CN105607010A - Method for estimating health state of power battery of electric vehicle - Google Patents

Abstract

The invention relates to the field of estimation of a state of a power battery of an electric vehicle, especially relates to a method for estimating a health state of a power battery of an electric vehicle, and brings forward a method for estimating a health state of a power battery of an electric vehicle, for solving the problems of low estimation precision, instability, long consumed time, large workload, high estimation cost and too high reliance of an estimation result on the corresponding relation between the charge state and the open-circuit voltages of a power battery and the accuracy of an equivalent circuit model during estimation of the health state of the power battery in the prior art. Atual measurement end voltages V0 and charge and discharge currents I of the power battery, open-circuit voltages V<100%SoC> of the power battery at an electric quantity full-charge state and open-circuit voltages V<0%SoC> at an electric quantity full-discharge state are acquired; an equivalent circuit model of the power battery is established, and an simulated value C<b><^> of a storage capacitor C<b> of the power battery is identified; and an estimated value C<cap><^> of a maximum available capacity C<cap> of the power battery is estimated according to an expression shown in the descriptions. The method provided by the invention is simple in calculation, small in calculation amount, high in precision and high in adaptability.

Description

Method for estimating state of health of power battery of electric vehicle

Technical Field

The invention relates to the field of state estimation of vehicle-mounted power batteries of electric vehicles, in particular to a method for estimating the state of health of a power battery of an electric vehicle.

Background

The state of health (SOH) of an on-board power battery of an electric vehicle is an important index for evaluating the current performance of the power battery. During the aging process of the power battery, the state of health of the power battery is mainly reflected by the attenuation of the maximum available capacity and the increase of the internal resistance of the power battery. The aging of the power battery is affected by many factors, such as temperature, charge and discharge rate, and discharge depth, which cause great uncertainty and nonlinearity in the changes of the maximum available capacity and internal resistance of the power battery, and further brings great challenges to a power Battery Management System (BMS) for detecting the maximum available capacity and internal resistance of the power battery in the management process.

In addition, when the power battery is used in a group, the capacity information and the internal resistance information of the power battery cannot be directly acquired through an experimental means, the maximum available capacity of the power battery directly affects the estimated value of the state of charge (SOC) of the power battery, and the estimation precision of the SOC of the power battery directly relates to whether the power battery is safe to use, so that the power battery management system must accurately estimate the maximum available capacity of the power battery, that is, the health state of the power battery, in order to ensure the safe use of the power battery.

The maximum available capacity of the power battery refers to the amount of electricity charged (discharged) when the power battery is charged (discharged) to the cut-off voltage of the power battery at a nominal rate under a certain temperature and aging degree. Since the difference between the maximum available capacity of the power battery at the time of charging and the maximum available capacity at the time of discharging is less than 1%, the average value of the two is usually taken as the maximum available capacity of the power battery in the current state.

Currently, methods for estimating the maximum available capacity of a power battery can be roughly classified into four methods:

1. the method for estimating the maximum available capacity of the power battery based on the open-circuit voltage of the power battery estimates the state of charge of the power battery by utilizing the open-circuit voltage of the power battery according to the unique monotone change relation existing between the state of charge of the power battery and the open-circuit voltage of the power battery, and under the premise of considering the aging of the power battery, the state of charge of the power battery refers to the percentage of the residual capacity of the power battery and the maximum available capacity of the power battery in the current state, and then the maximum available capacity of the power battery in the current state is estimated through the measured residual capacity and the measured state of charge of the power battery.

2. The method for estimating the maximum available capacity of the power battery based on the equivalent circuit model of the power battery constructs a dynamic equation of the power battery through the equivalent circuit model of the power battery, and estimates the maximum available capacity of the power battery as an unknown state.

3. The method for estimating the maximum available capacity of a power battery based on capacity gain analysis and power supply differential analysis focuses on describing the extent of chemical reactions inside the power battery in a laboratory environment.

4. The method for estimating the maximum available capacity of the power battery based on the aging of the power battery mainly analyzes the influence of different aging factors on the degradation of the maximum available capacity of the power battery through experimental data, so that a capacity degradation model or a residual service life model of the power battery is established to estimate the maximum available capacity of the power battery.

The four estimation methods can be divided into an offline estimation method and an online estimation method, wherein when the offline estimation method is used for estimating the maximum available capacity of the power battery, if less statistical data is used for analysis, the estimation deviation is larger, the estimation precision is low and unstable, and if a large amount of statistical data is used for analysis, although the estimation precision requirement can be met, the data accumulation consumes long time and the data analysis workload is large. When the online estimation method is adopted to estimate the maximum available capacity of the power battery, real-time estimation can be carried out and the estimation precision is ensured, however, the online estimation has high requirement on the computing capacity of a power battery management system, so that the estimation cost is high, and the estimation result has strong dependence on the corresponding relation between the state of charge and the open-circuit voltage of the power battery and the accuracy of an equivalent circuit model of the power battery.

Disclosure of Invention

In order to solve the problems that in the prior art, when the health state of a power battery, namely the maximum available capacity, is estimated, the estimation precision is low and unstable, the time consumption is long, the workload is large, or the calculation capacity of a power battery management system is excessively high, the estimation cost is high, and the dependence of the estimation result on the corresponding relation between the state of charge and the open-circuit voltage of the power battery and the accuracy of an equivalent circuit model is excessively strong, the invention provides a method for estimating the health state of the power battery of an electric vehicle, which comprises the following steps:

step 1, in the process of charging and discharging the power battery, actually measured terminal voltage V of the power battery₀Sampling the charging and discharging current I, wherein the sampling time interval is delta t, and acquiring the open-circuit voltage V of the power battery in the state that the electric quantity is fully charged_100％SoCAnd the electricity quantity is discharged in the photoelectric stateOpen circuit voltage V_0％SoC；

Step 2, selecting an RC model as an equivalent circuit model of the power battery, and when the collected charging and discharging current I is input into the RC model, collecting the actually-measured terminal voltage V₀Identifying the storage capacitor C in the equivalent circuit model of the power battery for the output of the RC model_bIs estimated value of

Step 3, utilizing a storage capacitor C in an equivalent circuit of the power battery_bIs estimated value ofAccording toEstimating the maximum available capacity C of the power battery_capIs estimated value of

When the method carries out off-line estimation on the maximum available capacity of the power battery, the actually measured terminal voltage and the charge-discharge current of the power battery are obtained through sampling, the charge-discharge current in the sampled data is input into a system equation of an RC model of the power battery, model parameters of the RC model of the power battery are identified by using an optimization algorithm, and then the identified storage capacitor C of the power battery is estimated_bIs estimated value ofAnd estimating the maximum available capacity C of the power battery according to the one-to-one correspondence relationship between the storage capacitor and the maximum available capacity of the power battery_capIs estimated value ofThe calculation is simple, the calculation amount is small, and the estimation precision is high; the maximum available capacity of the power battery under different aging degrees can be estimated, the estimation precision is ensured, and the applicability is strong.

Preferably, in step 1, the sampling time interval Δ t is a fixed value so as to collect data. Further, the sampling time interval Δ t is 1 s.

Preferably, the system equation of the RC model of the power battery is:

$\left\{\begin{array}{c}{V}_0=\frac{R_e}{(R_s+R_e)}{V}_b+\frac{R_s}{(R_s+R_e)}{V}_s+\frac{(R_t{R}_s+R_t{R}_e+R_e{R}_s)}{(R_s+R_e)}I\\ {\stackrel{\&CenterDot;}{V}}_b=\frac{V_s}{C_b(R_s+R_e)}-\frac{V_b}{C_b(R_s+R_e)}+\frac{R_s}{C_b(R_s+R_e)}I\\ {\stackrel{\&CenterDot;}{V}}_s=\frac{V_b}{C_s(R_s+R_e)}-\frac{V_s}{C_s(R_s+R_e)}+\frac{R_e}{C_b(R_s+R_e)}I\end{array}\right.,$

wherein,

R_erepresents the termination resistance of the power cell,

R_trepresents the ohmic resistance of the power cell,

V_brepresenting the voltage across the storage capacitor Cb of the power cell,

R_srepresents the polarization resistance of the power cell,

V_srepresents the polarization voltage of the power cell,

C_srepresenting the polarization capacitance of the power cell.

Preferably, a genetic algorithm is used to identify model parameters of the equivalent circuit model of the power battery, and the model parameters ξ to be identified are [ R ═ R_tR_sC_sR_eC_b]^TWhere T denotes a matrix transpose.

Preferably, an objective function is set when identifying model parameters of the equivalent circuit model of the power battery $F=\min \left\{\underset{i=1}{\overset{N}{\&Sigma;}}{(V_{0,i}-{\hat{V}}_{0,i}(\mathrm{\&xi;}\left)\right)}^2\right\},$

Wherein,

n represents the length of the sampled data,

V_0,ithe measured terminal voltage at the moment i in the charging and discharging process of the power battery is shown,

representing the estimated terminal voltage at the moment i in the charging and discharging process of the power battery,

represents an estimate of the model quantity ξ to be identified.

When the method carries out off-line estimation on the maximum available capacity of the power battery, the actually measured terminal voltage and the charge-discharge current of the power battery are obtained through sampling, the charge-discharge current in the sampled data is input into a system equation of an RC model of the power battery, model parameters of the RC model of the power battery are identified by using an optimization algorithm, and then the identified storage capacitor C of the power battery is estimated_bIs estimated value ofAnd estimating the maximum available capacity C of the power battery according to the one-to-one correspondence relationship between the storage capacitor and the maximum available capacity of the power battery_capIs estimated value ofThe method has the advantages of simple calculation, small calculation amount and higher estimation precision. Compared with a parallel RC equivalent circuit model, the RC model has unique advantages. When the maximum available capacity of the power battery with different aging degrees is estimated, the maximum available capacity of the power battery can be accurately estimated, and the applicability is strong.

The invention also provides a device for estimating the state of health of the power battery of the electric vehicle by adopting any one of the methods for estimating the state of health of the power battery of the electric vehicle.

Drawings

FIG. 1 is a flow chart of the present invention for estimating the state of health of a power battery of an electric vehicle;

FIG. 2 is an equivalent circuit diagram of an RC model of a power cell;

FIG. 3 is an equivalent circuit diagram of a parallel RC equivalent circuit model of a power battery;

FIG. 4 is a distribution diagram of charge and discharge currents of a power battery under a DST working condition after 0-time circulation;

FIG. 5 is a graph of measured terminal voltage versus time for the power cell of FIG. 4;

FIG. 6 is a graph of estimated terminal voltage versus time for the power cell shown in FIG. 4;

FIG. 7 is a graph showing variation of estimation error of terminal voltage of the power battery shown in FIG. 4;

FIG. 8 is a distribution diagram of charge and discharge currents of a power battery under a DST working condition after 200 cycles;

FIG. 9 is a graph of measured terminal voltage versus time for the power cell of FIG. 8;

FIG. 10 is a graph of estimated values of terminal voltage of the power cell shown in FIG. 8 over time;

fig. 11 is a graph showing a variation in estimation error of the terminal voltage of the power battery shown in fig. 8.

Detailed Description

The method of estimating the state of health of the power battery of the electric vehicle of the present invention will be described in detail with reference to fig. 1 to 11.

Since the state of health of the power battery is mainly represented by the attenuation of the maximum available capacity of the power battery and the increase of the internal resistance, and when the internal resistance is increased, the maximum available capacity of the power battery is correspondingly reduced, the state of health of the power battery is estimated only by estimating the maximum available capacity of the power battery.

As shown in fig. 1, a charge-discharge test is performed on a power battery, and during the test, the measured terminal voltage (measured value of terminal voltage) and charge-discharge current of the power battery, and the open-circuit voltage V of the power battery in a fully charged state are collected_100％SoCOpen circuit voltage V under the state of discharging electricity and light_0％SoC(ii) a Establishing an equivalent circuit model of the power battery, establishing a system equation of the power battery according to the established equivalent circuit model, inputting the acquired charging and discharging current into the system equation of the power battery, identifying model parameters of the equivalent circuit model of the power battery according to the requirements of a target function, and further estimating the maximum available capacity of the power battery in the current state so as to estimate the health state of the power battery.

The method comprises the following specific steps:

step 1, collecting actually measured terminal voltage V0 and charging and discharging current I of a power battery in the charging and discharging process and open-circuit voltage of the power battery

Carrying out charge and discharge tests on the power battery at the same temperature, and carrying out actual measurement on the terminal voltage V of the power battery in the charge and discharge process₀Sampling with the charging and discharging current I, wherein a sampling time interval, namely a time interval between two adjacent sampling moments, is delta t, for example, a time interval between the moment I and the moment I +1 is a sampling time interval delta t, and acquiring an open-circuit voltage V of the power battery in a fully charged state_100％SoCOpen circuit voltage V under the state of discharging electricity and light_0％SoC. Preferably, the sampling time interval Δ t is a constant value, such as 1 second(s).

Step 2, establishing an equivalent circuit model of the power battery, and identifying model parameters of the equivalent circuit model

In the aspect of describing the available capacity of the power battery, the RC model shown in the equivalent circuit diagram of fig. 2 has unique advantages over the parallel RC equivalent circuit model shown in fig. 3, so the RC model is selected as the equivalent circuit model of the power battery. Wherein, the storage capacitor C in the equivalent circuit_bIs used for representing the capacity of the power battery for storing the electric quantity, and the electric storage capacitor C_bMaximum available capacity C of power battery_capEstablishing a one-to-one corresponding relation; ohmic internal resistance R_tRepresenting the contact resistance of electrode materials, electrolyte, diaphragm resistance and other parts in the power battery; r_eRepresenting the termination resistance of the power battery; r_sRepresenting the polarization resistance of the power battery; v_sRepresenting the polarization voltage, C, of the power cell_sRepresenting the polarization effect of the power cell. According to kirchhoff's law, the following can be obtained:

$\left\{\begin{array}{c}{V}_0={\mathrm{IR}}_t+I_b{R}_e+V_b\\ V_0={\mathrm{IR}}_t+I_s{R}_s+V_s\\ I_b=C_b{\stackrel{\&CenterDot;}{V}}_b\\ I_s=C_s{\stackrel{\&CenterDot;}{V}}_s\end{array}\right.$

wherein,

I_brepresents the resistance R of the power battery during the charging and discharging processes_eAnd an electric storage capacitor C_bThe current on the power storage branches formed in series,

V_bstorage capacitor C for representing power battery_bThe voltage at the two ends, namely the storage voltage of the power battery,

I_sexpressed by polarization resistance R in the power battery during charging and discharging_sAnd a polarization capacitor C_sCurrent on the polarized branch formed in series.

And further obtaining a system equation of the power battery, wherein the system equation comprises a measurement equation of the power battery:

$V_0=\frac{R_e}{(R_s+R_e)}{V}_b+\frac{R_s}{(R_s+R_e)}{V}_s+\frac{(R_t{R}_s+R_t{R}_e+R_e{R}_s)}{(R_s+R_e)}I$

the dynamic equation is as follows:

$\{\begin{array}{c}{\stackrel{\&CenterDot;}{V}}_b=\frac{V_s}{C_b(R_s+R_e)}-\frac{V_b}{C_b(R_s+R_e)}+\frac{R_s}{C_b(R_s+R_e)}I\\ {\stackrel{\&CenterDot;}{V}}_s=\frac{V_b}{C_s(R_s+R_e)}-\frac{V_s}{C_s(R_s+R_e)}+\frac{R_e}{C_b(R_s+R_e)}I\end{array}.$

sampling the charge-discharge current I and the actually measured terminal voltage V of the power battery₀System input and system output of the equivalent circuit model of the power battery, and identifying a model parameter ξ ═ R of the equivalent circuit model of the power battery by an optimization algorithm, such as a genetic algorithm_tR_sC_sR_eC_b]^TThe model parameter ξ of the equivalent circuit model of the power battery is changed into [ R ] by adopting a genetic algorithm_tR_sC_sR_eC_b]^TWhen identification is carried out, firstly, the charging and discharging current Ii of the power battery at the moment i obtained by sampling is input into a dynamic equation of the power batteryObtaining the estimated value of the polarization voltage of the power battery at the moment iAnd the estimated value of the stored voltageAnd further obtaining the estimated terminal voltage of the power battery at the moment iSum voltage estimation vector ${\hat{U}}_i={\left[\begin{array}{cc}{\hat{V}}_{s,i}& {\hat{V}}_{b,i}\end{array}\right]}^T,$ Wherein T represents a matrix transpose; then, the dynamic equation of the power battery obtains a voltage estimation vectorAmount of change ofFurther obtain the voltage at the next moment, i +1 momentEstimating a vectorThen, the voltage at the time of i +1 is estimated as a vectorThe voltage is brought into an RC model of the power battery, and an estimated value of the terminal voltage of the power battery at the moment i +1 is estimatedJudging whether i is greater than or equal to N, when i is greater than or equal to N, continuously utilizing the sampled measured terminal voltage and charge-discharge current of the power battery to estimate the terminal voltage of the power battery and obtain an estimated value until i is greater than or equal to N; then, the voltage V is measured according to the actually measured terminal voltage V of the power battery at the moment i_0,iAnd estimating terminal voltageCalculating the square sum of estimation errors of the terminal voltage of the power batteryAnd isFinally, an objective function F is set to minimize the sum of the squares of the estimation errors of the terminal voltage of the battery, i.e. to minimize the sum of the square of the terminal voltage of the batteryOrThereby identifying an estimated value of a model parameter of an RC model of the power batteryFurther obtaining the storage capacitor C of the power battery_bIs estimated value of

Step 3, because the storage capacitor C of the power battery_bMaximum available capacity C of the power battery_capThere is a one-to-one correspondence between them, i.e. $C_b=\frac{2\&times;C_{cap}\&times;V_{100\%SoC}}{(V_{100\%SoC}^2-V_{0\%SoC}^2)}.$

Therefore, the storage capacitor C of the power battery is identified_bIs estimated value ofThen, the power battery can pass through the storage capacitor C_bWith maximum available capacity C_capThe maximum available capacity C of the power battery is obtained by estimating the one-to-one correspondence relationship between the maximum available capacity C and the maximum available capacity C_capIs estimated value of

In the following, a lithium ion battery with a nominal capacity of 25Ah and a nominal voltage of 3.7 volts (V) was used as a test object to verify the advantage of the present invention in estimating the maximum available capacity of a power battery on an electric vehicle, i.e., the state of health of the power battery. Taking power batteries with aging degrees of 0 cycle and 200 cycles as examples respectively, the estimation effect of the method of the invention when the maximum available capacity of the power battery is estimated is described.

Eg1, power battery with aging degree of 0 cycle

Firstly, carrying out Dynamic Stress Test (DST) on the power battery, wherein the ambient temperature is 10 ℃, sampling the actually measured terminal voltage V0 and the charging and discharging current I of the power battery in the test process, wherein the sampling time interval delta t is 1s, and collecting the open-circuit voltage V of the power battery in the state of full charge of electric quantity_100％SoCOpen circuit voltage V under the state of discharging electricity and light_0％SoC. The maximum available capacity of the power battery is 25.75Ah through experiment. Basic information of the power battery when the power battery is cycled for 0 times is shown in table 1; the time-varying curve of the charging and discharging current I of the power battery obtained by sampling is shown in FIG. 4, and the actually measured terminal voltage V₀The time-dependent curve is shown in fig. 5.

TABLE 1

	Maximum available capacity (Ah)	V100％SoC(V)	V0％SoC(V)
				Circulation 0 times	25.75	4.1298	3.3678

Identifying and obtaining model parameter ξ ═ R of RC model of power battery by using genetic algorithm_tR_sC_sR_eC_b]^TThe estimated terminal voltage of the power battery is estimated as shown in table 2The time-varying curve is shown in fig. 6, and the estimated error of the terminal voltage of the power battery is shown in fig. 7, so as to obtain the storage capacitor C of the power battery_bIs estimated value ofThereby according to the storage capacitor C of the power battery_bWith maximum available capacity C_capThe maximum available capacity C of the power battery is obtained by estimating the one-to-one correspondence relationship between the maximum available capacity C and the maximum available capacity C_capIs estimated value of25.7620 Ah.

TABLE 2

Therefore, when the method for estimating the state of health of the power battery of the battery electric vehicle is adopted to estimate the maximum available capacity of the power battery circulating for 0 times, the estimation error is only 0.0466%, and the estimation precision is high.

Eg2, power battery with aging degree of 200 cycles

Firstly, the dynamic stress test is carried out on the power battery at the ambient temperature of 10 ℃, and the actually measured terminal voltage V of the power battery is measured₀Sampling the charging and discharging current I, wherein the sampling time interval delta t is a fixed value, and acquiring the open-circuit voltage V of the power battery in the state of full charge of electric quantity_100％SoCOpen circuit voltage V under the state of discharging electricity and light_0％SoC. The maximum available capacity of the power battery is measured to be 24.58Ah through experiments. Basic information of the power battery when the power battery is cycled for 200 times is shown in table 3; the time-varying curve of the charging and discharging current I of the power battery obtained by sampling is shown in FIG. 8, and the actually measured terminal voltage V₀The time-dependent curve is shown in fig. 9.

TABLE 3

	Maximum available capacity (Ah)	V100％SoC(V)	V0％SoC(V)
				Circulating for 200 times	24.58	4.1298	3.3678

Identifying and obtaining model parameter ξ ═ R of RC model of power battery by using genetic algorithm_tR_sC_sR_eC_b]^TThe estimated terminal voltage of the power battery is estimated as shown in table 4The time-varying curve is shown in fig. 10, and the time-varying curve of the estimated error of the terminal voltage of the power battery is shown in fig. 11, so as to obtain the storage capacitor C of the power battery_bIs estimated value ofAccording to the storage capacitance C_bWith maximum available capacity C_capThe maximum available capacity C of the power battery is obtained by estimating the one-to-one correspondence relationship between the maximum available capacity C and the maximum available capacity C_capIs estimated value of24.3584 Ah.

TABLE 4

Therefore, when the method for estimating the state of health of the power battery of the battery electric vehicle is adopted to estimate the maximum available capacity of the power battery which circulates for 200 times, the estimation error is-0.9014%, and the estimation precision is high.

In conclusion, when the method for estimating the health state of the power battery of the electric vehicle is used for estimating the maximum available capacity of the power battery, the estimation is accurate, and when the maximum available capacity of the power battery under different aging degrees is estimated, the maximum available capacity of the power battery can be accurately estimated, so that the method is high in applicability.

Claims (7)

1. A method of estimating the state of health of a power cell of an electric vehicle, characterized in that the method comprises the steps of:

step 1, in the process of charging and discharging the power battery, actually measured terminal voltage V of the power battery₀Sampling the charging and discharging current I, wherein the sampling time interval is delta t, and acquiring the open-circuit voltage V of the power battery in the state that the electric quantity is fully charged_100％SoCOpen circuit voltage V under the state of discharging electricity and light_0％SoC；

Step 2, selecting an RC model asThe equivalent circuit model of the power battery, and the acquired actually-measured terminal voltage V when the acquired charging and discharging current I is input into the RC model₀Identifying the storage capacitor C in the equivalent circuit model of the power battery for the output of the RC model_bIs estimated value of

Step 3, utilizing a storage capacitor C in an equivalent circuit of the power battery_bIs estimated value ofAccording toEstimating the maximum available capacity C of the power battery_capIs estimated value of

2. Method of estimating the state of health of a power cell of an electric vehicle according to claim 1, characterized in that in step 1 the sampling time interval Δ t is constant.

3. Method of estimating the state of health of a power battery of an electric vehicle according to claim 2, characterized in that said sampling time interval Δ t is 1 s.

4. Method of estimating the state of health of a power battery of an electric vehicle according to any of claims 1-3, characterized in that the system equation of the RC model of the power battery is:

$\left\{\begin{array}{c}{V}_0=\frac{R_e}{(R_s+R_e)}{V}_b+\frac{R_s}{(R_s+R_e)}{V}_s+\frac{(R_t{R}_s+R_t{R}_e+R_e{R}_s)}{(R_s+R_e)}I\\ {\stackrel{\&CenterDot;}{V}}_b=\frac{V_s}{C_b(R_s+R_e)}-\frac{V_b}{C_b(R_s+R_e)}+\frac{R_s}{C_b(R_s+R_e)}I\\ {\stackrel{\&CenterDot;}{V}}_s=\frac{V_b}{C_s(R_s+R_e)}-\frac{V_s}{C_s(R_s+R_e)}+\frac{R_e}{C_b(R_s+R_e)}I\end{array}\right.,$

wherein,

R_erepresents the termination resistance of the power cell,

R_trepresents the ohmic resistance of the power cell,

V_brepresents the storage capacitor C of the power battery_bThe voltage across the two terminals is such that,

R_srepresents the polarization resistance of the power cell,

V_srepresents the polarization voltage of the power cell,

C_srepresenting the polarization capacitance of the power cell.

5. The method of estimating state of health of a power cell of an electric vehicle of claim 4, characterized in that a genetic algorithm is used to identify model parameters of an equivalent circuit model of the power cell, and the model parameter ξ to be identified is [ R ═ R_tR_sC_sR_eC_b]^TWhere T denotes a matrix transpose.

6. The method of estimating the state of health of a power battery of an electric vehicle according to claim 5, characterized in that an objective function is set when identifying model parameters of an equivalent circuit model of the power battery $F=\min \left\{\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{(V_{0,i}-{\hat{V}}_{0,i}(\widehat{\mathrm{\&xi;}}\left)\right)}^2\right\},$

Wherein,

n represents the length of the sampled data,

V_0,ithe measured terminal voltage at the moment i in the charging and discharging process of the power battery is shown,

representing the estimated terminal voltage at the moment i in the charging and discharging process of the power battery,

represents an estimate of the model quantity ξ to be identified.

7. An apparatus for estimating a state of health of a power battery of an electric vehicle using the method of estimating a state of health of a power battery of an electric vehicle of any one of claims 1 to 6.