基于等效电路模型和数据驱动模型融合的SOC和SOH联合估计方法

doi:10.19595/j.cnki.1000-6753.tces.230261

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Abstract

The estimation results of SOC will be seriously affected by battery aging. And the calibration of SOH will be misled by inaccurate SOC estimation results. Most of the existing studies estimate SOC and SOH separately.The close relationship between the two is ignored, resulting in the estimation accuracy is not high.Aiming at the above problems, this paper proposes a joint estimation method of SOC and SOH based on the fusion of equivalent circuit model and data-driven model.The influence mechanism between battery SOC and SOH is revealed, which can solve the mutual influence between them to a certain extent and improve the accuracy of SOC and SOH estimation.
Firstly,by constructing a second-order RC equivalent circuit model of the battery considering aging and SOC,the recursive least square method with forgetting factor was used to identify the parameters of the battery online under different SOC and SOH conditions.Secondly, the required time from 20%SOC to the end of constant-current charging stage was extracted.The Pearson and Spearman relationships between constant current charge time and SOH of lithium-ion batteries were calculated.Thirdly, the actual time required from 20%SOC to the end of constant-current charging phase of lithium-ion batteries was taken as input and the battery SOH value was taken as output to train the GPR model offline.The GPR model obtained by training was optimized by hyperparameter and used for SOH prediction.Finally,the estimated SOH output was multiplied by the rated capacity of the cell to obtain the actual capacity of the cell,which was used to update the second order RC state space equation. Based on the constructed second-order RC equivalent circuit model, the battery SOC was estimated by the EKF.
The Oxford University battery degradation data set and NASA random battery data set are used to verify the joint estimation method.The results show that the average MAE and RMSE of SOC estimated by the proposed joint estimation method are basically less than 0.04. Even though Cell1~Cell8 and RW3~RW6 are aging experiments conducted under different working conditions, their average MAE and average RMSE are relatively stable, which indicates that the combined estimation method is suitable. The actual initial SOC value is 1, and the initial value is set to 0.7 in this paper. With the decline of battery capacity, the joint estimate of battery SOC can follow the actual SOC more accurately. However, when EKF is used to estimate SOC separately, the error between the estimated SOC value and the actual SOC value becomes larger and larger. This proves that the joint estimation algorithm is robust and accurate.Meanwhile, the reservation-one method is used to verify the Gaussian process regression model. The MAE and RMSE predicted by SOH for Cell1~Cell8 are less than 0.5%, and the MAE and RMSE predicted by SOH for RW3~RW6 are about 0.5. All the predicted SOH values are in the confidence interval, and the confidence interval is narrow, indicating that the GPR model established in this paper has high accuracy and reliability for SOH prediction.
The following conclusions can be drawn from the simulation analysis:1) Compared with the existing battery model, the dynamic second-order RC equivalent circuit model considering battery aging and SOC is constructed in this paper. In the case of battery aging, the voltage obtained by fitting the identified circuit parameters can track the actual voltage better.2) Compared with SOC estimation alone, the joint estimation method applies the real-time online modified battery parameters and battery SOH to SOC estimation to ensure that the battery SOC is adjusted with the aging of the battery, and the SOC estimation is more accurate.3) The combined method applies the estimated SOC to the battery SOH estimation, which can ensure the extraction of effective health features and improve the accuracy of SOH prediction.

Key words： Lithium-ion battery state of charge state of health gaussian process regression forgetting factor recursive leastsquares

Received: 07 March 2023

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TM912

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	Liu Ping
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Cite this article:

Liu Ping,Li Zewen,Cai Yusi等. Joint Estimation Method of SOC and SOH Based on the Fusion of Equivalent Circuit Model, Data-driven Model[J]. Transactions of China Electrotechnical Society, 0, (): 9020-20.

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