TY - GEN
T1 - A cycle-based recurrent neural network for state-of-charge estimation of li-ion battery cells
AU - Savargaonkar, M.
AU - Chehade, A.
AU - Shi, Z.
AU - Hussein, A.A.
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - This paper proposes a neural network model for state-of-charge (SOC) estimation in lithium-ion battery cells. The proposed deep neural network model is a cycle-based recurrent model that leverages relevant information from historical cycles to provide reliable estimates of the state-of-charge of on-going cycles within a mean-absolute error (MAE) of 1%. In addition, the proposed model can be trained in a relatively short time. Details on the model followed by experimental verification are provided.
AB - This paper proposes a neural network model for state-of-charge (SOC) estimation in lithium-ion battery cells. The proposed deep neural network model is a cycle-based recurrent model that leverages relevant information from historical cycles to provide reliable estimates of the state-of-charge of on-going cycles within a mean-absolute error (MAE) of 1%. In addition, the proposed model can be trained in a relatively short time. Details on the model followed by experimental verification are provided.
UR - https://www.scopus.com/pages/publications/85092208406
UR - https://www.scopus.com/pages/publications/85092208406
UR - https://www.mendeley.com/catalogue/6f755261-75b9-3878-9bd0-ebf74e897b6e/
U2 - 10.1109/ITEC48692.2020.9161587
DO - 10.1109/ITEC48692.2020.9161587
M3 - Conference contribution
SN - 9781728146294
T3 - 2020 IEEE Transportation Electrification Conference and Expo, ITEC 2020
SP - 584
EP - 587
BT - 2020 IEEE Transportation Electrification Conference and Expo, ITEC 2020
ER -