Abstract: Disclosed are an excellent-durability carbon-based catalyst for a fuel cell, a preparation method therefor, and a proton exchange membrane fuel cell comprising same, the excellent-durability carbon-based catalyst for a fuel cell, in order to protect a carbon-based support which rapidly corrodes in a fuel cell operating environment, having a ceramic material having strong corrosion resistance coated so as to form a ceramic coating layer of which a portion protrudes in the form of needles.

Abstract: The present disclosure relates to a method of preparing an electrode assembly capable of improving life characteristics and rapid charging performance by minimizing a space between a folding separator and an electrode. The method of preparing the electrode assembly includes disposing a plurality of unit cells on a first surface of a folding separator, fixing the unit cells on the first surface of the folding separator, providing a binder composition to at least one end portion of a second surface of the folding separator, and stacking the unit cells by folding the folding separator.

Abstract: A negative electrode for a secondary battery in which a negative electrode active material layer is formed on at least one surface of a current collector. The continuous adhesive force in the thickness direction of the negative electrode for the secondary battery is 30 gf/20 mm or more, and/or the breaking stress of the negative electrode is 3.6 N or more. The breaking stress is a value measured by pressing the negative electrode active material layer with a linear tip having a width of 2.5 mm and a sharp end at a speed of 10 ?m/s.

Abstract: A battery management apparatus according to an embodiment of the present disclosure includes: a feature value extracting unit configured to extract a feature value of a learning battery at every charging and discharging cycle; a first state judging unit configured to judge a state of the learning battery at every charging and discharging cycle, based on the feature value extracted by the feature value extracting unit and a criterion value preset to correspond to the feature value; a labelling unit configured to set a label for the feature value of the learning battery at every charging and discharging cycle, based on the state of the learning battery judged by the first state judging unit; a model learning unit configured to learn a classification model for judging a state of an analysis battery based on the feature value for which the label is set by the labelling unit; and a second state judging unit configured to judge the state of the analysis battery based on the classification model learned by the model