Saurabh Saxena
Cited by
Cited by
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
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
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
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
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
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
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
Exploding e-cigarettes: A battery safety issue
S Saxena, L Kong, MG Pecht
IEEE Access 6, 21442-21466, 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
Batteries in portable electronic devices: A user's perspective
S Saxena, G Sanchez, M Pecht
IEEE Industrial Electronics Magazine 11 (2), 35-44, 2017
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
Evaluation of present accelerated temperature testing and modeling of batteries
W Diao, Y Xing, S Saxena, M Pecht
Applied Sciences 8 (10), 1786, 2018
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
Derating guidelines for lithium-ion batteries
Y Sun, S Saxena, M Pecht
Energies 11 (12), 3295, 2018
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
The explosive nature of tab burrs in Li-ion batteries
XY Yao, S Saxena, L Su, MG Pecht
IEEE Access 7, 45978-45982, 2019
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
Analysis of specified capacity in power banks
W Diao, S Saxena, MG Pecht
IEEE Access 8, 21326-21332, 2020
PHM of Li‐ion Batteries
S Saxena, Y Xing, MG Pecht
Prognostics and Health Management of Electronics: Fundamentals, Machine …, 2018
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
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