基于传递熵与JS-BP神经网络的锂离子电池容量预测模型

doi:10.11930/j.issn.1004-9649.202310039

中国电力 ›› 2025, Vol. 58 ›› Issue (2): 186-192, 215.DOI: 10.11930/j.issn.1004-9649.202310039

基于传递熵与JS-BP神经网络的锂离子电池容量预测模型

吴小忠¹(), 肖立华¹, 童超², 夏向阳³(), 袁翎¹, 甘星¹, 江志文¹, 黄湘源¹

1. 国网湖南省电力有限公司，湖南长沙　410004
2. 南京南瑞继保工程技术有限公司，江苏南京　211100
3. 长沙理工大学电气与信息工程学院，湖南长沙　410114

收稿日期:2023-11-15 接受日期:2024-07-18 出版日期:2025-02-28 发布日期:2025-02-25
作者简介:吴小忠（1974—），男，高级工程师，从事新能源安全保护与控制研究，E-mail：wuxiaozhong@126.com
夏向阳（1968—），男，通信作者，教授，博士生导师，从事柔性直流输电控制和储能安全控制研究，E-mail：307351045@qq.com
基金资助:
国家自然科学基金资助项目（51977014）。

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

Xiaozhong WU¹(), Lihua XIAO¹, Chao TONG², Xiangyang XIA³(), Ling YUAN¹, Xing GAN¹, Zhiwen JIANG¹, Xiangyuan HUANG¹

1. State Grid Hunan Electric Power Co., Ltd., Changsha 410004, China
2. Nanjing NARI-Relays Engineering & Technology Co., Ltd., Nanjing 211100, China
3. School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114, China

Received:2023-11-15 Accepted:2024-07-18 Online:2025-02-28 Published:2025-02-25
Supported by:
This work is supported by National Natural Science Foundation of China (No.51977014)

摘要/Abstract

摘要：

实现储能系统安全运行，对锂离子电池可用容量的准确预测非常关键。通过对储能电池相关参数进行信息熵分析，筛选出对于电池可用容量具有显著影响的健康因子，将信息熵筛选出的健康因子与水母搜索反向传播神经网络（jellyfish search-back propagation neural network，JS-BP）相结合，建立电池可用容量预测模型。基于美国航空航天局（National Aeronautics and Space Administration，NASA）公开的相关老化数据集与电池老化实验平台的老化数据展开综合分析，其结果表明所提模型具有较高的电池容量预测精度，平均绝对误差（mean absolute error，MAE）、均方根误差（root mean square error，RMSE）均处于较低水平，验证了该模型的准确性。

关键词: 传递熵, JS-BP神经网络, 健康因子, 可用容量

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

吴小忠, 肖立华, 童超, 夏向阳, 袁翎, 甘星, 江志文, 黄湘源. 基于传递熵与JS-BP神经网络的锂离子电池容量预测模型[J]. 中国电力, 2025, 58(2): 186-192, 215.

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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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202310039

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

图/表 16

图 1 各电池容量变化示意

Fig.1 Schematic diagram of changes in battery capacity

图 2 各电池充电最高温度

Fig.2 Maximum charging temperature of each battery

图 3 各电池放电最高温度

Fig.3 Maximum discharge temperature of each battery

图 4 各电池内阻Rct变化示意

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

图 5 各电池内阻Re变化示意

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

图 6 电池可用容量预测整体架构

Fig.6 Overall architecture for predicting the battery usable capacity

表 1 熵值计算结果

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

图 7 B0005电池容量预测结果

Fig.7 Battery capacity prediction results of B0005

图 8 B0006电池容量预测结果

Fig.8 Battery capacity prediction results of B0006

图 9 B0007电池容量预测结果

Fig.9 Battery capacity prediction results of B0007

表 2 预测误差统计

Table 2 Prediction error statistics

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

图 10 实验平台

Fig.10 Experimental platform

表 3 电池老化过程阈值

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

图 11 充放电过程中部分电流/电压曲线

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

图 12 电池容量预测结果

Fig.12 Battery capacity prediction results

表 4 预测结果

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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基于传递熵与JS-BP神经网络的锂离子电池容量预测模型

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

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基于传递熵与JS-BP神经网络的锂离子电池容量预测模型

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

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