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Electric vehicles (EV’s) are gaining increasing popularity because of fast depleting fossil fuels and increasing environmental hazards caused by vehicles that use fossil fuels as a source of propulsion. One of the key factors in EV development is battery management and time required to fully charge the battery. Constant-Current (CC) and Constant-Voltage (CV) are the most common charging strategies used for the charging of batteries. High charging current can cause the temperature of the battery to rise and shorten the battery life. At low State of Charge (SoC) levels, the open circuit voltage (OCV) of the battery is low and CV strategy may cause high charging current, adversely affecting the battery life. Therefore, CCCV is the most commonly used charging strategy, in which battery is charged by CC at low SOC levels and after the OCV reaches a limit, the CV method is applied for charging.

Innovation in the field of intelligent autonomous systems of the automotive sector has been ever increasing. Accurate tracking of vehicles is an important aspect in the design of applications such as smart route planning or collision avoidance systems. In practical applications, tracking of vehicle using radar technology suffers from serious problem due to noisy measurements. It introduces major limit on the accuracy of the tracking system. This paper discusses a case study scenario where the robustness of vehicle tracking can be improved using Extended Kalman Filtering. Noisy radar measurement is simulated through model based design (MBD) using MATLAB. Analysis and design of Extended Kalman Filter to mitigate the noise is discussed. An efficient system architecture to implement the algorithm in autonomous smart vehicle tracking system is also identified.

Hybrid and electric vehicles are becoming increasingly popular these days owing to concerns over exhaustion of conventional fuel sources, pollution from combustion, as well as high carbon foot print of these fuels. Lithium-ion batteries are widely preferred as the source of power for hybrid and electric vehicles because of their high monomer voltage and high energy density. Accurate estimation of the State of Charge (SoC) of battery is crucial in the electric vehicle. It provides the information on the range of operation of the vehicle. It also ensures the safety and reliability of the battery unit. Accurate State of Charge estimation also enables more optimized battery pack design for the electric vehicle. Conventional methods for State of Charge estimation such as Coulomb counting and Open Circuit Voltage (OCV) measurement suffer from inaccuracies and is affected by noise during the vehicle operation.