Enhancing Electric Vehicle Performance and Connectivity through Internet of Things Integration

Internet of Things (IoT) technology in Electric Vehicles (EVs) has the potential to enhance performance, connectivity, and the overall user experience. This connection improves EV efficiency, battery life, user interaction, charging infrastructure, and traffic management systems. Dependable communication networks, system compatibility, and data security are all essential. Several concerns must be solved before manufacturers can use the IoT in EVs. Internet of Things-based Accurate Estimation Monitoring Analysis (IoT-AEMA) is presented in this paper as a solution to address these problems. Intending to enhance energy management, safety, and predictive maintenance, the IoT-AEMA has taken the initiative. Electric vehicle (EV) performance can be monitored comprehensively and in real-time with the help of IoT-AEMA, which utilizes IoT technology. This technology makes monitoring metrics like energy use and battery health more accurate. Proactive maintenance is made possible, and communication with smart infrastructure is improved. Improving electric vehicle (EV) connection and efficiency has never been easier than with this scalable solution that prioritizes sustainability. This objective will be accomplished by providing extensive analysis and monitoring of vehicle parameters in real-time. These applications use this technology to enhance data accuracy, the decision-making process for drivers and manufacturers, and the development of intelligent transportation networks. The effectiveness of IoT-AEMA has been demonstrated through simulation studies in various circumstances. By giving accurate insights and encouraging collaboration, this research implies that the electric vehicle industry is on the verge of experiencing a paradigm change. According to the information presented in this article, the IoT and advanced energy management have the potential to make EVs more dependable, efficient, and integrated into the infrastructure of smart cities. The proposed method increases the Energy Management Optimization ratio by 97.6%, Data Accuracy ratio by 90.2%, Predictive Maintenance ratio by 95.7%, System Compatibility ratio by 93.4% and Reliability Analysis ratio by 98.4% compared to other existing methods.