Joint modeling for early predictions of Li-ion battery cycle life and degradation trajectory

Accurate early prediction of Li-ion battery aging facilitates new product optimization and application manage-ment. Here, a joint modeling scheme is proposed. It is dedicated to decoupling cell-to-cell variability and cycle -by-cycle nonlinear aging in Li-ion batteries, enabling accurate cycle life and capacity trajectory predictions. An aging end-point early predictor is established based on the multiple output Gaussian process (MOGP). It enables better performance by sharing knowledge across outputs, including cycle life and life capacity. For degradation trajectory prediction, an aging process trajectory predictor is constructed based on the 'prompt-learning' para-digm neural network, and the life capacity from MOGP is used as the prompt input. Moreover, a Bayesian search strategy is proposed to obtain optimal feature combinations, and feature alignment is used to narrow the gap between source and target domains. The proposed scheme is developed on the source domain with 41 cells and tested on three special target domains with 128 cells. As a result, the predicted error of cycle life can be within 7.87%, and that of the capacity trajectory can be within 6.53%. The prediction results prove its high adaptability and effectiveness. The achievements will promote high-dimensional parameter design and optimization, accel-erating battery development and reducing testing costs.