Development and validation of a Kalman filter to identify lateral crosswind loads on road vehicles in real operating environments

An accurate method is needed to estimate the lateral loads acting externally on road vehicles such that transient aerodynamic loads and driver response can be studied in-situ. The aim of this work is to develop a Kalman filter that can be practically applied to estimate external lateral loads using measurements from sensors that are commonly installed by manufacturers on modern road vehicles. An appraisal of the accuracy of the estimates – and the estimate uncertainties – is presented using real-world experiments performed with a test vehicle in the presence of crosswinds. A network of surface pressure taps was installed on the vehicle body to provide a reference estimation of the aerodynamic loads. The effect of making different assumptions about the process, measurement and cross-covariance matrices – as well as a Gaussian random walk and a latent force model of the unknown loads – on the accuracy and precision of the estimates is discussed and recommendations are given for best practice. Given a calibrated single-track model, the method can be applied to any road vehicle legally operating on public roads and has potential to be used as a low-cost method to collect large datasets describing road and crosswind disturbances on public road networks. • Up to 79 % correlation with pressure-derived force estimates. • Uses simple 5-parameter model of system dynamics. • Uses only sensors that are widely available, often already on vehicles. • Provides forces derived from road undulations in addition to crosswinds.