期刊
IEEE CONTROL SYSTEMS LETTERS
卷 5, 期 4, 页码 1387-1392出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LCSYS.2020.3038131
关键词
Lithium-ion batteries; model predictive control; physics-based model
This study combines model predictive control with reduced-order electrochemical models to achieve advanced control of lithium-ion batteries, addressing performance issues during fast charging.
Most state-of-the art battery-control strategies rely on voltage-based design limits to address performance and lifetime concerns. Such approaches are inherently conservative. However, by exploiting internal electrochemical quantities, it is possible to control battery performance right up to true physical bounds. This letter develops an extensible framework that combines model predictive control (MPC) with computationally efficient realization algorithm (xRA)-generated reduced-order electrochemical models for the advanced control of lithium-ion batteries. The approach is demonstrated on the fast-charge problem where hard constraints are imposed on problem variables to avoid lithium plating induced performance degradation. This letter establishes a general mathematical foundation for the incorporation of electrochemically rich reduced-order models directly into an MPC framework.
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