TY - GEN
T1 - Power Capability Prediction of Lithium-Ion Batteries Using Physics-Based Model and NMPC
AU - Li, Yang
AU - Wei, Zhongbao
AU - Vilathgamuwa, Mahinda
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - A model-based battery power capability prediction method is reported to prevent the battery from moving into harmful situations during its operation for its health and safety. The method incorporates a high-fidelity electrochemical-thermal battery model, with which not only the external limitations on current, voltage, and power, but also the internal constraints such as lithium plating and thermal runaway, can be readily taken into account. The online prediction of maximum power is accomplished by formulating and successively solving a constrained nonlinear optimization problem. Due to the relatively high system order, high model nonlinearity, and long prediction horizon, an accurate and computationally efficient scheme based on nonlinear model predictive control is designed.
AB - A model-based battery power capability prediction method is reported to prevent the battery from moving into harmful situations during its operation for its health and safety. The method incorporates a high-fidelity electrochemical-thermal battery model, with which not only the external limitations on current, voltage, and power, but also the internal constraints such as lithium plating and thermal runaway, can be readily taken into account. The online prediction of maximum power is accomplished by formulating and successively solving a constrained nonlinear optimization problem. Due to the relatively high system order, high model nonlinearity, and long prediction horizon, an accurate and computationally efficient scheme based on nonlinear model predictive control is designed.
KW - lithium-ion batteries
KW - model predictive control
KW - power capability
UR - http://www.scopus.com/inward/record.url?scp=85161263333&partnerID=8YFLogxK
U2 - 10.1109/ONCON56984.2022.10126930
DO - 10.1109/ONCON56984.2022.10126930
M3 - Conference contribution
AN - SCOPUS:85161263333
T3 - 1st IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2022
BT - 1st IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2022
Y2 - 9 December 2022 through 11 December 2022
ER -