Operating Point Optimization of Auxiliary Power Unit Using Adaptive Multi-Objective Differential Evolution Algorithm

Abstract

Series hybrid electric vehicles improvements in fuel consumption and emissions directly depend on the operating point of the auxiliary power unit (APU). In order to balance the conflicting goals of fuel consumption and emissions reduction in the choice of operating point, the APU operating point optimization problem is formulated as a constrained multi-objective optimization problem with competing objectives of fuel-electricity conversion cost, HC emissions, CO emissions, and NOx emissions first. Then, the adaptive multi-objective differential evolution (AMODE) algorithm is proposed to solve the APU operating point multi-objective optimization problem. It adopts an external elitist archive to store the nondominated solutions that are found during the evolutionary process. The innovative adaptive mutation operator of the AMODE ensures its high searching ability, and the adaptive grids mechanism improves the diversity of the resulted Pareto solutions effectively. Finally, bench experiments under four typical driving cycles are performed and comparisons are made between the results of the proposed multi-objective optimization approach (MOA), the exponential weighted approach, and the traditional single objective approach. The experimental results show that the proposed AMODE-based MOA improves the APU emissions significantly, at the expense of a slight drop in fuel efficiency.