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Cloud-Based AI Framework for EV Charging Forecasting and Infrastructure Optimization
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DOI：10.3390/electronics15112283.png)
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Abstract
En 中文
The growing use of electric vehicles (EVs) has created a strong need for smart, data-driven charging management systems that can support large-scale and sustainable infrastructure. This study introduces a modular cloud-based framework that combines artificial intelligence and machine learning to provide predictive insights for energy demand and station expansion. The system mainly consists of two complementary models. The first is an AutoRegressive Integrated Moving Average (ARIMA) model that forecasts charging energy demand using transactional data from Palo Alto. The second is a Light Gradient Boosting Machine (LightGBM) model that predicts optimal charging-station locations using spatial data from the U.S. Department of Energy’s Alternative Fuels Data Center (AFDC). Both models were deployed as scalable containerized microservices and were validated for accuracy and efficiency within the cloud environment. This proposed framework establishes a predictive link between energy-demand trends and infrastructure planning. It demonstrates the viability of cloud-native, AI-enabled systems to proactively manage EV charging networks and future smart-grid applications.
Keywords:
electric vehicle
machine learning
cloud computing
charging infrastructure
artificial intelligence