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5 June 2024 Predictive energy management of fuel cell vehicle based on global driving condition construction
Heyang Ma, Biao Liang, Chao Sun, Jinlong Wu
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Proceedings Volume 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024); 1316320 (2024) https://doi.org/10.1117/12.3030631
Event: International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 2024, Xi'an, China
Abstract
Combined with the current development trend of the Internet of Vehicles, this paper proposes a predictive energy management strategy based on working conditions for plug-in fuel cell vehicles. First, relying on the micro-traffic simulation platform, based on the use of neural networks to predict changes in road traffic in the future, a method for constructing global operating conditions is proposed. Combining vehicle speed conditions with predictive energy management strategies, introducing final value constraints in the control time domain into model predictive control (MPC), which improves the energy-saving effect of real-time control strategies from a global perspective. The simulation results show that the comprehensive energy-saving effect of the proposed fuel cell vehicle predicted energy management strategy based on working conditions can reach 93.99% of the theoretical optimal control effect.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Heyang Ma, Biao Liang, Chao Sun, and Jinlong Wu "Predictive energy management of fuel cell vehicle based on global driving condition construction", Proc. SPIE 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 1316320 (5 June 2024); https://doi.org/10.1117/12.3030631
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KEYWORDS
Power consumption
Neural networks
Computer programming
Mathematical optimization
Hybrid vehicles
Hydrogen fuel cells
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