In India, transport becomes a basic commodity of daily life. As transportation starts increasing, safety has become a major concern for consumers. This paper mainly aims at reducing the fatalities caused due to accidents occurring in roadways. In general, many lives could be saved if emergency service could get accurate accident location and rescue the injured people at the minimum possible time. The Internet of Things has revlontinsed the modern world in recent times. As Global Positioning System has become an integral part of any vehicle system, this effective method is utilized to monitor the location of vehicles and send accident locations to an Accidents Monitoring and Rescue Services Centre (AMRSC) using GSM. The Accelerometer located in the vehicle system gives the live status of the vehicle position while the vehicle is in motion. Whenever an accident occurs, the signal from the accelerometer is fed to the controller. The Node MCU controller is programmed to check whether the accident has occurred and given the information to the user and AMRSC as soon as possible. Now, the system will also send the accident location acquired from the GPS along with the vehicle details through the GSM network to AMRSC. After receiving the alert message from the infected user vehicle system, the rescue team will reach the accident location as soon as possible by reading the data from the server.
The battery pack powers the electric motor in a battery-operated electric vehicle. To achieve the required power, the cells are connected in series and parallel combinations to form a battery pack. The battery pack is monitored using the battery management system. During the charging and discharging process, imbalance occurs in the cells due to intrinsic and extrinsic properties of the battery chemistry. This cell imbalance induces problems, such as an under-discharge, over-charge, increase in charging time and reduction in battery lifecycle. The passive and active balancing technique is employed to balance the individual cells in the battery pack. In this paper, the adaptive passive cell balancing is performed for a battery pack of six series-connected Li-ion cells of rating 3.6[Formula: see text]V, 4[Formula: see text]Ah under ideal, charging, discharging and drive cycle conditions using MATLAB/Simscape. In this proposed adaptive passive cell balancing methodology, a dynamic resistance is selected based on the threshold values to balance the individual cells in the battery pack. For this battery pack, the proposed design achieves 34% reduction in balancing time, 17% reduction in energy loss, and 14% reduction in power loss under ideal conditions. The experimental verification is also done and shows that the balancing time is about 2400[Formula: see text]s. The capacity fade factor of the battery pack is also analyzed.
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