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.

Auxiliary power units (APUs) are widely used for electric power generation in various types of electric vehicles, improvements in fuel economy and emissions of these vehicles directly depend on the operating point of the APUs. In order to balance the conflicting goals of fuel consumption and emissions reduction in the process of operating point choice, the APU operating point optimization problem is formulated as a constrained multi-objective optimization problem (CMOP) firstly. The four competing objectives of this CMOP are fuel-electricity conversion cost, hydrocarbon (HC) emissions, carbon monoxide (CO) emissions and nitric oxide (NO x ) emissions. Then, the multi-objective particle swarm optimization (MOPSO) algorithm and weighted metric decision making method are employed to solve the APU operating point multi-objective optimization model. Finally, bench experiments under New European driving cycle (NEDC), Federal test procedure (FTP) and high way fuel economy test (HWFET) driving cycles show that, compared with the results of the traditional fuel consumption single-objective optimization approach, the proposed multi-objective optimization approach shows significant improvements in emissions performance, at the expense of a slight drop in fuel efficiency.