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...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-10-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/21/7139 |
| _version_ | 1797511770287374336 |
|---|---|
| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:52:46Z |
| format | Article |
| id | doaj.art-457fd2d432384d908f258e36fc565764 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T05:52:46Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-457fd2d432384d908f258e36fc5657642023-11-22T21:37:11ZengMDPI AGSensors1424-82202021-10-012121713910.3390/s21217139Feasibility of a Neural Network-Based Virtual Sensor for Vehicle Unsprung Mass Relative Velocity EstimationEldar Šabanovič0Paulius Kojis1Šarūnas Šukevičius2Barys Shyrokau3Valentin Ivanov4Miguel Dhaens5Viktor Skrickij6Transport and Logistics Competence Centre, Transport Engineering Faculty, Vilnius Gediminas Technical University, 10223 Vilnius, LithuaniaTransport and Logistics Competence Centre, Transport Engineering Faculty, Vilnius Gediminas Technical University, 10223 Vilnius, LithuaniaDepartment of Mobile Machinery and Railway Transport, Transport Engineering Faculty, Vilnius Gediminas Technical University, 08101 Vilnius, LithuaniaDepartment of Cognitive Robotics, Delft University of Technology, 2628 CD Delft, The NetherlandsAutomotive Engineering Group, Technische Universität Ilmenau, 98693 Ilmenau, GermanyTenneco Automotive Europe, 3800 Sint-Truiden, BelgiumTransport and Logistics Competence Centre, Transport Engineering Faculty, Vilnius Gediminas Technical University, 10223 Vilnius, LithuaniaWith 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.https://www.mdpi.com/1424-8220/21/21/7139virtual sensorautomotive controlactive suspensionvehicle state estimationneural networksdeep learning |
| spellingShingle | 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 Sensors virtual sensor automotive control active suspension vehicle state estimation neural networks deep learning |
| title | 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_short | 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 |
| topic | virtual sensor automotive control active suspension vehicle state estimation neural networks deep learning |
| url | https://www.mdpi.com/1424-8220/21/21/7139 |
| 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 |