Multi-objective optimization of constrained parallel Hybrid Electric Vehicles

Hybrid Electric Vehicles (HEVs), surrounded by high complexity, nonlinear constraint and large amount of coupling design parameters, provides fairly higher fuel economy with lower emissions than conventional vehicles. It is significant to optimize HEV's parameters to enhance its performance. Considering the disadvantage of the methods transforming multi-objective functions into a single objective evaluation function, this paper reports a methodological approach for multi-objective optimization of parallel hybrid vehicle. Firstly, a model of parallel hybrid electric vehicle for optimal simulation is established. Secondly, based on the non-dominated sorting genetic algorithms II, a methodological approach for the simultaneous optimization of HEV parameters to minimize the fuel consumption and emissions was proposed, which adopts ADVISOR to simulate. Taking Insight as a case, the simulation results show that this approach can obtain a set of Pareto-optimal solutions with better performance.