Maximizing Electric Vehicle Battery Efficiency:  A Multi-Model Machine Learning Approach for RUL Prediction of NMC-LCO Batteries

S. Sivakumar; Moazzam Haidari; A. M. Arun Mohan; M. Bheemalingaiah; R. Jayasudha; Senthilkumar C

doi:10.63278/mme.v31i1.1239

Authors

S. Sivakumar Department of Electrical and Electronics Engineering, Vel Tech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology, Avadi, Chennai, Tamilnadu, India.
Moazzam Haidari Department of Electrical Engineering, Saharsa College of Engineering, Saharsa, Bihar, India
A. M. Arun Mohan Department of Civil Engineering, Sethu institute of Technology, Pulloor, Kariapatti Tamilnadu, India
M. Bheemalingaiah Department of CSE, J.B. Institute of Engineering and Technology, Hyderabad, India
R. Jayasudha Department of Mathematics, Dr.N.G.P.Institute of Technology, Coimbatore, India
Senthilkumar C Department of Mathematics, Jeppiaar Institute of Technology (Autonomous), Sriperumbhudhur, Chennai, India

DOI:

https://doi.org/10.63278/mme.v31i1.1239

Keywords:

Electric Vehicle, ML, Regression, Prediction Method, Battery, Electric Vehicle.

Abstract

Electric vehicles (EV) are becoming more prevalent because they are good for the environment and don't cost much to run. One big problem with EVs, though, is that their batteries don't last long. There is a complete way to figure out how long Nickel Manganese Cobalt-Lithium Cobalt Oxide (NMC-LCO) batteries will still work after this study. The information used in this study comes via the Hawaii Natural Energy Institute consist of 15 different batteries that were put through over 1000 rounds of controlled settings. A method with several steps is used, starting with collecting data and preparing it, then choosing features and getting rid of outliers. The RUL forecast method is made with machine learning (ML) methods like Bagging Regressor, XG Boost, Cat Boost, Light GBM and Extra Trees Regressor. Feature value analysis helps find important factors that affect the health and lives of a battery. Statistical tests show that there are no missing as well as duplicate data points and getting rid of outliers makes the method more accurate. Not surprisingly, XG Boost turned out to be the best algorithm, making predictions that were very close to being correct. This study shows how important RUL forecast is for improving battery lifetime management, especially in electric car uses, to make sure that resources are used in the best way possible, costs are kept low, and the environment is protected.

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