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Design and Analysis of Distributed Energy Management Systems pp 27–45Cite as

Probabilistic Model and Prediction of Vehicle Daily Use

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Part of the book series: Power Electronics and Power Systems ((PEPS))

Abstract

Electric vehicles (EVs) are expected to work, not only as transportation means, but also as power storage units in energy management systems (EMS) given their high capacity batteries. To utilize the in-vehicle battery in an EMS, considering the acceptance by the users, the EMS should be able to identify when the vehicle is being driven and when it is parked, which represents the profile of departure and travel time. This chapter presents a method to predict the most probable car use profile over 1 day, based on statistics of the customer’s daily car use. The prediction problem is formulated as a maximum-likelihood estimation problem and the usefulness of the proposed method is evaluated by numerical experiments.

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Acknowledgement

This research was supported by JST CREST Grant NumberJPMJCR15K3.

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Authors and Affiliations

  1. Department of Mechanical Systems Engineering, Nagoya University, Nagoya, Aichi, Japan

    Shinkichi Inagaki & Tatsuya Suzuki

Authors
  1. Shinkichi Inagaki
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  2. Tatsuya Suzuki
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Corresponding author

Correspondence to Shinkichi Inagaki .

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Editors and Affiliations

  1. Graduate School of Engineering, Nagoya University, Nagoya, Aichi, Japan

    Tatsuya Suzuki

  2. Graduate School of Engineering, Nagoya University, Nagoya, Aichi, Japan

    Shinkichi Inagaki

  3. Graduate School of Engineering, Osaka Prefecture University, Sakai, Osaka, Japan

    Yoshihiko Susuki

  4. Graduate School of Engineering, Nagoya University, Nagoya, Aichi, Japan

    Anh Tuan Tran

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Inagaki, S., Suzuki, T. (2020). Probabilistic Model and Prediction of Vehicle Daily Use. In: Suzuki, T., Inagaki, S., Susuki, Y., Tran, A. (eds) Design and Analysis of Distributed Energy Management Systems. Power Electronics and Power Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-33672-1_2

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  • DOI: https://doi.org/10.1007/978-3-030-33672-1_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33671-4

  • Online ISBN: 978-3-030-33672-1

  • eBook Packages: EngineeringEngineering (R0)

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