| Machine learning pipeline for battery state-of-health estimation D Roman, S Saxena, V Robu, M Pecht, D Flynn Nature Machine Intelligence 3 (5), 447-456, 2021 | 442 | 2021 |
| Cycle life testing and modeling of graphite/LiCoO2 cells under different state of charge ranges S Saxena, C Hendricks, M Pecht Journal of Power Sources 327, 394-400, 2016 | 206 | 2016 |
| Accelerated cycle life testing and capacity degradation modeling of LiCoO2-graphite cells W Diao, S Saxena, M Pecht Journal of Power Sources 435, 226830, 2019 | 161 | 2019 |
| Accelerated degradation model for C-rate loading of lithium-ion batteries S Saxena, Y Xing, D Kwon, M Pecht International journal of electrical power & energy systems 107, 438-445, 2019 | 134 | 2019 |
| Feature engineering for machine learning enabled early prediction of battery lifetime NH Paulson, J Kubal, L Ward, S Saxena, W Lu, SJ Babinec Journal of Power Sources 527, 231127, 2022 | 108 | 2022 |
| Algorithm to determine the knee point on capacity fade curves of lithium-ion cells W Diao, S Saxena, B Han, M Pecht Energies 12 (15), 2910, 2019 | 108 | 2019 |
| Analysis of manufacturing-induced defects and structural deformations in lithium-ion batteries using computed tomography Y Wu, S Saxena, Y Xing, Y Wang, C Li, WKC Yung, M Pecht Energies 11 (4), 925, 2018 | 107 | 2018 |
| A convolutional neural network model for battery capacity fade curve prediction using early life data S Saxena, L Ward, J Kubal, W Lu, S Babinec, N Paulson Journal of Power Sources 542, 231736, 2022 | 72 | 2022 |
| Exploding e-cigarettes: A battery safety issue S Saxena, L Kong, MG Pecht IEEE Access 6, 21442-21466, 2018 | 52 | 2018 |
| Batteries in portable electronic devices: A user's perspective S Saxena, G Sanchez, M Pecht IEEE Industrial Electronics Magazine 11 (2), 35-44, 2017 | 36 | 2017 |
| Evaluation of present accelerated temperature testing and modeling of batteries W Diao, Y Xing, S Saxena, M Pecht Applied Sciences 8 (10), 1786, 2018 | 33 | 2018 |
| Battery stress factor ranking for accelerated degradation test planning using machine learning S Saxena, D Roman, V Robu, D Flynn, M Pecht energies 14 (3), 723, 2021 | 32 | 2021 |
| A novel approach for electrical circuit modeling of Li-ion battery for predicting the steady-state and dynamic I–V characteristics S Saxena, SR Raman, B Saritha, V John Sādhanā 41, 479-487, 2016 | 32 | 2016 |
| Derating guidelines for lithium-ion batteries Y Sun, S Saxena, M Pecht Energies 11 (12), 3295, 2018 | 27 | 2018 |
| The explosive nature of tab burrs in Li-ion batteries XY Yao, S Saxena, L Su, MG Pecht Ieee Access 7, 45978-45982, 2019 | 25 | 2019 |
| Anomaly detection during lithium-ion battery qualification testing S Saxena, M Kang, Y Xing, M Pecht 2018 IEEE International Conference on Prognostics and Health Management …, 2018 | 24 | 2018 |
| A machine learning degradation model for electrochemical capacitors operated at high temperature D Roman, S Saxena, J Bruns, R Valentin, M Pecht, D Flynn IEEE Access 9, 25544-25553, 2021 | 22 | 2021 |
| Analysis of specified capacity in power banks W Diao, S Saxena, MG Pecht IEEE Access 8, 21326-21332, 2020 | 13 | 2020 |
| Role of the rest period in capacity fade of Graphite/LiCoO2 batteries S Saxena, Y Ning, R Thompson, M Pecht Journal of Power Sources 484, 229246, 2021 | 11 | 2021 |
| PHM of Li‐ion Batteries S Saxena, Y Xing, MG Pecht Prognostics and Health Management of Electronics: Fundamentals, Machine …, 2018 | 11 | 2018 |
Michael G. PechtUniversity of MarylandVerified email at umd.edu