[0053] The above is only an overview of the technical solutions of the present invention. In order to better understand the technical means of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0054] The method for estimating the state of health of the power battery system provided by the present invention is as attached figure 1 As shown, it specifically includes the following steps:

[0055] 1). Establish a fractional-order model of the power battery system, and discretize the model;

[0056] 2). Carry out real vehicle data collection, and use the least square method based on forgetting factor for online identification of parameter matrix;

[0057] 3). Extract open circuit voltage and other impedance parameters in real time from the parameter matrix obtained by the identification.

[0058] 4). Estimating the state of health (SOH) of the battery system using the open circuit voltage curve combined with the incremental capacity method (ICA).

[0059] In a preferred embodiment of the present application, establishing a fractional model of the power battery system and discretizing the model in combination with the theory of fractional calculus includes the following steps:

[0060] (1-1). Determine the fractional-order model and its transfer function;

[0061] (1-2) Perform inverse Laplace transform on the transfer function and transform it into a differential equation;

[0062] (1-3). Calculate the fractional differential in the differential equation.

[0063] In a preferred embodiment of the present application, the fractional-order model determined in the step (1-1) is as attached figure 2 As shown, it is composed of an equivalent electrochemical polarization internal resistance R ct Parallel a constant phase angle element Q 1 , And the equivalent ohmic resistance R i And the voltage source OCV is connected in series. In the model, I represents the current, charging is positive, V t Represents the battery terminal voltage.

[0064] Among them, the constant phase angle element includes the size Q 1 And the differential order α two parameters. The impedance transfer function expression is

[0065]

[0066] Where OCV is the open circuit voltage value of the voltage source, and s is the Laplace transform operator;

[0067] According to Kirchhoff's law and Laplace transform, the mathematical expression of the fractional model is established:

[0068]

[0069] The transfer function obtained is:

[0070] Vt(s)-OCV(s)+R ct Q 1 s α (Vt(s)-OCV(s))=(R ct +R i )I(s)+R ct R i Q 1 s α I(s) (3)

[0071] Perform inverse Laplace transform on the transfer function obtained in the step (1-2) to obtain the differential equation:

[0072] Vt(t)-OCV(t)+R ct Q 1 D (α) (Vt(t)-OCV(t))=(R ct +R i )I(t)+R ct R i Q 1 D (α) I(t) (4)

[0073] Among them, D (α) Means to differentiate the variable α times, α is a rational number:

[0074] At the kth sampling point:

[0075] Vt(k)=(R ct +R i )I(k)+R ct R i Q 1 D (α) I(k)+OCV(k)+R ct Q 1 D (α) (OCV(k)-Vt(k)) (5)

[0076] Organize it into the form of matrix parameter matrix and data matrix multiplication, namely:

[0077]

[0078] In a preferred embodiment of the present application, the fractional differential of the differential equation in the step (1-3) is solved based on the Grunwald-Letnikov definition:

[0079]

[0080] Among them, L is the memory length selected by oneself, and T s Is the sampling interval.

[0081] In a preferred embodiment of the present application, the actual vehicle data collection described in step 2) specifically includes: real-time collection of power battery cells through the battery management system BMS data collector in the power battery system when the electric vehicle is running. The voltage, current and temperature information of the body and the power battery pack are stored in the corresponding memory to establish a complete power battery system processing basic data source.

[0082] In a preferred embodiment of the present application, the online identification of the parameter matrix using the least square method based on the forgetting factor in the step 2) adopts the following iterative process:

[0083]

[0084] In the formula, μ is the forgetting factor, y k Is the model output, Φ k Is the data matrix, Is the parameter matrix, K Ls,k Is the gain of the algorithm, P Ls,k Is the error covariance matrix of the state estimate.

[0085] In a preferred embodiment of the present application, the following formula is used to implement the real-time extraction of open circuit voltage and other impedance parameters from the parameter matrix obtained from the identification in step 3):

[0086]

[0087] In a preferred embodiment of the present application, the step 4) using the open circuit voltage curve combined with the incremental capacity method (ICA) to estimate the state of health (SOH) of the battery system specifically includes the following steps:

[0088] (4-1). Establish the relationship between the capacity retention rate of the power battery and the peak value of the capacity increment (IC) curve through testing; wherein the capacity retention rate refers to the ratio of the remaining capacity of the battery to the initial capacity in a certain aging state, The capacity increment curve refers to the relationship curve between the derivative of the electric quantity to the open circuit voltage value and the open circuit voltage value;

[0089] (4-2) Obtain the capacity increment curve and its normalized peak value according to the collected real vehicle data;

[0090] (4-3). Predict the remaining battery capacity based on the relationship between the capacity retention rate and the peak value of the capacity increment curve established in step (4-1).

[0091] The essential features of the present invention are further clarified by examples below:

[0092] The battery used in this example is the NMC ternary material lithium-ion battery, and the establishment is as follows figure 2 The equivalent model shown. The specific parameters of the battery are as follows:

[0093] Table 1NMC lithium ion battery parameters

[0094]

[0095] Take the electric vehicle dynamic stress test condition (DST condition) as an example for algorithm verification. The current excitation condition of DST condition is as attached image 3 Shown. The relative error of the terminal voltage obtained by the above identification method for DST working conditions is as attached Figure 4 As shown, the identification results of each element in the parameter matrix are as attached Figure 5 Shown. The open circuit voltage and other impedance parameters are extracted in real time from the parameter matrix obtained by the identification, such as Image 6 Shown.

[0096] In this example, two new battery cells No. 1 and No. 2 are selected, the capacity of each battery cell is tested, the capacity data of each battery cell is obtained, and the actual operating conditions of the battery are simulated using DST conditions, and then OCV is performed test. Carry out battery cycle aging, and carry out a capacity test, DST working condition test and OCV test every 100 cycles. The voltage and current curve of OCV test is as attached Figure 7 As shown, the overall test process is as attached Figure 8 Shown.

[0097] The obtained real-time OCV and real-time SOC are fitted with an 8-order polynomial to obtain the OCV-SOC curve. The comparison between the fitted curve and the OCV experimental results is shown in the attachment Picture 9 Shown.

[0098] A linear relationship is used to fit the relationship between the capacity retention rate and the peak value of the normalized capacity increment curve. The fitting relation expression is:

[0099] C=aP+b (10)

[0100] In the formula, C is the capacity retention rate, P is the normalized IC curve peak value, and a and b are the coefficients to be fitted.

[0101] Attached Picture 10 It is the relationship between the capacity increase curve of No. 2 battery and the capacity retention rate, attached Picture 11 It is the relationship between the peak value of the normalized capacity increment curve of No. 2 battery and the capacity retention rate.

[0102] The linear relationship is used to fit the relationship between the peak value of the capacity increment curve of the No. 2 battery and the capacity retention rate. The fitting result is:

[0103] Table 2 The fitting results of the peak value of the capacity increment curve and the capacity retention rate of the No. 2 battery

[0104]

[0105] The obtained real-time OCV and real-time SOC are fitted with an 8-order polynomial to obtain the OCV-SOC curve, and the capacity increment method is used for processing to obtain the capacity increment curve, and the peak value is extracted and normalized.

[0106] Attached Picture 12 It shows the result of predicting the capacity retention rate of the No. 1 battery through the IC curve peak-capacity retention rate relationship obtained from the No. 2 battery in the test. It can be seen that before the capacity retention rate drops to 70%, the estimated error of the battery capacity retention rate can be controlled within 4%, and a better technical effect is achieved by the method provided by the present invention.

[0107] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, and substitutions can be made to these embodiments without departing from the principle and spirit of the present invention. And variations, the scope of the present invention is defined by the appended claims and their equivalents.