As the demand for more efficient and compact power converters grows, the trend points to a progressive raise of their switching frequency. However, even with wide-bandgap semiconductors, switching losses limit the maximum frequency at which they can operate, requiring zero-voltage switching (ZVS) to maximize efficiency and to prevent overheating. Existing studies state that the conditions to achieve ZVS depend only on the stored charge in the output parasitic capacitances of the transistors, but this statement is not applicable for the asymmetrical half-bridge (AHB) structure defined in this article, commonly found in topologies such as the buck and the asymmetrical half-bridge flyback. This article addresses this limitation by deriving a novel condition for achieving complete ZVS in the AHB, showing that both, the stored charge and the stored energy in the transistors must be considered. It also proposes a simplified circuit for analyzing dead times and discusses the issue of having different switches in a half-bridge, stating the advantage of placing high capacitance transistors in the low-side. Furthermore, this article proposes some consideration for dimensioning the converter that facilitate achieving ZVS. Experimental measurements validate the theoretical analysis, demonstrating its potential in the design of high efficiency and high power density converters.

This article proposes the novel use of the asymmetrical half-bridge flyback (AHBF) topology with a bidirectional behavior. AHBF has seen an increase in industrial use in recent years due to its advantages over other topologies, such as its high power density, low component count and wide voltage range by exploiting the resonance between the transformer leakage inductance and an added resonant tank. Up to now, the AHFB topology has been used as a step-down converter; furthermore, by making use of its synchronous rectifier, a resonant flyback-forward topology with step-up function is obtained. The proposed isolated bidirectional converter can achieve high voltage conversion rate with elevated efficiency and power density. To test the theoretical analysis, a 240 W prototype with 320 to 400 V on the high voltage side and 12 to 24 V on the low voltage side built with GaN technology is carried out in the laboratory. A high boost and buck capability with wide voltage range is obtained, as well as reaching a maximum efficiency in step-down mode of 97.97% and of 97.76% in step-up configuration. This makes AHFB an attractive alternative for bidirectional conversion applications.

Conventional Maximum Power Point Tracking (MPPT) algorithms, such as Perturb and Observe (P&O), suffer from steady-state oscillations and slow convergence under rapidly varying environmental conditions, leading to suboptimal energy extraction and unnecessary switching activity. To address these limitations, we propose a predictive control strategy in which the DC–DC converter control signal is adaptively updated only when significant deviations are detected between measured and model-predicted voltage and current values. The approach leverages power-of-two quantized Artificial Neural Networks ($2^{Q}ANNs$), enabling highly accurate inference with extreme weight quantization (2–3 bits) while remaining suitable for MPPT. A dataset-driven evaluation using year-long climatic records from geographically distinct locations indicates annual energy yields of up to 99.90% of the ideal maximum under the adopted modeling assumptions. Under the adopted fixed-condition evaluation protocol, compared with conventional P&O implementations, the proposed method requires 20–40× fewer internal control updates to approach the same efficiency region. Additionally, a robustness experiment with perturbed voltage and current measurements further shows that the recurrent $2^{Q}ANN$ controllers remain above 98% aggregated efficiency even under the strongest tested sensing-noise condition, without retraining. Finally, post-place-and-route FPGA implementation estimates on a highly resource-constrained device indicate that the resulting architecture supports low-resource edge-oriented implementation.

Distributed energy systems open up a vast field of research in power electronics. Local solar power generation requires DC-DC converters that adapt the energy generated by the panels to on-site distribution buses. In addition, the control of the power converter to obtain the maximum possible energy from the solar source is crucial for the correct deployment of these distributed grids. In this work, system-level solutions are proposed for this application as follows: On the one hand, the use of novel resonant forward-flyback converters allows for a higher energy density than that of a conventional flyback and more relaxed withstand voltages on the switching elements. On the other hand, the implementation of maximum power point tracking algorithms for solar energy using Edge AI enables the deployment of algorithms that maximize the energy obtained locally. These improvements are shown by means of a prototype demonstrator, using cutting-edge microcontrollers and the implementation of a DC-DC power converter based on the proposed topology.

The transition of the USB Power Delivery (USB-PD) standard from fixed 5–20 V profiles to the dynamic extended power range (EPR) of up to 48 V and 240 W has redefined the constraints of isolated AC–DC conversion for high-density adapters and chargers. Delivering high efficiency simultaneously at low (5 V) and high (28–48 V) outputs, while maximising volumetric power density and meeting stringent no-load and average-efficiency regulations, has become the central design challenge. This review provides a structured, comparative analysis of the isolated topologies competing in the sub-240 W adapter space: the quasi-resonant and active-clamp flyback converters, the two-transistor flyback, the resonant hybrid (asymmetrical half-bridge) flyback, and conventional and reconfigurable LLC resonant converters. Each is assessed against a common set of metrics (soft-switching capability, output-voltage-range flexibility, control complexity, component count, and achievable power density), supported by a quantitative meta-analysis of representative published prototypes. The enabling roles of wide-bandgap gallium nitride (GaN) devices, planar magnetics, and advanced digital control are examined. The analysis delineates the region of the power and output-voltage space in which each topology is optimal and proposes a common figure of merit for benchmarking future high-density adapters.