A system for reducing at least one of motor loss or motor drive loss in a vehicle. The system includes a motor designed to convert electrical energy into torque. The system also includes a sensor designed to detect motor data corresponding to at least one of a motor torque or a motor speed of the motor. The system also includes a memory designed to store testing data including optimized current commands for multiple combinations of motor torques that were determined during testing of the motor or a similar motor. The system also includes a speed or torque controller coupled to the motor, the sensor, and the memory and designed to receive a speed or torque command and to determine a current command signal usable to control the motor based on the speed or torque command, the testing data, the detected motor data, and an artificial intelligence algorithm.

Deep neural network (DNN) based driving assistance system is disclosed. For one example, a vehicle data processing system includes one or more sensors and a driving assistance system. The one or more sensors obtain data describing an environment around a vehicle. The driving assistance system is coupled to the one or more sensors and configured to detect continuously a designated object in the environment around the vehicle based on the captured data from the one or more sensors using a deep neural network (DNN). The driving assistance system is also configured to output commands from the DNN used to autonomously steer the vehicle to the designated object in the environment to enable coupling of the vehicle with the designated object, e.g., a charging pad for wireless charging.

A vehicle includes an electric machine and a controller. The electric machine is configured to draw energy from a battery to propel the vehicle and to recharge the battery during regenerative braking. The controller is programmed to, in response to identifying a regenerative braking opportunity along an upcoming road segment based on a classification of driver behavior and a classification of the upcoming road segment, operate the electric machine to recharge the battery along the upcoming road segment.

An extended-range fuel cell electric vehicle power device includes a driving motor, a bidirectional converter, a chopper, a power cell, a fuel cell, a high-pressure hydrogen storage tank, an electric control valve, a controller, an accelerator pedal and a brake pedal. An output of the driving motor is connected to a transmission shaft of an electric vehicle through a speed change gearbox, and an input of the driving motor is connected to an alternating current output end of the bidirectional converter; a direct current input end of the bidirectional converter is connected in parallel to an output of the power cell and an output of the chopper, and an input of the chopper is connected to a power source output of the fuel cell.

Presented are intelligent vehicle systems and control logic for predictive charge planning and powertrain control of electric-drive vehicles, methods for manufacturing/operating such systems, and electric-drive vehicles with smart charge planning and powertrain control capabilities. Systems and methods of AI-based predictive charge planning for smart electric vehicles use machine-learning (ML) driver models that draws on available traffic, location, and roadway map information to estimate vehicle speed and propulsion torque requirements to derive a total energy consumption for a given trip. Systems and methods of AI-based predictive powertrain control for smart hybrid vehicles use ML driver models with deep learning techniques to derive a drive cycle profile defined by a preview route with available traffic, geopositional, geospatial, and map data. ML-generated driver models are developed with collected data to replicate driver behavior and predict the drive cycle profile, including predicted vehicle speed, propulsion torque, and accelerator/brake pedal positions for a preview route.

Disclosed are a vehicle control unit (VCU) and an operation method thereof that calculate a speed variation of a vehicle based on input information, predict an average speed of the vehicle based on the calculated speed variation, generate a first speed profile based on the predicted average speed, and generate a second speed profile by applying speed noise information to the first speed profile.

Disclosed are a vehicle control unit (VCU) and an operation method thereof that calculate a speed variation of a vehicle based on input information, predict an average speed of the vehicle based on the calculated speed variation, generate a first speed profile based on the predicted average speed, and generate a second speed profile by applying speed noise information to the first speed profile.

Presented are intelligent vehicle systems and control logic for predictive charge planning and powertrain control of electric-drive vehicles, methods for manufacturing/operating such systems, and electric-drive vehicles with smart charge planning and powertrain control capabilities. Systems and methods of AI-based predictive charge planning for smart electric vehicles use machine-learning (ML) driver models that draws on available traffic, location, and roadway map information to estimate vehicle speed and propulsion torque requirements to derive a total energy consumption for a given trip. Systems and methods of AI-based predictive powertrain control for smart hybrid vehicles use ML driver models with deep learning techniques to derive a drive cycle profile defined by a preview route with available traffic, geopositional, geospatial, and map data. ML-generated driver models are developed with collected data to replicate driver behavior and predict the drive cycle profile, including predicted vehicle speed, propulsion torque, and accelerator/brake pedal positions for a preview route.

A system for reducing at least one of motor loss or motor drive loss in a vehicle. The system includes a motor designed to convert electrical energy into torque. The system also includes a sensor designed to detect motor data corresponding to at least one of a motor torque or a motor speed of the motor. The system also includes a memory designed to store testing data including optimized current commands for multiple combinations of motor torques that were determined during testing of the motor or a similar motor. The system also includes a speed or torque controller coupled to the motor, the sensor, and the memory and designed to receive a speed or torque command and to determine a current command signal usable to control the motor based on the speed or torque command, the testing data, the detected motor data, and an artificial intelligence algorithm.

The disclosure relates to a method for operating a movement apparatus having a first assembly and a second assembly. The first assembly includes a base and several permanent-magnet arrangements that are connected to the base via actuators such that they move as a whole relative to the base in at least one degree of freedom by the assigned actuator, the second assembly including a base and a permanent-magnet arrangement arranged firmly relative to the base. Position controllers are provided, each with a controlled variable and with a correcting variable. The controlled variable is one of six possible degrees of freedom with regard to a relative position between the first and second assembly. The correcting variable represents a force or a torque that has been assigned to the degree of freedom. Desired positions of the actuators are computed from the correcting variables and the actuators are set accordingly.