Data-Driven Lithium-Ion Battery Degradation Evaluation Under Overcharge Cycling Conditions

Accurately assessing degradation and detecting abnormalities of overcharged lithium-ion batteries is critical to ensure the health and safe adoption of electric vehicles. This article proposed a data-driven lithium-ion battery degradation evaluation framework. First, a multilevel overcharge cycling experiment was conducted. Second, the battery degradation behaviors and features were analyzed and extracted using incremental capacity analysis and Pearson correlation coefficient. Above all, a data-driven lithium-ion battery degradation evaluation method based on machine learning and model integration method was developed. The proposed integrated model was compared with other state-of-the-art methods and reached a mean squared error of 1.26 x 10(-4). Finally, based on prediction results, rate of degradation was calculated and classified to different degrees, and overcharged cells can be effectively identified. Moreover, to verify the feasibility of the proposed overall framework, this article carried out an experiment by connecting overcharge-induced degraded cell and fresh cells in series to simulate the real-world battery assembly and function of battery management systems. Based on the proposed scheme, the overcharged batteries in the battery series can be detected efficiently likewise.

Automated summary

This article proposes a data-driven framework for evaluating degradation of overcharged lithium-ion batteries. It includes a multilevel overcharge cycling experiment to analyze battery degradation behaviors and features, and develops a machine learning and model integration method for evaluation. The proposed method achieves a mean squared error of 1.26 x 10(-4) and effectively identifies overcharged cells.