CN105607010A - A method of estimating the state of health of a traction battery of an electric vehicle - Google Patents

Abstract

The invention relates to the field of estimating the power battery state of an electric vehicle, in particular to a method for estimating the state of health of a power battery of an electric vehicle. In order to solve the problem of estimating the state of health of the power battery in the prior art, the estimation accuracy is low and unstable, it takes a long time and the workload is high, the estimation cost is high, and the estimation result has a corresponding relationship with the state of charge of the power battery-open circuit voltage and The accuracy of the equivalent circuit model depends too much on the problem that the present invention proposes a method for estimating the state of health of the power battery of an electric vehicle, which collects the measured terminal voltage _V0 and the charge and discharge current I of the power battery, and the current I of the power battery. The open circuit voltage V _{100% SoC} in the fully charged state and the open circuit voltage V _{0% SoC} in the light discharge state; establish the equivalent circuit model of the power battery, and identify the estimated value of the power storage capacitor C _b of the power battery according to Estimate the estimated value of the maximum available capacity C _cap of the power battery The method is simple in calculation, small in calculation amount, high in precision and strong in applicability.

Description

A method of estimating the state of health of a traction battery of an electric vehicle

technical field

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

Background technique

The state of health (State of Health, SOH for short) of the vehicle-mounted 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 health status of the power battery is mainly reflected in the attenuation of the maximum available capacity of the power battery and the increase of internal resistance. However, the aging of power batteries is affected by many factors, such as temperature, charge-discharge rate, and depth of discharge, which lead to great uncertainty and nonlinearity in the maximum available capacity and internal resistance of the power battery. The management system (battery management system, referred to as BMS) brings huge challenges to the detection of the maximum available capacity and internal resistance of the power battery during the management process.

In addition, when the power batteries are used in groups, the capacity information and internal resistance information of the power batteries cannot be directly obtained through experimental means, and 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 accuracy of the state of charge of the power battery is directly related to whether the use of the power battery is safe. Therefore, in order to ensure the safe use of the power battery, the power battery management system must accurately estimate the maximum available capacity of the power battery, that is, the power The state of health of the battery is accurately estimated.

The maximum available capacity of the power battery refers to the amount of electricity charged (discharged) when the power battery is charged (discharged) at a nominal rate to the cut-off voltage of the power battery at a certain temperature and aging degree. Since the difference between the maximum available capacity of the power battery during charging and the maximum available capacity during discharge 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.

At present, the methods for estimating the maximum available capacity of power batteries can be roughly divided into the following four types:

1. The method of estimating the maximum available capacity of the power battery based on the open circuit voltage of the power battery. According to the unique monotonous change relationship between the state of charge of the power battery and its open circuit voltage, the open circuit voltage of the power battery is used to estimate the power battery. The state of charge 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 remaining power of the power battery and its maximum available capacity in the current state, and then through the measured remaining power of the power battery The capacity and state of charge estimate the maximum available capacity of the traction battery in its current state.

2. The method of estimating the maximum available capacity of the power battery based on the equivalent circuit model of the power battery, constructing the dynamic equation of the power battery through the equivalent circuit model of the power battery, and taking the maximum available capacity of the power battery as an unknown state is estimated.

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

4. The method of estimating the maximum available capacity based on the aging of the power battery is mainly to analyze the influence of different aging factors on the decline of the maximum available capacity of the power battery through experimental data, so as to establish the capacity decline model of the power battery or the remaining usage The life model estimates the maximum available capacity of the power battery.

The above four estimation methods can be divided into two categories: offline estimation and online estimation. Among them, when the offline estimation method is used to estimate the maximum available capacity of the power battery, if less statistical data is used for analysis, it may lead to large estimation deviations. , the estimation accuracy is low and unstable. If a large amount of statistical data is used for analysis, although the estimation accuracy requirements can be met, the data accumulation takes a long time and the workload of data analysis is heavy. When the online estimation method is used to estimate the maximum available capacity of the power battery, real-time estimation can be performed and the estimation accuracy can be guaranteed. However, the online estimation requires high computing power of the power battery management system, resulting in high estimation costs, and the estimation results are not accurate. The corresponding relationship between the state of charge of the power battery and the open circuit voltage and the accuracy of the equivalent circuit model of the power battery have a strong dependence.

Contents of the invention

In order to solve the problem of estimating the state of health of the power battery, that is, the maximum available capacity in the prior art, either the estimation accuracy is low and unstable, or it takes a long time and the workload is heavy, or it is caused by excessive requirements on the computing power of the power battery management system. The cost of estimation is high and the estimation results are too dependent on the corresponding relationship between the state of charge of the power battery and the open circuit voltage and the accuracy of the equivalent circuit model. The present invention proposes a method for estimating the state of health of the power battery of an electric vehicle method comprising the steps of:

Step 1. During the charging and discharging process of the power battery, the measured terminal voltage _V0 and the charging and discharging current I of the power battery are sampled, and the sampling time interval is Δt, and the power battery is in a fully charged state. The open circuit voltage V _{100% SoC} under and the open circuit voltage V _{0% SoC} under the light state of electric discharge;

Step 2, select the RC model as the equivalent circuit model of the power battery, and when the collected charging and discharging current I is input into the RC model, the measured terminal voltage V _collected is the output of the RC model, Identify the estimated value of the storage capacitor C _b in the equivalent circuit model of the power battery

Step 3, using the estimated value of the storage capacitor C _b in the equivalent circuit of the power battery according to Estimated to obtain the estimated value of the maximum available capacity C _cap of the power battery

When the method of the present invention estimates the maximum available capacity of the power battery offline, the measured terminal voltage and charge and discharge current of the power battery are obtained by sampling, and the charge and discharge current in the sampled data is input into the system equation of the RC model of the power battery , use the optimization algorithm to identify the model parameters of the RC model of the power battery, and then estimate the estimated value of the power storage capacitor C _b of the power battery obtained from the identification And the one-to-one correspondence between the power storage capacitor and the maximum available capacity of the power battery is used to estimate the estimated value of the maximum available capacity C _cap of the power battery The calculation is simple, the calculation amount is small, and the estimation accuracy is high; the maximum available capacity of the power battery under different aging degrees can be estimated, and the estimation accuracy is guaranteed, and the applicability is strong.

Preferably, in the step 1, the sampling time interval Δt is a constant value, so as to facilitate data collection. Further, the sampling time interval Δt is 1s.

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

$\left\{\begin{array}{c}{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; +</span>\frac{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; +</span>\frac{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; )</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; )</span>}\mathrm{<span\; class="notranslate">\; I</span>}\\ {\stackrel{<span\; class="notranslate">\; \&CenterDot;</span>}{\mathrm{<span\; class="notranslate">\; V</span>}}}_{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}}{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span>\frac{{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}}{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>\frac{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}}{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; )</span>}\mathrm{<span\; class="notranslate">\; I</span>}\\ {\stackrel{<span\; class="notranslate">\; \&CenterDot;</span>}{\mathrm{<span\; class="notranslate">\; V</span>}}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}}{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span>\frac{{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}}{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>\frac{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}}{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; )</span>}\mathrm{<span\; class="notranslate">\; I</span>}\end{array}\right.<span\; class="notranslate">\; ,</span>$

in,

R _e represents the termination resistance of the power battery,

R _t represents the ohmic resistance of the power battery,

V _b represents the voltage across the storage capacitor Cb of the power battery,

R _s represents the polarization resistance of the power battery,

V _s represents the polarization voltage of the power battery,

C _s represents the polarization capacitance of the power battery.

Preferably, the genetic algorithm is used to identify the model parameters of the equivalent circuit model of the power battery, and the model parameters to be identified ξ=[R _t R _s C _s R _e C _b ] ^T , where T represents the matrix transformation place.

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

in,

N represents the length of the sampled data,

V _0,i represents the measured terminal voltage at moment i during the charging and discharging process of the power battery,

Indicates the estimated terminal voltage at time i during the charging and discharging process of the power battery,

represents the estimated value of the model parameter ξ to be identified.

When the method of the present invention estimates the maximum available capacity of the power battery offline, the measured terminal voltage and charge and discharge current of the power battery are obtained by sampling, and the charge and discharge current in the sampled data is input into the system equation of the RC model of the power battery , use the optimization algorithm to identify the model parameters of the RC model of the power battery, and then estimate the estimated value of the power storage capacitor C _b of the power battery obtained from the identification And the one-to-one correspondence between the power storage capacitor and the maximum available capacity of the power battery is used to estimate the estimated value of the maximum available capacity C _cap of the power battery The calculation is simple, the calculation amount is small, and the estimation accuracy is high. Using the RC model to describe the available capacity of the power battery has unique advantages compared with the parallel RC equivalent circuit model. When estimating the maximum available capacity of the power battery with different aging degrees, the maximum available capacity of the power battery can be accurately estimated, and the applicability is strong.

The present invention also proposes 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.

Description of drawings

Fig. 1 is a flowchart of the present invention estimating the state of health of a power battery of an electric vehicle;

Fig. 2 is the equivalent circuit diagram of the RC model of the power battery;

Fig. 3 is the equivalent circuit diagram of the parallel RC equivalent circuit model of the power battery;

Figure 4 is a distribution diagram of the charge and discharge current of the power battery with 0 cycles under the DST working condition;

Fig. 5 is a graph showing the measured terminal voltage of the power battery shown in Fig. 4 as a function of time;

Fig. 6 is a graph showing the estimated terminal voltage of the power battery shown in Fig. 4 changing with time;

Fig. 7 is a curve diagram showing the variation of the estimation error of the terminal voltage of the power battery shown in Fig. 4;

Figure 8 is a distribution diagram of the charge and discharge current of the power battery cycled 200 times under the DST working condition;

Fig. 9 is a graph showing the measured terminal voltage of the power battery shown in Fig. 8 as a function of time;

Fig. 10 is a graph showing the estimated value of the terminal voltage of the power battery shown in Fig. 8 changing with time;

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

detailed description

Next, the method for estimating the state of health of a power battery of an electric vehicle according to the present invention will be described in detail with reference to FIGS. 1-11 .

Since the state of health of the power battery is mainly manifested as the attenuation of the maximum available capacity of the power battery and the increase of internal resistance, and when the internal resistance increases, the maximum available capacity of the power battery decreases accordingly, so the present invention only uses the maximum available capacity of the power battery The capacity is estimated to estimate the state of health of the power battery.

As shown in Figure 1, the power battery is charged and discharged, and the measured terminal voltage (measured value of the terminal voltage) and charge and discharge current of the power battery are collected during the test, as well as the open circuit voltage of the power battery in a fully charged state. V _{100% SoC} and the open circuit voltage V _{0% SoC} under the light state of electricity discharge; establish the equivalent circuit model of the power battery, and establish the system equation of the power battery according to the established equivalent circuit model, and collect the charging and discharging The current is input into the system equation of the power battery, and the model parameters of the equivalent circuit model of the power battery are identified according to the requirements of the objective function, and then the maximum available capacity of the power battery in the current state is estimated, thereby estimating the health of the power battery state.

Specific steps are as follows:

Step 1. Collect the measured terminal voltage V0 and charge and discharge current I of the power battery during charging and discharging, as well as the open circuit voltage of the power battery

At the same temperature, the power battery is charged and discharged, and the measured terminal voltage V ₀ and the charge and discharge current I of the power battery are sampled during the charging and discharging process. The sampling time interval is the interval between two adjacent sampling moments. The time interval is Δt, for example, the time interval between time i and time i+1 is a sampling time interval Δt, and the open circuit voltage V _{100% SoC} of the power battery in the fully charged state and the light state of power discharge are collected under the open circuit voltage V _{0% SoC} . Preferably, the sampling time interval Δt is a constant value, such as 1 second (s).

Step 2. Establish the equivalent circuit model of the power battery, and identify the model parameters of the equivalent circuit model

In terms of describing the available capacity of the power battery, since the RC model shown in Figure 2 has unique advantages over the parallel RC equivalent circuit model shown in Figure 3, the RC model is selected as the equivalent of the power battery. circuit model. Among them, the storage capacitor C _b in the equivalent circuit is used to represent the power storage capacity of the power battery, and the storage capacitor C _b can establish a one-to-one correspondence with the maximum available capacity C _cap of the power battery; the ohmic internal resistance R _t Represents the contact resistance of the electrode material, electrolyte, diaphragm resistance and other parts in the power battery; R _e represents the termination resistance of the power battery; R _s represents the polarization resistance of the power battery; V _s represents the polarization voltage of the power battery, C _s Indicates the polarization effect of the power battery. According to Kirchhoff's law:

$\left\{\begin{array}{c}{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; IR</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}\\ {\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; IR</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}\\ {\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}{\stackrel{<span\; class="notranslate">\; \&Center\; Dot;</span>}{\mathrm{<span\; class="notranslate">\; V</span>}}}_{\mathrm{<span\; class="notranslate">\; b</span>}}\\ {\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}{\stackrel{<span\; class="notranslate">\; \&Center\; Dot;</span>}{\mathrm{<span\; class="notranslate">\; V</span>}}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}\end{array}\right.$

in,

I _b represents the current on the power storage branch formed by the termination resistor R _e and the storage capacitor C _b in series in the power battery during the charge and discharge process,

V _b represents the voltage across the power storage capacitor C _b of the power battery, that is, the power storage voltage of the power battery,

I _s represents the current on the polarization branch formed by the series connection of polarization resistance R _s and polarization capacitance C _s in the power battery during charging and discharging.

Then the system equation of the power battery can be obtained, which includes the measurement equation of the power battery:

${\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; +</span>\frac{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; +</span>\frac{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; )</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; )</span>}\mathrm{<span\; class="notranslate">\; I</span>}$

Dynamic equation:

$<span\; class="notranslate">\; \{</span>\begin{array}{c}{\stackrel{<span\; class="notranslate">\; \&CenterDot;</span>}{\mathrm{<span\; class="notranslate">\; V</span>}}}_{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}}{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span>\frac{{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}}{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>\frac{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}}{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; )</span>}\mathrm{<span\; class="notranslate">\; I</span>}\\ {\stackrel{<span\; class="notranslate">\; \&Center\; Dot;</span>}{\mathrm{<span\; class="notranslate">\; V</span>}}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}}{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span>\frac{{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}}{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>\frac{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}}{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; )</span>}\mathrm{<span\; class="notranslate">\; I</span>}\end{array}<span\; class="notranslate">\; .</span>$

The sampled charging and discharging current I of the power battery and the measured terminal voltage V ₀ are used as the system input and system output of the equivalent circuit model of the power battery, and the model of the equivalent circuit model of the power battery is identified through an optimization algorithm such as a genetic algorithm Parameter ξ=[R _t R _s C _s R _e C _b ] ^T . When the genetic algorithm is used to identify the model parameter ξ=[R _t R _s C _s R _e C _b ] ^T of the equivalent circuit model of the power battery, firstly, the charging and discharging current Ii of the power battery at time i obtained by sampling Input it into the dynamic equation of the power battery to obtain the estimated value of the polarization voltage of the power battery at time i and the storage voltage estimate Then get the estimated terminal voltage of the power battery at time i and the voltage estimation vector ${\hat{u}}_i={\left[\begin{array}{cc}{\hat{V}}_{\mathrm{the\; s},i}& {\hat{V}}_{b,i}\end{array}\right]}^T,$ Among them, T represents the matrix transpose; then, the dynamic equation of the power battery derives the voltage estimation vector The amount of change And then get the voltage estimation vector at the next moment, that is, the moment i+1 Then, the voltage estimation vector at time i+1 Bring it into the RC model of the power battery to estimate the estimated value of the terminal voltage of the power battery at time i+1 And judge whether i≥N is established, when i≥N is not established, continue to use the measured terminal voltage and charge and discharge current of the power battery obtained by sampling to estimate the terminal voltage of the power battery and obtain an estimated value until i≥N is established; then , according to the measured terminal voltage V _0,i and estimated terminal voltage of the power battery at time i Calculate the sum of squares of the estimation error of the terminal voltage of the power battery and Finally, the objective function F is set to minimize the sum of squares of the estimation errors of the terminal voltage of the mobile battery, namely or In order to identify the estimated value of the model parameters of the RC model of the power battery Then the estimated value of the power storage capacitor C _b of the power battery can be obtained

Step 3. Since there is a one-to-one correspondence between the power storage capacitor C _b of the power battery and the maximum available capacity C _cap of the power battery, that is $C_b=\frac{2\&times;C_{cap}\&times;V_{100\%SoC}}{(V_{100\%SoC}^2-V_{0\%SoC}^2)}.$

Therefore, after identifying the estimated value of the storage capacitor C _b of the power battery Finally, the estimated value of the maximum available capacity C _cap of the power battery can be obtained by estimating the one-to-one correspondence between the storage capacitor C _b of the power battery and the maximum available capacity C _cap

Below, be 25Ah with nominal capacity, the lithium-ion battery that nominal voltage is 3.7 volts (V) is as test object, verify that the present invention exists when estimating the maximum usable capacity of the power battery on the electric vehicle, namely the state of health of the power battery Advantage. Taking power batteries whose aging degrees are 0 cycles and 200 cycles respectively as examples, the estimation effect of using the method of the present invention to estimate the maximum available capacity of the power battery is illustrated.

Eg1, a power battery with an aging degree of 0 cycles

First, the dynamic stress test (Dynamic Stress Test, DST for short) is carried out on the power battery, and the ambient temperature is 10°C, and the measured terminal voltage V0 and the charge and discharge current I of the power battery are sampled during the test, and the sampling time interval Δt is 1s, and the open circuit voltage V _100%SoC of the power battery in the fully charged state and the open circuit voltage V _0%SoC in the light discharge state of the power battery are collected. The maximum usable capacity of the power battery is measured to be 25.75Ah through experiments. The basic information of the power battery at cycle 0 is shown in Table 1; the curve of the charging and discharging current I of the power battery obtained by sampling as a function of time is shown in Figure 4, and the curve of the measured terminal voltage V ₀ changing with time As shown in Figure 5.

Table 1

Maximum usable capacity (Ah) V _{100% SoC} (V) V _{0% SoC} (V) Cycle 0 times 25.75 4.1298 3.3678

The model parameter ξ=[R _t R _s C _s R _e C _b ] ^T of the RC model of the power battery identified by the genetic algorithm is shown in Table 2, and the estimated terminal voltage of the power battery is estimated The time-varying curve is shown in Figure 6, and the time-varying curve of the estimation error of the estimated terminal voltage of the power battery is shown in Figure 7, and then the estimated value of the power storage capacitor _Cb of the power battery can be obtained Therefore, the estimated value of the maximum available capacity C _cap of the power battery can be obtained by estimating the one-to-one correspondence between the power storage capacitor C _b of the power battery and the maximum available capacity C _cap It is 25.7620Ah.

Table 2

It can be seen that when the method for the state of health of the power battery of the battery electric vehicle proposed by the present invention is used to estimate the maximum available capacity of the power battery with 0 cycles, the estimation error is only 0.0466%, and the estimation accuracy is high.

Eg2, a power battery with an aging degree of 200 cycles

First, under the ambient temperature of 10°C, the dynamic stress test is carried out on the power battery, and the measured terminal voltage V ₀ and the charge and discharge current I of the power battery are sampled, and the sampling time interval Δt is a fixed value, and the power battery The open circuit voltage V _{100% SoC} in the fully charged state and the open circuit voltage V _{0% SoC} in the discharged light state. The maximum usable capacity of the power battery is 24.58Ah measured through experiments. The basic information of the power battery when it is cycled 200 times is shown in Table 3; the curve of the charging and discharging current I of the power battery obtained by sampling is shown in Figure 8, and the curve of the measured terminal voltage _V0 changing with time As shown in Figure 9.

table 3

Maximum usable capacity (Ah) V _{100% SoC} (V) V _{0% SoC} (V) Cycle 200 times 24.58 4.1298 3.3678

The model parameter ξ=[R _t R _s C _s R _e C _b ] ^T of the RC model of the power battery identified by the genetic algorithm is shown in Table 4, and the estimated terminal voltage of the power battery is estimated The time-varying curve is shown in Figure 10, and the time-varying curve of the estimation error of the estimated terminal voltage of the power battery is shown in Figure 11, and then the estimated value of the power storage capacitor C _b of the power battery is obtained Therefore, the estimated value of the maximum available capacity C _cap of the power battery can be obtained by estimating the one-to-one correspondence between the storage capacitor C _b and the maximum available capacity C _cap It is 24.3584Ah.

Table 4

It can be seen that when using the health state method of the power battery of the battery electric vehicle proposed by the present invention to estimate the maximum available capacity of the power battery that has cycled 200 times, the estimation error is -0.9014%, and the estimation accuracy is high.

In summary, when the method for estimating the state of health of the power battery of an electric vehicle proposed by the present invention is used to estimate the maximum available capacity of the power battery, the estimation is accurate, and the maximum available capacity of the power battery under different aging degrees is estimated. When estimating, the maximum available capacity of the power battery can be accurately estimated, and the applicability is strong.

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.