{"title":"Bi-Level Optimization Framework for Heavy-Duty Electric Truck Charging Station Design","authors":"Derek Jackson, Yue Cao, I. Beil","doi":"10.1109/ITEC53557.2022.9813815","DOIUrl":null,"url":null,"abstract":"Heavy-duty commercial electric vehicle (HDEV) charging stations, such as for freight trucks, must handle large peak power demands. Installing on-site energy storage can reduce the peak charging demand to avoid expensive and oversized utility-managed distribution equipment. To ensure optimal design of charging infrastructure, the trade-off between energy storage size and grid equipment ratings should be considered. This paper presents a bi-level multi-objective optimization framework to discover Pareto optimal designs, under the constraint of optimally sized power electronic converters and realistic power loss models. Under these considerations, the bi-level approach can greatly simplify the design process by breaking up charging station optimization into a system-level problem and multiple converter-level problems. Using industry-based HDEV arrival times and charging conditions, this bi-level approach is demonstrated for a 9port charging station. The resulting Pareto front showcases equipment sizing trade-offs that are necessary for informed charging infrastructure development decisions. The bi-level optimization Pareto front is compared the Pareto fronts of traditional, fixed efficiency converter models.","PeriodicalId":275570,"journal":{"name":"2022 IEEE Transportation Electrification Conference & Expo (ITEC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE Transportation Electrification Conference & Expo (ITEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITEC53557.2022.9813815","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Heavy-duty commercial electric vehicle (HDEV) charging stations, such as for freight trucks, must handle large peak power demands. Installing on-site energy storage can reduce the peak charging demand to avoid expensive and oversized utility-managed distribution equipment. To ensure optimal design of charging infrastructure, the trade-off between energy storage size and grid equipment ratings should be considered. This paper presents a bi-level multi-objective optimization framework to discover Pareto optimal designs, under the constraint of optimally sized power electronic converters and realistic power loss models. Under these considerations, the bi-level approach can greatly simplify the design process by breaking up charging station optimization into a system-level problem and multiple converter-level problems. Using industry-based HDEV arrival times and charging conditions, this bi-level approach is demonstrated for a 9port charging station. The resulting Pareto front showcases equipment sizing trade-offs that are necessary for informed charging infrastructure development decisions. The bi-level optimization Pareto front is compared the Pareto fronts of traditional, fixed efficiency converter models.