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Article

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

1
Transport and Logistics Competence Centre, Transport Engineering Faculty, Vilnius Gediminas Technical University, 10223 Vilnius, Lithuania
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Department of Mobile Machinery and Railway Transport, Transport Engineering Faculty, Vilnius Gediminas Technical University, 08101 Vilnius, Lithuania
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Department of Cognitive Robotics, Delft University of Technology, 2628 CD Delft, The Netherlands
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Automotive Engineering Group, Technische Universität Ilmenau, 98693 Ilmenau, Germany
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Tenneco Automotive Europe, 3800 Sint-Truiden, Belgium
*
Author to whom correspondence should be addressed.
Academic Editor: Francesco Bellotti
Sensors 2021, 21(21), 7139; https://doi.org/10.3390/s21217139
Received: 23 September 2021 / Revised: 16 October 2021 / Accepted: 25 October 2021 / Published: 27 October 2021
(This article belongs to the Section Intelligent Sensors)
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. View Full-Text
Keywords: virtual sensor; automotive control; active suspension; vehicle state estimation; neural networks; deep learning; long-short term memory; sequence regression virtual sensor; automotive control; active suspension; vehicle state estimation; neural networks; deep learning; long-short term memory; sequence regression
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MDPI and ACS Style

Šabanovič, E.; Kojis, P.; Šukevičius, Š.; Shyrokau, B.; Ivanov, V.; Dhaens, M.; Skrickij, V. Feasibility of a Neural Network-Based Virtual Sensor for Vehicle Unsprung Mass Relative Velocity Estimation. Sensors 2021, 21, 7139. https://doi.org/10.3390/s21217139

AMA Style

Šabanovič E, Kojis P, Šukevičius Š, Shyrokau B, Ivanov V, Dhaens M, Skrickij V. Feasibility of a Neural Network-Based Virtual Sensor for Vehicle Unsprung Mass Relative Velocity Estimation. Sensors. 2021; 21(21):7139. https://doi.org/10.3390/s21217139

Chicago/Turabian Style

Šabanovič, Eldar, Paulius Kojis, Šarūnas Šukevičius, Barys Shyrokau, Valentin Ivanov, Miguel Dhaens, and Viktor Skrickij. 2021. "Feasibility of a Neural Network-Based Virtual Sensor for Vehicle Unsprung Mass Relative Velocity Estimation" Sensors 21, no. 21: 7139. https://doi.org/10.3390/s21217139

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