Abstract: Vehicle control apparatus and vehicle control method are described. The vehicle control apparatus may include a compressor and a controller. The controller may determine a thermal load level at one or more of: a first time point at which an engine is switched to an ON state from an OFF state, a second time point at which a first discharge amount of the battery exceeds a first reference value while the engine is in the OFF state, or a third time point at which a second discharge amount of the battery exceeds a second reference value smaller than the first reference value and a discharge rate associated with the battery exceeds a third reference value while the engine is in the OFF state. The controller may control the compressor using a control value corresponding to the thermal load level.

Abstract: A method for controlling an electric heater of a vehicular heating, ventilation, and air conditioning (HVAC) system includes turning on the electric heater; determining whether an ambient air temperature of a vehicle is higher than or equal to a threshold ambient air temperature, and a battery temperature is lower than or equal to a threshold battery temperature; determining whether battery efficiency is lower than or equal to threshold efficiency when the ambient air temperature of the vehicle is higher than or equal to the threshold ambient air temperature, and the battery temperature is lower than or equal to the threshold battery temperature; and turning off the electric heater when the battery efficiency is lower than or equal to the threshold efficiency, wherein the electric heater is configured to receive electric energy from the battery.

Abstract: Disclosed is a battery cell, which includes a battery case having an accommodation portion in which an electrode assembly is mounted, and a sealing portion formed by sealing an outer periphery thereof; an electrode lead electrically connected to an electrode tab included in the electrode assembly and protruding out of the battery case via the sealing portion; and a lead film located at a portion corresponding to the sealing portion in at least one of an upper portion and a lower portion of the electrode lead, wherein a gas discharge guiding unit is inserted in the lead film, the battery case includes a cover portion extending from the sealing portion, and the cover portion is located on the lead film and protrudes in an outer direction of the battery case.

Abstract: The present disclosure relates to a secondary battery comprising an electrode assembly having an electrode lead attached thereto, a case comprising a receiving portion in which the electrode assembly is received and a sealing portion comprising a sealant resin and configured to seal the electrode assembly, a lead film configured to cover a portion of an outer surface of the electrode lead and interposed between the electrode lead and the case, a vent area disposed in at least a portion of the case, and a vent member comprising a first layer comprising a resin having lower melting point than the sealant resin, and a second layer disposed on at least one surface of the first layer and comprising an adhesive material. The vent member may be inserted into the vent area. A thickness of the second layer may be equal to or smaller than 5 ?m.

Abstract: A method for predicting performance of a vehicle NVH system based on deep learning is provided. The method includes preprocessing learning data collected for each channel associated with noise and vibration while driving, learning a model forming a correlation function between multiple inputs and multiple outputs corresponding to the preprocessed learning data using an artificial neural network, and predicting performance using a vehicle NVH system model formed through the learned model.

Abstract: A method for predicting performance of a vehicle NVH system based on deep learning is provided. The method includes preprocessing learning data collected for each channel associated with noise and vibration while driving, learning a model forming a correlation function between multiple inputs and multiple outputs corresponding to the preprocessed learning data using an artificial neural network, and predicting performance using a vehicle NVH system model formed through the learned model.