Concurrent optimization for parameters of hybrid electric vehicle based on Non-dominated Sorting Genetic Algorithms

The optimizing design of hybrid electric vehicle (HEV) aims at improving fuel economy and decreasing emissions subject to the satisfaction of its drivability. The concurrent optimization for main parameters of powertrain components and control system is the key to implement this objective. However, this problem is challenging due to the large amount of coupling design parameters, conflicting design objectives and nonlinear constraints. A comprehensive methodology based on the Non-dominated Sorting Genetic Algorithms (NSGA) is presented in this paper to achieve parameters optimization for powertrain and control system simultaneously and find the Pareto-optimal solutions set successfully. This optimal solutions set provides a wide range of choices for the design, which can improve the fuel economy and reduce emissions without sacrificing vehicle performance. A case simulation is carried out and simulated by ADVISOR, the results demonstrate the effectiveness of the algorithms proposed in this paper.