A multi-objective coupling parameter hierarchical optimization framework for a novel dual-motor powertrain system of electric vehicle

Abstract

Collaborative optimization of the coupling parameters of powertrain is essential to improve the performance of electric vehicles. However, complex coupling relationships and multi-objective trade-offs bring challenges to traditional heuristic optimization algorithms, limiting the exploitation of system performance. To improve optimization accuracy and the performance of vehicles, a global parameter optimization framework for multi-power source systems is proposed. Specifically, the optimization framework consists of three layers, the middle and bottom layers respectively perform multi-disciplinary and multi-objective collaborative optimization of the coupling parameters to obtain a Pareto front formed by the optimal combination of parameters. Furthermore, the decision layer utilizes the Technique for Order Preference by Similarity to Ideal Solution to perform a comprehensive evaluation of the solution on the Pareto front to scientifically obtain the best solution and the weight coefficient range. The simulation results demonstrate that the optimized optimal solution improves dynamic performance by 15.77% while reducing operating costs by 7.37% compared to the initial parametric solution, resulting in a significant improvement in vehicle economy. Meanwhile, the parameter optimization design regularities of the dual-motor system are summarized.