IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v171y2021icp881-901.html
   My bibliography  Save this article

Energy management and optimization of PEMFC/battery mobile robot based on hybrid rule strategy and AMPSO

Author

Listed:
  • Xueqin Lü,
  • Wu, Yinbo
  • Lian, Jie
  • Zhang, Yangyang

Abstract

Due to the flexibility of seam tracking robot in special environment, independent power supply with different characteristics is adopted. Power allocation of hybrid power system composed of proton exchange membrane fuel cell and lithium battery is mainly researched. According to the requirements of economy and safety, hybrid rule fuzzy state machine control is designed to realize the optimal operation and fast response of power supply system. On the basis of energy allocation, further reduction of hydrogen consumption and power fluctuation is considered, which improves the power supply scheme. The sensitivity analysis method is used to screen the optimization variables, which reduces the complexity of the strategy; in addition, an adaptive mutation particle swarm optimization is studied to optimize the strategy, which combines the mutation idea, adaptively changes the weight and learning factor, and realizes the minimization of power fluctuation and equivalent hydrogen consumption. The results show that the stability of fuel cell and the rationality of lithium battery charging and discharging are greatly improved, and the fuel economy and service life of hybrid power system are improved.

Suggested Citation

  • Xueqin Lü, & Wu, Yinbo & Lian, Jie & Zhang, Yangyang, 2021. "Energy management and optimization of PEMFC/battery mobile robot based on hybrid rule strategy and AMPSO," Renewable Energy, Elsevier, vol. 171(C), pages 881-901.
    • Handle: RePEc:eee:renene:v:171:y:2021:i:c:p:881-901
    DOI: 10.1016/j.renene.2021.02.135
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148121003141
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2021.02.135?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. Sulaiman, N. & Hannan, M.A. & Mohamed, A. & Ker, P.J. & Majlan, E.H. & Wan Daud, W.R., 2018. "Optimization of energy management system for fuel-cell hybrid electric vehicles: Issues and recommendations," Applied Energy, Elsevier, vol. 228(C), pages 2061-2079.
    2. Ettihir, K. & Boulon, L. & Agbossou, K., 2016. "Optimization-based energy management strategy for a fuel cell/battery hybrid power system," Applied Energy, Elsevier, vol. 163(C), pages 142-153.
    3. Kang, Sanggyu & Zhao, Li & Brouwer, Jacob, 2019. "Dynamic modeling and verification of a proton exchange membrane fuel cell-battery hybrid system to power servers in data centers," Renewable Energy, Elsevier, vol. 143(C), pages 313-327.
    4. Peng, Fei & Zhao, Yuanzhe & Chen, Ting & Zhang, Xuexia & Chen, Weirong & Zhou, Donghua & Li, Qi, 2018. "Development of robust suboptimal real-time power sharing strategy for modern fuel cell based hybrid tramways considering operational uncertainties and performance degradation," Applied Energy, Elsevier, vol. 226(C), pages 503-521.
    5. Daud, W.R.W. & Rosli, R.E. & Majlan, E.H. & Hamid, S.A.A. & Mohamed, R. & Husaini, T., 2017. "PEM fuel cell system control: A review," Renewable Energy, Elsevier, vol. 113(C), pages 620-638.
    6. 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).
    7. 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.
    8. Fathy, Ahmed & Rezk, Hegazy & Nassef, Ahmed M., 2019. "Robust hydrogen-consumption-minimization strategy based salp swarm algorithm for energy management of fuel cell/supercapacitor/batteries in highly fluctuated load condition," Renewable Energy, Elsevier, vol. 139(C), pages 147-160.
    9. Devin Fowler & Vladimir Gurau & Daniel Cox, 2019. "Bridging the Gap between Automated Manufacturing of Fuel Cell Components and Robotic Assembly of Fuel Cell Stacks," Energies, MDPI, vol. 12(19), pages 1-14, September.
    10. Li, Xiaoyu & Yuan, Changgui & Li, Xiaohui & Wang, Zhenpo, 2020. "State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression," Energy, Elsevier, vol. 190(C).
    11. Peng, Fei & Zhao, Yuanzhe & Li, Xiaopeng & Liu, Zhixiang & Chen, Weirong & Liu, Yang & Zhou, Donghua, 2017. "Development of master-slave energy management strategy based on fuzzy logic hysteresis state machine and differential power processing compensation for a PEMFC-LIB-SC hybrid tramway," Applied Energy, Elsevier, vol. 206(C), pages 346-363.
    12. Benmouna, Amel & Becherif, Mohamed & Depernet, Daniel & Ebrahim, Mohamed A., 2018. "Novel Energy Management Technique for Hybrid Electric Vehicle via Interconnection and Damping Assignment Passivity Based Control," Renewable Energy, Elsevier, vol. 119(C), pages 116-128.
    13. Fathabadi, Hassan, 2019. "Combining a proton exchange membrane fuel cell (PEMFC) stack with a Li-ion battery to supply the power needs of a hybrid electric vehicle," Renewable Energy, Elsevier, vol. 130(C), pages 714-724.
    14. Arya, Yogendra, 2019. "Impact of hydrogen aqua electrolyzer-fuel cell units on automatic generation control of power systems with a new optimal fuzzy TIDF-II controller," Renewable Energy, Elsevier, vol. 139(C), pages 468-482.
    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. Lü, Xueqin & Meng, Ruidong & Deng, Ruiyu & Long, Liyuan & Wu, Yinbo, 2022. "Energy economy optimization and comprehensive performance improvement for PEMFC/LIB hybrid system based on hierarchical optimization," Renewable Energy, Elsevier, vol. 193(C), pages 1132-1149.
    2. Wang, Congyu & Song, Jiwei, 2023. "Performance assessment of the novel coal-fired combined heat and power plant integrating with flexibility renovations," Energy, Elsevier, vol. 263(PC).
    3. Lü, Xueqin & Deng, Ruiyu & Chen, Chao & Wu, Yinbo & Meng, Ruidong & Long, Liyuan, 2022. "Performance optimization of fuel cell hybrid power robot based on power demand prediction and model evaluation," Applied Energy, Elsevier, vol. 316(C).
    4. Fan, Lixin & Liu, Yang & Luo, Xiaobing & Tu, Zhengkai & Chan, Siew Hwa, 2023. "Comparison and evaluation of mega watts proton exchange membrane fuel cell combined heat and power system under different waste heat recovery methods," Renewable Energy, Elsevier, vol. 210(C), pages 295-305.

    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. Sun, Zhendong & Wang, Yujie & Chen, Zonghai & Li, Xiyun, 2020. "Min-max game based energy management strategy for fuel cell/supercapacitor hybrid electric vehicles," Applied Energy, Elsevier, vol. 267(C).
    2. Bizon, Nicu, 2019. "Real-time optimization strategies of Fuel Cell Hybrid Power Systems based on Load-following control: A new strategy, and a comparative study of topologies and fuel economy obtained," Applied Energy, Elsevier, vol. 241(C), pages 444-460.
    3. Bizon, Nicu, 2019. "Efficient fuel economy strategies for the Fuel Cell Hybrid Power Systems under variable renewable/load power profile," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    4. 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).
    5. Nicu Bizon & Phatiphat Thounthong, 2021. "A Simple and Safe Strategy for Improving the Fuel Economy of a Fuel Cell Vehicle," Mathematics, MDPI, vol. 9(6), pages 1-29, March.
    6. Peng, Hujun & Chen, Zhu & Li, Jianxiang & Deng, Kai & Dirkes, Steffen & Gottschalk, Jonas & Ünlübayir, Cem & Thul, Andreas & Löwenstein, Lars & Pischinger, Stefan & Hameyer, Kay, 2021. "Offline optimal energy management strategies considering high dynamics in batteries and constraints on fuel cell system power rate: From analytical derivation to validation on test bench," Applied Energy, Elsevier, vol. 282(PA).
    7. 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.
    8. Ioan-Sorin Sorlei & Nicu Bizon & Phatiphat Thounthong & Mihai Varlam & Elena Carcadea & Mihai Culcer & Mariana Iliescu & Mircea Raceanu, 2021. "Fuel Cell Electric Vehicles—A Brief Review of Current Topologies and Energy Management Strategies," Energies, MDPI, vol. 14(1), pages 1-29, January.
    9. Kim, Youngki & Figueroa-Santos, Miriam & Prakash, Niket & Baek, Stanley & Siegel, Jason B. & Rizzo, Denise M., 2020. "Co-optimization of speed trajectory and power management for a fuel-cell/battery electric vehicle," Applied Energy, Elsevier, vol. 260(C).
    10. Bizon, Nicu, 2019. "Fuel saving strategy using real-time switching of the fueling regulators in the proton exchange membrane fuel cell system," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    11. Kang, Sanggyu & Zhao, Li & Brouwer, Jacob, 2019. "Dynamic modeling and verification of a proton exchange membrane fuel cell-battery hybrid system to power servers in data centers," Renewable Energy, Elsevier, vol. 143(C), pages 313-327.
    12. 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).
    13. Quan, Shengwei & Wang, Ya-Xiong & Xiao, Xuelian & He, Hongwen & Sun, Fengchun, 2021. "Feedback linearization-based MIMO model predictive control with defined pseudo-reference for hydrogen regulation of automotive fuel cells," Applied Energy, Elsevier, vol. 293(C).
    14. Rudravaram Venkatasatish & Dhanamjayulu Chittathuru, 2023. "Coyote Optimization Algorithm-Based Energy Management Strategy for Fuel Cell Hybrid Power Systems," Sustainability, MDPI, vol. 15(12), pages 1-21, June.
    15. Ma, Shuai & Lin, Meng & Lin, Tzu-En & Lan, Tian & Liao, Xun & Maréchal, François & Van herle, Jan & Yang, Yongping & Dong, Changqing & Wang, Ligang, 2021. "Fuel cell-battery hybrid systems for mobility and off-grid applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    16. Lü, Xueqin & Meng, Ruidong & Deng, Ruiyu & Long, Liyuan & Wu, Yinbo, 2022. "Energy economy optimization and comprehensive performance improvement for PEMFC/LIB hybrid system based on hierarchical optimization," Renewable Energy, Elsevier, vol. 193(C), pages 1132-1149.
    17. Laura Zecchi & Giulia Sandrini & Marco Gadola & Daniel Chindamo, 2022. "Modeling of a Hybrid Fuel Cell Powertrain with Power Split Logic for Onboard Energy Management Using a Longitudinal Dynamics Simulation Tool," Energies, MDPI, vol. 15(17), pages 1-18, August.
    18. Nicu Bizon & Mihai Oproescu, 2018. "Experimental Comparison of Three Real-Time Optimization Strategies Applied to Renewable/FC-Based Hybrid Power Systems Based on Load-Following Control," Energies, MDPI, vol. 11(12), pages 1-32, December.
    19. Feng, Yanbiao & Dong, Zuomin, 2020. "Integrated design and control optimization of fuel cell hybrid mining truck with minimized lifecycle cost," Applied Energy, Elsevier, vol. 270(C).
    20. 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).

    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:renene:v:171:y:2021:i:c:p:881-901. 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.journals.elsevier.com/renewable-energy .

    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.