Energy-Efficient Model Predictive Control of Industrial Induction-Motor Drives: A Systematic Review of Loss-Minimizing, Thermal-Aware, and Data-Driven Strategies

Induction motors (IMs) consume most of the electricity used in industry, and the efficiency of their drives is classified through the IE and IES classes of IEC 60034-30-1 and IEC 61800-9-2. This PRISMA 2020 systematic review examines energy-efficient model predictive control (MPC) for IM drives using a three-axis taxonomy: efficiency objective (electrical loss, thermal/economic, degradation), solution paradigm (model-based, data-driven, learning-augmented), and uncertainty handling (nominal, observer-based, adaptive/robust). Of the 65 included studies, 43 concern induction-machine drives, but only three of these report an industrial, process, or marine load, and 20 of the 65 address traction or transport. The largest reported effects, 49–65% reductions in drive-cycle loss or energy and light-load efficiency gains of up to about 30 percentage points on a 0.75 kW machine, were obtained in simulation. Hardware-validated IM results mostly amount to about 0.7–4% in efficiency at fixed operating points and 8–18% in drive-cycle energy relative to other MPC variants. These figures are not directly comparable because loss boundaries, operating points, baselines, and validation methods differ. No included study combines a thermal/economic or degradation objective with a data-driven or learning paradigm and adaptive/robust uncertainty handling. Open problems and a research agenda on thermally adaptive economic MPC are outlined.