Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/160281
Title: Improved short-term speed prediction using spatiotemporal-vision-based deep neural network for intelligent fuel cell vehicles
Authors: Zhang, Yuanzhi
Huang, Zhiyu
Zhang, Caizhi
Lv, Chen
Deng, Chenghao
Hao, Dong
Chen, Jinrui
Ran, Hongxu
Keywords: Engineering::Mechanical engineering
Issue Date: 2020
Source: Zhang, Y., Huang, Z., Zhang, C., Lv, C., Deng, C., Hao, D., Chen, J. & Ran, H. (2020). Improved short-term speed prediction using spatiotemporal-vision-based deep neural network for intelligent fuel cell vehicles. IEEE Transactions On Industrial Informatics, 17(9), 6004-6013. https://dx.doi.org/10.1109/TII.2020.3033980
Journal: IEEE Transactions on Industrial Informatics
Abstract: In this article, an improved short-term speed prediction method is proposed to predict short-term future speed and analyze future energy consumption of intelligent fuel cell vehicles. The short-term future speed is predicted by the proposed Inflated 3-D Inception long short-term memory (LSTM) network, which takes the spatiotemporal-vision information and vehicle motion states. Specifically, the spatiotemporal-vision-based deep neural network utilizes image sequences captured by a front-facing camera as environmental information and historical speed series as motion information to improve the prediction accuracy. Then, a case study of the proposed speed prediction method, with rule-based energy management strategy to calculate future energy consumption, is presented. The simulation results show that short-term speed prediction based on the Inflated 3-D Inception LSTM network can achieve high accuracy of speed prediction in various traffic densities, as well as low prediction errors of future energy consumption including the hydrogen consumption and state-of-charge attenuation.
URI: https://hdl.handle.net/10356/160281
ISSN: 1551-3203
DOI: 10.1109/TII.2020.3033980
Schools: School of Mechanical and Aerospace Engineering 
Rights: © 2020 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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