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Electric Vehicle Battery Lifetime Extension through an Intelligent Double-Layer Control Scheme

Author

Listed:
  • Omid Rahbari

    (MOBI Research Group, ETEC Department, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussels, Belgium
    Flanders Make, 3001 Heverlee, Belgium)

  • Noshin Omar

    (MOBI Research Group, ETEC Department, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussels, Belgium
    Flanders Make, 3001 Heverlee, Belgium)

  • Joeri Van Mierlo

    (MOBI Research Group, ETEC Department, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussels, Belgium
    Flanders Make, 3001 Heverlee, Belgium)

  • Marc A. Rosen

    (Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, Oshawa, ON L1G 0C5, Canada)

  • Thierry Coosemans

    (MOBI Research Group, ETEC Department, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussels, Belgium
    Flanders Make, 3001 Heverlee, Belgium)

  • Maitane Berecibar

    (MOBI Research Group, ETEC Department, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussels, Belgium
    Flanders Make, 3001 Heverlee, Belgium)

Abstract

Electric vehicles (EVs) are recognized as promising options, not only for the decarbonization of urban areas and greening of the transportation sector, but also for increasing power system flexibility through demand-side management. Large-scale uncoordinated charging of EVs can impose negative impacts on the existing power system infrastructure regarding stability and security of power system operation. One solution to the severe grid overload issues derived from high penetration of EVs is to integrate local renewable power generation units as distributed generation units to the power system or to the charging infrastructure. To reduce the uncertainties associated with renewable power generation and load as well as to improve the process of tracking Pareto front in each time sequence, a predictive double-layer optimal power flow based on support vector regression and one-step prediction is presented in this study. The results demonstrate that, through the proposed control approach, the rate of battery degradation is reduced by lowering the number of cycles in which EVs contribute to the services that can be offered to the grid via EVs. Moreover, vehicle to grid services are found to be profitable for electricity providers but not for plug-in electric vehicle owners, with the existing battery technology and its normal degradation.

Suggested Citation

  • Omid Rahbari & Noshin Omar & Joeri Van Mierlo & Marc A. Rosen & Thierry Coosemans & Maitane Berecibar, 2019. "Electric Vehicle Battery Lifetime Extension through an Intelligent Double-Layer Control Scheme," Energies, MDPI, vol. 12(8), pages 1-24, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:8:p:1525-:d:225101
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    References listed on IDEAS

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    1. de Hoog, Joris & Timmermans, Jean-Marc & Ioan-Stroe, Daniel & Swierczynski, Maciej & Jaguemont, Joris & Goutam, Shovon & Omar, Noshin & Van Mierlo, Joeri & Van Den Bossche, Peter, 2017. "Combined cycling and calendar capacity fade modeling of a Nickel-Manganese-Cobalt Oxide Cell with real-life profile validation," Applied Energy, Elsevier, vol. 200(C), pages 47-61.
    2. Dawoud, Samir M. & Lin, Xiangning & Okba, Merfat I., 2018. "Hybrid renewable microgrid optimization techniques: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2039-2052.
    3. Zeng, Jianwu & Qiao, Wei, 2013. "Short-term solar power prediction using a support vector machine," Renewable Energy, Elsevier, vol. 52(C), pages 118-127.
    4. McPherson, Madeleine & Ismail, Malik & Hoornweg, Daniel & Metcalfe, Murray, 2018. "Planning for variable renewable energy and electric vehicle integration under varying degrees of decentralization: A case study in Lusaka, Zambia," Energy, Elsevier, vol. 151(C), pages 332-346.
    5. Ahmadian, Ali & Sedghi, Mahdi & Elkamel, Ali & Fowler, Michael & Aliakbar Golkar, Masoud, 2018. "Plug-in electric vehicle batteries degradation modeling for smart grid studies: Review, assessment and conceptual framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2609-2624.
    6. Seddig, Katrin & Jochem, Patrick & Fichtner, Wolf, 2017. "Integrating renewable energy sources by electric vehicle fleets under uncertainty," Energy, Elsevier, vol. 141(C), pages 2145-2153.
    7. Drude, Lukas & Pereira Junior, Luiz Carlos & Rüther, Ricardo, 2014. "Photovoltaics (PV) and electric vehicle-to-grid (V2G) strategies for peak demand reduction in urban regions in Brazil in a smart grid environment," Renewable Energy, Elsevier, vol. 68(C), pages 443-451.
    8. Li, J. & Adewuyi, K. & Lotfi, N. & Landers, R.G. & Park, J., 2018. "A single particle model with chemical/mechanical degradation physics for lithium ion battery State of Health (SOH) estimation," Applied Energy, Elsevier, vol. 212(C), pages 1178-1190.
    9. Grover-Silva, Etta & Girard, Robin & Kariniotakis, George, 2018. "Optimal sizing and placement of distribution grid connected battery systems through an SOCP optimal power flow algorithm," Applied Energy, Elsevier, vol. 219(C), pages 385-393.
    10. Rahbari, Omid & Vafaeipour, Majid & Omar, Noshin & Rosen, Marc A. & Hegazy, Omar & Timmermans, Jean-Marc & Heibati, Seyedmohammadreza & Bossche, Peter Van Den, 2017. "An optimal versatile control approach for plug-in electric vehicles to integrate renewable energy sources and smart grids," Energy, Elsevier, vol. 134(C), pages 1053-1067.
    11. Berecibar, M. & Gandiaga, I. & Villarreal, I. & Omar, N. & Van Mierlo, J. & Van den Bossche, P., 2016. "Critical review of state of health estimation methods of Li-ion batteries for real applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 572-587.
    12. Rahbari, Omid & Omar, Noshin & Firouz, Yousef & Rosen, Marc A. & Goutam, Shovon & Van Den Bossche, Peter & Van Mierlo, Joeri, 2018. "A novel state of charge and capacity estimation technique for electric vehicles connected to a smart grid based on inverse theory and a metaheuristic algorithm," Energy, Elsevier, vol. 155(C), pages 1047-1058.
    13. Uddin, Kotub & Jackson, Tim & Widanage, Widanalage D. & Chouchelamane, Gael & Jennings, Paul A. & Marco, James, 2017. "On the possibility of extending the lifetime of lithium-ion batteries through optimal V2G facilitated by an integrated vehicle and smart-grid system," Energy, Elsevier, vol. 133(C), pages 710-722.
    14. Yilun Shang & Yamei Ye, 2017. "Leader-Follower Fixed-Time Group Consensus Control of Multiagent Systems under Directed Topology," Complexity, Hindawi, vol. 2017, pages 1-9, March.
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