电化学阻抗谱机器学习评估动力电池状态研究进展

Research progress of evaluating the status of power lithium batteries based on electrochemical impedance spectroscopy combined with machine learning

退役动力锂电池(额定容量80%以上)梯次利用可有效缓解电池回收和环境污染压力,提高资源利用率和经济效益,然而对其进行快速、无损和准确的状态评估仍是一个挑战。与其他已报道的方法相比,电化学交流电测量电池并收集数据绘制阻抗谱图是研究电池状态的核心方法,具有快速、无损这两种优势。通过这种方式检测的电池可以建立起内部阻抗和状态相关性,快速完成电池状态评测。电化学阻抗谱图的分析方法主要包括依靠测量数据和机器学习的方法进行阻抗的预测,依靠等效电路图对电路各个等效元件的变化情况进行分析,以及用积分算法将阻抗谱图转化为更直观的弛豫时间分布图谱。这些方法都提供了电池内部老化情况的分析方法,为电池内部阻抗和健康状态之间的联系提供了电化学方面的基础。基于此,综述了电化学阻抗谱结合机器学习评估动力锂电池状态在国内外最新研究进展,重点针对电化学阻抗谱、等效电路模型、弛豫时间分布和机器学习之间的关系进行总结和探讨。

The cascading utilization of retired power lithium batteries(with a rated capacity of over 80%)can effectively alleviate the pressure of battery recycling and environmental pollution,and improve resource utilization efficiency and economic benefits.However,conducting rapid,nondestructive,and accurate state assessment of the retired batteries remains a challenge.Compared with other reported methods,electrochemical alternating current measurement of batteries and collecting data to draw impedance spectra are the core methods for studying battery states,which have two advantages:fast and non-destructive.The battery detected in this way can establish internal impedance and state correlation,and quickly complete battery state evaluation.The analysis methods of electrochemical impedance spectroscopy mainly include predicting impedance based on measurement data and machine learning methods,analyzing the changes in various equivalent components of the circuit based on equivalent circuit diagrams,and using integration algorithms to convert impedance spectroscopy into a more intuitive relaxation time distribution spectroscopy.These methods all provide analytical methods for the internal aging of batteries,providing an electrochemical basis for the relationship between the internal impedance and health status of batteries.Based on this,this article reviewed the latest research progress in combining electrochemical impedance spectroscopy with machine learning to evaluate the state of power lithium batteries both domestically and internationally,with a focus on summarizing and exploring the relationship between electrochemical impedance spectroscopy,equivalent circuit models,relaxation time distribution,and machine learning.