Feasibility of a Neural Network-Based Virtual Sensor for Vehicle Unsprung Mass Relative Velocity Estimation

With the automotive industry moving towards automated driving, sensing is increasingly important in enabling technology. The virtual sensors allow data fusion from various vehicle sensors and provide a prediction for measurement that is hard or too expensive to measure in another way or in the case...

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Autores principales:	Eldar Šabanovič, Paulius Kojis, Šarūnas Šukevičius, Barys Shyrokau, Valentin Ivanov, Miguel Dhaens, Viktor Skrickij
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	virtual sensor automotive control active suspension vehicle state estimation neural networks deep learning Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/457fd2d432384d908f258e36fc565764
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spelling	oai:doaj.org-article:457fd2d432384d908f258e36fc5657642021-11-11T19:08:18ZFeasibility of a Neural Network-Based Virtual Sensor for Vehicle Unsprung Mass Relative Velocity Estimation10.3390/s212171391424-8220https://doaj.org/article/457fd2d432384d908f258e36fc5657642021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7139https://doaj.org/toc/1424-8220With the automotive industry moving towards automated driving, sensing is increasingly important in enabling technology. The virtual sensors allow data fusion from various vehicle sensors and provide a prediction for measurement that is hard or too expensive to measure in another way or in the case of demand on continuous detection. In this paper, virtual sensing is discussed for the case of vehicle suspension control, where information about the relative velocity of the unsprung mass for each vehicle corner is required. The corresponding goal can be identified as a regression task with multi-input sequence input. The hypothesis is that the state-of-art method of Bidirectional Long–Short Term Memory (BiLSTM) can solve it. In this paper, a virtual sensor has been proposed and developed by training a neural network model. The simulations have been performed using an experimentally validated full vehicle model in IPG Carmaker. Simulations provided the reference data which were used for Neural Network (NN) training. The extensive dataset covering 26 scenarios has been used to obtain training, validation and testing data. The Bayesian Search was used to select the best neural network structure using root mean square error as a metric. The best network is made of 167 BiLSTM, 256 fully connected hidden units and 4 output units. Error histograms and spectral analysis of the predicted signal compared to the reference signal are presented. The results demonstrate the good applicability of neural network-based virtual sensors to estimate vehicle unsprung mass relative velocity.Eldar ŠabanovičPaulius KojisŠarūnas ŠukevičiusBarys ShyrokauValentin IvanovMiguel DhaensViktor SkrickijMDPI AGarticlevirtual sensorautomotive controlactive suspensionvehicle state estimationneural networksdeep learningChemical technologyTP1-1185ENSensors, Vol 21, Iss 7139, p 7139 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	virtual sensor automotive control active suspension vehicle state estimation neural networks deep learning Chemical technology TP1-1185
spellingShingle	virtual sensor automotive control active suspension vehicle state estimation neural networks deep learning Chemical technology TP1-1185 Eldar Šabanovič Paulius Kojis Šarūnas Šukevičius Barys Shyrokau Valentin Ivanov Miguel Dhaens Viktor Skrickij Feasibility of a Neural Network-Based Virtual Sensor for Vehicle Unsprung Mass Relative Velocity Estimation
description	With the automotive industry moving towards automated driving, sensing is increasingly important in enabling technology. The virtual sensors allow data fusion from various vehicle sensors and provide a prediction for measurement that is hard or too expensive to measure in another way or in the case of demand on continuous detection. In this paper, virtual sensing is discussed for the case of vehicle suspension control, where information about the relative velocity of the unsprung mass for each vehicle corner is required. The corresponding goal can be identified as a regression task with multi-input sequence input. The hypothesis is that the state-of-art method of Bidirectional Long–Short Term Memory (BiLSTM) can solve it. In this paper, a virtual sensor has been proposed and developed by training a neural network model. The simulations have been performed using an experimentally validated full vehicle model in IPG Carmaker. Simulations provided the reference data which were used for Neural Network (NN) training. The extensive dataset covering 26 scenarios has been used to obtain training, validation and testing data. The Bayesian Search was used to select the best neural network structure using root mean square error as a metric. The best network is made of 167 BiLSTM, 256 fully connected hidden units and 4 output units. Error histograms and spectral analysis of the predicted signal compared to the reference signal are presented. The results demonstrate the good applicability of neural network-based virtual sensors to estimate vehicle unsprung mass relative velocity.
format	article
author	Eldar Šabanovič Paulius Kojis Šarūnas Šukevičius Barys Shyrokau Valentin Ivanov Miguel Dhaens Viktor Skrickij
author_facet	Eldar Šabanovič Paulius Kojis Šarūnas Šukevičius Barys Shyrokau Valentin Ivanov Miguel Dhaens Viktor Skrickij
author_sort	Eldar Šabanovič
title	Feasibility of a Neural Network-Based Virtual Sensor for Vehicle Unsprung Mass Relative Velocity Estimation
title_short	Feasibility of a Neural Network-Based Virtual Sensor for Vehicle Unsprung Mass Relative Velocity Estimation
title_full	Feasibility of a Neural Network-Based Virtual Sensor for Vehicle Unsprung Mass Relative Velocity Estimation
title_fullStr	Feasibility of a Neural Network-Based Virtual Sensor for Vehicle Unsprung Mass Relative Velocity Estimation
title_full_unstemmed	Feasibility of a Neural Network-Based Virtual Sensor for Vehicle Unsprung Mass Relative Velocity Estimation
title_sort	feasibility of a neural network-based virtual sensor for vehicle unsprung mass relative velocity estimation
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/457fd2d432384d908f258e36fc565764
work_keys_str_mv	AT eldarsabanovic feasibilityofaneuralnetworkbasedvirtualsensorforvehicleunsprungmassrelativevelocityestimation AT pauliuskojis feasibilityofaneuralnetworkbasedvirtualsensorforvehicleunsprungmassrelativevelocityestimation AT sarunassukevicius feasibilityofaneuralnetworkbasedvirtualsensorforvehicleunsprungmassrelativevelocityestimation AT barysshyrokau feasibilityofaneuralnetworkbasedvirtualsensorforvehicleunsprungmassrelativevelocityestimation AT valentinivanov feasibilityofaneuralnetworkbasedvirtualsensorforvehicleunsprungmassrelativevelocityestimation AT migueldhaens feasibilityofaneuralnetworkbasedvirtualsensorforvehicleunsprungmassrelativevelocityestimation AT viktorskrickij feasibilityofaneuralnetworkbasedvirtualsensorforvehicleunsprungmassrelativevelocityestimation
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Feasibility of a Neural Network-Based Virtual Sensor for Vehicle Unsprung Mass Relative Velocity Estimation

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