Hybrid hydraulic vehicle parameter optimisation using multi-objective genetic algorithm

Abstract

The hybrid hydraulic vehicle (HHV) setup combines compressed-fluid energy in parallel with internal combustion engine (ICE) to deliver the propelling energy to the wheels. During power assist mode, the compressed fluid assists the ICE to propel the vehicle at relatively less energy, hence improving fuel economy. Obviously, in this case the engine torque and fuel economy are two conflicting parameters in which high-torque operation results in poor fuel economy. These conflicting objectives cannot be solved using a classical single-objective optimisation method. Therefore, a multi-objective genetic algorithm (MOGA) is proposed to optimise the power split between an ICE and a hydraulic motor to improve fuel economy. The simulation runs on three operating modes namely, engine only, power assist and regenerative modes considering both highway and city drive cycles. Using a single unified formulation, the objectives can be simultaneously optimised through a systematic search algorithm within a diverse parameter space to provide a set of non-dominated solutions along the Pareto optimal front. Overall, the HHV contribution is significantly observed at low-torque operations when the hydraulic motor assists the ICE in both drive cycles. In conclusion, the improvement achieved by the HHV in terms of fuel economy is recorded as much as 5.55% for highway and 6.50% for city drive cycles.