Sidharth Jangra, C. Chung, Qingzhi Lai, Xinfan Lin
{"title":"Optimal Maintenance of Electric Vehicle Battery System Through Overnight Home Charging","authors":"Sidharth Jangra, C. Chung, Qingzhi Lai, Xinfan Lin","doi":"10.1115/dscc2019-9004","DOIUrl":null,"url":null,"abstract":"\n Plug-in electric vehicle (PEV) is emerging as one of the most viable choices for the transportation sector to reduce fossil fuel consumption and CO2 emission. As the most critical component of PEV, battery systems require intensive management and diagnostics to ensure safety, efficiency, and endurance. Most existing studies focus on battery management when PEVs are under operation while none has explored battery maintenance during overnight parking, which accounts for a majority of the time (> 12 hours per day). Battery states during this period significantly affect the lifetime due to the side reactions that occur even when the battery is not in use. The process occurs at an accelerated rate when the battery energy level or temperature is too high or too low. In this paper, we propose to utilize an existing infrastructure available to PEV owners, the home charging unit, for intelligent battery maintenance during overnight parking. We will design an optimal charging profile that would charge the battery to a specific level while maintaining its states at optimal conditions to minimize degradation due to side reactions over the whole overnight period. Optimal charging profiles are created for different ambient temperatures and at different stages of battery life to investigate various scenarios. To demonstrate the effectiveness of the designed optimal charging profiles, the total capacity loss overnight is compared with those of three standard charging profiles.","PeriodicalId":41412,"journal":{"name":"Mechatronic Systems and Control","volume":null,"pages":null},"PeriodicalIF":1.0000,"publicationDate":"2019-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mechatronic Systems and Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/dscc2019-9004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 2
Abstract
Plug-in electric vehicle (PEV) is emerging as one of the most viable choices for the transportation sector to reduce fossil fuel consumption and CO2 emission. As the most critical component of PEV, battery systems require intensive management and diagnostics to ensure safety, efficiency, and endurance. Most existing studies focus on battery management when PEVs are under operation while none has explored battery maintenance during overnight parking, which accounts for a majority of the time (> 12 hours per day). Battery states during this period significantly affect the lifetime due to the side reactions that occur even when the battery is not in use. The process occurs at an accelerated rate when the battery energy level or temperature is too high or too low. In this paper, we propose to utilize an existing infrastructure available to PEV owners, the home charging unit, for intelligent battery maintenance during overnight parking. We will design an optimal charging profile that would charge the battery to a specific level while maintaining its states at optimal conditions to minimize degradation due to side reactions over the whole overnight period. Optimal charging profiles are created for different ambient temperatures and at different stages of battery life to investigate various scenarios. To demonstrate the effectiveness of the designed optimal charging profiles, the total capacity loss overnight is compared with those of three standard charging profiles.
期刊介绍:
This international journal publishes both theoretical and application-oriented papers on various aspects of mechatronic systems, modelling, design, conventional and intelligent control, and intelligent systems. Application areas of mechatronics may include robotics, transportation, energy systems, manufacturing, sensors, actuators, and automation. Techniques of artificial intelligence may include soft computing (fuzzy logic, neural networks, genetic algorithms/evolutionary computing, probabilistic methods, etc.). Techniques may cover frequency and time domains, linear and nonlinear systems, and deterministic and stochastic processes. Hybrid techniques of mechatronics that combine conventional and intelligent methods are also included. First published in 1972, this journal originated with an emphasis on conventional control systems and computer-based applications. Subsequently, with rapid advances in the field and in view of the widespread interest and application of soft computing in control systems, this latter aspect was integrated into the journal. Now the area of mechatronics is included as the main focus. A unique feature of the journal is its pioneering role in bridging the gap between conventional systems and intelligent systems, with an equal emphasis on theory and practical applications, including system modelling, design and instrumentation. It appears four times per year.