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Abstract-This work implements a smart boost converter to enable an electric bicycle to be powered by a battery/supercapacitor hybrid combination. A 36V, 250W front hub motor was retrofitted onto a normal geared bike powered by a 36V; 12Ah lithium ion phosphate battery pack. A 16.2V, 58F supercapacitor module was connected in parallel to the battery pack via a custom made microcontroller-based boost converter which arbitrates power between the battery and supercapacitor. The control algorithm for the boost converter was developed using a practical approach by using various sensor inputs (battery/supercapacitor current and voltage, bike speed) and comparing the robustness of control scheme. Also energy efficient components were used in designing the boost converter to ensure maximum power transfer efficiency.Based on the implemented system experimental results show an improvement in the up-hill acceleration of the bicycle as a direct result of the boost converter being responsive enough to harvest the extra current from the high power complementary supercapacitor module avoiding deep discharges from the battery. This enhanced battery life. The maximum speed remained unchanged while the improvement in range per charge was subjective to the terrain i.e. flat land; not significant improvement, hilly terrain; significant. However, recharging the supercapacitor via regenerative braking proved to be an arduous task since the boost converter was not designed to be bi-directional. Copyright Form of EVS25.

This paper analyses the application of supercapacitors in a standalone off-grid solar PV system. The solar PV system at University of Nottingham Malaysia Campus (UNMC) was tested using a programmable load. The programmable load was used to apply various load values to the system. The results on the effect of using different loads will be analysed and tested with and without a supercapacitor bank. Results show that the supercapacitor can supply peak current demand and preserve battery state of charge during the day. This system can be implemented in rural areas or small industries.

The aim of this is to demonstrate the capability of Kalman Filter to reduce Support Vector Machine classification errors in classifying pipeline corrosion depth. In pipeline defect classification, it is important to increase the accuracy of the SVM classification so that one can avoid misclassification which can lead to greater problems in monitoring pipeline defect and prediction of pipeline leakage. In this paper, it is found that noisy data can greatly affect the performance of SVM. Hence, Kalman Filter + SVM hybrid technique has been proposed as a solution to reduce SVM classification errors. The datasets has been added with Additive White Gaussian Noise in several stages to study the effect of noise on SVM classification accuracy. Three techniques have been studied in this experiment, namely SVM, hybrid of Discrete Wavelet Transform + SVM and hybrid of Kalman Filter + SVM. Experiment results have been compared to find the most promising techniques among them. MATLAB simulations show Kalman Filter and Support Vector Machine combination in a single system produced higher accuracy compared to the other two techniques