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Isaiah O. Oyewole
Isaiah O. Oyewole
PhD. Candidate, Industrial & Systems Engineering, University of Michigan-Dearborn
Verified email at umich.edu
Title
Cited by
Cited by
Year
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
552022
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
222020
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, 2022
102022
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
72021
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
62021
Optimal model reduction of lithium-ion battery systems using particle swarm optimization
I Oyewole
42019
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
12022
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
12019
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
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Articles 1–9