SOC adaptive correction method, device and system for lithium battery pack

Abstract

The present application discloses a method, device and system for self-adapting correction of the SOC of a lithium battery pack, and relates to the technical field of electrical performance testing. The method for self-adapting correction of the SOC of the lithium battery pack comprises: obtaining the usage data of the lithium battery pack; obtaining the abnormal weight of each battery cell based on the usage data; performing negative correlation normalization processing on each abnormal weight to obtain the normalized weight; and adjusting the load of each battery cell based on the normalized weight. The present application calls and analyzes the usage data of the lithium battery pack, first constructs a first coefficient sequence inside the battery cell, then performs a correlation analysis based on the motor load of the electric vehicle loaded with the lithium battery pack, and finally obtains the abnormal weight of each battery cell, and performs active balancing management based on the abnormal weight, so as to achieve the purpose of improving the accuracy of subsequent SOC correction by improving the consistency of the voltage of each battery cell.

Description

Technical Field

[0001] The present application relates to the technical field of electrical performance testing, and specifically to a method, device and system for adaptively correcting the SOC of a lithium battery pack. Background Art

[0002] As a power source with high energy density, lithium-ion batteries are widely used in electric vehicles, portable electronic devices and energy storage systems. As the usage time increases, the performance of lithium-ion batteries will gradually decline, which is mainly manifested in the reduction of battery capacity, the increase of internal resistance and the attenuation of electrochemical performance. In order to maintain the performance of lithium-ion batteries and prevent overcharging and over-discharging, the state of charge (SOC) estimation in the battery management system (BMS) becomes particularly important. SOC is one of the key parameters in the battery management system. It indicates the percentage of the battery's current remaining power and is crucial for the prediction of the range of electric vehicles and the health management of batteries.

[0003] The existing SOC adaptive correction method for lithium battery packs in electric vehicles is usually based on the BMS obtaining the electrical parameters of the battery pack as a whole and multiple battery cells in the battery pack, such as voltage and current, and then inputting these electrical parameters into the estimation model to obtain the SOC estimation value. However, due to the continuous cycle of charge and discharge of the lithium battery pack, the inconsistency between several battery cells inside it gradually increases, and the obtained electrical parameters are easily affected by battery aging, environmental factors and other factors. At this time, it is usually necessary to frequently recalibrate the parameters of the estimation model, which leads to low SOC estimation accuracy. Summary of the invention

[0004] The purpose of the present application is to provide a method, device and system for self-adapting correction of the SOC of a lithium battery pack, so as to solve the technical problem that the SOC estimated by the self-adapting correction method of the prior art has low accuracy.

[0005] To achieve the above objectives, this application provides the following technical solutions:

[0006] In a first aspect, the present application proposes a SOC adaptive correction method for a lithium battery pack, which is applied to an electric vehicle; the SOC adaptive correction method for a lithium battery pack comprises:

[0007] Acquire usage data of the lithium battery pack; the lithium battery pack includes a plurality of battery cells; the usage data includes electrical parameters of the lithium battery pack, electrical parameters of each battery cell, and parameters of the electric vehicle motor;

[0008] Based on the usage data, an abnormal weight of each battery cell is obtained; the abnormal weight is at least used to indicate the abnormal degree of the corresponding battery cell;

[0009] Perform negative correlation normalization processing on each abnormal weight to obtain the normalized weight;

[0010] Based on the normalized weight, the load of each battery cell is adjusted.

[0011] As a specific solution in the technical solution of the present application, the abnormal weight of each battery cell is obtained based on the usage data, including:

[0012] Based on the usage data, current discharge segment data is obtained; the start time of the current discharge segment data is the stop time of the last charge, and the end time of the current discharge segment data is the start time of the current charge;

[0013] Based on each battery cell, a first battery cell is obtained; the first battery cell is any one of the battery cells;

[0014] Based on the current discharge segment data, a first voltage and a second voltage are obtained; the first voltage is the voltage of the first battery cell at any time in the current discharge segment data; the second voltage is the average value of the voltages of each battery cell at the same time as the first voltage;

[0015] Based on the first voltage and the second voltage, a first coefficient is obtained; the first coefficient is used to at least represent the voltage difference between the first battery cell and the remaining battery cells;

[0016] Based on the first coefficient, the abnormal weight of each battery cell is obtained.

[0017] As a specific solution in the technical solution of the present application, the step of obtaining the abnormal weight of each battery cell based on the first coefficient includes:

[0018] Based on the current discharge segment data, acquiring a time-series temperature value of the first battery cell;

[0019] Based on each time series temperature value, obtain the maximum temperature value;

[0020] Based on each time series temperature value and the maximum temperature value, a second coefficient is obtained; the second coefficient is at least used to represent the difference between each time series temperature value and the maximum temperature value;

[0021] Based on the first coefficient and the second coefficient, obtaining a matching degree between voltage and temperature change of the first battery cell;

[0022] Based on the change matching degree, the abnormal weight of each battery cell is obtained.

[0023] As a specific solution in the technical solution of the present application, the obtaining of the voltage and temperature change matching degree of the first battery cell based on the first coefficient and the second coefficient includes:

[0024] Using a dynamic time warping algorithm to match each first coefficient with each second coefficient, and obtaining a data distance between each first coefficient and the corresponding second coefficient;

[0025] Based on the data distance, a matching degree between the voltage and the temperature change of the first battery cell is obtained.

[0026] As a specific solution in the technical solution of the present application, the calculation formula for obtaining the matching degree of the voltage and temperature change of the first battery cell based on the data distance is as follows:

[0027] ;

[0028] in, Indicates the matching degree of voltage and temperature change of the nth battery cell in the mth discharge segment data; Indicates the data distance between the tth first coefficient and the second coefficient of the nth battery cell in the mth discharge segment data; Indicates the data value of the t-th first coefficient of the n-th battery cell in the m-th discharge segment data; represents the maximum temperature value of the nth battery cell in the mth discharge segment data; T represents the number of time series data values ​​of the nth battery cell in the mth discharge segment data; b is any positive number greater than or equal to 0.

[0029] As a specific solution in the technical solution of the present application, the abnormal weight of each battery cell is obtained based on the change matching degree, including:

[0030] Based on the first coefficients of each time series of the first battery cell, obtaining a first coefficient sequence;

[0031] Based on the change matching degree and the first coefficient sequence, obtaining an abnormal factor of the first battery cell by means of a principal component analysis algorithm;

[0032] Based on the abnormal factors, the abnormal weight of each battery cell is obtained.

[0033] As a specific solution in the technical solution of the present application, the calculation formula for obtaining the abnormal factor of the first battery cell based on the change matching degree and the first coefficient sequence with the help of the principal component analysis algorithm is as follows:

[0034] ;

[0035] in, Indicates the abnormal factor of the nth battery cell in the mth discharge segment data; Indicates the matching degree of voltage and temperature change of the nth battery cell in the mth discharge segment data; Indicates the slope of the principal component direction of the nth battery cell in the mth discharge segment data; Represents a normalization function, which is used to normalize the values ​​in brackets.

[0036] As a specific solution in the technical solution of the present application, the abnormal weight of each battery cell is obtained based on the abnormal factor, including:

[0037] Based on the current discharge segment data, acquiring the motor speed data of the electric vehicle; the motor speed data at least includes a plurality of time series values ​​of the motor speed;

[0038] Based on the motor speed data, a plurality of data sets are obtained; each data set is a set of time series values ​​of all motor speeds with a corresponding moment in the motor speed data as the center and a local range of ten seconds;

[0039] Based on multiple data sets, obtain the standard deviation corresponding to each data set;

[0040] Based on each standard deviation, obtain information entropy;

[0041] Based on the Pearson function, the Pearson coefficient is obtained from the abnormal factor and the information entropy;

[0042] Based on the Pearson coefficient, the abnormal weight of each battery cell is obtained.

[0043] In a second aspect, the present application provides a SOC adaptive correction device for a lithium battery pack, the SOC adaptive correction device for the lithium battery pack comprising:

[0044] A reading module, used to obtain the usage data of the lithium battery pack; the lithium battery pack includes a plurality of battery cells; the usage data includes the electrical parameters of the lithium battery pack, the electrical parameters of each battery cell, and the parameters of the electric vehicle motor;

[0045] A processing module, configured to obtain an abnormality weight of each battery cell based on the usage data; the abnormality weight is at least used to indicate an abnormality degree of the corresponding battery cell;

[0046] And, negative correlation normalization processing is performed on each abnormal weight to obtain a normalized weight;

[0047] The control module is used to adjust the load of each battery cell based on the normalized weight.

[0048] As a specific solution in the technical solution of the present application, the processing module is further used to obtain current discharge segment data based on the usage data; the start time of the current discharge segment data is the stop time of the last charging, and the end time of the current discharge segment data is the start time of the current charging;

[0049] And, based on each battery cell, obtaining a first battery cell; the first battery cell is any one of the battery cells;

[0050] And, based on the current discharge segment data, a first voltage and a second voltage are obtained; the first voltage is the voltage of the first battery cell at any time in the current discharge segment data; the second voltage is the average value of the voltages of each battery cell at the same time as the first voltage;

[0051] And, based on the first voltage and the second voltage, a first coefficient is obtained; the first coefficient is at least used to represent the voltage difference between the first battery cell and the remaining battery cells;

[0052] And, based on the first coefficient, the abnormal weight of each battery cell is obtained.

[0053] As a specific solution in the technical solution of the present application, the processing module is further used to obtain the time series temperature value of the first battery cell based on the current discharge segment data;

[0054] And, based on each time series temperature value, obtaining a maximum temperature value;

[0055] And, based on each time series temperature value and the maximum temperature value, a second coefficient is obtained; the second coefficient is at least used to represent the difference between each time series temperature value and the maximum temperature value;

[0056] and, based on the first coefficient and the second coefficient, obtaining a matching degree between voltage and temperature change of the first battery cell;

[0057] And, based on the change matching degree, the abnormal weight of each battery cell is obtained.

[0058] As a specific solution in the technical solution of the present application, the processing module is further used to match each first coefficient with each second coefficient using a dynamic time warping algorithm to obtain a data distance between each first coefficient and the corresponding second coefficient;

[0059] And, based on the data distance, the matching degree between the voltage and temperature change of the first battery cell is obtained.

[0060] As a specific solution in the technical solution of the present application, the processing module obtains the calculation formula of the voltage and temperature change matching degree of the first battery cell based on the data distance as follows:

[0061] ;

[0062] in, Indicates the matching degree of voltage and temperature change of the nth battery cell in the mth discharge segment data; Indicates the data distance between the tth first coefficient and the second coefficient of the nth battery cell in the mth discharge segment data; Indicates the data value of the t-th first coefficient of the n-th battery cell in the m-th discharge segment data; represents the maximum temperature value of the nth battery cell in the mth discharge segment data; T represents the number of time series data values ​​of the nth battery cell in the mth discharge segment data; b is any positive number greater than or equal to 0.

[0063] As a specific solution in the technical solution of the present application, the processing module is further used to obtain a first coefficient sequence based on the first coefficient of each time series of the first battery cell;

[0064] and, based on the change matching degree and the first coefficient sequence, obtaining an abnormal factor of the first battery cell by means of a principal component analysis algorithm;

[0065] And, based on the abnormal factor, the abnormal weight of each battery cell is obtained.

[0066] As a specific solution in the technical solution of the present application, the processing module obtains the calculation formula of the abnormal factor of the first battery cell based on the change matching degree and the first coefficient sequence with the help of the principal component analysis algorithm as follows:

[0067] ;

[0068] in, Indicates the abnormal factor of the nth battery cell in the mth discharge segment data; Indicates the matching degree of voltage and temperature change of the nth battery cell in the mth discharge segment data; Indicates the slope of the principal component direction of the nth battery cell in the mth discharge segment data; Represents a normalization function, which is used to normalize the values ​​in brackets.

[0069] As a specific solution in the technical solution of the present application, the processing module is further used to obtain the motor speed data of the electric vehicle based on the current discharge segment data; the motor speed data at least includes a plurality of motor speed time series values;

[0070] And, based on the motor speed data, a plurality of data sets are obtained; each data set is a set of time series values ​​of all motor speeds with a corresponding moment in the motor speed data as the center and a local range of ten seconds;

[0071] And, based on the multiple data sets, obtaining a standard deviation corresponding to each data set;

[0072] And, based on each standard deviation, obtain the information entropy;

[0073] And, based on the Pearson function, obtaining the Pearson coefficient from the abnormal factor and the information entropy;

[0074] And, based on the Pearson coefficient, the abnormal weight of each battery cell is obtained.

[0075] In a third aspect, the present application proposes a SOC adaptive correction system for a lithium battery pack, the SOC adaptive correction system for a lithium battery pack comprising:

[0076] A reader, used to obtain usage data of the lithium battery pack; the lithium battery pack includes a plurality of battery cells; the usage data includes electrical parameters of the lithium battery pack, electrical parameters of each battery cell, and parameters of the electric vehicle motor;

[0077] A processor, configured to obtain an abnormality weight of each battery cell based on the usage data; the abnormality weight is at least used to indicate an abnormality degree of the corresponding battery cell;

[0078] And, negative correlation normalization processing is performed on each abnormal weight to obtain a normalized weight;

[0079] The controller is used to adjust the load of each battery cell based on the normalized weight.

[0080] As a specific solution in the technical solution of the present application, the processor is further used to obtain current discharge segment data based on the usage data; the start time of the current discharge segment data is the stop time of the last charging, and the end time of the current discharge segment data is the start time of the current charging;

[0081] And, based on each battery cell, obtaining a first battery cell; the first battery cell is any one of the battery cells;

[0082] And, based on the current discharge segment data, a first voltage and a second voltage are obtained; the first voltage is the voltage of the first battery cell at any time in the current discharge segment data; the second voltage is the average value of the voltages of each battery cell at the same time as the first voltage;

[0083] And, based on the first voltage and the second voltage, a first coefficient is obtained; the first coefficient is at least used to represent the voltage difference between the first battery cell and the remaining battery cells;

[0084] And, based on the first coefficient, the abnormal weight of each battery cell is obtained.

[0085] As a specific solution in the technical solution of the present application, the processor is further used to obtain the time series temperature value of the first battery cell based on the current discharge segment data;

[0086] And, based on each time series temperature value, obtaining a maximum temperature value;

[0087] And, based on each time series temperature value and the maximum temperature value, a second coefficient is obtained; the second coefficient is at least used to represent the difference between each time series temperature value and the maximum temperature value;

[0088] and, based on the first coefficient and the second coefficient, obtaining a matching degree between voltage and temperature change of the first battery cell;

[0089] And, based on the change matching degree, the abnormal weight of each battery cell is obtained.

[0090] As a specific solution in the technical solution of the present application, the processor is further used to match each first coefficient with each second coefficient using a dynamic time warping algorithm to obtain a data distance between each first coefficient and the corresponding second coefficient;

[0091] And, based on the data distance, the matching degree between the voltage and temperature change of the first battery cell is obtained.

[0092] As a specific solution in the technical solution of the present application, the processor obtains the calculation formula of the voltage and temperature change matching degree of the first battery cell based on the data distance as follows:

[0093] ;

[0094] in, Indicates the matching degree of voltage and temperature change of the nth battery cell in the mth discharge segment data; Indicates the data distance between the tth first coefficient and the second coefficient of the nth battery cell in the mth discharge segment data; Indicates the data value of the t-th first coefficient of the n-th battery cell in the m-th discharge segment data; represents the maximum temperature value of the nth battery cell in the mth discharge segment data; T represents the number of time series data values ​​of the nth battery cell in the mth discharge segment data; b is any positive number greater than or equal to 0.

[0095] As a specific solution in the technical solution of the present application, the processor is further used to obtain a first coefficient sequence based on the first coefficient of each time series of the first battery cell;

[0096] and, based on the change matching degree and the first coefficient sequence, obtaining an abnormal factor of the first battery cell by means of a principal component analysis algorithm;

[0097] And, based on the abnormal factor, the abnormal weight of each battery cell is obtained.

[0098] As a specific solution in the technical solution of the present application, the processor obtains the calculation formula of the abnormal factor of the first battery cell based on the change matching degree and the first coefficient sequence with the help of the principal component analysis algorithm as follows:

[0099] ;

[0100] in, Indicates the abnormal factor of the nth battery cell in the mth discharge segment data; Indicates the matching degree of voltage and temperature change of the nth battery cell in the mth discharge segment data; Indicates the slope of the principal component direction of the nth battery cell in the mth discharge segment data; Represents a normalization function, which is used to normalize the values ​​in brackets.

[0101] As a specific solution in the technical solution of the present application, the processor is further used to obtain the motor speed data of the electric vehicle based on the current discharge segment data; the motor speed data at least includes a plurality of time series values ​​of the motor speed;

[0102] And, based on the motor speed data, a plurality of data sets are obtained; each data set is a set of time series values ​​of all motor speeds with a corresponding moment in the motor speed data as the center and a local range of ten seconds;

[0103] And, based on the multiple data sets, obtaining a standard deviation corresponding to each data set;

[0104] And, based on each standard deviation, obtain the information entropy;

[0105] And, based on the Pearson function, obtaining the Pearson coefficient from the abnormal factor and the information entropy;

[0106] And, based on the Pearson coefficient, the abnormal weight of each battery cell is obtained.

[0107] Compared with the prior art, the beneficial effects of this application are:

[0108] The present application calls and analyzes the usage data of the lithium battery pack, first constructs the first coefficient sequence inside the battery cell, analyzes based on the electrochemical reaction characteristics inside the battery cell, obtains the abnormal factors in each battery cell, and then performs correlation analysis based on the motor load of the electric vehicle loaded with the lithium battery pack. Finally, the abnormal weight of each battery cell is obtained, and active balancing management is performed based on the abnormal weight, so as to achieve the purpose of improving the subsequent SOC correction accuracy by improving the consistency of the voltage of each battery cell. BRIEF DESCRIPTION OF THE DRAWINGS

[0109] Figure 1 A schematic diagram of a flow chart of a method for adaptively correcting the SOC of a lithium battery pack proposed in an embodiment of the present application;

[0110] Figure 2 A schematic diagram of the structure of a SOC adaptive correction device for a lithium battery pack proposed in an embodiment of the present application;

[0111] Figure 3 A structural schematic diagram of a SOC adaptive correction system for a lithium battery pack proposed in an embodiment of the present application. DETAILED DESCRIPTION

[0112] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0113] The terms "first", "second", etc. in the specification of the embodiment of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence, such as the first coefficient and the second coefficient proposed below, which belong to different coefficients. It should be understood that the coefficients used in this way can be interchanged where appropriate, so that the embodiments described here can be implemented in an order other than the content illustrated or described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device including a series of steps or modules need not be limited to those steps or modules clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. The division of the modules that appear in the embodiment of the present application is only a logical division. There may be other division methods when implemented in practical applications, such as multiple modules can be combined or integrated into another system, or some features can be ignored, or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, and the indirect coupling or communication connection between modules can be electrical or other similar forms, which are not limited in the embodiment of the present application. Furthermore, the modules or sub-modules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed in multiple circuit modules, and some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiment of the present application.

[0114] In order to solve the technical problem that the SOC estimated by the SOC adaptive correction method of the prior art proposed in the background technology is of low accuracy, the present application proposes an embodiment of a SOC adaptive correction method for a lithium battery pack, and the SOC adaptive correction method for a lithium battery pack is applied to electric vehicles. Figure 1 As shown, the SOC adaptive correction method of the lithium battery pack includes steps S100 to S400.

[0115] Step S100: Acquire usage data of the lithium battery pack.

[0116] It should be noted that in this embodiment, the lithium battery pack includes multiple battery cells. The usage data includes electrical parameters of the lithium battery pack, electrical parameters of each battery cell, and parameters of the electric vehicle motor. Acquiring these parameters is a mature technology and will not be described in detail here.

[0117] Step S200: based on the usage data, obtaining the abnormal weight of each battery cell.

[0118] It should be noted that the battery cells inside the lithium battery pack will have certain inconsistencies due to process and other reasons during the manufacturing process. With the use of cyclic charge and discharge, the aging rates of different battery cells may be different, which will cause the inconsistency of various performance parameters of the battery cells (such as internal resistance or capacity, etc.) to gradually expand with continuous charge and discharge cycles. At this time, for such battery cells, active battery balancing management is required to transfer the energy of high-energy batteries to low-energy batteries, thereby reducing the gap between cells and maintaining the balance of the electrical parameters actually output by each battery cell.

[0119] In the application scenarios of electric vehicles, since the operating conditions of the vehicle where the lithium battery pack is located are usually relatively complex, the corresponding battery discharge state changes are extremely frequent. For example, inaccurate calculation of the SOC value is usually caused by a variety of driving behaviors and working conditions. If the electric vehicle needs to accelerate suddenly, the lithium battery pack needs to release a large discharge current, which will cause the battery voltage to drop rapidly. The calculation of SOC is often related to voltage, so the rapid acceleration of the electric vehicle may cause the SOC display to jump or be inaccurate. Similarly, the recovery of energy through the regenerative braking system during rapid deceleration may also affect the accurate estimation of SOC. Or long-term high-load driving, such as climbing or high-speed driving, will increase the battery discharge rate, which will also cause the SOC to drop faster than expected, affecting the accurate calculation of SOC. The above behaviors will lead to the expansion of the inconsistency between the battery cells in the lithium battery. And because the operating parameters between the battery cells themselves are different, the conventional abnormal identification method usually makes an incorrect abnormal judgment on the voltage data of the battery cell. Therefore, it is necessary to perform a long-term correlation analysis based on the historical electrical parameters of the current lithium battery pack and the corresponding vehicle drive motor parameters, and identify the battery cells with deviations in voltage performance based on the correlation. In the embodiment of the present application, the abnormality degree is used to evaluate the abnormality degree of the battery cell, that is, the abnormality weight mentioned above is at least used to represent the abnormality degree of the corresponding battery cell.

[0120] In an embodiment of the present application, any suitable method may be used to obtain the abnormal weight of each battery cell based on the usage data. For example, in an embodiment of the present application, step S200, based on the usage data, obtains the abnormal weight of each battery cell, including steps S201 to S205.

[0121] Step S201: based on the usage data, obtaining current discharge segment data.

[0122] In an embodiment of the present application, the start time of the current discharge segment data is the stop time of the last charge, and the end time of the current discharge segment data is the start time of the current charge.

[0123] Step S202: based on each battery cell, obtain a first battery cell.

[0124] In the embodiment of the present application, the first battery cell is any one of the battery cells. That is, in the embodiment of the present application, the quality of each battery cell in the lithium battery pack needs to be evaluated, that is, the abnormal weight of each battery cell needs to be obtained.

[0125] Step S203: Based on the current discharge segment data, a first voltage and a second voltage are acquired.

[0126] In an embodiment of the present application, the first voltage is the voltage of the first battery cell at any time in the current discharge segment data, and the second voltage is the average value of the voltages of each battery cell at the same time as the first voltage.

[0127] It should be noted that in the scenario of battery cell discharge, when the voltage data of a battery cell deviates significantly from the other battery cells, it indicates that the consistency of the internal operating parameters of the battery cell at this time is greatly different from that of the other batteries, and the difference will increase with the increase of usage time, mainly manifested as the heat generated by the electrochemical reaction inside the battery cell gradually accumulates, further causing the degree of deviation to show an increasing trend. In the embodiment of the present application, the abnormal weight of the first battery cell can be characterized by the difference between the first voltage and the second voltage.

[0128] Step S204: obtaining a first coefficient based on the first voltage and the second voltage.

[0129] It should be clear that the first coefficient is at least used to indicate the voltage difference between the first battery cell and the other battery cells. In the embodiment of the present application, the first coefficient can be the ratio of the first voltage to the second voltage, or the absolute value of the difference between the first voltage and the second voltage. It is easy to understand that the larger the first coefficient is, the more abnormal the first battery cell is.

[0130] Step S205: based on the first coefficient, obtaining the abnormal weight of each battery cell.

[0131] In the embodiment of the present application, the first coefficient can be directly used as the abnormal weight of the first battery cell. As can be seen from the foregoing, the larger the first coefficient is, the more abnormal the first battery cell is.

[0132] It should be noted that if the first battery cell is abnormal, the temperature of the first battery cell must be different from the temperature of other battery cells during use. Therefore, in order to accurately obtain the abnormal weight of the first battery cell, in the embodiment of the present application, the abnormal weight of each battery cell can also be obtained through the temperature of each battery cell. If the change and fluctuation of the battery cell voltage shows a local abnormality, this abnormality is relatively different from the trend deviation mentioned above, and its deviation value in the local range shows a large mutation change. This situation is caused by the internal battery cell working condition of the battery cell. At this time, based on the voltage deviation obtained at this moment (that is, the first coefficient in the previous text), when performing correlation analysis with the temperature below, interference will occur. In order to eliminate these interferences, in the embodiment of the present application, step S205, based on the first coefficient, obtains the abnormal weight of each battery cell, including steps S206 to S210.

[0133] Step S206: Based on the current discharge segment data, the time-series temperature value of the first battery cell is obtained.

[0134] It should be clear that the current discharge segment data includes the time-series temperature values ​​of each battery cell in the corresponding time period. It is a mature technology to screen and obtain the time-series temperature value of the first battery cell from the time-series temperature values ​​of multiple battery cells, which will not be described in detail here.

[0135] Step S207: based on each time series temperature value, obtain the maximum temperature value.

[0136] It should be clear that obtaining the maximum value of these values ​​from various values ​​is also a mature technology and will not be elaborated here.

[0137] Step S208: obtaining a second coefficient based on each time series temperature value and the maximum temperature value.

[0138] It should be noted that, as can be seen from the foregoing, the voltage of each battery cell is in direct proportion to the amount of electricity contained therein, that is, the more electricity a battery cell has, the higher the voltage of the battery cell; the less electricity a battery cell has, the lower the voltage of the battery cell. As can be seen from the foregoing, when an electric vehicle is rapidly accelerated, the current of each battery cell increases, the amount of electricity in each battery cell will be rapidly lost, and the voltage of each battery cell will also drop rapidly. It is easy to understand that during the rapid acceleration of an electric vehicle, the temperature of each battery cell will also rise accordingly. If there is a problem with a battery itself, the corresponding temperature will also be abnormal with a high probability. Therefore, in an embodiment of the present application, the second coefficient is at least used to represent the difference between each timing temperature value and the maximum temperature value.

[0139] In the embodiment of the present application, the second coefficient can be obtained based on each sequential temperature value and the maximum temperature value in any reasonable manner. For example, the second coefficient can be the ratio of the sequential temperature value to the maximum temperature value, or the second coefficient can be the absolute value of the difference between the sequential temperature value and the maximum temperature value.

[0140] Step S209: Based on the first coefficient and the second coefficient, obtain the matching degree between the voltage and temperature change of the first battery cell.

[0141] As can be seen from the foregoing, for the first battery cell, there will be a first coefficient and a second coefficient for each timing. And there is a correlation between the first coefficient and the second coefficient. Relative to the voltage of the first battery cell, the temperature test of the first battery cell has a lag. For example, if the electric vehicle performs emergency acceleration, the voltage of the first battery cell will drop immediately, but the temperature of the first battery cell will not rise until a period of heat conduction. In order to enable each first coefficient and each second coefficient of the first battery cell to match in timing, in one embodiment of the present application, step S209, based on the first coefficient and the second coefficient, obtains the voltage and temperature change matching degree of the first battery cell, including step S211 and step S212.

[0142] Step S211: Use a dynamic time warping algorithm to match each first coefficient with each second coefficient to obtain the data distance between each first coefficient and the corresponding second coefficient.

[0143] It should be noted that the Dynamic Time Warping (DTW) algorithm is a method for measuring the similarity between two time series, and is particularly suitable for processing time series data of unequal lengths. The algorithm calculates the nonlinear mapping relationship between the two sequences to minimize the cumulative distance between the two, thereby evaluating the similarity of the sequences. Since it is a mature technology, it will not be described in detail here.

[0144] Step S212: Based on the data distance, obtaining the matching degree between the voltage and temperature change of the first battery cell.

[0145] It is easy to understand that if the data distance between the first coefficient and the second coefficient is close, it means that the voltage and temperature changes of the first battery cell are caused by the parameter characteristics of the battery itself; if the data distance between the first coefficient and the second coefficient is far, it means that the voltage and temperature changes of the first battery cell are not caused by the parameter characteristics of the battery itself. In an embodiment of the present application, the data distance can be directly used as the matching degree of the voltage and temperature changes of the first battery cell. In order to more accurately obtain the matching degree of the voltage and temperature changes of the first battery cell, in one embodiment of the present application, step S212, based on the data distance, obtains the calculation formula for the matching degree of the voltage and temperature changes of the first battery cell as follows:

[0146] ;

[0147] in, Indicates the matching degree of voltage and temperature change of the nth battery cell in the mth discharge segment data; Indicates the data distance between the tth first coefficient and the second coefficient of the nth battery cell in the mth discharge segment data; Indicates the data value of the t-th first coefficient of the n-th battery cell in the m-th discharge segment data; represents the maximum temperature value of the nth battery cell in the mth discharge segment data; T represents the number of time series data values ​​of the nth battery cell in the mth discharge segment data; b is any positive number greater than or equal to 0.

[0148] It should be clear that Indicates the data distance between the tth first coefficient and the second coefficient of the nth battery cell in the mth discharge segment data. The smaller the data distance, the higher the correlation between the first coefficient sequence characterizing the first battery cell and the corresponding second coefficient sequence, that is, the more obvious the abnormal working condition inside the first battery cell is compared with the other battery cells, that is, the lower the consistency between the first battery cell and the other battery cells, that is, the more abnormal the first battery cell is. The purpose of obtaining the data distance is to avoid the random mutation of the first battery cell during the discharge process, which may cause the first coefficient sequence of the first battery cell to be unable to accurately match the corresponding second data sequence, resulting in inaccurate abnormal weights. This is to avoid heat transfer from other battery cells that may have abnormalities, which may increase the temperature of the first battery cell and cause the subsequent acquired abnormal weight to be inaccurate, which will not be elaborated here.

[0149] Step S210: based on the change matching degree, obtaining the abnormal weight of each battery cell.

[0150] It should be clear that in the embodiments of the present application, the change matching degree can be directly used as the abnormal weight of each battery cell. It should be noted that, as can be seen from the foregoing, if a battery cell is abnormal, the difference between its voltage and the voltage of the remaining battery cells will gradually increase during the discharge process, which is the first coefficient in the foregoing. In order to more accurately obtain the abnormal weight of each battery cell, in the embodiments of the present application, step S210, based on the change matching degree, obtains the abnormal weight of each battery cell, including steps S213 to S215.

[0151] Step S213: acquiring a first coefficient sequence based on the first coefficients of each time sequence of the first battery cell.

[0152] It should be noted that the first coefficient of each time sequence of the first battery cell is obtained as described in step S201 to step S204 above, and will not be described in detail here.

[0153] Step S214: Based on the change matching degree and the first coefficient sequence, an abnormal factor of the first battery cell is obtained by means of a principal component analysis algorithm.

[0154] It should be clear that the principal component analysis algorithm (PCA) is a statistical method mainly used for dimensionality reduction and feature extraction. The basic idea is to convert the original data into a set of new orthogonal bases through linear transformation. These new orthogonal bases are called principal components. Each principal component is a linear combination of the original data, but they are independent of each other. The goal of PCA is to find these principal components so that the first principal component has the largest variance, the second principal component is uncorrelated with the first principal component and has the second largest variance, and so on. Since the principal component analysis algorithm is a mature technology, it will not be elaborated. Specifically, in step S214, based on the change matching degree and the first coefficient sequence, the calculation formula for obtaining the abnormal factor of the first battery cell with the help of the principal component analysis algorithm is as follows:

[0155] ;

[0156] in, Indicates the abnormal factor of the nth battery cell in the mth discharge segment data; Indicates the matching degree of voltage and temperature change of the nth battery cell in the mth discharge segment data; Indicates the slope of the principal component direction of the nth battery cell in the mth discharge segment data; Represents a normalization function, which is used to normalize the values ​​in brackets.

[0157] It should be noted that if the principal component direction slope The larger it is, the more serious the abnormality of the operating parameters inside the first battery cell compared with the other battery cells in the same period of time, and the higher the degree of matching with the temperature, indicating that the abnormal operating parameters are usually caused mainly by self-heating. At this time, the first battery cell has a higher inconsistency difference in the lithium battery pack than the other battery cells.

[0158] Step S215: based on the abnormal factors, obtaining the abnormal weight of each battery cell.

[0159] It should be clear that in the embodiments of the present application, the abnormal factor can be used as the abnormal weight of the corresponding battery cell. It should be noted that the abnormal factor of each battery cell in a certain discharge segment mainly characterizes the performance of the operating parameters of the battery cell in this discharge segment, and when the operating parameters of the battery cell are abnormal, they will gradually accumulate and increase with the increase in the number of charge and discharge cycles. At this time, the inconsistency between the above-mentioned battery cells will become more and more obvious. And the power consumption behavior corresponding to each discharge segment, that is, the complex situation of the electric vehicle motor drive, will also increase the abnormal performance of its operating parameters. In order to obtain a more accurate abnormal weight, in one embodiment of the present application, step S215, based on the abnormal factor, obtains the abnormal weight of each battery cell, including steps S216 to S221.

[0160] Step S216: Based on the current discharge segment data, the motor speed data of the electric vehicle is obtained.

[0161] It should be understood that, in this embodiment, the motor speed data at least includes a plurality of motor speed time series values.

[0162] Step S217: Based on the motor speed data, multiple data sets are acquired.

[0163] It should be understood that, in this embodiment, each data set is a collection of time series values ​​of all motor speeds with a corresponding moment in the motor speed data as the center and ten seconds as the local range.

[0164] Step S218: Based on the multiple data sets, obtain the standard deviation corresponding to each data set.

[0165] It should be clear that based on multiple data values ​​in a data set, obtaining the standard deviation of each data value is a mature technology and will not be described in detail here.

[0166] Step S219: Obtain information entropy based on each standard deviation.

[0167] It should be clear that obtaining information entropy based on multiple standard deviations is also a mature technology and will not be elaborated here. It is easy to understand that if the information entropy is more complex, it means that the load of the battery cell is higher, and the abnormal accumulation and accumulation speed of its operating parameters are relatively higher. In other words, if the information entropy is more complex, the abnormal factor of the abnormal battery cell should also be larger. In other words, if the battery cell is abnormal, the corresponding information entropy and abnormal factor are positively correlated.

[0168] Step S220: Based on the Pearson function, the Pearson coefficient is obtained from the abnormal factor and the information entropy.

[0169] It should be clear that obtaining the Pearson coefficient based on the Pearson function from the abnormal factor and the information entropy is a mature technology and will not be described in detail here.

[0170] Step S221: Based on the Pearson coefficient, obtain the abnormal weight of each battery cell.

[0171] It is easy to understand that in this embodiment, the larger the Pearson coefficient, the more obvious the abnormal performance of the electrochemical reaction in the internal battery cell of the corresponding battery cell is as the number of discharges increases, and the higher the correlation between the abnormal degree and the load brought by the corresponding motor speed, which further indicates that the abnormal performance of the battery cell is more serious than that of the other battery cells. In other words, the higher the Pearson coefficient, the greater the abnormal weight of the corresponding battery cell. In this embodiment, the obtained Pearson coefficient can be directly used as the abnormal weight of the battery cell.

[0172] Step S300: performing negative correlation normalization processing on each abnormal weight to obtain a normalized weight.

[0173] It should be clear that normalizing multiple data values ​​is a mature technology and will not be described in detail here.

[0174] Step S400: adjusting the load of each battery cell based on the normalized weight.

[0175] It should be noted that after obtaining the abnormal weight of each battery cell, the BMS battery management system adjusts the actual load borne by each battery cell based on the abnormal weight. For example, electronic switches and power devices (such as inductors, capacitors, transformers, converters, etc.) are used to transfer the energy of the high-voltage battery to the low-voltage battery, thereby ensuring that the actual output voltage is as close as possible to the voltage predicted by the system. That is, the more accurate the voltage parameters are, the more accurate the adaptive correction accuracy of the SOC of the lithium battery pack can be achieved.

[0176] It should be clear that the embodiment of the SOC adaptive correction method for the lithium battery pack proposed in the present application, by calling and analyzing the usage data of the lithium battery pack, first constructs the first coefficient sequence inside the battery cell, analyzes based on the electrochemical reaction characteristics inside the battery cell, and then obtains the abnormal factors in each battery cell, and then performs correlation analysis based on the motor load of the electric vehicle loaded with the lithium battery pack, and finally obtains the abnormal weight of each battery cell, and performs active balancing management based on the abnormal weight, so as to achieve the purpose of improving the subsequent SOC correction accuracy by improving the consistency of the voltage of each battery cell.

[0177] After introducing the SOC adaptive correction method of the lithium battery pack proposed in the embodiment of the present application, the following introduces a SOC adaptive correction device of the lithium battery pack proposed in the present application, such as Figure 2 As shown, the SOC adaptive correction device 10 of the lithium battery pack includes:

[0178] The reading module 11 is used to obtain the usage data of the lithium battery pack; the lithium battery pack includes a plurality of battery cells; the usage data includes the electrical parameters of the lithium battery pack, the electrical parameters of each battery cell, and the parameters of the electric vehicle motor;

[0179] The processing module 12 is used to obtain the abnormal weight of each battery cell based on the usage data; the abnormal weight is at least used to indicate the abnormal degree of the corresponding battery cell;

[0180] And, negative correlation normalization processing is performed on each abnormal weight to obtain a normalized weight;

[0181] The control module 13 is used to adjust the load of each battery cell based on the normalized weight.

[0182] As a specific embodiment of the present application, the processing module 12 is further used to obtain current discharge segment data based on the usage data; the start time of the current discharge segment data is the stop time of the last charging, and the end time of the current discharge segment data is the start time of the current charging;

[0183] And, based on each battery cell, obtaining a first battery cell; the first battery cell is any one of the battery cells;

[0184] And, based on the current discharge segment data, a first voltage and a second voltage are obtained; the first voltage is the voltage of the first battery cell at any time in the current discharge segment data; the second voltage is the average value of the voltages of each battery cell at the same time as the first voltage;

[0185] And, based on the first voltage and the second voltage, a first coefficient is obtained; the first coefficient is at least used to represent the voltage difference between the first battery cell and the remaining battery cells;

[0186] And, based on the first coefficient, the abnormal weight of each battery cell is obtained.

[0187] As a specific embodiment of the present application, the processing module 12 is further used to obtain the time series temperature value of the first battery cell based on the current discharge segment data;

[0188] And, based on each time series temperature value, obtaining a maximum temperature value;

[0189] And, based on each time series temperature value and the maximum temperature value, a second coefficient is obtained; the second coefficient is at least used to represent the difference between each time series temperature value and the maximum temperature value;

[0190] and, based on the first coefficient and the second coefficient, obtaining a matching degree between voltage and temperature change of the first battery cell;

[0191] And, based on the change matching degree, the abnormal weight of each battery cell is obtained.

[0192] As a specific embodiment of the present application, the processing module 12 is further used to match each first coefficient with each second coefficient using a dynamic time warping algorithm to obtain a data distance between each first coefficient and the corresponding second coefficient;

[0193] And, based on the data distance, the matching degree between the voltage and temperature change of the first battery cell is obtained.

[0194] As a specific embodiment of the present application, the processing module 12 obtains the calculation formula of the voltage and temperature change matching degree of the first battery cell based on the data distance as follows:

[0195] ;

[0196] in, Indicates the matching degree of voltage and temperature change of the nth battery cell in the mth discharge segment data; Indicates the data distance between the tth first coefficient and the second coefficient of the nth battery cell in the mth discharge segment data; Indicates the data value of the t-th first coefficient of the n-th battery cell in the m-th discharge segment data; represents the maximum temperature value of the nth battery cell in the mth discharge segment data; T represents the number of time series data values ​​of the nth battery cell in the mth discharge segment data; b is any positive number greater than or equal to 0.

[0197] As a specific embodiment of the present application, the processing module 12 is further configured to obtain a first coefficient sequence based on the first coefficient of each time series of the first battery cell;

[0198] and, based on the change matching degree and the first coefficient sequence, obtaining an abnormal factor of the first battery cell by means of a principal component analysis algorithm;

[0199] And, based on the abnormal factor, the abnormal weight of each battery cell is obtained.

[0200] As a specific embodiment of the present application, the processing module 12 obtains the calculation formula of the abnormal factor of the first battery cell based on the change matching degree and the first coefficient sequence by means of the principal component analysis algorithm as follows:

[0201] ;

[0202] in, Indicates the abnormal factor of the nth battery cell in the mth discharge segment data; Indicates the matching degree of voltage and temperature change of the nth battery cell in the mth discharge segment data; Indicates the slope of the principal component direction of the nth battery cell in the mth discharge segment data; Represents a normalization function, which is used to normalize the values ​​in brackets.

[0203] As a specific embodiment of the present application, the processing module 12 is further used to obtain the motor speed data of the electric vehicle based on the current discharge segment data; the motor speed data at least includes a plurality of time series values ​​of the motor speed;

[0204] And, based on the motor speed data, a plurality of data sets are obtained; each data set is a set of time series values ​​of all motor speeds with a corresponding moment in the motor speed data as the center and a local range of ten seconds;

[0205] And, based on the multiple data sets, obtaining a standard deviation corresponding to each data set;

[0206] And, based on each standard deviation, obtain the information entropy;

[0207] And, based on the Pearson function, obtaining the Pearson coefficient from the abnormal factor and the information entropy;

[0208] And, based on the Pearson coefficient, the abnormal weight of each battery cell is obtained.

[0209] It should be clear that the embodiment of the SOC adaptive correction device for the lithium battery pack proposed in the present application, by calling and analyzing the usage data of the lithium battery pack, first constructs the first coefficient sequence inside the battery cell, analyzes based on the electrochemical reaction characteristics inside the battery cell, and then obtains the abnormal factors in each battery cell, and then performs correlation analysis based on the motor load of the electric vehicle loaded with the lithium battery pack, and finally obtains the abnormal weight of each battery cell, and performs active balancing management based on the abnormal weight, so as to achieve the purpose of improving the subsequent SOC correction accuracy by improving the consistency of the voltage of each battery cell.

[0210] After introducing the SOC adaptive correction device of the lithium battery pack proposed in the embodiment of the present application, the following introduces a SOC adaptive correction system of the lithium battery pack proposed in the present application, such as Figure 3 As shown, the SOC adaptive correction system 20 of the lithium battery pack includes:

[0211] The reader 21 is used to obtain the usage data of the lithium battery pack; the lithium battery pack includes a plurality of battery cells; the usage data includes the electrical parameters of the lithium battery pack, the electrical parameters of each battery cell, and the parameters of the electric vehicle motor;

[0212] The processor 22 is used to obtain an abnormality weight of each battery cell based on the usage data; the abnormality weight is at least used to indicate the abnormality degree of the corresponding battery cell;

[0213] And, negative correlation normalization processing is performed on each abnormal weight to obtain a normalized weight;

[0214] The controller 23 is used to adjust the load of each battery cell based on the normalized weight.

[0215] As a specific embodiment of the present application, the processor 22 is further used to obtain current discharge segment data based on the usage data; the start time of the current discharge segment data is the stop time of the last charging, and the end time of the current discharge segment data is the start time of the current charging;

[0216] And, based on each battery cell, obtaining a first battery cell; the first battery cell is any one of the battery cells;

[0217] And, based on the current discharge segment data, a first voltage and a second voltage are obtained; the first voltage is the voltage of the first battery cell at any time in the current discharge segment data; the second voltage is the average value of the voltages of each battery cell at the same time as the first voltage;

[0218] And, based on the first voltage and the second voltage, a first coefficient is obtained; the first coefficient is at least used to represent the voltage difference between the first battery cell and the remaining battery cells;

[0219] And, based on the first coefficient, the abnormal weight of each battery cell is obtained.

[0220] As a specific embodiment of the present application, the processor 22 is further configured to obtain a time series temperature value of the first battery cell based on the current discharge segment data;

[0221] And, based on each time series temperature value, obtaining a maximum temperature value;

[0222] And, based on each time series temperature value and the maximum temperature value, a second coefficient is obtained; the second coefficient is at least used to represent the difference between each time series temperature value and the maximum temperature value;

[0223] and, based on the first coefficient and the second coefficient, obtaining a matching degree between voltage and temperature change of the first battery cell;

[0224] And, based on the change matching degree, the abnormal weight of each battery cell is obtained.

[0225] As a specific embodiment of the present application, the processor 22 is further configured to match each first coefficient with each second coefficient using a dynamic time warping algorithm to obtain a data distance between each first coefficient and the corresponding second coefficient;

[0226] And, based on the data distance, the matching degree between the voltage and temperature change of the first battery cell is obtained.

[0227] As a specific embodiment of the present application, the processor 22 obtains the calculation formula of the voltage and temperature change matching degree of the first battery cell based on the data distance as follows:

[0228] ;

[0229] in, Indicates the matching degree of voltage and temperature change of the nth battery cell in the mth discharge segment data; Indicates the data distance between the tth first coefficient and the second coefficient of the nth battery cell in the mth discharge segment data; Indicates the data value of the t-th first coefficient of the n-th battery cell in the m-th discharge segment data; represents the maximum temperature value of the nth battery cell in the mth discharge segment data; T represents the number of time series data values ​​of the nth battery cell in the mth discharge segment data; b is any positive number greater than or equal to 0.

[0230] As a specific embodiment of the present application, the processor 22 is further configured to obtain a first coefficient sequence based on the first coefficient of each time series of the first battery cell;

[0231] and, based on the change matching degree and the first coefficient sequence, obtaining an abnormal factor of the first battery cell by means of a principal component analysis algorithm;

[0232] And, based on the abnormal factor, the abnormal weight of each battery cell is obtained.

[0233] As a specific embodiment of the present application, the processor 22 obtains the calculation formula of the abnormal factor of the first battery cell based on the change matching degree and the first coefficient sequence by means of the principal component analysis algorithm as follows:

[0234] ;

[0235] in, Indicates the abnormal factor of the nth battery cell in the mth discharge segment data; Indicates the matching degree of voltage and temperature change of the nth battery cell in the mth discharge segment data; Indicates the slope of the principal component direction of the nth battery cell in the mth discharge segment data; Represents a normalization function, which is used to normalize the values ​​in brackets.

[0236] As a specific embodiment of the present application, the processor 22 is further used to obtain the motor speed data of the electric vehicle based on the current discharge segment data; the motor speed data at least includes a plurality of motor speed time series values;

[0237] And, based on the motor speed data, a plurality of data sets are obtained; each data set is a set of time series values ​​of all motor speeds with a corresponding moment in the motor speed data as the center and a local range of ten seconds;

[0238] And, based on the multiple data sets, obtaining a standard deviation corresponding to each data set;

[0239] And, based on each standard deviation, obtain the information entropy;

[0240] And, based on the Pearson function, obtaining the Pearson coefficient from the abnormal factor and the information entropy;

[0241] And, based on the Pearson coefficient, the abnormal weight of each battery cell is obtained.

[0242] It should be clear that the embodiment of the SOC adaptive correction system for the lithium battery pack proposed in the present application, by calling and analyzing the usage data of the lithium battery pack, first constructs the first coefficient sequence inside the battery cell, analyzes based on the electrochemical reaction characteristics inside the battery cell, and then obtains the abnormal factors in each battery cell, and then performs correlation analysis based on the motor load of the electric vehicle loaded with the lithium battery pack, and finally obtains the abnormal weight of each battery cell, and performs active balancing management based on the abnormal weight, so as to achieve the purpose of improving the subsequent SOC correction accuracy by improving the consistency of the voltage of each battery cell.

[0243] After introducing the SOC adaptive correction system of the lithium battery pack proposed in the embodiments of the present application, a storage medium proposed in the present application is introduced below. The storage medium stores a corresponding computer program. After the computer program is run by the processor, the SOC adaptive correction method of the lithium battery pack proposed in any one of the above embodiments is executed.

[0244] It should be clear that the computer-readable storage media in this application include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs or other data. Examples of computer storage media include, but are not limited to, phase change memory, static random access memory, dynamic random access memory, other types of random access memory, read-only memory, electrically erasable programmable read-only memory, flash memory or other memory technology, read-only compact disc read-only memory, digital versatile disc or other optical storage, magnetic cassettes, disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include temporary computer-readable media, such as modulated data signals and carrier waves.

[0245] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0246] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the methods, devices and equipment described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0247] In the several embodiments provided in the embodiments of the present application, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0248] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0249] In addition, each functional module in each embodiment of the present application can be integrated into a processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0250] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product.

[0251] The computer program product includes one or more computer instructions. When the computer program is loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, a computer, a server, or a data center to another website site, a computer, a server, or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or a data center that includes one or more available media integrated therein. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disk), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

[0252] Although the embodiments of the present application have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles of the present application.

Claims

1. A method for adaptively correcting the SOC of a lithium battery pack, applied to electric vehicles; characterized in that: include: Obtaining usage data of the lithium battery pack; The lithium battery pack includes a plurality of battery cells; the usage data includes electrical parameters of the lithium battery pack, electrical parameters of each battery cell, and parameters of the motor of the electric vehicle; Based on the usage data, obtaining an abnormal weight of each battery cell; The abnormal weight is used to at least indicate the abnormal degree of the corresponding battery cell; Perform negative correlation normalization processing on each abnormal weight to obtain the normalized weight; Based on the normalized weight, adjusting the load of each battery cell; The obtaining of the abnormal weight of each battery cell based on the usage data includes: Based on the usage data, current discharge segment data is obtained; the start time of the current discharge segment data is the stop time of the last charge, and the end time of the current discharge segment data is the start time of the current charge; Based on each battery cell, a first battery cell is obtained; the first battery cell is any one of the battery cells; Based on the current discharge segment data, a first voltage and a second voltage are obtained; the first voltage is the voltage of the first battery cell at any time in the current discharge segment data; the second voltage is the average value of the voltages of each battery cell at the same time as the first voltage; Based on the first voltage and the second voltage, a first coefficient is obtained; the first coefficient is used to at least represent the voltage difference between the first battery cell and the remaining battery cells; Based on the first coefficient, obtaining the abnormal weight of each battery cell; Based on the first coefficient, the abnormal weight of each battery cell is obtained, including: Based on the current discharge segment data, acquiring a time-series temperature value of the first battery cell; Based on each time series temperature value, obtain the maximum temperature value; Based on each time series temperature value and the maximum temperature value, a second coefficient is obtained; the second coefficient is at least used to represent the difference between each time series temperature value and the maximum temperature value; Based on the first coefficient and the second coefficient, obtaining a matching degree between voltage and temperature change of the first battery cell; Based on the change matching degree, the abnormal weight of each battery cell is obtained.

2. The SOC adaptive correction method of a lithium battery pack according to claim 1, characterized in that: The obtaining, based on the first coefficient and the second coefficient, a matching degree between the voltage and the temperature change of the first battery cell includes: Using a dynamic time warping algorithm to match each first coefficient with each second coefficient, and obtaining a data distance between each first coefficient and the corresponding second coefficient; Based on the data distance, a matching degree between the voltage and the temperature change of the first battery cell is obtained.

3. The SOC adaptive correction method of a lithium battery pack according to claim 2, characterized in that: The calculation formula for obtaining the matching degree of the voltage and temperature change of the first battery cell based on the data distance is as follows: ； in, Indicates the matching degree of voltage and temperature change of the nth battery cell in the mth discharge segment data; Indicates the data distance between the tth first coefficient and the second coefficient of the nth battery cell in the mth discharge segment data; Indicates the data value of the t-th first coefficient of the n-th battery cell in the m-th discharge segment data; represents the maximum temperature value of the nth battery cell in the mth discharge segment data; T represents the number of time series data values ​​of the nth battery cell in the mth discharge segment data; b is any positive number greater than or equal to 0.

4. The SOC adaptive correction method of a lithium battery pack according to any one of claims 1 to 3, characterized in that: The obtaining of the abnormal weight of each battery cell based on the change matching degree includes: Based on the first coefficients of each time series of the first battery cell, obtaining a first coefficient sequence; Based on the change matching degree and the first coefficient sequence, obtaining an abnormal factor of the first battery cell by means of a principal component analysis algorithm; Based on the abnormal factors, the abnormal weight of each battery cell is obtained.

5. The SOC adaptive correction method of a lithium battery pack according to claim 4, characterized in that: The calculation formula for obtaining the abnormal factor of the first battery cell based on the change matching degree and the first coefficient sequence by means of the principal component analysis algorithm is as follows: ； in, Indicates the abnormal factor of the nth battery cell in the mth discharge segment data; Indicates the matching degree of voltage and temperature change of the nth battery cell in the mth discharge segment data; Indicates the slope of the principal component direction of the nth battery cell in the mth discharge segment data; Represents a normalization function, which is used to normalize the values ​​in brackets.

6. The SOC adaptive correction method of a lithium battery pack according to claim 4, characterized in that: The step of obtaining the abnormal weight of each battery cell based on the abnormal factor includes: Based on the current discharge segment data, acquiring the motor speed data of the electric vehicle; the motor speed data at least includes a plurality of time series values ​​of the motor speed; Based on the motor speed data, a plurality of data sets are obtained; each data set is a set of time series values ​​of all motor speeds with a corresponding moment in the motor speed data as the center and a local range of ten seconds; Based on multiple data sets, obtain the standard deviation corresponding to each data set; Based on each standard deviation, obtain information entropy; Based on the Pearson function, the Pearson coefficient is obtained from the abnormal factor and the information entropy; Based on the Pearson coefficient, the abnormal weight of each battery cell is obtained.

7. A SOC adaptive correction device for a lithium battery pack, the device being used to execute the steps of the SOC adaptive correction method for a lithium battery pack according to any one of claims 1 to 6, characterized in that: include: A reading module, used to obtain usage data of the lithium battery pack; The lithium battery pack includes a plurality of battery cells; the usage data includes electrical parameters of the lithium battery pack, electrical parameters of each battery cell, and parameters of the electric vehicle motor; A processing module, configured to obtain an abnormal weight of each battery cell based on the usage data; The abnormal weight is used to at least indicate the abnormal degree of the corresponding battery cell; And, negative correlation normalization processing is performed on each abnormal weight to obtain a normalized weight; The control module is used to adjust the load of each battery cell based on the normalized weight.

8. A SOC adaptive correction system for a lithium battery pack, the system being used to execute the steps of the SOC adaptive correction method for a lithium battery pack as claimed in any one of claims 1 to 6, characterized in that: include: A reader, used to obtain usage data of the lithium battery pack; The lithium battery pack includes a plurality of battery cells; the usage data includes electrical parameters of the lithium battery pack, electrical parameters of each battery cell, and parameters of the motor of the electric vehicle; A processor, configured to obtain an abnormal weight of each battery cell based on the usage data; The abnormal weight is used to at least indicate the abnormal degree of the corresponding battery cell; And, negative correlation normalization processing is performed on each abnormal weight to obtain a normalized weight; The controller is used to adjust the load of each battery cell based on the normalized weight.

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