ABSTRACT Digital twin (DT) plays a vital role across various applications, notably in electric vehicles (EVs). It serves as a virtual counterpart to physical systems. Repurposing legacy EV propulsion systems—those developed prior to the rise of connected vehicle technologies—can reduce electronic waste and support sustainability goals. However, adapting DTs in such systems is challenging due to limited connectivity, incomplete schematics, and performance degradation over time. This paper presents a hybrid‐driven DT modelling framework for an EV repurposed with a legacy propulsion system, where some components, like the drive system, have uncertain parameters. A physics‐based model is developed for the motor, leveraging its well‐defined parameters, while a data‐driven model is applied to the drive system due to its uncertainty. The data‐driven model was developed using a nonlinear autoregressive neural network with exogenous inputs (NARX). It was trained on physical test bench data and achieved a validation RMSE of 0.04 on unseen data. A hybrid‐driven model, combining the NARX‐based drive system with a physics‐based motor model, was first validated offline in MATLAB/Simulink, then deployed on a speedgoat baseline target machine for real‐world testing. The deployment enabled validation under real world vehicle conditions beyond the test bench and assessment of its real‐time capability. Real‐time testing demonstrated high steady‐state accuracy and reliable performance, with an average execution cycle of 8 ms, 60% CPU load, and 300 MB memory usage. Communication via user datagram protocol confirmed the model's real‐time readiness and suitability for practical DT integration.
Ibrahim et al. (Wed,) studied this question.
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