Results in Engineering (Mar 2024)
A thermal management control using particle swarm optimization for hybrid electric energy system of electric vehicles
Abstract
A metaheuristic algorithm, Particle Swarm Optimization (PSO), was employed for developing the optimal control strategies for an innovative hybrid thermal management system (IHTMS) in a proton exchange membrane fuel cell (PEMFC)/battery electric vehicles (EVs). The goals were to shorten the period of low-efficiency temperatures during the initial startup of EVs, and to maintain temperatures of PEMFCs and batteries at their optimal-efficiency zones, where significantly enhances the traveling range and power output of EVs. Prior to simulation for benefit analysis, eight IHTMS subsystems were mathematically constructed. For the multi-input-multi-output PSO control strategy, two inputs were the fuel cell and battery coolant temperatures; while two outputs were the coolant mass flow rate and the flow rate ratio between two energy sources. A rule-based (RB) control strategy for four actuators was designed as the baseline case. Another RB using the PSO to derive the initial conditions (PSOi) was developed as well. In this research, the IHTMS was tested under two driving patterns, WLTP and NEDC, where outstanding thermal management performance was exhibited. The results demonstrate that: in WLTP driving cycle, to compare PSO and PSOi-RB with the RB strategies, the rise time of optimal temperature decreased 13.655 % and 9.505 % for the PEMFC; while 8.77 % and 4.385 % for the battery. For the NEDC driving cycle, the rise time of optimal temperature decreased 8.908 % and 7.318 % for the PEMFC, while 5.226 % and 3.136 % for the battery. The improvements of average temperature errors of the PEMFC were 19.759 % and 11.023 %; the improvements of the average temperature errors of the battery were 57.027 % and 3.67 %. For NEDC driving cycle, the improvements of average temperature errors of the PEMFC were 18.879 % and 9.551 %; the improvements of the average temperature errors of the battery were 29.144 % and 20.221 %. In the future work, the IHTMS will be integrated to a hybrid-energy EV for experimental verification.
