Application of genetic algorithm for simultaneous optimisation of HEV component sizing and control strategy
This paper describes a methodological approach for the simultaneous optimisation of Hybrid Electric Vehicle (HEV) component sizing and control strategy using Genetic Algorithm (GA). In this approach, using a parallel HEV configuration and an Electric Assist Control Strategy (EACS), the optimisation problem is formulated. The whole set of sizing and control variables is then encoded as the chromosomes. The multi-objective target is also defined to minimise the Fuel Consumption (FC) and emissions. In addition, the PNGV performance requirements are considered as constraints. Finally, to evaluate the objective function, three driving cycles, ECE-EUDC, FTP and TEH-CAR, are employed. The simulation results obtained in this study show that the approach is effective, resulting in improvement of the objective value and FC.
Keywords: optimisation, genetic algorithms, component sizes, control strategy, hybrid electric vehicles, HEV control, alternative propulsion, fuel consumption, emissions reduction, simulation