With an increasing number of driver assistance functions and the upcoming trend of autonomous driving, knowledge of the current state and changeable parameters of the vehicle becomes more and more important. One particularly significant parameter with regard to vehicle dynamics is the center of gravity (COG) height, which mainly accounts for its roll dynamics in combination with the mass of the vehicle. Thus, a highly increased vehicle mass and COG height might lead to rollover during cornering, which underlines the need for accurate knowledge of the load of the vehicle. Based on that, an improved rollover prevention could be implemented, for instance, by enhancing the electronic stability program (ESP) of the vehicle. Therefore, the main contribution of this work is the model-based online estimation of the additional load of the vehicle, comprising its COG position and mass. This is achieved by applying a joint extended Kalman Filter (EKF) for the simultaneous state and parameter estimation. Based on a nonlinear model with roll, pitch, and vertical dynamics, an accurate and reliable estimation is possible. One major novelty of this work is the consideration of air suspension systems on top of conventional steel spring suspension systems. Therefore, a nonlinear air spring model with sufficient complexity is proposed, making it suitable for real-time applications. Further, a system theoretical observability analysis allows for an online adaptation of the Kalman Filter weights in order to account for different driving situations. The proposed estimation method is tested and validated by considering a wide range of driving situations, considering distinct loading conditions on both a test track and public roads. The estimation accuracy lies within roughly 50 kg and 1.5 cm for the vehicle mass and COG height, respectively.