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面向部分充电条件的不确定性加权贝叶斯融合SVGMR鲁棒电池健康状态估计框架
Papers | 更新时间:2026-10-02
    • 面向部分充电条件的不确定性加权贝叶斯融合SVGMR鲁棒电池健康状态估计框架

    • An uncertainty-weighted Bayesian-fusion SVGMR framework for robust battery SOH estimation under partial charging conditions

    • 可靠性科学与工程学报(英文)   2026年2卷第3期 页码:1-15
    • 作者机构:

      School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, People’s Republic of China

      Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hong Kong SAR, People’s Republic of China

      Department of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People’s Republic of China

      State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, People’s Republic of China

      State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, Chongqing, People’s Republic of China

    • DOI:10.1088/3050-2454/ae9e30    

      中图分类号:
    • 收稿:2026-05-30,

      修回:2026-07-11,

      录用:2026-08-25,

      网络首发:2026-09-09,

      纸质出版:2026-09

    移动端阅览

  • Hongjie Lv, Xingchen Liu, Dong Wang, Bingchang Hou, Dong Zhen and Jinzhen Kong. 面向部分充电条件的不确定性加权贝叶斯融合SVGMR鲁棒电池健康状态估计框架[J]. 可靠性科学与工程学报(英文), 2026, 2: 035304. DOI: 10.1088/3050-2454/ae9e30.

    Hongjie Lv, Xingchen Liu, Dong Wang, Bingchang Hou, Dong Zhen and Jinzhen Kong. An uncertainty-weighted Bayesian-fusion SVGMR framework for robust battery SOH estimation under partial charging conditions[J]. Journal of Reliability Science and Engineering, 2026, 2: 035304. DOI: 10.1088/3050-2454/ae9e30.

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