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Guo, Hao (author), Zhao, C. (author), Zhou, Dong (author), Wang, Jianlin (author), Ma, Xiaobai (author), Gao, Jianxiang (author), Jiao, Xuesheng (author), Hu, Xufeng (author), Bai, Xuedong (author), Sun, Kai (author), Chen, Dongfeng (author)
Layered O3-type oxides are one of the most promising cathode materials for Na-ion batteries owing to their high capacity and straightforward synthesis. However, these materials often experience irreversible structure transitions at elevated cutoff voltages, resulting in compromised cycling stability and rate performance. To address such...
journal article 2024
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Guo, Hao (author), Zhao, C. (author), Gao, Jianxiang (author), Yang, Wenyun (author), Hu, Xufeng (author), Ma, Xiaobai (author), Jiao, Xuesheng (author), Yang, Jinbo (author), Sun, Kai (author), Chen, Dongfeng (author)
To realize concurrently the high-energy density and excellent cycling stability, maximum utilization of redox couple, minimization of detrimental phase transition, and structural degradation of O3-type layered oxide cathodes are critical for developing Na-ion batteries. Ni<sup>2+</sup>/Ni<sup>4+</sup> redox couple showing multielectron...
journal article 2023
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Xu, Jingyi (author), Li, Z. (author), Gao, Li (author), Ma, Junyi (author), Liu, Qi (author), Zhao, Yanan (author)
The deep reinforcement learning-based energy management strategies (EMS) have become a promising solution for hybrid electric vehicles (HEVs). When driving cycles are changed, the neural network will be retrained, which is a time-consuming and laborious task. A more efficient way of choosing EMS is to combine deep reinforcement learning (DRL)...
conference paper 2022