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This paper presents a time-series point-to-point generative adversarial network (TS-p2pGAN) for synthesizing realistic electric vehicle (EV) driving data. The model accurately generates four critical operational parameters—battery state of charge (SOC), battery voltage, mechanical acceleration, and vehicle torque—as multivariate time-series data. Evaluation on 70 real-world driving trips from an open battery dataset reveals the model’s exceptional accuracy in estimating SOC values, particularly under complex stop-and-restart scenarios and across diverse initial SOC levels. The model delivers high accuracy, with root mean square error (RMSE), mean absolute error (MAE), and dynamic time warping (DTW) consistently below 3%, 1.5%, and 2.0%, respectively. Qualitative analysis using principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) demonstrates the model’s ability to preserve both feature distributions and temporal dynamics of the original data. This data augmentation framework offers significant potential for advancing EV technology, digital energy management of lithium-ion batteries (LIBs), and autonomous vehicle comfort system development.

This study introduces a Multiscale Dual-Attention U-Net (TS-MSDA U-Net) model for long-term time series synthesis. By integrating multiscale temporal feature extraction and dual-attention mechanisms into the U-Net backbone, the model captures complex temporal dependencies more effectively. The model was evaluated in two distinct applications. In the first, using multivariate datasets from 70 real-world electric vehicle (EV) trips, TS-MSDA U-Net achieved a mean absolute error below 1% across key parameters, including battery state of charge, voltage, acceleration, and torque—representing a two-fold improvement over the baseline TS-p2pGAN. While dual-attention modules provided only modest gains over the basic U-Net, the multiscale design enhanced overall performance. In the second application, the model was used to reconstruct high-resolution signals from low-speed analog-to-digital converter data in a prototype resonant CLLC half-bridge converter. TS-MSDA U-Net successfully learned nonlinear mappings and improved signal resolution by a factor of 36, outperforming the basic U-Net, which failed to recover essential waveform details. These results underscore the effectiveness of transformer-inspired U-Net architectures for high-fidelity multivariate time series modeling in both EV analytics and power electronics.

An end-to-end approach to autonomous navigation that is based on deep reinforcement learning (DRL) with a survival penalty function is proposed in this paper. Two actor–critic (AC) frameworks, namely, deep deterministic policy gradient (DDPG) and twin-delayed DDPG (TD3), are employed to enable a nonholonomic wheeled mobile robot (WMR) to perform navigation in dynamic environments containing obstacles and for which no maps are available. A comprehensive reward based on the survival penalty function is introduced; this approach effectively solves the sparse reward problem and enables the WMR to move toward its target. Consecutive episodes are connected to increase the cumulative penalty for scenarios involving obstacles; this method prevents training failure and enables the WMR to plan a collision-free path. Simulations are conducted for four scenarios—movement in an obstacle-free space, in a parking lot, at an intersection without and with a central obstacle, and in a multiple obstacle space—to demonstrate the efficiency and operational safety of our method. For the same navigation environment, compared with the DDPG algorithm, the TD3 algorithm exhibits faster numerical convergence and higher stability in the training phase, as well as a higher task execution success rate in the evaluation phase.

In this paper, we develop a variable-frequency pulse width modulation (VFPWM) circuit for input control of 6.78-MHz resonant wireless power transfer (WPT) systems. The zero-voltage switching control relies on the adjustments of both duty cycle and switching frequency for the class-E amplifier used in the WPT as the power transmission unit. High-frequency pulse wave modulation integrated circuits exist, but some have insufficiently high frequency or unfavorable resolution for duty cycle tuning. The novelty of this work is the VFPWM circuit design that we put together. A voltage-controlled oscillator (VCO) of radio frequency and capacitor-coupled difference amplifiers are used to simultaneously perform the frequency and duty cycle tuning required in resonant WPT applications. Different circuit topologies of VFPWM are compared analytically and numerically. The most favorable circuit topology, enabling independent control of the frequency and duty cycle, is employed in experiments. The experimental results demonstrate the validity of the novel VFPWM, which is capable of operating at 6.78-MHz and has a duty ratio adjustable from 20% to 45% of the range applicable in the resonant WPT applications.

A new SOC (State-Of-Charge)–VOC (Voltage-of-Open-Circuit) mathematical model was proposed in this paper, which is particularly useful in parallel lithium battery modeling. When the battery strings are charged in parallel connection, the batteries can be deemed as capacitors with different capacitances, and the one with larger capacitance always obtains the higher current. According to this mathematical model, the parallel battery charging with different peak capacitances can result in different voltage slew rates on different battery strings during the constant current control. Different parallel battery strings are charged with different currents, of which the battery string under higher current can induce higher power loss and higher temperature. The conventional solution can use this model to switch the constant current charging into the constant voltage charging with the correct timing to avoid overcurrent charging. Other battery pack protection methods including current sense resistor, resettable thermal cutoff device, or resettable fuse can also use this mathematical model to improve the protection. In the experiments, three kinds of batteries including LiFePO4 battery, EV Type-1 battery, and ternary battery were examined. The experiments showed good consistency with the simulation results derived from the mathematical model.

This paper presents a 250 kHz bidirectional battery charger circuit using a GaN HEMT. The charger is subjected to a high-/low-side constant voltage at 200 V/20 V. The charger circuit is a hybrid of the LLC and flyback circuit topologies. Both the power output analysis and efficiency control of this circuit are simplified when the magnetization current is minimized using the low-resistance GaN HEMT. The switching frequency is controlled to match the series resonance in a way that is analogous to conventional LLC circuit controls, while the duty ratio that determines the power output and the dead time, which determines the zero voltage switching, is controlled in an analogous manner to the flyback circuit control. The charging and discharging modes were altered by applying a double-throw relay that changes the transformer turn ratio, which is different from conventional LLC designs using the switching frequency adjustment. A nominal turn ratio with Np = 35 and Ns = 3.5 for a 200 V/20 V converter can only produce an internal series resonance with no current flowing in any charging direction. The proposed circuit using a transformer with multiple windings (Np = 35, Ns,F = 4, and Ns,R = 3) was fabricated to deliver 125 W output power from the power grid battery to the vehicle battery in the forward (charging) mode and 90 W in the reverse (discharging) mode. The conversion efficiency was calculated to be as high as 97% in the forward mode and 95% in the reverse mode. The high conversion efficiency is due to the characteristics of the GaN HEMT, including low resistive and switching losses. The equations derived in this paper associate these losses with the series resonant frequency and power conversion rate, which highlight the advantages of using a GaN HEMT in this CLLC design.

This paper presents a design of an intelligent remote electrical power supervisory control and data acquisition (SCADA) system based on the Internet of Things (IoT), with Internet Information Services (IIS) for setting up web servers, an ASP.NET model-view- controller (MVC) for establishing a remote electrical power monitoring and control system by using responsive web design (RWD), and a Microsoft SQL Server as the database. With the web browser connected to the Internet, the sensing data is sent to the client by using the TCP/IP protocol, which supports mobile devices with different screen sizes. The users can provide instructions immediately without being present to check the conditions, which considerably reduces labor and time costs. The developed system incorporates a remote measuring function by using a wireless sensor network and utilizes a visual interface to make the human-machine interface (HMI) more instinctive. Moreover, it contains an analog input/output and a basic digital input/output that can be applied to a motor driver and an inverter for integration with a remote SCADA system based on IoT, and thus achieve efficient power management.

Mason’s gain formula can grow factorially because of growth in the enumeration of paths in a directed graph. Each of the (n − 2)! permutation of the intermediate vertices includes a path between input and output nodes. This paper presents a novel method for analyzing the loop gain of a signal flow graph based on the transform matrix approach. This approach only requires matrix determinant operations to determine the transfer function with complexity O(n3) in the worst case, therefore rendering it more efficient than Mason’s gain formula. We derive the transfer function of the signal flow graph to the ratio of different cofactor matrices of the augmented matrix. By using the cofactor expansion, we then obtain a correspondence between the topological operation of deleting a vertex from a signal flow graph and the algebraic operation of eliminating a variable from the set of equations. A set of loops sharing the same backward edges, referred to as a loop group, is used to simplify the loop enumeration. Two examples of feedback networks demonstrate the intuitive approach to obtain the transfer function for both numerical and computer-aided symbolic analysis, which yields the same results as Mason’s gain formula. The transfer matrix offers an excellent physical insight, because it enables visualization of the signal flow.

This paper presents a simple behavioral model with experimentally extracted parameters for packaged cascode gallium nitride (GaN) field-effect transistors (FETs). This study combined a level-1 metal–oxide–semiconductor field-effect transistor (MOSFET), a junction field-effect transistor (JFET), and a diode model to simulate a cascode GaN FET, in which a JFET was used to simulate a metal-insulator-semiconductor high-electron-mobility transistor (MIS-HEMT). Using the JFET to simulate the MIS-HEMT not only ensures that the curve fits an S-shape transfer characteristic but also enables the pinch-off voltages extracted from the threshold voltage of the MIS-HEMT to be used as a watershed to distinguish where the drop in parasitic capacitance occurs. Parameter extraction was based on static and dynamic characteristics, which involved simulating the behavior of the created GaN FET model and comparing the extracted parameters with experimental measurements to demonstrate the accuracy of the simulation program with an integrated circuit emphasis (SPICE) model. Cascode capacitance was analyzed and verified through experimental measurements and SPICE simulations. The analysis revealed that the capacitance of low-voltage MOSFETs plays a critical role in increasing the overall capacitance of cascode GaN FETs. The turn-off resistance mechanism effectively described the leakage current, and a double-pulse tester was used to evaluate the switching performance of the fabricated cascode GaN FET. LTspice simulation software was adopted to compare the experimental switching results. Overall, the simulation results were strongly in agreement with the experimental results.

This study examined the output electrical characteristics—current-voltage (I-V) output, threshold voltage, and parasitic capacitance—of novel gallium nitride (GaN) power transistors. Experimental measurements revealed that both enhanced- and depletion-mode GaN field-effect transistors (FETs) containing different components of identical specifications yielded varied turn-off impedance; hence, the FET quality was inconsistent. Establishing standardized electrical measurements can provide necessary information for designers, and measuring transistor electrical characteristics establishes its equivalent-circuit model for circuit simulations. Moreover, high power output requires multiple parallel power transistors, and sorting the difference between similar electrical characteristics is critical in a power system. An isolated gate driver detection method is proposed for sorting the uniformity from the option of the turn-off characteristic. In addition, an equivalent-circuit model for GaN FETs is established on the basis of the measured electrical characteristics and verified experimentally.