Raffaele Marotta;Sebastiaan van Aalst;Kylian Praet;Miguel Dhaens;Valentin Ivanov;Salvatore Strano;Mario Terzo;Ciro Tordela
{"title":"通过将基于模型的估算器与人工智能相结合,增强道路车辆车轮垂直位移估算能力","authors":"Raffaele Marotta;Sebastiaan van Aalst;Kylian Praet;Miguel Dhaens;Valentin Ivanov;Salvatore Strano;Mario Terzo;Ciro Tordela","doi":"10.1109/OJVT.2024.3431449","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"979-989"},"PeriodicalIF":5.3000,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10605031","citationCount":"0","resultStr":"{\"title\":\"Enhancing Wheel Vertical Displacement Estimation in Road Vehicles Through Integration of Model-Based Estimator With Artificial Intelligence\",\"authors\":\"Raffaele Marotta;Sebastiaan van Aalst;Kylian Praet;Miguel Dhaens;Valentin Ivanov;Salvatore Strano;Mario Terzo;Ciro Tordela\",\"doi\":\"10.1109/OJVT.2024.3431449\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":34270,\"journal\":{\"name\":\"IEEE Open Journal of Vehicular Technology\",\"volume\":\"5 \",\"pages\":\"979-989\"},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2024-07-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10605031\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Open Journal of Vehicular Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10605031/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Open Journal of Vehicular Technology","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10605031/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
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 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.