Optimal design of a parallel Hybrid Electric Vehicle using multi-objective genetic algorithms

Hybrid Electric Vehicles (HEVs) provide fairly high fuel economy with lower emissions compared to conventional vehicles. To enhance HEV performance in terms of fuel economy and emissions, subject to the satisfaction of driving performance, optimal powertrain component sizing is inevitable. This paper presents an efficient multi-objective genetic algorithm (MOGA), to optimize powertrain component sizes as well as fuel economy and emissions, including HC, CO, and NOx, for a parallel HEV. The main target is to find the trade-off solutions, known as pareto-optimal set, from among the objectives. Simulation results show the potential of the proposed optimization technique in terms of improved fuel economy and low emissions.