锂离子电池高精度机理建模、参数辨识与寿命预测研究进展

doi:10.3901/JME.2022.22.019

摘要/Abstract

摘要： 拥有高能量密度、低自放电率和长寿命的锂离子电池是电动车辆的主要储能单元,其性能直接影响了车辆的动力性和安全性。然而,锂离子电池是复杂的电化学系统,其内部状态具有时变性和不可观测性。此外,电池在使用过程中性能将不断衰减,将给车辆的安全性带来隐患。为保证电池在车用工况下的高效、安全和可靠运行,需要对电池实施有效管理。电池模型是管理算法的理论基础,参数辨识是模型应用的前提,而寿命预测是保证电池安全的关键技术。针对上述实际应用需求,综述了锂离子电池高精度电化学-热耦合机理建模、模型参数辨识和寿命预测的最新研究进展。重点关注宏观电化学模型中模型重构和模型简化两种模型降阶方法,对比分析参数辨识中试验测量和非拆解式辨识方法的特点,全面总结寿命预测中基于模型、基于数据驱动和融合式算法的算法架构。在此基础上,总结现有研究的不足并对未来研究方向提出展望。

关键词: 锂离子电池, 电化学模型, 热模型, 参数辨识, 寿命预测

Abstract: Lithium-ion batteries with high energy density, low self-discharge rate, and long cycle life are the main energy storage devices in electric vehicles. The performance of lithium-ion batteries has direct effects on the power and security of electric vehicles.However, lithium-ion battery is a complex electrochemical system, and its internal states are time-varying and unmeasurable. Besides,the performance and life of the lithium-ion battery gradually deteriorate during use, which brings potential safety hazards. To operate the battery efficiently, safely, and reliably in demanding vehicle driving conditions, effective battery management is required. Battery modeling is the theoretical basis of management algorithms, and parameter identification is the premise of model application.Moreover, battery lifetime prediction is the key technology to ensuring battery safety. Aiming at real-world applications, a comprehensive review of high-fidelity electrochemical-thermal coupled modeling, non-destructive parameter identification, and battery lifetime prognostics is presented. The model reformulation and simplification of the macro-scale electrochemical models are introduced. The characteristics of experimental measurement and non-destructive methods for parameter identification are compared and analyzed. The algorithm architectures of the model-based, data-driven, and hybrid approaches for lifetime prediction are summarized. On these bases, upcoming challenges and future research directions are identified and discussed.

Key words: lithium-ion battery, electrochemical model, thermal model, parameter identification, lifetime prognostics

中图分类号:

TM912

徐乐, 邓忠伟, 谢翌, 胡晓松. 锂离子电池高精度机理建模、参数辨识与寿命预测研究进展[J]. 机械工程学报, 2022, 58(22): 19-36.

XU Le, DENG Zhong-wei, XIE Yi, HU Xiao-song. Review on Research Progress in High-fidelity Modeling, Parameter Identification and Lifetime Prognostics of Lithium-ion Battery[J]. Journal of Mechanical Engineering, 2022, 58(22): 19-36.

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