Automated engine calibration of hybrid electric vehicles

We present a method for automated engine calibration, by optimizing engine management settings and power-split control of a hybrid electric vehicle. The problem, which concerns minimization of fuel consumption under a NOx constraint, is formulated as an optimal control problem. By applying Pontryagin's maximum principle, this study shows that the problem is separable in space. In the case where the limits of battery state of charge are not activated, we show that the optimization problem is also separable in time. The optimal solution is obtained by iteratively solving the power-split control problem using dynamic programming or the Equivalent Consumption Minimization Strategy. In addition, we present a computationally efficient suboptimal solution, which aims at reducing the number of power-split optimizations required. An example is provided concerning optimization of engine management settings and power-split control of a parallel hybrid electric vehicle

Fuel Shortages During Hurricanes: Epidemiological Modeling and Optimal Control

Hurricanes are powerful agents of destruction with significant socioeconomic impacts. A persistent problem due to the large-scale evacuations during hurricanes in the southeastern United States is the fuel shortages during the evacuation. Fuel shortages often lead to stranded vehicles and exacerbate the evacuation efforts. Computational models can aid in emergency preparedness and help mitigate the impacts of hurricanes. In this thesis, the hurricane fuel shortages are modeled using the Susceptible-Infected-Recovered (SIR) epidemic model. Crowd-sourced data corresponding to Hurricane Irma and Florence are utilized to parametrize the model. An estimation technique based on Unscented Kalman filter (UKF) is employed to evaluate the SIR dynamic parameters. Finally, an optimal control approach for refueling based on a vaccination analogue is presented to effectively reduce the fuel shortages under a resource constraint. The control model estimates the duration and the level of intervention required to mitigate this epidemic. This approach could be useful for emergency management of future hurricanes. A predictive model is then proposed where the UKF can be utilized to evaluate the SIR dynamic parameters from incoming fuel shortage during the initial stages of the hurricane. Due to the nature of the Ordinary Differential Equations (ODE) of SIR dynamics, only one of the parameters can be accurately estimated from the data collection of initial stages of the evacuation. The Basic Reproduction number (R0) value is then varied to produces predictive trends and the optimal refueling strategy is applied to these probable fuel shortage trends to demonstrate possible countermeasures

Generalized Kuhn-Tucker Conditions for N-Firm Stochastic Irreversible Investment under Limited Resources

In this paper we study a continuous time, optimal stochastic investment problem under limited resources in a market with N firms. The investment processes are subject to a time-dependent stochastic constraint. Rather than using a dynamic programming approach, we exploit the concavity of the profit functional to derive some necessary and sufficient first order conditions for the corresponding Social Planner optimal policy. Our conditions are a stochastic infinite-dimensional generalization of the Kuhn-Tucker Theorem. The Lagrange multiplier takes the form of a nonnegative optional random measure on [0,T] which is flat off the set of times for which the constraint is binding, i.e. when all the fuel is spent. As a subproduct we obtain an enlightening interpretation of the first order conditions for a single firm in Bank (2005). In the infinite-horizon case, with operating profit functions of Cobb-Douglas type, our method allows the explicit calculation of the optimal policy in terms of the `base capacity' process, i.e. the unique solution of the Bank and El Karoui representation problem (2004).Comment: 25 page

Cooperative look-ahead control for fuel-efficient and safe heavy-duty vehicle platooning

The operation of groups of heavy-duty vehicles (HDVs) at a small inter-vehicular distance (known as platoon) allows to lower the overall aerodynamic drag and, therefore, to reduce fuel consumption and greenhouse gas emissions. However, due to the large mass and limited engine power of HDVs, slopes have a significant impact on the feasible and optimal speed profiles that each vehicle can and should follow. Therefore maintaining a short inter-vehicular distance as required by platooning without coordination between vehicles can often result in inefficient or even unfeasible trajectories. In this paper we propose a two-layer control architecture for HDV platooning aimed to safely and fuel-efficiently coordinate the vehicles in the platoon. Here, the layers are responsible for the inclusion of preview information on road topography and the real-time control of the vehicles, respectively. Within this architecture, dynamic programming is used to compute the fuel-optimal speed profile for the entire platoon and a distributed model predictive control framework is developed for the real-time control of the vehicles. The effectiveness of the proposed controller is analyzed by means of simulations of several realistic scenarios that suggest a possible fuel saving of up to 12% for the follower vehicles compared to the use of standard platoon controllers.Comment: 16 pages, 16 figures, submitted to journa