基于片段充电数据和DEKF-WNN-WLSTM的锂电池健康状态实时估计

doi:10.19595/j.cnki.1000-6753.tces.222241

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Abstract

As a clean technology to solve carbon emissions, electric vehicles have been widely used in modern vehicles. Due to its high energy density, light weight, long life and low self discharge, lithium-ion batteries have become the main energy storage equipment of electric vehicles. Real time and accurate evaluation of the state of health (SOH) of the lithium batteries is critical to the stable driving of electric vehicles. However, most traditional SOH forecast methods are offline, which makes it difficult to obtain the SOH of the batteries in real time. Recently, some methods were presented to forecast the SOH of lithium-ion batteries, but most of them suffered from inconvenient adjustment of battery model parameters and accumulation of errors. To address these issues, this paper proposes a battery full charging time estimation model and dual extended Kalman filters-wavelet neural network-wavelet long short-term memory neural network (DEKF-WNN-WLSTM). By taking the daily segment charging data of lithium batteries as input, to predict the full time charging of the battery, and then get the SOH in real time.
Firstly, based on the strong robustness of wavelet neural network (WNN) and the ability of long short term memory (LSTM) to extract the time series features of the data, the neural network of WNN-WLSTM is designed. Secondly, two WNN-WLSTM networks are trained with one full charging data and three fragment data of lithium batteries, respectively. Thirdly, a real-time estimation algorithm named DEKF is constructed, in which the first EKF is used to estimate the full charging time corresponding to the segment data, and the second EKF is used to predict the error between the estimated and measured battery full charging time under the current cycle. Then the two trained networks are integrated into DEKF to provide corresponding output values for the cyclic recursion of EKF. Finally, a real-time SOH estimation model based on daily segment charging data is designed. The segment data from constant current charging to full charging at any time is used as the input of DEKF-WNN-WLSTM, to estimate the current full charging time of lithium batteries, then calculate the SOH of the battery at the current time. In this real-time model, the WNN-WLSTM alleviates the inconvenient adjustment of battery model parameters problem, addresses the long-term dependence problem. The DEKF uses the daily segment charging data as the input, which extends the practical application of the model.
Simulation results on the actual battery charging and discharging data show that, the mean relative error of the predictions for the entire 80 cycles is 0.010 1, the estimated error for the first 50 cycles is completely less than 2%, and less than 1% at most times. The comparison between DEKF-WNN-WLSTM and extended Kalman filter and Gaussian process regression (EKF-GPR) shows that, the mean relative error of EKF-GPR is 0.017 6, which is higher than DEKF-WNN-WLSTM, especially in the 170~180 cycles, which indicates that the model of DEKF-WNN-WLSTM can alleviate certain error growth with the increase of cycles. The proposed method has a better estimation effect under the condition that no artificial full recharge operation is performed to update the initial full charging time value.
The following conclusions can be drawn from the simulation analysis: (1)The proposed method integrates WNN-WLSTM neural network, which address the problems of long-term dependence and the inconvenient adjustment of battery model parameters. (2) Compared with EKF-GPR, the DEKF-WNN-WLSTM not only improves the prediction accuracy, but also alleviates the error accumulation. (3) The proposed model only needs the daily segment charging data. In this sense, it is practical in the real world.

Key words： State of health segment data dual extended Kalman filter wavelet neural network wavelet long short-term memory

Received: 28 December 2022

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	Song Xianhua
	Yao Quanzheng

Cite this article:

Song Xianhua,Yao Quanzheng. Real-Time State of Health Estimation for Lithium-Ion Batteries Based on Daily Segment Charging Data and Dual Extended Kalman Filters-Wavelet Neural Network-Wavelet Long Short-Term Memory Neural Network[J]. Transactions of China Electrotechnical Society, 2024, 39(5): 1565-1576.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.222241 OR https://dgjsxb.ces-transaction.com/EN/Y2024/V39/I5/1565