Data-Driven Battery Aging Mechanism Analysis and Degradation Pathway Prediction

Capacity decline is the focus of traditional battery health estimation as it is a significant external manifestation of battery aging. However, it is difficult to depict the internal aging information in depth. To achieve the goal of deeper online diagnosis and accurate prediction of battery aging, this paper proposes a data-driven battery aging mechanism analysis and degradation pathway prediction approach. Firstly, a non-destructive aging mechanism analysis method based on the open-circuit voltage model is proposed, where the internal aging modes are quantified through the marine predator algorithm. Secondly, through the design of multi-factor and multi-level orthogonal aging experiments, the dominant aging modes and critical aging factors affecting the battery capacity decay at different life phases are determined using statistical analysis methods. Thirdly, a data-driven multi-factor coupled battery aging mechanism prediction model is developed. Specifically, the Transformer network is designed to establish nonlinear relationships between factors and aging modes, and the regression-based data enhancement is performed to enhance the model generalization capability. To enhance the adaptability to variations in aging conditions, the model outputs are set to the increments of the aging modes. Finally, the experimental results verify that the proposed approach can achieve satisfactory performances under different aging conditions.