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Comparison of semi-active hybrid battery system configurations for electric taxis application

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  • Zhuang, Weichao
  • Ye, Jianwei
  • Song, Ziyou
  • Yin, Guodong
  • Li, Guangmin

Abstract

This paper proposes two semi-active configurations for hybrid battery system (HBS) in which LiFePO4 (LFP) and Li4Ti5O12 (LTO) batteries are combined to prolong the life span of LFP batteries used in electric vehicles (EVs). To protect the LFP batteries from frequent peak power demands, the first configuration (HBS #1) uses diodes and switches, while the second (HBS #2) adopts a bidirectional DC/DC convertor to decouple various batteries. To make a fair comparison of the two configurations, their component sizes are firstly determined using brute-force search considering energy capacity and cost. Then, a unified fuzzy-logic energy management strategy was designed and optimized for each configuration to mitigate LFP battery degradation. Simulated implementation of HBSs #1 and 2 in an electric taxi with standard daily operation (driving nearly 400 km per day) and charging patterns revealed that both have longer LFP lifespans (44.4% and 45.4% improvement, respectively), lower annual costs (12.49% and 11.52% reduction, respectively) and reduced distance-based costs (39.41% and 39.18% reduction, respectively) than a single-LFP battery configuration. Although HBS #2 demonstrated more battery life improvement, HBS #1 was found to be cheaper in EV application from the perspectives of total and distance-based cost.

Suggested Citation

  • Zhuang, Weichao & Ye, Jianwei & Song, Ziyou & Yin, Guodong & Li, Guangmin, 2020. "Comparison of semi-active hybrid battery system configurations for electric taxis application," Applied Energy, Elsevier, vol. 259(C).
    • Handle: RePEc:eee:appene:v:259:y:2020:i:c:s0306261919318586
    DOI: 10.1016/j.apenergy.2019.114171
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    Cited by:

    1. 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.
    2. Niu, Junyan & Zhuang, Weichao & Ye, Jianwei & Song, Ziyou & Yin, Guodong & Zhang, Yuanjian, 2022. "Optimal sizing and learning-based energy management strategy of NCR/LTO hybrid battery system for electric taxis," Energy, Elsevier, vol. 257(C).
    3. Qi, Nanjian & Yin, Yajiang & Dai, Keren & Wu, Chengjun & Wang, Xiaofeng & You, Zheng, 2021. "Comprehensive optimized hybrid energy storage system for long-life solar-powered wireless sensor network nodes," Applied Energy, Elsevier, vol. 290(C).
    4. He, Hongwen & Meng, Xiangfei & Wang, Yong & Khajepour, Amir & An, Xiaowen & Wang, Renguang & Sun, Fengchun, 2024. "Deep reinforcement learning based energy management strategies for electrified vehicles: Recent advances and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).

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