Electrochemical Model-Based Fast Charging: Physical Constraint-Triggered PI Control

Yang Li, Mahinda Vilathgamuwa, Evelina Wikner, Zhongbao Wei, Xinan Zhang, Torbjorn Thiringer, Torsten Wik, Changfu Zou

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)3208-3220
Number of pages13
JournalIEEE Transactions on Energy Conversion
Issue number4
Publication statusPublished - 1 Dec 2021


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