The disclosure relates to a method for generating a reference trajectory within a lane for a vehicle. The method comprises receiving at least one vehicle current state parameter describing a current state of the vehicle (S11). The current state of the vehicle comprises at least a current position of the vehicle. Furthermore, a destination parameter describing a destination to be reached by the vehicle (S12), and at least one route parameter describing a route for reaching the destination (S13) are received. Moreover, the method comprises estimating a power loss being caused when traveling from the current position of the vehicle to the destination (S14). The reference trajectory within the lane is determined such that it minimizes the power loss and leads to the destination (S15). Additionally, a method for operating a vehicle is presented. According to this method, a reference trajectory is generated in accordance with the above method (S21) and at least one control signal is provided for controlling a motion of the vehicle along the reference trajectory (S22). Furthermore, a data processing apparatus, a vehicle and a computer-readable medium are presented.

A method for populating a controller for a motor vehicle with data includes providing a controller with a storage device, and generating a projected mathematical model of at least one section of a powertrain, including a transmission. The projected mathematical model describes the section of the powertrain with a gear ratio of 1 and is applicable to different transmissions. The projected mathematical model is stored in the storage device of the controller. A motor vehicle is also provided and operated accordingly.

It is determined whether fuel efficiency of a vehicle is improved by operating a first rotating machine to generate electricity to such an extent that an electrical path amount becomes a desired electrical path amount for controlling an operating point of an engine to a desired operating point and driving and operating a second rotating machine as a second power source, the electrical path amount being a magnitude of electric power in an electrical path through which the electric power is transferred between the first rotating machine and the second rotating machine. When the electronic control device determines that the fuel efficiency of the vehicle is improved, the first rotating machine is operated to generate electricity to such an extent that the electrical path amount becomes the desired electrical path amount and the second rotating machine is driven and operated as the second power source.

A processor unit (3) is configured for executing an MPC algorithm (13) for model predictive control of a first component (18) of a motor vehicle (1) and of a second component (19) of the motor vehicle (1). The MPC algorithm (13) includes a cost function (15) to be minimized and a dynamic model (14) of the motor vehicle (1). The dynamic model (14) includes a loss model (27) of the motor vehicle (1). The loss model (27) describes an overall loss of the motor vehicle (1). The cost function (15) includes a first term, which represents the overall loss of the motor vehicle (1). The overall loss depends on a combination of operating values, which includes a first value of a first operating parameter and a second value of a second operating parameter. The processor unit (3) is also configured for determining, by executing the MPC algorithm (13) as a function of the loss model (14), that combination of operating values, by which the first term of the cost function (15) is minimized.

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.

A method for estimating cardan shaft moments in a vehicle includes performing a state space modelling of a physical model for force transmission in at least one drive train The at least one drive train is formed with at least one drive machine, at least one axle and at least two axle shafts each with a respective wheel. The method further includes selecting the physical model as a torsional oscillator chain in which a respective drive machine inertia moment is assigned to the respective drive train and a respective wheel inertia moment is assigned to the respective wheel. The respective drive machine inertia moment is connected by a respective spring-damper element to the respective wheel inertia moment of the respective wheel which is connected to the respective axle shaft. A vehicle mass is connected by a respective spring-damper element to the respective wheel inertia moment of the respective wheel.

A method for determines the drive train sensitivity of a drive train of a motor vehicle. A vehicle body is placed in longitudinal oscillations in the direction of travel and a parameter for the drive train sensitivity is determined as a function of the determined longitudinal accelerations of the vehicle body and the resultant angular accelerations of a transmission input shaft of a transmission of the motor vehicle.

A system and method for controlling driving of an electronic 4-wheel drive hybrid vehicle appropriately executes torque distribution and compensation to front wheels and rear wheels in each gear position to satisfy driver's requested torque depending on selected driving mode of the electronic 4-wheel drive hybrid vehicle in which an engine and a front wheel motor are connected to the front wheels and a rear wheel motor is connected to the rear wheels, thereby being capable of increasing acceleration performance when a sports mode is selected as the driving mode and realizing acceleration linearity when a comfort mode is selected as the driving mode.

A computer-implemented method for training a machine learning system to generate driving profiles of a vehicle. The method includes first travel routes are selected from a first database having travel routes, a generator of the machine learning system receives the first travel routes and generates first driving profiles for each of the first travel routes, travel routes and associated driving profiles determined during vehicle operation are stored in a second database, second travel routes and respective associated second driving profiles determined during vehicle operation are selected from the second database, a discriminator of the machine learning system receives pairs made up of one of the first travel routes with the respective associated first generated driving profile and pairs made up of second travel routes with the respective associated second driving profile determined during vehicle operation, as input variables.

The present invention relates to a universal powertrain for controlling an effort request and/or a flow request to a powertrain based on a demanded effort or demanded flow for the powertrain. The universal controller includes a configurable powertrain model and a configurable optimiser module. The universal controller is configurable to control a class of generic powertrains comprising J generic power sources, K generic power sinks, and L generic couplings. The universal controller is arranged to receive an input file of a plurality of input parameters to configure the universal controller to control a specific powertrain having a powertrain architecture with N power sources, M power sinks, and X couplings, the configurable powertrain model comprising: (a) a generic powertrain component library configured to provide a model of each of the N power sources, M power sinks and X couplings of the specific powertrain, and (b) a connection parameter module configured to define a model architecture of the N power source models, M power sink models and X coupling models which is representative of the powertrain architecture based on flow weight parameters and effort weight parameters of the input file, the configurable optimiser module comprising: a generic performance objective function library comprising a plurality of configurable performance objective functions from which a cost function is configurable based on input parameters of the input file, wherein the configurable optimiser module is configurable to calculate at least one of an optimised effort request or an optimised flow request for each of the N power sources of the specific powertrain based on: the cost function, the powertrain model of the specific powertrain, the demanded effort request of demanded flow request.