Search Results (522)

The increasing adoption of electric vehicles (EVs) demands highly efficient bidirectional DC–DC converters capable of seamless energy transfer between the grid and vehicle batteries. This paper introduces a Fixed-Frequency Dual-Active-Bridge (DAB) resonant converter featuring four degrees of freedom, achieved through a combination of triple phase-shift (TPS) modulation and a current-controlled variable inductor (VI). The proposed control strategy aims to minimize conduction and switching losses by simultaneously managing reactive power, RMS current, and soft-switching conditions across wide variations in voltage and power. Unlike conventional phase-shift or variable-frequency modulations, the fixed-frequency operation maintains full zero-voltage switching (ZVS) for the two bridges, and zero-current switching (ZCS) in the bridge that is receiving energy, enhancing overall system reliability and control simplicity. The proposed converter is validated through simulations and experimental results from a SiC MOSFET-based 14 kW prototype operating at 122 kHz, demonstrating peak efficiencies above 97% under both charging and discharging modes. The experimental results confirm that the proposed DAB topology and modulation scheme significantly improve efficiency and controllability, making it a promising solution for next-generation on-board chargers and vehicle-to-grid (V2G) applications. Full article

To address the voltage regulation problem of the DC-DC buck converter under multi-source disturbances, this paper proposes a composite anti-disturbance control strategy integrating a Chebyshev-based self-evolving fuzzy neural network (SECFNN) and an arctangent super-twisting sliding mode control (ASTSMC). First, to construct the composite anti-disturbance framework, a load algebraic reconstruction compensator (LARC) is utilized to analytically estimate real-time load dynamics, providing active feedforward compensation for extreme load steps. Second, targeting the unmodeled nonlinearities and parameter uncertainties, the SECFNN is deeply integrated into the control loop. It employs a bidirectional structural learning mechanism—dynamically growing and pruning fuzzy rules—to achieve high-precision adaptive approximation and intelligent compensation. Furthermore, serving as the robust inner-loop core of this composite strategy, the ASTSMC is introduced. By replacing the traditional discontinuous sign function with a continuous arctangent operator, it effectively mitigates sliding mode chattering while ensuring the rapid finite-time convergence of the current tracking error. Ultimately, by synergistically fusing feedforward disturbance rejection (LARC), intelligent nonlinear approximation (SECFNN), and robust tracking (ASTSMC), the proposed strategy significantly reduces transient voltage drops and achieves smoother steady-state performance. Comparative simulation experiments demonstrate the superiority of the proposed method, achieving a rapid startup settling time of 6.5 ms, limiting the maximum transient voltage drop to 15 mV, and completing dynamic reference tracking in 1.2 ms. Furthermore, hardware experimental results confirm its practical engineering feasibility, demonstrating a fast startup of 8.3 ms with zero overshoot, effectively mitigating transient voltage drops during load step changes, and completing dynamic tracking in just 2.2 ms, which verifies its reliable dynamic agility and strong robustness under various test conditions. Full article

This study details the modelling and simulation analyses performed on a mathematically modelled permanent magnet-assisted synchronous reluctance motor (PMa-SynRM) driven by a field-oriented controlled (FOC) voltage source inverter (VSI) coupled with a half-bridge bidirectional buck-boost DC/DC converter for two-wheeler electric vehicle (EV) applications. The 5 kW, 1500 rpm PMa-SynRM employed here has a shorter response time and is also naturally lighter and cost-effective, making it suitable for two-wheeler EVs. Field-oriented control simplifies the control strategy for PMa-SynRM by decoupling torque and flux, effectively matching the behaviour of a DC motor. A half-bridge buck-boost converter is a DC-DC converter capable of bidirectional power flow, stepping up and down voltages. This makes it ideal for both motoring and regenerative braking in electric vehicles. The buck-boost converter with its controller effectively adjusts the inverter and battery voltage for efficient power flow during motoring and maximum power recovery during regenerating braking. The developed model aims at demonstrating forward and reverse motoring, as well as forward and reverse braking to validate the four-quadrant torque-speed characteristics of two-wheeler EVs. The proposed model attains less than 2% torque ripple and less than 1% speed ripple, respectively. Further, the current ripples are minimised to reduce losses and to improve efficiency. The work presented in this paper implements a PMa-SynRM-based drive system for EVs, a technology which is in the exploratory stage and not commercially widespread. This adds novelty to the proposed work. A MATLAB Simulink environment was used for modelling and simulation. Full article

The existing studies on Small-Scale Embedded Generation (SSEG) have not addressed the net billing framework behavior that applies to different import and export tariff rates. This paper presents the simulation and modeling of a smart net billing electricity meter for SSEG in MATLAB/Simulink R2018b. The model integrates a PV array, MPPT controller, DC-DC boost converter, three-phase voltage source inverter (VSI), LC filter, synchronous generator, and a bidirectional energy meter. A smart billing subsystem was developed to compute real-time energy costs using differential tariff rates consistent with South African utility policies. Simulations were conducted under fixed irradiance, with electrical performance evaluated over a short interval and billing dynamics assessed over an extended period. Results show stable PV generation, proper inverter synchronization with the utility grid, and accurate tracking of imported and exported energy. The system effectively calculates the net bill, demonstrating transparency, automation, and economic accuracy in line with policy-driven net billing frameworks. These outcomes validate the technical feasibility and practical relevance of smart net billing meters in modern grid-connected renewable energy applications. Full article

Cascaded H-bridge converters are the prevalent option for classic energy storage converters due to their excellent battery integration and current sharing capabilities. However, this scheme requires numerous IGBT switchings and exhibits high losses in low-voltage, high-power applications due to high current flowing through the batteries. Furthermore, the limited DC voltage regulation capability makes it difficult to obtain sufficient DC voltage for modulation when the battery is continuously discharging, resulting in shortened continuous discharge duration. To address these issues, this paper proposes a novel energy storage converter based on controllable DC buses. The proposed controllable DC bus consists of cascaded half-bridges and a bidirectional DC converter, where the former topology is designed to preserve voltage and current balancing between batteries, as well as boost the DC voltage—thereby reducing the current flowing through the batteries and minimizing losses. The latter topology is implemented to maintain DC bus voltage during battery discharge, thereby increasing the continuous operating time of the proposed energy storage converter. Moreover, the control and modulation of the proposed controllable DC bus have been optimized, and its effectiveness and performance are verified through simulation results. Full article

Photovoltaic (PV) power generation is inherently intermittent due to unpredictable irradiance variations, posing significant challenges for grid integration. While conventional power smoothing strategies mitigate short-term fluctuations, they do not explicitly enforce the tracking of a scheduled power trajectory. This paper proposes a dispatchable PV framework that integrates a hybrid convolutional neural network-long short-term memory (CNN-LSTM) model for precise day-ahead power forecasting with a real-time supercapacitor (SC) compensation strategy. The CNN-LSTM network captures complex spatiotemporal meteorological dependencies to generate a robust day-ahead reference trajectory. Concurrently, a supercapacitor energy storage system (SC-ESS) integrated at the DC-link level via a bidirectional buck–boost converter actively balances the instantaneous mismatch between this forecast trajectory and the actual PV generation. Unlike filter-based hybrid methods, the SC-ESS is employed as a direct forecast error actuator in a closed-loop control scheme. This strategy strictly enforces real-time forecast tracking while preserving maximum power point tracking (MPPT) and DC-link voltage stability. Simulations and laboratory experiments under rapidly varying irradiance confirm that the proposed method significantly reduces power deviations from the forecast reference and improves short-term power predictability without imposing excessive stress on the SC. This forecast-aware strategy effectively enhances the dispatchability of PV systems, providing a practical solution for grid-supportive operation. Full article

This paper proposes a low weight hybrid battery–supercapacitor energy storage system interfaced with bidirectional DC/DC converters with high power/current capability for aircraft applications. The supercapacitor converter having high power uses two pairs of interleaved coupled inductors to reduce the overall current ripple whilst increasing the converter’s power density. Due to the sensitive performance to saturation of the coupled inductors, a phase current balancing strategy is proposed to counter the effect current imbalance in the channels that would cause saturation degrading overall performance. A power management strategy (PMS) is implemented along with a low pass filter to separate the supercapacitor high frequency power component reference from the battery low frequency power component; therefore, separating the energy and power requirement for the energy storage system contributing to minimizing its weight whilst ensuring the current/power stresses are correctly handled. The validity of the system design is validated by a series of transient tests is conducted both in a simulation model as well as experimentally. Full article

This paper proposes a co-design methodology for the power and control stages of a bidirectional battery charger/discharger based on a boost converter topology. The approach ensures safe operation by limiting the battery current derivative, preventing abrupt transients that could degrade battery lifespan. The control strategy combines a cascade structure with an inner sliding mode current controller (for robustness and fast response) and an outer adaptive PI voltage loop (to regulate the DC-link voltage under varying load conditions). Additionally, the design constrains the switching frequency to reduce power losses. Experimental validation on a prototype converter demonstrates the effectiveness of the co-design framework, showing precise current/voltage regulation, adherence to switching frequency limits, and compliance with battery charging/discharging requirements. The results highlight the methodology’s potential to enhance efficiency and reliability in energy storage systems. The dynamic restrictions, overshoot lower than 5%, settling time shorter than 5 ms, and a battery current limitation less than 50 A/ms were always met with SMC and, in some cases, with the PI controller, but the results with SMC were always better: lower overshoot, shorter settling time, and greater restriction on the derivative of the battery current. In addition, the SMC system was 2.5–5.0% more efficient than the PI controller. Full article

Power converters are fundamental components in vehicle electrification systems. However, their inherently nonlinear and time-varying condition requires complex design procedures when conventional control strategies based on linear small-signal models are employed. This work proposes a simplified and hardware-oriented DC-DC converter control methodology that combines fuzzy logic and Neural Networks in a sequential manner. A fuzzy logic fuzzy controller is first used to generate a dataset of control actions under closed-loop operation. A lightweight neural network is then trained using the obtained data to approximate this mapping and subsequently replace the fuzzy controller in real-time operation. To validate the approach, a bidirectional buck–boost DC-DC converter is designed for applications in the powertrain of electric vehicles with 500 kHz switching frequency and 13 kW power rating. The control algorithm is embedded in an FPGA to demonstrate its suitability for hardware deployment. The experimental results show a reduction in RMSE of 33.7% and a decrease in the settling time of at least 51.7% when compared with a benchmark PID control. Full article

In recent years, the rapid expansion of electric vehicle (EV) charging infrastructure and the increasing penetration of renewable energy sources require highly efficient and dynamically robust power electronic interfaces. In photovoltaic (PV)-assisted EV charging stations and DC microgrids, bidirectional DC-DC converters (BDCs) are essential for managing power flow between PV arrays, battery energy storage systems, and the DC bus supplying EV chargers. This paper presents a novel voltage and current control design for a BDC operating in a PV-powered DC microgrid oriented to EV charging applications. Following a detailed mathematical model of the converter, a digital current controller and a predictive voltage regulator were developed using Model-Based Predictive Control (MBPC). The proposed cascade control structure enables accurate DC bus voltage regulation and seamless bidirectional power flow under dynamic load variations representative of EV charging and discharging scenarios. The control scheme was evaluated in MATLAB/SIMULINK^® and experimentally validated through Field-Programmable Gate Array (FPGA)-based test benches using an OPAL-RT real-time (RT) simulator, integrating the RT-LAB and RT-eFPGAsim environments. The predictive controller achieved precise regulation in both buck and boost modes, reaching efficiencies of 97.07% and 98.57%, respectively. The results demonstrate that integrating MBPC with RT validation provides high performance, fast dynamic response, and computational efficiency, making the proposed approach suitable for renewable-integrated EV charging stations and next-generation DC microgrid-based mobility systems. Full article

This paper presents an integrated techno–economic framework for coordinated grid-to-vehicle and vehicle-to-grid (G2V–V2G) operation in unbalanced distribution networks. A hardware-compatible bidirectional charger with nested AC/DC and DC/DC control loops, together with a rule-based energy management system (EMS), enables seamless mode transitions while enforcing state-of-charge (SoC) and network constraints. A probabilistic Monte Carlo study on the IEEE 13-bus feeder shows that uncoordinated G2V charging induces adverse grid impacts such as voltage stress, line-ampacity violations, and transformer overloading, whereas EMS-driven V2G support improves voltage by 2–4%, reduces line loading by 15–25%, and lowers transformer stress by up to 10%. To align these technical benefits with economic incentives, a bi-level Stackelberg model is formulated where the utility updates locational energy prices based on combined voltage, line ampacity, transformer loading stress indices and EVs choose profit-maximizing nodes, modes and power levels. The interaction converges to a Stackelberg equilibrium with a clear win–win situation; the feeder’s average locational energy price falls entirely within the win–win region, yielding positive per-session profits for both the EV (≈$0.80) and the utility (≈$0.48) while reducing feeder stress. These results demonstrate that stress-aware locational pricing, combined with detailed converter-level control provides a technically robust and economically sustainable pathway for large-scale EV integration. Full article

DC-bias may accumulate in bidirectional full-bridge LLC converters during reverse power transfer because the magnetizing branch lacks an inherent DC-blocking mechanism. This bias may cause asymmetric flux excitation in the transformer core, thereby increasing magnetic stress and even leading to core saturation. To address this issue, a confidence-scheduled hybrid DC-bias estimation and suppression method is proposed. An integration-based indicator is constructed for sensitive weak-bias detection, while a reduced-order extended Kalman filter (EKF) is introduced to improve noise immunity and dynamic tracking under strong-bias conditions. Moreover, a confidence-scheduling mechanism is developed to adaptively fuse the two estimates according to bias severity. Based on the fused estimate, a two-level suppression strategy is implemented for severe- and weak-bias conditions. Simulations and experiments on a 2 kW prototype verify that the proposed strategy achieves fast detection, highly accurate robust estimation with a steady-state error of less than 2%, and effective suppression over a wide operating range without additional bulky DC-blocking hardware. Full article

Bidirectional AC/DC converters in hybrid microgrids are prone to DC-bus voltage instability caused by source-side, grid-side, and load-side disturbances. Conventional linear active disturbance rejection control (LADRC) suffers from a trade-off between transient overshoot suppression and disturbance rejection capability, which limits its practical application. To address this issue, an improved LADRC strategy for bidirectional AC/DC converters is proposed in this paper. First, a linear tracking differentiator (LTD) is introduced to smooth the DC-bus voltage reference and suppress overshoot caused by abrupt command changes. Second, a proportional-derivative (PD) term is embedded into the linear extended state observer (LESO) to introduce phase lead compensation, thereby improving the observer phase characteristics without excessively increasing the observation bandwidth or amplifying high-frequency noise. Frequency domain analysis, MATLAB/Simulink simulations, and full-hardware prototype experiments are carried out to validate the proposed method. The simulation study covers grid voltage sag, photovoltaic-side source fluctuation, and DC-side load disturbance conditions. To further strengthen the experimental verification, hardware tests are conducted under grid voltage dip, PV-side voltage reduction, and DC-side load-switching conditions. The results consistently show that the proposed strategy can effectively reduce DC-bus voltage fluctuation and improve transient recovery performance compared with conventional LADRC. Therefore, the improved LADRC provides a practical and robust control solution for stabilizing bidirectional converters in AC/DC hybrid microgrids. Full article

Extreme fast charging (XFC) infrastructure is becoming increasingly necessary as the number of electric vehicles continues to grow. However, deploying such stations introduces several challenges related to power quality and compliance with regulatory standards. This work presents an alternative XFC station designed for charging multiple vehicles while ensuring low harmonic distortion in the grid currents, without the need for sinusoidal filters, by employing the Zero Harmonic Distortion (ZHD) converter. The proposed system offers galvanic isolation for each charging interface and supports additional functionalities, including the integration of Distributed Energy Resources (DERs) and the provision of ancillary services. These features are enabled through the combination of a bidirectional grid-connected active front-end operating at low switching frequency with high-frequency silicon carbide (SiC)-based dc/dc converters on the vehicle side. Hardware-in-the-loop (HIL) simulation results demonstrate a total demand distortion (TDD) of 1.12% for charging scenarios involving both 400 V and 800 V battery systems, remaining within the limits specified by IEEE 519-2022. Full article

The large-scale integration of distributed energy resources poses numerous challenges to distribution networks. At present, multi-terminal flexible interconnection has become a key development trend for active distribution networks integrated with high-penetration distributed energy resources. Conventional unified power flow controllers (UPFCs) are mainly designed for high-voltage transmission networks and lack distribution-adapted control strategies, making it difficult for them to meet the networking requirements for multi-terminal interconnection. Moreover, most existing studies still focus on two-terminal devices, soft open points and improved UPFC topologies for transmission networks. Existing multi-port schemes mostly adopt only shunt-side structures without series compensation branches, which fail to regulate voltage magnitude and phase difference, thus failing to suppress closing inrush currents and mitigate busbar voltage sags. Meanwhile, such schemes struggle with three-phase imbalance, feeder load imbalance and bidirectional power flow fluctuations in distribution networks, and lack adaptive power allocation capability among multiple ports. To solve the above problems, this paper proposes a VSC-based series–shunt hybrid multi-terminal flexible interconnection converter. The proposed topology consists of one series-side VSC and n − 1 shunt-side VSCs connected through a common DC capacitor; it removes the shunt-side transformer, and effectively reduces cost and volume, while achieving phase shifting, voltage regulation and power flow control. Meanwhile, dual closed-loop PI cross-decoupling control and a flexible closing strategy are adopted to independently regulate the active and reactive power of each feeder, adapt to three-phase imbalance and load imbalance conditions, suppress inrush currents, and realize flexible power mutual support among multiple ports, thereby significantly enhancing adaptability to distribution networks. Full article