CN113158947A - Power battery health scoring method, system and storage medium - Google Patents
Power battery health scoring method, system and storage medium Download PDFInfo
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Abstract
The invention provides a method for scoring the health of an electric vehicle battery, which comprises the following steps: and S1, collecting vehicle signal data and preprocessing the data. S2 obtains and analyzes the current battery state of health of the vehicle as a base health score. S3 analyzes user behavior and environmental conditions, and influences on battery health. S4 scores the user behavior of S3 using a scoring card model. S5 analyzes the vehicle battery life decay trend. S6: and establishing a machine learning model and predicting the attenuation degree of the vehicle battery along with time. And S7, predicting and scoring the battery attenuation according to the battery health attenuation model obtained in the S6. And S8, setting the weight of each score according to the scoring results of S2, S4 and S7, and carrying out weighted summation to obtain the final battery health score. According to the invention, the SOH of the battery, the driving habits and working conditions of a user, the remaining quality-guaranteed mileage and the future attenuation condition are comprehensively considered when the health score of the battery is evaluated, so that the current health degree of the battery of the vehicle is more objectively reflected, and a more objective basis is provided for the evaluation of the residual value of the battery.
Description
Technical Field
The invention relates to the technical field of power batteries of vehicles, in particular to a power battery health state evaluation technology.
Background
The core power of the electric automobile comes from the battery, and the health degree of the battery directly influences the performance, the endurance and the safety of the whole automobile. The battery has a limited life and will gradually degrade over time and with cycling until it is discarded and unusable. The cost of replacing batteries is enormous, so the health of the batteries becomes the focus of major concern for manufacturers and owners of the vehicles. Meanwhile, it is also an urgent need to be able to effectively and accurately evaluate the health degree of the battery.
The health of a battery is related to a number of factors. On the one hand, the energy comes from the battery, such as capacity, power, internal resistance, charging and discharging depth, recycling times and the like. And on the other hand, the user car usage habits from the owner of the user car, such as driving habits, charging habits, ambient temperature, daily maintenance and the like. The correct and normative use habit can keep the health state of the battery and prolong the service life of the battery.
The current method for reflecting the health degree of the battery is to detect and check the SOH value of the battery. The SOH value of a battery is generally calculated from the internal capacity, internal resistance, and the like of the battery. Only can reflect the influence factors of the battery, but not the influence of the vehicle using habit of the vehicle owner on the health of the battery. However, most owner users lack professional knowledge for internal management of the battery, use and maintenance of the battery, and the like. Therefore, the influence of the daily use habit of a user vehicle owner on the health of the battery can be reflected while the health degree of the battery can be effectively and accurately evaluated, and the influence has important significance and value in helping the user improve and improve the use habit, improve the battery maintenance knowledge, prolong the service life of the battery and the like.
Disclosure of Invention
The invention aims to establish a more objective method and a more objective system for grading the battery health of an electric vehicle, which are based on the big data of the Internet of vehicles, comprehensively consider the SOH of a battery, the driving habits and working conditions of a user, the remaining quality protection mileage and the future attenuation condition when evaluating the battery health score, more objectively reflect the current battery health degree of the vehicle, and provide more objective basis for evaluating the residual value of the battery.
The health of a vehicle battery is influenced by the vehicle owner's habits, in addition to the characteristics of the battery itself. Therefore, in the evaluation formula of the battery health of the electric vehicle, various factors affecting the battery health need to be comprehensively considered, such as: the battery self-factor, the environmental working condition, the user behavior habit of using the vehicle, the daily maintenance and other factors. In the invention, the influence result of the battery self factor is obtained by the SOH value of the battery, and the results of other factors are obtained by the relevant data in the vehicle signal through big data analysis and machine learning modeling. Each factor will get a score and will be assigned a corresponding weight and finally weighted and summed to get the final total score of the battery health.
In order to achieve the above purpose, the present invention proposes the following technical solutions based on the above ideas.
A method for scoring the health of an electric automobile battery is based on Internet of vehicles big data and comprises the following steps:
s1: collecting vehicle signal data and carrying out data preprocessing; the vehicle signal data comprises data of three categories including the inside of a battery, user behaviors and environmental conditions, and is big data obtained through the Internet of vehicles.
S2: the current battery state of health of the vehicle is acquired and analyzed as a base health score.
S3: and analyzing the influence of user behaviors and environmental conditions on the health of the battery.
S4: the user behavior of S3 is scored using a scoring card model.
S5: and analyzing the life decay trend of the vehicle battery.
S6: and establishing a machine learning model and predicting the attenuation degree of the vehicle battery along with time.
S7: and predicting and scoring the battery attenuation according to the battery health attenuation model obtained in the step S6.
S8: and setting the weight of each score in the processes of S2, S4 and S7 according to the scoring results of S2, S4 and S7, and weighting and summing to obtain the final battery health score.
Further, the step S1 includes the following steps:
s1-1: the method comprises the steps of collecting signal data such as vehicle types, usage types, battery BMS, vehicle owner behavior data and environmental conditions in a big data mode, wherein the vehicle signal data are big data based on the Internet of vehicles.
S1-2: and classifying the data into three categories, namely the interior of the battery, the user behavior and the environmental working condition according to factors influencing the health of the battery.
S1-3: preprocessing data, and deleting abnormal data such as null values, noise, invalid values and the like.
Further, the step S2 includes the following steps:
s2-1: the data of the battery internal category classified and processed in the S1 process is acquired.
S2-2: and checking the SOH value of the current battery health state and analyzing the internal state data of the battery. Including current battery capacity, rated capacity, internal resistance, self-discharge rate, etc.
S2-3: and carrying out basic scoring on the health internal state of the battery according to the current SOH value of the battery and the analysis result of the internal state of the battery.
Further, the step S3 includes the following steps:
s3-1: and acquiring data of the user behavior class and the environmental condition class after being classified and processed in the S1 process.
S3-2: and analyzing and counting user behavior data, including accumulated driving mileage, vehicle usage, charging behavior, discharging behavior, DOD depth of discharge, overcharging behavior, usage ratio of fast and slow charging and the like.
S3-3: and analyzing and counting environmental working condition data, including temperature environment, under-voltage and over-voltage conditions, high SOC standing conditions and the like.
Further, the step S4 includes the following steps:
s4-1: constructing a scoring card model sample data set for training and evaluation, wherein the scoring card model sample data set comprises positive and negative sample data;
s4-2: dividing a sample data set into a training set and a test set;
s4-3: performing characteristic engineering treatment, namely performing variable binning and discretization;
s4-4: computing WOE (weight Of evaluation) values and IV (information value) values Of characteristic variables, and performing characteristic screening and box classification test;
s4-5: training a classification model on a training data set, and verifying the effect of the classification model in a test set;
s4-6: and (4) score conversion is carried out on the scoring card, and scores and model scores of all the variable sub-boxes are output.
Further, the step S5 includes the following steps:
s5-1: acquiring data of three categories which are classified and processed in the S1 process;
s5-2: marking the internal category data of the battery as a dependent variable to form a dependent variable pool;
s5-3: marking the user behavior category and the environmental working condition category data as independent variables to form an independent variable pool;
s5-4: and analyzing the correlation between the variables in the independent variable pool and the dependent variable pool and the influence degree of each variable on each dependent variable.
Further, the step S6 includes the following steps:
s6-1: constructing a sample data set of a battery health decay model for training and evaluation;
s6-2: dividing a sample data set into a training set and a test set;
s6-3: performing characteristic screening according to an analysis result in the S5 process;
s6-4: performing feature fusion and filtering processing to generate a health factor HI (health index) for representing the health attenuation of the battery;
s6-5: analyzing the influence and trend of the change of the health factor HI along with time;
s6-6: training a battery health attenuation model on a training data set to generate a battery health attenuation model pool;
s6-7: and (5) verifying the battery health attenuation model on the test data set, and evaluating the model effect.
Further, the step S7 includes the following steps:
s7-1: constructing variables for inputting the battery health decay model from the vehicle data;
s7-2: and inputting the variables into the model, outputting the battery health prediction result by the model, and converting the battery health prediction result into a score.
The invention also provides a battery health scoring system for an electric vehicle, which comprises a memory and a processor, wherein the memory stores instructions for enabling the processor to execute the battery health scoring method for the electric vehicle.
The invention also provides a machine-readable storage medium, wherein the machine-readable storage medium is stored with instructions for enabling a machine to execute the electric vehicle battery health scoring method.
The method utilizes the internal mechanism of the battery and a machine learning model to analyze and mine various factors influencing the health of the battery from multiple angles, and converts the influence degree of the factors into a score form. The system has the advantages that the health degree of the battery is accurately evaluated and predicted, meanwhile, the vehicle owner is helped to locate the reason influencing the decline of the health of the battery in a grading mode, improvement and optimization suggestions of the vehicle owner in the aspects of vehicle using behaviors, environmental working conditions, daily maintenance and the like are given, and finally the vehicle owner is helped to prolong the service life of the vehicle battery.
Drawings
FIG. 1 is a schematic diagram of a scoring method for assessing the health of a battery of an electric vehicle according to an embodiment of the present invention;
Detailed Description
The invention is further described with reference to the drawings and examples in the following description:
referring to fig. 1, the present embodiment provides a method for scoring the health of a battery of an electric vehicle,
the method comprises the following specific steps:
s1: the method comprises the steps of collecting vehicle signal data and preprocessing the data, wherein the vehicle signal data comprise three categories of battery interior, user behavior and environment working conditions.
S2: acquiring and analyzing the current battery health state of the vehicle as a basic score;
s3: analyzing the influence of user behaviors and environmental conditions on the health of the battery;
s4: scoring the user behavior of S3 using a scoring card model;
s5: analyzing the life decay trend of the vehicle battery;
s6: establishing a machine learning model, and predicting the attenuation degree of the vehicle battery along with time;
s7: predicting and scoring the battery attenuation according to the battery health attenuation model obtained in the step S6;
s8: and calculating the total battery health score according to the scoring results of S2, S4 and S7.
In a further embodiment of the present invention, the step S1 further includes the following steps:
s1-1: collecting signal data such as vehicle type, use type, battery BMS, vehicle use behavior data of a vehicle owner, environmental working conditions and the like;
s1-2: classifying data according to factors influencing the health of the battery, and dividing the data into three categories, namely battery interior, user behavior and environmental working conditions;
s1-3: preprocessing data, and deleting abnormal data such as null values, noise, invalid values and the like.
In a further embodiment of the present invention, the step S2 further includes the following steps:
s2-1: acquiring data of the classified and processed internal categories of the battery in the S1 process;
s2-2: and checking the SOH value of the current battery health state, and analyzing the internal state data of the battery. Including current capacity, rated capacity, internal resistance, self-discharge rate, etc.;
s2-3: and carrying out basic scoring on the health internal state of the battery according to the current SOH value of the battery and the analysis result of the internal state of the battery.
The SOH value in the BMS signal of the vehicle battery represents the current state of health of the battery, and by analyzing the relationship between the internal state of the battery and the SOH value of the current state of health of the battery, it can be found that: the state data of the residual capacity, the internal resistance and the like of the battery are strongly related to the SOH value. For example, the smaller the remaining battery capacity, the lower the SOH value, and the larger the internal resistance, the lower the SOH value. The SOH value may be used to adequately represent the current internal state of the battery and the state of health of the battery. Therefore, in this step, only the SOH value is used to derive the base score, which is expressed as follows:
scorebase＝soh*100
wherein, scorebaseIndicates a basal score at 0,100]Within the interval.
In a further embodiment of the present invention, the step S3 further includes the following steps:
s3-1: acquiring data of the user behavior category and the environmental working condition category which are classified and processed in the S1 process;
s3-2: analyzing and counting user behavior data, including accumulated driving mileage, vehicle usage, charging behavior, discharging behavior, DOD depth of discharge, overcharge behavior, proportion of fast and slow charging, and the like;
s3-3: and analyzing and counting environmental working condition data, including temperature environment, under-voltage and over-voltage conditions, high SOC standing conditions and the like.
Wherein, the environmental condition class data need carry out the case discretization. For example, the temperature data are labeled as discrete bins of [10 ℃, 20 ℃ ], [20 ℃, 35 ℃ ], [35 ℃, 40 ℃ ], and [ others ].
In a further embodiment of the present invention, the step S4 further includes the following steps:
s4-1: constructing a scoring card model sample data set for training and evaluation, wherein the scoring card model sample data set comprises positive and negative sample data; these sample data are derived from the relevant data of user behavior and environmental conditions in step S3.
S4-2: dividing a sample data set into a training set and a test set;
s4-3: performing characteristic engineering treatment, namely performing variable binning and discretization;
and performing box discretization on continuous characteristic variables in all data sets, and processing by using modes such as equal-frequency/equal-distance box separation, chi-square box separation, supervised box separation and the like according to different characteristics.
S4-4: computing WOE (weight Of evaluation) values and IV (information value) values Of characteristic variables, and performing characteristic screening and box classification test;
WOE (weight Of evidence) represents evidence weight, and a larger value indicates a larger difference in the proportion Of positive and negative samples in the bin. IV (information value) is a measure of the amount of information of a variable, the larger the value is, the larger the amount of information is. In feature screening, IV values were used for selection. Removing characteristic variables with too small and too large IV values.
S4-5: and training a classification model on the training data set, and verifying the effect of the classification model in the test set.
Further, the specific steps of training and testing in step S4-5 are as follows:
s4-5-1: training a classification model by using a training set and a cross validation mode to find an optimal parameter model;
the classification in this process belongs to a two-classification problem, using a logistic regression classification model:
z＝θ0+θ1x1+θ2x2+…+θnxn
where θ represents a parameter and x represents a characteristic variable. h isθRepresenting Sigmoid function, e, used in logistic regression model-zIs an exponential function with a base number of euler numbers e and an exponent of-z.
S4-5-2: using the test set data, the effect of the model was assessed by observing the AUC values of the confusion matrix, ROC curve.
S4-6: and (4) score conversion is carried out on the scoring card, and scores and model scores of all the variable sub-boxes are output.
The formula of the conversion score of the scoring card is as follows:
A＝P0-B*ln(θ0)
score＝A+B*ln(odds)
where p represents the negative sample probability and odds represents the occurrence ratio. P0PDO is constant, A and B have no practical meaning, and belong to an intermediate value in calculating the fraction, and can be represented by P0And calculating the PDO. score represents the converted score.
In a further embodiment of the present invention, the step S5 further includes the following steps:
s5-1: acquiring data of three categories which are classified and processed in the S1 process;
s5-2: marking the internal category data of the battery as a dependent variable to form a dependent variable pool;
s5-3: marking the user behavior category and the environmental working condition category data as independent variables to form an independent variable pool;
s5-4: and analyzing the correlation between the variables in the independent variable pool and the dependent variable pool and the influence degree of each variable on each dependent variable.
In a further embodiment of the present invention, the step S6 further includes the following steps:
s6-1: constructing a sample data set of a battery health decay model for training and evaluation;
s6-2: dividing a sample data set into a training set and a test set;
s6-3: performing characteristic screening according to an analysis result in the S5 process;
s6-4: and performing feature fusion and filtering processing to generate a health factor HI (health index) for representing the health attenuation of the battery.
Further, the specific steps of feature fusion and filtering processing in step S6-4 are as follows:
s6-4-1: constructing a data set due to feature fusion;
the state of health at the beginning of battery life is defined as 1 and the state of health at the end of battery life is defined as 0. Data in the training set soh > 97 are labeled as 1, and data in soh ≦ 80 are labeled as 0. The labeled data is then extracted from the training set to form a data set ω for feature fusion:
ω＝{(X，y)}
ω＝{(xi，0)|soh≤80}∪{(xi，1)|soh＞97}
s6-4-2: performing feature fusion by using linear regression to generate a health factor HI;
in this process, the health factors were generated using linear regression fusion, the fusion model being as follows:
wherein, yHIRepresenting the health factor (HI), alpha, beta representing the parameters in the model, xiRepresenting the feature vector, and epsilon is a noise term to control the overfitting.
S6-4-3: filtering the health factor;
because the health factor (HI) generated after fusion is noisy and has large fluctuation, the Sav _ gol can be further used for filtering processing to reduce the error of health attenuation.
S6-5: the influence and trend of the change of the health factor HI along with the time are analyzed.
S6-6: training a battery health attenuation model on a training data set to generate a battery health attenuation model pool;
a plurality of battery attenuation models trained in the process are formed, and a battery health attenuation model pool is formed. The model formula is as follows:
wherein y represents a dependent variable health factor (HI), a, b, c represent parameters in the model, and T representssdjRepresenting the time period of the independent variable, epsilon is a noise term to control the overfitting.
S6-7: verifying a battery health attenuation model on a test data set, and evaluating the effect of the model;
the step of validating the model in the test dataset in step S6-7 is as follows:
s6-7-1: filtering out soh characteristic variables in the test set;
s6-7-2: performing S6-4 feature fusion and filtering to generate health factor HI for test datatest；
S6-7-3: mixing HItestPerforming similarity comparison with HI of each model in the model pool of the S6-6 step;
similarity comparison uses a way of calculating the euclidean distance of the feature vectors, with closer distances being higher similarities.
S6-7-4: sorting the obtained similarity according to a descending order, and filtering out a model with the similarity smaller than a 3-quantile numerical value;
s6-7-5: respectively predicting by using the rest models, and taking the average value of the predicted values of the models as a final health state value;
s6-7-5: comparing the obtained predicted value with the value soh which is not filtered in S6-7-1, the smaller the error is, the better the model effect is.
In a further embodiment of the present invention, the step S7 further includes the following steps:
s7-1: constructing variables for inputting the battery health decay model from the vehicle data;
s7-2: inputting the variables into a model, outputting a battery health prediction result by the model, and converting the battery health prediction result into a score;
the conversion score formula is as follows:
score＝ypredicted*100
wherein score represents a score, yprediotedRepresenting the model predicted battery state of health value.
In a further embodiment of the present invention, the step S8 further includes the following steps:
s8-1: obtaining scoring results in the processes of S2, S4 and S7;
s8-2: setting the weight of each score in the processes of S2, S4 and S7, and weighting and summing to obtain a final battery health score;
the final battery health score formula is as follows:
scorefinal＝w1s1+w2s2+w3s3
wherein, scorefinalScore for Battery health, w1s1Representing base scores and corresponding weights, w2s2Representing user behavior, environmental condition scores and corresponding weights, w3s3Representing the battery health decay score and corresponding weight.
According to the detailed content of the embodiment, the battery health score is obtained from different angles by respectively adopting the basic health score, the user vehicle using behavior habit score and the battery attenuation score, and the comprehensive and comprehensive battery health score is carried out from different angles and different methods. Therefore, the health degree of the battery can be effectively and accurately evaluated, meanwhile, the influence of the daily use habit of a user owner on the health of the battery can be reflected, a basis is provided, and the user is helped to improve the use habit, improve the battery maintenance knowledge, prolong the service life of the battery and the like.
Claims (12)
1. A method for scoring the health of an electric vehicle battery is characterized by comprising the following steps:
s1: collecting vehicle signal data and carrying out data preprocessing; the vehicle signal data comprises data of three categories including the inside of a battery, user behaviors and environmental working conditions;
s2: acquiring and analyzing the current battery health state of the vehicle as a basic score;
s3: analyzing the influence of user behaviors and environmental conditions on the health of the battery;
s4: scoring the user behavior of S3 using a scoring card model;
s5: analyzing the life decay trend of the vehicle battery;
s6: establishing a machine learning model, and predicting the attenuation degree of the vehicle battery along with time;
s7: predicting and scoring the battery attenuation according to the battery health attenuation model obtained in the step S6;
s8: and setting the weight of each score in the processes of S2, S4 and S7 according to the scoring results of S2, S4 and S7, and weighting and summing to obtain the final battery health score.
2. The electric vehicle battery health scoring method according to claim 1, wherein the step S1 includes:
s1-1: collecting vehicle signal data such as vehicle type, use type, battery BMS, vehicle behavior data of a vehicle owner, environmental conditions and the like; the vehicle signal data is big data based on the internet of vehicles;
s1-2: classifying data according to factors influencing the health of the battery, and dividing the data into three categories, namely battery interior, user behavior and environmental working conditions;
s1-3: preprocessing data, and deleting abnormal data such as null values, noise, invalid values and the like.
3. The electric vehicle battery health scoring method according to claim 1, wherein the step S2 includes:
s2-1: acquiring data of the classified and processed internal categories of the battery in the S1 process;
s2-2: and analyzing the internal state data of the battery according to the SOH value of the current battery state of health. Including current battery capacity, rated capacity, internal resistance, self-discharge rate, etc.;
s2-3: and carrying out basic scoring on the internal state of health of the battery according to the current SOH value of the battery and the analysis result of the internal state data of the battery.
4. The electric vehicle battery health scoring method according to claim 3, wherein the base score is derived from an SOH value, and the formula is as follows:
scorebase＝soh*100
wherein, scorebaseIndicates a basal score at 0,100]Within the interval.
5. The electric vehicle battery health scoring method according to claim 1, wherein the step S3 includes:
s3-1: acquiring data of the user behavior category and the environmental working condition category which are classified and processed in the S1 process;
s3-2: analyzing and counting user behavior data, including accumulated driving mileage, vehicle usage, charging behavior, discharging behavior, DOD depth of discharge, overcharge behavior, proportion of fast and slow charging, and the like;
s3-3: and analyzing and counting environmental working condition data, including temperature environment, under-voltage and over-voltage conditions, high SOC standing conditions and the like.
6. The electric vehicle battery health scoring method according to claim 1, wherein the step S4 includes:
s4-1: constructing a scoring card model sample data set for training and evaluation, wherein the scoring card model sample data set comprises positive and negative sample data;
s4-2: dividing a sample data set into a training set and a test set;
s4-3: performing characteristic engineering treatment, namely performing variable binning and discretization;
s4-4: calculating an evidence weight WOE value and an information quantity IV value of the characteristic variable, and performing characteristic screening and box separation inspection;
s4-5: training a classification model on a training data set, and verifying the effect of the classification model in a test set;
s4-6: and (4) score conversion is carried out on the scoring card, and scores and model scores of all the variable sub-boxes are output.
7. The electric vehicle battery health scoring method according to claim 1, wherein the step S5 includes:
s5-1: acquiring data of three categories which are classified and processed in the S1 process;
s5-2: marking the internal category data of the battery as a dependent variable to form a dependent variable pool;
s5-3: marking the user behavior category and the environmental working condition category data as independent variables to form an independent variable pool;
s5-4: and analyzing the correlation between the variables in the independent variable pool and the dependent variable pool and the influence degree of each variable on each dependent variable.
8. The electric vehicle battery health scoring method according to claim 1, wherein the step S6 includes:
s6-1: constructing a sample data set of a battery health decay model for training and evaluation;
s6-2: dividing a sample data set into a training set and a test set;
s6-3: performing characteristic screening according to an analysis result in the S5 process;
s6-4: performing feature fusion and filtering processing to generate a health factor HI (health index) for representing the health attenuation of the battery;
s6-5: analyzing the influence and trend of the change of the health factor HI along with time;
s6-6: training a battery health attenuation model on a training data set to generate a battery health attenuation model pool;
the model formula is as follows:
wherein y represents a dependent variable health factor (HI), a, b, c represent parameters in the model, and T representsadjRepresenting the time period of the independent variable, epsilon is a noise term to control the overfitting;
s6-7: and (5) verifying the battery health attenuation model on the test data set, and evaluating the model effect.
9. The electric vehicle battery health scoring method according to claim 1, wherein the step S6-4 comprises:
s6-4-1: constructing a data set for feature fusion;
defining the state of health of the beginning of the battery life as 1, the state of health of the end of the battery life as 0, marking the data with soh > 97 in the training set as 1, marking the data with soh ≦ 80 as 0, and then extracting the marked data from the training set to form a data set omega for feature fusion:
ω＝{(X，y)}
ω＝{(xi，0)|soh≤80}∪{(xi，1)|soh＞97}
where X represents a characteristic of a sample in the dataset, y represents a label (i.e., battery state of health) for the sample, and XiRepresenting a feature of sample i;
s6-4-2: performing feature fusion by using linear regression to generate a health factor HI;
the fusion model is as follows:
wherein, yHIRepresenting the health factor (HI), alpha, beta representing parameters in the model, x representing characteristic variables, xiA feature vector representing sample i, ε being a noise term used to control the overfitting;
s6-4-3: and (5) filtering the health factor.
10. The electric vehicle battery health scoring method according to claim 1, wherein the step S7 includes:
s7-1: constructing variables for inputting the battery health decay model from the vehicle data;
s7-2: inputting the variables into a model, outputting a battery health prediction result by the model, and converting the battery health prediction result into a score;
the conversion score formula is as follows:
score＝ypredicted*100
wherein score represents a score, ypredictedRepresenting the model predicted battery state of health value.
11. An electric vehicle battery health scoring system, characterized in that the system comprises a memory and a processor, wherein the memory stores instructions for enabling the processor to execute the electric vehicle battery health scoring method according to any one of claims 1 to 9.
12. A machine-readable storage medium having instructions stored thereon for enabling a machine to perform the electric vehicle battery health scoring method according to any one of claims 1-9.
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