Validation of robustness and fuel efficiency of a universal model-based energy management strategy for fuel cell hybrid trains: From analytical derivation via simulation to measurement on test bench

Abstract Fuel cell hybrid trains are being commercialized to replace trains powered by combustion engine to reduce carbon dioxide emission without high investment cost in overhead catenaries. In this context, this paper presents a universal model-based strategy for the operation of fuel cell hybrid trains based on adaptive Pontryagin’s minimum principle (APMP). Different from all other work, the implementation of Pontryagin’s minimum principle (PMP) considers the relaxation process due to the resistance-capacitor branches in the batteries to provide a precise reference for the evaluation of the robustness and fuel economy of the APMP-based strategy. Furthermore, a formula to physically estimate the costate is inspired by the offline PMP results and derived by using the energy conservation principle. Moreover, the robustness of the strategy against fuel cell aging, battery aging, inaccurate fuel cell modeling, and deviations introduced through fitting experimental data is investigated through simulation. Compared to the offline results, a maximum 1.5% higher hydrogen consumption is observed by simulation under different aging and uncertain operating conditions. Finally, the effectiveness and the robustness of the strategy are validated through measurement on the test bench at the Center for Mobile Propulsion of the RWTH Aachen University. A maximum of 2.7% more hydrogen consumption is measured compared to the offline PMP results under various conditions of uncertainty.

[1]  Kay Hameyer,et al.  An Efficient Optimum Energy Management Strategy Using Parallel Dynamic Programming for a Hybrid Train Powered by Fuel-Cells and Batteries , 2019, 2019 IEEE Vehicle Power and Propulsion Conference (VPPC).

[2]  Kay Hameyer,et al.  A scalable, causal, adaptive energy management strategy based on optimal control theory for a fuel cell hybrid railway vehicle , 2020 .

[3]  Suresh G. Advani,et al.  Power management system for a fuel cell/battery hybrid vehicle incorporating fuel cell and battery degradation , 2019, International Journal of Hydrogen Energy.

[4]  Hongwen He,et al.  Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus , 2019, Applied Energy.

[5]  Fei Peng,et al.  Development of robust suboptimal real-time power sharing strategy for modern fuel cell based hybrid tramways considering operational uncertainties and performance degradation , 2018, Applied Energy.

[6]  A. Vahidi,et al.  A review of the main parameters influencing long-term performance and durability of PEM fuel cells , 2008 .

[7]  Yujie Wang,et al.  Adaptive energy management strategy for fuel cell/battery hybrid vehicles using Pontryagin's Minimal Principle , 2019, Journal of Power Sources.

[8]  Qi Li,et al.  Optimal Energy Management and Control in Multimode Equivalent Energy Consumption of Fuel Cell/Supercapacitor of Hybrid Electric Tram , 2019, IEEE Transactions on Industrial Electronics.

[9]  Alexandre Ravey,et al.  Online adaptive equivalent consumption minimization strategy for fuel cell hybrid electric vehicle considering power sources degradation , 2019, Energy Conversion and Management.

[10]  Lifan Sun,et al.  Optimization based energy management strategy for fuel cell/battery/ultracapacitor hybrid vehicle considering fuel economy and fuel cell lifespan , 2020, International Journal of Hydrogen Energy.

[11]  Jeffrey B. Burl,et al.  Estimation of the ECMS Equivalent Factor Bounds for Hybrid Electric Vehicles , 2018, IEEE Transactions on Control Systems Technology.

[12]  Yingfeng Cai,et al.  An adaptive ECMS with driving style recognition for energy optimization of parallel hybrid electric buses , 2019 .

[13]  Jeffrey B. Burl,et al.  A New Real-Time Optimal Energy Management Strategy for Parallel Hybrid Electric Vehicles , 2019, IEEE Transactions on Control Systems Technology.

[14]  Kay Hameyer,et al.  A scalable, causal, adaptive rule-based energy management for fuel cell hybrid railway vehicles learned from results of dynamic programming , 2020 .

[15]  Zoran Filipi,et al.  Parametric study on reinforcement learning optimized energy management strategy for a hybrid electric vehicle , 2020 .

[16]  S.M.T. Bathaee,et al.  Improving fuel economy and performance of a fuel-cell hybrid electric vehicle (fuel-cell, battery, and ultra-capacitor) using optimized energy management strategy , 2018 .