A method of estimating a life of a battery, the method comprising: acquiring density data based on battery sensing data; wherein the density data is data representing a spatial distribution of battery sensing data over time; determining a density feature based on the density data using clusters generated by clustering a density data set based on a plurality of battery management profiles; wherein the density data set is a combination of density data based on the plurality of battery management profiles; estimating the life of the battery based on the density feature; wherein the determining of the density feature comprises determining the density feature based on counting the number of pieces of density data included in each of the clusters, and wherein the determining of the density feature further comprises determining the pieces of the density data included in each of the clusters based on a centroid of each of the clusters; wherein the determining of the density feature comprises converting the density data to a density vector based on the clusters, wherein the density vector comprises elements respectively corresponding to the clusters; and each of the elements is the numbers of pieces of the density data in a respective one of the clusters; wherein estimating the life of the battery comprises using a battery life estimation model based on machine learning; and further comprising: acquiring a further density data set by combining further density data acquired based on further battery sensing data; clustering the further density data in the further density data set into a further plurality of clusters; determining a further density feature based on training data using the further plurality of clusters; training the battery life estimation model based on the further density feature. acquiring density data based on battery sensing data; wherein the density data is data representing a spatial distribution of battery sensing data over time; determining a density feature based on the density data using clusters generated by clustering a density data set based on a plurality of battery management profiles; wherein the density data set is a combination of density data based on the plurality of battery management profiles; estimating the life of the battery based on the density feature; wherein the determining of the density feature comprises determining the density feature based on counting the number of pieces of density data included in each of the clusters, and wherein the determining of the density feature further comprises determining the pieces of the density data included in each of the clusters based on a centroid of each of the clusters; wherein the determining of the density feature comprises converting the density data to a density vector based on the clusters, wherein the density vector comprises elements respectively corresponding to the clusters; and each of the elements is the numbers of pieces of the density data in a respective one of the clusters; wherein estimating the life of the battery comprises using a battery life estimation model based on machine learning; and further comprising: acquiring a further density data set by combining further density data acquired based on further battery sensing data; clustering the further density data in the further density data set into a further plurality of clusters; determining a further density feature based on training data using the further plurality of clusters; training the battery life estimation model based on the further density feature. acquiring a further density data set by combining further density data acquired based on further battery sensing data; clustering the further density data in the further density data set into a further plurality of clusters; determining a further density feature based on training data using the further plurality of clusters; training the battery life estimation model based on the further density feature. <EOS>