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

IF 6.4 2区工程技术 Q1 THERMODYNAMICS

Case Studies in Thermal Engineering Pub Date : 2025-04-19 DOI:10.1016/j.csite.2025.106136

Yu-Hsuan Lin, Yi-Hsuan Hung

{"title":"Integrated thermal and energy management systems using particle swarm optimization for energy optimization in electric vehicles","authors":"Yu-Hsuan Lin, Yi-Hsuan Hung","doi":"10.1016/j.csite.2025.106136","DOIUrl":null,"url":null,"abstract":"<div><div>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<sub>RB</sub>/TM<sub>RB</sub> is the baseline, the temperature rise-time improvements for EM<sub>RB</sub>/TM<sub>PSO</sub> were 11.55 % and 1.94 %, and the average temperature errors improvements were 80.73 % and 81.12 %. When EM<sub>PSO</sub>/TM<sub>RB</sub> is the baseline, the temperature rise-time improvements for EM<sub>PSO</sub>/TM<sub>PSO</sub> 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.</div></div>","PeriodicalId":9658,"journal":{"name":"Case Studies in Thermal Engineering","volume":"71 ","pages":"Article 106136"},"PeriodicalIF":6.4000,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Case Studies in Thermal Engineering","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214157X2500396X","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"THERMODYNAMICS","Score":null,"Total":0}

引用次数: 0

Abstract

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.

查看原文本刊更多论文

基于粒子群优化的电动汽车热能与能量集成管理系统

本文采用粒子群优化（PSO）技术，构建了燃料电池/纯电动汽车（EV）能量管理系统（EMS）和热管理系统（TMS）的三变量控制系统。目的是提高温度稳定性，缩短温升时间，同时降低双能源的总能耗。TMS和EMS的控制策略被开发并使用PSO建模，包括5个输入和3个输出。模型输入之前的实验数据。结果表明，与NEDC和WLTP工况下EMS和TMS均采用基于规则的（RB）控制策略相比，EMS和TMS均采用PSO控制策略，能耗分别提高12.33%和24.19%。以EMRB/TMRB为基准，EMRB/TMPSO的温升时间改善率分别为11.55%和1.94%，平均温度误差改善率分别为80.73%和81.12%。以EMPSO/TMRB为基准时，EMPSO/TMPSO的温升时间改善率分别为10.56%和20.82%，平均温度误差改善率分别为32.21%和21.30%。在未来的工作中，开发的TMS和EMS将应用于实际车辆进行效益验证。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Case Studies in Thermal Engineering Chemical Engineering-Fluid Flow and Transfer Processes

CiteScore

8.60

自引率

11.80%

发文量

812

审稿时长

76 days

期刊介绍： Case Studies in Thermal Engineering provides a forum for the rapid publication of short, structured Case Studies in Thermal Engineering and related Short Communications. It provides an essential compendium of case studies for researchers and practitioners in the field of thermal engineering and others who are interested in aspects of thermal engineering cases that could affect other engineering processes. The journal not only publishes new and novel case studies, but also provides a forum for the publication of high quality descriptions of classic thermal engineering problems. The scope of the journal includes case studies of thermal engineering problems in components, devices and systems using existing experimental and numerical techniques in the areas of mechanical, aerospace, chemical, medical, thermal management for electronics, heat exchangers, regeneration, solar thermal energy, thermal storage, building energy conservation, and power generation. Case studies of thermal problems in other areas will also be considered.