Authors

Yuzhu Chen, Jian Chen

Abstract

This article proposes an adaptive control for the power distribution of fuel cell (FC) hybrid electric vehicles. port-Hamiltonian framework is utilized to describe the fuel cell hybrid system dynamics. Then, the controller is designed in the interconnection and damping assignment passivity-based control approach to distribute the power flow between the fuel cell and the battery pack, and the stability of designed controller has been proved. Moreover, an adaptation law is utilized to improve the fuel economy and durability of energy sources, and the battery state of charge is online estimated by a quasi-sliding-mode observer. Finally, simulation and experiments are conducted to verify the performance of proposed controller compared with an equivalent consumption minimization strategy.

Citation

  • Journal: IEEE Transactions on Industrial Informatics
  • Year: 2025
  • Volume: 21
  • Issue: 6
  • Pages: 4863–4873
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • DOI: 10.1109/tii.2025.3547028

BibTeX

@article{Chen_2025,
  title={{Adaptive Control of Fuel Cell-Battery Hybrid Systems Considering Power Sources Degradation}},
  volume={21},
  ISSN={1941-0050},
  DOI={10.1109/tii.2025.3547028},
  number={6},
  journal={IEEE Transactions on Industrial Informatics},
  publisher={Institute of Electrical and Electronics Engineers (IEEE)},
  author={Chen, Yuzhu and Chen, Jian},
  year={2025},
  pages={4863--4873}
}

Download the bib file

References

  • Moghadari, M., Kandidayeni, M., Boulon, L. & Chaoui, H. Operating Cost Comparison of a Single-Stack and a Multi-Stack Hybrid Fuel Cell Vehicle Through an Online Hierarchical Strategy. IEEE Trans. Veh. Technol. 72, 267–279 (2023) – 10.1109/tvt.2022.3205879
  • Lü, X., Meng, R., Deng, R., Long, L. & Wu, Y. Energy economy optimization and comprehensive performance improvement for PEMFC/LIB hybrid system based on hierarchical optimization. Renewable Energy 193, 1132–1149 (2022) – 10.1016/j.renene.2022.05.007
  • Ogungbemi, E., Wilberforce, T., Ijaodola, O., Thompson, J. & Olabi, A. G. Selection of proton exchange membrane fuel cell for transportation. International Journal of Hydrogen Energy 46, 30625–30640 (2021) – 10.1016/j.ijhydene.2020.06.147
  • Yang, L., Li, X., Sun, M. & Sun, C. Hybrid Policy-Based Reinforcement Learning of Adaptive Energy Management for the Energy Transmission-Constrained Island Group. IEEE Trans. Ind. Inf. 19, 10751–10762 (2023) – 10.1109/tii.2023.3241682
  • Kumar, K. & Bae, S. Coordinated Dynamic Power Management for Renewable Energy-Based Grid-Connected Microgrids Using Model Predictive Control. IEEE Trans. Ind. Inf. 19, 9596–9608 (2023) – 10.1109/tii.2022.3231409
  • Lian, R., Peng, J., Wu, Y., Tan, H. & Zhang, H. Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle. Energy 197, 117297 (2020) – 10.1016/j.energy.2020.117297
  • Chen, J., Xu, C., Wu, C. & Xu, W. Adaptive Fuzzy Logic Control of Fuel-Cell-Battery Hybrid Systems for Electric Vehicles. IEEE Trans. Ind. Inf. 14, 292–300 (2018) – 10.1109/tii.2016.2618886
  • Pereira, D. F., Lopes, F. da C. & Watanabe, E. H. Nonlinear Model Predictive Control for the Energy Management of Fuel Cell Hybrid Electric Vehicles in Real Time. IEEE Trans. Ind. Electron. 68, 3213–3223 (2021) – 10.1109/tie.2020.2979528
  • Feng, J., Han, Z., Wu, Z. & Li, M. A Dynamic ECMS Method Considering Vehicle Speed Pattern and Minimum Engine Operation Time for a Range-Extender Electric Vehicle (Jan. 2022). IEEE Trans. Veh. Technol. 71, 4788–4800 (2022) – 10.1109/tvt.2022.3148268
  • Mehraban, A., Farjah, E., Ghanbari, T. & Garbuio, L. Integrated Optimal Energy Management and Sizing of Hybrid Battery/Flywheel Energy Storage for Electric Vehicles. IEEE Trans. Ind. Inf. 19, 10967–10976 (2023) – 10.1109/tii.2023.3242580
  • Gan, J., Li, S., Wei, C., Deng, L. & Tang, X. Intelligent Learning Algorithm and Intelligent Transportation-Based Energy Management Strategies for Hybrid Electric Vehicles: A Review. IEEE Trans. Intell. Transport. Syst. 24, 10345–10361 (2023) – 10.1109/tits.2023.3283010
  • Jia, C., Qiao, W., Cui, J. & Qu, L. Adaptive Model-Predictive-Control-Based Real-Time Energy Management of Fuel Cell Hybrid Electric Vehicles. IEEE Trans. Power Electron. 38, 2681–2694 (2023) – 10.1109/tpel.2022.3214782
  • Chen, H., Chen, J., Lu, H., Yan, C. & Liu, Z. A Modified MPC-Based Optimal Strategy of Power Management for Fuel Cell Hybrid Vehicles. IEEE/ASME Trans. Mechatron. 25, 2009–2018 (2020) – 10.1109/tmech.2020.2993811
  • Li, H., Chaoui, H. & Gualous, H. Cost Minimization Strategy for Fuel Cell Hybrid Electric Vehicles Considering Power Sources Degradation. IEEE Trans. Veh. Technol. 69, 12832–12842 (2020) – 10.1109/tvt.2020.3031000
  • Zhu, L. et al. Multiobjective Optimization of Safety, Comfort, Fuel Economy, and Power Sources Durability for FCHEV in Car-Following Scenarios. IEEE Trans. Transp. Electrific. 9, 1797–1808 (2023) – 10.1109/tte.2022.3193806
  • Jia, Z., Qiao, L. & Zhang, W. Adaptive tracking control of unmanned underwater vehicles with compensation for external perturbations and uncertainties using Port-Hamiltonian theory. Ocean Engineering 209, 107402 (2020) – 10.1016/j.oceaneng.2020.107402
  • Hilairet, M. et al. A passivity-based controller for coordination of converters in a fuel cell system. Control Engineering Practice 21, 1097–1109 (2013) – 10.1016/j.conengprac.2013.04.003
  • Kong, S., Bressel, M., Hilairet, M. & Roche, R. Advanced passivity-based, aging-tolerant control for a fuel cell/super-capacitor hybrid system. Control Engineering Practice 105, 104636 (2020) – 10.1016/j.conengprac.2020.104636
  • Wu, C., Chen, J., Xu, C. & Liu, Z. Real-Time Adaptive Control of a Fuel Cell/Battery Hybrid Power System With Guaranteed Stability. IEEE Trans. Contr. Syst. Technol. 25, 1394–1405 (2017) – 10.1109/tcst.2016.2611558
  • Benmouna, A., Becherif, M., Depernet, D. & Ebrahim, M. A. Novel Energy Management Technique for Hybrid Electric Vehicle via Interconnection and Damping Assignment Passivity Based Control. Renewable Energy 119, 116–128 (2018) – 10.1016/j.renene.2017.11.051
  • Benmouna, A., Becherif, M., Depernet, D., Dépature, C. & Boulon, L. Nonlinear control and optimization of hybrid electrical vehicle under sources limitation constraints. International Journal of Hydrogen Energy 45, 11255–11266 (2020) – 10.1016/j.ijhydene.2018.12.227
  • Xun, Q., Murgovski, N. & Liu, Y. Joint Component Sizing and Energy Management for Fuel Cell Hybrid Electric Trucks. IEEE Trans. Veh. Technol. 71, 4863–4878 (2022) – 10.1109/tvt.2022.3154146
  • Ming, W. et al. A systematic review of machine learning methods applied to fuel cells in performance evaluation, durability prediction, and application monitoring. International Journal of Hydrogen Energy 48, 5197–5228 (2023) – 10.1016/j.ijhydene.2022.10.261
  • Fletcher, T., Thring, R. & Watkinson, M. An Energy Management Strategy to concurrently optimise fuel consumption & PEM fuel cell lifetime in a hybrid vehicle. International Journal of Hydrogen Energy 41, 21503–21515 (2016) – 10.1016/j.ijhydene.2016.08.157
  • Chen, H., Pei, P. & Song, M. Lifetime prediction and the economic lifetime of Proton Exchange Membrane fuel cells. Applied Energy 142, 154–163 (2015) – 10.1016/j.apenergy.2014.12.062
  • Li, H., Zhou, Y., Gualous, H., Chaoui, H. & Boulon, L. Optimal Cost Minimization Strategy for Fuel Cell Hybrid Electric Vehicles Based on Decision-Making Framework. IEEE Trans. Ind. Inf. 17, 2388–2399 (2021) – 10.1109/tii.2020.3003554
  • Hu, Z. et al. Multi-objective energy management optimization and parameter sizing for proton exchange membrane hybrid fuel cell vehicles. Energy Conversion and Management 129, 108–121 (2016) – 10.1016/j.enconman.2016.09.082
  • Wang, J. et al. Cycle-life model for graphite-LiFePO4 cells. Journal of Power Sources 196, 3942–3948 (2011) – 10.1016/j.jpowsour.2010.11.134
  • Chen, Z., Xiong, R., Liu, B., Wang, Z. & Yu, Q. Pontryagin’s Minimum Principle-Based Power Management of Plug-In Hybrid Electric Vehicles to Enhance the Battery Durability and Thermal Safety. IEEE Trans. Transp. Electrific. 9, 5039–5048 (2023) – 10.1109/tte.2022.3201029
  • Hilairet, M. et al. Experimental Validation of a Sampled-Data Passivity-Based Controller for Coordination of Converters in a Fuel Cell System. IEEE Trans. Ind. Electron. 62, 5187–5194 (2015) – 10.1109/tie.2014.2362497
  • Hu, X. et al. State estimation for advanced battery management: Key challenges and future trends. Renewable and Sustainable Energy Reviews 114, 109334 (2019) – 10.1016/j.rser.2019.109334
  • Zhang, Z. et al. High-Precision and Robust SOC Estimation of LiFePO4 Blade Batteries Based on the BPNN-EKF Algorithm. Batteries 9, 333 (2023) – 10.3390/batteries9060333
  • Ortega, R., van der Schaft, A., Maschke, B. & Escobar, G. Interconnection and damping assignment passivity-based control of port-controlled Hamiltonian systems. Automatica 38, 585–596 (2002) – 10.1016/s0005-1098(01)00278-3
  • Butcher, Numerical Differential Equation Methods Numerical (2016)
  • Li, X., Li, Q., Wang, T., Chen, W. & Zhang, S. Adaptive Power Transient Smoothing Control Considering Performance Degradation for Multistack Fuel Cell Hybrid Power Systems. IEEE Trans. Transp. Electrific. 10, 7501–7512 (2024) – 10.1109/tte.2023.3339165
  • Zeng, T. et al. Optimization-oriented adaptive equivalent consumption minimization strategy based on short-term demand power prediction for fuel cell hybrid vehicle. Energy 227, 120305 (2021) – 10.1016/j.energy.2021.120305
  • Polycarpou, M. M. & Ioannou, P. A. A robust adaptive nonlinear control design. Automatica 32, 423–427 (1996) – 10.1016/0005-1098(95)00147-6