Development of an intelligent perception system for an automotive brake-by-wire system

This thesis considers the problem of estimating clamp force in an electro-mechanical brake (EMB) for an automotive brake-by-wire (BBW) system. A clamp force sensor is typically used in EMB designs and the elimination of this component is strongly demanded due to implementation difficulties and cost issues. The motivation behind this thesis is to make developmental inroads into the deficiencies provided by previous attempts to estimate clamp force. Previous attempts have deficiencies for high speed braking applications as well as handling thermally dependent parameter variations. A dynamic stiffness model to estimate clamp force is developed that relies on the actuator resolver sensor and two additional temperature sensors. Previous attempts to estimate clamp force have been stiffness based and have not been capable of successfully modelling parameter variations in response to heating. This thesis introduces new developments on how to model stiffness parameter variations under the influence of heating. Two temperature sensors are required to be employed in this new approach. These additional sensors will not have a considerable impact towards the cost savings created by omitting a clamp force sensor. A torque balance model to estimate clamp force is also developed that relies on the actuator resolver sensor and actuator motor current sensors. A training strategy is used for the dynamic stiffness and torque balance models to estimate clamp force so that wear dependent parameters can be adapted. The two independent models to estimate clamp force are fused using various sensor fusion algorithms to give improved estimates of clamp force. A maximum-likelihood estimator is used to optimize the root-mean-square error (RMSE) of estimation. This is followed by implementing a Kalman filter to estimate clamp force in an EMB. The dynamic stiffness model is used as the state space equation in the Kalman filter, whilst the torque balance model is used to give measurement updates. Experimental verification showed that the Kalman filter was more accurate than the maximum-likelihood estimator as expected, however the Kalman filter required more computational burden. A RMSE of 0.5 kN was attained for the Kalman filter and 0.56 kN for the maximum-likelihood estimator