A vehicle includes a high-voltage system circuit including a high-voltage battery, a low-voltage system circuit including a low-voltage battery and an updater, a DC-DC converter coupled between the high-voltage system circuit and the low-voltage system circuit, and a controller. The low-voltage battery has a lower output voltage than the high-voltage battery. The updater updates a program of an update-target device with electric power supplied from the low-voltage or high-voltage battery. The DC-DC converter is capable of reducing in voltage output electric power of the high-voltage battery and then supplying the electric power to the updater. The controller sets a target SOC range of the high-voltage battery and controls charging of the high-voltage battery based on the target SOC range. The controller changes a target SOC lower-limit value of the high-voltage battery to a value higher than a normal value when updating of the program of the update-target device is scheduled.

A vehicle includes a high-voltage system circuit including a high-voltage battery, a low-voltage system circuit including a low-voltage battery and an updater, a DC-DC converter coupled between the high-voltage system circuit and the low-voltage system circuit, and a controller. The low-voltage battery has a lower output voltage than the high-voltage battery. The updater updates a program of an update-target device with electric power supplied from the low-voltage or high-voltage battery. The DC-DC converter is capable of reducing in voltage output electric power of the high-voltage battery and then supplying the electric power to the updater. The controller sets a target SOC range of the high-voltage battery and controls charging of the high-voltage battery based on the target SOC range. The controller changes a target SOC lower-limit value of the high-voltage battery to a value higher than a normal value when updating of the program of the update-target device is scheduled.

A method for classifying an underlying surface travelled by an agricultural utility vehicle includes acquiring a detail of a surface of the underlying surface in the form of optical data, classifying the optical data in a data processing unit with respect to different underlying surface classes, and determining an underlying surface class on the basis of the classifying step. Output data is output from the data processing unit representative of the determined underlying surface class as a classification result. A technical feature of the utility vehicle is adapted as a function of the classification result.

A control system and method for controlling a vehicle subsystem are provided. The control system includes a remote parameter sensor configured to generate a remote parameter signal indicative of a value of a universal parameter associated with an environment in which a vehicle is operating. The system further includes a local parameter sensor configured to generate a local parameter signal indicative of the value of the universal parameter and a local controller. The controller is configured to receive the local parameter signal along a first signal path, receive the remote parameter signal and a command signal configured for controlling a function of the vehicle subsystem along a second signal path, compare the local and remote parameter signals and implement the function of the vehicle subsystem responsive to the command signal if the remote parameter signal meets a predetermined condition relative to the local parameter signal.

The present invention discloses a real-time optimization control method for an electro-mechanical transmission system, and relates to the field of electro-mechanical transmission technologies. The method includes the following steps: (S0) starting; (S1) state observation: a current operating state of each element of the electro-mechanical transmission system is obtained through state observation; (S2) dynamic prediction: a feasible operating range of each element of the electro-mechanical transmission system is obtained through dynamic prediction; (S3) optimal decision: an optimal control command of each element in the optimal decision is formulated and executed; (S4) feedback correction: feedback correction is performed on control amounts of a motor and an engine of the electro-mechanical transmission system by using state deviations; and (S5) determining whether feedback correction meets a requirement, and if feedback correction meets the requirement, ending the process, or if feedback correction does not meet the requirement, repeating (S1).

The present invention discloses a real-time optimization control method for an electro-mechanical transmission system, and relates to the field of electro-mechanical transmission technologies. The method includes the following steps: (S0) starting; (S1) state observation: a current operating state of each element of the electro-mechanical transmission system is obtained through state observation; (S2) dynamic prediction: a feasible operating range of each element of the electro-mechanical transmission system is obtained through dynamic prediction; (S3) optimal decision: an optimal control command of each element in the optimal decision is formulated and executed; (S4) feedback correction: feedback correction is performed on control amounts of a motor and an engine of the electro-mechanical transmission system by using state deviations; and (S5) determining whether feedback correction meets a requirement, and if feedback correction meets the requirement, ending the process, or if feedback correction does not meet the requirement, repeating (S1).

Described herein are various techniques, including a method that uses high-rate acceleration data for computing an accident score indicative of a potential collision and triggering an action in response to determining that the accident score indicates a potential collision. The method includes filtering out undesired high-rate acceleration trigger events such as noise and harsh braking events prior to determining the accident score. The accident score is based on contexts or scores computed from high-rate acceleration data, speed, and GPS data captured by a telematics monitor deployed in a vehicle.

After a driver parks a vehicle and has left the vehicle, a system relocates the autonomous vehicle into an optimized parking spot. The system obtains availability of other parking spots within a parking lot in which the vehicle is located. The system estimates whether any alternate parking spot would optimize cabin temperature and whether the energy consumed to complete an autonomous relocation of the vehicle will obtain a net savings in fuel economy relative to the energy expended by the relocation. The system determines the benefits of these optimized parking spots by using shaded and unshaded areas, GPS location of the vehicle, current date and time, and weather and fuel consumption estimates. If a favorable parking spot is determined, the autonomous vehicle relocates into the optimal alternate parking spot and informs the driver of the new location.

Embodiments of the present invention provide an electric machine control system for a vehicle, the electric machine control system comprising one or more controllers, wherein the vehicle comprises an electric machine arranged to be selectively coupleable to provide torque to at least one wheel of an axle of the vehicle, the control system comprising input means arranged to receive a speed signal (410) indicative of a speed of the vehicle and a status signal (470) indicative of a status of a coupling of the electric machine to the at least one wheel of an axle of the vehicle, output means (340) arranged to output a coupling signal to control coupling of the electric machine to the at least one wheel of the axle, and processing means arranged to determine (1210) a coupling state of the electric machine to the at least one wheel of the axle and to control the output means to output (1220) a coupling signal indicative of the determined coupling state, wherein the processing means is arranged, in dependence on the status signal being indicative of a failure (1230) to change the coupling state of the electric machine to the at least one wheel of the axle in dependence on a change in the determined coupling state, to control the output means to output the coupling signal (345) indicative of a retry (1250) of the change in the coupling state in dependence on the speed signal.

Described herein are various techniques, including a system that uses high-rate acceleration data for computing an accident score indicative of a potential collision and triggering an action in response to determining that the accident score indicates a potential collision. The system is configured to filter out undesired high-rate acceleration trigger events such as noise and harsh braking events prior to determining the accident score. The accident score is based on contexts or scores computed from high-rate acceleration data, speed, and GPS data captured by a telematics monitor deployed in a vehicle.