Different from the typical topology derivation of power electronics converters (PECs) depending on manual effort, this article aims to explore a programmable method based on graph theory for multiport dc–dc converters with reduced switches. By mathematically modeling converters with graph theory and transforming their working criteria into the corresponding graph constraints, the topology derivation can be automatically and conveniently solved with the aid of computer program. In this article, first, the principle of the proposed graph theory-based programmable method is introduced in detail. Then, it is employed to derive multiport dc–dc converters with reduced switches, and a diversity of new topologies are simultaneously excavated, among which a favorable one is selected to be theoretically introduced and experimentally verified. The proposed method remains relatively simple with the increasing number of ports, as the graph constraints of working criteria are almost the same for different number of ports. More importantly, because PECs can be essentially regarded as graphs with different components and their connecting relationships, the proposed method is applicable for many various converters.

An intelligent energy management strategy (EMS) based on an improved Reinforcement Learning (RL) algorithm is developed to enhance the adaptability of the EMS and to further improve the fuel efficiency of a Plug-in Parallel Hybrid Electric Vehicle (PHEV). Both the numerical model and the energy management strategy of a plug-in PHEV are described. The improved RL with Q-learning algorithm is implemented to acquire the optimal control strategies for improving fuel economy. The Markov Chain is employed to calculate the Transition Probability Matrix of the required power. A Kullback-Leibler (KL) divergence rate is designed to activate the update of EMS, when a new corresponding driving cycle is expected. An Exploration Factor (EF) is proposed to overcome the disadvantages of the normal RL algorithm in convergence rate and reward cost evaluation. The diverse KL divergence rates are examined to seek optimal solutions. The normal-RL strategy, rule-based strategy, and dynamic programming strategy are implemented as benchmark strategies to verify the effectiveness of the proposed strategy. The validation results indicate that the improved RL algorithm with EF makes it possible to promote the EMS capable of significantly improving the energy efficiency of a plug-in PHEV.

The past 20 years witnessed the invention of numerous converters by utilizing various topology derivation methods. Unfortunately, most of these methods are limited by pre-existing topologies or specific cells, causing the omission of some potentially valuable topologies. To break the limitations, a fundamental topology derivation method, namely components to converters (C2C), is proposed for nonresonant dc–dc converters. The basic idea of C2C is intuitively to derive topologies by combining separate components and filtering out valid combinations. Theoretically, C2C can derive converters more comprehensively since its results are not restricted by firm connections of the existing topologies or cells. However, C2C faces a heavy computing load caused by the massive combinations of components. Hence, a two-stage C2C topology derivation strategy is designed to alleviate the computing load. Furthermore, graph theory and dynamic programming are applied to computerize and optimize the above two-stage C2C. The two-stage C2C is utilized to derive single-switch two-port converters and single-inductor multiple-port converters. The derivation results show that all existing topologies with given components and numerous new topologies are derived automatically and simultaneously. Compared with the existing topology derivation methods, the proposed two-stage C2C is more thorough and automatic, facilitating more converters to meet various demands in practical applications.

When autonomous underwater vehicles (AUVs) replenish energy through wireless power transfer (WPT) technology on the underwater charging platform, there are practical issues such as excessive transmission gaps and unstable positioning. Additionally, the fixed size of the charging platform leads to insufficient compatibility. This paper proposes a high compatibility WPT system based on a reconfigurable magnetic coupler (RMC) for AUVs replenishing underwater energy. The transmitter of the RMC is combined with the charging platform cage and can follow the charging platform cage in radial dimensions. RMC can provide better coupling performance, and its necessity and effectiveness have been verified through finite element simulation. When the proposed high-compatibility charging platform supplies energy to AUVs of different sizes, the RMC will reconfigure its structure, leading to changes in the transmitter's self-inductance. To address this issue, a switch-controlled capacitor (SCC) method was incorporated into the system to achieve dynamic tuning. Finally, an experimental prototype was built to test the functionality and performance of the proposed RMC structure and SCC method. Compared to existing methods, this approach significantly enhances the practical application capabilities of WPT technology in underwater charging platforms and positively contributes to the cluster deployment of AUVs.

Equivalent power electronics converters (PECs), which have same performance characteristics but dissimilar configurations, are easily mistaken as different converters to be repeatedly studied in the practice, resulting in extra workload. Therefore, it is essential to implement the equivalence identification to avoid the undesired repeated research. In order to accomplish this goal, a computer-aided solution is proposed in this letter, aiming to quickly and precisely identify the equivalent PECs. First, two identification rules are figured out, with which different PECs can be systematically judged to be equivalent or not. Then, software Altium Designer is used to automatically acquire the information of PECs, including the components and the connecting relationships. After that, software MATLAB is utilized to further complete the data process, circuit loop search, and final judgment. Compared with the conventional manual identification method, which is totally dependent on researcher's effort, the proposed computer-aided scheme is more convenient, accurate, and practical, which is beneficial for the topology research in both academic and industrial fields.

The classical Cuk, Sepic, and Zeta converters have found wide applications in various fields. However, there has been limited research exploring their relationships. This letter aims to uncover their relationships by focusing on their capacitor-less circuits (CLCs). Initially, it is observed that these three converters exhibit highly similar performance characteristics, such as equal voltage gains and average component currents. Subsequently, this letter establishes that their equivalent CLCs are the reason behind these similarities, presenting a new perspective on understanding dc-dc converters with capacitors. Furthermore, based on the above findings, a novel topology derivation method is proposed by connecting capacitors to a CLC. Compared with the traditional methods, the proposed method can derive families of converters with comparable performance. Moreover, the proposed method is particularly suitable to leverage existing converter designs and modify them to meet specific requirements. The proposed method successfully derives several groups of converters from different CLCs. For a group of converters derived from an equivalent CLC, their effectiveness and their performance relationship are verified experimentally.