An power-train model based on the internet of things for thingspeak data tracking and EV performance evaluation

Abstract

Abstract Electric vehicles (EVs) are becoming essential transportation alternatives due to their pollution-free environment. Researchers use EV power train models to study battery monitoring, range estimation, and vehicle performance. The models are simulated using various drive cycles and use an IoT-based model to send vehicle data to the cloud for communication optimization. The Li-ion battery pack is used for the EV battery model. The model assesses the EV’s full powertrain and mathematical model, determining efficiency, motor output, and battery parameters as a function of torque and speed. Motor torque is crucial for a vehicle’s power consumption and mobility. Greater mass but higher SOC consumes more energy over the same distance—a higher wind speed results in 6.369 kWh of energy consumption and a balance of 61.52%. The proposed system tracks user operation data to train a remaining distance prediction model based on an electric car’s battery state of charge. The simulation outcomes summarize the vehicle’s distance travelled and the battery state of charge.