Research on Multi-Objective Parameter Matching and Stepwise Energy Management Strategies for Hybrid Energy Storage Systems

Electric vehicle technologies present promising solutions for achieving energy conservation and emission reduction goals. However, efficiently distributing power across hybrid energy storage systems (HESSs) remains a major challenge in enhancing overall system performance. To address this, this paper proposes an energy management strategy (EMS) based on stepwise rules optimized by Particle Swarm Optimization (PSO). The approach begins by applying a multi-objective optimization method, utilizing the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to fine-tune the parameters of lithium-ion batteries and ultracapacitors for an optimal balance in system performance. Additionally, an innovative stepwise-based EMS has been designed using adaptive PSO. This strategy builds a real-time control mechanism by dynamically adjusting the power distribution gradient threshold, taking into account the compensation for the state of charge (SOC). Comparative analysis across three typical operating conditions-urban, suburban, and highway-demonstrates that the stepwise-rule optimized strategy reduces the energy consumption of the HESS by 3.19%, 7.9%, and 5.37%.