The integration of artificial intelligence to transform the infrastructure for charging electric vehicles (EVs) is examined in this article. The suggested intelligent charge management system forecasts demand with 95....
详细信息
ISBN:
(数字)9798350374957
ISBN:
(纸本)9798350374964
The integration of artificial intelligence to transform the infrastructure for charging electric vehicles (EVs) is examined in this article. The suggested intelligent charge management system forecasts demand with 95.2% accuracy using sophisticated machine learning models, cutting average wait times to 3.5 minutes and increasing resource usage efficiency to 87.4%. By lowering average load variation to 12.5 kW, attaining a 93.8% load forecasting accuracy, and providing 2,500 MWh via vehicle-to-grid interactions which results in a 22.1% decrease in peak load the AI-based load management module improves grid stability. Regression approaches are used by predictive maintenance models to accurately predict equipment failures, reducing downtime and prolonging the life of charging infrastructure. Real-time sensor data, historical charging data, and V2G logs are all included in the collection, which was obtained from utility companies and smart grid pilot programs. In the age of electric transportation, the suggested method demonstrates significant gains in all parameters, opening the door for more effective and sustainable energy management. The results show notable improvements in grid integration and EV charging optimization, providing a solid answer for the future of electric vehicles.
For operational effectiveness and cost savings, electric vehicle (EV) fleets must receive proper maintenance. However, reactive repairs and scheduled inspections are frequently the mainstays of traditional maintenance...
详细信息
ISBN:
(数字)9798350374957
ISBN:
(纸本)9798350374964
For operational effectiveness and cost savings, electric vehicle (EV) fleets must receive proper maintenance. However, reactive repairs and scheduled inspections are frequently the mainstays of traditional maintenance methods, which raises costs and results in more downtime. The predictive maintenance solution for electric vehicle fleets that the research proposes uses artificial intelligence (AI) and real-time data analysis to predict possible faults. The proposed system gathers and analyzes data from different car components to predict maintenance needs ahead of time by integrating IoT devices and machine learning techniques. Comparing the proposed system with existing systems, the study shows that it performs better in terms of accuracy (9 0%), lower rates of false positives (10) and negatives (5), and improved performance metrics (accuracy: 90%, precision: 88%, recall: 85%, F1-score: 86%). The proposed system presents a viable way to improve fleet management in electric vehicle operations, ensuring higher uptime, sustainability, and dependability. Higher cross-validation findings (average accuracy: 88%, average F1-score: 85% and improved maintenance effectiveness further support the claim.
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