A Real-Time Load Prediction Control for Fuel Cell Hybrid Vehicle

Jun Fu, Linghong Zeng, Jingzhi Lei, Zhonghua Deng, Xiaowei Fu, Xi Li and Yan Wang
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Jun Fu: Key Laboratory of Image Processing and Intelligent Control of Education Ministry, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Linghong Zeng: Key Laboratory of Image Processing and Intelligent Control of Education Ministry, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Jingzhi Lei: Key Laboratory of Image Processing and Intelligent Control of Education Ministry, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Zhonghua Deng: Key Laboratory of Image Processing and Intelligent Control of Education Ministry, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Xiaowei Fu: Key Laboratory of Image Processing and Intelligent Control of Education Ministry, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Xi Li: Key Laboratory of Image Processing and Intelligent Control of Education Ministry, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Yan Wang: School of Electronic and Information Engineering, Jingchu University of Technology, Jingmen 448000, China

Energies, 2022, vol. 15, issue 10, 1-18

Abstract: The development of hydrogen energy is an effective solution to the energy and environmental crisis. Hydrogen fuel cells and energy storage cells as hybrid power have broad application prospects in the field of vehicle power. Energy management strategies are key technologies for fuel cell hybrid systems. The traditional optimization strategy is generally based on optimization under the global operating conditions. The purpose of this project is to develop a power allocation optimization method based on real-time load forecasting for fuel cell/lithium battery hybrid electric vehicles, which does not depend on specific working conditions or causal control methods. This paper presents an energy-management algorithm based on real-time load forecasting using GRU neural networks to predict load requirements in the short time domain, and then the local optimization problem for each predictive domain is solved using a method based on Pontryagin’s minimum principle (PMP). The algorithm adopts the idea of model prediction control (MPC) to transform the global optimization problem into a series of local optimization problems. The simulation results show that the proposed strategy can achieve a good fuel-saving control effect. Compared with the rule-based strategy and equivalent hydrogen consumption strategy (ECMS), the fuel consumption is lower under two typical urban conditions. In the 1800 s driving cycle, under WTCL conditions, the fuel consumption under the MPC-PMP strategy is 22.4% lower than that based on the ECMS strategy, and 10.3% lower than the rules-based strategy. Under CTLT conditions, the fuel consumption of the MPC-PMP strategy is 13.12% lower than that of the rule-based strategy, and 3.01% lower than the ECMS strategy.

Keywords: hybrid power; velocity forecasting; GRU neural network; MPC; PMP (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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