Abstract

Accelerated ageing is a significant issue for various lithium-ion battery applications such as electrical vehicles, energy storage and electronic devices. Effective early diagnosis is prominent to restrict battery failure. Typical battery classification data-driven methods are structured to capture features from data without considering the underlying ageing mechanism. On the other hand, analysis of the detailed ageing mechanism that can generate electrochemistry-based models can be highly complicated and may not be suitable for real-time battery management. In this paper, the accelerated ageing diagnosis method is systematically investigated. The accelerated ageing mechanisms of the Li[NiCoMn]O2 (NCM) battery is analyzed by the non-destructive quantitative diagnostic method. We prove the feasibility of accelerated ageing diagnosis based on the accelerated ageing mechanism analysis. An integrated framework of ageing mechanisms and data-driven methods (IFAMDM) is introduced for lithium-ion battery accelerated ageing diagnosis. Highly adaptable features reflecting the accelerated ageing mechanism are proposed for lithium-ion battery accelerated ageing. We propose a combination method with high interpretability, adaptability and accuracy to diagnose battery accelerated ageing. The IFAMDM was verified on two types of battery datasets. The IFAMDM is proved to be highly generic and accurate for lithium-ion battery accelerated ageing diagnosis at the 100th cycle.

Instructions:

We performed the NCM batteries ageing experiments and obtained worthy normal ageing and accelerated ageing batteries data. Most existing ageing datasets only include nonlinear or accelerated ageing data. Therefore, the NCM batteries dataset has highly valuable and comprehensive information on battery ageing samples.

The NCM batteries experiment dataset and early accelerated ageing diagnosis result in this paper are available with this paper. The LFP dataset in [12] and early diagnosis results of the LFP battery are also submitted with this paper.

If readers want to use the NCM dataset for research, please cite this paper. ‘X. Jia, C. Zhang, L. Wang, L. Zhang and X. Zhou, "Early Diagnosis of Accelerated Ageing for Lithium-Ion Batteries with an Integrated Framework of Ageing Mechanisms and Data-Driven Methods," in IEEE Transactions on Transportation Electrification, 2022, doi: 10.1109/TTE.2022.3180805.' Early Diagnosis of Accelerated Ageing for Lithium-Ion Batteries with an Integrated Framework of Ageing Mechanisms and Data-Driven Methods | IEEE Journals & Magazine | IEEE Xplore

If the reader has any problems with the dataset, you can email the authors. The email address:

xinyu.jia@bjtu.edu.cn. zhangcaiping@bjtu.edu.cn, lj.zhang@bjtu.edu.cn