| State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach E Chemali, PJ Kollmeyer, M Preindl, A Emadi Journal of Power Sources 400, 242-255, 2018 | 627 | 2018 |
| Long short-term memory networks for accurate state-of-charge estimation of Li-ion batteries E Chemali, PJ Kollmeyer, M Preindl, R Ahmed, A Emadi IEEE Transactions on Industrial Electronics 65 (8), 6730-6739, 2017 | 621 | 2017 |
| Machine learning applied to electrified vehicle battery state of charge and state of health estimation: State-of-the-art C Vidal, P Malysz, P Kollmeyer, A Emadi Ieee Access 8, 52796-52814, 2020 | 310 | 2020 |
| 800-V electric vehicle powertrains: Review and analysis of benefits, challenges, and future trends I Aghabali, J Bauman, PJ Kollmeyer, Y Wang, B Bilgin, A Emadi IEEE Transactions on Transportation Electrification 7 (3), 927-948, 2020 | 253 | 2020 |
| Panasonic 18650pf li-ion battery data P Kollmeyer Mendeley Data 1 (2018), 2018 | 183 | 2018 |
| xEV Li-ion battery low-temperature effects C Vidal, O Gross, R Gu, P Kollmeyer, A Emadi IEEE transactions on vehicular technology 68 (5), 4560-4572, 2019 | 126 | 2019 |
| Lithium-ion battery pack robust state of charge estimation, cell inconsistency, and balancing M Naguib, P Kollmeyer, A Emadi Ieee Access 9, 50570-50582, 2021 | 122 | 2021 |
| LG 18650HG2 Li-ion battery data and example deep neural network xEV SOC estimator script P Kollmeyer, C Vidal, M Naguib, M Skells Mendeley Data 3, 2020, 2020 | 91 | 2020 |
| Battery state-of-health sensitive energy management of hybrid electric vehicles: Lifetime prediction and ageing experimental validation PG Anselma, P Kollmeyer, J Lempert, Z Zhao, G Belingardi, A Emadi Applied Energy 285, 116440, 2021 | 80 | 2021 |
| Li-ion battery state of charge estimation using long short-term memory recurrent neural network with transfer learning C Vidal, P Kollmeyer, E Chemali, A Emadi 2019 IEEE transportation electrification conference and expo (ITEC), 1-6, 2019 | 75 | 2019 |
| A compact methodology via a recurrent neural network for accurate equivalent circuit type modeling of lithium-ion batteries R Zhao, PJ Kollmeyer, RD Lorenz, TM Jahns IEEE Transactions on Industry Applications 55 (2), 1922-1931, 2018 | 69 | 2018 |
| Mobile medical ventilator PJ Kollmeyer, SI Kutko, R Tham, N Rick, JL Woods US Patent 8,960,193, 2015 | 68 | 2015 |
| Robust xev battery state-of-charge estimator design using a feedforward deep neural network C Vidal, P Kollmeyer, M Naguib, P Malysz, O Gross, A Emadi SAE International Journal of Advances and Current Practices in Mobility 2 …, 2020 | 67 | 2020 |
| Li-ion battery model performance for automotive drive cycles with current pulse and EIS parameterization P Kollmeyer, A Hackl, A Emadi 2017 IEEE transportation electrification conference and expo (ITEC), 486-492, 2017 | 66 | 2017 |
| A comparative study between physics, electrical and data driven lithium-ion battery voltage modeling approaches Y Liang, A Emadi, O Gross, C Vidal, M Canova, S Panchal, P Kollmeyer, ... SAE Technical Paper, 2022 | 62 | 2022 |
| 3D FEA thermal modeling with experimentally measured loss gradient of large format ultra-fast charging battery module used for EVs Z Zhao, S Panchal, P Kollmeyer, A Emadi, O Gross, D Dronzkowski, ... SAE Technical Paper, 2022 | 59 | 2022 |
| Investigation of the influence of superimposed AC current on lithium-ion battery aging using statistical design of experiments LW Juang, PJ Kollmeyer, AE Anders, TM Jahns, RD Lorenz, D Gao Journal of Energy Storage 11, 93-103, 2017 | 56 | 2017 |
| Onboard unidirectional automotive G2V battery charger using sine charging and its effect on li-ion batteries R Prasad, C Namuduri, P Kollmeyer 2015 IEEE energy conversion congress and exposition (ECCE), 6299-6305, 2015 | 53 | 2015 |
| Improved nonlinear model for electrode voltage–current relationship for more consistent online battery system identification LW Juang, PJ Kollmeyer, TM Jahns, RD Lorenz IEEE Transactions on Industry Applications 49 (3), 1480-1488, 2013 | 48 | 2013 |
| A convolutional neural network approach for estimation of li-ion battery state of health from charge profiles E Chemali, PJ Kollmeyer, M Preindl, Y Fahmy, A Emadi Energies 15 (3), 1185, 2022 | 47 | 2022 |
Ali EmadiMcMaster University在 mcmaster.ca 的电子邮件经过验证
