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Prediction of Hybrid Electric Bus Speed Using Deep Learning Method
Technical Paper
2020-01-1187
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
The recent development pace of the automotive technology is so rapid worldwide. Especially in a green car, hybrid electric vehicles (HEVs) have been studied a lot due to their significant effects on the urban driving. In the vehicle energy management strategy study, the driving speed is assumed to be known in advance, however the speed is not given in a real world. Accordingly, the prediction of vehicle speed is very important. In this study, we study the prediction methodology for the speed prediction using deep learning. Based on the vehicle driving speed data, the supervised deep learning has been used and the speed prediction accuracy using deep learning shows accurate results comparing to the actual speed. The supervised deep learning is used which is suitable for driving cycle database. As a result, the speed prediction after few seconds is feasible.
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Authors
- Giyeon Hwang - Myongji University
- Jongmyung Kim - Myongji University
- Huy Nguyen - Myongji University
- Yeongha Hwang - Myongji University
- Minjae Kim - Myongji University
- Kyoungdoug Min - Seoul National University
- Jihwan Park - Seoul National University
- Seunghyup Shin - Seoul National University
- Sangyul Lee - Hoseo University
Citation
Hwang, G., Lee, S., Min, K., Park, J. et al., "Prediction of Hybrid Electric Bus Speed Using Deep Learning Method," SAE Technical Paper 2020-01-1187, 2020, https://doi.org/10.4271/2020-01-1187.Also In
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