Abstract
Discriminating internal multi-parameter difference between cells is a basis of battery screening, diagnosis, consistency assessment and early warning. However, the existing methods based on voltage features can only identify one or two parameter difference sources and usually work under specific conditions. To solve this problem, a modified multidimensional scaling (MDS) framework that incorporates the concept of shape dissimilarity is newly proposed here. First, the shape dissimilarities between voltage curves are presented by derivative dynamic time warping (DDTW) distances. Then, by utilizing MDS, the dissimilarities are converted into the relative locations between points in two-dimensional space. Finally, based on the complementarity of DDTW-MDS and traditional MDS, the parameter difference sources are determined visually on a spatial map. The effectiveness of proposed method is validated on 484 cells with different combinations of three internal parameters including capacity, state of charge and internal resistance, and various working conditions including constant charging, constant discharging and dynamic condition.
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