Electrothermal Dynamics-Conscious Many-Objective Modular Design for Power-Split Plug-in Hybrid Electric Vehicles

This article proposes an improved modular design methodology of a power-split plug-in hybrid electric vehicle (PHEV) that introduces advanced electrothermal coupled model and temperature-related subobjective to simultaneously reveal battery thermal electrical dynamics in the design. Considering provide customers with more optimal configuration solutions, Pareto-augmented collaborative optimization (PACO) scheme is designed integrates three benchmarking many-objective evolutionary algorithms (MOEAs) expand distribution approximated Pareto frontier composed best solution set. Two realistic worldwide harmonized light vehicles test cycles are separately reproduced by two trained drivers on chassis dynamometer robustness optimized system. The simulation results demonstrate MOEA based decomposition (MOEA/D) PACO main contributor for PHEV because it lessens generational distance at least 2.7% enlarges hypervolume 17.6%, compared elitist nondominated sorting genetic algorithm strength algorithm. In adaptation different user types, system can regulate cell temperatures ( $\mathbf{27}{{\bf .5}} - \mathbf{38}{{\bf .}}{\mathbf{3}^ \circ }\mathrm{C}$ ) all types within safe efficient working zone notation="LaTeX">$\mathbf{0} \mathbf{5}{\mathbf{5}^ ).