Abstract: A battery charging method, including obtaining a voltage capacity ratio for a reference charge C-rate and voltage capacity ratios for N (N an integer of 1 or more) charge C-rates greater than the reference charge C-rate, each of the voltage capacity ratios for the reference charge C-rate and the N charge C-rates being defined as a ratio of a voltage variance to a capacity variance depending on a change in state of charge (SOC) of a battery when the battery is charged at a corresponding one of the C-rates, comparing the voltage capacity ratio of the reference charge C-rate with each of the voltage capacity ratios of the N charge C-rates, and then setting a charge C-rate of the N charge C-rates so that a difference in voltage capacity ratio is minimized for each of SOC sections, and charging the battery with the charge C-rates corresponding to the SOC sections.

Abstract: An electrode for a rechargeable lithium battery includes an electrode active material and a copolymer represented by where A is selected from —O—(CFRf3—CFRf4)—, —(CFRf4—CFRf5)— and combinations thereof, each of Rf1 through Rf5 is independently selected from fluorine, C1-C4 alkyls and C1-C4 fluorinated alkyls, and each of x and y is an integer ranging from 1 to 100,000.

Abstract: A system for predicting the thickness of a battery is disclosed. In one aspect, the battery thickness predicting system includes a learning data input unit for receiving data on a previously manufactured battery. The thickness predicting system further includes an object data input unit for receiving data on a battery whose thickness is to be predicted. The system further comprises a mechanical learning unit connected to the learning data input unit to obtain a predicting function based on learning factors input to the learning data input unit and to provide weight values to the learning factors, respectively. The system further includes a thickness predicting unit connected to the object data input unit and the mechanical learning unit and using the weight values provided by the mechanical learning unit in order to predict the thickness of the battery whose thickness is to be predicted.

Abstract: A battery charging method, including obtaining a voltage capacity ratio for a reference charge C-rate and voltage capacity ratios for N (N an integer of 1 or more) charge C-rates greater than the reference charge C-rate, each of the voltage capacity ratios for the reference charge C-rate and the N charge C-rates being defined as a ratio of a voltage variance to a capacity variance depending on a change in state of charge (SOC) of a battery when the battery is charged at a corresponding one of the C-rates, comparing the voltage capacity ratio of the reference charge C-rate with each of the voltage capacity ratios of the N charge C-rates, and then setting a charge C-rate of the N charge C-rates so that a difference in voltage capacity ratio is minimized for each of SOC sections, and charging the battery with the charge C-rates corresponding to the SOC sections.

Abstract: A secondary battery includes a battery unit that has an accommodation space for an electrode assembly, a vent housing that defines a housing space connected with the accommodation space through a first vent hole, the vent housing including a second vent hole through which the housing space is connected with an outside of the secondary battery, a revolving member that is rotatably mounted between a closing position for closing the first and second vent holes and an opening position for opening the first and second vent holes, and first and second sealing caps that are formed at opposite sides of the revolving member with respect to a rotation axis of the revolving member. The first and second sealing caps respectively close the first and second vent holes, when the revolving member is mounted at the closing position.

Abstract: A system for predicting the thickness of a battery is disclosed. In one aspect, the battery thickness predicting system includes a learning data input unit for receiving data on a previously manufactured battery. The thickness predicting system further includes an object data input unit for receiving data on a battery whose thickness is to be predicted. The system further comprises a mechanical learning unit connected to the learning data input unit to obtain a predicting function based on learning factors input to the learning data input unit and to provide weight values to the learning factors, respectively. The system further includes a thickness predicting unit connected to the object data input unit and the mechanical learning unit and using the weight values provided by the mechanical learning unit in order to predict the thickness of the battery whose thickness is to be predicted.

Abstract: A secondary battery includes a battery unit that has an accommodation space for an electrode assembly, a vent housing that defines a housing space connected with the accommodation space through a first vent hole, the vent housing including a second vent hole through which the housing space is connected with an outside of the secondary battery, a revolving member that is rotatably mounted between a closing position for closing the first and second vent holes and an opening position for opening the first and second vent holes, and first and second sealing caps that are formed at opposite sides of the revolving member with respect to a rotation axis of the revolving member. The first and second sealing caps respectively close the first and second vent holes, when the revolving member is mounted at the closing position.

Abstract: An electrode for a rechargeable lithium battery includes an electrode active material and a copolymer represented by where A is selected from —O—(CFRf3—CFRf4)—, —(CFRf4—CFRf5)— and combinations thereof, each of Rf1 through Rf5 is independently selected from fluorine, C1-C4 alkyls and C1-C4 fluorinated alkyls, and each of x and y is an integer ranging from 1 to 100,000.