Multi-objective optimization-based energy control strategy of battery/super-capacitor hybrid power source for electric vehicles

This paper addresses the challenge of multi-objective coordination in battery/super-capacitor hybrid power sources (HPSs) by proposing strategies based on the nondominated sorting genetic algorithm II, multi-objective particle swarm optimization, and multi-objective grey wolf optimization. Three dynamic balancing objectives-power loss, battery current change rate, and energy efficiency are optimized simultaneously. A fitness function is constructed to correlate 3 objectives with battery current regulation. Through simulation under federal test procedure 75 driving conditions, the study obtains optimal trade-offs among the objectives and identifies the optimal battery current trajectory. The verification results show that the 3 strategies achieved average power losses of 6342.79 W, 6334.75 W, and 6432.98 W, with corresponding average battery current change rates of 1402.40, 1824.78, and 2401.71, average energy efficiencies of 91.29%, 91.33%, and 91.37%, and simulation runtimes ranging from approximately 6–13 s, 5–16 s, and 10–31 s, respectively. A comparative analysis of battery optimal current distribution, optimization objective mapping, and algorithm runtime reveals performance differences among control strategies using distinct multi-objective optimization algorithms. This study aims to provide theoretical support for energy control optimization in HPSs. • Propose 3 multi-objective optimization-based energy control strategies. • Take power loss, battery current change rate, and energy efficiency as objectives. • Verify the proposed scheme under typical driving conditions. • Compare 3 strategies in terms of optimization process and results. • Provide solution for control strategy optimization of hybrid power source.