Leveraging deep transfer learning and adaptive power models for enhanced charging time prediction in electric vehicles

The proliferation of electric vehicles (EVs) requires accurate and context-aware forecasting of charging times to maximize user satisfaction and optimize energy resource planning. Existing predictive models, however, sometimes ignore dynamic elements including battery health degradation, ambient temperature variations, and charger variability by depending just on static statistics and simple heuristics. This work presents a robust artificial intelligence-based system integrating data-driven modelling with computer vision for automatic recognition of EV models and adaptive charging time estimate. Multi-angle visual data helps to optimize a refined ResNet50 architecture for strong EV classification. The model guarantees consistent performance under real-world conditions including occlusion, lighting variation, and non-standard viewing angles by using transfer learning, residual feature propagation, and extensive data augmentation. With a top-1 classification accuracy of 96%, an F1-score of 96%, and a recall of 95%, experimental data show that the proposed ResNet50 model beats conventional models including VGG16, VGG19, and YOLOv8. Following recognition, a module driven by metadata retrieves important battery properties. These are then fed into a dynamic power-flow-based charging time calculator that modulates predictions depending on real-time criteria including state-of- charge (SoC), charger rating, and ambient conditions. Through reduction of idle charging times and improvement of user-level decision-making, this combined approach offers a scalable and intelligent answer to EV infrastructure planning. The integration of deep learning-based image recognition with real-time parameterized analytics demonstrates strong potential for advancing smart transportation systems and enabling more adaptive, personalized electric mobility experiences.