Vehicle Fleet Prediction for V2G System - Based on Left to Right Markov Model

Osamu Shimizu, Akihiko Kawashima, Shinkichi Inagaki, Tatsuya Suzuki

2018

Abstract

The regulations for internal combustion vehicles, CO2 or NOx emission or noise and so on, are strengthened. Therefore EV (electric vehicle)'s market is expanding. The amount of EV get more, the amount of electric get more and the impact for grid that are voltage fluctuation and frequency fluctuation is concerned. V2G (Vehicle to Grid) can solve this problem, but it has a constraint that EV’s battery can be used during it parked. So as the basic technology, the prediction the vehicles’ state that is driving or parked is important. In this research, machine learning algorithm for predicting vehicle fleet's states is developed. The data for study and test is obtained by person-trip survey. The algorithm is based on left to right Markov-model. The states are stay or drive from an area to an area. Future state probability is predicted using the latest observed state and state transition probability. As the result, the prediction error of stay is less than the prediction error of drive. Therefore study data and test data are separated into sunny day and rainy day, the prediction error becomes less.

Paper Citation

in Harvard Style

Shimizu O., Kawashima A., Inagaki S. and Suzuki T. (2018). Vehicle Fleet Prediction for V2G System - Based on Left to Right Markov Model.In Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-293-6, pages 417-422. DOI: 10.5220/0006762604170422

in Bibtex Style

@conference{vehits18,
author={Osamu Shimizu and Akihiko Kawashima and Shinkichi Inagaki and Tatsuya Suzuki},
title={Vehicle Fleet Prediction for V2G System - Based on Left to Right Markov Model},
booktitle={Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2018},
pages={417-422},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006762604170422},
isbn={978-989-758-293-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Vehicle Fleet Prediction for V2G System - Based on Left to Right Markov Model
SN - 978-989-758-293-6
AU - Shimizu O.
AU - Kawashima A.
AU - Inagaki S.
AU - Suzuki T.
PY - 2018
SP - 417
EP - 422
DO - 10.5220/0006762604170422