Enhancing Wheel Vertical Displacement Estimation in Road Vehicles Through Integration of Model-Based Estimator With Artificial Intelligence
In the automotive industry, the accurate estimation of wheel displacements is crucial for optimizing vehicle suspension systems. Traditional model-based approaches often face challenges in accurately predicting these displacements due to the complex dynamics of the road-vehicle interaction. To addre...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Vehicular Technology |
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| Online Access: | https://ieeexplore.ieee.org/document/10605031/ |
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| author | Raffaele Marotta Sebastiaan van Aalst Kylian Praet Miguel Dhaens Valentin Ivanov Salvatore Strano Mario Terzo Ciro Tordela |
| author_facet | Raffaele Marotta Sebastiaan van Aalst Kylian Praet Miguel Dhaens Valentin Ivanov Salvatore Strano Mario Terzo Ciro Tordela |
| author_sort | Raffaele Marotta |
| collection | DOAJ |
| description | In the automotive industry, the accurate estimation of wheel displacements is crucial for optimizing vehicle suspension systems. Traditional model-based approaches often face challenges in accurately predicting these displacements due to the complex dynamics of the road-vehicle interaction. To address this limitation, this study, conducted in the frame of the OWHEEL project, proposes the integration of a multi-output neural network capable of compensating for estimation errors inherent in model-based approaches, specifically those arising from road inputs. Leveraging only vertical acceleration measurements, the neural network operates in parallel with the model-based estimator, enhancing the overall accuracy of displacement estimation. Experimental validation using a sports vehicle demonstrates the efficacy of the proposed methodology, showcasing its ability to improve estimation accuracy beyond the capabilities of the model-based approach alone. |
| format | Article |
| id | doaj-art-5c99a2e62004458d9607412e4ef45cb2 |
| institution | Kabale University |
| issn | 2644-1330 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj-art-5c99a2e62004458d9607412e4ef45cb22025-01-30T00:04:06ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302024-01-01597998910.1109/OJVT.2024.343144910605031Enhancing Wheel Vertical Displacement Estimation in Road Vehicles Through Integration of Model-Based Estimator With Artificial IntelligenceRaffaele Marotta0https://orcid.org/0000-0002-9593-9509Sebastiaan van Aalst1https://orcid.org/0009-0006-0485-9586Kylian Praet2Miguel Dhaens3Valentin Ivanov4https://orcid.org/0000-0001-7252-7184Salvatore Strano5https://orcid.org/0000-0003-2697-2273Mario Terzo6https://orcid.org/0000-0001-8808-0743Ciro Tordela7https://orcid.org/0000-0001-5024-3703Department of Industrial Engineering, University of Naples Federico II, Naples, ItalyTenneco Automotive Europe BVBA, Poort Sint-Truiden, Sint-Truiden, BelgiumTenneco Automotive Europe BVBA, Poort Sint-Truiden, Sint-Truiden, BelgiumTenneco Automotive Europe BVBA, Poort Sint-Truiden, Sint-Truiden, BelgiumAutomotive Engineering Group, TU Ilmenau, Ilmenau, GermanyDepartment of Industrial Engineering, University of Naples Federico II, Naples, ItalyDepartment of Industrial Engineering, University of Naples Federico II, Naples, ItalyDepartment of Industrial Engineering, University of Naples Federico II, Naples, ItalyIn the automotive industry, the accurate estimation of wheel displacements is crucial for optimizing vehicle suspension systems. Traditional model-based approaches often face challenges in accurately predicting these displacements due to the complex dynamics of the road-vehicle interaction. To address this limitation, this study, conducted in the frame of the OWHEEL project, proposes the integration of a multi-output neural network capable of compensating for estimation errors inherent in model-based approaches, specifically those arising from road inputs. Leveraging only vertical acceleration measurements, the neural network operates in parallel with the model-based estimator, enhancing the overall accuracy of displacement estimation. Experimental validation using a sports vehicle demonstrates the efficacy of the proposed methodology, showcasing its ability to improve estimation accuracy beyond the capabilities of the model-based approach alone.https://ieeexplore.ieee.org/document/10605031/Wheel displacementvertical displacementestimationroad vehiclesmodel-basedartificial intelligence |
| spellingShingle | Raffaele Marotta Sebastiaan van Aalst Kylian Praet Miguel Dhaens Valentin Ivanov Salvatore Strano Mario Terzo Ciro Tordela Enhancing Wheel Vertical Displacement Estimation in Road Vehicles Through Integration of Model-Based Estimator With Artificial Intelligence IEEE Open Journal of Vehicular Technology Wheel displacement vertical displacement estimation road vehicles model-based artificial intelligence |
| title | Enhancing Wheel Vertical Displacement Estimation in Road Vehicles Through Integration of Model-Based Estimator With Artificial Intelligence |
| title_full | Enhancing Wheel Vertical Displacement Estimation in Road Vehicles Through Integration of Model-Based Estimator With Artificial Intelligence |
| title_fullStr | Enhancing Wheel Vertical Displacement Estimation in Road Vehicles Through Integration of Model-Based Estimator With Artificial Intelligence |
| title_full_unstemmed | Enhancing Wheel Vertical Displacement Estimation in Road Vehicles Through Integration of Model-Based Estimator With Artificial Intelligence |
| title_short | Enhancing Wheel Vertical Displacement Estimation in Road Vehicles Through Integration of Model-Based Estimator With Artificial Intelligence |
| title_sort | enhancing wheel vertical displacement estimation in road vehicles through integration of model based estimator with artificial intelligence |
| topic | Wheel displacement vertical displacement estimation road vehicles model-based artificial intelligence |
| url | https://ieeexplore.ieee.org/document/10605031/ |
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