Robust GenUT-UKF for state of charge estimation of Li-ion battery against data and model uncertainty

Accurate state-of-charge (SoC) estimation of lithium-ion (Li-ion) batteries is essential for the reliable operation of the battery management system (BMS) in electric vehicles (EVs). Conventional unscented transformation based unscented Kalman filters (UT-UKF) are very useful for moderately non-linear systems with Gaussian noise distribution data during state update. However, it provides satisfactory results only for highly non-linear and nonGaussian data. This paper provides a robust generalized unscented transformation based unscented Kalman filter (RGenUT-UKF) for SoC accurate estimation of real-world drive cycle. This proposed approach effectively captures highly non-Gaussian and non-linear characteristics using distribution-free non-linear transformation with optimally tuned noise covariance matrices ensuring reliability against unseen real-time data. The generalized unscented transformation technique uses higher order moments, in addition to mean and covariance for sigma points selection for enhancement of filter performance. This study deals with the development of an equivalent circuit model (ECM)-based GenUT-UKF for the estimation of SoC of Turnigy Graphene Li-ion battery for different drive cycles that include LA92, US06, and UDDS drive cycle. The efficacy of the proposed algorithm has been demonstrated by comparing it with UT-UKF and GenUT-extended Kalman filter (GenUT-EKF). Additionally, analysis of the robustness of the proposed algorithm has been made with LA92 drive cycle at 40 degrees C against data uncertainties such as 5 % sensor noise to the battery current, ambient temperature variations (0 degrees C, 10 degrees C and 25 degrees C) and model uncertainty in the noise covariance matrices (Q and R). The proposed GenUT-UKF showed outperformance over UT-UKF and GenUT-EKF against sensor noise, ambient temperature variations, and model uncertainty with RMSE of 0.2780%, 0.7842 %, and 0.7843 % respectively. Finally, a RGenUT-UKF has been designed by optimally tuned noise covariance matrices for the LA92 drive cycle using the Bayesian optimization technique. Improved performance of the proposed method has been ensured with a minimum RMSE of 0.4099 %. In addition, the efficacy of the proposed algorithm has been demonstrated under complex working conditions through simulation as well as experimental studies. The practicability of realizing the algorithm has been demonstrated with RT-LAB-based real-time simulator using software-in-the-loop (SIL) configuration.