Hybrid Hydraulic Vehicle Parameter Optimization using Multi-Objective Genetic Algorithm

Abstract

Parameter optimizations of HHV torque distribution must deal with conflicting objectives between the engine torque and fuel economy without compromising the vehicle driving quality. The torque generation from an internal combustion engine (ICE)  is directly influenced by the amount of fuel burnt, hence cannot be solved using a classical single-objective optimization method. In this paper, multi-objective genetic algorithm (MOGA) is used to optimize the power split of a parallel hybrid hydraulic vehicle (HHV) that utilizes an ICE and a hydraulic motor. The simulation runs on three operating modes, engine only, power assist and regenerative modes to optimize two conflicting objectives, engine torque and fuel economy considering both highway and city drive cycles. Using a single unified formulation, a number of design objectives can be simultaneously optimized through a systematic search algorithm within a diverse parameter space. Simulation results have shown both objectives have good compromises that lie along the Pareto optimal front. In comparison, it is observed that there is a significant improvement on fuel economy for HHV as compared to a conventional ICE especially at low-torque operation when the hydraulic motor assists the vehicle for both highway and city drive cycles.