Weighting Optimization for Fuel Cell Hybrid Vehicles: Lifetime-Conscious Component Sizing and Energy Management
Source abstract
Abstract
Fuel economy and system durability are critical yet interdependent performance metrics for fuel cell hybrid vehicles (FCHVs). This paper devises an integrated framework for optimizing component sizing and energy management in a fuel cell/battery hybrid passenger vehicle. A unified cost function is proposed, combining fuel economy and system durability through a weighting coefficient, based on a comprehensive model of the hydrogen consumption and degradation characteristics of fuel cells and batteries. Utilizing the dynamic programming (DP) algorithm, the total cost is optimized to derive the optimal weighting factors and component sizing, effectively addressing the multi-objective optimization problem and balancing efficiency and durability. Furthermore, the impact of power prices on the optimal parameters is carefully examined. The simulation results indicate that a battery capacity of 44 Ah and a fuel cell maximum power of 80 kW represent the optimal sizing configuration. A weighting factor of 0.5 achieves the minimum equivalent total cost by effectively balancing fuel economy and system durability for the light-duty fuel cell passenger vehicle. Additionally, the battery price affects the weighting factor, indicating that future reductions in power source costs will shift focus away from system durability to fuel economy in FCHV optimization. These findings provide recommendations for FCHV manufacturers to advance the application of fuel cells in passenger vehicles.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Battery Technologies Research · Electric and Hybrid Vehicle Technologies · Electric Vehicles and Infrastructure
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Nuclear Science, Energy and Power Engineering, Shandong University, Jinan 250061, China
Department of Mechanical Engineering, University of Bath, Bath BA2 7AY, UK
Shenzhen Research Institute of Shandong University, Shenzhen 518057, China
Funding
Guangdong Basic and Applied Basic Research Foundation · Natural Science Foundation of Shandong Province
References (35)
Show source-provided references
1. An overview: Current progress on hydrogen fuel cell vehicles10.1016/j.ijhydene.2022.10.156 ↗
2. Comparative global warming impact and NOX emissions of conventional and hydrogen automotive propulsion systems10.1016/j.enconman.2020.113137 ↗
3. Li, D., Leung, M.F., Tang, J., Wang, Y., Hu, J., and Wang, S. (2025). Generative Self-Supervised Learning for Cyberattack-Resilient EV Charging Demand Forecasting. IEEE Trans. Intell. Transp. Syst., 1–10.10.1109/TITS.2025.3545577 ↗
4. A modeling framework for predicting the effect of the operating conditions and component sizing on fuel cell degradation and performance for automotive applications10.1016/j.apenergy.2022.119137 ↗
5. A comprehensive review on hybrid power system for PEMFC-HEV: Issues and strategies10.1016/j.enconman.2018.06.065 ↗
6. Prospects of fuel cell technologies10.1093/nsr/nww099 ↗
7. Investigation of drive cycle simulation performance for electric, hybrid, and fuel cell powertrains of a small-sized vehicle10.1016/j.ijhydene.2023.08.358 ↗
8. Optimized rule-based energy management for a polymer electrolyte membrane fuel cell/battery hybrid power system using a genetic algorithm10.1016/j.ijhydene.2021.12.121 ↗
9. Approximate Cost-Optimal Energy Management of Hydrogen Electric Multiple Unit Trains Using Double Q-Learning Algorithm10.1109/TIE.2021.3113021 ↗
10. Energy management strategy based on fuzzy logic for a fuel cell hybrid bus10.1016/j.jpowsour.2008.06.083 ↗
11. A wavelet-fuzzy logic based energy management strategy for a fuel cell/battery/ultra-capacitor hybrid vehicular power system10.1016/j.jpowsour.2009.04.072 ↗
12. Teng, Y.-T., and Wang, F.-C. (2016, January 13–15). Cost Analyses and Optimization of a PEMFC Electric Vehicle Model. Proceedings of the 2016 IEEE/SICE International Symposium on System Integration (SII), Sapporo, Japan.10.1109/SII.2016.7844112 ↗
13. Jalil, N., Kheir, N.A., and Salman, M. (1997, January 6). A rule-based energy management strategy for a series hybrid vehicle. Proceedings of the American Control Conference, Albuquerque, NM, USA.
14. Twin delayed deep deterministic policy gradient-based deep reinforcement learning for energy management of fuel cell vehicle integrating durability information of powertrain10.1016/j.enconman.2022.116454 ↗
15. Health-conscious deep reinforcement learning energy management for fuel cell buses integrating environmental and look-ahead road information10.1016/j.energy.2023.130146 ↗
16. Model predictive control energy management strategy integrating long short-term memory and dynamic programming for fuel cell vehicles10.1016/j.ijhydene.2023.12.245 ↗
17. Application of Pontryagin’s Minimal Principle to the energy management strategy of plugin fuel cell electric vehicles10.1016/j.ijhydene.2013.05.125 ↗
18. Convex programming energy management and components sizing of a plug-in fuel cell urban logistics vehicle10.1016/j.jpowsour.2019.03.044 ↗
19. Xu, L., Ouyang, M., Li, J., and Yang, F. (2012, January 28–31). Dynamic Programming Algorithm for minimizing operating cost of a PEM fuel cell vehicle. Proceedings of the 2012 IEEE International Symposium on Industrial Electronics, Hangzhou, China.10.1109/ISIE.2012.6237311 ↗
20. Power management and design optimization of fuel cell/battery hybrid vehicles10.1016/j.jpowsour.2006.12.038 ↗
21. Dominguez, R., Solano, J., and Jacome, A. (2018, January 27–30). Sizing of Fuel Cell—Ultracapacitors Hybrid Electric Vehicles Based on the Energy Management Strategy. Proceedings of the 2018 IEEE Vehicle Power and Propulsion Conference (VPPC), Chicago, IL, USA.10.1109/VPPC.2018.8605018 ↗
22. Multi-objective energy management optimization and parameter sizing for proton exchange membrane hybrid fuel cell vehicles10.1016/j.enconman.2016.09.082 ↗
23. Research on energy management strategy for fuel cell hybrid electric vehicles based on improved dynamic programming and air supply optimization10.1016/j.energy.2024.131567 ↗
24. Optimal sizing of plug-in fuel cell electric vehicles using models of vehicle performance and system cost10.1016/j.apenergy.2012.10.010 ↗
25. Optimization and sizing of a fuel cell range extender vehicle for passenger car applications in driving cycle conditions10.1016/j.apenergy.2021.116469 ↗
26. Sizing for fuel cell/supercapacitor hybrid vehicles based on stochastic driving cycles10.1016/j.apenergy.2016.09.008 ↗
27. Optimal component sizing and performance of Fuel Cell—Battery powered vehicle over world harmonized and new european driving cycles10.1016/j.enconman.2023.117992 ↗
28. Sizing Stack and Battery of a Fuel Cell Hybrid Distribution Truck10.2516/ogst/2012014 ↗
29. Optimal dimensioning and power management of a fuel cell/battery hybrid bus via convex programming10.1109/TMECH.2014.2336264 ↗
30. Optimal Design of Hybrid Fuel Cell Vehicles10.1115/1.2890106 ↗
31. Multi-objective component sizing based on optimal energy management strategy of fuel cell electric vehicles10.1016/j.apenergy.2015.02.017 ↗
32. A quick evaluating method for automotive fuel cell lifetime10.1016/j.ijhydene.2008.04.048 ↗
33. Cycle-life model for graphite-LiFePO4 cells10.1016/j.jpowsour.2010.11.134 ↗
34. Energy storage deployment and innovation for the clean energy transition10.1038/nenergy.2017.125 ↗
35. (2016). Fuel Cell System Cost-2016, DOE Hydrogen and Fuel Cells Program Record, U.S. Department of Energy. Rec. No. 16020.