This paper presents a comparative analysis of scalar V/F and field-oriented control (FOC) control strategies for induction motor drives, including classical and AI-enhanced implementations. AI-based optimization is used to improve dynamic performance, energy efficiency, and robustness under various load conditions. The...
P. Stanchev, Nikolay Hinov, R. Kabakchieva· EEPES 2026· 0 citations
Accurate health assessment is essential for the reliable and cost-effective operation of lithium-ion battery energy storage systems. This study presents a physics-informed explainable machine-learning framework for hybrid State-of-Health (SoH) estimation and Remaining Useful Life (RUL) prediction under heterogeneous si...
P. Stanchev, Nikolay Hinov· Batteries· 0 citations
The Solid-State Transformer (SST) is a new generation of power conversion and management technology that integrates high-frequency isolation, advanced broadband semiconductor elements, and intelligent control systems. Thanks to its modular architecture and the combination of AC/DC, DC/DC, and DC/AC conversion stages, t...
P. Stanchev, Nikolay Hinov· TEM Journal· 0 citations
Vehicle speed affects exposure to a dynamic wireless charging (DWC) lane and propulsion demand. This study develops an analytically tractable homogeneous-lane model that separates received electrical energy, wheel work, drivetrain losses, and auxiliary demand. Under an explicitly assumed affine speed–efficiency relatio...
A. Perçuku, D. Minkovska, Nikolay Hinov· World Electric Vehicle Journ...· 0 citations
This paper proposes an adaptive, AI-driven control architecture for real-time regulation of Kaplan turbines in small and medium hydropower plants, targeting simultaneous power tracking, reservoir level stabilization, and cavitation-aware operation. A cascade control structure is adopted, where an outer level loop enfor...
P. Stanchev, Nikolay Hinov, A. Salimov· IOP Conference Series: Earth...· 0 citations
The results demonstrate high soil twin synchronization accuracy, with an overall RMSE of 1.47 percentage points and R2 of 0.981, based on experimental field measurements, demonstrating the potential of the proposed digital twin framework for integrated water–energy management in smart agriculture.
R. Kabakchieva, P. Stanchev, Nikolay Hinov· Electronics· 0 citations
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