Abstract:

A parameter optimization method for hybrid electric vehicle (HEV) was proposed to improve fuel
economy and reduce emission within requisite power performances. An orthogonal experimental design was
used with ADVISOR platform to find out the first fifth notable system parameters, which severely influence
the fuel economy and emission of HEV, among power components and control strategies. An optimization
model was built using a multi-objective genetic algorithm to obtain a set of Pareto-optimal solution. An optimal
parameter combination from the solution set was selected using a combination weighting method between
subjective and objective evaluation in a least squares sense. The results show that with the optimized
parameters, the fuel consumption per 100km is reduced by 25.3%, the CO emissions per kilometer is reduced
by 35.5%, and the total HC and NOx emission is reduced by 13.7%. These facts verify the effectiveness of the
method.

Key words: hybrid electric vehicle (HEV), orthogonal experimental design, multi-objective genetic algorithm, Pareto optimality, parameters optimization