Machine Learning Application in Battery Prediction: A Systematic Literature Review and Bibliometric Study

¹ Industrial Engineering Department, Faculty of Engineering, Bina Nusantara University, Jakarta, 11480, Indonesia
² Electrical Engineering Department, Faculty of Industrial Technology, Universitas Trisakti, Jakarta, 11440, Indonesia
³ Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia, 11480
⁴ Computer Science Department, BINUS Graduate Program - Master of Computer Science Program, Bina Nusantara University, Jakarta, Indonesia, 11480
⁵ Engineering Industrial Engineering Department, BINUS Graduate Program - Master of Industrial Engineering, Bina Nusantara University, Jakarta, Indonesia 11480
⁶ Department of Mechanical and Manufacturing Engineering and Technology, Oregon Institute of Technology, Klamath Falls, OR 97601, USA

^* Corresponding author: azure.kamul@binus.ac.id

Abstract

Recently, the popularity of li-ion batteries has attracted many researchers to carry out the battery’s maximum potential. Predicting batteries condition and behavior is part of the process that is considered challenging. ML algorithm is widely applied to overcome this challenge as it demonstrates a successful outcome in optimizing the complexity, accuracy, reliability, and efficiency of battery prediction. Yet, we believe there is a particular research area of battery prediction that can further be explored and enhanced with machine learning capability. Therefore, we perform a systematic literature review and bibliometric study to uncover the gap in the machine learning application in the battery prediction field. This study is divided into four stages: (1) literature search from the Scopus Database, (2) filtering the results based on keywords and prepared criteria using PRISMA method, (3) systematic review from filtered papers to provide further understanding, and (4) bibliometric analysis from visualization created in VOSViewer software. The analysis findings determine battery safety and performance prediction as a potential gap in the scope of machine learning for battery prediction research and provide some insightful information to assist future researchers. We envision this study to encourage further battery research, which will assist in the creation of better, cleaner, safer, and long-lasting energy resources.