Multi-Objective Optimization of Drive Gears for Power Split Device Using Surrogate Models


Power split device (PSD) is a key component in the energy coupling and decoupling of parallel-series hybrid electric vehicle. This paper proposes a multi-objective optimization method to achieve optimal balance solution among the volume, contact stress, and frictional energy dissipation of PSD drive gears, some of which are implicit with respect to design variables. To avoid the time-consuming problem of finite element analysis used to solve nonlinear responses, surrogate models are adopted to generate approximate expressions of design variables. Pareto-optimal solutions of PSD are obtained using multi-island genetic algorithm (MIGA), non-dominated sorting GA-II (NSGA-II), and multi-objective particle swarm optimization algorithm. The performances of PSD before and after optimization are compared. Results indicate that the proposed method is effective, and NSGA-II achieves higher optimizing efficiency in solving the multiobjective optimization problem of PSD than the other algorithms.