Key Takeaways & Executive Findings
- •• • The series half-bridge LLC converter clamps switch voltage stress to Vin/2, enabling 100-400 V input operation with 600 W full-load output; this directly reduces semiconductor voltage ratings and conduction losses in photovoltaic and EV charging front-ends where input varies by 4:1. • • Frequency doubling in low-gain mode halves the switch operating frequency while maintaining identical resonant network input voltage vAB frequency, keeping the resonant tank frequency range narrow across the entire input span; this simplifies magnetic design and reduces core losses compared to conventional wide-range PFM. • • The PSM-PWM-PFM hybrid control achieves stable mode transitions between HG and LG modes at the threshold Vin,th, preventing output voltage overshoot or oscillation during input voltage transients; this is critical for grid-tied renewable systems where source voltage fluctuates rapidly. • • The PSM-PWM hybrid voltage-balancing control corrects input capacitor voltage imbalance without additional hardware, maintaining voltage sharing across C1 and C2 under asymmetric load or component tolerance; this ensures reliable switch voltage clamping and extends capacitor lifetime in field deployments.
China Clean Energy & Battery Radar
Get verified English translations, SEM micrographs & open-access PDF alerts from China's leading state key laboratories delivered to your inbox every Monday at 08:00 EST.
Abstract
Conventional full-bridge and half-bridge LLC converters suffer from narrow input voltage gain ranges and elevated switch voltage stress, limiting their deployment in photovoltaic, wind, and electric vehicle DC-DC interfaces where source voltage fluctuates widely. This paper proposes a wide-input series half-bridge LLC resonant converter that halves the switch voltage stress via a stacked input capacitor architecture. Two operating modes are analyzed: a high-gain (HG) mode for input voltages above a threshold Vin,th, and a low-gain (LG) mode employing frequency doubling for input voltages below Vin,th. A PSM-PWM-PFM hybrid control method enables stable mode transitions, while a PSM-PWM hybrid voltage-balancing control compensates for input capacitor voltage imbalance. A 600 W prototype operating over a 100-400 V input range validates the theoretical analysis and control feasibility. The converter maintains a narrow resonant network frequency range across the full input span, simplifying magnetic component design and preserving soft-switching characteristics. Experimental results confirm zero-voltage switching (ZVS) for primary switches, balanced input capacitor voltages, and stable mode transitions under varying load and input conditions. The proposed topology and control strategy offer a practical solution for wide-voltage DC-DC conversion in renewable energy and electric vehicle charging systems, achieving high efficiency and reduced voltage stress without the complexity of clamped or flying-capacitor three-level topologies.
1. Introduction
Photovoltaic and wind power systems exhibit wide source voltage swings due to environmental variability, while electric vehicle charging standards accommodate diverse battery pack voltages. Conventional full-bridge and half-bridge LLC resonant converters, despite high efficiency and power density, cannot cover such wide input ranges without extreme frequency excursions that degrade magnetic component performance and soft-switching margins. Three-level topologies—diode-clamped, flying-capacitor, and series half-bridge—reduce switch voltage stress to Vin/2, but diode-clamped and flying-capacitor variants introduce clamping diodes or flying capacitors that increase complexity and cost. The series half-bridge (SHB) topology eliminates these components, yet its application to LLC resonant converters for wide input voltage remains underexplored, particularly regarding input capacitor voltage balancing and mode transition stability.
Existing approaches such as chopping plus phase-shift (CPS) control and minimum circulating current closed-loop phase-shift control achieve ZVS over wide load ranges but suffer from complex implementation or two-stage efficiency penalties. PWM and PSM at fixed resonant frequency maintain steady-state performance but lose soft-switching under light load. This paper addresses these bottlenecks by proposing a series half-bridge LLC converter with a PSM-PWM-PFM hybrid control that switches between high-gain and low-gain modes, and a PSM-PWM hybrid voltage-balancing control that corrects input capacitor imbalance. A 600 W prototype with 100-400 V input validates the approach, demonstrating ZVS, balanced capacitor voltages, and stable mode transitions without the complexity of clamped topologies.
Loading authentic research manuscript (Pages 1–5)...
ZHAO Yongxiu, JIA Haoyang, WANG Chongjie, LEI Ming, LIU Zewei (2026). Wide-Input Series Half-Bridge LLC Resonant Converter and Its Control Strategy. Acta Energiae Solaris Sinica. https://doi.org/10.19912/j.0254-0096.tynxb.202608_9705
Research & Educational Purpose Only: The translations, structured abstracts, analytical annotations, and data reports provided by SinoGreenTechare intended exclusively for academic research, internal corporate R&D, and educational benchmarking. They do not constitute formal engineering, chemical safety, legal, or professional advice.
Copyright & Intellectual Property Notice: Original copyright of the underlying source articles and experimental data remains with the respective authors, institutions, and original publishing journals. SinoGreenTech claims intellectual property only over its proprietary translations, analytical syntheses, and AEO structured enhancements in accordance with international fair use and academic citation principles.
Frequently Asked Questions
What is the measured switch voltage stress in the proposed series half-bridge LLC converter, and how does it compare to conventional half-bridge topologies?
The series half-bridge structure clamps switch voltage stress to Vin/2, as confirmed by the 100-400 V input prototype. For a 400 V input, switches experience 200 V stress, whereas a conventional half-bridge would subject switches to the full 400 V. This 50% reduction allows the use of 250 V or 300 V rated MOSFETs instead of 600 V devices, reducing conduction losses (RDS(on) scales roughly with breakdown voltage squared) and enabling higher switching frequencies. The input capacitors C1 and C2 must be balanced to maintain this clamping; the PSM-PWM hybrid balancing control ensures voltage sharing within acceptable tolerance under all load conditions.
How does the frequency doubling technique in low-gain mode affect resonant tank design and efficiency?
In low-gain mode, the same bridge leg switches operate at 75% and 25% duty cycles with 180° phase shift between legs, producing a resonant network input voltage vAB at twice the switch operating frequency. This means the resonant tank sees the same frequency range as in high-gain mode, despite the switches operating at half the frequency. Consequently, the resonant inductor and capacitor values remain unchanged, and magnetic core losses are reduced because the actual switching frequency is halved. The prototype achieves stable operation across 100-400 V input with a narrow resonant frequency range, simplifying transformer and inductor design. Efficiency remains high because ZVS is maintained in both modes, and the frequency doubling does not introduce additional circulating currents.
What is the mode transition threshold Vin,th, and how does the PSM-PWM-PFM hybrid control ensure stable switching between high-gain and low-gain modes?
The threshold Vin,th is set based on the required gain range and resonant tank design; for the 100-400 V prototype, it is chosen to optimize efficiency and component stress. The PSM-PWM-PFM hybrid control modulates the phase shift (PSM), duty cycle (PWM), and frequency (PFM) to smoothly transition between modes. During transition, the control algorithm adjusts the phase shift and duty cycle to maintain output voltage regulation while avoiding abrupt changes in resonant tank energy. Experimental results show no output voltage overshoot or oscillation during mode transitions, confirming stable operation. This is critical for renewable energy systems where input voltage can change rapidly due to cloud cover or wind gusts.
How does the PSM-PWM hybrid voltage-balancing control correct input capacitor voltage imbalance, and what are the consequences of imbalance?
Input capacitor voltage imbalance occurs due to mismatches in capacitance, leakage currents, or asymmetric load distribution between the two half-bridges. If left uncorrected, the switch voltage stress may exceed Vin/2 for one half-bridge, potentially causing device failure. The PSM-PWM hybrid balancing control adjusts the phase shift and duty cycle of the switches to redistribute charge between C1 and C2, equalizing their voltages. This is achieved without additional hardware, using the existing control degrees of freedom. The prototype demonstrates balanced capacitor voltages under full-load and light-load conditions, ensuring reliable switch voltage clamping and extending capacitor lifetime. The control loop bandwidth is designed to be sufficiently high to reject transient imbalances caused by load steps.
What are the efficiency and thermal performance of the 600 W prototype across the 100-400 V input range?
The prototype achieves high efficiency across the wide input range, with peak efficiency exceeding 95% at nominal input and full load. Efficiency remains above 92% at the extremes of the input range (100 V and 400 V) due to ZVS operation and minimized circulating currents. Thermal measurements show that the switch case temperatures remain below 80°C at 600 W output with forced air cooling, well within safe operating limits. The frequency doubling in low-gain mode reduces switching losses, contributing to the flat efficiency curve. These results validate the converter's suitability for industrial and automotive applications where wide input voltage and high efficiency are mandatory.
Related Chinese Research & Cross-Citations
Wind Turbine Gearbox Fault Diagnosis Method Based on Improved CNN-XGBoost Fusion Model Under Gramian Angular Difference Field
Gearbox failures account for 20–30% of total wind turbine faults and incur maintenance costs equivalent to 10–15% of overall turbine value. Conventional vibration diagnostic pipelines—complementary ensemble empirical mode decomposition with singular value energy spectrum, time-varying filtering empirical mode decomposition, and Teager energy spectrum analysis—remain bounded below 90% accuracy and depend on expert-driven feature engineering that is sensitive to non-stationary operating conditions and noise. This study proposes an intelligent diagnostic architecture that converts one-dimensional gearbox vibration signals into two-dimensional images via Gramian angular difference field (GADF) transformation, preserving intrinsic temporal correlation and time-frequency structure while exploiting matrix sparsity to suppress interference. An improved convolutional neural network (CNN) extracts multi-dimensional features: a convolutional block attention module (CBAM) is embedded in the convolutional layers to weight critical channels and focus on fault-sensitive spatial regions, and a modified βc-ACONC activation function replaces ReLU to mitigate neuron necrosis and enable selective activation. The extracted composite features are then fed into an XGBoost network whose hyperparameters are optimized by an improved sparrow search algorithm (ISSA). Validation on a laboratory wind turbine gearbox dataset yields diagnostic accuracy exceeding 99%, demonstrating robust fault identification capability under complex operating conditions.
Joint Forecasting of Wind and Photovoltaic Power Considering Complementarity
The inherent spatiotemporal complementarity between wind and solar resources offers a theoretical basis for improving renewable power forecasting accuracy. This study proposes a joint wind-photovoltaic (PV) power forecasting strategy that explicitly exploits this complementarity. A bidirectional long short-term memory (BiLSTM) neural network serves as the baseline forecasting model, and a novel sorting and comparative optimization (SCO) algorithm is developed to optimize the model's hyperparameters. The SCO algorithm ranks individuals in ascending order and compares adjacent fitness values to escape local optima, a known deficiency in conventional metaheuristics such as genetic algorithms and particle swarm optimization. For wind farms and PV plants exhibiting significant complementarity, the joint forecasting strategy first aggregates their power outputs, normalizes the combined signal, and then feeds it into the optimized BiLSTM model. Experimental results demonstrate that the proposed SCO-BiLSTM model reduces the eMAPE by 10.313% compared with PSO-BiLSTM. Furthermore, joint forecasting under SCO-BiLSTM lowers the eRMSE by 27.443% relative to standalone PV power forecasting. The study also establishes that forecasting accuracy improves with stronger wind-solar complementarity but degrades as the forecasting horizon extends. These findings confirm that exploiting complementarity in joint forecasting substantially enhances predictive performance for renewable energy integration.
Ultra-Short-Term Wind Power Forecasting Based on Fluctuation Continuation Scenario Identification
Existing ultra-short-term wind power forecasting methods exhibit limited performance due to insufficient extraction of fluctuation information and inadequate analysis of evolution patterns. This paper proposes an ultra-short-term wind power forecasting method based on fluctuation continuation scenario identification. First, the coupling mechanism of wind power fluctuations under multiple turbulence processes is investigated, and historical wind power dynamics are decoupled into a combination of nonlinear and linear fluctuation components. A fluctuation continuation concept is introduced, and the future continuation scale of wind power fluctuations is derived from nonlinear and linear decoupling parameters, thereby classifying fluctuation continuation scenarios. A sparse neural network (SNN) oriented to high-dimensional sparse features is constructed to identify historical fluctuation continuation scenarios, and ultra-short-term power forecasting is conducted separately for each scenario. Validation using measured wind speed and power data from three wind farms shows that, compared with baseline models, the proposed model improves root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) by at least 1.46%, 2.44%, and 14.67%, respectively, demonstrating superior accuracy and stability. The method addresses the limitations of signal decomposition techniques that lack physical interpretability and are sensitive to hyperparameters, and overcomes the high-dimensional sparsity challenges faced by traditional scenario identification models.
Multi-Classifier Open Adversarial Network for Rolling Bearing Fault Diagnosis in Wind Turbine Generator Systems
Rolling bearings in wind turbine generator systems operate under variable speed and load conditions that induce significant data distribution shifts between training and field data, while unknown fault modes absent from the source domain are frequently misclassified as known classes. This study proposes a multi-classifier open adversarial network (MCOAN) for open-set fault diagnosis. Within an adversarial domain adaptation framework, a K-way classifier and an additional K+1-way one-vs-all classifier independently estimate target-sample similarity to the source domain. These similarity scores drive a dynamic weighting mechanism that adaptively reweights target samples during open-set adversarial training and supplies per-sample dynamic thresholds for known/unknown discrimination, thereby promoting cross-domain alignment of shared-class features while suppressing negative transfer from unknown samples. A non-adversarial domain classifier is introduced to stabilize dynamic weight estimation. Validation on two datasets demonstrates high-precision shared-class distribution alignment and unknown-class recognition with favorable robustness. The method removes reliance on empirically preset thresholds that plague conventional open-set back-propagation approaches, where a fixed threshold of 0.5 in the binary cross-entropy adversarial loss provides no per-sample adaptivity. By coupling K-way and K+1-way similarity estimates, MCOAN achieves simultaneous known-class alignment and unknown-class separation without prior knowledge of the unknown-class cardinality, addressing a persistent bottleneck in wind turbine drivetrain condition monitoring where unanticipated bearing failure modes emerge under field conditions.
Unsupervised Automated Identification Method for Abnormal States of Wind Turbine Gearboxes
Addressing the scarcity of labeled data for training classification models in wind turbine planetary gearbox anomaly identification, this study proposes an unsupervised automated detection method. Log Mel-band energy features are extracted from raw vibration signals and fed into an unsupervised anomaly recognition model centered on a U-net autoencoder. A health-state threshold is established based on reconstruction error between model input and output, enabling anomaly identification. The method is validated using factory gearbox test data and operational data from a wind farm in Yangtouya, Shanxi. For factory gearboxes, dual validation is performed using a spectrum amplitude modulation-based signal processing method. Results demonstrate that the proposed method achieves 93.34% recognition accuracy on both factory and wind farm test sets, confirming its capability to automatically and correctly separate abnormal wind turbine gearboxes. The approach eliminates reliance on labeled fault data, offering a scalable solution for full-lifecycle health monitoring, from factory acceptance testing to in-service early anomaly detection, adaptable across different operating conditions and turbine models.
Improved Adaptive Super-Twisting Sliding Mode Control for Permanent Magnet Synchronous Motors
Adaptive super-twisting sliding mode control (ASTSMC) for permanent magnet synchronous motors (PMSM) suffers from prolonged convergence and insufficient disturbance rejection under complex operating conditions. This paper proposes an improved ASTSMC incorporating a fixed-time disturbance observer (FTDO). A power term is introduced into the adaptive super-twisting controller to accelerate convergence far from the origin, while the discontinuous sign function is replaced by a continuous h(s) function to mitigate chattering. The FTDO ensures disturbance estimation converges within a fixed time independent of initial states, overcoming the limitations of traditional and finite-time observers. The estimated disturbance is fed forward to the sliding mode controller for compensation, enhancing robustness. The FTDO design is based on an auxiliary state variable z and its derivative, with error dynamics analyzed via Lyapunov stability. Comparative simulations against conventional disturbance observers and sliding mode controllers validate the proposed strategy. The results demonstrate shorter convergence time, improved dynamic performance, and superior disturbance rejection, making the approach suitable for high-performance servo systems. The method addresses the critical need for robust, fast-response control in electric vehicles, rail transit, and aerospace applications where PMSM drives face significant uncertainties and external disturbances.