Simultaneous multi-objective optimization of a PHEV power management system and component sizing in real world traffic condition

Due to emission concerns as well as fuel shortages and expenses, plug-in hybrid electric vehicles (PHEVs) have become public and successful in the market. The performance of such vehicles can be mainly associated with energy management, powertrain system component sizes, and costs which are considered as the most effective factors improving fuel economy and reducing emissions. Since there is an interaction between the performances of these sub-systems, simultaneous optimization of control strategy and component sizing were developed in the presence of multi-objective optimization such as fuel consumption, emissions, as well as operating costs based on a genetic algorithm. For this purpose, a multi input fuzzy logic controller (MFLC) was designed at the first step for energy management system with regard to energy efficiency and batteries performance. Then, a novel simultaneous multi-objective constrained optimization approach was implemented to enhance optimally various coupling design parameters (optimization variables), conflicting design objectives (fuel economy, costs, and emissions), as well as non-linear constraints (vehicle dynamic and batteries performance). Accordingly, the simulation results showed that the proposed instantaneous optimization method was sufficient for improving fuel economy despite increases in the optimization variables and time taking multiple objectives and constraints into account. Besides, the results demonstrated that the designed multi-objective simultaneous optimization algorithm could respectively improve overall energy efficiency and emission reduction of the PHEV up to 7% and 10% for real-world driving cycle and also decreases the operational costs up to 12% approving its applicability. In the optimization process the batteries performance and vehicle dynamic were observed as the constraints to enhance the batteries safety and driver required performance. Finally, the sensitivity and the robustness of the proposed algorithm were verified through the variation of vehicle parameters as well as road and traffic conditions. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

Research has shown that using a multi-objective optimization approach can effectively improve the energy efficiency, reduce emissions, and lower operational costs of plug-in hybrid electric vehicles.