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Particle Swarm-Optimized Fuzzy Logic Energy Management of Hybrid Energy Storage in Electric Vehicles

Author

Listed:
  • Joseph Omakor

    (Intelligent Robotic and Energy Systems Research Group, Faculty of Engineering and Design, Carleton University, Ottawa, ON K1S 5B6, Canada)

  • Mohamad Alzayed

    (Intelligent Robotic and Energy Systems Research Group, Faculty of Engineering and Design, Carleton University, Ottawa, ON K1S 5B6, Canada)

  • Hicham Chaoui

    (Intelligent Robotic and Energy Systems Research Group, Faculty of Engineering and Design, Carleton University, Ottawa, ON K1S 5B6, Canada)

Abstract

A lithium-ion battery–ultracapacitor hybrid energy storage system (HESS) has been recognized as a viable solution to address the limitations of single battery energy sources in electric vehicles (EVs), especially in urban driving conditions, owing to its complementary energy features. However, an energy management strategy (EMS) is required for the optimal performance of the HESS. In this paper, an EMS based on the particle swarm optimization (PSO) of the fuzzy logic controller (FLC) is proposed. It aims to minimize battery current and power peak fluctuations, thereby enhancing its capacity and lifespan, by optimizing the weights of formulated FLC rules using the PSO algorithm. This paper utilizes the battery temperature as the cost function in the optimization problem of the PSO due to the sensitivity of lithium-ion batteries (LIBs) to operating temperature variations compared to ultracapacitors (UCs). An evaluation of optimized FLC using PSO and a developed EV model is conducted under the Urban Dynamometer Driving Schedule (UDDS) and compared with the unoptimized FLC. The result shows that 5.4% of the battery’s capacity was conserved at 25.5 °C, which is the highest operating temperature attained under the proposed strategy.

Suggested Citation

  • Joseph Omakor & Mohamad Alzayed & Hicham Chaoui, 2024. "Particle Swarm-Optimized Fuzzy Logic Energy Management of Hybrid Energy Storage in Electric Vehicles," Energies, MDPI, vol. 17(9), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:9:p:2163-:d:1387189
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    References listed on IDEAS

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    1. Chen, Zeyu & Xiong, Rui & Cao, Jiayi, 2016. "Particle swarm optimization-based optimal power management of plug-in hybrid electric vehicles considering uncertain driving conditions," Energy, Elsevier, vol. 96(C), pages 197-208.
    2. da Silva, Samuel Filgueira & Eckert, Jony Javorski & Corrêa, Fernanda Cristina & Silva, Fabrício Leonardo & Silva, Ludmila C.A. & Dedini, Franco Giuseppe, 2022. "Dual HESS electric vehicle powertrain design and fuzzy control based on multi-objective optimization to increase driving range and battery life cycle," Applied Energy, Elsevier, vol. 324(C).
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    Cited by:

    1. Bin Huang & Wenbin Yu & Minrui Ma & Xiaoxu Wei & Guangya Wang, 2025. "Artificial-Intelligence-Based Energy Management Strategies for Hybrid Electric Vehicles: A Comprehensive Review," Energies, MDPI, vol. 18(14), pages 1-42, July.
    2. C. Treesatayapun & A. J. Munoz-Vazquez & S. K. Korkua & B. Srikarun & C. Pochaiya, 2025. "Electric Vehicle Energy Management Under Unknown Disturbances from Undefined Power Demand: Online Co-State Estimation via Reinforcement Learning," Energies, MDPI, vol. 18(15), pages 1-20, July.

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