1 |
胡天中, 余建波. 基于多尺度分解和深度学习的锂电池寿命预测[J]. 浙江大学学报(工学版), 2019, 53(10): 1852-1864.
|
|
HU T Z, YU J B. Life prediction of lithium-ion batteries based on multiscale decomposition and deep learning[J]. Journal of Zhejiang University (Engineering Science), 2019, 53(10): 1852-1864.
|
2 |
梁海峰, 袁芃, 高亚静. 基于CNN-Bi-LSTM网络的锂离子电池剩余使用寿命预测[J]. 电力自动化设备, 2021, 41(10): 213-219.
|
|
LIANG H F, YUAN P, GAO Y J. Remaining useful life prediction of lithium-ion battery based on CNN-Bi-LSTM network[J]. Electric Power Automation Equipment, 2021, 41(10): 213-219.
|
3 |
李超然, 肖飞, 樊亚翔, 等. 基于卷积神经网络的锂离子电池SOH估算[J]. 电工技术学报, 2020, 35(19): 4106-4119.
|
|
LI C R, XIAO F, FAN Y X, et al. An approach to lithium-ion battery SOH estimation based on convolutional neural network[J]. Transactions of China Electrotechnical Society, 2020, 35(19): 4106-4119.
|
4 |
KHODADADI SADABADI K, JIN X, RIZZONI G. Prediction of remaining useful life for a composite electrode lithium ion battery cell using an electrochemical model to estimate the state of health[J]. Journal of Power Sources, 2021, 481: doi: 10.1016/j.jpowsour. 2020.228861.
|
5 |
严干贵, 蔡长兴, 段双明, 等. 锂离子储能电池成组方式优化[J]. 电力自动化设备, 2021, 41(4): 148-153.
|
|
YAN G G, CAI C X, DUAN S M, et al. Grouping mode optimization of lithium-ion energy storage battery[J]. Electric Power Automation Equipment, 2021, 41(4): 148-153.
|
6 |
杨新波, 郑岳久, 高文凯, 等. 基于改进等效电路模型的高比能量储能锂电池系统功率状态估计[J]. 电网技术, 2021, 45(1): 57-66.
|
|
YANG X B, ZHENG Y J, GAO W K, et al. Power state estimation of high specific energy storage lithium battery system based on extended equivalent circuit model[J]. Power System Technology, 2021, 45(1): 57-66.
|
7 |
谢滟馨, 王顺利, 史卫豪, 等. 一种用于高保真锂电池SOC估计的无迹粒子滤波新方法[J]. 储能科学与技术, 2021, 10(2): 722-731.
|
|
XIE Y X, WANG S L, SHI W H, et al. A new method of unscented particle filter for high-fidelity lithium-ion battery SOC estimation[J]. Energy Storage Science and Technology, 2021, 10(2): 722-731.
|
8 |
焦自权, 范兴明, 张鑫, 等. 基于改进粒子滤波算法的锂离子电池状态跟踪与剩余使用寿命预测方法[J]. 电工技术学报, 2020, 35(18): 3979-3993.
|
|
JIAO Z Q, FAN X M, ZHANG X, et al. State tracking and remaining useful life predictive method of Li-ion battery based on improved particle filter algorithm[J]. Transactions of China Electrotechnical Society, 2020, 35(18): 3979-3993.
|
9 |
姚芳, 张楠, 黄凯. 锂离子电池状态估算与寿命预测综述[J]. 电源学报, 2020, 18(3): 175-183.
|
|
YAO F, ZHANG N, HUANG K. Review of state estimation and life prediction for lithiumion batteries[J]. Journal of Power Supply, 2020, 18(3): 175-183.
|
10 |
杨彦茹, 温杰, 史元浩, 等. 基于CEEMDAN和SVR的锂离子电池剩余使用寿命预测[J]. 电子测量与仪器学报, 2020, 34(12): 197-205.
|
|
YANG Y R, WEN J, SHI Y H, et al. Remaining useful life prediction for lithium-ion battery based on CEEMDAN and SVR[J]. Journal of Electronic Measurement and Instrumentation, 2020, 34(12): 197-205.
|
11 |
李练兵, 李思佳, 李洁, 等. 基于差分电压和Elman神经网络的锂离子电池RUL预测方法[J]. 储能科学与技术, 2021, 10(6): 2373-2384.
|
|
LI L B, LI S J, LI J, et al. RUL prediction of lithium-ion battery based on differential voltage and Elman neural network[J]. Energy Storage Science and Technology, 2021, 10(6): 2373-2384.
|
12 |
肖浩逸, 何晓霞, 梁佳佳, 等. 一种基于模态分解和机器学习的锂电池寿命预测方法[J]. 储能科学与技术, 2022, 11(12): 3999-4009.
|
|
XIAO H Y, HE X X, LIANG J J, et al. A lithium battery life-prediction method based on mode decomposition and machine learning[J]. Energy Storage Science and Technology, 2022, 11(12): 3999-4009.
|
13 |
YU J B. State of health prediction of lithium-ion batteries: Multiscale logic regression and Gaussian process regression ensemble[J]. Reliability Engineering & System Safety, 2018, 174: 82-95.
|
14 |
LI X Y, ZHANG L, WANG Z P, et al. Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks[J]. Journal of Energy Storage, 2019, 21: 510-518.
|
15 |
黄凯, 丁恒, 郭永芳, 等. 基于数据预处理和长短期记忆神经网络的锂离子电池寿命预测[J]. 电工技术学报, 2022, 37(15): 3753-3766.
|
|
HUANG K, DING H, GUO Y F, et al. Prediction of remaining useful life of lithium-ion battery based on adaptive data preprocessing and long short-term memory network[J]. Transactions of China Electrotechnical Society, 2022, 37(15): 3753-3766.
|
16 |
张少凤, 张清勇, 杨叶森, 等. 基于滑动窗口和LSTM神经网络的锂离子电池建模方法[J]. 储能科学与技术, 2022, 11(1): 228-239.
|
|
ZHANG S F, ZHANG Q Y, YANG Y S, et al. Lithium-ion battery model based on sliding window and long short term memory neural network[J]. Energy Storage Science and Technology, 2022, 11(1): 228-239.
|
17 |
刘芊彤, 邢远秀. 基于VMD-PSO-GRU模型的锂离子电池剩余寿命预测[J]. 储能科学与技术, 2023, 12(1): 236-246.
|
|
LIU Q T, XING Y X. Remaining life prediction of lithium-ion battery based on VMD-PSO-GRU model[J]. Energy Storage Science and Technology, 2023, 12(1): 236-246.
|
18 |
WU Z H, HUANG N E. Ensemble empirical mode decomposition: A noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1(1): 1-41.
|
19 |
SAHA B, GOEBEL K. Battery data set[R]. NASA Ames Prognostics Data Repository, 2007.
|
20 |
BIRKL C. Diagnosis and prognosis of degradation in lithium-ion batteries[D]. Oxford, South East England, UK: University of Oxford, 2017.
|