State of Charge Estimation of Lithium-ion Batteries using Hybrid Machine Learning Technique

The pivotal features of low self-discharge, high energy density and long calendar life lead the Lithium-ion (Li-ion) batteries as being a mainstream energy storage source in electric vehicles (EVs). A meticulous estimation of the state of charge (SOC) is indispensable for ensuring safe and reliable operations in battery powered EVs. However, SOC estimation of Li-ion battery with high accuracy have become a major challenge in the automotive industry. To fulfill reliable operation in EVs, researchers have proposed numerous SOC estimators through model based or machine learning techniques. This paper presents an improved SOC estimation of Li-ion battery using random forest (RF) regression, which is robust and effective for controlling dynamic systems. To ensure good resilience and accuracy, a Gaussian filter is adopted at the final stage to minimize the variations in the SOC estimation. The proposed SOC estimator is verified on the experimental data of the Liion battery under Federal test driving schedules and different operating temperatures. Results show that the proposed SOC estimator displays sufficient accuracy and outperforms the traditional artificial intelligence based approaches.