A method for controlling wheel slip of a vehicle includes: observing and estimating equivalent inertia information of a driving system in real time based on operation information of the driving system by receiving the operation information of the driving system for driving the vehicle; calculating the compensated amount for compensating a torque command of a driving device from the equivalent inertia information of the driving system observed and estimated by a controller; compensating the torque command of the driving device by using the calculated compensated amount; and performing a control of a torque applied to a driving wheel according to the compensated torque command.

A vehicle control device mounted on a four-wheel drive vehicle including a driving force transmission system includes an electronic control unit. The electronic control unit calculates a command torque based on vehicle information. The electronic control unit estimates a temperature of a heat generating location in the driving force transmission system based on the command torque. The electronic control unit estimates the temperature of the heat generating location based on an estimated value of a driving force input to an input rotating member, when it is not possible for the driving force corresponding to the command torque to be transmitted to auxiliary drive wheels due to a magnitude of the driving force generated by a drive source or occurrence of a wheel slip.

The present invention discloses an ECMS-based PHEV four-drive torque distribution method. The method specifically comprises the following steps: step 1, calculating an equivalent fuel consumption factor according to the residual electric quantity of a power battery; step 2, calculating instantaneous total equivalent fuel consumption rate; step 3, converting all operating torque combinations of an engine, a BSG motor and a rear axle motor into the operating torque of a driving wheel, and determining the operating torque range of each power source according to the operating torque range of the driving wheel; step 4, solving the minimum value of the instantaneous total equivalent fuel consumption rate within the actual operating torque range of each power source; and step 5, taking the operating torque of each power source corresponding to the minimum instantaneous total equivalent fuel consumption rate as the PHEV optimal operating torque for distribution.

The present invention discloses a method for constructing linear luenberger observer for vehicle control. The method for constructing linear luenberger observer for vehicle control comprises the following steps: step 1: building a state-space equation of a driving system of a vehicle to judge observability of the driving system; step 2: dividing the state of the driving system into blocks, and reconstructing state components of the driving system to obtain an rewritten state observation equation of the driving system; step 3: introducing transformation into the rewritten state equation of the driving system to obtain an expression equation and an error equation of the Luenberger observer. The linear luenberger observer constructed by the present invention has low implementation difficulty. High-frequency noise in an output signal of a rotational speed sensor is reduced.

A computer-implemented method for training a machine learning system to generate driving profiles of a vehicle. The method includes first travel routes are selected from a first database having travel routes, a generator of the machine learning system receives the first travel routes and generates first driving profiles for each of the first travel routes, travel routes and associated driving profiles determined during vehicle operation are stored in a second database, second travel routes and respective associated second driving profiles determined during vehicle operation are selected from the second database, a discriminator of the machine learning system receives pairs made up of one of the first travel routes with the respective associated first generated driving profile and pairs made up of second travel routes with the respective associated second driving profile determined during vehicle operation, as input variables.

A computer-implemented method for training a machine learning system for generating driving profiles and/or driving routes of a vehicle including: a generator obtains first random vectors and generates first driving routes and associated first driving profiles related to the first random vectors, driving routes and respectively associated driving profiles recorded in driving mode are stored in a data base, second driving routes and respectively associated second driving profiles recorded in driving mode are selected from the database, a discriminator obtains first pairs made up of first generated driving routes and respectively associated first generated driving profiles and second pairs made up of second driving routes and respectively associated second driving profiles recorded in driving mode, the discriminator calculates outputs that characterize each pair, and a target function is optimized as a function of the outputs of the discriminator.

A method for reducing exhaust gas emissions of a drive system of a vehicle including an internal combustion engine, including generating first driving profiles using a computer-implemented machine learning system, the statistical distribution of the first driving profiles being a function of a statistical distribution of second driving profiles measured during real driving operation, calculating respective exhaust gas emissions for the first driving profiles using a computer-implemented modeling of the vehicle or the drive system, adapting the drive system as a function of at least one of the calculated exhaust gas emissions, the adaptation taking place as a function of a level or of a profile of the calculated exhaust gas emissions and of a statistical frequency of the corresponding first driving profile, the statistical frequency of the corresponding first driving profile being ascertained with the aid of the statistical distribution of the first driving profiles.

A method for estimation of a vehicle tire force includes: receiving, by a controller of a vehicle, a measured vehicle acceleration of the vehicle; receiving, by the controller, a measured wheel speed and a measured yaw rate of the vehicle; forming, by the controller, inertia matrices based on an inertia of rotating components of the vehicle; calculating torques at corners of the vehicle using the inertia matrices; estimating tire forces of the vehicle based on the measured vehicle acceleration, the measured wheel speed, and the inertia matrices; and controlling, by the controller, the vehicle, based on the plurality of estimated longitudinal and lateral tire forces.

A hybrid system includes a transmission control module, a power source, a transmission, and a drive train. The transmission control module partially operates the hybrid system and receives operating information from various components of the system, calculates power losses in the drive train, and calculates the driving torque needed to reach a target power profile determined from a driver's input.

A method of operating a vehicle, comprising: receiving ambient air information; receiving size, distance and relative velocity information about a vehicle in proximity to the vehicle; receiving road surface properties information; receiving wind velocity and direction information; computing an air density ratio factor using the ambient air information; computing an aerodynamic drag ratio factor using the size, distance and relative velocity information; computing a rolling resistance ratio factor using the information road surface properties information; computing effective velocity of the vehicle using the wind velocity and direction information; combining at least one of the air density ratio factor, the aerodynamic drag ratio factor and the rolling resistance ratio factor with vehicle loss coefficients to determining new vehicle loss coefficients; computing an energy loss or power loss of the vehicle using the new vehicle loss coefficients and the effective velocity of the vehicle; and controlling the vehicle to improve fuel economy.