Design and development of advanced machine learning algorithms for lithium-ion battery state-of-charge estimation

Abstract

Batteries have been becoming more and more popular because of their long life and lightweight. Accurate estimation of the SOC help in making plans in an application to conserve and further enhance battery life. State of Charge (SOC) estimation is a difficult task made more challenging by changes in battery characteristics over time and their nonlinear behavior. In recent years, intelligent schemes for the estimation of the SOC have been proposed because of the absence of the formula for calculating SOC which is hard to deduce because of the effect of external factors like temperature. As the traditional methods only considered certain aspects which with the aging and degradation of the battery results in errors. To tackle this problem several methods were proposed which made use of now evolving artificial intelligence technologies. This paper presents a new SOC estimation algorithm based on kNearest neighbor and random forest regression and a comparison study is done using four algorithms Support Vector Regression, Neural Network Regression, Random Forest Regression and kNearest Neighbor. Their performance is evaluated using data from two drive cycles.