Study of control strategy parameters and component sizing in hybrid electric vehicles using particle swarm optimization

Abstract

This paper studies the effect of different control strategy parameters on fuel economy for hybrid electric vehicles. The parameters with significant effect as well as the key component sizing are used in the optimization algorithm to reduce fuel consumption. The optimization algorithm applied in this paper is the PSO algorithm with a proposed approximation approach. The approximation approach is an interpolation method used to generate search-space in the early optimization stage to improve the computational efficiency. Simulations are carried out in Powertrain System Analysis Toolkit (PSAT). The results show that the computational load of the optimization algorithm is greatly reduced for both parallel and series HEVs. This method could be further applied to investigate the optimized parameters for different driving cycles. In this paper, three driving cycles are applied: 2 NEDC, 4 NEDC, and 6 NEDC. The optimized parameters from each of these three driving cycles are used to calculate the fuel economy for all the three driving cycles. Comparing the average and the standard deviation of fuel economy, it is suggested that the parameters optimized from the driving cycle with longer distance provide a better result. This study could be further investigated on the relation between optimized parameters and characteristics of driving cycles to achieve an interactive control strategy parameter advising system in the future.