Digital-Twin-Inspired IoT-Assisted Intelligent Performance Analysis Framework for Electric Vehicles

The significance of intelligent transportation is increasing in modern societies. The development of electric mobility is a result of extensive research and industrial needs. Conspicuously, the current study proposes a smart electric vehicular (EV) performance system for the transportation industry that uses IoT–fog–cloud (IFC) computing technology to provide an effective analysis of domestic and commercial EVs. The system analyzes real-time EV-oriented attributes to present a performance analysis measure (PAM). The framework uses a Bayesian belief model (BBM) to classify EV-related attributes in different categories over a temporal scale. Finally, a two-level threshold-based decision tree model is proposed for an overall assessment of the EV. Experimental simulations were performed to validate its effectiveness over challenging data sets with nearly 56365 data instances. Comparative to state-of-the-art techniques, the proposed framework registered enhanced performance for statistical metrics of delay assessment (126.68 s), statistical classification analysis [specificity (96.97%), precision (95.56%), and sensitivity (96.44%)], decision-making efficiency (97.53%), reliability (92.69%), and stability (0.73).