Unmanned and Autonomous Ground Vehicle (UAGV) is a smart vehicle that capable of doing tasks without the need of human operator. The automated vehicle can work during off and on road navigation and also used in military operation such as detecting bombs, border patrol, carrying cargos, search, rescue etc reducing soldier’s exposure to danger, freeing them to perform other duties. This type of vehicle mainly uses sensors to observe the environment and automatically take decisions on its own in unpredictable situation and with unknown information or pass this information to the operator who control the UAGV through various communication when it requires support. This UAGV can send visual feedbacks to the operator at the ground station. An onboard sensor gives the complete environment of the vehicle as signals to the operator.

<span lang="EN-US">This paper proposes increasing the efficiency of the autonomous Photovoltaic (PV) system by utilizing zeta converter alongside neutral point clamped multilevel inverters (NPC-MLI) based on innovative PWM scheme. The PV system acts as an input source and the relevant control of zeta converter through maximum power point tracking (MPPT) offers the maximum available power from the PV array connected to DC-link. To obtain a high voltage gain we need to exhaust the dc-link voltage as much as possible and reduce stress on the switches. For this the NPC-MLI algorithm approaches PWM technique to perform capacitive charging in parallel and discharging in series to obtain maximum voltage gain. The proposed scheme is designed and verified via detailed simulations in the MATLAB/Simulink environment. </span>

This paper deals the implementation of 3-level output voltage using dual 2-level inverter with control of sub-region based Space Vector Modulation (SR-SVM). Switching loss and voltage stress are the most important issues in multilevel inverters, for keep away from these problems dual inverter system executed. Using this proposed system, the conventional 3-level inverter voltage vectors and switching vectors can be located. In neutral point clamped multilevel inverter, it carries more load current fluctuations due to the DC link capacitors and it requires large capacitors. Based on the sub-region SVM used to control IGBT switches placed in the dual inverter system. The proposed system improves the output voltage with reduced harmonic content with improved dc voltage utilisation. The simulation and hardware results are verified using matlab/simulink and dsPIC microcontroller.

Batteries are one of the most compact and reliable sources of sustainable energy. Lead-Acid batteries are the battery-powered sort of batteries concocted during the 1980s. The significant utilization of lead-acid battery is in beginning, lighting and start frameworks of vehicles.To guarantee the health and to dodge potential disappointments of a battery it is important to examine its Territory of health precisely. This examination expects to give efficiently evaluating the accessible writing on the condition of health estimation techniques. This study focuses on many factors and provides a suggestion for the defended battery manufacturing process. This study provides increasing efforts toward the advancement of battery interms of specific power, energy density, durability, invulnerability, economics, and performance in various applications.

An Artificial Neural Network (ANN) based Space Vector Pulse Width Modulation (SVPWM) for five level cascaded H-bridge inverter (CHBI) fed grid connected photovoltaic (PV) system. The multilevel inverter topologies are offers better performance compare conventional two level inverter like reduced total harmonic distortion, less electromagnetic interferences and voltage stresses across switches are low. The ANN based SVPWM generates the switching pulses for cascaded H-bridge inverter; it improves the accuracy in reference vectors tuning and identification, which leads to improve the inverter output voltage, better utilization of dc-link voltage and controlled output current. The ANN control makes the implementation of SVPWM becomes simple and minimizes the intricacy in tracking reference vector and calculation of switching time; it is suitable for any type of non-linear systems. This proposed system is energized using PV system and it is boosted using dc-dc boost converter, and the output of CHBI is synchronized with grid connected system using coupled inductor. The simulation and experimental results of ANN based SVPWM for CHBI is verified using simulink-matlab and DSP processor.