A migration-based method for non-invasive revelation of microscopic degradation mechanisms and health prognosis of lithium-ion batteries

Abstract

Precise health prognosis is essential to guarantee efficient and safe battery operation. However, the non-invasive analysis of the microscopic aging mechanism of batteries has always been a challenge. To address this problem, a macro–micro non-invasive health prognosis method for lithium-ion batteries is proposed based on aging mechanisms migration in this paper. Firstly, an improved reduced order physics-based model considering hysteresis is established. Besides, a multi-step full parameter identification method is designed based on the multi-verse optimizer. Secondly, the critical mechanism parameters are determined for model lightweighting, through the correlation and sensitivity analysis, which can dominate the electrical performance and capture the aging modes. Then, an aging mechanism migration method is designed for macro–micro state of health (SOH) estimation and remaining useful life (RUL) prediction. The superiority of the proposed method is that it can predict not only macroscopic capacity-defined SOH, but also microscopic aging mechanisms non-invasively, including the loss of lithium inventory, loss of positive/negative active material, and reaction kinetics decline. Finally, experiments are carried out to demonstrate the effectiveness of proposed model and method under different aging conditions.