基于短时搁置端电压压降的快速锂离子电池健康状态预测

doi:10.19595/j.cnki.1000-6753.tces.181206

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摘要最大可用容量是衡量锂离子电池寿命状态的重要依据。在分析电池寿命实验和开路电压实验特性的基础上,提出一种能够表征锂离子电池最大可用容量的健康因子。由于该健康因子仅利用电池充电或放电至某电压后,搁置10min内端电压压降信息,与传统方法相比,不限制电池的运行工况,能够快速预测电池健康状态,且获取健康因子时不需要对电池放电。论文进一步采用多种神经网络回归预测方法,建立健康因子与电池可用容量和寿命状态之间的关系,在对实验结果进行比较分析的基础上,提出一种加权混合神经网络模型。实验结果表明,所提出的健康因子能够用于表征电池寿命状态,且对实验工况有较强的鲁棒性,提出的加权混合神经网络模型能够获得高精度健康状态预测结果。

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	郭永芳
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关键词 ：锂离子电池, 端电压压降, 寿命状态, 回归预测

Abstract：The maximum available capacity is an important basis for indicating the State of Healthy (SOH) of lithium-ion battery. Upon analyzing the experiments results of the cycle life and the open circuit voltage, a novel healthy factor could represent the maximum available capacity was proposed in this paper to predict the SOH of the battery. The healthy factor of the paper mainly used the data relative with the terminal voltage drop during the battery rest for 10min after the battery was charged or discharge. Comparing with the traditional methods, the method of the paper does not limit the working conditions of the battery, and it can predict the SOH quickly. Besides, it does not need to discharge the battery to obtain the health factor. Furthermore, four regression prediction methods were used to model the relationship between health factors and the battery available capacity. Upon analyzing the performance and results of the regression neural networks, an improved weighted hybrid neural network was proposed. The experimental results show that the proposed health factor can be used to characterize SOH of the battery and it is robust to the working conditions. The weighted hybrid neural network of the paper can achieve high precision for SOH prediction.

Key words： Lithium-ion battery terminal voltage drop SOH regression prediction

收稿日期: 2018-07-11 出版日期: 2019-10-12

PACS:

TM912

基金资助:国家自然科学基金（51377044）、河北省自然科学基金重点项目（E2017202284）和河北省教育厅青年基金（QN2017314,QN2017316）资助项目

通讯作者: 黄凯男,1980年生,博士,讲师,硕士生导师,研究方向为锂离子电池建模、寿命状态估计。E-mail：huangkai@hebut.edu.cn

作者简介: 郭永芳女,1979年生,博士,副教授,硕士生导师,研究方向为智能算法、锂离子电池建模与寿命估计。E-mail: guoyongfang@hebut.edu.cn

引用本文:

郭永芳, 黄凯, 李志刚. 基于短时搁置端电压压降的快速锂离子电池健康状态预测[J]. 电工技术学报, 2019, 34(19): 3968-3978. Guo Yongfang, Huang Kai, Li Zhigang. Fast State of Health Prediction of Lithium-Ion Battery Based on Terminal Voltage Drop During Rest for Short Time. Transactions of China Electrotechnical Society, 2019, 34(19): 3968-3978.

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https://dgjsxb.ces-transaction.com/CN/10.19595/j.cnki.1000-6753.tces.181206 https://dgjsxb.ces-transaction.com/CN/Y2019/V34/I19/3968

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