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An efficient object detection by autonomous vehicle using deep learning Kolukula, Nitalaksheswara Rao; Kalapala, Rajendra Prasad; Ivaturi, Sundara Siva Rao; Tammineni, Ravi Kumar; Annavarapu, Mahalakshmi; Pyla, Uma
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4287-4295

Abstract

The automation industries have been developing since the first demonstration in the period 1980 to 2000 it is mainly used on automated driving vehicle. Now a day’s automotive companies, technology companies, government bodies, research institutions and academia, investors and venture capitalists are interested in autonomous vehicles. In this work, object detection on road is proposed, which uses deep learning (DL) algorithms. You only look once (YOLO V3, V4, V5). In this system object detection on the road data set is taken as input and the objects are mainly on-road vehicles, traffic signals, cars, trucks and buses. These inputs are given to the models to predict and detect the objects. The Performance of the proposed system is compared with performance of deep learning algorithms convolution neural network (CNN). The proposed system accuracy greater than 76.5% to 93.3%, mean average precision (Map) and frame per second (FPS) are 0.895 and 43.95%.
Bidirectional power converter for electrical vehicle with battery charging and smart battery management system Rajanna, Bodapati Venkata; Krishnaiah, Kondragunta Rama; Reddy, Ganta Raghotham; Ahammad, Shaik Hasane; Najumunnisa, Mohammad; Inthiyaz, Syed; Eragamreddy, Gouthami; Sudhakar, Ambarapu; Kolukula, Nitalaksheswara Rao
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i4.pp2592-2604

Abstract

In electric vehicles (EVs), efficient energy management is critical for reliable power transfer between the battery and motor. This paper presents the design and implementation of a bidirectional DC-DC converter equipped with a smart battery management system (BMS). The system supports bidirectional power flow, operating in boost mode during acceleration and buck mode during regenerative braking, thereby enhancing overall energy efficiency and vehicle performance. A PIC microcontroller governs the system, performing real-time monitoring of key battery parameters such as state of charge (SOC), state of health (SOH), voltage, and temperature. Safety features include automatic cooling fan activation when the temperature exceeds 45 °C and generator startup when battery voltage falls below 23 V. Real-time data is displayed via an LCD interface to improve user interaction and system transparency. The proposed system achieved a conversion efficiency of 90-93% during experimental testing, with stable switching, reliable automation, and effective thermal protection. The embedded energy management system optimizes charging and discharging cycles while preventing overcharging, deep discharge, and thermal stress. This intelligent, automated power converter enhances battery life, improves EV reliability, and contributes to sustainable transportation by enabling features like vehicle-to-grid (V2G) energy transfer. The proposed architecture is well-suited for integration into modern EV infrastructure. Although the system architecture supports future V2G integration, V2G functionality was not implemented or tested in the present experimental setup.