TY - JOUR
T1 - Electrochemical Model-Based Fast Charging
T2 - Physical Constraint-Triggered PI Control
AU - Li, Yang
AU - Vilathgamuwa, Mahinda
AU - Wikner, Evelina
AU - Wei, Zhongbao
AU - Zhang, Xinan
AU - Thiringer, Torbjorn
AU - Wik, Torsten
AU - Zou, Changfu
PY - 2021/12/1
Y1 - 2021/12/1
N2 - This paper proposes a new fast charging strategy for lithium-ion batteries. The approach relies on an experimentally validated high-fidelity model describing battery electrochemical and thermal dynamics that determine the fast charging capability. Such a high-dimensional nonlinear dynamic model can be intractable to compute in real-time if it is fused with the extended Kalman filter or the unscented Kalman filter that is commonly used in the community of battery management. To significantly save computational efforts and achieve rapid convergence, the ensemble transform Kalman filter (ETKF) is selected and tailored to estimate distributed battery states. Then, a health- and safety-aware charging protocol is proposed based on successively applied proportional-integral (PI) control actions. The controller regulates charging rates using online battery state information and the imposed constraints, in which each PI control action automatically comes into play when its corresponding constraint is triggered. The proposed physical constraint-triggered PI charging control strategy with the ETKF is evaluated and compared with several prevalent alternatives. It shows that the derived controller can achieve close to the optimal solution in terms of charging time and trajectory, as determined by a nonlinear model predictive controller, but at a drastically reduced computational cost.
AB - This paper proposes a new fast charging strategy for lithium-ion batteries. The approach relies on an experimentally validated high-fidelity model describing battery electrochemical and thermal dynamics that determine the fast charging capability. Such a high-dimensional nonlinear dynamic model can be intractable to compute in real-time if it is fused with the extended Kalman filter or the unscented Kalman filter that is commonly used in the community of battery management. To significantly save computational efforts and achieve rapid convergence, the ensemble transform Kalman filter (ETKF) is selected and tailored to estimate distributed battery states. Then, a health- and safety-aware charging protocol is proposed based on successively applied proportional-integral (PI) control actions. The controller regulates charging rates using online battery state information and the imposed constraints, in which each PI control action automatically comes into play when its corresponding constraint is triggered. The proposed physical constraint-triggered PI charging control strategy with the ETKF is evaluated and compared with several prevalent alternatives. It shows that the derived controller can achieve close to the optimal solution in terms of charging time and trajectory, as determined by a nonlinear model predictive controller, but at a drastically reduced computational cost.
KW - Batteries
KW - Computational modeling
KW - Electrochemical model
KW - Electrodes
KW - ensemble transform Kalman filter (ETKF)
KW - fast charging
KW - Integrated circuit modeling
KW - Kalman filters
KW - lithium plating
KW - Lithium-ion batteries
KW - lithiumion (Li-ion) battery
KW - Mathematical model
UR - http://www.scopus.com/inward/record.url?scp=85103212335&partnerID=8YFLogxK
U2 - 10.1109/TEC.2021.3065983
DO - 10.1109/TEC.2021.3065983
M3 - Article
AN - SCOPUS:85103212335
SN - 0885-8969
VL - 36
SP - 3208
EP - 3220
JO - IEEE Transactions on Energy Conversion
JF - IEEE Transactions on Energy Conversion
IS - 4
ER -