Paper Info
Title
Analysis of Optimal Machine Learning Approach for Battery Life Estimation of Li-Ion Cell
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.
Year
DOI
Venue
2021
10.1109/ACCESS.2021.3130994
IEEE ACCESS
Keywords
DocType
Volume
Batteries, Predictive models, Estimation, Analytical models, State of charge, Lithium-ion batteries, Hardware, Battery management system, deep neural network, Li-ion batteries, long short time memory, state of health, remaining useful life, state of charge
Journal
9
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Prakash Venugopal100.68
S. Siva Shankar200.34
C. Phillip Jebakumar300.34
Rishab Agarwal400.34
Hassan Haes Alhelou5813.42
S. Sofana Reka600.34
Mohamad Esmail Hamedani Golshan700.68