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For commercial vehicles, reliability is key since the vehicle is typically linked to the daily earnings of the owner. To ensure continuous vehicle operation, early diagnostics of critical issues and proactive maintenance are important. However, an electric vehicle is a complex and dynamic system consisting of numerous components interacting with each other and with external environments such as road conditions, traffic, weather, and driving behavior. Thus, vehicle operation and performance are highly contextual and for identifying an abnormal operation (diagnostics) the solution must consider the conditions under which it is driven. To address this, the paper proposes an AI-based digital twin of an electric three-wheeler vehicle. TabNet a deep-learning based model is used to learn and generate near-ideal vehicle behavior. The focus of the paper is motor subsystem. The model is trained using appx 200 vehicles first 1500 km driven data.

Good driving practices, encompassing actions like maintaining smooth acceleration, sustaining a consistent speed, and avoiding aggressive maneuvers, can yield several benefits. These practices enhance energy efficiency, reduce accident risks, and significantly lower maintenance costs. Consequently, the presence of a system capable of providing actionable insights to promote such driving behavior is crucial. Addressing this need, the Drive-GPT model is introduced, representing an AI-based generative pre-trained transformer. Within this study, the transformative potential of deep learning networks, specifically based on transformers, is showcased in capturing the typical driving patterns exhibited by individuals in diverse road, traffic, weather, and vehicle health scenarios.

Truck and car manufacturers are required to satisfy certain emission standards while driving regulatory prescribed driving cycles on a vehicle chassis dynamometer. In India, the requirement is to use the regulatory Modified Indian Driving Cycle (MIDC), derived from the European Driving Cycle. The MIDC is a modal driving cycle with protracted periods at constant speed and uniform acceleration and deceleration patterns. It does not emulate typical road driving. In this study we instrument vehicles with off-the-shelf On-Board-Diagnostics (OBD) loggers to record actual drive data. The recorded vehicle speed profiles are then used as inputs for the vehicle simulation model we develop. The simulation model uses vehicle speed as an input and then calculates power required at the wheel, gear box, and Internal Combustion Engine (ICE) for the vehicle to achieve the measured speed profile. We use Willans Approximation to model the ICE fuel flow based on torque and speed.