Lithium-ion battery state of health monitoring based on an adaptive variable fractional order multivariate grey model

Accurate assessment of the state of health of lithium-ion batteries using relevant factors is crucial for the maintenance of lithium-ion batteries in electric vehicles. Firstly, data features are extracted from University of Maryland public dataset and dataset is pre-processed. Secondly, the extracted features were analysed using a grey relational analysis model to identify the most significant factors affecting the state of health. Thirdly, this paper proposed an adaptive variable fractional order multivariate grey prediction model to accurately estimate the state of health of lithium-ion batteries. The comparative results demonstrate the overall superiority of the proposed model. •A framework of lithium-ion battery health monitoring model is established.•A new grey prediction model is proposed.•The proposed model is validated on a publicly available dataset.