锂离子电池的多状态模型剩余寿命预测方法  

Method for Predicting Remaining Useful Life of Lithium-ion Battery Based on Multi-state Model

作　　者：陈万 蔡艳平 苏延召 姜柯 黄华 CHEN Wan;CAI Yan-ping;SU Yan-zhao;JIANG Ke;HUANG Hua(No.305 Teaching and Research Section,The Rocket Force Engineering University,Xi’an 710025,China)

机构地区：[1]火箭军工程大学305教研室,西安710025

出　　处：《科学技术与工程》2021年第10期4078-4083,共6页Science Technology and Engineering

摘　　要：针对锂离子电池的容量恢复现象导致的剩余寿命预测精度不高的问题,提出了一种锂离子电池的多状态模型剩余寿命预测方法。首先通过分析锂电池的衰退数据将锂离子电池的退化过程分为正常退化、容量恢复和加速退化三种状态,然后分别对三种状态的退化过程进行建模并验证了模型的有效性,将3种状态的模型组合得到锂离子电池多状态容量衰退模型。然后基于建立的模型提出了粒子群优化粒子滤波算法,用于多状态容量衰退模型进行参数识别和状态更新。最后实现了锂离子电池的剩余寿命预测和预测结果的不确定性表达。与其他方法相比,实验结果表明:所提出方法精度更高,鲁棒性更强。Aiming at solving the problem of low accuracy of remaining useful life prediction caused by the phenomenon of lithium-ion battery capacity recovery, a method for predicting the remaining useful life of the lithium-ion battery based on the multi-state model was proposed. First, by analyzing lithium batteries’ degradation data, the degradation process of lithium-ion batteries was divided into three states: normal degradation, capacity recovery, and accelerated degradation. Then, the three states’ degradation processes were modeled, and the validity of the model was verified. The lithium-ion battery multi-state capacity decline model was obtained by combining the three-state models. Based on the established model, the particle swarm optimization-particle filter algorithm was proposed, which was used for parameter identification and state update of the multi-state capacity decay model. Finally, the remaining useful life prediction of the lithium-ion battery and the uncertainty expression of the prediction results were realized. Compared with other methods, the experimental results show that the proposed method has higher accuracy and stronger robustness.

关 键 词：锂离子电池 剩余寿命预测 多状态模型 容量恢复 粒子群优化粒子滤波算法 

分 类 号：TM912.9[电气工程—电力电子与电力传动]