Vehicle Mass and Road Grade Estimation Using Kalman Filter
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
This Master's thesis presents a method for on-line estimation of vehicle mass and road grade using Kalman filter. Many control strategies aiming for better fuel economy, drivability and safety in today's vehicles rely on precise vehicle operating information. In this context, vehicle mass and road grade are crucial parameters. The method is based on an extended Kalman filter (EKF) and a longitudinal vehicle model. The main advantage of this method is its applicability on drivelines with continuous power output during gear shifts and cost effectiveness compared to hardware solutions. The performance has been tested on both simulated data and on real measurement data, collected with a truck on road. Two estimators were developed; one estimates both vehicle mass and road grade and the other estimates only vehicle mass using an inclination sensor as an additional measurement. Tests of the former estimator demonstrate that a reliable mass estimate with less than 5 % error is often achievable within 5 minutes of driving. Furthermore, the root mean square error of the grade estimate is often within 0.5°. Tests of the latter estimator show that this is more accurate and robust than the former estimator with a mass error often within 2 %. A sensitivity analysis shows that the former estimator is fairly robust towards minor modelling errors. Also, an observability analysis shows under which circumstances simultaneous vehicle mass and road grade is possible.
Place, publisher, year, edition, pages
2011. , 38 p.
Vehicle mass estimation, Road grade estimation, Extended Kalman filter, Vehicle model, Nonlinear observability, Truck sensors
IdentifiersURN: urn:nbn:se:liu:diva-70266ISRN: LiTH-ISY-EX--11/4491--SEOAI: oai:DiVA.org:liu-70266DiVA: diva2:437583
Subject / course
2011-08-16, Algoritmen, Linköping, 10:15 (Swedish)
Nilsson, Tomas, Ph.D. Student
Åslund, Jan, Associate Professor