Dynamic behavior prediction of modules in crushing via FEA-DNN technique for durable battery-pack system design
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DOI: 10.1016/j.apenergy.2022.119527
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Cited by:
- Zhang, Xiaoxi & Pan, Yongjun & Zhou, Junxiao & Li, Zhixiong & Liao, Tianjun & Li, Jie, 2024. "Forward and reverse design of adhesive in batteries via dynamics and machine learning algorithms for enhanced mechanical safety," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
- Zhang, Xiaoxi & Pan, Yongjun & Xiong, Yue & Zhang, Yongzhi & Tang, Mao & Dai, Wei & Liu, Binghe & Hou, Liang, 2024. "Deep learning-based vibration stress and fatigue-life prediction of a battery-pack system," Applied Energy, Elsevier, vol. 357(C).
- Xu, Dongxin & Pan, Yongjun & Zhang, Xiaoxi & Dai, Wei & Liu, Binghe & Shuai, Qi, 2024. "Data-driven modelling and evaluation of a battery-pack system’s mechanical safety against bottom cone impact," Energy, Elsevier, vol. 290(C).
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Keywords
Battery-pack system; Battery packs; Battery modules; Crushing; Stress prediction; Deep neural networks;All these keywords.
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