A novel online method for predicting the remaining useful life of lithium-ion batteries considering random variable discharge current
A novel online method for predicting the remaining useful life of lithium-ion batteries considering random variable discharge current
复制标题
考虑随机可变放电电流的锂离子电池剩余使用寿命在线预测新方法
DOI：
10.1016/j.energy.2020.119490
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发表时间：
2021-03
期刊：
影响因子：
9
通讯作者：
Zhen Peng
中科院分区：
文献类型：
--
作者：
Dongxu Shen;Lifeng Wu;Guoqing Kang;Yong Guan;Zhen Peng
锂离子电池广泛应用于许多电子和电气设备中，准确预测其剩余使用寿命对于确保系统的安全可靠运行至关重要。锂离子电池在实际环境中的放电电流在一个充放电循环中是随机变化的，随机变化的电流对电池寿命的影响较大。现有的预测方法很少考虑到这一点。因此，本文提出了一种新的预测可变放电电流的锂离子电池的剩余使用寿命的方法。首先，通过模拟蓄电池的工作状态，设计了变放电电流下的蓄电池老化实验，并采集了容量数据。其次，建立了一个新的两阶段维纳过程模型，以描述不同阶段的锂离子电池的退化特性的差异。最后，引入无迹粒子滤波算法，使模型中的所有参数和锂离子电池的剩余使用寿命分布随最新的在线测量值自适应更新。实验结果表明，与前两种方法相比，该方法能够获得更准确、鲁棒的结果，验证了该方法的有效性和鲁棒性。
Lithium-ion batteries are widely used in many electronic and electrical devices, and accurately predicting their remaining useful life is essential to ensure the safe and reliable operation of the systems. The discharge current of lithium-ion batteries in the actual environment changes randomly during one charge and discharge cycle, and the randomly changing current has a greater impact on battery life. Existing prediction methods rarely take this into account. Therefore, this paper proposes a new method for predicting the remaining useful life of lithium-ion batteries with variable discharge current. First, the battery aging experiment under variable discharge current is designed by simulating the operation state of batteries and capacity data is collected. Secondly, a novel two-stage Wiener process model is established to describe the differences in the degradation characteristics of lithium-ion batteries at different stages. Finally, the unscented particle filtering algorithm is introduced so that all parameters in the model and the remaining useful life distribution of lithium-ion batteries are adaptively updated with the latest on-line measurements. The experimental results demonstrate that the proposed method can achieve more accurate and robust results compared with the two previous methods, which verifies the effectiveness and robustness of the proposed method.
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DOI：
10.1016/j.energy.2019.116732
发表时间：
2019-01
期刊：
ArXiv
影响因子：
--
作者：
Ziyou Song;Xiaogang Wu;Xuefeng Li;Jun Hou;H. Hofmann;Jing Sun
通讯作者：
Ziyou Song;Xiaogang Wu;Xuefeng Li;Jun Hou;H. Hofmann;Jing Sun
影响因子：
11.2
作者：
Liang Li;Sixiong You;Chao Yang;Bingjie Yan;Jian Song;Zheng Chen
通讯作者：
Liang Li;Sixiong You;Chao Yang;Bingjie Yan;Jian Song;Zheng Chen
影响因子：
9
作者：
Chen, Lin;Wang, Huimin;Pan, Haihong
通讯作者：
Pan, Haihong
影响因子：
7.7
作者：
Wuzhao Yan;Bin Zhang;Guangquan Zhao;J. Weddington;Guangxing Niu
通讯作者：
Wuzhao Yan;Bin Zhang;Guangquan Zhao;J. Weddington;Guangxing Niu
基于改进布谷鸟搜索粒子滤波器和新型荷电状态估计方法的锂离子电池剩余寿命预测
影响因子：
9.2
作者：
Qiu Xianghui;Wu Weixiong;Wang Shuangfeng
通讯作者：
Wang Shuangfeng