In this paper, a Radial Basis Function Network (RBFN) controlled Sensorless Hybrid Electric Vehicle (HEV) using a Brushless DC Motor (BLDC) is presented. In this context, hybrid energy sources (Photovoltaic& Fuel cell) are designed for supplying electrical energy to the HEV. This involves the design of a Maximum Power Point Tracking (MPPT) system using a Fuzzy Logic controller (FLC) in order to track the maximum power obtained from hybrid energy sources under various weather conditions. The speed of HEV drives is efficiently controlled by using the RBFN-ANN controller in comparison with sensor based control techniques. The performance of the proposed HEV was tested for two types of controllers, namely the Proportional Integral (PI) and the RBFN-ANN controller. The performance of the system is analysed with respect to the three different load torque conditions. The performance comparison is made in terms of generated torque ripple and speed response. The proposed hybrid electric vehicle using a RBFN-ANN controller is validated through the MATLAB/Simulink environment.

The automobile industry is focusing on renewable power sources for driving Electric Vehicles (EVs), which results in the reduction of pollution. This paper presents an Artificial Neural Network (ANN) optimized hybrid Energy Management System (EMS), which was designed for solar Photovoltaic (PV) Electric Vehicles (EVs). In the proposed EMS, two DC-DC converters are utilized, namely a High Gain Interleaved Boost Converter (HGIBC) and a conventional boost converter. The main use of the HGIBC is to harvest maximum power from the solar PV panel which is accomplished with the help of a Model Predictive Controller (MPC) and the other DC-DC converter is used for maintaining the DC link voltage constant. The model predictive controller not only controls the parameters involved,but it can also predict a future change in these parameters, which cannot be performed by conventional controllers. The purpose of this paper is to proposea hybrid energy supply systemfor EVs based on a Battery and an Ultra-Capacitor. The energy of the battery and of the UC is controlled by an ANNcontrollerand also evaluated by means of a conventional PI controller. Based on the simulation results, it can be concluded thatthe ANN controller showed a better performance in comparison with the Proportional Integral (PI) controller. The entire structure wasanalysedfor various conditions of the State of Charge (SoC) of the Battery using MATLAB/Simulink.

Electricity, a readily usable form of energy is much in demand in developing countries like India due to the increase in human population and their improved lifestyles. One of the criteria to achieve the developed country tag is per capita consumption of electricity. It means that all developed countries have energy-intensive economies. With climate change effects are coming into fore, countries started scrutinizing the sources of energy and electricity too. Resource intensive traditional or conventional sources of energy with matured technologies on one side and alternative energy technologies that include renewable with developed or yet to fully developed technologies on the other side are competing for electricity pie in almost in every country. This study adopts the Life Cycle Assessment methodology to determine the energy efficiency and environmental impact assessment of two competing electric power generation systems, one based on conventional energy source and another one based on the renewable energy source. It is a common knowledge that renewable systems have better energy efficiency as they do not consume fuel energy as input and also have lower carbon emissions. This study projects what extent they are better for a unit of electricity produced by both competing systems in terms of predefined metrics such as ERR/EPBT, GWP and GWMP in terms of CO2 equivalents and Green Rating for comparative purposes. The study results may be useful for policymakers as a component in the comprehensive decision-making process for energy planning to meet the future human need or greed for electricity with sustainability and also to achieve circular economy goals.

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The present society suffers with the problem of the greenhouse effect due to the emission of huge amount of carbon dioxide. And almost 70% of emission of carbon dioxide will be due to the usage of automotive vehicles. It is required to reduce the utilization of automotive vehicles to protect the life of earth for the coming years. This manuscript presents the design of electric vehicle with the utilization of renewable energy source like solar energy with the combination of fuel cell energy. It involves the design of maximum power point tracking system with intelligent fuzzy controller to track the maximum power for various weather conditions. The proposed electric vehicle drives the brush less DC motor whose speed has been monitored with the sensor less speed control technique. The speed control technique has been realized with two types of controller’s namely proportional intelligent controller and radial basis function neural network. Also the speed control technique has been analysed with the performance comparison of the two controllers in terms of speed, torque generated and also errors of speed and current to improve the performance of speed by 5% with the help of MATLAB/Simulink.