Real-Time Optimal Lithium-Ion Battery Charging Based on Explicit Model Predictive Control

The rapidly growing use of lithium-ion batteries across various industries highlights the pressing issue of optimal charging control, as charging plays a crucial role in the health, safety, and life of batteries. The literature increasingly adopts model predictive control (MPC) to address this issue, taking advantage of its capability of performing optimization under constraints. However, the computationally complex online constrained optimization intrinsic to MPC often hinders real-time implementation. This article is thus proposed to develop a framework for real-time charging control based on explicit MPC (eMPC), exploiting its advantage in characterizing an explicit solution to an MPC problem, to enable real-time charging control. This article begins with the formulation of MPC charging based on a nonlinear equivalent circuit model. Then, multisegment linearization is conducted to the original model, and applying the eMPC design to the obtained linear models leads to a charging control algorithm. The proposed algorithm shifts the constrained optimization to offline by precomputing explicit solutions to the charging problem and expressing the charging law as piecewise affine functions. This drastically reduces not only the online computational costs in the control run but also the difficulty of coding. Extensive numerical simulation and experimental results verify the effectiveness of the proposed eMPC charging control framework and algorithm. The research results can potentially meet the needs for real-time battery management running on embedded hardware.

Automated summary

The article proposes a real-time charging control framework based on explicit MPC, which shifts constrained optimization offline and expresses charging law as piecewise affine functions, reducing online computational costs and coding difficulty. Extensive numerical simulation and experimental results verify the effectiveness of the proposed eMPC charging control framework and algorithm. This research has the potential to meet the needs for real-time battery management running on embedded hardware.