Model predictive control with robust stability for voltage regulation of auxiliary loads in electric vehicles

This paper proposes a unified methodological framework for a novel model predictive controller for regulating the output voltage of DC–DC buck converters supplying auxiliary loads in electric vehicles. The proposed controller explicitly incorporates engineering-oriented design specifications and guarantees robust stability and performance in terms of bounded overshoot and settling time, with zero steady-state error in the presence of parametric uncertainties in the converter. The optimal control action is analytically obtained by minimising a cost function based on quadratic terms of the voltage error, the integral of this error, and the inductor current. Only two scalar weighting factors need to be tuned offline, through a systematic and reproducible design procedure, in order to meet the desired performance specifications under parametric uncertainties, based on closed-loop eigenvalue location inside a prescribed sector in the unit circle. The resulting controller corresponds to a low-complexity robust state feedback with optimal gains obtained in closed form. The robust stability of the closed-loop system against time-varying parametric uncertainties is also ensured based on Lyapunov functions systematically constructed using linear matrix inequalities. The proposed methodology further integrates systematic tuning via spectral-radius-based analysis and offline robustness verification under parametric uncertainty. To demonstrate the effectiveness of the proposal, a comprehensive experimental validation is carried out using a Control Hardware-in-the-Loop (C-HIL) framework. The main case study provides results for a buck converter supplying low-power auxiliary loads of electric vehicles at regulated 5 V under sudden variations of load and input voltage. The controller is implemented in real time on a low-cost microcontroller operating at 50 kHz, demonstrating practical feasibility. Comparisons with a nonlinear adaptive controller widely used in the literature highlight the superiority of the proposed approach in terms of transient performance, robustness, and computational efficiency.