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 temper...
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Elsevier
2025-07-01
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| Series: | Case Studies in Thermal Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X2500396X |
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| author | Yu-Hsuan Lin Yi-Hsuan Hung |
| author_facet | Yu-Hsuan Lin Yi-Hsuan Hung |
| author_sort | Yu-Hsuan Lin |
| collection | DOAJ |
| description | 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 EMRB/TMRB is the baseline, the temperature rise-time improvements for EMRB/TMPSO were 11.55 % and 1.94 %, and the average temperature errors improvements were 80.73 % and 81.12 %. When EMPSO/TMRB is the baseline, the temperature rise-time improvements for EMPSO/TMPSO 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. |
| format | Article |
| id | doaj-art-1a52700b7db043faa14d51e4fdb041d1 |
| institution | OA Journals |
| issn | 2214-157X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj-art-1a52700b7db043faa14d51e4fdb041d12025-08-20T02:01:39ZengElsevierCase Studies in Thermal Engineering2214-157X2025-07-017110613610.1016/j.csite.2025.106136Integrated thermal and energy management systems using particle swarm optimization for energy optimization in electric vehiclesYu-Hsuan Lin0Yi-Hsuan Hung1Undergraduate Program of Vehicle and Energy Engineering, National Taiwan Normal University, Taipei City, 106, TaiwanCorresponding author. Undergraduate Program of Vehicle and Energy Engineering at National Taiwan Normal University, 162, Section 1, Heping E. Rd., Taipei City, 106, Taiwan.; Undergraduate Program of Vehicle and Energy Engineering, National Taiwan Normal University, Taipei City, 106, TaiwanIn 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 EMRB/TMRB is the baseline, the temperature rise-time improvements for EMRB/TMPSO were 11.55 % and 1.94 %, and the average temperature errors improvements were 80.73 % and 81.12 %. When EMPSO/TMRB is the baseline, the temperature rise-time improvements for EMPSO/TMPSO 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.http://www.sciencedirect.com/science/article/pii/S2214157X2500396XThermal management systemParticle swarm optimizationFuel cellBatteryElectric vehicleEnergy management system |
| spellingShingle | Yu-Hsuan Lin Yi-Hsuan Hung Integrated thermal and energy management systems using particle swarm optimization for energy optimization in electric vehicles Case Studies in Thermal Engineering Thermal management system Particle swarm optimization Fuel cell Battery Electric vehicle Energy management system |
| title | Integrated thermal and energy management systems using particle swarm optimization for energy optimization in electric vehicles |
| title_full | Integrated thermal and energy management systems using particle swarm optimization for energy optimization in electric vehicles |
| title_fullStr | Integrated thermal and energy management systems using particle swarm optimization for energy optimization in electric vehicles |
| title_full_unstemmed | Integrated thermal and energy management systems using particle swarm optimization for energy optimization in electric vehicles |
| title_short | Integrated thermal and energy management systems using particle swarm optimization for energy optimization in electric vehicles |
| title_sort | integrated thermal and energy management systems using particle swarm optimization for energy optimization in electric vehicles |
| topic | Thermal management system Particle swarm optimization Fuel cell Battery Electric vehicle Energy management system |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X2500396X |
| work_keys_str_mv | AT yuhsuanlin integratedthermalandenergymanagementsystemsusingparticleswarmoptimizationforenergyoptimizationinelectricvehicles AT yihsuanhung integratedthermalandenergymanagementsystemsusingparticleswarmoptimizationforenergyoptimizationinelectricvehicles |