| Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries S Shen, M Sadoughi, M Li, Z Wang, C Hu Applied Energy 260, 114296, 2020 | 351 | 2020 |
| A deep learning method for online capacity estimation of lithium-ion batteries S Shen, M Sadoughi, X Chen, M Hong, C Hu Journal of Energy Storage 25, 100817, 2019 | 287 | 2019 |
| A physics-informed deep learning approach for bearing fault detection S Shen, H Lu, M Sadoughi, C Hu, V Nemani, A Thelen, K Webster, M Darr, ... Engineering Applications of Artificial Intelligence 103, 104295, 2021 | 122 | 2021 |
| Physics-based prognostics of implantable-grade lithium-ion battery for remaining useful life prediction YH Lui, M Li, A Downey, S Shen, VP Nemani, H Ye, C VanElzen, G Jain, ... Journal of Power Sources 485, 229327, 2021 | 71 | 2021 |
| Integrating physics-based modeling and machine learning for degradation diagnostics of lithium-ion batteries A Thelen, YH Lui, S Shen, S Laflamme, S Hu, H Ye, C Hu Energy Storage Materials 50, 668-695, 2022 | 44 | 2022 |
| Correlating capacity fade with film resistance loss in fast charging of lithium-ion battery P Gargh, A Sarkar, YH Lui, S Shen, C Hu, S Hu, IC Nlebedim, P Shrotriya Journal of Power Sources 485, 229360, 2021 | 32 | 2021 |
| A hybrid machine learning model for battery cycle life prediction with early cycle data S Shen, V Nemani, J Liu, C Hu, Z Wang 2020 IEEE Transportation Electrification Conference & Expo (ITEC), 181-184, 2020 | 25 | 2020 |
| Online estimation of lithium-ion battery capacity using deep convolutional neural networks S Shen, MK Sadoughi, X Chen, M Hong, C Hu International design engineering technical conferences and computers and …, 2018 | 19 | 2018 |
| Kriging-based reliability analysis considering predictive uncertainty reduction M Li, S Shen, V Barzegar, M Sadoughi, C Hu, S Laflamme Structural and Multidisciplinary Optimization 63, 2721-2737, 2021 | 18 | 2021 |
| Online estimation of lithium-ion battery capacity using transfer learning S Shen, M Sadoughi, C Hu 2019 IEEE Transportation Electrification Conference and Expo (ITEC), 1-4, 2019 | 17 | 2019 |
| Remaining useful life prediction of lithium-ion batteries using multi-model gaussian process M Li, M Sadoughi, S Shen, C Hu 2019 IEEE International Conference on Prognostics and Health Management …, 2019 | 11 | 2019 |
| Physics-informed machine learning for degradation diagnostics of lithium-ion batteries A Thelen, YH Lui, S Shen, S Laflamme, S Hu, C Hu International design engineering technical conferences and computers and …, 2021 | 6 | 2021 |
| DynamicDeepFlow: An Approach for Identifying Changes in Network Traffic Flow Using Unsupervised Clustering S Shen, M Kiran, B Mohammed International Conference on Machine Learning for Networking, 98-116, 2021 | | 2021 |
| Controlling Laser Beam Combining via an Active Reinforcement Learning Algorithm M Kiran, B Mohammed, Q Du, D Wang, S Shen, R Wilcox Advanced Solid State Lasers, JM3A. 44, 2021 | | 2021 |
| Expected Uncertainty Reduction for Sequential Kriging-Based Reliability Analysis M Li, S Shen, V Barzegar, M Sadoughi, S Laflamme, C Hu International Design Engineering Technical Conferences and Computers and …, 2020 | | 2020 |
| Data-driven approaches for battery state estimation and early cycle life prediction S Shen Iowa State University, 2020 | | 2020 |
| On-Board Prediction of Remaining Useful Life of Lithium-Ion Battery C Hu, S Shen, Y Li Midwest Transportation Center, 2019 | | 2019 |
| Ensemble Prognostics With Degradation-Dependent Weights: Prediction of Remaining Useful Life for Aircraft Engines Z Li, D Wu, C Hu, J Terpenny, S Shen International Design Engineering Technical Conferences and Computers and …, 2017 | | 2017 |
