Integrated thermal and energy management systems using particle swarm optimization for energy optimization in electric vehicles

In this study, a three-variable control system with an energy management system (EMS) and a thermal management system (TMS) of a fuel cell/battery electric vehicle (EV) was developed using particle swarm optimization (PSO). The objectives are to enhance the temperature stability, decrease the temperature rise time, while reducing total energy consumption of dual energy sources. The control strategies for TMS and EMS were developed and modeled using a PSO, incorporating five inputs and three outputs. Previous experimental data were input for the model. The results demonstrate that, compared to the rule-based (RB) control strategies applied to both EMS and TMS under the NEDC and WLTP cycles, the PSO control strategies applied to both EMS and TMS led to energy consumption improvements of 12.33 % and 24.19 %. With EM RB /TM RB is the baseline, the temperature rise-time improvements for EM RB /TM PSO were 11.55 % and 1.94 %, and the average temperature errors improvements were 80.73 % and 81.12 %. When EM PSO /TM RB is the baseline, the temperature rise-time improvements for EM PSO /TM PSO were 10.56 % and 20.82 %, while the average temperature error improvements were 32.21 % and 21.30 %. In future work, the developed TMS and EMS will be applied to real vehicles for benefit verification. • Particle Swarm Optimization was utilized to optimize EMS and TMS in EVs. • Design of a new control-oriented fuel cell/battery vehicle and thermal dynamic model and simulator. • Integrating EMS and TMS for optimal energy management and temperature control in EVs. • Simulation for four control cases under two driving cycles. • Maximum 20+% temperature rising, 81+% temperature stability and 24+% energy consumption improvements have been achieved.