关键词: 主元分析, 相关向量机, 线性相关性, 寿命预测

Abstract:

Aiming at the problem that the existing online life prediction of lithium?ion batteries based on the correlation vector machine has a single consideration factor, which results in unsatisfactory prediction accuracy, a method based on principal component analysis (PCA) for weighted construction of characteristic factor variables was proposed. In this method, a variety of characteristic factor variables are taken as the research object to find the matrix of the score vector after the linear transformation. The feature coverage degree of different score vectors to the original variable data matrix is analyzed, and the corresponding feature vectors are constructed by weighted fusion. Using the vector as input, a prediction model is established by the relevance vector machine and the online prediction of lithium?ion battery life is performed, and the prediction results are finally obtained. International public battery data was used as the research object, and MATLAB experiments were used to verify that the method has the feasibility of multivariate prediction of battery life, and the prediction effect is better.

Key words: Principal component analysis, Relevance vector machine, Linear correlation, Life prediction