Machine Learning-Based Digital Twin for Optimizing Automotive Components

This paper presents a Digital Twin approach based on Machine Learning (ML), aimed at creating software-based sensors to reduce the auxiliary devices of the vehicle and enabling predictive maintenance, thus reducing carbon footprint. The solution is applied to the Electric Lubrication Oil Pump (ELOP), a crucial component within a vehicle's powertrain system. The proposed ELOP Digital Twin integrates ML-based sensors to estimate critical parameters such as temperature, pressure and flow rate, reducing the reliance on physical sensors and associated hardware. This estimation approach minimizes manufacturing complexity and cost, enhancing energy efficiency during both production and operation. Furthermore, the digital twin facilitates predictive maintenance by continuously monitoring the component's performance, enabling early detection of potential failures and optimizing maintenance schedules. This leads to lower energy consumption and reduced emissions throughout the component's lifecycle. The Digital Twin architecture is constructed using a detailed simulation model of the ELOP, which is validated against empirical data to ensure high fidelity in replicating real-world behavior. The simulation generates a comprehensive dataset capturing various operational scenarios, which is then used to train ML models for the estimation of physical quantities and the prediction of remaining useful life of the component. These predictive capabilities are crucial for vehicle-level applications that can significantly impact overall vehicle efficiency. While the focus of this paper is on the ELOP, the methodology can be extended to other components and systems, demonstrating a scalable approach to the use of ML for optimizing vehicle performance, providing a pathway to more sustainable automotive design and operation.