Abstract The shift towards electromobility worldwide is the key approach to counter the 25% of Aglobal carbon dioxide emissions produced by the transportation sector. But the massive adoption of electric vehicles is obstructed by "range anxiety," long waiting times, and the absence of real-time infrastructure information. This paper describes a complete Internet of Things (IoT)-based framework for smart electric vehicle charging management, combining diverse sensing hardware with cloud-based reservation algorithms. We test the performance of the SCT013 current sensor and ZMPT101B voltage sensor, observing an average error of only 0.036A and 1.66V, respectively. Moreover, we examine AI-powered scheduling models, namely Long Short-Term Memory (LSTM) and Random Forest networks, which provide an accuracy of 87.4% in availability forecasting and minimize urban waiting times to an average of 7.8 minutes. The research also measures the sustainability value of solar-integrated stations, proving that a 10-panel solar photovoltaic system can completely compensate for the standard user's daily 37-mile commute, ensuring a 100% carbon-neutral footprint. The results prove that smart coordination can decrease the mean travel time per trip by 9.8% and minimize station peak loads by 25%. Keywords: Electric Vehicles (EV); Internet of Things (IoT); Smart Charging; LSTM; Load Balancing; Sustainable Smart Cities; Renewable Energy.
Sudharshan.S et al. (2026) studied this question.