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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

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  • Rahbari, Omid
  • Omar, Noshin
  • Firouz, Yousef
  • Rosen, Marc A.
  • Goutam, Shovon
  • Van Den Bossche, Peter
  • Van Mierlo, Joeri

Abstract

Increasing interest in the successful coordination of electric vehicles and renewable energy sources has recently been shown by researchers and power generation companies, in large part due to its impact on de-carbonization of urban areas and its capability of contributing towards ancillary services. Nevertheless, this coordination requires a bi-directional communication infrastructure, between combined electric vehicles, renewable energy systems, and power plants since one of the main reasons of this combination is to address the temporal fluctuations in renewable power generation. This bi-directional communication enables the power grid to adapt to different power source structures and improves the acceptability of intermittent renewable energy generation. Whereas electric vehicles equipped with lithium-ion batteries appear to be feasible options for stationary energy storage systems, known as a new distributed generation, the flexibility of electric vehicles in vehicle-to-grid connections is completely dependent on the maximum practical capacity and state of charge of each vehicle. Hence this infrastructure for the integration of electric vehicles as new distributed generation and renewable energy systems with electrical grids, emphasizes the need for an off-board state estimation of electric vehicles in aggregators. Moreover, an accurate estimation of the state of health and state of charge when electric vehicles are not charged or discharged by a constant current profile is required to overcome the challenges of existing methods. Each of introduced methods has different limitations, which are presented in this article. This article proposes a novel off-board state estimation technique for such parameters as state of charge and maximum practical capacity by employing a metaheuristic algorithm and an adaptive neuro-fuzzy inference system to overcome the limitations of existing methods. Due to drawbacks of filtering techniques, inverse theory is used in this article to convert the filtering problem to an optimization problem in order to take advantage of its capability. The results exhibit not only a high convergence rate (low settling time) but also a high robustness.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:155:y:2018:i:c:p:1047-1058
    DOI: 10.1016/j.energy.2018.05.079
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    References listed on IDEAS

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    Cited by:

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    2. Taysa Millena Banik Marques & João Lucas Ferreira dos Santos & Diego Solak Castanho & Mariane Bigarelli Ferreira & Sergio L. Stevan & Carlos Henrique Illa Font & Thiago Antonini Alves & Cassiano Moro , 2023. "An Overview of Methods and Technologies for Estimating Battery State of Charge in Electric Vehicles," Energies, MDPI, vol. 16(13), pages 1-18, June.
    3. Linghu, Jinqing & Kang, Longyun & Liu, Ming & Luo, Xuan & Feng, Yuanbin & Lu, Chusheng, 2019. "Estimation for state-of-charge of lithium-ion battery based on an adaptive high-degree cubature Kalman filter," Energy, Elsevier, vol. 189(C).
    4. Wang, Shun-Li & Fernandez, Carlos & Zou, Chuan-Yun & Yu, Chun-Mei & Chen, Lei & Zhang, Li, 2019. "A comprehensive working state monitoring method for power battery packs considering state of balance and aging correction," Energy, Elsevier, vol. 171(C), pages 444-455.
    5. Dong, Zhe & Liu, Miao & Guo, Zhiwu & Huang, Xiaojin & Zhang, Yajun & Zhang, Zuoyi, 2019. "Adaptive state-observer for monitoring flexible nuclear reactors," Energy, Elsevier, vol. 171(C), pages 893-909.
    6. 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.
    7. Sanchari Deb & Xiao-Zhi Gao, 2022. "Prediction of Charging Demand of Electric City Buses of Helsinki, Finland by Random Forest," Energies, MDPI, vol. 15(10), pages 1-18, May.

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