Open Access
Issue
JNWPU
Volume 38, Number 4, August 2020
Page(s) 814 - 821
DOI https://doi.org/10.1051/jnwpu/20203840814
Published online 06 October 2020
  1. LIPU M H, HANNAN M, HUSSAIN A, et al. A Review of State of Health and Remaining Useful Life Estimation Methods for Lithium-Ion Battery in Electric Vehicles:Challenges and Recommendations[J]. Journal of Cleaner Production, 2018, 205: 115– 133 [Article] [CrossRef] [Google Scholar]
  2. YANG D, ZHANG X, PAN R, et al. A Novel Gaussian Process Regression Model for State-of-Health Estimation of Lithium-Ion Battery Using Charging Curve[J]. Journal of Power Sources, 2018, 384: 387– 395 [Article] [CrossRef] [Google Scholar]
  3. DENG L M, HSU Y C, LI H X. An Improved Model for Remaining Useful Life Prediction on Capacity Degradation and Regene- ration of Lithium-Ion Battery[C]//Proceedings of the Annual Conference of the Prognostics and Health Management Society, Saint Petersburg, FL, USA, 2017: 2–7 [Google Scholar]
  4. WU J, ZHANG C, CHEN Z H. An Online Method for Lithium-Ion Battery Remaining Useful Life Estimation Using Importance Sampling and Neural Networks[J]. Applied Energy, 2016, 173: 134– 140 [Article] [CrossRef] [Google Scholar]
  5. WIDODO A, SHIM M C, CAESARENDRA W, et al. Intelligent Prognostics for Battery Health Monitoring Based on Sample Entropy[J]. Expert Systems with Applications, 2011, 38 (9): 11763– 11769 [Article] [CrossRef] [Google Scholar]
  6. LIU D, WANG H, PENG Y, et al. Satellite Lithium-Ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator Extraction[J]. Energies, 2013, 6 (8): 3654– 3668 [Article] [CrossRef] [Google Scholar]
  7. HUSSEIN A. Capacity Fade Estimation in Electric Vehicle Li-Ion Batteries Using Artificial Neural Networks[J]. IEEE Trans on Industry Applications, 2014, 51 (3): 2321– 2330 [Article] [CrossRef] [Google Scholar]
  8. LU S, WANG F, PIAO C H, et al. Health State Prediction of Lithium-Ion Battery Based on Deep Learning Method[C]//IOP Conference Series: Materials Science and Engineering, 2020 [Google Scholar]
  9. PARK K, CHOI Y, WON J C, et al. LSTM-Based Battery Remaining Useful Life Prediction with Multi-Channel Charging Profiles[J]. IEEE Access, 2020, (8): 20768– 20798 [Article] [Google Scholar]
  10. LI P H, ZHANG Z J, XIONG Q Y, et al. State-of-Health Estimation and Remaining Useful Life Prediction for the Lithium-Ion Battery Based on a Variant Long Short Term Memory Neural Network[J]. Journal of Power Sources, 2020, 459: 1– 12 [Article] [Google Scholar]
  11. LI Danqi, MEI Fei, ZHANG Huanyu, et al. Deep Belief Network Based Method for Feature Extraction and Source Identification of Voltaege Sag[J]. Automation of Electric Power Systems, 2019, 43: 1– 9 [Article] [Google Scholar]
  12. JIN Qi, WANG Youren, WANG Jun. Planetary Gearbox Fault Diagnosis Based on Multiple Feature Extraction and Information Fusion Combined with Deep Learning[J]. China Mechanical Engineering, 2019, 20 (2): 196– 204 [Article] [Google Scholar]
  13. ZHAO Chunhua, HU Hengxing, CHEN Baojia, et al. Bearing Fault Diagnosis Based on the Deep Learning Feature Extraction and WOA SVM State Recognition[J]. Journal of Vibration and Shock, 2019, 38 (10): 31– 37 [Article] [Google Scholar]
  14. AYINDE B O, ZURADA J M. Deep Learning of Constrained Autoencoders for Enhanced Understanding of Data[J]. IEEE Trans on Neural Networks Learning Systems, 2017, 29 (9): 3969– 3979 [Article] [CrossRef] [Google Scholar]
  15. HE Y J, SHEN J N, SHEN J F, et al. State of Health Estimation of Lithium-Ion Batteries:a Multiscale Gaussian Process Regression Modeling Approach[J]. AIChE Journal, 2015, 61 (5): 1589– 1600 [Article] [CrossRef] [Google Scholar]
  16. YU J. 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 [Article] [CrossRef] [Google Scholar]
  17. WANG Zhuqing. Research on RUL Prediction Method of Lithium-Ion Battery Based on Neural Network[D]. Taiyuan: North University of China, 2019(in Chinese) [Google Scholar]
  18. DRAGOMIRETSKIY K, ZOSSO D. Variational Mode Decomposition[J]. IEEE Trans on Signal Processing, 2013, 62 (3): 531– 544 [Article] [NASA ADS] [CrossRef] [Google Scholar]
  19. LI J, WANG H, ZHANG J, et al. Impact Fault Detection of Gearbox Based on Variational Mode Decomposition and Coupled Underdamped Stochastic Resonance[J]. ISA Transactions, 2019, 95: 320– 329 [Article] [CrossRef] [Google Scholar]
  20. SAHA B, GOEBEL K. Modeling Li-ion Battery Capacity Depletion in a Particle Fittering Framework[C]//Proceedings of the Annual Conference of the Prognotics and Health Management Society, 2009 [Google Scholar]
  21. SONG Y, LIU D, PENG Y, et al. Self-Adaptive Indirect Health Indicators Extraction within Prognosis of Satellite Lithium-Ion Battery[C]//Prognostics and System Health Management Conference. 2017: 1–7 [Google Scholar]
  22. WEN J, ZHONG Z F, ZHANG Z, et al. Adaptive Locality Preserving Regression[J]. IEEE Trans on Circuits and Systems for Video Technology, 2020, 30 (1): 75– 88 [Article] [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.