Abstract: An integrated speed prediction framework based on historical traffic data mining and real-time V2I communications for CAVs. The present framework provides multi-horizon speed predictions with different fidelity over short and long horizons. The present multi-horizon speed prediction is integrated with an economic model predictive control (MPC) strategy for the battery thermal management (BTM) of connected and automated electric vehicles (EVs) as a case study. The simulation results over real-world urban driving cycles confirm the enhanced prediction performance of the present data mining strategy over long prediction horizons. Despite the uncertainty in long-range CAV speed predictions, the vehicle level simulation results show that 14% and 19% energy savings can be accumulated sequentially through eco-driving and BTM optimization (eco-cooling), respectively, when compared with normal-driving and conventional BTM strategy.

Abstract: A system and method to optimize the loading of vehicle haulers and their transportation routes is described herein. The system considers various loading constraints (e.g., dimension, weight, etc.), legally allowed daily drive hours and vehicle destination proximity, and generates a loading plan that minimizes the number of trucks to be used. The disclosed embodiments will also incorporate real-time and predicted traffic information to generate an optimal route to deliver the vehicles to multiple destinations.

Abstract: An integrated speed prediction framework based on historical traffic data mining and real-time V2I communications for CAVs. The present framework provides multi-horizon speed predictions with different fidelity over short and long horizons. The present multi-horizon speed prediction is integrated with an economic model predictive control (MPC) strategy for the battery thermal management (BTM) of connected and automated electric vehicles (EVs) as a case study. The simulation results over real-world urban driving cycles confirm the enhanced prediction performance of the present data mining strategy over long prediction horizons. Despite the uncertainty in long-range CAV speed predictions, the vehicle level simulation results show that 14% and 19% energy savings can be accumulated sequentially through eco-driving and BTM optimization (eco-cooling), respectively, when compared with normal-driving and conventional BTM strategy.

Abstract: An engine and cabin thermal management system for use with a vehicle having an engine, a cabin heating system configured to thermally heat a cabin of the vehicle, a coolant system operably coupled to the engine and to the cabin heating system to thermally manage a temperature of the engine and a temperature of the cabin. The coolant system having one or more coolant thermal storage units fluidly coupled with a radiator and heater core of the coolant system forming a coolant loop. The system further having a control system configured to monitor and maintain at least a predetermined coolant temperature at the cabin heating system even during a coolant temperature decrease at the engine stops.

Abstract: An engine and cabin thermal management system for use with a vehicle having an engine, a cabin heating system configured to thermally heat a cabin of the vehicle, a coolant system operably coupled to the engine and to the cabin heating system to thermally manage a temperature of the engine and a temperature of the cabin. The coolant system having one or more coolant thermal storage units fluidly coupled with a radiator and heater core of the coolant system forming a coolant loop. The system further having a control system configured to monitor and maintain at least a predetermined coolant temperature at the cabin heating system even during a coolant temperature decrease at the engine stops.

Abstract: System for controlling an internal combustion engine having a variable geometry turbocharger, an EGR valve and an EGR throttle. The system includes a controller and an engine. In each sampling period, the controller inputs sensor values and estimators that are calculated based on the sensor values and dynamic models and records the sensor values and the estimators in a memory of the controller. The controller incorporates the sensor values and the estimators into conditions for optimality associated with a constrained optimal control problem, maps the conditions for optimality to a non-smooth system using Fischer-Burmeister function, performs Newton method iterations on a smoothed system approximating the non-smooth system in order to converge on a solution, and issues commands that control the EGR valve, the EGR throttle and the variable geometry turbocharger during engine operation.

Abstract: Systems and methods for controlling an engine airpath include receiving, at a supervisory controller, an engine speed corresponding to a present engine speed, a fuel target corresponding to a request for torque from a driver and one or more state estimates generated by an estimator. The supervisory controller predicts, over a prediction horizon, a constraint violation in response to the engine speed, the fuel target, and the one or more state estimates using a prediction model, adjusts an EGR rate target to a modified value, when the constraint violation is predicted, and maintains the EGR rate target at a nominal value when the constraint violation is not predicted. A nonlinear predictive controller generates one or more actuator commands based on the EGR rate target, where the one or more actuator commands control an engine actuator such that an EGR rate of the engine airpath tracks the EGR rate target.

Abstract: Systems and methods for controlling an engine airpath include receiving, at a supervisory controller, an engine speed corresponding to a present engine speed, a fuel target corresponding to a request for torque from a driver and one or more state estimates generated by an estimator. The supervisory controller predicts, over a prediction horizon, a constraint violation in response to the engine speed, the fuel target, and the one or more state estimates using a prediction model, adjusts an EGR rate target to a modified value, when the constraint violation is predicted, and maintains the EGR rate target at a nominal value when the constraint violation is not predicted. A nonlinear predictive controller generates one or more actuator commands based on the EGR rate target, where the one or more actuator commands control an engine actuator such that an EGR rate of the engine airpath tracks the EGR rate target.

Abstract: Methods and systems for use of model predictive control (MPC) controllers utilizing hybrid, quadratic solvers to solve a linear feasibility problem corresponding to a nonlinear problem for an internal combustion engine plant such as a diesel engine air path. The MPC solves a convex, quadratic cost function having optimization variables and constraints and directs the plant per the output solutions to optimize plant operation while adhering to regulations and constraints. The problem includes a combination of iterative and direct calculations in the primal space depending on whether a partial step (iterative) or a full step (direct) is attempted. Further, primal and dual space array matrices are pre-computed and stored offline and are retrieved via use of a unique identifier associated with a specific active set for a set of constraints. Such hybrid and/or offline calculations allow for a reduction in computational power while still maintaining accuracy of solution results.

Abstract: System for controlling an internal combustion engine having a variable geometry turbocharger, an EGR valve and an EGR throttle. The system includes a controller and an engine. In each sampling period, the controller inputs sensor values and estimators that are calculated based on the sensor values and dynamic models and records the sensor values and the estimators in a memory of the controller. The controller incorporates the sensor values and the estimators into conditions for optimality associated with a constrained optimal control problem, maps the conditions for optimality to a non-smooth system using Fischer-Burmeister function, performs Newton method iterations on a smoothed system approximating the non-smooth system in order to converge on a solution, and issues commands that control the EGR valve, the EGR throttle and the variable geometry turbocharger during engine operation.

Abstract: Methods and systems for use of model predictive control (MPC) controllers utilizing hybrid, quadratic solvers to solve a linear feasibility problem corresponding to a nonlinear problem for an internal combustion engine plant such as a diesel engine air path. The MPC solves a convex, quadratic cost function having optimization variables and constraints and directs the plant per the output solutions to optimize plant operation while adhering to regulations and constraints. The problem includes a combination of iterative and direct calculations in the primal space depending on whether a partial step (iterative) or a full step (direct) is attempted. Further, primal and dual space array matrices are pre-computed and stored offline and are retrieved via use of a unique identifier associated with a specific active set for a set of constraints. Such hybrid and/or offline calculations allow for a reduction in computational power while still maintaining accuracy of solution results.

Abstract: A discrete time rate-based model predictive controller for air path control for a diesel engine regulates VGT position and EGR valve position to specified set points by coordinated control of intake manifold air pressure and EGR rate. The controller may be configured to measure or estimate at least one of the intake manifold pressure and EGR rate. A non-linear discrete time rate-based predictive model may be used, as developed by the controller.

Abstract: Methods and systems for use of model predictive control (MPC) controllers utilizing hybrid, quadratic solvers to solve a linear feasibility problem corresponding to a nonlinear problem for an internal combustion engine plant such as a diesel engine air path. The MPC solves a convex, quadratic cost function having optimization variables and constraints and directs the plant per the output solutions to optimize plant operation while adhering to regulations and constraints. The problem includes a combination of iterative and direct calculations in the primal space depending on whether a partial step (iterative) or a full step (direct) is attempted. Further, primal and dual space array matrices are pre-computed and stored offline and are retrieved via use of a unique identifier associated with a specific active set for a set of constraints. Such hybrid and/or offline calculations allow for a reduction in computational power while still maintaining accuracy of solution results.

Abstract: A method for controlling an internal combustion engine using a controller that controls an air flow path by adjusting at least one of a variable geometry turbine (VGT) and an exhaust gas recirculation (EGR) flow rate during engine operation. The method determines inputs, such as engine speed and fuel rate from the sensor data, and employs a switch based gain-scheduled explicit model predictive controller (MPC) responsive to the inputs to determine the air flow path.

Abstract: A method for controlling an internal combustion engine using a controller that controls an air flow path by adjusting at least one of a variable geometry turbine (VGT) and an exhaust gas recirculation (EGR) flow rate during engine operation. The method determines inputs, such as engine speed and fuel rate from the sensor data, and employs a switch based gain-scheduled explicit model predictive controller (MPC) responsive to the inputs to determine the air flow path.

Abstract: A discrete time rate-based model predictive controller for air path control for a diesel engine regulates VGT position and EGR valve position to specified set points by coordinated control of intake manifold air pressure and EGR rate. The controller may be configured to measure or estimate at least one of the intake manifold pressure and EGR rate. A non-linear discrete time rate-based predictive model may be used, as developed by the controller.

Abstract: Methods and systems are provided for improving surge control. When surge conditions are approached, a reference governor reduces engine airflow at a slower rate and to a higher level than the engine airflow required to meet the reduced torque demand. The excess torque resulting from the extra airflow is offset by applying a negative torque on the driveshaft via an electric machine coupled to the engine or via alternate engine actuator adjustments.

Abstract: Methods and systems are provided for improving surge control. When surge conditions are approached, a reference governor reduces engine airflow at a slower rate and to a higher level than the engine airflow required to meet the reduced torque demand. The excess torque resulting from the extra airflow is offset by applying a negative torque on the driveshaft via an electric machine coupled to the engine or via alternate engine actuator adjustments.

Abstract: Vehicle apparatus adjusts a vehicle powertrain of the vehicle in response to a speed setpoint. An optimizer selects a control policy to periodically generate speed adjustments for applying to the speed setpoint to operate at increased efficiency. The control policy is based on a value function providing an optimized solution for a cost model and a transition probability model. The transition probability model corresponds to a driving state defined according to a plurality of dimensions including a time-of-day dimension and a geographic region dimension. The transition probability model and the control policy have inputs based on road grade and speed. The optimizer collects road grade data during routine driving of the vehicle to construct a observed transition probability model and uses divergence between the observed transition probability model and a set of predetermined transition probability models to identify a control policy for use during the routine driving.

Abstract: Vehicle apparatus adjusts a vehicle powertrain of the vehicle in response to a speed setpoint. An optimizer selects a control policy to periodically generate speed adjustments for applying to the speed setpoint to operate at increased efficiency. The control policy is based on a value function providing an optimized solution for a cost model and a transition probability model. The transition probability model corresponds to a driving state defined according to a plurality of dimensions including a time-of-day dimension and a geographic region dimension. The transition probability model and the control policy have inputs based on road grade and speed. The optimizer collects road grade data during routine driving of the vehicle to construct a observed transition probability model and uses divergence between the observed transition probability model and a set of predetermined transition probability models to identify a control policy for use during the routine driving.