| 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 | 606 | 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 | 598 | 2017 |
| Electrochemical and electrostatic energy storage and management systems for electric drive vehicles: State-of-the-art review and future trends E Chemali, M Preindl, P Malysz, A Emadi IEEE Journal of Emerging and Selected Topics in Power Electronics 4 (3 …, 2016 | 309 | 2016 |
| 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 | 74 | 2019 |
| 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 | 44 | 2022 |
| Hybrid energy storage system state-of-charge estimation using artificial neural network for micro-hybrid applications C Vidal, M Haußmann, D Barroso, PM Shamsabadi, A Biswas, E Chemali, ... 2018 IEEE Transportation Electrification Conference and Expo (ITEC), 1075-1081, 2018 | 36 | 2018 |
| Optimal performance of a full scale li-ion battery and li-ion capacitor hybrid energy storage system for a plug-in hybrid vehicle P Kollmeyer, M Wootton, J Reimers, T Stiene, E Chemali, M Wood, ... 2017 IEEE Energy Conversion Congress and Exposition (ECCE), 572-577, 2017 | 32 | 2017 |
| Minimizing battery wear in a hybrid energy storage system using a linear quadratic regulator E Chemali, L McCurlie, B Howey, T Stiene, MM Rahman, M Preindl, ... IECON 2015-41st Annual Conference of the IEEE Industrial Electronics Society …, 2015 | 30 | 2015 |
| A novel hybrid energy storage system using the multi-source inverter L Dorn-Gomba, E Chemali, A Emadi 2018 IEEE Applied Power Electronics Conference and Exposition (APEC), 684-691, 2018 | 28 | 2018 |
| On the concept of a novel Reconfigurable Multi-Source Inverter E Chemali, A Emadi 2017 IEEE Transportation Electrification Conference and Expo (ITEC), 707-713, 2017 | 22 | 2017 |
| Real-time control of a full scale Li-ion battery and Li-ion capacitor hybrid energy storage system for a plug-in hybrid vehicle PJ Kollmeyer, M Wootton, J Reimers, DF Opila, H Kurera, M Kadakia, ... IEEE Transactions on Industry Applications 55 (4), 4204-4214, 2019 | 17 | 2019 |
| A neural network energy management controller applied to a hybrid energy storage system using multi-source inverter J Ramoul, E Chemali, L Dorn-Gomba, A Emadi 2018 IEEE Energy Conversion Congress and Exposition (ECCE), 2741-2747, 2018 | 15 | 2018 |
| Neural-network state-of-charge estimation E Chemali, M Preindl US Patent 11,171,498, 2021 | 10 | 2021 |
| High-fidelity state-of-charge estimation of Li-ion batteries using machine learning W Wang, NW Brady, C Liao, YA Fahmy, E Chemali, AC West, M Preindl arXiv preprint arXiv:1909.02448, 2019 | 10 | 2019 |
| Intelligent state-of-charge and state-of-health estimation framework for Li-ion batteries in electrified vehicles using deep learning techniques E Chemali | 9 | 2018 |
| Neural-network state-of-charge and state of health estimation E Chemali, M Preindl US Patent 11,637,331, 2023 | 3 | 2023 |
| Neural-network state-of-charge and state of health estimation E Chemali, M Preindl US Patent App. 18/118,231, 2023 | | 2023 |
| Neural network state-of-charge estimation E Chemali, M Preindl US Patent App. 17/497,537, 2022 | | 2022 |
| 堀場雅夫賞特別賞受賞者論文 High-Fidelity State-of-Charge Estimation of Li-Ion Batteries using Machine Learning M Preindl, W Wang, NW Brady, C Liao, YA Fahmy, E Chemali, AC West Readout: Horiba technical reports, 36-45, 2019 | | 2019 |
| High-Fidelity State-of-Charge Estimation of Li-Ion Batteries using Machine Learning M Preindl, W Wang, NW Brady, C Liao, YA Fahmy, E Chemali, AC West | | |
