重庆理工大学学报(自然科学) ›› 2023, Vol. 37 ›› Issue (5): 265-272.

• 能源动力环境 • 上一篇    下一篇

融合电化学阻抗与容量增量曲线特征的锂电池健康状态算法研究

张兴红,徐 翊,巩泽浩   

  1. (重庆理工大学 两江国际学院,重庆 401135)
  • 出版日期:2023-06-21 发布日期:2023-06-21
  • 作者简介:张兴红,男,博士,教授,主要从事计算机辅助技术、智能检测与传感器技术,Email:zxh@cqut.edu.cn;通信作者 徐翊,女,硕士研究生,主要从事计算机辅助技术、新能源汽车电池健康管理,Email:XuYi_1623@stu.cqut. edu.cn。

Algorithm research on health state of a lithium battery by integrating the features of electrochemical impedance and incremental capacity curves

  • Online:2023-06-21 Published:2023-06-21

摘要: 针对锂离子电池的安全运行问题,提出了一种特征融合的锂电池健康状态预测算 法。该框架融合了电化学阻抗谱(electrochemicalimpedancespectroscopy,EIS)与容量增量分析 (incrementalcapacityanalysis,ICA)的健康特征,使用卷积神经网络(convolutionalneuralnet work,CNN)和改进型长短期记忆网络(longshorttermmemory,LSTM)建立特征与健康状态的映 射关系,利用量子粒子群优化(quantumparticleswarmoptimization,QPSO)算法对混合网络结构 进行超参数优化。最后,利用 NASAPCoE数据集验证了该方法的准确性与可靠性。

关键词: 锂离子电池, 健康状态, 电化学阻抗谱, 容量增量分析

Abstract: As the energy power of electric vehicles, batteries are the most critical component in the performance and safe operation of electric vehicles, whose state of health (SOH) is of great significance to improve the safety and availability of new energy electronic equipment. Aiming at safe operation of lithium-ion batteries, this paper proposes a health state prediction algorithm based on feature fusion. The framework combines the health features of electrochemical impedance spectroscopy (EIS) and incremental capacity analysis (ICA), uses convolutional neural network (CNN) and the improved long short term memory (LSTM) network to establish the mapping relationship between features and state of health, and uses quantum particle swarm optimization (QPSO) algorithm to optimize the hyperparameters of the hybrid network structure. Finally, the NASA PCoE dataset is used to verify the accuracy and reliability of the method.

中图分类号: 

  • TM911.4