IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v285y2021ics0306261920317803.html
   My bibliography  Save this article

Degradation adaptive energy management strategy using fuel cell state-of-health for fuel economy improvement of hybrid electric vehicle

Author

Listed:
  • Song, Ke
  • Ding, Yuhang
  • Hu, Xiao
  • Xu, Hongjie
  • Wang, Yimin
  • Cao, Jing

Abstract

Most studies on fuel cell hybrid electric vehicle energy management have focused on fuel economy. However, it is also important to consider the rapid degradation of the fuel cell. Therefore, a degradation-adaptive energy management strategy is proposed in this paper. The strategy can adaptively change the power distribution between different power sources using the fuel cell state-of-health. First, a novel degradation model is established for the fuel cell. The degradation model combines the polarisation curves of the fuel cell system under different state-of-health conditions and fuel cell efficiency models. An unbalanced degradation of the fuel cell at different current densities is shown in the degradation model. The proposed strategy is modified from an instantaneous optimisation energy management strategy by including state-of-health data. Accordingly, it is possible to provide optimised control based on the decrease in efficiency, thereby taking advantage of the unbalanced degradation. The proposed strategy can adaptively adjust the power distribution during degradation to get a higher energy efficiency over entire lifetime of fuel cell. The proposed strategy is adaptive to different degradation rates and consumes a small amount of computing resources, which ensure the feasibility of real-world implication. The performance of the proposed strategy is compared with that of the original strategy via simulation. The proposed strategy can optimise the fuel economy by 1.52–2.06% and 2.26–2.90% for a half and seriously degraded fuel cell, respectively. The results reveal that the proposed strategy provide an effective approach to improving the fuel economy of degraded fuel cell hybrid electric vehicles.

Suggested Citation

  • Song, Ke & Ding, Yuhang & Hu, Xiao & Xu, Hongjie & Wang, Yimin & Cao, Jing, 2021. "Degradation adaptive energy management strategy using fuel cell state-of-health for fuel economy improvement of hybrid electric vehicle," Applied Energy, Elsevier, vol. 285(C).
  • Handle: RePEc:eee:appene:v:285:y:2021:i:c:s0306261920317803
    DOI: 10.1016/j.apenergy.2020.116413
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920317803
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2020.116413?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ma, Rui & Yang, Tao & Breaz, Elena & Li, Zhongliang & Briois, Pascal & Gao, Fei, 2018. "Data-driven proton exchange membrane fuel cell degradation predication through deep learning method," Applied Energy, Elsevier, vol. 231(C), pages 102-115.
    2. Zhang, Tong & Wang, Peiqi & Chen, Huicui & Pei, Pucheng, 2018. "A review of automotive proton exchange membrane fuel cell degradation under start-stop operating condition," Applied Energy, Elsevier, vol. 223(C), pages 249-262.
    3. Jiang, Hongliang & Xu, Liangfei & Li, Jianqiu & Hu, Zunyan & Ouyang, Minggao, 2019. "Energy management and component sizing for a fuel cell/battery/supercapacitor hybrid powertrain based on two-dimensional optimization algorithms," Energy, Elsevier, vol. 177(C), pages 386-396.
    4. Hu, Xiaosong & Johannesson, Lars & Murgovski, Nikolce & Egardt, Bo, 2015. "Longevity-conscious dimensioning and power management of the hybrid energy storage system in a fuel cell hybrid electric bus," Applied Energy, Elsevier, vol. 137(C), pages 913-924.
    5. Wang, Yujie & Sun, Zhendong & Chen, Zonghai, 2019. "Energy management strategy for battery/supercapacitor/fuel cell hybrid source vehicles based on finite state machine," Applied Energy, Elsevier, vol. 254(C).
    6. Tribioli, Laura & Cozzolino, Raffaello & Chiappini, Daniele & Iora, Paolo, 2016. "Energy management of a plug-in fuel cell/battery hybrid vehicle with on-board fuel processing," Applied Energy, Elsevier, vol. 184(C), pages 140-154.
    7. Zhou, Yang & Ravey, Alexandre & Péra, Marie-Cecile, 2020. "Multi-mode predictive energy management for fuel cell hybrid electric vehicles using Markov driving pattern recognizer," Applied Energy, Elsevier, vol. 258(C).
    8. Bressel, Mathieu & Hilairet, Mickael & Hissel, Daniel & Ould Bouamama, Belkacem, 2016. "Extended Kalman Filter for prognostic of Proton Exchange Membrane Fuel Cell," Applied Energy, Elsevier, vol. 164(C), pages 220-227.
    9. Nyong-Bassey, Bassey Etim & Giaouris, Damian & Patsios, Charalampos & Papadopoulou, Simira & Papadopoulos, Athanasios I. & Walker, Sara & Voutetakis, Spyros & Seferlis, Panos & Gadoue, Shady, 2020. "Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty," Energy, Elsevier, vol. 193(C).
    10. Chen, Kui & Laghrouche, Salah & Djerdir, Abdesslem, 2019. "Degradation model of proton exchange membrane fuel cell based on a novel hybrid method," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Song, Ke & Huang, Xing & Huang, Pengyu & Sun, Hui & Chen, Yuhui & Huang, Dongya, 2024. "Data-driven health state estimation and remaining useful life prediction of fuel cells," Renewable Energy, Elsevier, vol. 227(C).
    2. Chen, Jinzhou & He, Hongwen & Wang, Ya-Xiong & Quan, Shengwei & Zhang, Zhendong & Wei, Zhongbao & Han, Ruoyan, 2024. "Research on energy management strategy for fuel cell hybrid electric vehicles based on improved dynamic programming and air supply optimization," Energy, Elsevier, vol. 300(C).
    3. Zeynali, Saeed & Nasiri, Nima & Marzband, Mousa & Ravadanegh, Sajad Najafi, 2021. "A hybrid robust-stochastic framework for strategic scheduling of integrated wind farm and plug-in hybrid electric vehicle fleets," Applied Energy, Elsevier, vol. 300(C).
    4. Piras, M. & De Bellis, V. & Malfi, E. & Novella, R. & Lopez-Juarez, M., 2024. "Hydrogen consumption and durability assessment of fuel cell vehicles in realistic driving," Applied Energy, Elsevier, vol. 358(C).
    5. Gong, Zhichao & Wang, Bowen & Xu, Yifan & Ni, Meng & Gao, Qingchen & Hou, Zhongjun & Cai, Jun & Gu, Xin & Yuan, Xinjie & Jiao, Kui, 2022. "Adaptive optimization strategy of air supply for automotive polymer electrolyte membrane fuel cell in life cycle," Applied Energy, Elsevier, vol. 325(C).
    6. Anselma, Pier Giuseppe & Belingardi, Giovanni, 2022. "Fuel cell electrified propulsion systems for long-haul heavy-duty trucks: present and future cost-oriented sizing," Applied Energy, Elsevier, vol. 321(C).
    7. Zhou, Yujie & Huang, Yin & Mao, Xuping & Kang, Zehao & Huang, Xuejin & Xuan, Dongji, 2024. "Research on energy management strategy of fuel cell hybrid power via an improved TD3 deep reinforcement learning," Energy, Elsevier, vol. 293(C).
    8. Lu, Dagang & Yi, Fengyan & Hu, Donghai & Li, Jianwei & Yang, Qingqing & Wang, Jing, 2023. "Online optimization of energy management strategy for FCV control parameters considering dual power source lifespan decay synergy," Applied Energy, Elsevier, vol. 348(C).
    9. Daeichian, Abolghasem & Ghaderi, Razieh & Kandidayeni, Mohsen & Soleymani, Mehdi & Trovão, João P. & Boulon, Loïc, 2021. "Online characteristics estimation of a fuel cell stack through covariance intersection data fusion," Applied Energy, Elsevier, vol. 292(C).
    10. Mubashir Rasool & Muhammad Adil Khan & Runmin Zou, 2023. "A Comprehensive Analysis of Online and Offline Energy Management Approaches for Optimal Performance of Fuel Cell Hybrid Electric Vehicles," Energies, MDPI, vol. 16(8), pages 1-33, April.
    11. Iqbal, Mehroze & Becherif, Mohamed & Ramadan, Haitham S. & Badji, Abderrezak, 2021. "Dual-layer approach for systematic sizing and online energy management of fuel cell hybrid vehicles," Applied Energy, Elsevier, vol. 300(C).
    12. Qilin Shuai & Yiheng Wang & Zhengxiong Jiang & Qingsong Hua, 2024. "Reinforcement Learning-Based Energy Management for Fuel Cell Electrical Vehicles Considering Fuel Cell Degradation," Energies, MDPI, vol. 17(7), pages 1-19, March.
    13. Quan, Shengwei & He, Hongwen & Chen, Jinzhou & Zhang, Zhendong & Han, Ruoyan & Wang, Ya-Xiong, 2023. "Health-aware model predictive energy management for fuel cell electric vehicle based on hybrid modeling method," Energy, Elsevier, vol. 278(PA).
    14. Pang, Ran & Zhang, Caizhi & Dai, Haifeng & Bai, Yunfeng & Hao, Dong & Chen, Jinrui & Zhang, Bin, 2022. "Intelligent health states recognition of fuel cell by cell voltage consistency under typical operating parameters," Applied Energy, Elsevier, vol. 305(C).
    15. Yanwei Liu & Jiansheng Liang & Jiaqing Song & Jie Ye, 2022. "Research on Energy Management Strategy of Fuel Cell Vehicle Based on Multi-Dimensional Dynamic Programming," Energies, MDPI, vol. 15(14), pages 1-20, July.
    16. He, Hongwen & Meng, Xiangfei & Wang, Yong & Khajepour, Amir & An, Xiaowen & Wang, Renguang & Sun, Fengchun, 2024. "Deep reinforcement learning based energy management strategies for electrified vehicles: Recent advances and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Kui & Laghrouche, Salah & Djerdir, Abdesslem, 2019. "Degradation model of proton exchange membrane fuel cell based on a novel hybrid method," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    2. Chen, Kui & Badji, Abderrezak & Laghrouche, Salah & Djerdir, Abdesslem, 2022. "Polymer electrolyte membrane fuel cells degradation prediction using multi-kernel relevance vector regression and whale optimization algorithm," Applied Energy, Elsevier, vol. 318(C).
    3. Li, Haolong & Chen, Qihong & Zhang, Liyan & Liu, Li & Xiao, Peng, 2023. "Degradation prediction of proton exchange membrane fuel cell based on the multi-inputs Bi-directional long short-term memory," Applied Energy, Elsevier, vol. 344(C).
    4. Alessandro Serpi & Mario Porru, 2019. "Modelling and Design of Real-Time Energy Management Systems for Fuel Cell/Battery Electric Vehicles," Energies, MDPI, vol. 12(22), pages 1-21, November.
    5. Kandidayeni, M. & Macias, A. & Boulon, L. & Kelouwani, S., 2020. "Investigating the impact of ageing and thermal management of a fuel cell system on energy management strategies," Applied Energy, Elsevier, vol. 274(C).
    6. Hua, Zhiguang & Zheng, Zhixue & Péra, Marie-Cécile & Gao, Fei, 2020. "Remaining useful life prediction of PEMFC systems based on the multi-input echo state network," Applied Energy, Elsevier, vol. 265(C).
    7. Zuo, Jian & Lv, Hong & Zhou, Daming & Xue, Qiong & Jin, Liming & Zhou, Wei & Yang, Daijun & Zhang, Cunman, 2021. "Deep learning based prognostic framework towards proton exchange membrane fuel cell for automotive application," Applied Energy, Elsevier, vol. 281(C).
    8. Chen, Hong & Zhan, Zhigang & Jiang, Panxing & Sun, Yahao & Liao, Liwen & Wan, Xiongbiao & Du, Qing & Chen, Xiaosong & Song, Hao & Zhu, Ruijie & Shu, Zhanhong & Li, Shang & Pan, Mu, 2022. "Whole life cycle performance degradation test and RUL prediction research of fuel cell MEA," Applied Energy, Elsevier, vol. 310(C).
    9. Zuo, Jian & Steiner, Nadia Yousfi & Li, Zhongliang & Hissel, Daniel, 2024. "Health management review for fuel cells: Focus on action phase," Renewable and Sustainable Energy Reviews, Elsevier, vol. 201(C).
    10. Liu, Hao & Chen, Jian & Hissel, Daniel & Lu, Jianguo & Hou, Ming & Shao, Zhigang, 2020. "Prognostics methods and degradation indexes of proton exchange membrane fuel cells: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 123(C).
    11. Aihua Tang & Yuanhang Yang & Quanqing Yu & Zhigang Zhang & Lin Yang, 2022. "A Review of Life Prediction Methods for PEMFCs in Electric Vehicles," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
    12. Desantes, J.M. & Novella, R. & Pla, B. & Lopez-Juarez, M., 2022. "A modeling framework for predicting the effect of the operating conditions and component sizing on fuel cell degradation and performance for automotive applications," Applied Energy, Elsevier, vol. 317(C).
    13. Quan, Shengwei & Wang, Ya-Xiong & Xiao, Xuelian & He, Hongwen & Sun, Fengchun, 2021. "Real-time energy management for fuel cell electric vehicle using speed prediction-based model predictive control considering performance degradation," Applied Energy, Elsevier, vol. 304(C).
    14. Zhuang Tian & Zheng Wei & Jinhui Wang & Yinxiang Wang & Yuwei Lei & Ping Hu & S. M. Muyeen & Daming Zhou, 2023. "Research Progress on Aging Prediction Methods for Fuel Cells: Mechanism, Methods, and Evaluation Criteria," Energies, MDPI, vol. 16(23), pages 1-21, November.
    15. Jinquan, Guo & Hongwen, He & Jianwei, Li & Qingwu, Liu, 2022. "Driving information process system-based real-time energy management for the fuel cell bus to minimize fuel cell engine aging and energy consumption," Energy, Elsevier, vol. 248(C).
    16. Deng, Zhihua & Chan, Siew Hwa & Chen, Qihong & Liu, Hao & Zhang, Liyan & Zhou, Keliang & Tong, Sirui & Fu, Zhichao, 2023. "Efficient degradation prediction of PEMFCs using ELM-AE based on fuzzy extension broad learning system," Applied Energy, Elsevier, vol. 331(C).
    17. Guarino, Antonio & Trinchero, Riccardo & Canavero, Flavio & Spagnuolo, Giovanni, 2022. "A fast fuel cell parametric identification approach based on machine learning inverse models," Energy, Elsevier, vol. 239(PC).
    18. Wang, Chu & Li, Zhongliang & Outbib, Rachid & Dou, Manfeng & Zhao, Dongdong, 2022. "Symbolic deep learning based prognostics for dynamic operating proton exchange membrane fuel cells," Applied Energy, Elsevier, vol. 305(C).
    19. Wang, Yujie & Sun, Zhendong & Chen, Zonghai, 2019. "Energy management strategy for battery/supercapacitor/fuel cell hybrid source vehicles based on finite state machine," Applied Energy, Elsevier, vol. 254(C).
    20. Xun, Qian & Murgovski, Nikolce & Liu, Yujing, 2022. "Chance-constrained robust co-design optimization for fuel cell hybrid electric trucks," Applied Energy, Elsevier, vol. 320(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:285:y:2021:i:c:s0306261920317803. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.