A processor unit (3) for model-based predictive control of a vehicle (1) taking into account an arrival time factor is configured to calculate a trajectory for the vehicle (1) based at least in part on at least one arrival time factor, with the trajectory including an entire route (20) to a specified destination (19) at which the vehicle (1) is to arrive, and with the at least one arrival time factor influencing an arrival time of the vehicle (1) at the specified destination (19). Additionally, the processor unit (3) is configured to optimize a section of the trajectory for the vehicle (1) for a sliding prediction horizon by executing a model-based predictive control (MPC) algorithm (13), where the MPC algorithm (13) includes a longitudinal dynamic model (14) of a drive train (7) of the vehicle (1) and a cost function (15) to be minimized.

The present disclosure relates to systems and methods of automatically distributing powertrain demand between tandem drive axles in a tandem drive axle system for balanced tire wear between the tandem drive axles. Examples described herein analyze input signals collected from various vehicle sensors about operating conditions of the front and back tandem drive axles, and automatically bias a distribution of torque demand between the front and back tandem drive axles to correct for asymmetric wear based on known normal wear of the tires under like operating conditions.

A method of controlling driving force of a vehicle, includes providing a first filter for removing or reducing a natural frequency component of the vehicle suspension pitch motion, and a second filter for extracting or increasing the natural frequency component of the vehicle suspension pitch motion to a controller of the vehicle, determining a required driving force command based on vehicle driving information collected during driving of the vehicle, determining a driving force command after filter application through a processing process by the first filter taking the determined required driving force command as input thereof, determining a driving force correction amount through a processing process by the second filter taking feedback driving force as input thereof, and correcting the driving force command after filter application using the driving force correction amount and controlling driving force applied to a driving wheel of the vehicle by a driving device of the vehicle using the driving force command after the correction.

A method for monitoring and identifying sensor faults in an electric drive system of a vehicle includes collecting corresponding data using sensors in the electric drive system, inputting the collected data to an already-established sensor fault mode identification model, and determining whether a fault mode exists and a fault mode type based on the collected data using the sensor fault mode identification model. The method quickly determines the fault mode caused by a sensor fault in the electric drive system, and the fault mode type of the sensor fault.

A computer system comprising processing circuitry configured to obtain topographic data containing information about the topography of the road along which a heavy-duty vehicle is currently travelling, the topographic data including information about an upcoming downhill slope; acquire prediction data indicative of the braking requirements for the upcoming downhill slope, the braking requirements including how much brake power and/or brake energy that will be needed in the upcoming downhill slope to maintain the speed of the heavy-duty vehicle at or below a selected speed limit of the heavy-duty vehicle throughout the travel in the downhill slope; determine a brake blending combination which allows a Selective Catalytic Reduction System (SCR) of the heavy-duty vehicle to be kept as warm as possible while still fulfilling the braking requirements; and apply the determined brake blending combination to the heavy-duty vehicle while travelling along the downhill slope.

Based on a steering control input, a measured value of the steering control attribute and a measured value of the traction control attribute received by a steering application, determining: a first setpoint value of the steering control attribute; a target steering angle of the steered wheel of the vehicle. Based on receiving, by a traction application executing on the vehicle control module of the vehicle: a traction speed control input to control the traction wheel of the vehicle, and the target steering angle, from the steering application, determining, by the traction application: a second setpoint value of the traction control attribute.

A traction application executing on a vehicle control module receives a traction speed control input to control a traction wheel of the vehicle. Based on the traction speed control input, the traction application determines a first setpoint value of a control attribute related to the traction wheel. A first diagnostic supervisor receives a measured value of the control attribute related to the traction wheel, and the first setpoint value from the traction application. The first diagnostic supervisor comprises a first model of a traction system of the vehicle. Based on the first setpoint value and the first model, the first diagnostic supervisor calculates a first virtual value of the control attribute related to the traction wheel. Based on the first virtual value and the measured value of the control attribute, the first diagnostic supervisor determines a first operating condition of the traction system of the vehicle.

A steering application executing on a vehicle control module receives a steering control input to control a steered wheel of a vehicle. Based on the steering control input, the steering application determines a traction threshold value associated with a traction control attribute related to a traction wheel of the vehicle. A first diagnostic supervisor executing on the vehicle control module receives a measured value of the traction control attribute and the traction threshold value. When the measured value of the traction control attribute exceeds the traction threshold value, the first diagnostic supervisor repeatedly calculates a respective difference between the traction threshold value and the measured value of the traction control attribute to generate a set comprising a plurality of the respective differences. Based on the plurality of respective differences, the first diagnostic supervisor determines a first operating condition of a traction system of the vehicle.

A vehicle may include battery cells of a battery and at least one controller configured to control the vehicle based on a state observation associated with the battery according to battery model parameters for the cells received from a computing device external to the vehicle and responsive to measurements relating to a battery model of the cells sent to the computing device.

A method for integrated control of handling stability of a distributed drive electric vehicle is provided, in which a Magic Formula tire model is subjected to piecewise linear fitting to obtain a piecewise affine tire model; a hybrid logical dynamic model is established based on the piecewise affine tire model; a hierarchical integrated control strategy is adopted to obtain an upper-layer hybrid model predictive controller and a lower-layer four-wheel torque optimal allocation controller, so as to calculate an additional yaw moment, an additional front-wheel steering angle and a wheel drive torque. Related devices for implementing the integrated control method are also provided.