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| Title | Enhancing Urban Electric Vehicle (Ev) Fleet Management Efficiency In Smart Cities: A Predictive Hybrid Deep Learning Framework |
|---|---|
| ID_Doc | 24056 |
| Authors | Aldossary M. |
| Year | 2024 |
| Published | Smart Cities, 7, 6 |
| DOI | http://dx.doi.org/10.3390/smartcities7060142 |
| Abstract | Highlights: What are the main findings? The GNN-ViGNet model achieved 98.9% accuracy in forecasting EV charging loads, setting a new standard in predictive analytics. The Coati–Northern Goshawk Optimization hybrid algorithm reduced fleet travel distances to 511 km, outperforming established optimization techniques. What is the implication of the main finding? Enhanced forecasting and route optimization significantly cut EV fleet energy consumption and costs. Real-time IoT data integration supports adaptive, sustainable urban transportation. Rapid technology advances have made managing charging loads and optimizing routes for electric vehicle (EV) fleets, especially in cities, increasingly important. IoT sensors in EV charging stations and cars enhance prediction and optimization algorithms with real-time data on charging behaviors, traffic, vehicle locations, and environmental factors. These IoT data enable the GNN-ViGNet hybrid deep learning model to anticipate electric vehicle charging needs. Data from 400,000 IoT sensors at charging stations and vehicles in Texas were analyzed to identify EV charging patterns. These IoT sensors capture crucial parameters, including charging habits, traffic conditions, and other environmental elements. Frequency-Aware Dynamic Range Scaling and advanced preparation methods, such as Categorical Encoding, were employed to improve data quality. The GNN-ViGNet model achieved 98.9% accuracy. The Forecast Accuracy Rate (FAR) and Charging Load Variation Index (CLVI) were introduced alongside Root-Mean-Square Error (RMSE) and Mean Square Error (MSE) to assess the model’s predictive power further. This study presents a prediction model and a hybrid Coati–Northern Goshawk Optimization (Coati–NGO) route optimization method. Routes can be real-time adjusted using IoT data, including traffic, vehicle locations, and battery life. The suggested Coati–NGO approach combines the exploratory capabilities of Coati Optimization (COA) with the benefits of Northern Goshawk Optimization (NGO). It was more efficient than Particle Swarm Optimization (919 km) and the Firefly Algorithm (914 km), reducing the journey distance to 511 km. The hybrid strategy converged more quickly and reached optimal results in 100 rounds. This comprehensive EV fleet management solution enhances charging infrastructure efficiency, reduces operational costs, and improves fleet performance using real-time IoT data, offering a scalable and practical solution for urban EV transportation. © 2024 by the author. |
| Author Keywords | deep learning; dynamic fleet management; EV charging load forecasting; hybrid optimization algorithm; route optimization; smart cities |
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