Oct 2026· IEEE Journal of Emerging and Selected Topics in Industrial Electronics· Vol 7, pp. 1932-1946· 0 citations· 36 references
Abstract
Deep reinforcement learning (DRL) has demonstrated promising results in power electronics control, particularly for current regulation of voltage source inverters (VSIs). However, its practical adoption remains limited due to the perceived complexity of neural network design, training, and real-time implementation. This work presents a practical, step-by-step, engineering-oriented guideline for implementing Deep Q-Learning (DQN) control in three-phase VSI applications. The proposed methodology encompasses the definition of state and action spaces, reward function design, neural network architecture selection, simulation-based training, and deployment on embedded real-time hardware. The guideline is specifically tailored for power electronics engineers without prior expertise in artificial intelligence. Experimental results obtained on a real-time test bench demonstrate model-free operation, accurate current tracking and real-time feasibility, supporting the applicability of the proposed approach for practical converter control.
The traction inverter is a critical component in Electric Vehicle (EV) powertrains, responsible for efficient energy conversion and precise motor control. Traditional control strategies, such as Proportional-Integral (PI) controllers and Model Predictive Control (MPC), often struggle with parameter variations, non-line...
R. Chekhmestruk, O. Halushchak, D. Halushchak· Automation, Control, and Inf...· 0 citations
Photovoltaic power conversion systems require maximum power point tracking (MPPT) strategies capable of fast dynamic response with low computational burden, while remaining reliable under variable environmental conditions. While neural-network-based methods have been widely investigated, their practical deployment is o...
Javed Jamshed, L. Becchi, M. Bindi et al.· Electronics· 0 citations
The rapid expansion of renewable energy and electric vehicles has increased the need for intelligent, high-performance DC–AC converters. Conventional control methods such as PI, PID, and MPC struggle under nonlinear and grid-interactive conditions, leading to higher THD and reduced stability. This paper reviews deep le...
Shakir Hussein_Ali, Khalaf SalloumGaeid· Dijlah Journal of Engineerin...· 0 citations
Owing to the bidirectional power transfer, galvanic isolation and high conversion efficiency, the Dual Active Bridge (DAB) converter has become a popular topology for bidirectional DC-DC power conversion. It applies to electric vehicles, battery energy storage systems and DC microgrids due to its properties. The non-li...
K. G. Babu, J. S. Prasad, V. Vasudevan· International Journal of Eng...· 0 citations
This paper proposes a lightweight, simplified Q-learning energy management strategy for extended-range electric vehicles (REEVs), successfully implemented on an STM32 microcontroller, fully satisfying strict on-board embedded system constraints and providing a highly feasible solution for intelligent REEV energy manage...
Jun Guo· European Conference on Elect...· 0 citations
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