A deep learning-based power battery capacity estimation model assisted with feature vector construction

This study aims to answer the key question on how to detect the capacity degradation of the power batteries in electric vehicles (EVS) with emerging intelligent methods. Focusing on the random and incomplete charging process in actual EVs usage, the new estimation method only needs a specific charging current segment rather than data from the whole charging process for model training and estimation. Given that, this paper proposes a novel feature vector construction method to extract the key features from the current segment firstly. On this basis, a fully connected neural network is established to recognize the constructed feature vectors and obtain the mapping between the charging information and capacity degradation. Battery cycling data measured from 718650Li-ion cells was utilized for model training and performance verification. Compared with the traditional data-driven capacity estimation methods proposed previously, the proposed method demonstrates the great superiority in terms of the method applicability and estimation accuracy for online capacity estimation in actual battery usage.