A method for analyzing the causes of rapid battery aging

Abstract

The present invention discloses a method for analyzing the cause of rapid battery aging, which relates to the technical field of electric vehicle power batteries. The method comprises the following steps: obtaining the charge and discharge data of each vehicle battery; calculating the SOH variation of each vehicle battery, and dividing the aging of the vehicle battery into different levels; constructing an index for evaluating the charge and discharge state of the battery, and calculating the values ​​of each index of each vehicle, and determining the subdivision of the state of each vehicle under each index; forming association rules between the subdivision of the state of the index and the aging of the vehicle battery; calculating the support, confidence, and lift of the association rules, thereby extracting effective association rules for analyzing the causes of different aging conditions of the vehicle battery. The present invention can analyze the causes of vehicle battery aging, thereby identifying bad vehicle use habits, and giving car owners some good suggestions on driving habits and charging habits.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicle power batteries, and in particular to a method for analyzing causes of rapid battery aging. Background Art

[0002] As electric vehicle technology continues to mature and electric vehicles become more and more popular, different car owners have different car-using habits, such as driving habits and charging habits. If bad car-using habits can be identified in time, good suggestions on driving and charging habits can be given to car owners, and car owners can be reminded, it can delay battery aging, reduce car owners' car costs, and maintain the company's good image. At the same time, bad car-using habits will cause the battery to age too quickly, and car owners who think that the vehicle has been purchased for a short time and continue to drive may bring about driving safety.

[0003] The existing technologies all predict the aging state of vehicle batteries, but do not analyze the causes of vehicle battery aging. Summary of the invention

[0004] In order to overcome the defects in the above-mentioned prior art, the present invention provides a method for analyzing the causes of rapid battery aging, which can analyze the causes of vehicle battery aging, thereby identifying bad vehicle use habits and giving car owners some good suggestions on driving habits and charging habits.

[0005] To achieve the above object, the present invention adopts the following technical solutions, including:

[0006] A method for analyzing the cause of rapid battery aging, comprising the following steps:

[0007] S1, select M vehicles of the same model and driving years, and obtain the charging and discharging data of the batteries of each vehicle;

[0008] S2, calculate the SOH change of each vehicle battery during the driving years;

[0009] S3, classifying the aging conditions of the vehicle battery into different levels according to the SOH change of the vehicle battery;

[0010] S4, constructing an index for evaluating the battery charging and discharging state, and calculating various index values ​​of each vehicle according to the charging and discharging data of the battery of each vehicle;

[0011] S5, according to the values ​​of the various indicators of each vehicle, sub-classify the status of each indicator, and determine the sub-classification of the status of each vehicle under each indicator;

[0012] S6. Form an association rule between the detailed classification of the status of the indicator and the aging condition of the vehicle battery. The association rule includes a factor part and a result part, denoted as {A -> B}, where factor A is composed of the detailed classification of the status of the indicator, and the aging condition of the vehicle battery is taken as result B.

[0013] S7. Calculate the support of all association rules based on the aging condition of the batteries of each vehicle and the detailed classification of the status of each indicator, and filter out the association rules with a support greater than the minimum support threshold, thereby forming a frequent item set.

[0014] S8. Calculate the confidence of the association rules in the frequent item set, and filter out all association rules with a confidence greater than the minimum confidence threshold as strong association rules.

[0015] S9. Calculate the lift of the strong association rules, and filter out all strong association rules with a lift greater than the minimum lift threshold as effective association rules.

[0016] S10. Analyze the formation reasons for different aging conditions of the vehicle battery according to the extracted effective association rules, that is, the factor part in the effective association rules.

[0017] Preferably, in step S3, the aging condition of the vehicle battery is divided into: rapid aging of the vehicle battery caused by extremely damaged vehicle use, rapid aging of the vehicle battery caused by damaged vehicle use, aging of the vehicle battery caused by general damaged vehicle use, natural aging of the vehicle battery caused by normal vehicle use, and natural aging of the vehicle battery caused by good vehicle use.

[0018] Preferably, in step S4, the indicators for evaluating the charge and discharge status of the battery include: low-temperature charging duration, high-temperature charging duration, high-temperature discharge duration, low-temperature discharge duration, high-current charging duration, high-current discharge duration, duration with discharge depth soc < s1%, duration with discharge depth soc < s2%, and duration with discharge depth soc < s3%; where high temperature means higher than 35°C, low temperature means lower than 5°C, and high current means higher than 50A; 15% > s1% > s2% > s3% > 0.

[0019] Preferably, in step S5, the status of each indicator is classified in detail. Among them, according to a certain indicator value of the vehicle, the detailed classification of the status of the vehicle under this indicator is confirmed as follows:

[0020] If the indicator value ≥ x2, the duration of this indicator is too long; if x1 ≤ indicator value < x2, the duration of this indicator is moderate; if the indicator value < x1, the duration of this indicator is acceptable; where x1 and x2 are the status judgment thresholds of this indicator.

[0021] ∞ > x2 > x1 > 0.

[0022] Preferably, the state judgment threshold of the indicator is determined by arranging the indicator values ​​of the M vehicles in descending order, and taking the values ​​at one-third and two-thirds as the state judgment thresholds x2 and x1 of the indicator respectively.

[0023] Preferably, in step S7, the support calculation of the association rule is as follows:

[0024] Support = m1 / M;

[0025] Among them, Support is the support of the association rule {A—>B}, and m1 is the number of association rule {A—>B} appearing in M ​​vehicles.

[0026] Preferably, in step S8, the confidence calculation of the association rule is as follows:

[0027] Confidence = m1 / m2;

[0028] Among them, m1 is the number of association rules {A—>B} appearing in M ​​vehicles; m2 is the number of factor A appearing in M ​​vehicles; Confidence is the confidence of the association rule {A—>B}.

[0029] Preferably, in step S9, the lifting degree of the association rule is calculated as follows:

[0030] Promotion = Confidence / Support_B;

[0031] Among them, Promotion is the promotion degree of the association rule {A—>B}; Confidence is the confidence degree of the association rule {A—>B}; Support_B is the support degree of result B, that is, the number of times result B appears in M ​​vehicles.

[0032] Preferably, in step S2, the current SOH of each vehicle battery is calculated using the capacity change method, and the SOH change of each vehicle battery, i.e., Δsoh, is calculated, as shown below:

[0033] The current SOH of the vehicle battery is:

[0034] ;

[0035] Among them, Q now is the total capacity of the battery in its current state, Q rated is the total capacity of the battery when it leaves the factory, soh Q is the current SOH value of the vehicle battery;

[0036] ;

[0037] Among them, ΔQ is the charging capacity of the battery during a period of time; ΔSOC is the change in the SOC of the battery during this period of time; SOC1 is the SOC value at the end of charging, which is 100%; SOC2 is the SOC value at the beginning of charging;

[0038] The SOH change of the vehicle battery is:

[0039] Δsoh = soh1-soh Q ;

[0040] Among them, soh1 is the factory capacity of the vehicle battery, which is 100%.

[0041] A computer program product includes a computer program / instruction, which implements the above-mentioned method for analyzing the cause of rapid battery aging when the computer program / instruction is executed by a processor.

[0042] The advantages of the present invention are:

[0043] (1) The present invention provides a method for analyzing the causes of rapid battery aging based on correlation analysis, which can identify bad car-using habits and give car owners some good suggestions on driving habits and charging habits, thereby delaying battery aging, reducing car owners' car-using costs, and maintaining the company's good image.

[0044] (2) The method of the present invention is simple to implement, has high accuracy of results, is applicable to a variety of vehicle models, and is easy to promote and apply.

[0045] (3) The present invention analyzes the causes of vehicle battery aging and provides suggestions. The method can help vehicle owners adjust their driving and charging habits, thereby delaying battery aging. This not only extends the battery life, but also reduces the frequency of battery replacement, reducing the maintenance costs of vehicle owners. In addition, by optimizing charging and discharging behavior, energy consumption can be reduced and the vehicle's economy can be improved.

[0046] (4) The present invention can identify bad driving habits, thereby providing early warning and avoiding driving safety hazards caused by excessive battery aging, thereby improving safety.

[0047] (5) The present invention improves the car owner's car experience by providing personalized driving and charging suggestions. Car owners can adjust their car usage habits based on the suggestions to improve the vehicle's endurance and performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 The present invention is a flow chart of a method for analyzing the cause of rapid battery aging. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] The meanings of the relevant terms in the present invention are explained as follows:

[0051] SOC: state of charge, battery state of charge, the percentage of the remaining battery power to the rated power.

[0052] SOH: state of health, battery health, the percentage of the battery's current capacity to the factory capacity.

[0053] Depend on Figure 1 As shown, a method for analyzing the cause of rapid battery aging includes the following steps:

[0054] S1, data acquisition and data cleaning: select M (M>30) vehicles of the same type, which were manufactured and put into use in the market at the same time (two years ago), have been maintained normally within two years, have no battery replacement, and have a mileage of about 40,000 kilometers, and obtain the charging and discharging data of the batteries of each vehicle, including the temperature, current, soc and time during charging and discharging. Use the box plot method to eliminate outliers and remove null and invalid values.

[0055] S2. Use the capacity change method to calculate the current SOH of each vehicle battery at this moment (two years later), and calculate the SOH change of each vehicle battery within two years, i.e., Δsoh, as shown below:

[0056] The current SOH of the vehicle battery is calculated as follows:

[0057] ;

[0058] Among them, Q now is the total capacity of the battery in its current state, Q rated is the total capacity of the battery when it leaves the factory, soh Q is the current SOH value of the vehicle battery;

[0059] ;

[0060] Among them, ΔQ is the charging capacity of the battery during a period of time; ΔSOC is the change in the SOC of the battery during this period of time; SOC1 is the SOC value at the end of charging, which is 100%; SOC2 is the SOC value at the beginning of charging.

[0061] The calculation method of the SOH change of the vehicle battery is as follows:

[0062] Δsoh = soh1-soh Q ;

[0063] Among them, soh1 is the factory capacity of the vehicle battery, which is 100%.

[0064] S3, according to the change of SOH of the vehicle battery, sets the judgment threshold of the aging condition, and divides the aging condition of the vehicle battery into different levels, specifically: rapid aging of the vehicle battery caused by extremely damaged vehicle use, rapid aging of the vehicle battery caused by damaged vehicle use, aging of the vehicle battery caused by general damaged vehicle use, natural aging of the vehicle battery caused by normal vehicle use, and natural aging of the vehicle battery caused by good vehicle use.

[0065] Among them, if Δsoh∈(α4,100%], it means that the vehicle battery ages rapidly due to extremely damaged vehicle use; if Δsoh∈(α3,α4], it means that the vehicle battery ages rapidly due to damaged vehicle use; if Δsoh∈(α2,α3], it means that the vehicle battery ages due to generally damaged vehicle use; if Δsoh∈(α1,α2], it means that the vehicle battery ages naturally due to normal vehicle use; if Δsoh∈[0,α1], it means that the vehicle battery ages naturally due to good vehicle use.

[0066] The values ​​of the above thresholds α1, α2, α3, and α4 can be determined according to different vehicle models, 0<α1<α2<α3<α4<100%. For example, for a fast-charging electric vehicle with a capacity of 100AH, α1, α2, α3, and α4 are 5, 8, 12, and 15 respectively.

[0067] S4, construct indicators for evaluating the battery charging and discharging status, including: low temperature charging time, high temperature charging time, high temperature discharging time, low temperature discharging time, high current charging time, high current discharging time, discharge depth soc<10% duration, discharge depth soc<5% duration, discharge depth soc<3% duration. According to the temperature, current, soc and time of each vehicle battery during charging and discharging collected in step S1, calculate the various indicator values ​​of each vehicle.

[0068] For example, for a fast-charging electric vehicle with a capacity of 100AH, based on the vehicle performance and subjective experience, high temperature refers to above 35°C, low temperature refers to below 5°C, and high current refers to above 50A.

[0069] S5, setting the state judgment threshold of each indicator, sub-classifying the state of each indicator, and determining the state sub-classification of each vehicle under each indicator according to the value of each indicator of each vehicle, as shown below:

[0070] If the high temperature charging time ∈[β2,∞), the high temperature charging is too long; if the high temperature charging time ∈[β1,β2), the high temperature charging is moderate; if the high temperature charging time ∈[0,β1), the high temperature charging is acceptable; among them, β1 and β2 are the state judgment thresholds of high temperature charging, β1<β2.

[0071] If the low-temperature charging time is ∈[η2,∞), the low-temperature charging is too long; if the low-temperature charging time is ∈[η1,η2), the low-temperature charging is moderate; if the low-temperature charging time is ∈[0,η1), the low-temperature charging is acceptable; wherein η1 and η2 are the state judgment thresholds for low-temperature charging, η1<η2.

[0072] If the high-temperature discharge time ∈[μ2,∞), the high-temperature discharge is too long; if the high-temperature discharge time ∈[μ1,μ2), the high-temperature discharge is moderate; if the high-temperature discharge time ∈[0,μ1), the high-temperature discharge is acceptable; among them, μ1 and μ2 are the state judgment thresholds of high-temperature discharge, μ1<μ2.

[0073] If the low-temperature discharge time ∈[θ2,∞), the low-temperature discharge is too long; if the low-temperature discharge time ∈[θ1,θ2), the low-temperature discharge is moderate; if the low-temperature discharge time ∈[0,θ1), the low-temperature discharge is acceptable; wherein θ1 and θ2 are the state judgment thresholds of the low-temperature discharge, θ1<θ2.

[0074] If the large current discharge time ∈[c2,∞), the large current discharge time is too long; if the large current discharge time ∈[c1,c2), the large current discharge time is moderate; if the large current discharge time ∈[0,c1), the large current discharge time is acceptable; where c1 and c2 are the state judgment thresholds of large current discharge, c1 <c2。

[0075] If the high current charging time ∈[d2,∞), the high current charging time is too long; if the high current charging time ∈[d1,d2), the high current charging time is moderate; if the high current charging time ∈[0,d1), the high current charging time is acceptable; where d1 and d2 are the state judgment thresholds for high current charging, d1 <d2。

[0076] If the duration of the discharge depth soc<10%∈[e2,∞), the duration of the discharge depth soc<10% is too long; if the duration of the discharge depth soc<10%∈[e1,e2), the duration of the discharge depth soc<10% is moderate; if the duration of the discharge depth soc<10%∈[0,e1), the duration of the discharge depth soc<10% is acceptable; where e1 and e2 are the state judgment thresholds of the discharge depth soc<10%, e1 <e2。

[0077] If the duration of the discharge depth soc<5% is ∈[f2,∞), the duration of the discharge depth soc<5% is too long; if the duration of the discharge depth soc<5% is ∈[f1,f2), the duration of the discharge depth soc<5% is moderate; if the duration of the discharge depth soc<5% is ∈[0,f1), the duration of the discharge depth soc<5% is acceptable; where f1 and f2 are the state judgment thresholds of the discharge depth soc<5%, and f1 is <f2。

[0078] If the duration of the discharge depth soc<3%∈[h2,∞), the duration of the discharge depth soc<3% is too long; if the duration of the discharge depth soc<3%∈[h1,h2), the duration of the discharge depth soc<3% is moderate; if the duration of the discharge depth soc<3%∈[0,h1), the duration of the discharge depth soc<3% is acceptable; where h1 and h2 are the state judgment thresholds of the discharge depth soc<3%, and h1 <h2。

[0079] The state judgment thresholds of each indicator are determined as follows:

[0080] For a certain indicator, the indicator values ​​of M vehicles are arranged in descending order, and the values ​​at one-third and two-thirds are used as the state judgment thresholds of the indicator.

[0081] S6, forming association rules by subdividing the indicator status and the vehicle battery aging condition. The association rules include a factor part and a result part, which are recorded as {A—>B}. The factor A is composed of the subdivided status of one or more indicators, and the vehicle battery aging condition is taken as the result B.

[0082] According to the SOH change of each vehicle battery calculated in step S2, the aging of each vehicle battery is determined; according to the index value of each vehicle calculated in step S4, the subdivision of the state of each vehicle under each index is determined; and the association rules are obtained by combining the aging of the vehicle battery and the subdivision of the state of each index. For example: association rule = {high temperature charging for too long, low temperature charging for moderate, high temperature discharge for too long, low temperature discharge for acceptable, large current discharge for too long, large current charging for acceptable, discharge depth soc<10% for too long, discharge depth soc<5% for moderate, discharge depth soc<3% for acceptable —> extremely damaged vehicle battery aging caused by rapid}; association rule = {high temperature charging for too long, discharge depth soc<3% for too long —> extremely damaged vehicle battery aging caused by rapid}.

[0083] S7, according to the battery aging conditions of each vehicle and the status of each indicator, the support of all association rules is calculated, and the association rules with support greater than the minimum support threshold are screened out to form a frequent item set.

[0084] The support reflects the frequency of occurrence of association rules. Only when the support reaches a certain level is it worth studying. Calculate the support of each association rule. For example, calculate the support of the association rule "{High-temperature charging for too long, discharge depth soc<3% for too long—> Extremely damaged vehicle use leads to rapid aging of vehicle batteries}" as follows:

[0085] Support = m1 / M;

[0086] Among them, Support is the support of the association rule, and m0 is the number of occurrences of the association rule in M ​​vehicles.

[0087] The minimum support represents the lowest statistical importance of the association rule. If the support of an association rule is greater than the minimum support threshold, the association rule is a frequent item. Therefore, all association rules are screened according to the minimum support threshold, and all association rules with support greater than the minimum support threshold are screened out to obtain frequent item sets.

[0088] The minimum support threshold can be determined according to the size and sparsity of the data set collected for different models. If the data set is very large, the support threshold can be relatively low, such as 0.1, because even a low support may represent a certain number. On the contrary, if the data set is small or very sparse, a higher support threshold is needed to filter out too rare rules, such as 0.7.

[0089] S8, calculate the confidence of the association rules in the frequent item set, and filter out all association rules whose confidence is greater than the minimum confidence threshold as strong association rules.

[0090] Confidence reflects the reliability of the association rule. If the confidence of the association rule {A—>B} (when A occurs, B also occurs) is high, it means that when A occurs, B is likely to occur as well, which may bring research value. For example, when {high-temperature charging for too long, the duration of discharge depth soc<3% is too long} occurs, the probability of {vehicle battery rapid aging caused by extreme vehicle damage} is calculated, that is, the confidence of {high-temperature charging for too long, the duration of discharge depth soc<3% is too long—>vehicle battery rapid aging caused by extreme vehicle damage} is:

[0091] Confidence = m1 / m2;

[0092] Among them, m1 is the number of {high-temperature charging for too long, discharge depth soc<3% for too long—> extremely violent driving and rapid aging} that appear in M ​​vehicles; m2 is the number of {high-temperature charging for too long, discharge depth soc<3% for too long} that appear in M ​​vehicles; Confidence is the confidence of this association rule.

[0093] The association rules in the frequent item set are screened according to the minimum confidence threshold, and all association rules with confidence greater than the minimum confidence threshold are screened out, that is, strong association rules. The minimum confidence represents the lowest reliability of the association rule.

[0094] S9, calculating the lift of the strong association rules, and screening out all the strong association rules whose lift is greater than the minimum lift threshold as valid association rules.

[0095] The effective association rules are obtained according to the value of lift. In the association rule {A—>B}, lift refers to the confidence of the association rule {A—>B} divided by the support of B, that is:

[0096] Promotion = Confidence / Support_B;

[0097] Among them, Promotion is the promotion degree of the association rule {A—>B}; Confidence is the confidence degree of the association rule {A—>B}; Support_B is the support degree of result B, that is, the number of times result B appears in M ​​vehicles.

[0098] The minimum lift threshold is set to 1. If the lift is greater than 1, it means that the association rule {A—>B} is valuable and is a valid association rule; otherwise, it means that the association rule {A—>B} is worthless.

[0099] S10. Analyze the causes of different aging conditions of vehicle batteries according to the extracted effective association rules, that is, the factors in the effective association rules.

[0100] For example, after step S10, the following valid association rules are obtained:

[0101] {Excessive high-temperature charging and long-term discharge depth soc<3%} —> {Rapid aging of vehicle battery due to extreme damage to the vehicle};

[0102] {High temperature charging is moderate, and the duration of discharge depth soc<3% is too long} —> {Rapid aging of vehicle battery caused by damaged vehicle};

[0103] {Moderate high temperature charging and moderate duration of discharge depth soc<3%}—>{Natural aging of vehicle battery caused by normal use}.

[0104] The meaning of the first effective association rule is that when the high-temperature charging time in the charging habit is ∈[β2,∞), and the duration of the discharge depth soc<3% in the driving habit is ∈[h2,∞), it is an extremely damaged vehicle and the vehicle battery will age rapidly; the meaning of the second effective association rule is that when the high-temperature charging time in the charging habit is ∈[β1,β2), and the duration of the discharge depth soc<3% in the driving habit is ∈[h2,∞), it is a damaged vehicle and the vehicle battery will age rapidly; the meaning of the third effective association rule is that when the high-temperature charging time in the charging habit is ∈[β1,β2), and the duration of the discharge depth soc<3% in the driving habit is ∈[h1,h2), it is a normal vehicle and the vehicle battery will age naturally.

[0105] Furthermore, based on the above two effective association rules, it can be seen that the high-temperature charging time of the vehicle battery should not be greater than β2, otherwise it will aggravate the damage to the vehicle battery life. The duration of the vehicle battery's discharge depth soc<3% should not be greater than h2, otherwise it will also aggravate the damage to the vehicle battery life.

[0106] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for analyzing the cause of rapid battery aging, characterized in that: The following steps are involved: S1, select M vehicles of the same model and driving years, and obtain the charging and discharging data of the batteries of each vehicle; S2, calculate the SOH change of each vehicle battery during the driving years; S3, classifying the aging conditions of the vehicle battery into different levels according to the SOH change of the vehicle battery; S4, constructing an index for evaluating the battery charging and discharging state, and calculating various index values ​​of each vehicle according to the charging and discharging data of the battery of each vehicle; S5, according to the values ​​of the various indicators of each vehicle, sub-classify the status of each indicator, and determine the sub-classification of the status of each vehicle under each indicator; S6, forming an association rule by subdividing the indicator status and the vehicle battery aging condition, wherein the association rule includes a factor part and a result part, which is recorded as {A—>B}, wherein the indicator status subdividing condition constitutes factor A, and the vehicle battery aging condition is taken as result B; S7, according to the battery aging conditions of each vehicle and the status of each indicator, the support of all association rules is calculated, and the association rules with support greater than the minimum support threshold are screened out to form a frequent item set; S8, calculating the confidence of the association rules in the frequent item set, and screening out all association rules whose confidence is greater than the minimum confidence threshold as strong association rules; S9, calculating the lift of the strong association rules, and screening out all the strong association rules whose lift is greater than the minimum lift threshold as valid association rules; S10, analyzing the causes of different aging conditions of vehicle batteries according to the extracted effective association rules, that is, the factor part of the effective association rules; In step S7, the support calculation of the association rule is as follows: Support = m1 / M; Among them, Support is the support of the association rule {A—>B}, and m1 is the number of association rules {A—>B} appearing in M ​​vehicles; In step S8, the confidence calculation of the association rule is as follows: Confidence = m1 / m2; Among them, m1 is the number of association rules {A—>B} appearing in M ​​vehicles; m2 is the number of factor A appearing in M ​​vehicles; Confidence is the confidence of the association rule {A—>B}; In step S9, the lift calculation of the association rule is as follows: Promotion = Confidence / Support_B; Among them, Promotion is the promotion degree of the association rule {A—>B}; Confidence is the confidence degree of the association rule {A—>B}; Support_B is the support degree of result B, that is, the number of times result B appears in M ​​vehicles; In step S3, the vehicle battery aging conditions are divided into: rapid aging of the vehicle battery due to extremely damaged vehicle use, rapid aging of the vehicle battery due to damaged vehicle use, aging of the vehicle battery due to generally damaged vehicle use, natural aging of the vehicle battery due to normal vehicle use, and natural aging of the vehicle battery due to good vehicle use; In step S4, the indicators for evaluating the charge and discharge state of the battery include: low-temperature charging duration, high-temperature charging duration, high-temperature discharging duration, low-temperature discharging duration, high-current charging duration, high-current discharging duration, the duration when the depth of discharge soc < s1%, the duration when the depth of discharge soc < s2%, and the duration when the depth of discharge soc < s3%; where high temperature means higher than 35 °C, low temperature means lower than 5 °C, and high current means higher than 50 A; 15% > s1% > s2% > s3% > 0.

2. The method for analyzing the cause of rapid battery aging according to claim 1, characterized in that: In step S5, the states of each indicator are classified in detail. Among them, according to a certain indicator value of the vehicle, the state sub-classification of the vehicle under this indicator is confirmed as follows: If the indicator value ≥ x2, the duration of this indicator is too long; if x1 ≤ indicator value < x2, the duration of this indicator is moderate; if the indicator value < x1, the duration of this indicator is acceptable; where x1 and x2 are the state judgment thresholds of this indicator, and ∞ > x2 > x1 > 0.

3. The method for analyzing the cause of rapid battery aging according to claim 2, characterized in that: The value-taking method of the state judgment threshold of the indicator is: sort the indicator values of M vehicles in descending order, and take the values at the one-third and two-thirds positions as the state judgment thresholds x2 and x1 of this indicator respectively.

4. The method for analyzing the cause of rapid battery aging according to claim 1, characterized in that: In step S2, the current SOH of each vehicle's battery is calculated by the capacity change method, and the SOH change amount of each vehicle's battery, that is, Δsoh, is calculated as follows: The current SOH of the vehicle battery is: ； Among them, Q now is the total capacity of the battery in its current state, Q rated is the total capacity of the battery when it leaves the factory, soh Q is the current SOH value of the vehicle battery; ； where ΔQ is the charging capacity of the battery within a period of time; ΔSOC is the SOC change amount of the battery during this period; SOC1 is the SOC value at the end of charging, which is 100%; SOC2 is the SOC value at the start of charging; The SOH change amount of the vehicle battery is: Δsoh = soh1-soh Q ; where soh1 is the factory capacity of the vehicle battery, which is 100%.

5. A computer program product, characterized in that It includes a computer program / instructions, and when the computer program / instructions are executed by a processor, it implements the method for analyzing the reasons for rapid battery aging according to any one of claims 1 to 4.

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