Analysis of Optimal Machine Learning Approach for Battery Life Estimation of Li-Ion Cell

State of health (SOH) and remaining useful life (RUL) are two major key parameters which plays a major role in battery management system. In recent years, various machine learning approaches have been proposed to estimate SOH and RUL effectively for establishing the battery conditions. In the proposed work establishes an effective method to predict the battery aging process with accurate battery health estimation with real time simulations and hardware approach. This paper effectively exhibits a process to estimate SOH and RUL of a Li-Ion 18650 cell which are based on various factors like state of charge, discharge voltage transfers characteristics, internal resistance and capacity. To identify an optimal SOH and RUL machine learning based estimation approach, various battery's statistical models are developed and implemented on a standalone hardware platform. The experimental results in this real time application shows that SOH is predicted by deep neural network approach which are found to be within the accepted error rate of +/- 5% and long short time memory neural network model estimates a battery's RUL effectively with an accuracy of +/- 10 cycles. This approach exhibits various machine learning models in an realistic hardware platform which establishes optimal battery life.

Automated summary

This study proposes an effective method for predicting battery aging process and accurately estimating battery health and remaining useful life. Through various factors, SOH and RUL estimation for Li-Ion 18650 cell are achieved, with experimental results showing high accuracy in predicting battery state.