A method for controlling an actuator system of a motor vehicle includes utilizing a model predictive control (MPC) module with an MPC solver to determine optimal positions of one or more actuators of the actuator system. The method further includes receiving a plurality of actuator system parameters, and triggering the MPC solver to generate one or more control commands from plurality of actuator system parameters. The method further includes applying a cost function to reduce a steady-state tracking error in the one or more control commands from the MPC solver and applying the one or more control commands to alter positions of the one or more actuators, and applying a penalty term to the steady-state predictions of positions of the plurality of actuators to limit a difference between a steady-state prediction of the actuator system and a solution from the MPC solver.

A system includes a battery, an engine, and a processor. The processor is configured to plan, according to a model, an activation action of the engine of a vehicle for a next road segment subsequent to a current road segment; and activate, for the next road segment, the engine according to the activation action. The model includes a state space that includes a navigation map, which includes the current road segment of the vehicle, a current charge level of the battery, and whether the engine is currently on or off. The activation action is selected from a set comprising a first action to turn on the engine to charge the battery and a second action to turn off the engine.

A system and method of detecting and responding to a failure of one or more vehicle components, the method including: receiving system input at a failure detection module regarding the one or more vehicle components; determining a system state through use of one or more onboard vehicle sensors; obtaining a nominal state transition matrix and a nominal state input matrix; calculating a present state transition matrix estimate and a present state input matrix estimate based on the nominal state transition matrix, the nominal state input matrix, the system input, and a sampled state derivative; detecting a failure of at least one of the vehicle components based on one or more component parameters of the present state transition matrix estimate and/or the present state input matrix estimate; and performing a vehicle action in response to the detection of the failure.

A method for vehicle following control based on the real-time calculation of dynamic safe following distance. A preset vehicle deceleration model with three preset behavior adjustment parameters is used to obtain the absolute braking distance models of the leading and following vehicles, then to further establish the dynamic safe following distance model for calculating the dynamic safe following distance between the following vehicle and the leading vehicle in real time. In the process of vehicle following operation, the current dynamic safe following distance is compared with the current actual following distance to determine whether to adjust the following behavior of the following vehicle and how to control the following vehicle to move in safety, efficiency and smoothness.

A failure diagnostic system includes an instrument and a failure diagnostic device. The instrument makes measurement of a measured value regarding behavior of a diagnosis target. The failure diagnostic device has a model of the diagnostic target and performs simulation based on the model. The failure diagnostic device offers a user a proposal for execution of a special operation on the diagnostic target, on the condition that a difference between a result of the simulation and the measured value is greater than a predetermined error range but the difference provides an insufficient basis to determine whether or not the diagnosis target has a failure. The result of the simulation is calculated with the model supplied with a same input as an input to the diagnosis target at the time of the measurement by the instrument of the measured value regarding the behavior of the diagnosis target.

The disclosure relates to a method for operating a drive train of a working machine, wherein a traction drive of the drive train is driven by an electric traction motor via a transmission and wherein, when a gear stage of the transmission is changed, a rotational speed of the traction motor is synchronised with the gear stage being engaged. The method according to the disclosure further includes a computational model of the traction motor that is used for the rotational speed synchronisation. The model, taking into account a moment of inertia of the traction motor, describes a torque to be delivered for the rotational speed synchronisation. The disclosure further relates to a corresponding drive train and to a working machine.

A model of a system of an intelligent vehicle is trained and optimized using system operation data of the intelligent vehicle in a normal running state. The system operation data of the intelligent vehicle in a running state is collected in real time. Sensor data of the system operation data is de-noised, and feature extraction and screening are performed for a fatal sensor fault to reconstruct the system operation data. The reconstructed system operation data is inputted into the trained model to output system state data of the intelligent vehicle in the running state. The system state data is compared with a set threshold. If the system state data exceeds the set threshold, an actuator corresponding to the system state data is determined to have a fault. In addition, a system for a fault diagnosis of the intelligent vehicle is further provided.

The invention relates to a method of operating a vehicle (1) comprising at least a first vehicle retarding subsystem (3; 5; 13) controllable to retard the vehicle (1), and processing circuitry (15) coupled to the at least first vehicle retarding subsystem (3; 5; 13), the method comprising the steps of: acquiring (S10), by the processing circuitry (15) from the first vehicle retarding subsystem (3; 5; 13), at least one value indicative of current energy accumulation by the first vehicle retarding subsystem (3; 5; 13); and determining (S11), by the processing circuitry (15), a measure indicative of a retardation energy capacity currently available for retardation of the vehicle (1), based on: the acquired at least one value indicative of current energy accumulation by the first vehicle retarding subsystem (3; 5; 13); a predefined model of retardation energy accumulation by the first vehicle retarding subsystem (3; 5; 13); and a predefined limit indicative of a maximum allowed energy accumulation by the first vehicle retarding subsystem (3; 5; 13).

A vehicle and control method include a traction battery, a temperature sensor, current sensor, and voltage sensor associated with the traction battery, an electric machine powered by the traction battery to provide propulsive power to the vehicle, and a controller configured to control at least one of the electric machine and the traction battery in response to a battery state of charge (SOC) estimated using a battery model having parameters including a first resistance in series with a second resistance and a capacitance in parallel to the second resistance. The battery model parameters are adjusted during vehicle operation using a Kalman filter and reinitialized to new values in response to a vehicle key-on, in response to a change in the battery current exceeding a corresponding threshold, and/or in response to any of the parameter values crossing an associated limit.

A performance dimension is selected, and a gap in machine performance, according to the selected dimension, is identified. A target value is identified to improve machine control according to the selected dimension. A dependent dimension, which depends on the selected dimension, is selected and a dependency indicator, that indicates a dependency of the dependent dimension on the selected dimension, is accessed to identify a value of the dependent dimension that will change if the machine is controlled so that the value of the selected dimension is moved from a current value to the target value. The change in value of the selected dimension, and the dependent dimension are aggregated to determine whether machine control should be modified so the value of the selected dimension moves toward the target value. If so, a corresponding control operation is identified, and control signals are generated to control the machine to perform the identified control operation.