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This paper presents an economic model predictive control (eMPC) controller for the diesel engine airpath. The control objective is to maximize fuel economy while maintaining drivability and reducing emissions by manipulating the exhaust gas recirculation (EGR) valve, throttle, variable geometry turbine (VGT), and the fueling rate. This is achieved using a cascaded architecture wherein an upper level...
Condition monitoring and condition-based maintenance can reduce maintenance costs and improve operation safety of vehicles across the full spectrum of transportation domain, including automotive, rail, air and marine applications. In this paper, a set-membership condition monitoring framework is proposed to detect aging and health degradation for dual fuel engines based on simultaneous input and parameter...
This paper focuses on the modeling and control of a Free Piston Engine Generator (FPEG) which is an alternative to conventional internal combustion engines to be used as an auxiliary power unit (APU) in hybrid electric vehicles (HEV). A physics-based, continuous-time model and a discrete-time, control-oriented model are derived. A model predictive control approach is proposed to manage system constraints...
Precise control of piston turnaround location is essential for reliable free-piston engine (FPE) operation. In this paper we derive a discrete-time, control-oriented, implicit model that describes FPE clearance height behavior by applying an energy balance to the Otto cycle. We then design a state feedback controller using dynamic inversion that stabilizes the FPE system and ensures reference tracking...
This paper describes simple and suitable for real-time implementation algorithms for on-board learning of Markov Chain models of driving conditions (e.g., driver wheel torque request, vehicle speed, surrounding traffic speed, road grade, road curvature etc.). The use of Kullback-Liebler (KL) divergence is proposed as a stopping and re-initialization criterion for learning, permitting an evolving set...
This paper demonstrates a methodology, based on stochastic dynamic programming, for developing a control policy that prescribes vehicle speed to minimize on average a weighted sum of fuel consumption and travel time, while travelling along the same route or a set of routes in a given geographic area. Given the current road grade, traffic speed and vehicle speed, the control policy prescribes an offset...
This paper considers a range maximization problem for a direct methanol fuel cell powered and battery powered Mini Air Vehicle (MAV) operating in an uncertain environment. The control policy is developed to prescribe the power split between the fuel cell and the battery. This problem fits naturally into the Stochastic Drift Counteraction Optimal Control (SDCOC) framework. The approach accounts for...
This paper reports the development of vehicle fuel flow estimation algorithms based entirely on signals available through the standard OBD-II interface. The paper also illustrates the use of the resulting fuel flow estimates for adaptation and optimization. The fuel flow estimation algorithm functionality differs depending on the powertrain type (gasoline versus diesel, naturally aspirated versus...
A Model Predictive Control (MPC) approach is developed for spacecraft rendezvous and docking to a rotating/ tumbling platform and for debris avoidance maneuvers. With this approach, the constraints on thrust, approach velocity and spacecraft positioning within the Line-of-Sight cone from the docking port are systematically treated. The trajectories are simulated and time-to-dock and fuel consumption...
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