The Adaptive Fading Extended Kalman Filter SOC Estimation Method for Lithium-ion Batteries

SOC Estimation of a Rechargeable Li-Ion Battery Used in Fuel Cell Hybrid Electric Vehicles—Comparative Study of Accuracy and Robustness Performance Based on Statistical Criteria. Part II: SOC Estimators

“…A similar situation is reported in the literature, in reference [16], where the authors investigate an RC ECM Li-ion battery model, and the SOC accuracy performance and robustness are analyzed for 20% initialization of SOC error. The SOC estimated error of the AEKF SOC estimator is more significant than 2% during the steady-state for a considerable window length t ∈ (800, 2200) s of SOC residual [16] compared to the AEKF SOC estimator used in our research for which the SOC estimated error is 0.32% for third order resistor capacitor (3RC) equivalent circuit model (ECM), if a 20% initialization SOC error (SOCini = 50%) is under investigation. For performance comparison purposes, Figure 1 shows a complete picture of Li-ion battery AEKF SOC, i.e., accuracy and robustness performance, for a 20% initialization SOC error, such as is reported in several references in the literature.…”

“…In this section, an overview of two Kalman filter SOC estimators with adaptive function is provided, namely a linearized adaptive Kalman filter (AEKF) [5,6,[15][16][17][18][19] and an unscented adaptive Kalman filter (AUKF). A successful implementation of both SOC estimators is performed on the software platform MATLAB R2020a, which estimates the SOC of a Li-ion SAFT battery with a rated capacity of 6 Ah and a nominal voltage of 3.6 V. Both SOC estimators under investigation are model based; thus, a dynamic state space representation model of the Li-ion battery is required in order to develop a simulation model for the emulation of nonlinear battery behavior [5,6].…”

“…The AEKF SOC estimator is a standard EKF, such as those developed in [2,5,6,[15][16][17][18][19], with improved performance by using a memory fading factor [16] or adaptive correction of process and measurement noise covariances [6]. In [5], the SOC AEKF estimator combines both memory fading and noise correction.…”

“…In recent years, the lithium-ion battery has proven to be an ideal safety battery for hybrid electric vehicles, with high discharge power, environmental protection, low pollution, and long life [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Some details about its features, modelling, and hybrid combinations with different power sources in a fuel cell electric vehicle (FCEV) and power distribution controlled and optimized by an energy management system (EMS) are shown in Part 1 [20].…”

“…Typically, the Kalman filter SOC estimators are model based, so both battery models-the linear RC equivalent electrical circuit (ECM) and the nonlinear Simscape generic model developed and analyzed in Part 1 [20] are beneficial for designing and implementing a high accuracy SOC estimator [1][2][3][4][5][6][7][8][9][10][12][13][14][15][16][17][18][19]. For better documentation and information for the reader, the diagrams of both models represented in Part 1 [20] are taken over and repeated in Appendix A.1, Figure A1a-c.…”