Prediction of Battery Remaining Useful Life Using Machine Learning Algorithms

J. N. Chandra Sekhar (), Bullarao Domathoti and Ernesto D. R. Santibanez Gonzalez ()
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J. N. Chandra Sekhar: Department of Electrical and Electronics Engineering, Sri Venkateswara University, Tirupati 517502, India
Bullarao Domathoti: Department of Computer Science and Engineering, Shree Institute of Technological Education, Tirupati 517501, India
Ernesto D. R. Santibanez Gonzalez: Department of Industrial Engineering and CES 4.0, Faculty of Engineering, University of Talca, Curico 3340000, Chile

Sustainability, 2023, vol. 15, issue 21, 1-28

Abstract: 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.

Keywords: HNEI battery; machine learning algorithms; heat map; Mean Squared Error; Mean Absolute Error; Root Mean Squared Error; R-Squared Error (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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