Battery Lifespan Prediction Using Machine Learning and NASA Aging Dataset
Dr. Jogi John
Guide
PCE, Nagpur, India
jogi.john@pcenagpur.edu.in
Babita Prasad
Student 2
PCE, Nagpur, India
babitaprasad2230@gmail.com
Bhushan Murkute
Student4
PCE,Nagpur,India
bhushanmurkute56@gmail.com
Manav Patil
Student 1
PCE,Nagpur,India
manavpatil596@gmail.com
Aditya Agrawal
Student3
PCE, Nagpur, India
adityaagrawal297@gmail.com
Uday Shahu
Student 5
PCE.Nagpur,India
shahuuday11@gmail.com
Abstract— NASA Battery RLU 16.5 plays a crucial role in powering space missions, ensuring reliability and longevity under extreme conditions. Accurate estimation and control of its State of Health (SOH) are essential for maintaining its performance, particularly in the harsh and unpredictable environment of space. This review paper explores the latest advancements in SOH estimation for lithium-ion batteries, focusing on methods applicable to NASA Battery RLU 16.5. Key methods discussed include machine learning models such as Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNN), and hybrid deep learning models, which have shown promising results in accurately predicting SOH and Remaining Useful Life (RUL). Additionally, optimization techniques like ant lion optimization combined with support vector regression and incremental capacity analysis offer high precision in SOH predictions. Temperature-based SOH estimation and the integration of electrochemical models also emerge as essential methods for improving accuracy. Despite the significant progress in SOH estimation, challenges such as the unpredictability of space conditions remain, necessitating further research in hybrid modeling approaches. This paper provides a comprehensive overview of the state-of-the-art SOH estimation techniques and highlights the challenges and future directions in managing NASA’s lithium-ion batteries for long-term missions.
Keywords— Lithium-ion battery (LIB), Remaining Useful Life (RUL), Machine learning algorithms, Neural networks, LSTM (Long Short-Term Memory), CNN (Convolutional Neural Network), Battery degradation modeling, Hybrid neural networks, Optimization techniques, Space mission battery management
