Model Predictive Control Using an Artificial Neural Network for Fast-Charging Lithium-Ion Batteries

The increasing computational complexity of Model Predictive Control (MPC) in battery systems limits its practical adoption, despite its potential for optimizing performance under dynamic operating conditions. To address this challenge, this study introduces an Artificial Neural Network-based MPC framework (MPCANN) tailored for VTC6 3Ah lithium-ion cells, aiming to reduce computational burdens while retaining predictive accuracy. The framework synergizes MPC’s predictive capabilities with the daptive learning of Artificial Neural Network (ANN) by training the ANN offline using MPC-derived input–output data. Validation against prior MPC results demonstrates MPCANN’s ability to replicate MPC behavior across temperatures, achieving strong alignment in current and temperature predictions. While state of charge (SoC) estimation accuracy requires refinement at elevated temperatures, the framework reduces computation time by 94% compared to traditional MPC, highlighting its efficiency. These results underscore MPCANN’s potential to enable real-time implementation of advanced battery control strategies, offering a pathway to balance computational efficiency with performance in adaptive energy systems.