SOC Estimation of Large Capacity Lithium Batteries Based on LWOA-LSTM

doi:10.11930/j.issn.1004-9649.202312106

Abstract

Abstract:

Accurate prediction of the state of charge (SOC) of lithium batteries is crucial for their safe operation, and analyzing the SOC in different power grid modes is the basis for the comprehensive promotion of lithium batteries. This paper proposes a whale optimization algorithm based on Levy flight (LWOA) to optimize long short-term memory neural network (LSTM) for estimating the SOC of large capacity lithium-ion batteries in frequency modulation mode. Firstly, the LSTM neural network and LWOA algorithm are analyzed, and the LWOA-LSTM model is constructed to optimize the parameters. Then, the experimental data of the large capacity lithium-ion battery pack in frequency modulation mode are selected for data preprocessing and model training. Finally, SOC estimation of lithium batteries in frequency modulation mode is achieved. The experimental results show that the constructed model can accurately predict the SOC of lithium batteries. Compared with the WOA-LSTM model, the evaluation indicators RMSE and MAE are reduced by 25.55% and 28.71%, respectively, while R² increases by 0.76%.

Key words: state of charge, lithium batteries, whale optimization algorithm, LSTM, frequency modulation mode

Hongzhong MA, Wenjing XUAN, Muyu ZHU, Yuelin CHEN. SOC Estimation of Large Capacity Lithium Batteries Based on LWOA-LSTM[J]. Electric Power, 2024, 57(6): 37-44.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202312106

https://www.electricpower.com.cn/EN/Y2024/V57/I6/37

Figures/Tables 9

Fig.1 LSTM unit structure

Fig.2 Flow of SOC prediction based on LWOA-LSTM

Fig.3 Extraction of charging curves

Fig.4 Curves of frequency modulation working conditions

Table 1 WOA and LWOA optimization results for LSTM hyperparameters

网络模型	第1层隐藏层层数	第2层隐藏层层数	学习率	批量大小
WOA-LSTM	94	56	0.0084	57
LWOA-LSTM	50	85	0.0042	28

Fig.5 Prediction results of battery SOC using three network models

Fig.6 Battery SOC prediction results of test sets

Fig.7 Comparison of fitness curves of two algorithms

Table 2 Evaluation indicators of algorithms

模型	E_RMS	E_MA	E_MAP/%	R²
LSTM	0.0192880	0.0106760	3.5351	0.98412
WOA-LSTM	0.0127524	0.0071967	1.2462	0.98962
LWOA-LSTM	0.0094943	0.0051306	0.3894	0.99714

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