A Reduced-Order Method for Battery Modeling Considering Time-Varying Characteristics of the System

Physics-based electrochemical models offer great potential for battery management systems by accurately capturing internal states of the battery. However, their strong nonlinearity and coupled partial differential equations result in high computational complexity, limiting real-time applications. Model order reduction techniques address this challenge, yet traditional methods, such as Padé approximation and polynomial-based approaches, assume time-invariant parameters, leading to errors under dynamic conditions. This study systematically evaluates the representatively model order reduction techniques, highlighting their limitations in handling time-varying battery dynamics. To overcome these challenges, a novel order-reduction approach is proposed, integrating proper orthogonal decomposition with the finite difference method to improve computational efficiency and accuracy. Simulation results demonstrate that the proposed method significantly reduces computational cost while maintaining high accuracy. Experimental validation further confirms its robustness, establishing it as a promising candidate for real-time battery management system applications.