<p>This Paper proposes a method to improve the efficiency of charging the battery used in autonomous electric vehicle powered by foldable roof-mounted solar photovoltaic (PV) generation system. The conventional vehicle battery charging application from solar PV consists of a boost converter in the frontend followed by a full bridge converter with discrete switches. Here an attempt is made on the total scheme with a micro integrated package to have better conversion efficiency with high power density. The total system is controlled digitally incorporating zero voltage transition (ZVT) in the full bridge conversion. A typical specification with a power level of 300-400 W was targeted and achieved.</p>
This paper provides a power quality analysis of single phase full bridge rectifier fed DC motor drive. A single phase full bridge rectifier is generally used in low voltage distribution system. It is necessary to analyse harmonic level and power factor. Here, DC motor is considered as the rectifier's load. The THD and power factor analysis is done for different loading conditions. The first analysis is based on variation of delay angle and its effect on THD and power factor. The second analysis is based on variation of torque or the changes in mechanical load done in the motor while maintaining the delay angle as constant. These analyses are useful in quantizing the THD and power factor at various conditions. FFT analysis on the input current provides the information about the various harmonics present in the waveform. The overall analysis is done using MATLAB software.
Single point incremental forming (SPIF) is an advanced, flexible, and cost-effective approach for producing complicated sheet metal objects quickly. Because of its low equipment cost, the process is best suited toward low or medium quantity batch production as the conventional stamping method remains cost-effective only for mass manufacture. During SPIF formation of titanium grade 5 materials, a response surface methodology and artificial neural network (ANN) model was created to optimize and estimate wall angle (Ømax) and average surface roughness ( Ra). The ANN model is developed using feed forward back propagation networks. Various combinations of transfer functions and number of neurons were used to create the ANNs (3- n-1, 3- n-2). The confirmation runs have been used to ensure that the ANN anticipated and practical findings have been in agreement. The generated ANN model (3- n-1) was capable of accurately forecasting the results of the experiment, with an overall R-value and Mean Square Error (MSE) of 0.99987 and 0.010905 for Ra, and 0.99999 and 0.00700 for wall angle. The best 3- n-2 models has an average R-value of 0.99992 and MSE of 0.05532, respectively. As a consequence of rapid ANN modeling technique, it became discovered that technical effort inside the SPIF process could be decreased.
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