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Solar energy can function as a supplementary power supply for other renewable energy sources. On average, Vellore region experiences approximately six hours of daily sunshine throughout the year. Solar photovoltaic (PV) modules are necessary to monitor and fulfill the energy requirements of a given day. An artificial neural network (ANN) based maximum power point tracking (MPPT) controller is utilised to regulate the solar photovoltaic (PV) array and enhance its output. The utilisation of this controller can enhance the efficiency of the module even in severe circumstances, where reduced current and torque ripples will be observed on the opposite end. The motorised vehicle has the capability to function at its highest torque level in different load scenarios as a result. The proposed method is expected to provide advantages in various electric vehicle (EV) applications that require consistent velocity and optimal torque to satisfy the load conditions. The study employs a solar battery that is linked to an SVPWM inverter and subsequently a DC-DC boost converter to supply power to the load. An Artificial Neural Network (ANN) based Maximum Power Point Tracking (MPPT) control system is proposed for a solar battery powered Electric Vehicle (EV) and the system’s performance is evaluated by collecting and analysing data under adjustable load conditions to obtain constant parameters such as speed and torque. The MATLAB® Simulink® model was utilised for this purpose.

The purpose of this article is to model and validate the propulsion system of an electric tractor. Initially, the mathematical model of the ET and its load characteristics are developed based on the velocity profiles used in the agriculture ploughing operations. A PMSM is selected from the torque and power requirement of the ET. In order to replicate the ET load on the PMSM, the load is simulated in accordance with the specifications of the PMSM drive system. This article proposes a novel load-tracking control of the propulsion unit with the arrangement of a PMSM and dynamometer. The speed and torque of the propulsion motor are controlled separately using a decoupled control algorithm. The performance of the propulsion unit is verified under different load profiles of the ET. The load-tracking algorithm is used to improve the dynamic response of the propulsion motor. The propulsion system containing the mathematical model of the ET, PMSM and dynamometer is developed in MATLAB/SIMULINK and validated in a real-time environment using a hardware-in-the-loop (HIL) simulator (OPAL-RT OP5700). The result proves that the proposed load-tracking algorithm provided a better dynamic performance of the propulsion unit. [ABSTRACT FROM AUTHOR]

This paper aims to fabricate pineapple leaf fibre epoxy reinforced with three different orientations which are unidirectional, bidirectional and inclined 45°. E-glass fibre is also introduced in every orientation to observe the change in strength. Fabrication of all these epoxies reinforced pine apple fibre is being done by Hand Layup technique. Two tests are done on these composites that are Tensile Test and Flexural Test. The tensile strength of unidirectional oriented fibre is increased when the E-glass is introduced with composites. The tensile property of unidirectional fibre is reinforced when the E-glass is brought together with composites. The maximum tensile strength was observed in the E-glass unidirectional pineapple fibre composite. Optimum Flexural strength is observed for inclined 45° specimen without E-glass reinforcement, whereas with the introduction of E-glass unidirectional composite maximum flexural strength is yielded. From the results, it can be proposed that Pineapple Leaf Fibre (PALF) reinforced with epoxy and E-glass showed best characteristics for unidirectional orientation of fibre for tensile and in flexural loading. Based on the results obtained, this pineapple composite material is recommended to use for lightweight packaging applications like food industries. [ABSTRACT FROM AUTHOR]

This study aims at developing a novel method for condition monitoring technique for detection and classification of developing faults and increase the working life of continuous variable transmission (CVT) using Daubechies Wavelet 06 (DB-06). The vibration data is collected for 4 different types of faults and healthy condition. Using a magnetic accelerometer and signal analyser, vibration data is collected from the system in the time-domain which is then used as input for a MATLAB code producing the plot of the frequency-domain signal. Maximum frequency is determined to diagnose the faults which are induced over three different belts. Collected data for large scale automotive system (CVT) is used to train the network and then it is tested based on random data points. Faults were classified using ANN with a classification rate of 90.8 %. [ABSTRACT FROM AUTHOR]