Analytical Prediction of Battery Capacity Degradation Trajectories Considering Future Operating Condition Variability

Abstract

The accurate prediction of battery capacity degradation trajectories is essential for ensuring device safety; however, existing analytical models often overlook the uncertainties of future operating conditions. An analytical approach that incorporates future operating condition variability into capacity degradation trajectory prediction is presented. A dual-exponential model is used to model the nonlinear degradation behaviors, and Box-Cox transformation with variable coefficients is applied to describe the trajectory's dependence on future operating conditions. In addition, particle filtering is employed to dynamically predict the 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 $0.172\ \mathrm{A}\cdot \mathrm{h}$ using only the first 25 cycles, exhibiting a 52.8% improvement in accuracy over existing methods.