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Drive Force and Longitudinal Dynamics Estimation in Heavy-Duty Vehicles

1
Instituto de Diseño y Fabricación (IDF), Universitat Politècnica de València, Camino de Vera s/n,46022 Valencia, Spain
2
Instituto de Automática e Informática Industrial (AI2), Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(16), 3515; https://doi.org/10.3390/s19163515
Received: 21 June 2019 / Revised: 6 August 2019 / Accepted: 9 August 2019 / Published: 11 August 2019
(This article belongs to the Special Issue Sensor Data Fusion for IoT and Industrial Applications)
PDF [3794 KB, uploaded 11 August 2019]

Abstract

Modelling the dynamic behaviour of heavy vehicles, such as buses or trucks, can be very useful for driving simulation and training, autonomous driving, crash analysis, etc. However, dynamic modelling of a vehicle is a difficult task because there are many subsystems and signals that affect its behaviour. In addition, it might be hard to combine data because available signals come at different rates, or even some samples might be missed due to disturbances or communication issues. In this paper, we propose a non-invasive data acquisition hardware/software setup to carry out several experiments with an urban bus, in order to collect data from one of the internal communication networks and other embedded systems. Subsequently, non-conventional sampling data fusion using a Kalman filter has been implemented to fuse data gathered from different sources, connected through a wireless network (the vehicle’s internal CAN bus messages, IMU, GPS, and other sensors placed in pedals). Our results show that the proposed combination of experimental data gathering and multi-rate filtering algorithm allows useful signal estimation for vehicle identification and modelling, even when data samples are missing.
Keywords: sensor fusion; sampled-data; Kalman filter; dynamic systems; parameter identification; heavy vehicles; CAN bus; SAE J1939 sensor fusion; sampled-data; Kalman filter; dynamic systems; parameter identification; heavy vehicles; CAN bus; SAE J1939
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Girbés, V.; Hernández, D.; Armesto, L.; Dols, J.F.; Sala, A. Drive Force and Longitudinal Dynamics Estimation in Heavy-Duty Vehicles. Sensors 2019, 19, 3515.

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