Model-Predictive Control; Driver Assistance Systems; Electric Vehicles
[en] This thesis investigates a method to save energy and thus also extend the range of a series-production battery electric vehicle by influencing the driving style automatically with the help of a of a cruise controller. An exploration of existing methods shows that the contextual consideration of the current and upcoming driving situation is necessary to realise safe and energy-efficient driving. This limits the appropriate approaches to online methods using updated predictions of the vehicle behaviour. It turns out that the most suitable method for the intended purpose is model-predictive control (MPC).
The MPC generates controls for the accelerator pedal of the vehicle based on optimised predictions of the vehicle motion and energy consumption subject to the current and future road slope, curvature, speed limits and distance to an eventually preceding vehicle. The non-linear nature of the vehicle dynamics generally necessitates the use of a non-linear prediction model and solving a non-linear optimisation which goes along with difficulties in the online real-time implementation. However in this work - by exploiting and extending the tool sets of classical MPC - a controller based on a quadratic optimal control problem with linear constraints can be formulated that approximates the nonlinearities of the plant dynamics with equivalent accuracy as a non-linear formulation.
A linear prediction model of the vehicle motion is derived by a change of the model domain from time to position and a change of variables to predict the kinetic energy of the moving vehicle instead of the driving speed. Further, a convex piece-wise linear energy consumption model is included in the inequality constraints of the problem according to the methodology of separable programming to capture the consumption characteristics of the vehicle in different operating points. In this form, real-time capability and the energy-saving potential of the presented control approach can be demonstrated by simulations of the closed loop and by implementing the controller for driving experiments. A Smart ED series-production battery electric vehicle is chosen for the practical tests and all models and parameters are identified and adapted to the characteristics of the car. In this application case, a significant energy-saving potential could be demonstrated compared to human drivers.
To further reduce the computational burden and speed up the computation, the so-called move blocking method for input parameterisation of the MPC control trajectory is investigated and extended within this work to a flexible move blocking approach which enables a fast computation and at the same time high tracking performance.
Research center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT)