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Hierarchical model predictive control via deep learning vehicle speed predictions for oxygen stoichiometry regulation of fuel cells

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  • Wang, Xuechao
  • Chen, Jinzhou
  • Quan, Shengwei
  • Wang, Ya-Xiong
  • He, Hongwen

Abstract

Fuel cells are a promising solution for increasing driving range of electric vehicles. To guarantee the high efficiency and stable operation of fuel cells, the effective regulation of oxygen and hydrogen reactants is needed. During varied driving conditions, in which the drastic current demand changes may result in insufficient reactant, the fuel cell can even be damaged. In this paper, a hierarchical model predictive control (HMPC) strategy is proposed based on the deep learning for vehicle speed predictions. Speed variation predictions are considered by the MPC to regulate the air supply system and preventing oxygen starvation in the fuel cell stack. The problems of fuel cell oxygen stoichiometry control are, first, stated with the preliminary energy management description as well as with the limitations of the traditional MPC. The deep learning Back Propagation (BP) neural network was then designed as the first-level predictor to forecast the vehicle speed by training with integrated driving cycles, and predict the fuel cell current based on its cathode flow model. Subsequently, the second-level MPC used the current disturbance prediction and filling is introduced to regulate the oxygen mass flow. The simulation results for the MANHATTAN drive cycle demonstrated that the root mean square error (RMSE) for speed predictions was less than 1 km/h. Compared with the conventional MPC, HMPC offers better robustness in the face of influence from current changes induced by speed-variations, and the RMSE of the oxygen stoichiometry control was decreased by 63.37%.

Suggested Citation

  • Wang, Xuechao & Chen, Jinzhou & Quan, Shengwei & Wang, Ya-Xiong & He, Hongwen, 2020. "Hierarchical model predictive control via deep learning vehicle speed predictions for oxygen stoichiometry regulation of fuel cells," Applied Energy, Elsevier, vol. 276(C).
    • Handle: RePEc:eee:appene:v:276:y:2020:i:c:s0306261920309727
    DOI: 10.1016/j.apenergy.2020.115460
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    2. Yang, Jian & Zhang, Tiezhu & Hong, Jichao & Zhang, Hongxin & Zhao, Qinghai & Meng, Zewen, 2021. "Research on driving control strategy and Fuzzy logic optimization of a novel mechatronics-electro-hydraulic power coupling electric vehicle," Energy, Elsevier, vol. 233(C).
    3. Liu, Ze & Zhang, Baitao & Xu, Sichuan, 2022. "Research on air mass flow-pressure combined control and dynamic performance of fuel cell system for vehicles application," Applied Energy, Elsevier, vol. 309(C).
    4. Sylvain Rigal & Amine Jaafar & Christophe Turpin & Théophile Hordé & Jean-Baptiste Jollys & Paul Kreczanik, 2024. "Steady-State Voltage Modelling of a HT-PEMFC under Various Operating Conditions," Energies, MDPI, vol. 17(3), pages 1-18, January.
    5. Dong, Peng & Zhao, Junwei & Liu, Xuewu & Wu, Jian & Xu, Xiangyang & Liu, Yanfang & Wang, Shuhan & Guo, Wei, 2022. "Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    6. Hu, Haowen & Ou, Kai & Yuan, Wei-Wei, 2023. "Fused multi-model predictive control with adaptive compensation for proton exchange membrane fuel cell air supply system," Energy, Elsevier, vol. 284(C).
    7. Sylvain Rigal & Amine Jaafar & Christophe Turpin & Théophile Hordé & Jean-Baptiste Jollys & Paul Kreczanik, 2024. "An Air Over-Stoichiometry Dependent Voltage Model for HT-PEMFC MEAs," Energies, MDPI, vol. 17(12), pages 1-17, June.
    8. Zhang, Zhendong & Wang, Ya-Xiong & He, Hongwen & Sun, Fengchun, 2021. "A short- and long-term prognostic associating with remaining useful life estimation for proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 304(C).
    9. Yuguo Xu & Enyong Xu & Weiguang Zheng & Qibai Huang, 2023. "Hierarchical Model-Predictive-Control-Based Energy Management Strategy for Fuel Cell Hybrid Commercial Vehicles Incorporating Traffic Information," Sustainability, MDPI, vol. 15(17), pages 1-21, August.
    10. Mohammad AlElaiwi & Mugahed A. Al-antari & Hafiz Farooq Ahmad & Areeba Azhar & Badar Almarri & Jamil Hussain, 2022. "VPP: Visual Pollution Prediction Framework Based on a Deep Active Learning Approach Using Public Road Images," Mathematics, MDPI, vol. 11(1), pages 1-26, December.
    11. Min, Dehao & Song, Zhen & Chen, Huicui & Wang, Tianxiang & Zhang, Tong, 2022. "Genetic algorithm optimized neural network based fuel cell hybrid electric vehicle energy management strategy under start-stop condition," Applied Energy, Elsevier, vol. 306(PB).
    12. Quan, Shengwei & Wang, Ya-Xiong & Xiao, Xuelian & He, Hongwen & Sun, Fengchun, 2021. "Feedback linearization-based MIMO model predictive control with defined pseudo-reference for hydrogen regulation of automotive fuel cells," Applied Energy, Elsevier, vol. 293(C).
    13. Haubensak, Lukas & Strahl, Stephan & Braun, Jochen & Faulwasser, Timm, 2024. "Towards real-time capable optimal control for fuel cell vehicles using hierarchical economic MPC," Applied Energy, Elsevier, vol. 366(C).
    14. Messner, Wolfgang, 2024. "Exploring multilevel data with deep learning and XAI: The effect of personal-care advertising spending on subjective happiness," International Business Review, Elsevier, vol. 33(1).

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