Adaptive Constraints Model Active Mismatch Predictive Control for Power Converters of Energy Storage System

Model Predictive Control (MPC) is widely employed for power converters in energy storage systems. However, the limited sampling frequency of microcontrollers and model mismatches can cause trajectory tracking errors in both dynamic and steady states, potentially damaging electrical equipment and reducing the lifespan of electrical devices. To address these issues, this paper proposes an Adaptive Constraints Model Active Mismatch Predictive Control (AC-MAMPC) method for power converters. The inductance and capacitance values of the system model can be actively adjusted in the state-space model to eliminate current ripple caused by control delays. For model mismatches resulting from the proposed active model mismatch mechanism and perturbations, an adaptive constraints method for online optimization of MPC is introduced to handle plant model mismatches without updating model parameters. The performance of the proposed method is validated through simulation and hardware experiments.