Capacity Prediction Model of Lithium-Ion Batteries Based on Transfer Entropy and JS-BP Neural Network

doi:10.11930/j.issn.1004-9649.202310039

Abstract

Abstract:

Accurately predicting the available capacity of lithium-ion batteries is critical to ensuring the safe operation of energy storage systems. Therefore, this paper proposes a method for predicting the capacity of lithium-ion batteries in energy storage systems based on transfer entropy and JS-BP neural networks. Based on an analysis of the information entropy of relevant parameters of the energy storage batteries, the health factors that have a significant impact on the available capacity of batteries are selected, and a prediction model for the available capacity of batteries is established by combining the selected healthy factors with the JS-BP neural network. Finally, a comprehensive analysis is carried out based on the aging datasets from NASA and the battery aging experimental platform, and the results show that the proposed method has a high prediction accuracy of battery capacity, and the error indicators MAE and RMSE are at a low level, which verifies the accuracy of the model.

Key words: transfer entropy, JS-BP neural network, health factor, available capacity

Xiaozhong WU, Lihua XIAO, Chao TONG, Xiangyang XIA, Ling YUAN, Xing GAN, Zhiwen JIANG, Xiangyuan HUANG. Capacity Prediction Model of Lithium-Ion Batteries Based on Transfer Entropy and JS-BP Neural Network[J]. Electric Power, 2025, 58(2): 186-192, 215.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202310039

https://www.electricpower.com.cn/EN/Y2025/V58/I2/186

Figures/Tables 16

Fig.1 Schematic diagram of changes in battery capacity

Fig.2 Maximum charging temperature of each battery

Fig.3 Maximum discharge temperature of each battery

Fig.4 Schematic diagram of changes in internal resistance Rct of each battery

Fig.5 Schematic diagram of changes in internal resistance Re of each battery

Fig.6 Overall architecture for predicting the battery usable capacity

Table 1 Entropy calculation results

电池序号	健康特征	传递熵（健康特征至容量）
B0005	充电过程最高温度	0.4638
	放电过程最高温度	0.4453
	内阻R_ct	0.4884
	内阻R_e	0.4129
B0006	充电过程最高温度	0.4957
	放电过程最高温度	0.5583
	内阻R_ct	0.6076
	内阻R_e	0.5796
B0007	充电过程最高温度	0.3752
	放电过程最高温度	0.5211
	内阻R_ct	0.4553
	内阻R_e	0.3623

Fig.7 Battery capacity prediction results of B0005

Fig.8 Battery capacity prediction results of B0006

Fig.9 Battery capacity prediction results of B0007

Table 2 Prediction error statistics

电池序号		E_RMS/(A·h)
B0005		0.071083
B0006		0.080691
B0007		0.081517

Fig.10 Experimental platform

Table 3 Threshold value of aging progress

项目		阈值
充电截止电压/V		4.3
放电截止电压/V		2.7
老化终止条件		300次循环充放电

Fig.11 Partial current/voltage curve during charging and discharging process

Fig.12 Battery capacity prediction results

Table 4 Prediction results 单位：A·h

神经网络模型	E_MA	E_RMS
JS-BP	0.000471	0.000540
BP	0.001164	0.001525
RBF	0.001431	0.001804
CNN	0.001419	0.001810

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