Optimal Speed and Powertrain Control of a Heavy-Duty Vehicle in Urban Driving

Abstract: A major challenge in the transportation industry is how to reduce the emissions of greenhouse gases. One way of achieving this in vehicles is to drive more fuel-efficiently. One recently developed technique that has been successful in reducing the fuel consumption is the look-ahead cruise controller, which utilizes future conditions such as road topography. In this this thesis, similar methods are used in order to reduce the fuel consumption of heavy-duty vehicles driving in environments where the required and desired velocity vary. The main focus is on vehicles in urban driving, which must alter their velocity due to, for instance, changing legal speed restrictions and the presence of intersections. The driving missions of such vehicles are here formulated as optimal control problems. In order to restrict the vehicle to drive in a way that does not deviate too much from a normal way of driving, constraints on the velocity are imposed based on statistics from real truck operation.In a first approach, the vehicle model is based on forces and the cost function involves the consumed energy. This problem is solved both offline using Pontryagin's maximum principle and online using a model predictive controller with a quadratic program formulation. Simulations show that 7 % energy can be saved without increasing the trip time nor deviating from a normal way of driving.In a second approach, the vehicle model is extended to include an engine and a gearbox with the objective of minimizing the fuel consumption. A fuel map for the engine and a polynomial function for the gearbox losses are extracted from experimental data and used in the model. This problem is solved using dynamic programming taking into consideration gear changes, coasting with gear and coasting in neutral. Simulations show that by allowing the use of coasting in neutral gear, 13 % fuel can be saved without increasing the trip time or deviating from a normal way of driving.Finally, an implementation of a rule-based controller into an advanced vehicle model in highway driving is performed. The controller identifies sections of downhills where fuel can be saved by coasting in neutral gear.