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Battery Electric Vehicle

The Experts below are selected from a list of 6594 Experts worldwide ranked by ideXlab platform

Andrew G. Alleyne – 1st expert on this subject based on the ideXlab platform

  • Integrated Modeling for Battery Electric Vehicle Transcritical Thermal Management System
    2018 Annual American Control Conference (ACC), 2018
    Co-Authors: Sarah G. Garrow, Christopher T. Aksland, Sunny Sharma, Andrew G. Alleyne

    Abstract:

    Dynamic modeling approaches are presented for a Battery Electric Vehicle (BEV) transcritical thermal management system. In BEVs thermal management comprises of both temperature regulation of the passenger cabin and the Battery pack. This work proposes that a single vapor compression system may provide efficient and effective means to manage the temperature constraints on both systems. However, the transcritical vapor compression system, Battery pack, and cabin are complex systems with coupled behavior among Electrical and thermal domains. Dynamic and scalable models provide valuable insight to the coupling among systems, and allow for rapid thermal management architecture and control design. This potential is demonstrated with a simulation comparison of an air-cooled Battery pack with recirculated and exhausted return air and examples of parameter variation analysis important for controller robustness.

  • ACC – Integrated Modeling for Battery Electric Vehicle Transcritical Thermal Management System
    2018 Annual American Control Conference (ACC), 2018
    Co-Authors: Sarah G. Garrow, Christopher T. Aksland, Sunny Sharma, Andrew G. Alleyne

    Abstract:

    Dynamic modeling approaches are presented for a Battery Electric Vehicle (BEV) transcritical thermal management system. In BEVs thermal management comprises of both temperature regulation of the passenger cabin and the Battery pack. This work proposes that a single vapor compression system may provide efficient and effective means to manage the temperature constraints on both systems. However, the transcritical vapor compression system, Battery pack, and cabin are complex systems with coupled behavior among Electrical and thermal domains. Dynamic and scalable models provide valuable insight to the coupling among systems, and allow for rapid thermal management architecture and control design. This potential is demonstrated with a simulation comparison of an air-cooled Battery pack with recirculated and exhausted return air and examples of parameter variation analysis important for controller robustness.

Sarah G. Garrow – 2nd expert on this subject based on the ideXlab platform

  • Integrated Modeling for Battery Electric Vehicle Transcritical Thermal Management System
    2018 Annual American Control Conference (ACC), 2018
    Co-Authors: Sarah G. Garrow, Christopher T. Aksland, Sunny Sharma, Andrew G. Alleyne

    Abstract:

    Dynamic modeling approaches are presented for a Battery Electric Vehicle (BEV) transcritical thermal management system. In BEVs thermal management comprises of both temperature regulation of the passenger cabin and the Battery pack. This work proposes that a single vapor compression system may provide efficient and effective means to manage the temperature constraints on both systems. However, the transcritical vapor compression system, Battery pack, and cabin are complex systems with coupled behavior among Electrical and thermal domains. Dynamic and scalable models provide valuable insight to the coupling among systems, and allow for rapid thermal management architecture and control design. This potential is demonstrated with a simulation comparison of an air-cooled Battery pack with recirculated and exhausted return air and examples of parameter variation analysis important for controller robustness.

  • ACC – Integrated Modeling for Battery Electric Vehicle Transcritical Thermal Management System
    2018 Annual American Control Conference (ACC), 2018
    Co-Authors: Sarah G. Garrow, Christopher T. Aksland, Sunny Sharma, Andrew G. Alleyne

    Abstract:

    Dynamic modeling approaches are presented for a Battery Electric Vehicle (BEV) transcritical thermal management system. In BEVs thermal management comprises of both temperature regulation of the passenger cabin and the Battery pack. This work proposes that a single vapor compression system may provide efficient and effective means to manage the temperature constraints on both systems. However, the transcritical vapor compression system, Battery pack, and cabin are complex systems with coupled behavior among Electrical and thermal domains. Dynamic and scalable models provide valuable insight to the coupling among systems, and allow for rapid thermal management architecture and control design. This potential is demonstrated with a simulation comparison of an air-cooled Battery pack with recirculated and exhausted return air and examples of parameter variation analysis important for controller robustness.

Takayuki Morikawa – 3rd expert on this subject based on the ideXlab platform

  • Charge Timing Choice Behavior of Battery Electric Vehicle Users
    , 2020
    Co-Authors: Toshiyuki Yamamoto, Takayuki Morikawa

    Abstract:

    This paper aims to examine choice behavior in respect of the time at which Battery Electric Vehicle users charge their Vehicles. The focus is on normal charging after the last trip of the day, and the alternatives presented are no charging, charging immediately after arrival, nighttime charging, and charging at other times. A mixed logit model with unobserved heterogeneity is applied to panel data extracted from a two-year field trial on Battery Electric Vehicle usage in Japan. Estimation results, obtained using separate models for commercial and private Vehicles, suggest that state of charge, interval in days before the next travel day, and Vehicle-miles to be traveled on the next travel day are the main predictors for whether a user charges the Vehicle or not, and that users tend to charge during the nighttime in the latter half of the trial. On the other hand, experience of fast charging has little or no effect on commercial users, while it prompts private users to charge immediately after arrival at home and to charge at nighttime. Further, the correlations between alternatives in the estimation results, especially the highly positive correlation between no charging and nighttime charging, reveal that it may be possible to encourage charging during off-peak hours to lessen the load on the Electricity grid. This finding is supported by the high standard deviation for the alternative of nighttime charging.

  • Fast-charging station choice behavior among Battery Electric Vehicle users
    Transportation Research Part D: Transport and Environment, 2016
    Co-Authors: Xiao Hui Sun, Toshiyuki Yamamoto, Takayuki Morikawa

    Abstract:

    This study explores how Battery Electric Vehicle users choose where to fast-charge their Vehicles from a set of charging stations, as well as the distance by which they are generally willing to detour for fast-charging. The focus is on fast-charging events during trips that include just one fast-charge between origin and destination in Kanagawa Prefecture, Japan. Mixed logit models with and without a threshold effect for detour distance are applied to panel data extracted from a two-year field trial on Battery Electric Vehicle usage in Japan. Findings from the mixed logit model with threshold show that private users are generally willing to detour up to about 1750 m on working days and 750 m on non-working days, while the distance is 500 m for commercial users on both working and non-working days. Users in general prefer to charge at stations requiring a shorter detour and use chargers located at gas stations, and are significantly affected by the remaining charge. Commercial users prefer to charge at stations encountered earlier along their paths, while only private users traveling on working days show such preference and they turn to prefer the stations encountered later when choosing a station in peak hours. Only private users traveling on working days show a strong preference for free charging. Commercial users tend to pay for charging at a station within 500 m detour distance. The fast charging station choice behavior is heterogeneous among users. These findings provide a basis for early planning of a public fast charging infrastructure.

  • Charge timing choice behavior of Battery Electric Vehicle users
    Transportation Research Part D: Transport and Environment, 2015
    Co-Authors: Xiao Hui Sun, Toshiyuki Yamamoto, Takayuki Morikawa

    Abstract:

    This paper aims to examine choice behavior in respect of the time at which Battery Electric Vehicle users charge their Vehicles. The focus is on normal charging after the last trip of the day, and the alternatives presented are no charging, charging immediately after arrival, nighttime charging, and charging at other times. A mixed logit model with unobserved heterogeneity is applied to panel data extracted from a two-year field trial on Battery Electric Vehicle usage in Japan. Estimation results, obtained using separate models for commercial and private Vehicles, suggest that state of charge, interval in days before the next travel day, and Vehicle-kilometers to be traveled on the next travel day are the main predictors for whether a user charges the Vehicle or not, that the experience of fast charging negatively affects normal charging, and that users tend to charge during the nighttime in the latter half of the trial. On the other hand, the probability of normal charging after the last trip of a working day is increased for commercial Vehicles, while is decreased for private Vehicles. Commercial Vehicles tend not to be charged when they arrival during the nighttime, while private Vehicles tend to be charged immediately. Further, the correlations of nighttime charging with charging immediately and charging at other times reveal that it may be possible to encourage charging during off-peak hours to lessen the load on the Electricity grid. This finding is supported by the high variance for the alternative of nighttime charging.