CN108919129B - A life prediction method for power battery under time-varying operating conditions - Google Patents

Abstract

The invention provides a life prediction method for a power battery under time-varying working conditions, the method includes: based on the actual working conditions of the vehicle power battery, taking the three main factors T, C and DOD that affect the life of the power battery as variables, Combined with the time-varying factors of the vehicle operating conditions, firstly, in the time scale of a single cycle, a life prediction model of the power battery under time-varying current conditions is established, and then in the time scale of multiple cycles, the operating temperature and discharge are considered. The influence of deep DOD on battery life, and finally a life model of time-varying power battery that is closer to actual operating conditions, improves the accuracy of life prediction of vehicle power battery. The battery life calculated by this model can guide the vehicle. The use, maintenance and replacement of power batteries ensure the safe use of electric vehicles. In addition, the model is simple and easy to calculate, which is beneficial to improve the prediction efficiency.

Description

A life prediction method for power battery under time-varying operating conditions

technical field

The invention relates to the technical field of power batteries, in particular to a life prediction method of a power battery under time-varying working conditions.

Background technique

In recent years, all countries have been actively carrying out research on new energy vehicles, and lithium-ion batteries have the characteristics of high energy density, high operating voltage, long cycle life, low self-discharge rate and no memory effect, as driving energy in the field of power battery applications. more and more.

The development process of lithium-ion power batteries includes electrical performance, core functions, life and safety, etc., and life development is the top priority. In the process of battery use, there are many types of stress affecting its aging, including ambient temperature, humidity, mechanical pressure, radiation, current, voltage, SOC range, etc. Among many factors, the stress that has a major impact on battery aging is the ambient temperature and the (rate, DOD) during its use. During use, the capacity and internal resistance of the battery will change, and the law of battery capacity decay or internal resistance increase is usually used to characterize and predict battery life.

In order to obtain battery life data, most of the lithium-ion battery aging research is carried out under laboratory conditions, and the stress remains constant over time. Analyze the relationship between stress and life change. However, the vehicle operating conditions are varied and the temperature and current usually change with time, especially the current, and even change rapidly. Therefore, the life model established based on the laboratory operating conditions cannot directly predict the battery aging behavior under the actual operating conditions.

SUMMARY OF THE INVENTION

In view of this, the present invention proposes a life prediction method of power battery under time-varying working conditions, aiming to solve the problem that the existing power battery life prediction model does not conform to actual working conditions, resulting in low accuracy of prediction results.

The present invention provides a method for predicting the life of a power battery under a time-varying working condition, comprising the following steps: step S1, establishing a life prediction model of the power battery under a time-varying current working condition based on a single cycle; step S2, obtaining a time-varying current condition Under current conditions, the percentage of time that the power battery operates in different temperature intervals and the percentage of times of discharge in different depth of discharge intervals; step S3, based on multiple cycles, The percentage of time and the percentage of times in each of the depth-of-discharge intervals are substituted into the life prediction model under the corresponding working conditions in the step S1 to calculate the predicted life of the power battery.

Further, in the above-mentioned life prediction method, the function expression of the power battery life prediction model under the time-varying current condition based on a single cycle is as follows:

Among them, Cycles _C is the predicted life of the power battery under a certain time-varying operating condition based on a single cycle, T is the operating temperature of the power battery, DOD is the depth of discharge of the power battery, and C _i is the power battery under the ith operating condition. The discharge rate of , Ratio _Ci is the time ratio of the ith working condition to the whole working condition in a time-varying current condition, i is a positive integer greater than or equal to 1, and the ith working condition is expressed as (T, C _i , DOD ) _i .

Further, in the above-mentioned life prediction method, the function expression of the power battery life prediction model under the time-varying current condition based on a single cycle is obtained by the following steps:

Sub-step S11, using operating temperature T, depth of discharge DOD and discharge rate C as variables to establish a life prediction model Cycles=f(T, C, DOD) of the power battery under a single cycle; sub-step S12, obtaining time-varying operating condition current , the discharge rate C _i at a certain moment is obtained by conversion according to the magnitude of the current under the time-varying operating conditions; sub-step S13, for a single cycle, the discharge depth DOD is constant, assuming the operating temperature T Methods Establish a predictive life model of power battery based on a time-varying current condition under a single cycle

Further, in the above life prediction method, the step of determining Ratio _Ci in the power battery life prediction model based on a certain time-varying current condition under a single cycle is as follows: the time-varying current condition is discretized into the operating temperature and the discharge depth is constant. Under the condition of , the i combined working conditions of constant current discharge within the preset time, i is a positive integer greater than or equal to 1; determine the ith working condition (T, C _i , DOD) _i in a time-varying current working condition The time ratio of the whole working condition is Ratio _Ci =Δt/t, wherein the time of one time-varying current condition is t, and the time of each working condition (T, C _i , DOD) _i is Δt.

Further, in the above life prediction method, the expression Cycles=f(T, C, DOD) of the power battery life prediction model is a polynomial form or an exponential form.

Further, in the above life prediction method, the expression Cycles=f(T, C, DOD) of the power battery life prediction model is as follows:

Cycles=a ₀ +a ₁ *T+a ₂ *DOD+a ₃ *C+a ₄ *(TT ₀ )*(DOD-DOD ₀ )+a ₅ *(TT ₀ )*(CC ₀ )+a ₆ *C*DOD+a ₇ *(TT ₀ )*(TT ₀ )+a ₈ *(DOD-DOD ₀ )*(DOD-DOD ₀ )+a ₉ *(CC ₀ )*(CC ₀ )

In the formula, Cycles is the number of cycles when the capacity of the power battery decays to 80% of the initial capacity, T is the operating temperature of the power battery, DOD is the depth of discharge of the power battery, C is the discharge rate of the power battery, a ₀ , a ₁ , a ₂ , a ₃ , a ₄ , a ₅ , a ₆ , a ₇ , a ₈ , a ₉ , T ₀ , C ₀ , and DOD ₀ are fitting constants.

Further, in the above life prediction method, the expression Cycles=f(T, C, DOD) of the power battery life prediction model when the discharge rate, operating temperature and discharge depth are constant is as follows:

In the formula, Cycles is the number of cycles when the capacity of the power battery decays to 80% of the initial capacity; A ₀ , b, E _a , and c are fitting constants; R is the free gas constant, R=8.314J·K ^-1 ·mol ^{- 1} ; C is the discharge rate of the power battery.

Further, in the above life prediction method, the expression of the predicted life of the power battery based on the corresponding working conditions under multiple cycles is as follows:

Among them, Cycles _cell is the predicted life of the power battery based on the corresponding working conditions under multiple cycles, m, q, j are positive integers greater than or equal to 1, T _j is the middle value of the jth temperature interval, and Ratio _Tj is the th The time percentage of j temperature intervals in the total temperature data interval, DOD _q is the middle value of the qth depth of discharge interval, Ratio _DODq is the percentage of the qth depth of discharge interval in the total number of discharges, Cycles _{(Tj, DODq)} is the predicted life of the power battery based on a single cycle, under a certain temperature intermediate value and a certain depth of discharge intermediate value corresponding to the operating conditions.

Further, in the above life prediction method, in the step S2, the discharge depth interval of the power battery is obtained from the historical charging data.

Further, in the above-mentioned life prediction method, the depth of discharge interval DOD _(i) of the power battery each time is expressed as:

DOD _(i) =SOC _end(i-1) -SOC _ini(i) , where i is the number of times the power battery is charged, which is a positive integer greater than 1, and SOC _end(i-1) is the i-1th time The percentage of remaining power at the end of charging, and SOC _ini(i) is the percentage of remaining power at the starting point of the i-th charging.

In the present invention, based on the actual working conditions of the vehicle power battery, the three main factors T, C, and DOD that affect the life of the power battery are used as variables, and combined with the time-varying factors of the influencing factors under the vehicle working conditions, firstly in a single cycle In the time scale of 2000, the life prediction model of the power battery under time-varying current conditions is established, and then in the time scale of multiple cycles, the influence of operating temperature and depth of discharge DOD on battery life is considered, and finally a life prediction model that is closer to the actual operating conditions is obtained. The life model of power battery under time-varying working conditions improves the accuracy of life prediction of vehicle power battery. The battery life calculated by this model can guide the use, maintenance and replacement of vehicle power battery, ensuring the safe use of electric vehicles. . In addition, the model is simple and easy to calculate, which is beneficial to improve the prediction efficiency.

Description of drawings

Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are for the purpose of illustrating preferred embodiments only and are not to be considered limiting of the invention. Also, the same components are denoted by the same reference numerals throughout the drawings. In the attached image:

1 is a flowchart of a method for predicting the life of a power battery under a time-varying operating condition provided by an embodiment of the present invention;

2 is a graph of cell current changes over time collected in a real vehicle under NEDC operating conditions provided by an example of the present invention;

Fig. 3 is the current discharge rate change curve in the primary NEDC working condition provided by the example of the present invention;

Fig. 4 is the temperature change curve in the primary NEDC working condition provided by the example of the present invention;

Fig. 5 is the temperature distribution diagram of battery cell operation in a year of a city running vehicle provided by the example of the present invention;

FIG. 6 is the DOD range of the power battery provided by the example of the present invention when it is actually used for 1000 times of charging;

FIG. 7 is a distribution diagram of the DOD used by the power battery in each interval provided by the example of the present invention.

Detailed ways

Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure will be more thoroughly understood, and will fully convey the scope of the present disclosure to those skilled in the art. It should be noted that the embodiments of the present invention and the features of the embodiments may be combined with each other under the condition of no conflict. The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

Referring to FIG. 1 , the method for predicting the life of a power battery under a time-varying working condition provided by an embodiment of the present invention includes the following steps:

Step S1, based on a single cycle, establish a life prediction model of the power battery under the time-varying current condition.

Specifically, the power battery may be a lithium battery, a lead-acid battery, a nickel-based battery, or a sodium-sulfur battery, which is not limited in this embodiment.

The function expression of the power battery life prediction model based on the time-varying current condition under a single cycle is as follows:

Among them, Cycles _C is the predicted life of the power battery under a certain time-varying working condition under a single cycle, T is the operating temperature of the power battery, DOD is the depth of discharge of the power battery, and Ci is the power battery in the i-th operation.

The discharge rate under conditions (T, C _i , DOD) _i , Ratio _Ci is the time ratio of the i-th condition (T, C _i , DOD) _i to the whole condition in a time-varying current condition, and i is greater than A positive integer equal to 1.

In the specific implementation, the life prediction model of the power battery under the time-varying current condition is established according to the following steps:

In sub-step S11, a life prediction model Cycles=f(T, C, DOD) of the power battery under a single cycle is established with the operating temperature T, the depth of discharge DOD and the discharge rate C as variables.

Specifically, the level of (T, C, DOD) is reasonably set for the use range of the vehicle power battery. For example, the range of the depth of discharge DOD can be (60% to 100%), and the range of the operating temperature can be (- 30～50) ℃, the range of the discharge rate C can be (0.5～5).

In the specific implementation, a function expression with a high degree of agreement with the data change rule or a higher precision can be selected for fitting to obtain the expression Cycles=f(T) of the power battery life prediction model under constant (T, C, DOD) conditions , C, DOD) can be in polynomial form or exponential form. E.g:

Cycles=a ₀ +a ₁ *T+a ₂ *DOD+a ₃ *C+a ₄ *(TT ₀ )*(DOD-DOD ₀ )+a ₅ *(TT ₀ )*(CC ₀ )+a ₆ *C*DOD+a ₇ *(TT ₀ )*(TT ₀ )+a ₈ *(DOD-DOD ₀ )*(DOD-DOD ₀ )+a ₉ *(CC ₀ )*(CC ₀ )

In the formula, Cycles is the number of cycles when the capacity of the power battery decays to 80% of the initial capacity, T is the operating temperature of the power battery, DOD is the depth of discharge of the power battery, C is the discharge rate of the power battery, a ₀ , a ₁ , a ₂ , a ₃ , a ₄ , a ₅ , a ₆ , a ₇ , a ₈ , a ₉ , T ₀ , C ₀ and DOD ₀ are fitting constants, and each fitting constant can be determined according to the experimental conditions and life data results. Response surface fitting is obtained, that is, the corresponding life can be obtained by inputting any actual working condition (T, C, DOD).

The expression Cycles=f(T, C, DOD) of the power battery life prediction model when the discharge rate, operating temperature and discharge depth are constant can also be expressed as follows:

In the formula, Cycles is the number of cycles when the capacity of the power battery decays to 80% of the initial capacity; A ₀ , b, E _a , and c are fitting constants, and each fitting constant can be fitted according to the response surface through the experimental conditions and life data results. combined; R is the free gas constant, R=8.314J·K ^-1 ·mol ^-1 ; C is the discharge rate of the power battery.

In sub-step S12, the time-varying operating condition current is obtained, and the discharge rate C _i at a certain moment is obtained by conversion according to the magnitude of the time-varying operating condition current.

Specifically, the predicted life expression Cycles=f(T, C, DOD) obtained in sub-step S11 is suitable for the life prediction of the power battery under the conditions of constant discharge rate C, operating temperature T and discharge depth DOD, while the actual vehicle use The working conditions are more complicated, and the current size keeps changing during the discharge process. Therefore, in this embodiment, the current can be collected through the actual vehicle working condition, or a simulation experiment can be performed to simulate the actual vehicle working condition, and the time-varying working condition current can be obtained. The driving condition of the power battery can be any regular driving condition, including but not limited to NEDC, EUDC, US06, HWFET, UDDS, US06, etc.

In the specific implementation, the process of obtaining the discharge rate C in the NEDC working condition can be taken as an example. As shown in Figure 2, it can be seen that the magnitude of the current keeps changing during the discharge process, and the discharge rate under this working condition is obtained according to the magnitude of the current, namely C _i =I/C _n , where I is the magnitude of the current, and the unit is A; C _n is the rated capacity of the battery, and the unit is Ah.

Sub-step S13, for a single cycle, the depth of discharge DOD is constant, and assuming that the operating temperature T is constant, the life model of the power battery based on a time-varying current condition under a single cycle is established by the method of first discrete and then integrated

Specifically, the discrete method of the time-varying current condition may adopt time discrete, or may adopt methods such as fuzzy logic and cluster analysis according to the magnitude of the current. In this embodiment, a time-discrete method is used. The derivation process of the life model of the power battery under a certain time-varying current condition under a single cycle can specifically include the following sub-steps:

First, the time-varying current condition is discretized into i combined conditions of constant current discharge within a preset time under the condition of constant operating temperature and discharge depth, where i is a positive integer greater than or equal to 1. The i combined operating conditions are respectively (T, C ₁ , DOD) ₁ , . . . , (T, C _i , DOD) _i .

Then determine the ratio of each working condition (T, C _i , DOD) _i to the entire working condition time in a time-varying current working condition with time t as Ratio _Ci =Δt/t, where each working condition (T, The time of C _i , DOD) _i is Δt.

Assuming that the battery aging has no path dependence and memory effects, the cycle life under this condition is predicted by the integration method.

The following is a detailed description of the calculation process of the predicted life of the power battery under a certain time-varying current condition under a single cycle with a specific example:

First, obtain the current, set temperature and DOD under the demand condition. The settings of temperature and DOD take the median value of the interval in multiple cycle steps. The discharge rate and temperature change curves under one NEDC condition are shown in Figures 3 and 3. shown in Figure 4. It can be seen from Figure 4 that the temperature changes very little under one NEDC condition, so it can be considered that the temperature is constant under any one NEDC condition.

The NEDC working condition is simplified, and the life is calculated after integration. Specifically, the time of the NEDC working condition is calculated as 1298s. In order to facilitate the calculation, the working conditions are divided into 1298. That is, i=1298, and the time ratio corresponding to each working condition is Ratio _Ci =1/1298. Combined with Figure 2, the T, C, DOD data of the corresponding points are brought into the f(T, C, DOD) model to obtain the corresponding working condition. The life of the battery under the T and DOD conditions under the NEDC conditions can be obtained according to the following formula,

可计算出该条件下动力电池的循环寿命。For example, when T=25℃ and DOD=0.9, i changes from 1 to 1298, and the change curve of C _i is shown in Figure 3, which is represented by the following formula

The cycle life of the power battery under this condition can be calculated.

Similarly, when T=45℃, DOD=0.8, the NEDC life can be calculated according to the following formula:

In order to facilitate the calculation, the embodiment of the present invention divides T and DOD into intervals, and calculates with the median value of the respective interval. Therefore, the life of the power battery in the NEDC working condition under the conditions of different temperatures and depths of discharge can be obtained. The results are shown in Table 1 below:

Table 1 Life under constant T and DOD conditions under NEDC conditions

temperature/℃ DOD/% life/time 20 80 4788 20 70 5116 25 80 4653 25 70 4752 30 80 4347 30 70 4283

Step S2, obtaining the percentage of time that the power battery operates in different temperature ranges and the percentage of times of discharge in different depth-of-discharge ranges under the time-varying current condition.

Specifically, the operating temperature of the battery can be obtained from the battery management system BMS, the temperature range can be divided into multiple temperature intervals of equal width according to the requirements, and the ratio Ratio _T of each to the total temperature range is calculated, and the median value T _j of the interval is taken. Used as a representative temperature for subsequent prediction of battery life. For example, Figure 5 shows the temperature distribution of battery cells in a city that runs vehicles in one year. For example, for the temperature range (10-30) °C, the time percentage of the total temperature data can be calculated, and the median value of 20 °C is taken as the The representative temperature is used for subsequent lifetime calculations. Dividing the interval and taking the median value as the representative calculation is a choice made under the condition of comprehensive consideration of the accuracy of the result and the amount of calculation, which is beneficial to reduce the amount of calculation and quickly obtain the predicted value of the cycle life of the vehicle battery.

The depth of discharge DOD can be obtained from historical data. Historical data can come from the same vehicle, a certain model, or different vehicles in a certain area, which can meet the forecasting needs of different levels. Specifically, the depth of discharge data can come from T-box data uploaded by the vehicle, or can be recorded data from the charging pile. In specific implementation, the method for obtaining the depth of discharge DOD is as follows: by recording the initial and termination SOC points of each charge, the DOD interval for each use of the user's vehicle battery can be calculated, and the depth of discharge interval DOD _(i) of the power battery each time Expressed as:

DOD _(i) =SOC _end(i-1) -SOC _ini(i) , where i is the number of times the power battery is charged, which is a positive integer greater than 1, and SOC _end(i-1) is the i-1th time The percentage of remaining power at the end of charging, and SOC _ini(i) is the percentage of remaining power at the starting point of the i-th charging. The following table 2 is an example of the discharge depth interval of the power battery each time:

Table 2 Discharge depth interval of power battery each time

The depth of discharge DOD actually used by the user for 1000 times of charging and discharging is shown in Figure 6. Referring to Figure 7, the depth of discharge DOD is divided into multiple DOD intervals of equal width, and the distribution ratio of battery usage DOD in each interval is calculated. Ratio _DOD The value DOD _q is used as a representative depth of discharge DOD for subsequent prediction of battery life.

Step S3, based on multiple cycles, the time percentage of each of the temperature intervals and the percentage of times of each of the depth of discharge intervals in the above step 2 are substituted into the life prediction model under the corresponding working conditions in step 1, and the calculated result is obtained. The predicted life of the power battery.

Specifically, the expression of the predicted life of the power battery based on the corresponding operating conditions under multiple cycles is as follows:

Among them, Cycles _cell is the predicted life of the power battery based on the corresponding working conditions under multiple cycles, m, q, j are positive integers greater than or equal to 1, T _j is the middle value of the jth temperature interval, and Ratio _Tj is the th The time percentage of the j temperature intervals in the total temperature data interval, DOD _q is the middle value of the qth depth of discharge interval, Ratio _DODq is the percentage of the qth temperature interval in the total discharge times, Cycles _{(Tj,DODq )} is the predicted life of the power battery based on a single cycle, under a certain temperature intermediate value and a certain discharge depth intermediate value corresponding to the operating conditions.

When the time percentage of the operating temperature T and the number of times of the depth of discharge DOD are shown in Table 3 below, the predicted life of the power battery under the corresponding operating conditions under multiple cycles can be obtained by combining the data in Table 1.

Table 3 Operating temperature T and DOD ratio of depth of discharge

temperature/℃ Proportion DOD Proportion 20 0.8 0.8 0.1 25 0.1 0.7 0.9 30 0.1

The calculation process of power battery life is as follows:

Cycles _cell =4788*0.8*0.1+5116*0.8*0.9+4653*0.1*0.1+4752*0.1*0.9+4347*0.1*0.1+4283*0.1*0.9=4969, that is, the cycle life of the obtained power battery is 4969 times.

It can be clearly drawn from the above that the life prediction method of the power battery under the time-varying working conditions provided in this embodiment is based on the actual working conditions of the vehicle power battery, and the three main factors T, C, and DOD that affect the life of the power battery are: variables, combined with the time-varying factors of the vehicle operating conditions, firstly, within the time scale of a single cycle, establish a life prediction model for the power battery under time-varying current conditions, and then consider the operating temperature within the time scale of multiple cycles. and the influence of depth of discharge DOD on battery life, and finally obtain a life model of power battery under time-varying operating conditions that is closer to actual operating conditions, which improves the accuracy of life prediction of vehicle power battery. Guide the use, maintenance and replacement of vehicle power batteries to ensure the safe use of electric vehicles. In addition, the model is simple and easy to calculate, which is beneficial to improve the prediction efficiency.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit and scope of the invention. Thus, provided that these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims (9)

1. a life prediction method of power battery under a time-varying operating condition, is characterized in that, comprises the following steps: Step S1, based on a single cycle, establish a life prediction model of the power battery under the time-varying current working condition; the function expression of the power battery life prediction model under the time-varying current working condition based on the single cycle is as follows:

Among them, Cycles _C is the predicted life of the power battery under a certain time-varying operating condition based on a single cycle, T is the operating temperature of the power battery, DOD is the depth of discharge of the power battery, and C _i is the power battery under the ith operating condition. The discharge rate of , Ratio _Ci is the time ratio of the ith working condition to the whole working condition in a time-varying current condition, i is a positive integer greater than or equal to 1, and the ith working condition is expressed as (T, C _i , DOD ) _i ; Step S2, obtaining the percentage of time that the power battery operates in different temperature ranges and the percentage of times of discharge in different depth-of-discharge ranges under the time-varying current condition; Step S3, based on multiple cycles, substitute the time percentage of each of the temperature intervals and the frequency percentage of each of the depth of discharge intervals in the step S2 into the life prediction model under the corresponding operating conditions in the step S1, Calculate the predicted life of the power battery. 2. The life prediction method according to claim 1, wherein the function expression of the power battery life prediction model under the time-varying current working condition based on a single cycle is obtained by the following steps: Sub-step S11, using the operating temperature T, the depth of discharge DOD and the discharge rate C as variables to establish a life prediction model Cycles=f(T, C, DOD) of the power battery under a single cycle; Sub-step S12, obtaining the time-varying operating condition current, and converting the time-varying operating condition current to obtain the discharge rate C _i at a certain moment; Sub-step S13, for a single cycle, the depth of discharge DOD is constant, and assuming that the operating temperature T is constant, a predictive life model of the power battery based on a time-varying current condition under a single cycle is established by the method of first discrete and then integrated

3. The life prediction method according to claim 1 or 2, wherein the step of determining Ratio _Ci in the power battery life prediction model under a certain time-varying current condition based on a single cycle is as follows: Discrete the time-varying current working conditions into i combined working conditions of constant current discharge within a preset time under the condition of constant operating temperature and discharge depth, where i is a positive integer greater than or equal to 1; Determine the time ratio of each working condition (T, C _i , DOD) _i to the whole working condition in a time-varying current condition as Ratio _Ci =Δt/t, where the time of a time-varying current condition is t, and each time-varying current condition is t. The time for each operating condition (T, C _i , DOD) _i is Δt. 4 . The life prediction method according to claim 2 , wherein the expression Cycles=f(T, C, DOD) of the power battery life prediction model is in a polynomial form or an exponential form. 5 . 5. The life prediction method according to claim 4, wherein the expression Cycles=f(T, C, DOD) of the power battery life prediction model is as follows: Cycles=a ₀ +a ₁ *T+a ₂ *DOD+a ₃ *C+a ₄ *(TT ₀ )*(DOD-DOD ₀ )+a ₅ *(TT ₀ )*(CC ₀ )+a ₆ *C*DOD+a ₇ *(TT ₀ )*(TT ₀ )+a ₈ *(DOD-DOD ₀ )*(DOD-DOD ₀ )+a ₉ *(CC ₀ )*(CC ₀ ) In the formula, Cycles is the number of cycles when the capacity of the power battery decays to 80% of the initial capacity, T is the operating temperature of the power battery, DOD is the depth of discharge of the power battery, C is the discharge rate of the power battery, a ₀ , a ₁ , a ₂ , a ₃ , a ₄ , a ₅ , a ₆ , a ₇ , a ₈ , a ₉ , T ₀ , C ₀ , and DOD ₀ are fitting constants. 6. The life prediction method according to claim 4, wherein the expression Cycles=f(T, C, DOD) of the power battery life prediction model when the discharge rate, operating temperature and discharge depth are constant is as follows:

In the formula, Cycles is the number of cycles when the capacity of the power battery decays to 80% of the initial capacity, A ₀ , b, E _a , and c are fitting constants; R is the free gas constant, R=8.314J·K ^-1 ·mol ^{- 1} ; C is the discharge rate of the power battery. 7. The life prediction method according to claim 2, wherein the expression of the predicted life of the power battery based on corresponding operating conditions under multiple cycles is as follows:

Among them, Cycles _cell is the predicted life of the power battery based on the corresponding working conditions under multiple cycles, q and j are positive integers greater than or equal to 1, T _j is the middle value of the j-th temperature interval, and Ratio _Tj is the j-th The time percentage of the temperature interval to the total temperature data interval, DOD _q is the middle value of the qth depth of discharge interval, Ratio _DODq is the percentage of the qth depth of discharge interval to the total discharge times, Cycles _{(Tj, DODq)} is the power battery based on Under a single cycle, the predicted life under the corresponding working conditions at a certain temperature intermediate value and a certain discharge depth intermediate value. 8 . The life prediction method according to claim 1 , wherein in the step S2 , the depth of discharge interval of the power battery is obtained from historical charging data. 9 . 9. life prediction method according to claim 6, is characterized in that, the depth of discharge interval DOD _(i) of described power battery each time is expressed as: DOD _(i) =SOC _end(i-1) -SOC _ini(i) , where i is the number of times the power battery is charged, which is a positive integer greater than 1, and SOC _end(i-1) is the i-1th time The percentage of remaining power at the end of charging, and SOC _ini(i) is the percentage of remaining power at the starting point of the i-th charging.

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