Operating Point Optimization of Auxiliary Power Unit Based on Dynamic Combined Cost Map and Particle Swarm Optimization

Abstract

Series hybrid electric vehicles improvements in fuel consumption and emissions directly depend on the operating point of the auxiliary power unit (APU). A new APU operating point optimization approach based on dynamic combined cost map (DCM) and particle swarm optimization (PSO) is presented in this paper. The influence of coolant temperature, catalyst temperature, and air/fuel (A/F) ratio on fuel consumption characteristics and HC, CO, NOx emission characteristics are quantitatively analyzed first. Then, the DCM is derived by combining the individual cost maps with predefined weighting factors, so as to balance the potentially conflicting goals of fuel consumption and emissions reduction in the choice of operating point. The PSO is utilized to search the optimum APU operating point in the DCM. Finally, bench experiments under three typical driving cycles show that, compared with the results of the traditional static steady-state fuel consumption map-based APU operating point optimization approach, the proposed DCM and PSO-based approach shows significant improvements in emission performance, at the expense of a slight drop in fuel efficiency.