Analytical prediction of battery capacity degradation trajectories considering future operating condition variability

Accurate prediction of battery capacity degradation trajectories is essential for ensuring device safety, yet existing analytical models often overlook the uncertainties of future operating conditions. This paper presents an analytical approach that incorporates future operating condition variability into capacity degradation trajectory prediction. A dual-exponential model is used to model the nonlinear degradation behaviors, while Box-Cox transformation with variable coefficients is applied to describe the trajectory's dependence on future operating conditions. Additionally, particle filtering is employed to dynamically predict capacity degradation trajectories and confidence intervals under random future conditions. Experimental results show that the proposed approach achieves a median root mean square error below 0.172 Ah using only the first 25 cycles, representing a 52.8% improvement in accuracy over existing methods.