Methods and systems are provided for using the weights of cost functions to improve linear-program-based vehicle driveline architectures and systems. In some embodiments, the methods and systems may include establishing values for driveline controls of a linear program based on driveline requests of the linear program. The values of the driveline controls, which may be used to adjust driveline actuators, may be established based on values of a plurality of weights of a cost function of the linear program, the weights respectively corresponding with the plurality of driveline requests.

A vehicle control device includes an engine 10 capable of switching between reduced-cylinder operation and all-cylinder operation, an engine control mechanism that controls an engine torque, and a PCM 50 that executes vehicle posture control for generating vehicle deceleration by controlling the engine control mechanism to reduce the engine torque upon satisfaction of a condition that a vehicle is travelling and a steering angle-related value related to a steering angle of a steering device increases. In addition, this PCM 50 permits the execution of the vehicle posture control when an engine rotation speed is more than or equal to a first rotation speed Ne1 and permits the execution of the reduced-cylinder operation of the engine 10 when the engine rotation speed is more than or equal to a second rotation speed Ne2 that is more than the first rotation speed Ne1.

An automotive vehicle includes an actuator configured to control vehicle steering, a sensor configured to detect a yaw rate of the vehicle, and a controller. The controller is configured to estimate a yaw rate and lateral velocity of the vehicle via a vehicle dynamics model based on a measured longitudinal velocity of the vehicle, calculated road wheel angles of the vehicle, and estimated tire slip angles of the vehicle. The controller is configured to receive a measured yaw rate from the sensor, and to calculate a difference between the measured yaw rate and the estimated yaw rate. The controller is configured to apply a model correction to the vehicle dynamics model using a PID controller based on the difference, and to estimate a vehicle position based on the estimated lateral velocity and the measured longitudinal velocity. The controller is configured to automatically control the actuator based on the vehicle position.

A vehicle stability control method and a vehicle stability control device are provided. The method may be applied to an intelligent automobile field such as intelligent driving or autonomous driving, and is used to control lateral stability of a front axis and rear axis distributed driven vehicle. In this method, a yawing movement of the vehicle is considered, and an additional yawing moment for maintaining lateral stability of the vehicle is provided by compensating for front-axis and rear-axis slip ratios, to control lateral stability of the vehicle and therefore improve stability of the vehicle during driving.

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 system for monitoring vehicle dynamics and detecting adverse events during operation is presented. Position sensors attached to a vehicle are configured to identify a vehicle orientation (heading) as well as the vehicle's direction of travel (trajectory). A system controller connected to these position sensors can detect the difference between these two measurements. When the difference between these two measurements exceeds a safety threshold, it can be an indication of a slip event. A slip event can be caused by compromised traction or stability and may lead to a loss of vehicle control. The system controller can be configured to monitor various vehicle dynamics to detect these slip events. The system controller may be configured to track geolocations of slip events to create a database of historical slip events for determining location-based risk factors and prevention of future events.

Fuzzy logic based and machine learning enhanced vehicle dynamics determination is provided. A system can identify a previous longitudinal velocity and receive data from an inertia measurement unit. The system can determine a roll angle and a pitch angle. The system can determine a lateral acceleration and a longitudinal acceleration. The system can receive wheel speed sensor data, tire pressure sensor data, and steering angle sensor data, and use the data to determine a longitudinal velocity. The system can select one of a reduced-order non-linear Luenberger observer technique or a reduced-order Kalman filter technique. The system can determine a lateral velocity and a sideslip angle. The system can provide the lateral velocity and the sideslip angle to a vehicle controller.

A method for vehicle motion control includes receiving sensor data from a plurality of sensors of a vehicle and monitoring a vehicle response of the vehicle using the sensor data. The vehicle response is represented by a plurality of vehicle-response signals. The method further includes fusing the plurality of vehicle-response signals to obtain at least one fused signal. The method further includes determining whether to activate a vehicle stability control of the vehicle based on the at least one fused signal and commanding the vehicle to activate the vehicle stability control in response to determining to activate the vehicle stability control of the vehicle based on the at least one fused signal.

A control system for a steering system of a vehicle, the control system comprising: a processing module configured to obtain an indication of a proximity of a road wheel of the vehicle to a limit of adhesion, and to generate a driver feedback signal in the event that the road wheel is at or beyond the limit of adhesion; and an output arranged to issue the driver feedback signal.

A steering torque estimating device which estimates steering torque which is torque provided to a steering axis due to a vehicle body behavior, in a vehicle including a front wheel as a steered wheel, the vehicle being configured to turn in a bank state in which a vehicle body is tilted around a forward-rearward axis, includes a torque estimating section which estimates the steering torque based on a change over time of a bank angle and a rotational speed of the front wheel.