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. Neurocomputing, 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