Prediction of Battery Remaining Useful Life Using Machine Learning Algorithms

Electrified transportation systems are emerging quickly worldwide, helping to diminish carbon gas emissions and paving the way for the reduction of global warming possessions. Battery remaining useful life (RUL) prediction is gaining attention in real world applications to tone down maintenance expe...

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Bibliographic Details
Published in:Sustainability 2023-10, Vol.15 (21), p.15283
Main Authors: Sekhar, J. N. Chandra, Domathoti, Bullarao, Santibanez Gonzalez, Ernesto D. R
Format: Article
Language:English
Subjects:
Aging
Algorithms
Alternative energy sources
Analysis
Batteries
Clean technology
Climate change
Data mining
Electrolytes
Emissions
Energy storage
Global warming
Greenhouse gases
Lithium
Machine learning
Medical research
Medicine, Experimental
Neural networks
Parameter estimation
Renewable resources
Support vector machines
Sustainability
Sustainable development
Useful life
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Summary:Electrified transportation systems are emerging quickly worldwide, helping to diminish carbon gas emissions and paving the way for the reduction of global warming possessions. Battery remaining useful life (RUL) prediction is gaining attention in real world applications to tone down maintenance expenses and improve system reliability and efficiency. RUL forms the prominent component of fault analysis forecast and health management when the equipment operation life cycle is considered. The uprightness of RUL prediction is vital in providing the effectiveness of electric batteries and reducing the chance of battery illness. In assessing battery performance, the existing prediction approaches are unsatisfactory even though the battery operational parameters are well tabulated. In addition, battery management has an important contribution to several sustainable development goals, such as Clean and Affordable Energy (SDG 7), and Climate Action (SDG 13). The current work attempts to increase the prediction accuracy and robustness with selected machine learning algorithms. A Real battery life cycle data set from the Hawaii National Energy Institute (HNEI) is used to evaluate accuracy estimation using selected machine learning algorithms and is validated in Google Co-laboratory using Python. Evaluated error metrics such as Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-Squared, and execution time are computed for different L methods and relevant inferences are presented which highlight the potential of battery RUL prediction close to the most accurate values.
ISSN:2071-1050
2071-1050
DOI:10.3390/su152115283