In this paper, a novel variable step size (VSS) incremental conductance (INC) method with an adaptive scaling factor is proposed. The proposed technique utilizes the model-based state estimation method to calculate the irradiance level and then determine an appropriate scaling factor accordingly to enhance the capability of maximum power point tracking (MPPT). The fast and accurate tracking can be achieved by the presented method without the need for extra irradiance and temperature sensors. Only the voltage-and-current sets of any two operating points on the characteristic curve are needed to estimate the irradiance level. By choosing a proper scaling factor, the performance of the conventional VSS INC method can be improved. To validate the studied algorithm, a 600 W prototyping circuit is constructed and the performances are demonstrated experimentally. Compared to conventional VSS INC methods under the tested conditions, the tracking time is shortened by 31.8%. The tracking accuracy is also improved by 2.1% and 3.5%, respectively. Besides, tracking energy loss is reduced by 43.9% and 29.9%, respectively.
In this study, an adaptive driving method for synchronous rectification in bidirectional full-bridge LLC resonant converters used in railway applications is proposed. The drain to source voltage of the synchronous rectifier is utilized to detect the conduction of the body diode, and a suitable driving signal for synchronous rectification is generated accordingly. The proposed driving scheme is simple and can be realized using a low-cost digital signal processor (DSP). According to the experimental results, which averaged 0.4625% and 1.097%, improvement can be observed under charging and discharging mode, respectively.
In conventional adaptive variable step size (VSS) maximum power point tracking (MPPT) algorithms, a scaling factor is utilized to determine the required perturbation step. However, the performance of the adaptive VSS MPPT algorithm is essentially decided by the choice of scaling factor. In this paper, a neural network assisted variable step size (VSS) incremental conductance (IncCond) MPPT method is proposed. The proposed method utilizes a neural network to obtain an optimal scaling factor that should be used in current irradiance level for the VSS IncCond MPPT method. Only two operating points on the characteristic curve are needed to acquire the optimal scaling factor. Hence, expensive irradiance and temperature sensors are not required. By adopting a proper scaling factor, the performance of the conventional VSS IncCond method can be improved, especially under rapid varying irradiance conditions. To validate the studied algorithm, a 400 W prototyping circuit is built and experiments are carried out accordingly. Comparing with perturb and observe (P&O), α-P&O, golden section and conventional VSS IncCond MPPT methods, the proposed method can improve the tracking loss by 95.58%, 42.51%, 93.66%, and 66.14% under EN50530 testing condition, respectively.
The battery storage system (BSS) is one of the key components in many modern power applications, such as in renewable energy systems and electric vehicles. However, charge imbalance among batteries is very common in BSSs, which may impair the power efficiency, reliability, and safety. Hence, various battery equalization methods have been proposed in the literature. Among these techniques, switched-capacitor (SC)-based battery equalizers (BEs) have attracted much attention due to their low cost, small size, and controllability. In this paper, seven types of SC-based BEs are studied, including conventional, double-tiered, modularized, chain structure types I and II, series-parallel, and single SC-based BEs. Mathematical models that describe the charge–discharge behaviors are first derived. Next, a statistical analysis based on MATLAB simulation is carried out to compare the performance of these seven BEs. Finally, a summary of the circuit design complexity, balancing speed, and practical implementation options for these seven topologies is provided.
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