{"title":"Rule-Based Energy Management Strategy of Fuel Cell/Ultracapacitor/Battery Vehicles: winner of the IEEE VTS Motor Vehicles Challenge 2020","authors":"A. Ferrara, C. Hametner","doi":"10.1109/VPPC49601.2020.9330930","DOIUrl":null,"url":null,"abstract":"This paper focuses on the energy management of fuel cell/ultracapacitor/battery hybrid vehicles. A robust rule-based strategy is proposed to effectively reduce hydrogen consumption, increase vehicle lifetime, and handle multiple constraints. This strategy won the IEEE VTS Motor Vehicles Challenge 2020. The formulation of the control rules is heavily based on the vehicle model and the analysis of the assigned cost function. A stochastic generation of driving scenarios is proposed to deal with the limited information provided by the challenge, guarantying a robust design of the energy management strategy. The results are analyzed on a large set of synthetic driving cycles.","PeriodicalId":6851,"journal":{"name":"2020 IEEE Vehicle Power and Propulsion Conference (VPPC)","volume":"80 1","pages":"1-7"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Vehicle Power and Propulsion Conference (VPPC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VPPC49601.2020.9330930","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
This paper focuses on the energy management of fuel cell/ultracapacitor/battery hybrid vehicles. A robust rule-based strategy is proposed to effectively reduce hydrogen consumption, increase vehicle lifetime, and handle multiple constraints. This strategy won the IEEE VTS Motor Vehicles Challenge 2020. The formulation of the control rules is heavily based on the vehicle model and the analysis of the assigned cost function. A stochastic generation of driving scenarios is proposed to deal with the limited information provided by the challenge, guarantying a robust design of the energy management strategy. The results are analyzed on a large set of synthetic driving cycles.