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 propos...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.159616-159626
Main Authors: Venugopal, Prakash, Shankar, S. Siva, Jebakumar, C. Phillip, Agarwal, Rishab, Alhelou, Hassan Haes, Reka, S. Sofana, Golshan, Mohamad Esmail Hamedani
Format: Article
Language:English
Subjects:
Analytical models
Artificial neural networks
Batteries
Battery management system
Charge transfer
deep neural network
Estimation
Hardware
Li-ion batteries
Lithium-ion batteries
long short time memory
Machine learning
Neural networks
Predictive models
Real time
remaining useful life
State of charge
state of health
Statistical models
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3130994