This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2021.3130994, IEEE Access 1 Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000. Digital Object Identifier 10.1109/ACCESS.2017.Doi Number Analysis of Optimal Machine Learning Approach for Battery Life Estimation of Li-ion Cell Prakash Venugopal 1 , Siva Shankar S 1 , C Phillip Jebakumar 1 , Rishab Agarwal 1 , Hassan Haes Alhelou 2,3 (Senior Member, IEEE), S.Sofana Reka 1 , Mohamad Esmail Hamedani Golshan 4 (Senior Member, IEEE) 1 School of Electronics Engineering, Vellore Institute of Technology, Chennai, India 2 Department of Electrical Power Engineering, Tishreen University, Lattakia 2230, SY 3 School of Electrical and Electronic Engineering, University College Dublin, Dublin 4, D04 V1W8 Ireland 4 Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran Corresponding author: Hassan Haes Alhelou (alhelou@ieee.org) ABSTRACT 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. INDEX TERMS Battery Management System, Deep Neural Network, Li-ion batteries, Long Short Time Memory, State of Health, Remaining useful life, State of charge. LIST OF ABBREVIATIONS BMS – Battery Management System resistive-capacitive (RC) LSTM – Long Short Time Memory SOH – State of Health SOC- State of Charge RUL – Remaining useful life VTC- Discharge Voltage Transfer characteristics DNN – Deep Neural Network I. INTRODUCTION Li-ion Batteries (LiB) is one of the primary energy storage units widely used in many electrical and electronic applications due to its high life cycle, high capacity, high energy density and high specific energy. Majority of the devices are powered by a Li-ion based cell or battery with varying capacity for many applications including cell phones, spacecraft, electric vehicles. When compared to other types of batteries, Li-ion batteries require more advanced monitoring system to ensure safe operation of the battery with the help of battery management system (BMS). Various functions of BMS strongly depends on the complexity of the specific application. Understanding and analyzing the remaining life expectancy of the battery are greatly important to ensure proper functioning of them. An optimal method of battery life estimation for a Li-Ion cell [1] are needed to make efficient use of most of the