1 |
ROMAN D,SAXENA S,ROBU V,et al.Machine Learning Pipeline for Battery State-of-Health Estimation[J].Nature Machine Intelligence, 2021,3:447-456.
|
2 |
胡晓亚,郭永芳,张若可.锂离子电池健康状态估计方法研究综述[J].电源学报,2022,20(01):126-133.HU X Y, GUO Y F, ZHANG R K. Review of lithium-ion battery health status estimation methods [J].Journal of Power Supply,2022, 20(1):126-133.
|
3 |
王琛,闵永军.基于容量增量曲线与GWO-GPR的锂离子电池SOH估计[J].储能科学与技术,2023,12(11):3508-3518.WANG C,MIN Y J.SOH estimation of lithium-ion batteries based on capacity increment curve and GWO-GPR [J].Energy Storage Science and Technology,2023,12 (11): 3508-3518.
|
4 |
Li J, Adewuyi K, Yagin N L,et al.A Single Particle Model with Chemical/Mechanical Degradation Physics for Lithium Ion Battery State of Health (SOH) Estimation[J].Applied Energy,2018,212(15 February 2018):1178–1190.
|
5 |
Yang J, Cai Y, Pan C,et al.A novel resistor-inductor network-based equivalent circuit model of lithium-ion batteries under constant-voltage charging condition[J].Applied Energy,2019, 254:113726.
|
6 |
Bezha M, Bezha K, Nagaoka N.A Practical SoH Estimation using Adaptive ANN algorithm for the embedded EIS diagnosis in Industrial Applications[C]//2022 IEEE International Conference on Consumer Electronics-Taiwan.IEEE,2022:571-572.
|
7 |
韦荣阳,毛阗,高晗,彭建仁,杨健. 基于TWP-SVR的锂离子电池健康状态估计[J].储能科学与技术,2022,11(08):2585-2599.Wei R Y, Mao T, G H, Peng J R, Yang J.Health State Estimation of Lithium-Ion Batteries Based on TWP-SVR[J].Journal of Energy Storage Science and Technology,2022, 11(08): 2585-2599.
|
8 |
Lyu Z, Wang G, Gao R.Synchronous state of health estimation and remaininguseful lifetime prediction of Li-Ion battery through optimized relevance vectormachine framework[J].Energy,2022, 251: 123
|
9 |
SPOTNITZ R.Simulation of capacity fade in lithium-ion batteries[J].Journal of Power Sources,2003,113(1):72-80.
|
10 |
ZHAO L,WANG Y,CHENG J.A hybrid method for remaining useful life estimation of lithium-ion battery with regeneration phenomena[J].Applied Sciences,2019,9(9):1890.
|
11 |
Yuan Z, Tian T, Hao F, et al.A hybrid neural network based on variational mode decomposition denoising for predicting state-of-health of lithium-ion batteries[J].Journal of Power Sources,2024, 609: 234697.
|
12 |
Gao K, Huang Z, Lyu C, et al.Multi-scale prediction of remaining useful life of lithium-ion batteries based on variational mode decomposition and integrated machine learning[J].Journal of Energy Storage,2024, 99:113372.
|
13 |
M. Zhu, Q. Ouyang, Y. Wan, et al.Remaining Useful Life Prediction of Lithium-Ion Batteries:A Hybrid Approach of Grey–Markov Chain Model and Improved Gaussian Process[J].IEEEJournal of Emerging and Selected Topics in Power Electronics, 2023. 11(1): 143-153..
|
14 |
M. Wei, M. Ye, C. Zhang, et al.A multi-scale learning approach for remaining useful lifeprediction of lithium-ion batteries based on variational mode decomposition and Monte Carlosampling[J]. Energy,2023.283.
|
15 |
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.Lithium Battery Life Prediction Based on Multi-Scale Decomposition and Deep Learning[J].Journal of Zhejiang University (Engineering Edition),2019, 53(10): 1852-1864.
|
16 |
PAN H H, LU Z Q, WANG H Y, et al.Novel battery state-ofhealth online estimation method using multiple health indicators and an extreme learning machine[J].Energy,2018,160: 466-477.
|
17 |
谢旭,蒲娴怡,毕贵红,等.基于二层分解技术的锂离子电池容量评估方法[J].电源技术,2022,46(06):647-651.Xie X, Pu X Y, Bi G H, et al.Capacity Evaluation Method for Lithium-Ion Batteries Based on Two-Layer Decomposition Technique[J].Power Supply Technology,2022,46(06): 647-651.
|
18 |
Lin C, Xu J, Shi M, et al.Constant current charging time based fast state-of-health estimation for lithium-ion batteries[J].Energy,2022, 247: 123556.
|
19 |
李嘉波,王志璇,田迪,等.变模态分解下SSA-LSTM组合的锂离子电池剩余使用寿命预测方法[J/OL].储能科学与技术,1-14.Li J B, Wang Z X,Tian D,et al. The remaining service life prediction method of lithium-ion batteries with SSA-LSTM combination under variable mode decomposition[J/OL].Science and Technology forEnergyStorage,1-14.
|
20 |
孙中麟,李嘉波,田迪,等.基于COA-LSTM和VMD的锂离子电池剩余寿命预测[J].储能科学与技术,2024,13(09):3254-3265.Sun Zhonglin,Li Jiabo, Tian Di, et al. Remaining Useful Life Prediction of Lithium-Ion Batteries Based on COA-LSTM and VMD[J].Journal of Energy Storage Science and Technology,2024, 13(09):3254-3265.
|
21 |
张岸,杨春德.基于GAN-CNN-LSTM的锂电池SOH估计[J].电源技术,2021,45(07):902-906.Zhang A, Yang C D.SOH Estimation of Lithium Batteries Based on GAN-CNN-LSTM[J].Power Supply Technology,2021,45(07): 902-906.
|