Optimization of battery charging strategy based on nonlinear model predictive control
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DOI: 10.1016/j.energy.2021.122877
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Cited by:
- Yao, Jiachi & Han, Te, 2023. "Data-driven lithium-ion batteries capacity estimation based on deep transfer learning using partial segment of charging/discharging data," Energy, Elsevier, vol. 271(C).
- Shi, Haotian & Wang, Shunli & Fernandez, Carlos & Yu, Chunmei & Xu, Wenhua & Dablu, Bobobee Etse & Wang, Liping, 2022. "Improved multi-time scale lumped thermoelectric coupling modeling and parameter dispersion evaluation of lithium-ion batteries," Applied Energy, Elsevier, vol. 324(C).
- Fan, Zhaohui & Fu, Yijie & Liang, Hong & Gao, Renjing & Liu, Shutian, 2023. "A module-level charging optimization method of lithium-ion battery considering temperature gradient effect of liquid cooling and charging time," Energy, Elsevier, vol. 265(C).
- Olis, Walker & Rosewater, David & Nguyen, Tu & Byrne, Raymond H., 2023. "Impact of heating and cooling loads on battery energy storage system sizing in extreme cold climates," Energy, Elsevier, vol. 278(PB).
- Liu, Haoran & Yu, Jiaqi & Wang, Ruzhu, 2022. "Model predictive control of portable electronic devices under skin temperature constraints," Energy, Elsevier, vol. 260(C).
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Keywords
Charging optimization; Thermoelectric coupling model; Nonlinear model predictive control; Parameter identification; Lithium-ion batteries;All these keywords.
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