A fast active balancing strategy based on model predictive control for lithium-ion battery packs
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DOI: 10.1016/j.energy.2023.128028
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- 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).
- 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).
- 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:
- 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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Keywords
Cell balancing; Lithium-ion battery packs; Model predictive control;All these keywords.
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