正在加载图片...
994 工程科学学报,第43卷,第7期 [21]Qi H M.Research on Prediction Method of Remaining Life of [24]Yu Y,Hu C H,Si X S,et al.Averaged Bi-LSTM networks for Lithium Battery Based on Deep Learning [Dissertation].Harbin RUL prognostics with non-life-cycle labeled dataset.Neurocom- Harbin Institute of Technology,2019 puting,2020,402:134 (齐吴明,基于深度学习的锂电池剩余寿命预测方法研究学位 [25]Yang F F,Zhang S H,Li W H,et al.State of charge estimation of 论文]哈尔滨:哈尔滨工业大学,2019) lithium-ion batteries using LSTM and UKF.Energy,2020,201: [22]Li JL,Li X Y,He D.A directed acyclic graph network combined 117664 with CNN and LSTM for remaining useful life prediction./EEE [26]Liu J,Chen Z Q,Huang D Y,et al.Remaining useful life of Acces8.,2019,7:75464 lithium-ion batteries based on time interval of equal charging [23]Zhou Y T,Huang Y N.Pang J B,et al.Remaining useful life voltage difference.J Shanghai Jiaotong Univ,2019,53(9):1058 prediction for supercapacitor based on long short-term memory (刘健,陈自强,黄德扬,等.基于等压差充电时间的锂离子电池 neural network.Power Sources,2019,440:227149 寿命预测.上海交通大学学报,2019,53(9):1058)Qi  H  M. Research on Prediction Method of Remaining Life of Lithium Battery Based on Deep Learning [Dissertation].  Harbin: Harbin Institute of Technology, 2019 ( 齐昊明. 基于深度学习的锂电池剩余寿命预测方法研究[学位 论文]. 哈尔滨: 哈尔滨工业大学, 2019) [21] Li J L, Li X Y, He D. A directed acyclic graph network combined with  CNN  and  LSTM  for  remaining  useful  life  prediction. IEEE Access, 2019, 7: 75464 [22] Zhou  Y  T,  Huang  Y  N,  Pang  J  B,  et  al.  Remaining  useful  life prediction  for  supercapacitor  based  on  long  short-term  memory neural network. J Power Sources, 2019, 440: 227149 [23] Yu  Y,  Hu  C  H,  Si  X  S,  et  al.  Averaged  Bi–LSTM  networks  for RUL  prognostics  with  non-life-cycle  labeled  dataset. Neurocom￾puting, 2020, 402: 134 [24] Yang F F, Zhang S H, Li W H, et al. State of charge estimation of lithium-ion  batteries  using  LSTM  and  UKF. Energy,  2020,  201: 117664 [25] Liu  J,  Chen  Z  Q,  Huang  D  Y,  et  al.  Remaining  useful  life  of lithium-ion  batteries  based  on  time  interval  of  equal  charging voltage difference. J Shanghai Jiaotong Univ, 2019, 53(9): 1058 (刘健, 陈自强, 黄德扬, 等. 基于等压差充电时间的锂离子电池 寿命预测. 上海交通大学学报, 2019, 53(9):1058) [26] · 994 · 工程科学学报,第 43 卷,第 7 期
<<向上翻页
©2008-现在 cucdc.com 高等教育资讯网 版权所有