Global Optimization-Based Energy Management Strategy for Series–Parallel Hybrid Electric Vehicles Using Multi-objective Optimization Algorithm

Abstract

The study of series–parallel plug-in hybrid electric vehicles (PHEVs) has become a research hotspot in new energy vehicles. The global optimal Pareto solutions of energy management strategy (EMS) play a crucial role in the development of PHEVs. This paper presents a multi-objective global optimization algorithm for the EMS of PHEVs. The algorithm combines the Radau Pseudospectral Knotting Method (RPKM) and the Nondominated Sorting Genetic Algorithm (NSGA)-II to optimize both energy conservation and battery lifespan under the suburban driving conditions of the New European Driving Cycle. The driving conditions are divided into stages at evident mode switching points and the optimal objectives are computed using RPKM. The RPKM results serve as the fitness values in iteration through the NSGA-II method. The results of the algorithm applied to a PHEV simulation show a 26.74%–53.87% improvement in both objectives after 20 iterations compared to the solutions obtained using only RPKM. The proposed algorithm is evaluated against the weighting dynamic programming and is found to be close to the global optimality, with the added benefits of faster and more uniform solutions.