Realistic fault detection of li-ion battery via dynamical deep learning.

Zhang, Jingzhao; Wang, Yanan; Jiang, Benben; He, Haowei; Huang, Shaobo; Wang, Chen; Zhang, Yang; Han, Xuebing; Guo, Dongxu; He, Guannan; Ouyang, Minggao

Zhang, Jingzhao; Wang, Yanan; Jiang, Benben; He, Haowei; Huang, Shaobo; Wang, Chen; Zhang, Yang; Han, Xuebing; Guo, Dongxu; He, Guannan; Ouyang, Minggao.

Afiliación

Zhang J; IIIS Tsinghua University, Beijing, China.
Wang Y; Shanghai Qizhi Institute, Shanghai, China.
Jiang B; State Key Laboratory of Intelligent Green Vehicle and Mobility, School of Vehicle and Mobility, Tsinghua University, Beijing, China.
He H; Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China.
Huang S; IIIS Tsinghua University, Beijing, China.
Wang C; Beijing Circue Energy Technology Co. Ltd., Beijing, China.
Zhang Y; School of Automation Science and Electrical Engineering, Beihang University, Beijing, China.
Han X; Beijing Circue Energy Technology Co. Ltd., Beijing, China.
Guo D; State Key Laboratory of Intelligent Green Vehicle and Mobility, School of Vehicle and Mobility, Tsinghua University, Beijing, China.
He G; State Key Laboratory of Intelligent Green Vehicle and Mobility, School of Vehicle and Mobility, Tsinghua University, Beijing, China.
Ouyang M; Department of Industrial Engineering and Management, College of Engineering, Peking University, Beijing, China. gnhe@pku.edu.cn.

Nat Commun ; 14(1): 5940, 2023 Sep 23.

Article en En | MEDLINE | ID: mdl-37741826

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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2023 Tipo del documento: Article País de afiliación: China

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