Improved short-term speed prediction using spatiotemporal-vision-based deep neural network for intelligent fuel cell vehicles

• Main Authors: Zhang, Yuanzhi, Huang, Zhiyu, Zhang, Caizhi, Lv, Chen, Deng, Chenghao, Hao, Dong, Chen, Jinrui, Ran, Hongxu
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Mechanical engineering; Neural Networks; Lithium-Ion Batteries
• Online Access: https://hdl.handle.net/10356/160281
• Institution: Nanyang Technological University

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.