State‐of‐charge prediction of lithium ion battery through multivariate adaptive recursive spline and principal component analysis

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 R2 (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 SS2 of 93.69% and 94.23% for profile A and profile B, respectively.

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