Efficient estimation of state of charge of lithium-ion batteries

Abstract

The promotion of electric vehicles can help reduce carbon emissions and mitigate the greenhouse effect. Lithium-ion batteries are one of the main power sources for electric vehicles, whose state of charge is a key parameter of the battery management system to ensure the safe operation of electric vehicles. The complex structure of the battery makes the state of charge unable to be directly measured, so this paper establishes a mathematical model to estimate the state of charge based on four parameters (voltage, current, wavelength and light intensity), aiming to provide a high-precision model for state of charge measurement and establish a mathematical model which can help guide actual production. These four parameters can be measured by voltmeters, ammeters, and optical fibers. In this paper, the least squares method is used to perform global linear regression and global quadratic regression of experimental data, and a data pre-treatment is proposed to improve the behavior of the linear equation system. Also, the ridge regression method is used to achieve an effective solution of the ill-conditioned linear equation system. Then, the piecewise linear regression is introduced to optimize the global regression model, and effectively reduces the regression error by capturing the local features of the data. In order to meet the actual engineering requirements, constraints satisfying segmental continuity are added in the piecewise linear regression model. Finally, the regression results are compared with the results obtained by the Machine Learning method. Besides, the proposed methods outperform the Machine Learning method with a sufficient number of segments. The state of charge estimation model established in this paper has high approximation accuracy, strong interpretability, low operational complexity, and has certain engineering application value.