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A fast active balancing strategy based on model predictive control for lithium-ion battery packs

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  • Fan, Tian-E
  • Liu, Song-Ming
  • Yang, Hao
  • Li, Peng-Hua
  • Qu, Baihua

Abstract

The consistency of lithium-ion battery packs is extremely important to prolong battery life, maximize battery capacity and ensure safety operation in electric vehicles. In this paper, a model predictive control (MPC) method with a fast-balancing strategy is proposed to address the inconsistency issue of individual cell in lithium-ion battery packs. Firstly, an optimal energy transfer direction is investigated to improve equalization efficiency and reduce energy loss. Then, a MPC-based equalization algorithm is developed to obtain the optimal constant equalization current by directly minimizing equalization time of battery's SOC. Moreover, a fast-solving strategy for MPC is designed to reduce the computational burden of cells' equalization. Finally, the performance of proposed MPC algorithm has been compared with other MPC-based equalization methods in three different equalization topologies (cell-to-cell, cell-to-pack and module-based equalization topology), the results indicate that the proposed algorithm achieves faster equalization speed and less energy loss in three equalization topologies. Importantly, the proposed algorithm avoids the repeated charging and discharging of intermediate batteries effectively, and ensures the single-point convergence of cells' SOC. Furthermore, the effectiveness and accuracy of proposed fast-solving strategy for MPC algorithm is verified by comparison with common solving strategies, the results show the proposed method takes less computational time to obtain the accurate optimal balancing current, indicating that the proposed fast-solving strategy can improve computation speed and reduce computational burden.

Suggested Citation

  • Fan, Tian-E & Liu, Song-Ming & Yang, Hao & Li, Peng-Hua & Qu, Baihua, 2023. "A fast active balancing strategy based on model predictive control for lithium-ion battery packs," Energy, Elsevier, vol. 279(C).
    • Handle: RePEc:eee:energy:v:279:y:2023:i:c:s0360544223014226
    DOI: 10.1016/j.energy.2023.128028
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    References listed on IDEAS

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    1. Oyewole, Isaiah & Chehade, Abdallah & Kim, Youngki, 2022. "A controllable deep transfer learning network with multiple domain adaptation for battery state-of-charge estimation," Applied Energy, Elsevier, vol. 312(C).
    2. Wang, Shunli & Takyi-Aninakwa, Paul & Jin, Siyu & Yu, Chunmei & Fernandez, Carlos & Stroe, Daniel-Ioan, 2022. "An improved feedforward-long short-term memory modeling method for the whole-life-cycle state of charge prediction of lithium-ion batteries considering current-voltage-temperature variation," Energy, Elsevier, vol. 254(PA).
    3. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Li, Huan & Xu, Wenhua & Fernandez, Carlos, 2022. "An optimized relevant long short-term memory-squared gain extended Kalman filter for the state of charge estimation of lithium-ion batteries," Energy, Elsevier, vol. 260(C).
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    Cited by:

    1. Jia Xie & Huipin Lin & Jifeng Qu & Luhong Shi & Zuhong Chen & Sheng Chen & Yong Zheng, 2024. "Hierarchical Structure-Based Wireless Active Balancing System for Power Batteries," Energies, MDPI, vol. 17(18), pages 1-32, September.

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