| A controllable deep transfer learning network with multiple domain adaptation for battery state-of-charge estimation I Oyewole, A Chehade, Y Kim Applied Energy 312, 118726, 2022 | 65 | 2022 |
| Optimal discretization approach to the enhanced single-particle model for Li-ion batteries I Oyewole, KH Kwak, Y Kim, X Lin IEEE Transactions on Transportation Electrification 7 (2), 369-381, 2020 | 25 | 2020 |
| Uncorrelated sparse autoencoder with long short-term memory for state-of-charge estimations in lithium-ion battery cells M Savargaonkar, I Oyewole, A Chehade, AA Hussein IEEE Transactions on Automation Science and Engineering 21 (1), 15-26, 2022 | 16 | 2022 |
| A hybrid long short-term memory network for state-of-charge estimation of Li-ion batteries I Oyewole, M Savargaonkar, A Chehade, Y Kim 2021 IEEE Transportation Electrification Conference & Expo (ITEC), 469-473, 2021 | 7 | 2021 |
| Sparse autoencoded long short-term memory network for state-of-charge estimations M Savargaonkar, I Oyewole, A Chehade 2021 IEEE Transportation Electrification Conference & Expo (ITEC), 474-478, 2021 | 6 | 2021 |
| Optimal model reduction of lithium-ion battery systems using particle swarm optimization I Oyewole | 4 | 2019 |
| A Polynomial Regression Model with Bayesian Inference for State-of-Health Prediction of Li-ion Batteries I Oyewole, M Chelbi, A Chehade, AA Hussein 2022 IEEE Transportation Electrification Conference & Expo (ITEC), 970-974, 2022 | 1 | 2022 |
| On Simplification of a Solid-State Battery Model for State Estimation K Upreti, I Oyewole, X Lin, Y Kim 2019 IEEE Conference on Control Technology and Applications (CCTA), 487-492, 2019 | 1 | 2019 |
| A Two-Step Parameter Optimization Method for Low-Order Model-Based State-of-Charge Estimation................ I Oyewole, KH Kwak, Y Kim, X Lin, X Hu, Y Che, S Onori | | |
Youngki KimUniversity of Michigan-Dearborn在 umich.edu 的电子邮件经过验证
