Integrated brake control : downhill driving strategies

Abstract: In this thesis, downhill driving strategies that co–ordinate the different brake actuators in heavy duty vehicles have been developed. As a starting point, the vehicle features affected by the retardation system are investigated. The main conclusions are that the retardation power demand will increase in the future and that, therefore, optimization of the brake systems will come to play a major role. In particular, strategies for the integration of foundation brakes, auxiliary brakes, and gear box have to be developed. Furthermore, these strategies must take component wear cost into consideration. Additionally, a thorough description of the current situation in terms of driver behaviour and existing systems for driver assistance is given. Optimal control and nonlinear programming have been used for the generation of open–loop optimal driving strategies. Two different methods have been employed for the generation of implementable, closed–loop, driving strategies. First, a method that utilizes neural networks and genetic algorithms is presented. Second, in order to further enhance the controller transparency, and the possibility for robust implementation, the control problem is divided into two differentmodes of operation. Linear quadratic control using gain scheduling is then utilized for the controller design and generation of actuator reference values. Comparison with the open–loop optimal strategies is also made. It is shown that transport efficiency (i.e. mean speed) and retardation economy (i.e. component wear cost) can be improved significantly, even compared to what skilled drivers can achieve, by integrating the whole retardation system. It is furthermore shown that there is a trade–off between component wear cost and transport efficiency that must be balanced in order to achieve good brake performance. The main parameters that affect the longitudinal control of the vehicle are the level of vehicle utilization (mass) and road slope. Algorithms for estimation of vehicle mass and road slope are therefore developed and presented. Additionally, a downhill driving strategy is implemented and verified in a real truck.