Integrated State Estimation with Driving Dynamic Sensors and GPS Data to Evaluate Driving Dynamics Control Functions
The aim of this research project is to investigate the use of GPS data for test drives. Based on data of a multi-antenna GPS system and vehicle dynamic sensors, an information platform is performed. This platform includes the merged sensor signals and an
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Abstract The aim of this research project is to investigate the use of GPS data for test drives. Based on data of a multi-antenna GPS system and vehicle dynamic sensors, an information platform is performed. This platform includes the merged sensor signals and an estimation of vehicle states that are not measurable. In a state estimator the lateral dynamic model is combined with a navigation model. The state estimation is accomplished by coupling the signals in an extended Kalman Filter (EKF) which is a variant of the Kalman Filter (KF) for nonlinear dynamic systems. The double-track approach with a linear tire force model is used to describe the lateral vehicle dynamics. Pitch and roll movements are analyzed separately from each other. The unknown or time-variant vehicle parameters are estimated online by recursive estimation methods. In addition to the presentation of the developed methods, results from test drives with the research vehicle (BMW 540i) at the testing area of the Technische Universität Darmstadt are presented. Keywords Extended kalman filter estimation
Fusion INS/GPS
Parameter and state
1 Introduction and Motivation The current vehicle development is characterized by an increasing variety and complexity. In addition, the vehicles are more and more individualized. Certain vehicle models are available in up to 100 variants with different engines, gears, F2012-E15-013 M. Bauer (&) C. Ackermann R. Isermann Technische Universität Darmstadt, Darmstadt, Germany e-mail: [email protected]
SAE-China and FISITA (eds.), Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 196, DOI: 10.1007/978-3-642-33738-3_73, Ó Springer-Verlag Berlin Heidelberg 2013
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chassis adjustments and control systems [1]. The resulting tuning and testing efforts challenge the vehicle development and testing. In ever-shorter development time a higher number of vehicle models have to be calibrated. To face these challenges, automated calibration processes and a shifting of development steps to simulations are required. For the efficient tuning of vehicle dynamics control systems, an accurate determination of the vehicle dynamics is necessary. Therefore, a method for the integration of GPS measurement technology with a dynamic gyro box data is developed for research vehicles. Hence, the GPS system is not only used for navigation but also to determine the vehicle dynamics response. For several years, the use of GPS data for vehicle dynamics control systems has been studied [2–4]. The complementary characteristics of GPS data in terms of availability, long-term stability, and sampling frequency compared to vehicle dynamics sensors (VDS) motivate the use of GPS data to determine the dynamic state variables in addition to the actual navigation. In the research project ‘‘GNSS4FAS’’ [5] it could be proven in field test that the usability of GPS data (depending on the manufacturer) is up to 97 % on highway driving, 84 % on overland drives and 53 % o
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