Sensor fusion for estimating vehicle chassis movement

Sammanfattning: The aim of this thesis work is to investigate the possibility of applying a sensor fusion algorithm with a focus on estimating vehicle dynamic states, mainly the vehicle body accelerations. Modern passenger vehicles have several mechatronic systems such as active safety, comfort, driver assistance etc., which are highly dependant on accurate knowledge of such states. This work focuses on the mechatronic suspension system, which makes use of the body accelerations measurements to control the dynamics of the vehicle body in order to provide an improved driving experience. This work can be split up into two major parts, the first being the identification of available onboard sensors for measuring the vehicle body accelerations. Five different sensor combinations are considered and compared with each other. The next part is to develop a sensor fusion algorithm, in this case, a Kalman Filter (KF) based algorithm, which uses vehicle dynamic modelling knowledge to obtain accurate, reliable and less uncertain estimates of the states. Specifically, an Unscented Kalman Filter (UKF) and Cubature Kalman Filter (CKF) were built and compared with each other. Two different vehicle dynamic models, a vehicle planar dynamic model and a full car suspension model, were implemented to capture both the effects of road disturbances and drivingmanoeuvres on the vehicle body dynamics. Both these fusion algorithms were tested using simulation data and logged data and validated by comparing with an ideal sensing method to measure the body accelerations used currently at Volvo Car Corporation.