Dynamic Programming-Based Power Management Strategy for Battery/Supercapacitor Electric Vehicles Considering Thermal and Aging Impacts

Battery degradation remains a major challenge for battery electric vehicles (BEVs). The integration of supercapacitors as a secondary energy source to extend battery lifespan has gained significant research attention. This hybridization requires an Energy Management Strategy (EMS) to efficiently distribute power loads between onboard energy sources. This paper presents a Dynamic Programming (DP)-based EMS designed to optimize power distribution between the battery and supercapacitor. The DP algorithm is formulated to solve an optimal control problem while considering operational constraints, including power balance, voltage limits, and final state conditions. A multi-physics battery model is developed to assess the impact of the proposed EMS on battery state of health (SOH) and thermal stress. A comparative study is conducted using MATLAB/SIMULINK, evaluating the DP-based EMS against Frequency Decoupling (FD) EMS and If-Else rule-based EMS. The results demonstrate that the DP-based strategy effectively reduces current fluctuations, enhances battery thermal stability, and extends battery lifespan.