Road Slope Estimation
Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Knowledge about the current road slope can improve several applications in a heavy-duty vehicle such as predictive cruise control and automated gearbox control. In this thesis the possibility of estimating the road slope based on signals from a vehicles air suspension system has been studied. More specifically, the measurement consists of a pressure signal measuring the axle load, and a vertical distance sensor.
A variety of suspension systems can be mounted on a Scania truck. During this thesis, two discrete-time models based on two different rear axle air suspension systems have been proposed. The models use the effect of alternating axle load during a change in the road slope and the estimates are computed using an extended Kalman filter.
The first model is based on a rear axle suspension known as the 2-bellow system. This type of suspension is strongly affected by the driveshaft torque, which results in a behaviour where the rear end is pushed upwards and thus decreasing the rear axle load during uphill driving. A model was developed in order to compensate for this behaviour. Unfortunately, the estimates showed less promising results and all attempts to determine the error was unsuccessful.
The latter model is based on the 4-bellow system. This suspension system is not affected by the driveshaft torque and a less complex model could be derived. The experimental results indicated that road slope estimation was possible and with a fairly accurate result. However, more work is needed since the estimate is affected by road surface irregularities and since the algorithm requires knowledge about the vehicles mass and the location of the centre of gravity.
All the presented results have been estimated based on real data from a test track at Scania Technical Centre in Södertälje.
Place, publisher, year, edition, pages
2010. , 41 p.
Road Slope Estimation, Air Suspension, Heavy-duty Vehicle, Signal Processing, Modelling, Extended Kalman Filter
Engineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-53884ISRN: LiTH-ISY-EX--10/4307--SEOAI: oai:DiVA.org:liu-53884DiVA: diva2:293213
Christian, Lundqvist, PhDKasumovic, Ines, Development Engineer
Schön, Thomas, Associate Professor