Abstract: A system for modeling energy consumption efficiency of an electric vehicle and a method thereof are disclosed. The system includes a communication device that communicates with a plurality of electric vehicles and includes a controller that receives learning data from the plurality of electric vehicles, learns an energy consumption efficiency model based on the received learning data, and transmits the energy consumption efficiency model in which the learning is completed to the plurality of electric vehicles.

Abstract: A vehicular driving control system and a method for operating the same are provided, may include an information provider to provide information on a front road of a vehicle, a transmission controller to control gear shifting by predicting a condition on the front road based on the information on the front road, and a diagnosing device to diagnose a failure status by comparing the information on the front road with real front road information measured by a sensor, and to restrict a predictive gear-shifting controlling operation of the transmission controller, according to a result of the diagnosing.

Abstract: An apparatus for estimating a vehicle weight includes a memory storing computer-executable instructions and at least one processor that accesses the memory and executes the instructions. The at least one processor applies a real velocity and real acceleration to a trained real-to-virtual transformation model to obtain a virtual output set at a target time point, which includes a virtual gradient of a vehicle, a virtual velocity of the vehicle, and virtual longitudinal acceleration of the vehicle, and applies the virtual output set at the target time point and a real wheel torque at the target time point to a trained mass estimation model to obtain weight information of the vehicle. The real-to-virtual transformation model includes a first sub-model for obtaining an initial virtual velocity, a second sub-model for obtaining the virtual longitudinal acceleration, and a third sub-model for obtaining the virtual gradient.

Abstract: An energy efficiency improvement learning apparatus includes a processor configured to perform learning for vehicle control to minimize vehicle energy consumption required for vehicle driving under random conditions based on a reinforcement learning algorithm, and provide learned parameters of the reinforcement learning algorithm to a vehicle. The energy efficiency improvement learning apparatus also includes a storage medium configured to store data and algorithms, and the reinforcement learning algorithm executable by the processor.

Abstract: An embodiment server includes a processor configured to collect driving data based on a driver driving propensity or a driving situation from a plurality of vehicles and to change information for determining an output torque suitable for each of the vehicles by analyzing the driving data and a communication device configured to transmit the changed information for determining the output torque to the vehicles by communicating with the vehicles.

Abstract: An embodiment vehicle driving control apparatus includes a processor configured to determine an output torque of a driving source by using a driver driving propensity for each driver of a plurality of drivers or a driving situation based on a learning algorithm and a memory configured to store algorithms and data to be driven by the processor.

Abstract: According to an embodiment of the disclosure, an aperture ratio measurement device includes an actual aperture ratio measurement unit which image some mother substrates selected from a plurality of mother substrates and calculate an aperture ratio actual measurement value of a pixel for the imaged some mother substrates, an estimation aperture ratio measurement unit which learn sensing data for each of the plurality of mother substrates and calculate an aperture ratio prediction value of a pixel for all or a group of the plurality of mother substrates, and an addition unit which calculate a final aperture ratio prediction value based on the aperture ratio actual measurement value and the aperture ratio prediction value.

Abstract: An apparatus and a method for predicting a speed of a vehicle. The apparatus includes storage that stores past driving information of the vehicle. The apparatus also includes a controller that extracts feature information about a current state of the vehicle from the past driving information of the vehicle. The controller also generates a query corresponding to each target time point based on the feature information. The controller also determines forward information corresponding to each target time point by using each query. The controller also predicts the speed of the vehicle at each target time point based on the query corresponding to each target time point and the forward information corresponding to each target time point.

Abstract: Disclosed are an apparatus for distributing power of an electric vehicle and a method thereof capable of optimally improving the energy consumption efficiency of the electric vehicle by predicting a vehicle speed for a predetermined time using a learned vehicle speed prediction model, determining wheel power based on the vehicle speed, and distributing the wheel power to a front wheel drive motor and a rear wheel drive motor. The apparatus includes a storage that stores a vehicle speed prediction model in which learning is completed, and a controller that predicts a vehicle speed for a preset time using the vehicle speed prediction model, determines wheel power based on the vehicle speed, and distributes the wheel power to a front wheel drive motor and a rear wheel drive motor.

Abstract: A system for modeling energy consumption efficiency of an electric vehicle and a method thereof are disclosed. The system includes a communication device that communicates with a plurality of electric vehicles and includes a controller that receives learning data from the plurality of electric vehicles, learns an energy consumption efficiency model based on the received learning data, and transmits the energy consumption efficiency model in which the learning is completed to the plurality of electric vehicles.

Abstract: Disclosed are a system for modeling energy consumption efficiency of an electric vehicle and a method thereof. The system includes: a communication device that communicates with a plurality of electric vehicles, and a controller that receives a parameter set of an energy consumption efficiency model from the plurality of electric vehicles. The controller determines an average of received parameter sets as an optimal parameter set, and transmits the determined optimal parameter set to the plurality of electric vehicles.

Abstract: An apparatus and method are provided for controlling a powertrain of an autonomous vehicle. The apparatus includes an autonomous driving controller that controls autonomous driving of the vehicle and a transmission controller that determines whether the vehicle satisfies a pulse and glide (P&G) driving condition during the autonomous driving. The transmission controller alternately performs pulse driving and coasting in conjunction with the autonomous driving controller when the vehicle satisfies the P&G driving condition. The transmission controller causes a transmission to remain in a neutral state during the coasting.

Abstract: Disclosed are a multi-agent-based reinforcement learning system and method therefor. The multi-agent-based reinforcement learning system includes: a slave agent configured to: store a data set collected in each state of a first environment in a first buffer, store a data set received from a master agent in the first buffer, and learn a Q-function based on the data set stored in the first buffer; and the master agent configured to store a data set collected in each state of a second environment in a second buffer; transmit the data set to the slave agent; update a Q-function matched with the slave agent among a plurality of Q-functions; and perform reinforcement learning based on the data set stored in the second buffer.

Abstract: A system for controlling a vehicle battery includes a vehicle which includes a motor that provides a driving force or generates power through regenerative braking and a battery that provides power to the motor and calculates a battery usage according to use of the battery, and a portable terminal which obtains location information of a user, generates a battery charge/discharge guide based on the location information of the user and the battery usage received from the vehicle, and transmits the battery charge/discharge guide to the vehicle, thus minimizing anxiety and inconvenience to users when charging and discharging an electric vehicle, and also minimizing the maintenance cost of the electric vehicle.

Abstract: An apparatus and a method for controlling a transmission of a vehicle include an agent that determines a gear stage of the vehicle based on driving information of the vehicle, shape information of a road, and/or operation information of a driver, and include a controller that performs a reward with respect to the determination of the agent and controls the transmission of the vehicle based on the gear stage determined by the agent.

Abstract: A system and a method for controlling shifting stage of a hybrid vehicle may include a speed detecting device that detects a vehicle speed, an automatic transmission that changes a shifting stage of the vehicle, and a shift controller connected to the speed detecting device and the automatic transmission. The shift controller is configured to determine a start of shift control based on a travel environment and a travel state of the vehicle, compares the vehicle speed with a target vehicle speed when the start of the shift control is determined, and performs the shift control based on a state of charge of a battery of the vehicle and a regenerative braking amount of the vehicle when the shift controller concludes that the vehicle speed exceeds the target vehicle speed.

Abstract: A vehicle curation system includes: a vehicle that obtains driving information of a driver, a server that obtains the driving information from the vehicle and stores the driving information for each driver and specification information of a new vehicle, and a curation device that receives driving information corresponding to driver information from the server when the driver information is input. In particular, the curation device determines driver driving tendencies and a vehicle character based on the specification information of the new vehicle, and further determines a curation target vehicle based on a similarity between the driver driving tendencies and the vehicle character.

Abstract: A device for controlling an electric oil pump (EOP), which is configured to supply a hydraulic pressure to a vehicle, includes a controller that controls driving of the EOP based on lighting information of a traffic light corresponding to a travel direction of the vehicle in a state in which an engine of the vehicle is stopped as an Idle Stop and Go (ISG) system of the vehicle is operated.

Abstract: An apparatus of controlling a pulley of a continuously variable transmission and a method therefore is provided. The apparatus includes a non-transitory storage configured for storing a deep learning model, learning of which is completed and a controller that predicts a vehicle speed and an accelerator position sensor (APS) value for each future time point based on the deep learning model and controls the pulley of the continuously variable transmission based on a pulley ratio for each future time point, the pulley ratio corresponding to the predicted vehicle speed and the predicted APS value, thus preventing a reverse control phenomenon of the pulley ratio and increasing a tension of the belt in the continuously variable transmission.

Abstract: A shift control device and a shift control method for a vehicle may include a storage to store a table having a target speed corresponding to a speed of the vehicle at a starting point of a coasting operation of the vehicle and a speed profile corresponding to each of gearshifting stages of the vehicle with respect to each downhill slope, and a controller to control gearshifting of the vehicle traveling in the coasting operation on a downhill road, according to the table and the speed profile, to control the speed of the vehicle, according to a driver intent of accelerating the vehicle and a driver intent of traveling the vehicle at a constant speed.