A weighted Gaussian process regression model based on improved local outlier factor and its application in state of health estimation of lithium-ion battery

Battery state of health estimation is an important part of battery management system, which can improve the reliability and economy of battery use, and data-driven based estimation has become a hot topic in the field. It is accepted that data-driven modeling methods strongly rely on the accuracy of the acquired data, but it is inevitable that outliers will affect the original data measurement, which has an impact on data-driven modeling. This paper proposes a weighted Gaussian process regression model based on improved local outlier factor. Firstly, entropy weight method is introduced to calculate the contribution of each attribute of the sample to further construct a modified Euclidean distance, which reduce the discriminability of data in high-dimensional space in standard local outlier factor. Then, a density-based local outlier detection approach based on improved local outlier factor is developed to assign low weights for samples with high potential outlier, and the weight matrix is incorporated with the standard Gaussian process regression to construct weighted Gaussian process regression model, which solve the heteroscedasticity caused by outlier. Finally, the effectiveness of the proposed method is verified by comparative experiments, and the results illuminate that the proposed model has higher estimation accuracy compared with the existing methods, and achieves smaller error regarding multiple error indicators.