A suspension control device which controls an operation of a suspension of a vehicle includes an operation-induced state quantity estimation portion which estimates an operation-induced state quantity caused by an operation of a vehicle, a road surface-induced state quantity estimation portion which estimates a road surface-induced state quantity caused by a road surface, an operation-induced state quantity conversion portion which converts the operation-induced state quantity into an operation-induced required damping force, a road surface-induced state quantity conversion portion which converts the road surface-induced state quantity into a road surface-induced required damping force, and a current value calculation portion which determines a current value to be applied to the suspension with reference to the operation-induced required damping force and the road surface-induced required damping force.

A method of on-demand energy delivery to an active suspension system is disclosed. The suspension system includes an actuator body, a hydraulic pump, an electric motor, a plurality of sensors, an energy storage facility, and a controller. The method includes disposing an active suspension system in a vehicle between a wheel mount and a vehicle body, detecting a wheel event requiring control of the active suspension; and sourcing energy from the energy storage facility and delivering it to the electric motor in response to the wheel event.

A method of on-demand energy delivery to an active suspension system is disclosed. The suspension system includes an actuator body, a hydraulic pump, an electric motor, a plurality of sensors, an energy storage facility, and a controller. The method includes disposing an active suspension system in a vehicle between a wheel mount and a vehicle body, detecting a wheel event requiring control of the active suspension; and sourcing energy from the energy storage facility and delivering it to the electric motor in response to the wheel event.

A computer-implemented method for damping a vehicle, including: receiving external load data for the vehicle; receiving at least one damper velocity of a damper of the vehicle; providing an optimization model configured to describe a relation between external load data for a vehicle, at least one damper velocity of a damper of a vehicle, and at least one damper force of the at least one damper; determining at least one damper force of the damper for the vehicle by inputting the external load data and the at least one damper velocity into the optimization model; and providing the at least one damper force of the at least one damper of the vehicle.

A method is described for determining multiplicative faults of a sensor installed in a system comprising a plurality of sensors, comprising the steps of:detecting an effective target signal (s) from a target sensor, representative of a target quantity of the system;detecting one or more auxiliary signals respectively from one or more auxiliary sensors of the system besides the target sensor, representative of auxiliary quantities of the system;determining an estimated target signal (s*) representative of the target quantity from the one or more auxiliary signals;determining a first quadratic difference (r+) between the effective target signal (s) multiplied by a multiplicative positive factor (cr+) greater than 1, and the estimated target signal (s*);determining a second quadratic difference (r) between the effective target signal (s) and estimated target signal (s*);determining a third quadratic difference (r) between the effective target signal (s) multiplied by a positive multiplicative factor (c) smaller than 1, and the estimated target signal (s*);determining a first ratio (r/r+) between the second (r) and lirst quadratic differences (r+);determining a second ratio (r/r) between the second (r) and third quadratic differences (r);comparing the first (r/r+) and second ratios (r/r) with a first comparison factor (Kf);determining the square of the effective target signal (s); determining the square of the estimated target signal (s*); comparing the square of the effective target signal (s) and square of estimated target signatl (s*) with a second comparison factor (Ke); establishing the presence of multiplicative faults of target sensor if at least one between the first (r/r+) and second ratios (r/r) is greater than the first comparison factor (Kf), and at least one between the square of the effective target signal (s) and square of the estimated target signal (s*) is greater than said second comparison factor (Ke).

A system for determining a displacement velocity signal for controlling an active wheel suspension of a land vehicle by open-loop and/or closed-loop control includes at least one Kalman filter, and at least one acceleration sensor arranged on a sprung mass of the land vehicle to sense a vertical acceleration of the sprung mass and to generate a corresponding acceleration signal supplied to the Kalman filter. The Kalman filter includes a mathematical motion model of the sprung mass, and input states of the Kalman filter include a vertical acceleration of the sprung mass, a vertical displacement velocity of the sprung mass, and a vertical displacement distance of the sprung mass. A displacement measurement signal having a value 0 is supplied continuously to the Kalman filter to determine the displacement velocity signal. Constant noise variance values of a measurement noise covariance matrix of the Kalman filter that are assigned to the displacement measurement signal are, in each case, set at one half of a maximum vertical displacement distance of the sprung mass.

Realized is a technique for estimating a state quantity of a vehicle, which technique is applicable to estimation of a vehicle weight and allows an increase in accuracy and speed of the estimation. A state quantity estimating device includes a data storing section (101), a predictive quantity computing section (102), an obtaining section (107), a Kalman gain computing section (103), an estimated quantity computing section (104) which calculates an estimated state quantity and estimated covariance, and a process noise covariance correcting section (106) which corrects process noise covariance. The estimated state quantity, the estimated covariance, and the process noise covariance, each of which has been calculated or corrected, are written in the data storing section (101) as a state quantity, state covariance, and process noise covariance, respectively, and are used in a next computation for estimating a state quantity.

Proposed are an apparatus for and a method of controlling a vehicle suspension. The apparatus includes a sensor configured to acquire at least one of information on a road surface in front of a vehicle and state information of the vehicle; and a processor configured to predict a vehicular behavior based on the information acquired through the sensor and actuator information and to control at least one of a ride height of the vehicle, stiffness of an air spring, and a damping force of a damper based on the predicted vehicular behavior.

The present disclosure relates to a method for avoiding overheating of a vehicle subsystem, which comprises a compressor with a pressure chamber. The method comprises the steps of executing an Extended Kalman Filter on a control module that calculates an error between a predicted state model of the vehicle subsystem and a corresponding measured state model of the vehicle subsystem, and processes the calculated error to adjust the predicted state model, including adjusting the estimated ambient pressure, based on weighted uncertainties of the measured and estimated parameters. The method further comprises the steps of comparing the estimated ambient pressure to a predetermined ambient pressure default value; and reducing a cut-off pressure target value of the vehicle subsystem by a reduction amount for a period of time, when the estimated ambient pressure is less than or equal to the predetermined ambient pressure default value.

A method of on-demand energy delivery to an active suspension system is disclosed. The suspension system includes an actuator body, a hydraulic pump, an electric motor, a plurality of sensors, an energy storage facility, and a controller. The method includes disposing an active suspension system in a vehicle between a wheel mount and a vehicle body, detecting a wheel event requiring control of the active suspension; and sourcing energy from the energy storage facility and delivering it to the electric motor in response to the wheel event.