Real-Time Optimal Charging for Lithium-Ion Batteries via Explicit Model Predictive Control
Real-Time Optimal Charging for Lithium-Ion Batteries via Explicit Model Predictive Control
复制标题
DOI：
10.1109/isie.2019.8781259
复制
发表时间：
2019-06
期刊：
影响因子：
--
通讯作者：
Ning Tian;H. Fang;Yebin Wang
中科院分区：
文献类型：
--
作者：
Ning Tian;H. Fang;Yebin Wang
锂离子电池在各个行业的快速增长凸显了最佳充电控制的紧迫问题。文献中越来越多地采用模型预测控制（MPC）来执行充电控制，利用其在约束条件下执行优化的能力。然而，MPC中涉及的计算复杂的在线约束优化往往阻碍实时实现。因此，本文的动机是开发一种新的充电控制算法的基础上显式MPC（eMPC）。利用eMPC的优点，新算法可以通过预先计算最优充电控制问题的显式解并将控制律表示为分段函数来将约束优化转移到离线。这不仅大大降低了控制运行中的在线计算成本，而且还降低了编码算法的难度。数值仿真结果验证了所提出的充电控制算法的实用性，它可以潜在地满足嵌入式硬件上运行的实时电池管理的需求。
The rapidly growing use of lithium-ion batteries across various industries highlights the pressing issue of optimal charging control. The literature increasingly adopts model predictive control (MPC) to perform charging control, taking advantage of its capability of performing optimization under constraints. However, the computationally complex online constrained optimization involved in MPC often hinders real-time implementation. This paper is thus motivated to develop a new charging control algorithm based on explicit MPC (eMPC). Leveraging the merits of eMPC, the new algorithm can shift the constrained optimization to offline by precomputing explicit solutions to an optimal charging control problem and expressing the control law as piecewise functions. This drastically reduces not only the online computational costs in the control run but also the difficulty to code the algorithm. Numerical simulation results verify the utility of the proposed charging control algorithm, which can potentially meet the needs for real-time battery management running on embedded hardware.