第15讲:电池老化机理分析与诊断方法【AESA潘悦】
发表时间:2020-11-15     阅读次数:


分享人潘悦

现为AESA课题组硕士研究生,主要研究方向为电动汽车动力电池耐久性管理,在该领域发表SCI论文1篇。(个人网页)


讲座摘要

锂离子电池每用即衰,老化伴随着锂离子电池的整个使用过程。针对锂离子电池内部老化机理与外部因素间强耦合、难诊断的问题,本次讲座主要从锂离子电池内部老化副反应机理、现有的老化诊断方法和一些加速寿命实验结果三方面,详细介绍了锂离子电池的老化机理和诊断方法应用。


相关文献

[1] Xiong R, Pan Y, Shen W, et al. Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives[J]. Renewable and Sustainable Energy Reviews, 2020, 131: 110048. (点击下载)

[2] Xiong R, Li L, Tian J. Towards a smarter battery management system: A critical review on battery state of health monitoring methods[J]. Journal of Power Sources, 2018, 405: 18-29. (点击下载)

[3] Ma Z, Wang Z, Xiong R, et al. A mechanism identification model based state-of-health diagnosis of lithium-ion batteries for energy storage applications[J]. Journal of Cleaner Production, 2018, 193: 379-390. (点击下载)

[4] Xiong R, Zhang Y, Wang J, et al. Lithium-Ion Battery Health Prognosis Based on a Real Battery Management System Used in Electric Vehicles[J]. IEEE Transactions on Vehicular Technology, 2019, 68(5):4110-4121. (点击下载)

[5] Ouyang M, Feng X, Han X, et al. A dynamic capacity degradation model and its applications considering varying load for a large format Li-ion battery[J]. Applied Energy, 2016, 165(Mar.1):48-59. (点击下载)

[6] Battery Management Algorithm for Electric Vehicles[M]. Springer, 2020.

[7] Xiong R, Shen W. Advanced battery management technologies for electric vehicles[M]. John Wiley & Sons, 2019.

[8] 熊瑞. 动力电池管理系统核心算法[M]. 北京:机械工业出版社,2018.


上一篇:第16讲:滤波算法的概率论基础及其在电池容量预测中的应用【AESA王晨旭】
下一篇:第14讲:动力电池外部短路试验、机理与建模【AESA燕润博】
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