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A Battery Management Strategy in a Lead-Acid and Lithium-Ion Hybrid Battery Energy Storage System for Conventional Transport Vehicles

Author

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  • Andre T. Puati Zau

    (Department of Electrical Engineering, Tshwane University of Technology, Pretoria 0001, South Africa)

  • Mpho J. Lencwe

    (Department of Electrical Engineering, Tshwane University of Technology, Pretoria 0001, South Africa)

  • S. P. Daniel Chowdhury

    (Department of Electrical Engineering, Tshwane University of Technology, Pretoria 0001, South Africa)

  • Thomas O. Olwal

    (Department of Electrical Engineering, Tshwane University of Technology, Pretoria 0001, South Africa)

Abstract

Conventional vehicles, having internal combustion engines, use lead-acid batteries (LABs) for starting, lighting, and ignition purposes. However, because of new additional features (i.e., enhanced electronics and start/stop functionalities) in these vehicles, LABs undergo deep discharges due to frequent engine cranking, which in turn affect their lifespan. Therefore, this research study seeks to improve LABs’ performance in terms of meeting the required vehicle cold cranking current (CCC) and long lifespan. The performance improvement is achieved by hybridizing a lead-acid with a lithium-ion battery at a pack level using a fully active topology approach. This topology approach connects the individual energy storage systems to their bidirectional DC-DC converter for ease of control. Besides, a battery management strategy based on fuzzy logic and a triple-loop proportional-integral (PI) controller is implemented for these conversion systems to ensure effective current sharing between lead-acid and lithium-ion batteries. A fuzzy logic controller provides a percentage reference current needed from the battery and regulates the batteries’ state-of-charge (SoC) within the desired limits. A triple-loop controller monitors and limits the hybridized system’s current sharing and voltage within the required range during cycling. The hybridized system is developed and validated using Matlab/Simulink. The battery packs are developed using the battery manufacturers’ data sheets. The results of the research, compared with a single LAB, show that by controlling the current flow and maintaining the SoC within the desired limits, the hybrid energy storage system can meet the desired vehicle cold cranking current at a reduced weight. Furthermore, the lead-acid battery lifespan based on a fatigue cycle-model is improved from two years to 8.5 years, thus improving its performance in terms of long lifespan.

Suggested Citation

  • Andre T. Puati Zau & Mpho J. Lencwe & S. P. Daniel Chowdhury & Thomas O. Olwal, 2022. "A Battery Management Strategy in a Lead-Acid and Lithium-Ion Hybrid Battery Energy Storage System for Conventional Transport Vehicles," Energies, MDPI, vol. 15(7), pages 1-29, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2577-:d:785303
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    References listed on IDEAS

    as
    1. Mpho J. Lencwe & S. P. Daniel Chowdhury & Thomas O. Olwal, 2021. "An Effective Control for Lead-Acid Performance Enhancement in a Hybrid Battery-Supercapacitor System Used in Transport Vehicles," Sustainability, MDPI, vol. 13(24), pages 1-27, December.
    2. Mpho J. Lencwe & Shyama P. Chowdhury & Thomas O. Olwal, 2018. "A Multi-Stage Approach to a Hybrid Lead Acid Battery and Supercapacitor System for Transport Vehicles," Energies, MDPI, vol. 11(11), pages 1-16, October.
    3. Yiqun Liu & Y. Gene Liao & Ming-Chia Lai, 2020. "Lithium-Ion Polymer Battery for 12-Voltage Applications: Experiment, Modelling, and Validation," Energies, MDPI, vol. 13(3), pages 1-15, February.
    4. Tae-Won Noh & Jung-Hoon Ahn & Byoung Kuk Lee, 2019. "Cranking Capability Estimation Algorithm Based on Modeling and Online Update of Model Parameters for Li-Ion SLI Batteries," Energies, MDPI, vol. 12(17), pages 1-14, September.
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    7. Natascia Andrenacci & Elio Chiodo & Davide Lauria & Fabio Mottola, 2018. "Life Cycle Estimation of Battery Energy Storage Systems for Primary Frequency Regulation," Energies, MDPI, vol. 11(12), pages 1-24, November.
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    Cited by:

    1. Ji, Jie & Zhou, Mengxiong & Guo, Renwei & Tang, Jiankang & Su, Jiaoyue & Huang, Hui & Sun, Na & Nazir, Muhammad Shahzad & Wang, Yaodong, 2023. "A electric power optimal scheduling study of hybrid energy storage system integrated load prediction technology considering ageing mechanism," Renewable Energy, Elsevier, vol. 215(C).
    2. Hartani, Mohamed Amine & Rezk, Hegazy & Benhammou, Aissa & Hamouda, Messaoud & Abdelkhalek, Othmane & Mekhilef, Saad & Olabi, A.G., 2023. "Proposed frequency decoupling-based fuzzy logic control for power allocation and state-of-charge recovery of hybrid energy storage systems adopting multi-level energy management for multi-DC-microgrid," Energy, Elsevier, vol. 278(C).

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