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Robust optimization based energy management of a fuel cell/ultra-capacitor hybrid electric vehicle under uncertainty

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  • Koubaa, Rayhane
  • Bacha, Seddik
  • Smaoui, Mariem
  • krichen, Lotfi

Abstract

This paper investigates the energy management of a fuel cell/ultra-capacitor hybrid electric vehicle (FCHEV) under uncertainty. In addition to fuel economy and fuel cell (FC) system slow dynamics, solutions’ robustness is also considered as a key performance criterion. As previous studies focus on expected optimal performance under a deterministic framework and ignore possible data ambiguity, this paper considers uncertainties affecting power production, conversion and demand levels. Changing operation conditions, modeling and estimation claim several sources of errors. In practice, ignoring such uncertainties leads to high operation cost, poor overall system efficiency, performance failure and even infeasible solutions due to constraints violation. A robust optimization (RO) based energy management system (EMS) is studied in order to ensure optimal yet robust performance under uncertain parameters. The adopted RO based algorithm includes uncertainty in the cost function and constraints set and thus protects the system performance from feasibility and optimality issues. As RO approach is considered over conservative, conservatism level parameters are introduced to enable more flexible decision making and performance cost. Three different optimization case studies, namely classical deterministic, complete robust and variable conservatism level were investigated in order to assess the performance of the proposed approach.

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  • Koubaa, Rayhane & Bacha, Seddik & Smaoui, Mariem & krichen, Lotfi, 2020. "Robust optimization based energy management of a fuel cell/ultra-capacitor hybrid electric vehicle under uncertainty," Energy, Elsevier, vol. 200(C).
    • Handle: RePEc:eee:energy:v:200:y:2020:i:c:s036054422030637x
    DOI: 10.1016/j.energy.2020.117530
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    4. Mariano Gallo & Mario Marinelli, 2022. "The Impact of Fuel Cell Electric Freight Vehicles on Fuel Consumption and CO 2 Emissions: The Case of Italy," Sustainability, MDPI, vol. 14(20), pages 1-17, October.
    5. Coppitters, Diederik & De Paepe, Ward & Contino, Francesco, 2021. "Robust design optimization of a photovoltaic-battery-heat pump system with thermal storage under aleatory and epistemic uncertainty," Energy, Elsevier, vol. 229(C).
    6. Wang, Chenxu & Xiong, Rui & Tian, Jinpeng & Lu, Jiahuan & Zhang, Chengming, 2022. "Rapid ultracapacitor life prediction with a convolutional neural network," Applied Energy, Elsevier, vol. 305(C).
    7. Ju, Fei & Zhuang, Weichao & Wang, Liangmo & Zhang, Zhe, 2020. "Comparison of four-wheel-drive hybrid powertrain configurations," Energy, Elsevier, vol. 209(C).
    8. Iqbal, Mehroze & Laurent, Julien & Benmouna, Amel & Becherif, Mohamed & Ramadan, Haitham S. & Claude, Frederic, 2022. "Ageing-aware load following control for composite-cost optimal energy management of fuel cell hybrid electric vehicle," Energy, Elsevier, vol. 254(PA).
    9. Mariano Gallo & Mario Marinelli, 2020. "Sustainable Mobility: A Review of Possible Actions and Policies," Sustainability, MDPI, vol. 12(18), pages 1-39, September.
    10. Doh Dinga, Christian & Wen, Zongguo, 2022. "Many-objective optimization of energy conservation and emission reduction under uncertainty: A case study in China's cement industry," Energy, Elsevier, vol. 253(C).

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