOC avg is the average state of charge of all single batteries; a, b, g are the weight coefficients of voltage, temperature and state of charge respectively, and a+b+g=1.

[0061] Specifically, when calculating the group average value of each single battery parameter in the battery pack, the average voltage of 100 single batteries can be calculated based on the filtered voltage sequence. Assuming that each battery voltage value is between 3.65V and 3.75V, the group average value is 3.70V. This average value is used as a reference for subsequent deviation analysis. The calculation of the deviation sequence is obtained by comparing the smoothed voltage of each single battery with the group average value to obtain the deviation value. For example, the smoothed voltage of a certain battery is 3.68V, and the deviation is 3.68V-3.70V=-0.02V. If the voltage of a certain battery is 3.80V, the deviation is 0.10V. By these deviation values, a deviation distribution matrix is constructed, the rows represent single batteries, the columns represent time points, and the elements are deviation values. For example, the deviation of a certain battery at 5 time points is [-0.02V, 0.01V, -0.03V, 0.00V, 0.02V].

[0062] Further, the battery state classification of the power battery pack in the unbalanced state includes:

[0063] The filtered state parameter sequence is input into a support vector machine to obtain a classification result label, wherein the support vector machine is trained by historical state data with normal or abnormal labels;

[0064] According to the classification result label, the state parameter sequence with an abnormal label is subjected to feature extraction to obtain a change trend feature, which is input into a decision tree model to predict the state of the single battery and obtain a battery state label.

[0065] Specifically, when the support vector machine is used to classify the dynamic change of the deviation, the deviation feature set can be divided into two categories: normal and abnormal. In a possible implementation, the verification of the label classification accuracy can be realized by the cross-validation method. 100 groups of battery data are collected and divided into a training set and a test set, and the prediction accuracy of the classification model is calculated. If the preset threshold is 90% and the actual accuracy is 92%, the label validity is confirmed. This method ensures the reliability of the label and provides a solid foundation for subsequent strategy selection. For example, if the voltage deviation of a certain battery exceeds 0.3V for 5 consecutive times and the temperature deviation exceeds 5℃, the support vector machine can identify that the dynamic change of the battery belongs to an abnormal mode and mark it as an "abnormal" label. This classification method can effectively distinguish the running state of the battery and provide a basis for trend analysis.

[0066] Further, the feature extraction of the state parameter sequence with an abnormal label includes: extracting features through time series analysis to obtain change trend features including change rate and duration.

[0067] Specifically, when analyzing the dynamic change trend of the deviation, the change features can be extracted through time series analysis. Assuming that the voltage deviation of a certain battery gradually increases from 0.1V to 0.4V within 10 minutes, the trend feature set records the change rate and duration. This trend feature set can reflect the gradual change of the battery performance and provide key inputs for prediction. In a possible implementation, the decision tree model predicts the battery state according to the trend feature set. For example, the input features include a voltage deviation change rate of 0.03V / minute and a temperature rise rate of 0.5℃ / minute. The decision tree outputs a "overheating risk" state label through multiple layers of judgment. "Voltage deviation exceeding 0.3V and temperature exceeding 40℃" is an "overvoltage and overheating" abnormality. This prediction method can identify potential problems in advance and facilitate the adoption of measures.

[0068] Further, the balancing strategy according to the classification result includes: matching a corresponding adjustment scheme from a pre-established regulation library as the balancing strategy according to the classification result.

[0069] Specifically, when matching the differentiated balancing strategy from the regulation library, the preset strategy library can be searched according to the label type to obtain the balancing charging control parameter, wherein the balancing charging control parameter at least includes the adjustment current size, the adjustment time length and the adjustment interval of the target single battery.

[0070] Further, the preset balancing adjustment priority rule includes:

[0071] The single battery with the state of charge deviation value greater than the preset state of charge deviation value is preferentially adjusted; in the case that the state of charge deviation meets the requirement, the single battery with the voltage deviation value greater than the preset voltage deviation value is preferentially adjusted; in the case that the voltage deviation meets the requirement, the single battery with the temperature deviation value greater than the preset temperature deviation value is preferentially adjusted; for multiple single batteries of the same type of deviation, the adjustment priority is determined in the order of deviation value from large to small.

[0072] Specifically, the preset voltage deviation value is in the range of 0.05-0.1V; the preset temperature deviation threshold value is in the range of 2-5℃; and the preset state of charge deviation threshold value is in the range of 3%-8%.

[0073] In the control of the charging system to the target single battery for balancing charging adjustment operation, an independent balancing adjustment circuit is adopted, each target single battery corresponds to an independent adjustment branch, and an accurately controllable electronic switch and a current adjustment module are arranged in the adjustment branch; the current adjustment module can control the adjustment current accuracy to be within ±5mA according to the generated balancing charging control parameter; at the same time, in the adjustment process, the state parameter change rate of the target single battery is monitored in real time, when the change rate of a certain state parameter exceeds the preset safety change rate threshold value, the adjustment parameter is automatically adjusted or the adjustment is suspended, and after the parameter is stable, the adjustment is continued, the voltage safety change rate threshold value is 0.02V / s, the temperature safety change rate threshold value is 0.5℃ / s, and the state of charge safety change rate threshold value is 0.5% / s.

[0074] In a possible implementation, the method further judges whether the optimized balanced strategy meets the safety hidden danger threshold condition, obtains the final regulation instruction sequence through the simulation verification link, and includes the following steps. Real-time monitoring data is obtained, operation state parameters are collected from the target system through a sensor network to obtain an initial data input stream. According to the initial data input stream, a preset simulation environment configuration is used to execute the simulation verification link to generate a risk assessment result. If the risk assessment result exceeds the safety hidden danger threshold, adjustment parameters of the optimized balanced strategy are calculated according to the threshold determination standard to obtain strategy adjustment parameters. A preliminary regulation instruction sequence is generated through the strategy adjustment parameters and in combination with an instruction generation rule. The preliminary regulation instruction sequence is obtained, a feedback loop mechanism is used to verify the execution effect of the instruction sequence in the simulation environment to obtain a verified regulation instruction sequence. According to the verified regulation instruction sequence, secondary verification is performed through real-time monitoring data to judge whether the safety hidden danger threshold condition is met to obtain a final regulation instruction sequence. The operation state of the target system is adjusted through the final regulation instruction sequence to generate a control instruction of system optimization.

[0075] The above-described embodiments are merely descriptions of the preferred modes of the present application and are not intended to limit the scope of the present application. Various modifications and improvements to the technical solutions of the present application made by those of ordinary skill in the art without departing from the design spirit of the present application shall fall within the protection scope of the present application as defined by the claims.

Claims

1. An intelligent equalization charging adjustment method for new energy vehicle power batteries, characterized in that, The method comprises the following steps: obtaining a real-time state data set of each single battery from a power battery pack and performing filtering processing to obtain a filtered state parameter sequence, wherein the real-time state data set comprises voltage, temperature and state of charge parameters; calculating the deviation value of each single battery from the group average value based on the filtered state parameter sequence to obtain a deviation distribution matrix; if the deviation value of any single battery in the deviation distribution matrix exceeds a preset threshold value, the power battery pack is in an unbalanced state; classifying the battery state of the power battery pack in the unbalanced state and obtaining an equalization strategy according to the classification result; controlling the charging system and a preset equalization adjustment priority rule according to the equalization strategy to perform equalization charging adjustment operation on the target single battery. 2.The intelligent adjustment method for equalizing charging of new energy automobile power batteries according to claim 1, characterized in that, The filtering processing of the real-time state data set to obtain the filtered state parameter sequence comprises: format standardization of the real-time state data set to obtain a first state sequence; if the time stamp of the first state sequence is complete, state estimation of the first state sequence is performed by using a Kalman filtering algorithm to obtain a second state sequence; determination of the dynamic update increment of the state parameter based on the second state sequence to determine the state change trend; if the state change trend exceeds a preset threshold value, smoothing processing of the second state sequence is performed to obtain a third state sequence; extraction of the periodic characteristics of the third state sequence by time series analysis to generate a final parameter sequence; filling of the missing data by using an interpolation method based on the final parameter sequence to obtain the filtered state parameter sequence. 3.The intelligent adjustment method for equalizing charging of new energy automobile power batteries according to claim 1, characterized in that, The calculation of the deviation value of each single battery from the group average value based on the filtered state parameter sequence comprises: calculation of the group average value of the parameters of each single battery in the power battery based on the filtered state parameter sequence to obtain the group average value; calculation of the parameter deviation by using a multi-dimensional weighted calculation method based on the group average value to obtain a deviation sequence; construction of the deviation distribution matrix by using a matrix generation method based on the deviation sequence to obtain the deviation distribution matrix. 4.The intelligent adjustment method for equalizing charging of new energy automobile power batteries according to claim 3, characterized in that, The calculation of the parameter deviation E by using a multi-dimensional weighted calculation method comprises: E = a x (1 - (max(V i )-min(V i )) / V avg )+ b x (1 - (max(T i )-min(T i )) / T avg )+ g x (1 - (max(SOC i )-min(SOC i )) / SOC avg ) where V i is the voltage of the i-th cell, V avg is the average voltage of all cells; T i is the temperature of the i-th cell, T avg is the average temperature of all cells; SOC i is the state of charge of the i-th cell, SOC avg is the average state of charge of all cells; a, b, g are the weight coefficients of voltage, temperature, state of charge, respectively, and a+b+g = 1.

5. The equalization charging intelligent adjustment method for new energy vehicle power batteries according to claim 1, characterized in that, The battery state classification of the power battery pack in the unbalanced state comprises: inputting the filtered state parameter sequence into a support vector machine to obtain a classification result label, wherein the support vector machine is obtained by training historical state data with normal or abnormal labels; feature extraction of the state parameter sequence with abnormal labels based on the classification result label to obtain change trend features, inputting the change trend features into a decision tree model to predict the state of the single battery to obtain a battery state label. 6.The intelligent adjustment method for equalizing charging of new energy automobile power batteries according to claim 5, characterized in that, The feature extraction of the state parameter sequence with abnormal labels comprises: feature extraction by time series analysis to obtain the change trend features including change rate and duration. 7.The intelligent adjustment method for equalizing charging of new energy automobile power batteries according to claim 1, characterized in that, The equalization strategy obtained based on the classification result comprises: matching a corresponding adjustment scheme from a pre-established regulation library as the equalization strategy based on the classification result. 8.The intelligent adjustment method for equalizing charging of new energy automobile power batteries according to claim 1, characterized in that, The preset equalization adjustment priority rule comprises: The single battery with the state of charge deviation value greater than the preset state of charge deviation value is preferentially adjusted; in the case that the state of charge deviation meets the requirement, the single battery with the voltage deviation value greater than the preset voltage deviation value is preferentially adjusted; in the case that the voltage deviation meets the requirement, the single battery with the temperature deviation value greater than the preset temperature deviation value is preferentially adjusted; for multiple single batteries of the same type of deviation, the adjustment priority is determined in the order of the deviation value from large to small.

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