A battery health status assessment method and device based on vehicle driving data
By using a method based on vehicle driving data, empirical mode decomposition and Pearson correlation coefficient to extract battery health features, and constructing an integrated learning model, the problem of traditional methods' reliance on charging data is solved, and accurate assessment of battery health status is achieved.
Patent Information
- Application Number
- CN202411349509.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-26
- Publication Date
- 2025-09-26
- Estimated Expiration
- 2044-09-26
AI Technical Summary
Traditional battery health status assessment methods based on electric vehicle big data rely on charging data and ignore rich driving data, resulting in low assessment accuracy.
By obtaining historical driving data of electric vehicles, the target segments are selected using the vehicle identification code, latitude and longitude coordinates and driving speed. The battery health features are extracted by combining empirical mode decomposition and Pearson correlation coefficient, and an integrated learning model is constructed for evaluation.
It improves the utilization rate of real vehicle data, realizes accurate assessment of battery health status, reduces dependence on charging data, and improves assessment accuracy.
Smart Images
Figure CN119780761B_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of electric vehicle big data analysis, and in particular to a battery health status assessment method and device based on vehicle driving data. Background Art
[0002] The development of electric vehicles is one of the key means to solve the fossil energy crisis, alleviate global warming, and promote carbon neutrality. As a core component of electric vehicles, the state of health (SOH) of power batteries has a significant impact on the vehicle's driving performance and safety. Therefore, accurate estimation of power battery SOH is one of the key guarantees for the safe and stable operation of electric vehicles. Traditional battery SOH assessment methods based on electric vehicle big data mostly rely on relatively stable electric vehicle charging data, ignoring the more abundant data resources of electric vehicle driving data, resulting in low utilization of actual vehicle data and affecting the accuracy of the assessment. Summary of the Invention
[0003] Electric vehicles have significant advantages over traditional fuel vehicles in terms of on-board data collection and processing. If the battery health factor can be extracted based on actual vehicle driving data, the utilization rate of real vehicle big data can be improved, the reliance of existing technologies on vehicle charging data can be reduced, and the battery aging mechanism contained in the changes in vehicle power performance can be fully considered. Therefore, the purpose of this application is to provide a battery health status assessment method and equipment based on vehicle driving data, which can improve the utilization rate of real vehicle data and achieve accurate assessment of battery health status.
[0004] To achieve the above objectives, this application provides the following solutions:
[0005] In a first aspect, the present application provides a method for evaluating the battery health status based on vehicle driving data, comprising:
[0006] Acquire historical driving data of the electric vehicle and retain corresponding reference data items; the reference data items include: vehicle speed, cumulative mileage, current, remaining power, latitude and longitude coordinates, maximum temperature, minimum temperature and vehicle identification code;
[0007] Selecting a target segment from the historical driving data based on the vehicle identification code, latitude and longitude coordinates, and vehicle driving speed in the reference data item;
[0008] Calculating a battery health status reference value based on the cumulative mileage, maximum temperature, and minimum temperature of the target segment;
[0009] calculating a capacity consumption and a current peak value according to the current of the target segment;
[0010] Performing empirical mode decomposition on the capacity consumption and current peak of the target segment to obtain components of the capacity consumption and components of the current peak, and using the Pearson correlation coefficient to select components whose correlation with the battery health state reference value is within a set range as battery health features;
[0011] Determine a remaining power sequence and an average temperature sequence based on the remaining power, maximum temperature, and minimum temperature of the target segment, and use the Pearson correlation coefficient to select sequences whose correlation with the battery health feature is within a set range as feature influencing factors;
[0012] Constructing an integrated learning model, and training the integrated learning model according to the battery health characteristics, characteristic influencing factors, and corresponding battery health status reference values of the target segment to obtain a trained integrated learning model;
[0013] The trained integrated learning model is used to evaluate the battery health state based on the current driving data of the electric vehicle to obtain a battery health state prediction value.
[0014] Optionally, historical driving data of the electric vehicle is obtained, and corresponding reference data items are retained, including:
[0015] From the real vehicle data of electric vehicles collected from the big data platform, the historical driving data of electric vehicles is screened based on the vehicle driving speed not being equal to 0, and the corresponding reference data items are retained.
[0016] Optionally, selecting a target segment from the historical driving data according to the vehicle identification code, latitude and longitude coordinates, and vehicle driving speed in the reference data item includes:
[0017] Selecting a data set having the same vehicle model and vehicle travel route from the historical travel data according to the vehicle identification code and the latitude and longitude coordinates;
[0018] According to the latitude and longitude coordinates, a straight line trajectory having a mileage span less than or equal to a set distance is selected from the data set for segmentation to obtain a plurality of discharge segments;
[0019] The average vehicle speed of each discharge segment is calculated according to the vehicle running speed, the average vehicle speed interval is divided, and the discharge segment with the largest average vehicle speed interval proportion is selected as the target segment.
[0020] Optionally, calculating a battery health state reference value according to the cumulative mileage, maximum temperature, and minimum temperature of the target segment includes:
[0021] The battery health status calculation formula is SOH = 1.084M -0.01884 -0.04368+0.00572T m+1.657×10 - 7 M m -0.0001524T m 2 +6.964×10 -9 T m M m , calculate the battery health status reference value SOH according to the cumulative mileage, maximum temperature and minimum temperature of the target segment; where M represents the cumulative mileage of the target segment, T m represents the monthly average temperature calculated based on the maximum and minimum temperatures of the target segment, M m Indicates the monthly cumulative mileage calculated based on the cumulative mileage of the target segment.
[0022] Optionally, calculating the capacity consumption and the current peak value according to the current of the target segment includes:
[0023] Using the capacity consumption calculation formula Calculating the capacity consumption CC according to the current of the target segment; wherein I(i) represents the current of the i-th sampling point in the target segment, and n represents the number of sampling points in the target segment;
[0024] Use the current peak calculation formula I max =max(I(1), I(2), ..., I(n)), calculate the peak current I according to the current of the target segment max ; where (I(1), I(2), ..., I(n)) represents the current of all sampling points in the target segment.
[0025] Optionally, performing empirical mode decomposition on the capacity consumption and the current peak of the target segment to obtain components of the capacity consumption and components of the current peak includes:
[0026] Performing empirical mode decomposition on the capacity consumption of the target segment to obtain components of the capacity consumption; the components of the capacity consumption include: multiple intrinsic mode functions corresponding to the capacity consumption and a residual;
[0027] Performing empirical mode decomposition on the current peak of the target segment to obtain components of the current peak; the components of the current peak include: multiple intrinsic mode functions corresponding to the current peak and a residual.
[0028] Optionally, determining a remaining power sequence and an average temperature sequence according to the remaining power, maximum temperature, and minimum temperature of the target segment includes:
[0029] Extracting the remaining power of the target segment to obtain a remaining power sequence;
[0030] Extract the maximum and minimum temperatures of the target fragment and use the formula and Calculate the average temperature T of the target fragment a , get the average temperature series; where T i represents the average temperature of the i-th sampling point in the target segment, n represents the number of sampling points in the target segment, T imax represents the maximum temperature of the i-th sampling point in the target segment, T imin Indicates the minimum temperature of the i-th sampling point in the target segment.
[0031] Optionally, the basic models of the integrated learning model include: two Gaussian process regression models and two support vector machine models; the output of the integrated learning model is the weighted sum of the outputs of the four basic models; during the training process, the weights of the four basic models are updated once every 5,000 kilometers, every 10,000 kilometers or every 20,000 kilometers according to the error of each basic model.
[0032] Optionally, the battery health status assessment method based on vehicle driving data further includes:
[0033] A battery degradation trajectory is drawn based on the battery health status prediction value.
[0034] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the battery health status assessment method based on vehicle driving data.
[0035] According to the specific embodiments provided in this application, this application discloses the following technical effects:
[0036] The present application provides a battery health status assessment method and device based on vehicle driving data. By acquiring the historical driving data of an electric vehicle and retaining the corresponding reference data items, namely the vehicle driving speed, cumulative mileage, current, remaining power, latitude and longitude coordinates, maximum temperature, minimum temperature and vehicle identification code, the battery health status reference value, capacity consumption, current peak, remaining power sequence and average temperature sequence are determined based on the reference data items of the historical driving data. The empirical mode decomposition and Pearson correlation coefficient are used to assist in extracting the battery health characteristics and characteristic influencing factors during the driving process, and an integrated learning model is constructed and trained. The battery health degradation mechanism related to driving behavior, operating conditions, etc. contained in the driving data is used to improve the accuracy of the battery health status assessment from the dimension of vehicle power performance changes. Therefore, the present application can improve the utilization rate of real vehicle data and achieve accurate assessment of battery health status. BRIEF DESCRIPTION OF THE DRAWINGS
[0037] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.
[0038] Figure 1 A flowchart of the battery health status assessment method based on vehicle driving data provided in this application;
[0039] Figure 2 A graph showing the percentage of different average speed ranges for a certain vehicle model provided for this application;
[0040] Figure 3 Empirical Mode Decomposition of Capacity Consumption for this application;
[0041] Figure 4 Empirical mode decomposition diagram of the current peak provided for this application;
[0042] Figure 5 Framework diagram of the integrated learning model provided for this application. DETAILED DESCRIPTION
[0043] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.
[0044] Due to their high degree of electrification, electric vehicles generate massive amounts of real-world vehicle data during operation. This driving data contains a large number of battery health degradation mechanisms related to driving behavior, operating conditions, and so on. This helps to increase the accuracy of battery SOH assessment from the perspective of vehicle power performance changes. The organic combination of real-world vehicle operation data and battery health degradation mechanisms will help achieve accurate SOH estimation of electric vehicle power batteries. In view of this, this application provides a battery health status assessment method and device based on vehicle driving data, which can improve the utilization rate of real-world vehicle data and achieve accurate assessment of battery health status.
[0045] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.
[0046] In an exemplary embodiment, the present application provides a battery health status assessment method based on vehicle driving data, which is executed by a computer device, specifically a computer device such as a terminal or a server, or a terminal and a server. In the embodiment of the present application, Figure 1 As shown, the method includes the following steps 1 to 8.
[0047] Step 1: Obtain historical driving data of electric vehicles and retain corresponding reference data items.
[0048] Specifically, from the actual vehicle data of electric vehicles collected from the big data platform, the historical driving data of electric vehicles is screened based on the vehicle driving speed not being equal to 0, and the corresponding reference data items are retained.
[0049] The reference data items include: vehicle speed, accumulated mileage, current, remaining power, latitude and longitude coordinates, maximum temperature, minimum temperature and vehicle identification code; the latitude and longitude coordinates include: longitude and latitude.
[0050] Step 2: Select a target segment from the historical driving data based on the vehicle identification code, latitude and longitude coordinates, and vehicle driving speed in the reference data item.
[0051] Step 2.1: Based on the vehicle identification code and the latitude and longitude coordinates, a data set with the same vehicle model and vehicle driving route is selected from the historical driving data.
[0052] Step 2.2: Based on the latitude and longitude coordinates, select straight line tracks with a mileage span less than or equal to a set distance from the data set and segment them to obtain multiple discharge segments. The set distance is preferably 2 kilometers.
[0053] Step 2.3: Calculate the average vehicle speed of each discharge segment according to the vehicle's running speed, divide the average vehicle speed intervals, and select the discharge segment with the largest average vehicle speed interval as the target segment.
[0054] The calculation formula of the average vehicle speed is:
[0055]
[0056] in, represents the average vehicle speed in the discharge segment, V i represents the vehicle speed at the i-th sampling point in the discharge segment, and n represents the number of sampling points in the discharge segment (which is also equal to the number of sampling points in the target segment).
[0057] like Figure 2As shown in the figure, the average speed of a certain type of vehicle can be divided into three intervals, namely less than 20 km / h, 20-25 km / h and greater than 25 km / h. If an interval accounts for more than 1 / 3, it will be retained. If multiple intervals are retained in the previous screening step, the interval with the largest proportion, that is, the discharge segment in the 20-25 km / h interval, is selected as the target segment.
[0058] In this application, by selecting a straight trajectory, we can avoid excessive data fluctuations caused by vehicle oversteering. By limiting the length of the straight trajectory to select shorter segments, we can reduce the span of the data and avoid fluctuations caused by long vehicle travel times and many unstable factors. In addition, by retaining segments with a large proportion of speed ranges, we can ensure the richness of the data as much as possible.
[0059] Step 3: Calculate a battery health status reference value based on the cumulative mileage, maximum temperature, and minimum temperature of the target segment.
[0060] The calculation formula of the battery health status reference value is:
[0061] SOH=1.084M -0.01884 -0.04368+0.00572T m +1.657×10 -7 M m -0.0001524T m 2 +6.964×10 -9 T m M m
[0062] Among them, SOH represents the reference value of battery health status, M represents the cumulative mileage of the target segment, and T m represents the monthly average temperature calculated based on the maximum and minimum temperatures of the target segment, M m Indicates the monthly cumulative mileage calculated based on the cumulative mileage of the target segment.
[0063] The calculation formula for the monthly average temperature is:
[0064]
[0065]
[0066] in, Indicates the average temperature on the day of this month, T Times represents the average temperature of the Times sampling point in the day, k represents the number of sampling points in the day, and D represents the number of days in the month.
[0067] The monthly accumulated mileage is calculated as follows:
[0068] M m =M last -M first
[0069] Among them, M last Indicates the accumulated mileage at 24:00 on the last day of the month, M first Indicates the accumulated mileage at 0:00 on the first day of the month.
[0070] Step 4: Calculate the capacity consumption (CC) and the maximum current (I) according to the current of the target segment. max ), and further draw the evolution trajectory diagram.
[0071] The capacity consumption evolution trajectory diagram has the accumulated mileage as the horizontal axis and the capacity consumption as the vertical axis, see Figure 3 The first figure in the first column of the figure; the evolution trajectory of the current peak value is based on the accumulated mileage as the horizontal axis and the current peak as the vertical axis, see Figure 4 The first picture in the first column.
[0072] The calculation formula for the capacity consumption is:
[0073]
[0074] Where CC represents the capacity consumption of the target segment, I(i) represents the current at the i-th sampling point in the target segment, and n represents the number of sampling points in the target segment.
[0075] The calculation formula of the current peak is:
[0076] I max =max(I(1),I(2),...,I(n))
[0077] Among them, I max represents the peak current of the target segment, and (I(1), I(2), ..., I(n)) represents the current of all sampling points in the target segment.
[0078] Step 5: Perform EMD (Empirical Mode Decomposition) on the capacity consumption and current peak of the training segment to obtain the component CC of the capacity consumption EMD and the peak current component The Pearson correlation coefficient (PCC) is used to select components whose correlation with the battery health status reference value is within a set range as battery health indicators (HIs). The set range is set according to actual conditions, such as -1 to -0.9 and 0.9 to 1.
[0079] Specifically, on the one hand, the capacity consumption of the target segment is subjected to empirical mode decomposition to obtain the components of the capacity consumption; on the other hand, the current peak of the target segment is subjected to empirical mode decomposition to obtain the components of the current peak. The components of the capacity consumption include: multiple intrinsic mode functions corresponding to the capacity consumption and a residual, such as Figure 3 As shown; the components of the current peak include: multiple intrinsic mode functions corresponding to the current peak and a residual, such as Figure 4 shown.
[0080] The EMD method combines CC and I max The data is continuously decomposed into multiple intrinsic mode functions (IMFs) and a residual error (RES) to capture its changing trend. The calculation formula is as follows:
[0081]
[0082] Where x(t) represents the original data (in this application, it refers to the capacity consumption CC and the current peak I max ), c j (t) represents the jth IMF, r(t) represents RES, and J represents the number of IMFs.
[0083] The specific process of the EMD method is as follows:
[0084] 1. Extract extreme points: In EMD, first find the signal (i.e., capacity consumption CC and current peak I max ) may be the local maximum or minimum of the signal.
[0085] 2. Determine the upper and lower envelopes: Use interpolation to smooth the curve between the extreme points to obtain the upper and lower envelopes. These envelopes will help determine the IMFs.
[0086] 3. Extract IMF: Extract the first IMF from the signal, which is the average value between the local maximum and minimum values in the signal.
[0087] 4. Repeat this process until all IMFs are extracted from the signal. Each time, subtract the IMF from the extracted IMF until the remaining IMF is obtained.
[0088] 5. Subtract the original signal from all IMFs to obtain RES.
[0089] Afterwards, PCC was used to calculate the correlation coefficients among the original data, IMFs, RES, and the battery SOH reference value, and the components with greater correlation with the battery SOH reference value were selected to input into the model.
[0090] The PCC calculation formula is as follows:
[0091]
[0092] Among them, r xy represents the correlation coefficient, n represents the number of sampling points in the target segment, x i Indicates the i-th value in the reference sequence, and in this application indicates the marked battery SOH reference value, y i represents the i-th value in the comparison sequence, which in this application is the original data, IMFs and RES, and represent the average values of the reference sequence and the comparison sequence, respectively.
[0093] Step 6: Determine the remaining power sequence and the average temperature sequence based on the remaining power, maximum temperature, and minimum temperature of the target segment, and use the Pearson correlation coefficient to select sequences whose correlation with the battery health feature is within a set range as feature influencing factors.
[0094] Specifically, on the one hand, the remaining power of the target segment, i.e., the state of capacity (SOC), is extracted to obtain the remaining power sequence; on the other hand, the maximum temperature and minimum temperature of the target segment are extracted, and the formula is used. and Calculate the average temperature T of the target fragment a , get the average temperature series; where T i represents the average temperature of the i-th sampling point in the target segment, n represents the number of sampling points in the target segment, T imax represents the maximum temperature of the i-th sampling point in the target segment, T imin Indicates the minimum temperature of the i-th sampling point in the target segment.
[0095] Afterwards, PCC is used to calculate the correlation coefficients between the remaining power, average temperature, and battery health characteristics. The sequences with the highest correlation with the battery health characteristics are selected as input to the model.
[0096] Step 7: Construct an ensemble learning (EL) model and train the EL model based on the battery health characteristics, characteristic influencing factors and corresponding battery health status reference values of the target segment to obtain a trained EL model for evaluating battery SOH.
[0097] This step mainly includes: determining the number of basic models in the EL model, the offline training mode and the weight update strategy. In this application, the number of basic models is 4.
[0098] like Figure 5 As shown, the basic models for determining the EL model are two Gaussian process regression (GPR) models and two support vector machine (SVR) models. The offline training mode is: the first GPR is trained by the data of vehicle 1 collected by the big data platform, the second GPR is trained by the data of vehicle 2 collected by the big data platform, the first SVR is trained by the data of vehicle 1, and the second SVR is trained by the data of vehicle 2. The output of the integrated learning model is the weighted sum of the outputs of the four basic models. The weight update strategy is: every 5,000 kilometers, every 10,000 kilometers or every 20,000 kilometers, the weight of the basic model is updated according to the error of each basic model. The construction and weight update process of the EL model is as follows:
[0099] First, when a weight update event occurs, the error Error of each basic model model The calculation is as follows:
[0100]
[0101] in, and y model They represent the model output (i.e., the battery SOH prediction value) and the true SOH value (i.e., the battery SOH reference value). Each basic model outputs a different battery SOH prediction value, and the input of each basic model is the battery health characteristics and the characteristic influencing factors related to the battery health characteristics. model represents the sequence number of the basic model, and β represents the number of basic models. In this application, the value of β is 4.
[0102] The importance coefficients of the four basic models are then calculated as follows:
[0103]
[0104] Among them, r model Indicates the importance coefficient of the model-th basic model.
[0105] The obtained importance coefficients are then normalized to obtain the weight ω of each basic model model :
[0106]
[0107] Finally, the output of the EL model, namely the SOH evaluation result h f It can be expressed as:
[0108]
[0109] Among them, h model (x f ) represents the output of the model-th basic model, x f Represents the input of the base model.
[0110] Step 8: Using the trained ensemble learning model, the battery health status is evaluated based on the current driving data of the electric vehicle to obtain a battery health status prediction value.
[0111] Furthermore, the method further includes: drawing a battery degradation trajectory according to the battery health status prediction value.
[0112] In summary, the battery health status assessment method based on vehicle driving data provided by this application analyzes the driving data in the actual vehicle data, uses the EMD and PCC methods to assist in extracting the battery health factors (including battery health characteristics and characteristic influencing factors), and constructs an SOH assessment model based on the EL model to achieve accurate assessment of the battery SOH. Compared with the existing technology, this application improves the utilization rate of the original data and avoids the need to design a large number of calibration experiments to evaluate the SOH of the actual vehicle power battery, which causes the consumption of manpower, material resources and financial resources.
[0113] In an exemplary embodiment, the present application further provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.
[0114] In an exemplary embodiment, the present application further provides a computer-readable storage medium storing a computer program, which implements the steps in the above-mentioned method embodiments when executed by a processor.
[0115] In an exemplary embodiment, the present application further provides a computer program product, including a computer program, which implements the steps in the above-mentioned method embodiments when executed by a processor.
[0116] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant legal provisions.
[0117] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).
[0118] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.
[0119] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0120] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.
Claims
1. A battery health status assessment method based on vehicle driving data, characterized in that: include: Obtain historical driving data of electric vehicles and retain corresponding reference data items; The reference data items include: vehicle speed, accumulated mileage, current, remaining power, latitude and longitude coordinates, maximum temperature, minimum temperature and vehicle identification code; Selecting a target segment from the historical driving data based on the vehicle identification code, latitude and longitude coordinates, and vehicle driving speed in the reference data item; Calculating a battery health status reference value based on the cumulative mileage, maximum temperature, and minimum temperature of the target segment; calculating a capacity consumption and a current peak value according to the current of the target segment; Performing empirical mode decomposition on the capacity consumption and current peak of the target segment to obtain components of the capacity consumption and components of the current peak, and using the Pearson correlation coefficient to select components whose correlation with the battery health state reference value is within a set range as battery health features; Determine a remaining power sequence and an average temperature sequence based on the remaining power, maximum temperature, and minimum temperature of the target segment, and use the Pearson correlation coefficient to select sequences whose correlation with the battery health feature is within a set range as feature influencing factors; Constructing an integrated learning model, and training the integrated learning model according to the battery health characteristics, characteristic influencing factors, and corresponding battery health status reference values of the target segment to obtain a trained integrated learning model; The trained integrated learning model is used to evaluate the battery health state based on the current driving data of the electric vehicle to obtain a battery health state prediction value.
2. The battery health status assessment method based on vehicle driving data according to claim 1, characterized in that: Obtain historical driving data of electric vehicles and retain corresponding reference data items, including: From the real vehicle data of electric vehicles collected from the big data platform, the historical driving data of electric vehicles is screened based on the vehicle driving speed not being equal to 0, and the corresponding reference data items are retained.
3. The battery health status assessment method based on vehicle driving data according to claim 1, characterized in that: Selecting a target segment from the historical driving data according to the vehicle identification code, latitude and longitude coordinates, and vehicle driving speed in the reference data item includes: Selecting a data set having the same vehicle model and vehicle travel route from the historical travel data according to the vehicle identification code and the latitude and longitude coordinates; According to the latitude and longitude coordinates, a straight line trajectory having a mileage span less than or equal to a set distance is selected from the data set for segmentation to obtain a plurality of discharge segments; The average vehicle speed of each discharge segment is calculated according to the vehicle running speed, the average vehicle speed interval is divided, and the discharge segment with the largest average vehicle speed interval proportion is selected as the target segment.
4. The battery health status assessment method based on vehicle driving data according to claim 1, characterized in that: Calculating a battery health status reference value according to the cumulative mileage, maximum temperature, and minimum temperature of the target segment, including: The battery health status calculation formula is SOH = 1.084M -0.01884 -0.04368+0.00572T m +1.657×10 -7 M m -0.0001524T m 2 +6.964×10 -9 T m M m , calculate the battery health status reference value SOH according to the cumulative mileage, maximum temperature and minimum temperature of the target segment; where M represents the cumulative mileage of the target segment, T m represents the monthly average temperature calculated based on the maximum and minimum temperatures of the target segment, M m Indicates the monthly cumulative mileage calculated based on the cumulative mileage of the target segment.
5. The battery health status assessment method based on vehicle driving data according to claim 1, characterized in that: Calculating the capacity consumption and the current peak value according to the current of the target segment, including: Using the capacity consumption calculation formula Calculating the capacity consumption CC based on the current of the target segment; wherein I(i) represents the current of the i-th sampling point in the training segment, and n represents the number of sampling points in the target segment; Use the current peak calculation formula I max =max(I(1), I(2), ..., I(n)), calculate the peak current I according to the current of the target segment max ; where (I(1), I(2), ..., I(n)) represents the current of all sampling points in the target segment.
6. The battery health status assessment method based on vehicle driving data according to claim 1, characterized in that: Performing empirical mode decomposition on the capacity consumption and current peak of the target segment to obtain components of the capacity consumption and components of the current peak, including: Performing empirical mode decomposition on the capacity consumption of the target segment to obtain components of the capacity consumption; the components of the capacity consumption include: multiple intrinsic mode functions corresponding to the capacity consumption and a residual; Performing empirical mode decomposition on the current peak of the target segment to obtain components of the current peak; the components of the current peak include: multiple intrinsic mode functions corresponding to the current peak and a residual.
7. The battery health status assessment method based on vehicle driving data according to claim 1, characterized in that: Determining a remaining power sequence and an average temperature sequence according to the remaining power, maximum temperature, and minimum temperature of the target segment includes: Extracting the remaining power of the target segment to obtain a remaining power sequence; Extract the maximum and minimum temperatures of the target fragment and use the formula and Calculate the average temperature T of the target fragment a , get the average temperature series; where T i represents the average temperature of the i-th sampling point in the target segment, n represents the number of sampling points in the target segment, T imax represents the maximum temperature of the i-th sampling point in the target segment, T imin Indicates the minimum temperature of the i-th sampling point in the target segment.
8. The battery health status assessment method based on vehicle driving data according to claim 1, characterized in that: The basic models of the integrated learning model include: two Gaussian process regression models and two support vector machine models; the output of the integrated learning model is the weighted sum of the outputs of the four basic models; during the training process, the weights of the four basic models are updated once every 5,000 kilometers, every 10,000 kilometers or every 20,000 kilometers according to the error of each basic model.
9. The battery health status assessment method based on vehicle driving data according to claim 1, characterized in that: Also includes: A battery degradation trajectory is drawn based on the battery health status prediction value.
10. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the battery health status assessment method based on vehicle driving data as described in any one of claims 1 to 9.