A processor unit (3) is configured for executing an MPC algorithm (13) for model predictive control of a prime mover (8) and of at least one vehicle component influencing energy efficiency of a motor vehicle. The MPC algorithm (13) includes a longitudinal dynamic model (14) of the drive train (7) and of the vehicle component influencing the energy efficiency of the motor vehicle (1) as well as a cost function (15) to be minimized. The cost function (15) includes at least one first term. The processor unit (3) is configured for determining a particular input variable for the prime mover (8) and for the at least one vehicle component influencing the energy efficiency of the motor vehicle (1) by executing the MPC algorithm (13) as a function of a particular term such that the cost function (15) is minimized.

Operation data from one or more vehicle subsystems are input to a vehicle dynamics model. Predicted operation data of the one or more vehicle subsystems are output from the vehicle dynamics model. The operation data and the predicted operation data are input to an optimization program that is programmed to output control directives for the one or more vehicle subsystems. One or more vehicle subsystems are operated according to the output control directives.

A method for planning the motion of a vehicle includes: determining a nominal trajectory for the vehicle based on a desired maneuver to be carried out in a traffic space, on a current state of movement of the vehicle and on a detected state of a surrounding of the vehicle, and determining an abort trajectory branching off from the nominal trajectory and guiding the vehicle to a safe condition regardless of the desired maneuver, wherein the nominal trajectory and the abort trajectory are determined simultaneously using a single optimization process.

Disclosed are aspects of systems and computer-implemented methods for trajectory planning of a vehicle. In example embodiments, a method is carried out by computer hardware components and includes determining a reference for a trajectory of the vehicle for a prediction time horizon, with the reference varying over time during the prediction time horizon. The method also includes determining a location error based on the reference for the trajectory and determining a steering command for the vehicle based on the location error.

A computer implemented method for lateral control of a vehicle comprises the following steps carried out by computer hardware components: determining a location error of the vehicle; determining an orientation error of the vehicle; determining a cost function based on the location error and the orientation error using a circular transformation; and processing the cost function in a model predictive controller to control the vehicle laterally.

A processor unit (3) is configured for executing an MPC algorithm (13) for model predictive control of a motor vehicle (1). The MPC algorithm (13) includes a longitudinal dynamic model (14) of the motor vehicle (1) and a cost function (15) to be minimized. The cost function (15) includes multiple terms, a first term of which represents an output of the cooling pump (28). In addition, the processor unit (3) is configured for, by executing the MPC algorithm (13) as a function of the longitudinal dynamic model (14), ascertaining a speed trajectory of the motor vehicle (1) situated within a prediction horizon and simultaneously ascertaining a pump operating value trajectory situated within the prediction horizon such that the first term of the cost function (15) is minimized.

The operation of a hybrid powertrain system is optimized with respect to a desired state-of-charge trajectory, taking account of the estimated anticipated vehicle drive power. The hybrid powertrain system has an internal combustion engine and an electrically operated torque machine. The internal combustion engine and the torque machine are controlled by a control device and are connected to an output element via a hybrid transmission. Before the start of the prediction period Δt, an experience-based state-of-charge trajectory for the anticipated route, covering at least the prediction period Δt, is retrieved from an external database. The desired state-of-charge trajectory is established based on the experience-based state-of-charge trajectory by modifying it with at least one optimization constraint. The experience-based state-of-charge trajectory can be established based on operating data from hybrid powertrain systems of multiple vehicles and/or from operating data from multiple comparable journeys with the same vehicle.

A method of operating a vehicle, comprises determining, by a computer located in a first vehicle, that an initial distance between the first vehicle and a second vehicle is less than a first distance or a second distance, where the second vehicle is located in a same lane as and in front of the first vehicle; generating, in response to the determining, a sequence of position values and velocity values for the first vehicle, wherein each of the position values and each of the velocity values are associated with a time value; and causing the first vehicle to increase a distance between the first vehicle and the second vehicle by causing the first vehicle to move or operate according to the sequence of position values and velocity values.

A controller is provided for a vehicle having front and rear axles, each axle having two wheels, and first and second propulsion units. The controller controls the first and second propulsion units to generate a combined torque with reference to a total requested torque. The controller is configured to: receive a torque request signal; receive traction signals indicating available traction at at least one wheel; determine a traction torque range defined by a maximum and minimum torque for at least one of the at least first or second propulsion units in dependence on one or more of the traction signals; determine a proposed distribution of torque between each of the at least first and second propulsion units with reference to the total requested torque; and determine a proposed torque to be generated by each of the at least first and second propulsion units based on the proposed distribution of torque.

A controller and a method for controlling motion of a vehicle is provided. The method comprises acquiring motion information including a current state of the vehicle and a desired state of the vehicle, determining a combination of a steering angle of the wheels and motor forces for moving the vehicle from the current state into the desired state by using a first model of the motion of the vehicle and a second model of the motion of the chassis of the vehicle, determining a cost function of the motion of the vehicle, optimizing the cost function of the motion of the vehicle to compute a command signal for controlling the steering wheel and the plurality of electric motors, and controlling the steering angle of the wheels and the motor forces based on the command signal.