Yangbin Zeng

High-voltage gain DC-DC converters are critical in renewable energy systems, as photovoltaic cells and fuel cells have low output voltages while the DC bus voltages can reach 400V. This paper analyzes voltage-multiplier DC-DC converters based on an interleaved boost structure, utilizing the flux-balance principle and adjacency matrices. By automatically searching adjacency matrices, a series of non-isolated interleaved voltage-multiplier DC-DC converters can be systematically generated using computer programming methods. Using this method, voltage-multiplier DC–DC converters with an arbitrary number of capacitors and the minimum required number of diodes can be constructed. When combined with specific filtering criteria, converters with designated voltage gain, low capacitor voltage stress, and input-output common-ground characteristics can also be obtained. Using a five-capacitors configuration as an example, 24 non-isolated interleaved voltage-multiplier DC-DC converters were generated through the proposed method. Finally, a 200 W experimental prototype was developed for the topology exhibiting the best overall performance to validate the theoretical analysis and construction method.

Advancing the dynamics inference of power electronic systems (PES) to the real-time edge-side holds transformative potential for design, control, and monitoring. However, efficiently inferring the inherent hybrid continuous-discrete dynamics on resource-constrained edge hardware remains a significant challenge. This letter proposes a neural surrogate solver (NSS) approach, which is a neural-network-based framework aimed at rapid accurate inference with significantly reduced computational costs. Specifically, NSS leverages lightweight neural networks to surrogate time-consuming matrix operation and high-order numerical integration steps in traditional solvers, which transforms sequential bottlenecks into highly parallel operation suitable for edge hardware. Experimental validation on a multistage dc–dc converter demonstrates that NSS achieves 23x speedup and 60% hardware resource reduction compared with traditional solvers, paving the way for deploying edge inference of high-fidelity PES dynamics.

Edge digital twins (EDTs) deployed around power electronics systems can significantly enhance the monitoring and control of the systems. However, they are usually resource-constrained but require high fidelity between digital models and real systems. With limited computing resources, existing event-or-data-driven modeling methods implemented on EDTs fail to comprehensively capture hybrid dynamics (i.e., continuous modes and mode transitions), leading to safety risks. To address these challenges, this article proposes a physics-embedded neural ordinary differential equation (PENODE) framework that includes a multimode event automata to model continuous operation for each mode and neural networks with prior physics knowledge to model discrete mode switching with great accuracy. A deployment workflow based on the framework is designed to provide an effective guideline to transfer digital twins (DTs) from cloud to edge. The proposed PENODE framework has been implemented on the EDT of a high-frequency dc–dc converter. Experimental results verify that PENODE achieves significantly higher accuracy and largely reduced resources across white-box, gray-box, and black-box benchmarks, validating its physical interpretability, efficient edge deployment, and real-time control enhancement.

This article proposes a low-voltage-side parallel high-voltage-side series isolated bidirectional dc–dc converter for single-cell battery energy storage systems, addressing the critical challenges of high voltage gain requirements and low-voltage/high-current operation. Two parallel staggered shunt boost units are used on the low-voltage side to reduce the current ripple and the current stress. The voltage gain is further increased by applying an isolation capacitor on the high-voltage side, which enables the converter to accomplish high-gain boosting without a high transformer turns ratio. Since switches with different performance are used in the two parallel branches to achieve cost efficiency, this article further proposes a power active allocation strategy (PAAS) for the proposed converter. Despite the mixed use of switches with different conduction and switching characteristics, the proposed strategy dynamically optimizes the branch current allocation through duty cycle control while regulating the output voltage, thus improving the overall system efficiency, particularly enhancing efficiency under light-load conditions. Finally, a 250 W prototype with 3.2 V/48 V is built to verify the working principle of the proposed converter, and the overall efficiency is improved by an average of 1.22% after adopting the PAAS.

The large-scale installation of renewable energy sources (RES) causes real-time fluctuation in the power generation of the grid, which may damage the normal operation of critical loads (CL) that are sensitive to power (voltage) variation. To address this problem, this article proposes a novel smart load (SL) consisting of CL and back-to-back (B2B) converter. The proposed SL is named as fractional-order SL (FSL), since the B2B converter inside it is equivalent to a fractional-order capacitor (FOC) component. The equivalent FOC can achieve the dual objectives of CL voltage regulation and load-side power factor (PF) correction by adjusting the order in real time to control the power compensation. In addition, the proposed FSL can achieve cooperative group operation without complex cooperative strategies, and can be plug-and-play integrated into the grid. Meanwhile, the FSL is also beneficial in reducing the energy storage demand of the grid, as it uses B2B converter that do not require energy storage batteries. Finally, the effectiveness of the proposed FSL is fully verified by experiment.

Multi-cell configurations in new energy vehicles and energy storage systems have the issues of uneven energy distribution, low system efficiency, and high maintenance costs. To address these issues, this paper proposes a tapped interleaved isolated bidirectional high-gain DC-DC converter based on single cell battery, along with a dynamic current-sharing control strategy. This converter significantly reduces conduction losses at high currents on the low-voltage side by integrating a tapped transformer with an interleaved parallel structure, combined with magnetic integration and component reuse. Simultaneously, to address the issue of branch current distribution, a coordinated control strategy combining variable duty cycle difference and phase-shifting timing is proposed. A 330W prototype and a single-cell-driven motor experimental platform were constructed. Experiments demonstrate that the system achieves an average efficiency of 92.43% and a peak efficiency of 94.11% under 3.2V input voltage and 48V output voltage conditions. Furthermore, a converter extension scheme is proposed, offering a scalable architecture for kilowatt-level systems. The experimental results validate the feasibility of the proposed converter and control strategy, and the research findings can provide technical support for the development of high-energy-density, low-cost new energy power equipment.