Producing more fuel efficient vehicles as well as lowering emissions are of high importance among heavy duty vehicle manufactures. One functionality of lowering fuel consumption is to use a so called \emph{look-ahead control strategy}, which uses the GPS and topography data to determine the optimal velocity profile in the future. When driving downhill in slopes, no fuel is supplied to the engine which lowers the temperature in the aftertreatment system. This results in a reduced emission reduction capability of the aftertreatment system. This master thesis investigates the possibilities of using preheating look-ahead control actions to heat the aftertreatment system before entering a downhill slope, with the purpose of lowering fuel consumption and $NO_x$ emissions. A temperature model of a heavy duty aftertreatment system is produced, which is used to analyse the fuel consumption and $NO_x$ reduction performance of a Scania truck. A Dynamic Programming algorithm is also developed with the purpose of defining an optimal control trajectory for minimizing the fuel consumption and released $NO_x$ emissions. It is concluded that the Dynamic Programming optimization initiates preheating control actions with results of fuel consumption reduction as well as $NO_x$ emissions reductions. The best case for reducing the maximum amount of fuel consumption results in 0.14\% lower fuel consumption and 5.2\% lower $NO_x$ emissions.

Fuel consumption is one of the factors that are considered when deciding a vehicle’s optimal specification. In order to swiftly estimate the fuel consumed during real world driving scenarios, a simulation tool has been developed that is well suited for vehicle configuration exploration applications. The simulation method described in this paper differs from the static calculation method currently in use at Scania cv since the dynamic translation of the vehicle is considered, yet the simulation time is kept low. By adopting a more dynamic approach, the estimation accuracy is increased and simulation of fuel saving technology, e.g. intelli- gent driver support system, is enabled. In this paper, the modeling and implementation process is described. Different approaches is discussed and the choices made during the development is presented. In order to achieve a low simulation time and obtain a good compatability with Scania’s current software application, some of the influencial factors have been omitted from the model or described using simple relations. The validation of the fuel consumption estimation indicates an accuracy within three percent for motorway driving. Utilizing the newly devised simulation tool, a look-ahead cruise controller has been implemented and simulated. Instead of continuously finding the optimal control signals during the driving scenario like most look-aheadcontrollers, a dynamic programming algorithm is used to find a fuel efficient speed profile for the entire route. The speed profile is used as the reference speed for a conventional cruise controller and comparison with another simulation tool indicates that this is a fast and accurate way to emulate a real look-ahead controller.

The operation of groups of heavy-duty vehicles at small inter-vehicular distances, known as platoons, lowers the overall aerodynamic drag and, therefore, reduces fuel consumption and greenhouse gas emissions. Experimental tests conducted on a flat road and without traffic have shown that platooning has the potential to reduce the fuel consumption up to 10%. However, platoons are expected to drive on public highways with varying topography and traffic. Due to the large mass and limited engine power of heavy-duty vehicles, road slopes can have a significant impact on feasible and optimal speed profiles. Therefore, maintaining a short inter-vehicular distance without coordination can result in inefficient or even infeasible speed trajectories. Furthermore, external traffic can interfere by affecting fuel-efficiency and threatening the safety of the platooning vehicles. This thesis addresses the problem of safe and fuel-efficient control for heavy-duty vehicle platooning. We propose a hierarchical control architecture that splits this complex control problem into two layers. The layers are responsible for the fuel-optimal control based on look-ahead information on road topography and the real-time vehicle control, respectively. The top layer, denoted the platoon coordinator, relies on a dynamic programming framework that computes the fuel-optimal speed profile for the entire platoon. The bottom layer, denoted the vehicle control layer, uses a distributed model predictive controller to track the optimal speed profile and the desired inter-vehicular spacing policy. Within this layer, constraints on the vehicles' states guarantee the safety of the platoon. The effectiveness of the proposed controller is analyzed by means of simulations of several realistic scenarios. They suggest a possible fuel saving of up to 12% for the follower vehicles compared to the use of existing platoon controllers. Analysis of the simulation results shows how the majority of the fuel saving comes from a reduced usage of vehicles brakes. A second problem addressed in the thesis is model predictive control for obstacle avoidance and lane keeping for a passenger car. We propose a control framework that allows to control the nonlinear vehicle dynamics with linear model predictive control. The controller decouples the longitudinal and lateral vehicle dynamics into two successive stages. First, plausible braking and throttle profiles are generated. Second, for each profile, linear time-varying models of the lateral dynamics are derived and used to formulate a collection of linear model predictive control problems. Their solution provides the optimal control input for the steering and braking actuators. The performance of the proposed controller has been evaluated by means of simulations and real experiments. / <p>QC 20150911</p>

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This thesis presents a predictive energy management controller for long-haul hybrid trucks. In a receding horizon control framework, the vehicle speed reference, battery energy reference, and engine on/off decision are optimized over a prediction horizon. A mixed-integer quadratic program (MIQP) is formulated by performing modelling approximations and by including the binary engine on/off decision in the optimal control problem. The branch-and-bound algorithm is applied to solve this problem. Simulation results show fuel consumption reductions between 10-15%, depending on driving cycle, compared to a conventional truck. The hybrid truck without the predictive control saves significantly less. Fuel consumption is reduced by 3-8% in this case. A sensitivity analysis studies the effects on branch-and-bound iterations and fuel consumption when varying parameters related to the binary engine on/off decision. In addition, it is shown that the control strategy can maintain a safe time gap to a leading vehicle. Also, the introduction of the battery temperature state makes it possible to approximately model the dynamic battery power limitations over the prediction horizon. The main contributions of the thesis are the MIQP control problem formulation, the strategy to solve this with the branch-and-bound method, and the sensitivity analysis.

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