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Model predictive control with thermal constraints for fuel cell hybrid electric vehicle based on speed prediction
  • +1
  • Jiangtao Fu,
  • Bo Fan,
  • Zhumu Fu,
  • Shuzhong Song
Jiangtao Fu
Henan University of Science and Technology School of Basic Medicine and Forensic Medicine

Corresponding Author:fjtmengnan302@sina.com

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Bo Fan
Henan University of Science and Technology School of Basic Medicine and Forensic Medicine
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Zhumu Fu
Henan University of Science and Technology School of Basic Medicine and Forensic Medicine
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Shuzhong Song
Henan University of Science and Technology School of Basic Medicine and Forensic Medicine
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Abstract

Because of the soft dynamic performance of the fuel cell stack, the battery is usually integrated in the power system in fuel cell hybrid electric vehicles. In this paper, a real time energy management strategy considering thermal constraints based on speed prediction with neuron network is proposed. The main principle of the proposed control strategy is to get the future power requirement with model predictive control based on the historic speed information, and then optimize the objective function according to the state variables. The objective function is set to minimize the equivalent fuel consumption of the vehicle and extend the life span of the fuel cell stack based on thermal constraints. Contrasting with the control strategy without thermal constraints under the WLTC driving cycle, the proposed energy management is 0.9% higher, but the temperature of the fuel cell stack and the battery can be limited within an appropriate range. The total equivalent fuel consumption is 3.9% lower than dynamic programming control strategy, which proves the availability of the proposed control strategy can reduce the equivalent fuel consumption while prolonging the fuel cell stack life span. Hardware in loop (HIL) experiment is implemented to testify the real time application of the proposed control strategy.
27 May 2024Submitted to Optimal Control, Applications and Methods
28 May 2024Submission Checks Completed
28 May 2024Assigned to Editor
15 Jun 2024Review(s) Completed, Editorial Evaluation Pending
10 Jul 20241st Revision Received
10 Jul 2024Submission Checks Completed
10 Jul 2024Assigned to Editor
10 Jul 2024Review(s) Completed, Editorial Evaluation Pending
25 Jul 2024Reviewer(s) Assigned
25 Jul 2024Editorial Decision: Accept