Historical data-driven self-learning control of battery charging with convex mapping constraints

Thermal conditions significantly influence the battery performance and degradation, and thermal management is vital for the safe, efficient, and reliable operation of battery-powered systems such as grid-tied energy storage and electric vehicles. However, it is challenging to achieve rapid charging while minimizing the thermal impact on battery health, particularly the more critical internal battery temperature is often overlooked. Therefore, an intelligent charging control strategy is essential for effectively managing the thermal effects and enhancing charging efficiency. Given that battery charging is a highly repetitive process throughout the entire life of battery-powered systems, the historical operation data has a significant potential in the design of an effective charging control strategy. This paper proposes a novel historical data-driven self-learning control approach to iteratively optimize the battery charging strategy by applying convex mapping constraints derived from historical state information. This approach introduces a historical state convex mapping constraint, combined with a memory function to quantify the potential contribution of historical system state information and input data to improve the future control performance. The formulated historical data-based constraints and the memory function-enhanced cost function are then integrated into a model predictive control framework to optimize the battery charging current trajectories iteratively. Furthermore, to ensure that the constraints imposed on the battery electrical and thermal states are compatible with the self-learning control framework, a cascading linearized thermoelectric battery model is introduced to characterize the battery dynamics. Particularly, the internal temperature of the battery, which is not directly measurable in practical applications. Extensive simulation studies have been conducted, and the results demonstrate that the proposed control strategy can effectively regulate the internal temperature within a safe range while continuously optimizing the charging efficiency. In addition, the computation time variability is significantly reduced, with the standard deviation being decreased by approximately 80% compared to the standard MPC. The desirable control performance and continuous optimization capability make the proposed control strategy highly applicable to repetitive and complex engineering control problems. • A self-learning MPC framework is proposed for battery charging processes. • A cascading linearized thermo-electric battery model is developed. • A novel memory function is proposed to evaluate the contribution of the historical data. • A convex mapping constraint is incorporated into the framework. • An 80% reduction in computation-time variability is achieved.