Premium
State‐of‐charge prediction of lithium ion battery through multivariate adaptive recursive spline and principal component analysis
Author(s) -
Vyas Mayank,
Pareek Kapil,
Spare Shitanshu,
Garg Akhil,
Gao Liang
Publication year - 2021
Publication title -
energy storage
Language(s) - English
Resource type - Journals
ISSN - 2578-4862
DOI - 10.1002/est2.147
Subject(s) - principal component analysis , state of charge , multivariate adaptive regression splines , mars exploration program , battery (electricity) , multivariate statistics , estimator , spline (mechanical) , lithium ion battery , computer science , control theory (sociology) , statistics , artificial intelligence , engineering , machine learning , mathematics , nonparametric regression , power (physics) , physics , structural engineering , quantum mechanics , astronomy , control (management)
The main aim of this research work is to provide a comprehensible state of art for the intensification of the utmost decisive task performed by a modern BMS system to monitor and estimate battery states through a well‐entrenched statistical analysis method. In the present work, “multivariate adaptive regression splines” (MARS) method along with principal component analysis (PCA) has been used to develop a predictive model‐based state of charge (SoC) estimator for an NCR 18650PF Lithium ion battery at constant charging c‐rate of 0.3 C and 0.3 C and 0.5 C constant discharge profiles. Time‐weighing factors, that is, voltage‐current and temperature are employed as training datasets, to provide greater impact for developing a SoC MARS model of with high coefficient of correlation R 2 (0.9984). The SoC MARS model adequacy is then validated for voltage prediction of the same battery for two different profiles of discharging using NIPALS algorithm for principal component analysis (PCA) with SS 2 of 93.69% and 94.23% for profile A and profile B, respectively.