基于工况预测和动力源寿命衰减协同的燃料电池有轨电车能量管理策略

doi:10.19595/j.cnki.1000-6753.tces.240996

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Abstract In fuel cell hybrid systems, the degradation processes of fuel cells and power batteries are highly inconsistent. The excessive consumption and premature end of life of one power source can disrupt the balance of the power system, deplete the performance of the other power source, accelerate the aging of the entire power system, and negatively affect vehicle economy and system durability. Consequently, it becomes challenging to achieve optimal fuel economy and system durability simultaneously. To address this issue, an optimization strategy based on condition prediction and coordinated power source life degradation is proposed.
Firstly, to improve prediction accuracy, operating conditions are categorized into three typical states: low-speed, medium-speed, and high-speed. An upper-level Markov Chain Monte Carlo (MCMC) prediction model is established based on historical conditions to predict the tram's operating conditions. This prediction provides more information for the lower-level energy management strategy to optimize system energy distribution. Secondly, in the lower-level energy management strategy, the hydrogen consumption of the fuel cell and the equivalent hydrogen consumption of the auxiliary power source are analyzed. A continuous degradation model for the fuel cell and power battery is established, introducing optimization objectives and adaptively adjusting the weights of each objective online to optimize the multi-objective function. Finally, the proposed strategy is compared with the traditional equivalent consumption minimization strategy (ECMS) and the external energy maximization strategy (EEMS).
Results show that at the end of the entire operating condition, the proposed strategy's hydrogen consumption is 99.61 g, the degradation rate difference between the dual power sources is 0.000 66%, the system efficiency is 81.66%, the power fluctuation range is -800 W to 800 W, and the stress on the power battery and supercapacitor is 117.5 and 176.4 respectively. Compared to the ECMS strategy, with a hydrogen consumption of 115.1 g and system efficiency of 77.64%, the proposed strategy improves fuel economy and system efficiency by 15.6% and 5.2% respectively. Compared to the EEMS strategy, with a dual power source degradation rate difference of 0.014 4% and system efficiency of 79.77%, the proposed strategy reduces the degradation rate difference by 21.82 times and improves system efficiency by 2.4%. Additionally, the power fluctuation range under the proposed strategy is significantly reduced compared to the -1 000 W to 1 000 W range under both the ECMS and EEMS strategies, resulting in a smoother power source power curve. Under the ECMS strategy, the stress on the power battery and supercapacitor is 156.6 and 215 respectively, while under the EEMS strategy, the stress is 156.8 and 226.6 respectively. The proposed strategy reduces the stress on the auxiliary power source compared to the ECMS and EEMS strategies, decreasing excessive consumption and resulting in a more reasonable power distribution.
Comprehensive simulation analysis reveals the core advantages of the proposed strategy: (1) Establishing an MCMC prediction model for condition prediction improves the adaptability of the energy management strategy to operating conditions, achieving more reasonable, precise, and efficient energy control and reducing damage to the hybrid power system. (2) Overcoming the poor fuel economy of traditional ECMS and the high inconsistency in power source degradation of EEMS. (3) Achieving superior fuel economy and system durability, thereby extending the lifecycle of fuel cell hybrid systems.

Key words： hybrid tram driving cycle prediction energy management lifespan decay synergy

Received: 11 June 2024

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TM92

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	Gao Fengyang
	Su Honyu
	Zha pengtang
	Qiang Yaxin
	Liu Jia

Cite this article:

Gao Fengyang,Su Honyu,Zha pengtang等. Energy Management Strategy for Fuel Cell Hybrid Tram System Based on Driving Cycle Prediction and Power Source Lifespan Decay Synergy[J]. Transactions of China Electrotechnical Society, 2025, 40(13): 4316-4329.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.240996 OR https://dgjsxb.ces-transaction.com/EN/Y2025/V40/I13/4316

[1] 严锐浩, 许亮. 基于模糊控制优化小波分解的FCHEV能量管理策略[J/OL]. 电源学报, 1-12[2025-07-01].http://kns.cnki.net/kcms/detail/12.1420.tm.20240425.1853.034.html.
Yan Ruihao, Xu Liang. Fuzzy control based optimized wavelet decomposition for FCHEV energy management strategy[J/OL]. Journal of Power Supply, 1-12[2025-07-01]. http://kns.cnki.net/kcms/detail/12.1420.tm.20240425.1853.034.html.
[2] 陈杰, 陆宇鑫, 王雪钰, 等. 燃料电池/锂电池混合供电系统的分散式鲁棒滑模复合控制[J/OL].中国电机工程学报, 1-12[2025-07-07]. https://doi.org/10.13334/j.0258-8013.pcsee.240718.
Chen Jie, Lu Yuxin, Wang Xueyu, et al.Decentralized robust sliding mode composite control for the fuel cell/lithium batteryhybrid power supply system [J/OL]. Proceedings of the CSEE, 1-12 [2025-07-07]. https://doi.org/10.13334/j.0258-8013.pcsee.240718.
[3] 李奇, 刘鑫, 孟翔, 等. 基于近端策略优化算法的燃料电池混合动力系统综合价值损耗最小能量管理方法[J]. 中国电机工程学报, 2024, 44(12): 4788-4799.
Li Qi, Liu Xin, Meng Xiang, et al.Comprehensive value depletion minimization energy management method for fuel cell hybrid systems based on proximal policy optimization algorithm[J]. Proceedings of the CSEE, 2024, 44(12): 4788-4799.
[4] 蔡良东, 李奇, 刘强, 等. 考虑系统氢耗和耐久性的多堆燃料电池优化控制方法[J]. 中国电机工程学报, 2022, 42(10): 3670-3680.
Cai Liangdong, Li Qi, Liu Qiang, et al.Optimal control method for multi-stack fuel cells considering system hydrogen consumption and durability[J]. Proceedings of the CSEE, 2022, 42(10): 3670-3680.
[5] 敖文杰, 陈家伟, 陈杰, 等. 燃料电池-锂电池混合供电系统的无源控制策略及参数设计方法[J].电工技术学报, 2024, 39(2): 580-594.
Ao Wenjie, Chen Jiawei, Chen Jie, et al.Passivity-based control strategy and parameter design method forfuel cell-lithium battery hybrid power supply system[J]. Transactions of China Electrotechnical Society, 2024, 39(2): 580-594.
[6] 高锋阳, 宋志翔, 高建宁, 等. 计及光伏和储能接入的牵引供电系统能量管理策略[J]. 电工技术学报, 2024, 39(3):745-757.
Gao Fengyang, Song Zhixiang, Gao Jianning, et al.Energy management strategies for traction power systems with PV and energy storage access[J]. Transactions of China Electrotechnical Society, 2024, 39(3):745-757.
[7] Zhao Xiuliang, Wang Lei, Zhou Yinglong, et al.Energy management strategies for fuel cell hybrid electric vehicles: classification, comparison, and outlook[J]. Energy Conversion and Management, 2022, 270: 116179.
[8] Kandidayeni M, Macias A, Boulon L, et al.Investigating the impact of ageing and thermal management of a fuel cell system on energy management strategies[J]. Applied Energy, 2020, 274: 115293.
[9] 项鸿博, 裴晓飞, 刘杏, 等. 基于DP的混合动力汽车单踏板控制策略研究[J]. 武汉理工大学学报, 2023, 45(11): 122-131.
Xiang Hongbo, Pei Xiaofei, Liu Xing, et al.Research on single pedal control strategy of hybrid electric vehicle based on dynamic programming algorithm[J]. Journal of Wuhan University of Technology, 2023, 45(11): 122-131.
[10] Peng Hujun, Chen Zhu, Deng Kai, et al.A comparison of various universally applicable power distribution strategies for fuel cell hybrid trains utilizing component modeling at different levels of detail: from simulation to test bench measurement[J]. eTrans-portation, 2021, 9: 100120.
[11] 陈恺, 付宇, 孙毅, 等. 基于计算热点转移的5G车联网能量实时协同管理策略[J]. 电工技术学报, 2024, 39(23): 7481-7497.
Chen Kai, Fu Yu, Sun Yi, et al.Online energy management strategy for 5g-vehicle network basedon computing hotspot transfer[J]. Transactions of China Electrotechnical Society, 2024, 39(23): 7481-7497.
[12] 高锋阳, 高智山, 强雅昕, 等. 计及容量配置优化的有轨电车多阈值能量管理策略[J/OL]. 电源学报, 1-12[2025-07-01].http://kns.cnki.net/kcms/detail/12.1420.TM.20231219.1227.006.html.
[13] 高锋阳, 高翾宇, 张浩然, 等. 全局与瞬时特性兼优的燃料电池有轨电车能量管理策略[J]. 电工技术学报, 2023, 38(21): 5923-5938.
Gao Fengyang, Gao Xuanyu, Zhang Haoran, et al.Management strategy for fuel cell trams with both global and transient characteristics[J]. Transactions of China Electrotechnical Society, 2023, 38(21): 5923-5938.
[14] 高锋阳, 张浩然, 王文祥, 等. 氢燃料电池有轨电车混合储能系统的节能运行优化[J]. 电工技术学报, 2022, 37(3): 686-696.
Gao Fengyang, Zhang Haoran, Wang Wenxiang, et al.Energy saving operation optimization of hybrid energy storage system for hydrogen fuel cell tram[J]. Transactions of China Electrotechnical Society, 2022, 37(3): 686-696.
[15] Song Ke, Wang Xiaodi, Li Feiqiang, et al.Pontryagin’s minimum principle-based real-time energy management strategy for fuel cell hybrid electric vehicle considering both fuel economy and power source durability[J]. Energy, 2020, 205: 118064.
[16] Deng Kai, Peng Hujun, Dirkes S, et al.An adaptive PMP-based model predictive energy management strategy for fuel cell hybrid railway vehicles[J]. eTransportation, 2021, 7: 100094.
[17] Song Ke, Ding Yuhang, Hu Xiao, et al.Degradation adaptive energy management strategy using fuel cell state-of-health for fuel economy improvement of hybrid electric vehicle[J]. Applied Energy, 2021, 285: 116413.
[18] Tang Xiaolin, Zhou Haitao, Wang Feng, et al.Longevity-conscious energy management strategy of fuel cell hybrid electric vehicle based on deep reinforcement learning[J]. Energy, 2022, 238: 121593.
[19] Lu Dagang, Yi Fengyan, Hu Donghai, et al.Online optimization of energy management strategy for FCV control parameters considering dual power source lifespan decay synergy[J]. Applied Energy, 2023, 348: 121516.
[20] Xie Shanshan, He Hongwen, Peng Jiankun.An energy management strategy based on stochastic model predictive control for plug-in hybrid electric buses[J]. Applied Energy, 2017, 196: 279-288.
[21] Lin Xinyou, Xia Yutian, Huang Wei, et al.Trip distance adaptive power prediction control strategy optimization for a plug-in fuel cell electric vehicle[J]. Energy, 2021, 224: 120232.
[22] Zhang Man, Shi Shuming, Lin Nan, et al.High-efficiency driving cycle generation using a Markov chain evolution algorithm[J]. IEEE Transactions on Vehicular Technology, 2019, 68(2): 1288-1301.
[23] Pereira D F, da Costa Lopes F, Watanabe E H. Nonlinear model predictive control for the energy management of fuel cell hybrid electric vehicles in real time[J]. IEEE Transactions on Industrial Electronics, 2021, 68(4): 3213-3223.
[24] Ghaderi R, Kandidayeni M, Soleymani M, et al.Online health-conscious energy management strategy for a hybrid multi-stack fuel cell vehicle based on game theory[J]. IEEE Transactions on Vehicular Technology, 2022, 71(6): 5704-5714.
[25] Wang Tianhong, Li Qi, Wang Xiaotong, et al.A power allocation method for multistack PEMFC system considering fuel cell performance consistency[J]. IEEE Transactions on Industry Applications, 2020, 56(5): 5340-5351.