Optimization of energy management strategy for a novel electric-hydraulic vehicle based on NSGA-III genetic algorithm

This paper proposes a novel electrically powered hydraulic vehicle (EHDV) configuration to address issues like local overloading, limited adaptability, and low power density in pure electric vehicles. A rule-based energy management system (EMS) is developed for the EHDV, and its powertrain integration is completed. The system’s feasibility and efficiency are validated, demonstrating that the EHDV reduces battery consumption by 15.97% compared to conventional electric vehicles (EVs) under NEDC conditions. To further enhance performance, a multi-objective optimization model for the EHDV-EMS is formulated, aimed at maximizing the battery’s state of charge while minimizing speed errors. The model is solved using radial basis function approximation and the NSGA-III genetic algorithm. Simulation results show energy consumption reductions of 4.5%, 5.2%, 3.5%, and 8% under NEDC, FTP-75, WLTC, and real-world conditions, respectively. The optimized energy management strategy improves both the vehicle’s charge state and overall energy efficiency. These findings provide important insights for the future optimization of hybrid vehicle energy systems, highlighting the potential of EHDVs in achieving superior performance compared to conventional EVs.