Method for estimating a brake factor parameter, the brake factor parameter being defined as a ratio of a braking torque over a braking pressure, the braking torque being applied on a wheel of a vehicle by a braking wheel sub-system of a braking system of the vehicle, the braking pressure being applied by the braking wheel sub-system to achieve the braking torque on the wheel of the vehicle, the method comprising collecting input parameters and estimating the brake factor parameter as a function of the at least one input parameter, an output of the step of estimating being an open loop estimation of the brake factor parameter.

A vehicle sideslip estimation system includes sensors mounted on a vehicle and a kinematic model receiving signals from the sensors to estimate a lateral velocity of the vehicle. A compensated acceleration calculator calculates a compensated lateral acceleration as a measure of conditions that result in a deviation of a measured lateral acceleration. A lateral acceleration calculator determines, based on the compensated lateral acceleration and the measured lateral acceleration, if a lateral acceleration error is larger than a predefined threshold. A filter corrects the estimated lateral velocity of the vehicle when the lateral acceleration error is larger than the predefined threshold. A velocity output register registers the estimated lateral velocity of the vehicle when the lateral acceleration error is smaller than the predefined threshold, and a sideslip calculator calculates a sideslip angle of the vehicle in real time from the registered lateral velocity of the vehicle and a vehicle longitudinal velocity.

A method and a device for determining braking-related actual values of a train assembly including multiple carriages for carrying out a deceleration-controlled braking of the train assembly, in which the longitudinal deceleration and the longitudinal slope are considered to be actual values, from which an adjustment value balancing the control deviation is determined for a control element of the brake by a deceleration controller/deceleration force controller according to a predefined setpoint value of a desired braking deceleration.

A method for controlling propulsion of a heavy-duty vehicle includes. configuring a nominal shaft slip of the drive shaft in dependence of a desired longitudinal wheel force to be generated by the driven axle, wherein a shaft slip is indicative of a difference between a current vehicle velocity and a vehicle velocity corresponding to the rotation speed of the drive shaft, obtaining a rotation speed of the left wheel and a rotation speed of the right wheel, as function of a current shaft slip of the driven axle, estimating a peak shaft slip value associated with an open differential peak longitudinal force of the driven axle, based on the current shaft slip and on the corresponding obtained speeds of the left and right wheels, and controlling propulsion of the heavy-duty vehicle unit by setting the current shaft slip of the drive shaft based on the configured nominal shaft slip adjusted in dependence of the estimated peak shaft slip value.

Method for updating at least one vehicle model parameter and at least one tire parameter in at least one chassis control unit of a vehicle, based on tire sensor information collected by a tire sensor placed on a tire. The method includes the steps of: collecting tire sensor information; updating the at least one vehicle model parameter based on updating at least one tire parameter, updating one tire parameter being based on the tire sensor information.

The present disclosure provides a system and a method for correcting a friction coefficient of a brake pad for a vehicle, which can estimate a brake factor including a friction coefficient of a brake pad, and ultimately correct the brake factor through the calculation and the update of a brake factor offset based on the estimated brake factor, thereby enhancing the braking linearity of an electric brake system.

Methods, apparatus, systems, and articles of manufacture are disclosed herein that mitigate hard-braking events. An example apparatus includes a world generator to generate a deep learning model to identify and categorize an object in a proximity of a vehicle, a data analyzer to determine a danger level associated with the object, the danger level indicative of a likelihood of a collision between the vehicle and the object, a vehicle response determiner to determine, based on the danger level, a response of the vehicle to avoid a collision with the object, and an instruction generator to transmit instructions to a steering system or a braking system of the vehicle based on the determined vehicle response.

A system and method includes upgrading a metamodel for friction coefficient prediction of a brake, in which the metamodel for friction coefficient prediction may be constructed using various derivative parameters relating to the speed, temperature and pressure of a brake disc in addition to basic parameters, such as the speed, temperature and pressure of the brake disc, to greatly improve performance and accuracy in friction coefficient prediction using the metamodel for friction coefficient prediction and to improve accuracy in evaluation of the driving performance of a vehicle through an increase in accuracy of determination of brake torque.

An arithmetic model generation system includes a sensor information acquisition unit, a tire force calculator, and an arithmetic model update unit. The sensor information acquisition unit acquires acceleration of a tire. The tire force calculator includes an arithmetic model for calculating tire force F based on the acceleration, and calculates the tire force F by inputting the acceleration acquired by the sensor information acquisition unit. The arithmetic model update unit compares tire axial force measured by the tire and the tire force F calculated by the tire force calculator, and updates the arithmetic model.

A method for estimating the temperature of a component being monitored is described herein, comprising: inputting data related to the component being monitored into a brake thermal model; using the brake thermal model to predict a temperature of the component based on the input data; inputting a) actual temperature sensor measurement data of the component and b) the predicted temperature into an estimation algorithm, wherein the estimation algorithm combines the a) actual temperature sensor data and b) predicted temperature and generates an estimated brake temperature of the component based on the combined inputs. A computer-implemented system is also described.