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Energy Management of Parallel-Connected Cells in Electric Vehicles Based on Fuzzy Logic Control

Author

Listed:
  • Chuanxue Song

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China)

  • Yulong Shao

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China)

  • Shixin Song

    (School of Mechanical Science and Engineering, Jilin University, Changchun 130022, China)

  • Cheng Chang

    (College of Automotive Engineering, Jilin University, Changchun 130022, China)

  • Fang Zhou

    (College of Automotive Engineering, Jilin University, Changchun 130022, China)

  • Silun Peng

    (College of Automotive Engineering, Jilin University, Changchun 130022, China)

  • Feng Xiao

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China)

Abstract

Inconsistencies that are associated with parallel-connected cells used in electric vehicles induce varied states of charge ( SOCs ) in each cell. Thus, loop current in the battery pack is inevitable, and this reduces overall capacity, energy utilization rate, and pack lifetime. However, no method is available to address loop current. To reduce loop current and the resulting battery inconsistency, a parallel-connected cell pack (PCCP) model that considers thermal effects is established, and a novel Simscape model that is based on PCCP is successfully constructed. Furthermore, the strategy of parallel-connected cell energy management (PCCEM) is proposed to utilize fuzzy logic control (FLC) strategy, which automatically adjusts the number of cells in a circuit in accordance with the load demand, and turns on the first N switches in the corresponding SOC order. The New European Driving Cycle (NEDC) driving cycle simulation shows that the PCCEM strategy considerably reduces loop current and improves the consistency of battery performance and the utilization rate of battery power.

Suggested Citation

  • Chuanxue Song & Yulong Shao & Shixin Song & Cheng Chang & Fang Zhou & Silun Peng & Feng Xiao, 2017. "Energy Management of Parallel-Connected Cells in Electric Vehicles Based on Fuzzy Logic Control," Energies, MDPI, vol. 10(3), pages 1-13, March.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:3:p:404-:d:93610
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    References listed on IDEAS

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    Cited by:

    1. Bizhong Xia & Zheng Zhang & Zizhou Lao & Wei Wang & Wei Sun & Yongzhi Lai & Mingwang Wang, 2018. "Strong Tracking of a H-Infinity Filter in Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 11(6), pages 1-20, June.
    2. Saiteja, Pemmareddy & Ashok, B., 2022. "Critical review on structural architecture, energy control strategies and development process towards optimal energy management in hybrid vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    3. Phuong-Ha La & Sung-Jin Choi, 2020. "Novel Dynamic Resistance Equalizer for Parallel-Connected Battery Configurations," Energies, MDPI, vol. 13(13), pages 1-17, June.
    4. Neha Bhushan & Saad Mekhilef & Kok Soon Tey & Mohamed Shaaban & Mehdi Seyedmahmoudian & Alex Stojcevski, 2022. "Overview of Model- and Non-Model-Based Online Battery Management Systems for Electric Vehicle Applications: A Comprehensive Review of Experimental and Simulation Studies," Sustainability, MDPI, vol. 14(23), pages 1-31, November.
    5. Liu, Xuze & Fotouhi, Abbas & Auger, Daniel J., 2020. "Optimal energy management for formula-E cars with regulatory limits and thermal constraints," Applied Energy, Elsevier, vol. 279(C).

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