Embodiments herein can determine an optimal route for an autonomous electric vehicle. The system may score viable routes between the start and end locations of a trip using a numeric or other scale that denotes how viable the route is for autonomy. The score is adjusted using a variety of factors where a learning process leverages both offline and online data. The scored routes are not based simply on the shortest distance between the start and end points but determine the best route based on the driving context for the vehicle and the user.

A control device of a hybrid vehicle includes a pressing torque applying portion configured to control the first rotating machine to output a pressing torque acting in a predetermined backlash elimination direction in backlash elimination in which one of tooth surfaces of meshing gears is pressed against the other at a meshing portion during motor running, so that the pressing torque is applied on the electric transmission mechanism. When an electrical angle of the first rotating machine is within a predetermined electrical angle range in which a cogging torque of the first rotating machine generated in accordance with rotation of the first rotating machine is equal to or greater than a predetermined torque required for the backlash elimination in the predetermined backlash elimination direction, the pressing torque applying portion makes the pressing torque smaller as compared to when the electrical angle is outside the predetermined electrical angle range.

The dynamic vehicle charging system for electric vehicles includes one or more wind turbines mounted to a vehicle and positioned for exposure to the wind generated during the vehicle's natural driving motion. The wind turbine drives a generator such that when the vehicle moves through the wind, the wind strikes the wind turbine which generates electricity. This electricity is in turn passed through a power converter that converts the electrical energy from the generator to electrical energy that is used to recharge the vehicle batteries, used to offset the electricity consumption rate of the electric vehicle, or both.

Vehicle center of gravity (CoG) height and mass estimation techniques utilize a light detection and ranging (LIDAR) sensor configured to emit light pulses and capture reflected light pulses that collectively form LIDAR point cloud data and a controller configured to estimate the CoG height and the mass of the vehicle during a steady-state operating condition of the vehicle by processing the LIDAR point cloud data to identify a ground plane, identifying a height difference between (i) a nominal distance from the LIDAR sensor to the ground plane and (ii) an estimated distance from the LIDAR sensor to the ground plane using the processed LIDAR point cloud data, estimating the vehicle CoG height as a difference between (i) a nominal vehicle CoG height and the height difference, and estimating the vehicle mass based on one of (i) vehicle CoG metrics and (ii) dampening metrics of a suspension of the vehicle.

A processor unit (3) is configured for executing an MPC algorithm (13) for model predictive control of a first component (18) of a motor vehicle (1) and of a second component (19) of the motor vehicle (1). The MPC algorithm (13) includes a cost function (15) to be minimized and a dynamic model (14) of the motor vehicle (1). The dynamic model (14) includes a loss model (27) of the motor vehicle (1). The loss model (27) describes an overall loss of the motor vehicle (1). The cost function (15) includes a first term, which represents the overall loss of the motor vehicle (1). The overall loss depends on a combination of operating values, which includes a first value of a first operating parameter and a second value of a second operating parameter. The processor unit (3) is also configured for determining, by executing the MPC algorithm (13) as a function of the loss model (14), that combination of operating values, by which the first term of the cost function (15) is minimized.

A power adjustment system and a power adjustment method of an autonomous mobile device are provided. In the power adjustment method, two first current control signals respectively transmitted to two drivers are outputted by a control module. A tilt angle of the autonomous mobile device is detected by an inertial measurement module. A travel route is planned by a navigation module, and the control module obtains a steering angle of the autonomous mobile device during a traveling process. According to different weight values of the autonomous mobile device stored in a database module, a weight of the autonomous mobile device is estimated by the control module. According to the two first current control signals and the weight, the steering angle, and the tilt angle of the autonomous mobile device, two second current control signals respectively transmitted to the two drivers are outputted by the control module.

A vehicle slip control apparatus to be installed in a vehicle including a drive source configured to output power to a driving wheel of the vehicle and a gear pair interposed between an output shaft of the drive source and the driving wheel includes a rotating speed detector, a slip determination unit, and a slip determination prohibition unit. The rotating speed detector is configured to detect a rotating speed of the output shaft. The slip determination unit is configured to determine, when an absolute value of an angular acceleration of the rotating speed detected by the rotating speed detector exceeds a set threshold, that the driving wheel is in a slip state. The slip determination prohibition unit is configured to prohibit the determination by the slip determination unit until a predetermined time elapses after a direction of torque outputted from the drive source is inverted.

A transport robot may include a wheel driver, a power supply, and a processor. The transport robot may execute artificial intelligence (AI) algorithms and/or machine learning algorithms, and may communicate with other electronic devices in a 5G communication environment. Thus, user convenience can be improved.

A method for performing an on-board diagnostic function in a motor vehicle including at least one drive system, at least one operating element and at least one control unit with a processor, a control module and an on-board diagnostic function module. The method includes the steps of: activating an on-board diagnostic function in the on-board diagnostic function module; supplying a first signal profile (S1) of an operating value (CV) of the operating element to an analysis module; the analysis module analyzing the first signal profile (S1) of the operating value (CV); activating a filtering module in the event of a positive analysis result; and the filtering module filtering the first signal profile (S1) of the operating value (CV) with selected damping parameters (DP) upon activation of the filtering module in order to obtain a filtered second signal profile (S2) of the operating element.

An information processing device includes a processor configured to select, when the processor acquires power supply request information requesting supply of electric power from a vehicle equipped with a power supply device that supplies electric power to outside to a power supply target to which a plurality of electronic devices is connected, the electronic device to which electric power is supplied from among the electronic devices based on the power supply request information.