机译:一种剩余使用寿命预测和循环寿命测试优化不同配方锂离子电池的混合传递学习方案
Beihang Univ Sch Reliabil & Syst Engn Beijing Peoples R China|Natl Key Lab Sci & Technol Reliabil & Environm En Beijing Peoples R China;
Beihang Univ Sch Reliabil & Syst Engn Beijing Peoples R China|Natl Key Lab Sci & Technol Reliabil & Environm En Beijing Peoples R China;
Beihang Univ Sch Reliabil & Syst Engn Beijing Peoples R China|Natl Key Lab Sci & Technol Reliabil & Environm En Beijing Peoples R China;
Air Force Engn Univ Aviat Maintenance NCO Acad Xinyang Peoples R China;
Natl Key Lab Sci & Technol Reliabil & Environm En Beijing Peoples R China|Beihang Univ Sch Aeronaut Sci & Engn Beijing Peoples R China;
Beihang Univ Sch Reliabil & Syst Engn Beijing Peoples R China|Natl Key Lab Sci & Technol Reliabil & Environm En Beijing Peoples R China;
Beihang Univ Sch Reliabil & Syst Engn Beijing Peoples R China|Natl Key Lab Sci & Technol Reliabil & Environm En Beijing Peoples R China;
Beihang Univ Sch Reliabil & Syst Engn Beijing Peoples R China|Natl Key Lab Sci & Technol Reliabil & Environm En Beijing Peoples R China;
Beihang Univ Sch Reliabil & Syst Engn Beijing Peoples R China|Natl Key Lab Sci & Technol Reliabil & Environm En Beijing Peoples R China;
Contemporary Amperex Technol Co Ltd Ningde 352100 Fujian Peoples R China;
Contemporary Amperex Technol Co Ltd Ningde 352100 Fujian Peoples R China;
Contemporary Amperex Technol Co Ltd Ningde 352100 Fujian Peoples R China;
Contemporary Amperex Technol Co Ltd Ningde 352100 Fujian Peoples R China;
Lithium power battery; Remaining useful life prediction; Cycle life test optimization; Hybrid transfer learning; Transferable sample selection; Deep recurrent neural network;
机译:使用混合剩余使用寿命预测方法优化不同锂离子动力电池配方的循环寿命测试
机译:使用强大的深度学习方法
机译:通过考虑现实的测试曲线,研究石墨| NCA高能和LTO |金属氧化物高功率电池的循环寿命,评估混合动力电池系统降低电动汽车电池老化的潜力
机译:基于参数优化的混合数据驱动方法预测锂离子电池的剩余使用寿命
机译:在重型混合动力汽车的基于生命周期成本的设计优化中对电池退化的影响进行建模。
机译:具有出色循环寿命的大功率快速充电锂离子电池
机译:柔性路面剩余使用寿命预测的支持向量机和果蝇优化算法的混合机械学习模型
机译:通过模式识别应用于铅酸电池寿命循环测试数据的电池寿命预测。