Multi-Objective Optimization for Thermally-Constrained Energy Management in EV Hybrid Systems

Abstract

This paper presents an energy management strategy for electric vehicles (EVs) with a hybrid energy storage system (HESS), which combines a lithium-ion battery and a supercapacitor. The problem is formulated as a constrained multi-objective optimization (MOO) that aims to: (i) match the vehicle’s average power demand, (ii) reduce battery thermal stress, and (iii) maintain the supercapacitor’s state of charge near its nominal level. The Particle Swarm Optimization (PSO) algorithm allocates power optimally between sources under thermal, dynamic, and state of charge (SOC) constraints. Simulation results from a 400 -second driving cycle demonstrate that the proposed strategy lowers battery temperature by up to $9^{\circ} \mathrm{C}$ compared to a rulebased approach. Furthermore, energy sharing becomes more balanced, with the battery providing $65 \%$ of the power and the supercapacitor providing $35 \%$, as opposed to the baseline approach, where the battery provides $95 \%$ and the supercapacitor provides $5 \%$. This method improves energy efficiency, battery life, and thermal safety.