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Riding pattern identification by machine learning for electric motorcycles
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Faraji Niri, Mona, Dinh, Quang Truong and Marco, James (2022) Riding pattern identification by machine learning for electric motorcycles. In: 24th International Conference on Mechatronics Technology – ICMT 2021, Singapore (Virtual Conference), 18-22 Dec 2021. Published in: 2021 24th International Conference on Mechatronics Technology (ICMT) ISBN 9781665424592. doi:10.1109/ICMT53429.2021.9687179
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WRAP-Riding-pattern-identification-machine-learning-electric-motorcycles-2022.pdf - Accepted Version - Requires a PDF viewer. Download (677Kb) | Preview |
Official URL: https://doi.org/10.1109/ICMT53429.2021.9687179
Abstract
Identification of riding patterns is one of the key enablers to update energy consumption strategy, optimise the energy management system and increase the range of electric motorcycles despite their weight and space limits. Considering the varying driving conditions in real applications, improving accuracy of the riding pattern recognition without significant complexity is the main challenge. In this paper a simple and efficient online classification method is introduced based on features extracted only from the motorcycle speed. The recognition mechanism is firstly developed using support vector machine technique. The effect of validation method for removing the optimism in classification and the contribution of features to the accuracy of model is then investigated. Evaluation of the method on the real riding conditions in simulation environment shows the effectiveness of the approach.
| Item Type: | Conference Item (Paper) | ||||||
|---|---|---|---|---|---|---|---|
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TL Motor vehicles. Aeronautics. Astronautics |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||
| Journal or Publication Title: | 2021 24th International Conference on Mechatronics Technology (ICMT) | ||||||
| Publisher: | IEEE | ||||||
| ISBN: | 9781665424592 | ||||||
| Official Date: | 1 February 2022 | ||||||
| Dates: |
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| DOI: | 10.1109/ICMT53429.2021.9687179 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Reuse Statement (publisher, data, author rights): | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
| Date of first compliant deposit: | 5 January 2022 | ||||||
| Date of first compliant Open Access: | 5 January 2022 | ||||||
| Conference Paper Type: | Paper | ||||||
| Title of Event: | 24th International Conference on Mechatronics Technology – ICMT 2021 | ||||||
| Type of Event: | Conference | ||||||
| Location of Event: | Singapore (Virtual Conference) | ||||||
| Date(s) of Event: | 18-22 Dec 2021 | ||||||
| Related URLs: |
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