Joint Estimation of Battery Parameters and State of Charge Using an Extended Kalman Filter: A Single-Parameter Tuning Approach

Abstract

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State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

“…The feedback and correction mechanism are employed in KF to eliminate the noise and approach the real value continuously. Various KF-based extensions have been advanced to account for more complicated and higher nonlinear SOC estimation, and these solution representatives include extended KF (EKF) ( Beelen et al., 2021 ), adaptive EKF (AEKF) ( Shrivastava et al., 2021 ), unscented KF (UKF) ( Marelli and Corno, 2021 ), adaptive UKF (AUKF) ( Li et al., 2020b ), adaptive sigma-point KF (ASPKF) ( Sun et al., 2021 ), cubature KF (CKF) ( Peng et al., 2019 ), adaptive CKF (ACKF) ( Li et al., 2021c ), square root CKF (SRCKF) ( Shu et al., 2021a ), etc. Usually, two essential steps are involved in KF-based estimation, in which the state prediction is firstly implemented to estimate the current output, and then the estimation is updated and corrected to achieve more authentic output.…”

Skew t Distribution-Based Nonlinear Filter with Asymmetric Measurement Noise Using Variational Bayesian Inference

“…Due to inter-coupled parameters, it is infeasible to infer the posterior p(H|z 1:n ) analytically. From (19), the logarithmic marginal likelihood log p(z n |z 1:n−1 ) can be derived as [35] log p(z n |z 1:n−1 ) = KLD(q(H) p(H|z 1:n )) + L(q(H)), (20) where KLD(•) and L are the Kullback-Liebler divergence and the lower bound of log p(z n |z 1:n−1 ), respectively. Due to the non-negativity of the KLD, we can obtain the true posterior by minimizing the KLD between q(H) and p(H|z 1:n ) [35,36].…”

“…To deal with the unknown noise statistics, various adaptive and robust filters were designed for joint state estimation [17][18][19]. For example, a recursive state estimation method was presented with unknown Gaussian noise covariance for linear systems [20].…”

A novel intelligent weight decreasing firefly–particle filtering method for accurate state‐of‐charge estimation of lithium‐ion batteries

“…8 Therefore, a stable and reliable battery management system (BMS) must be adopted in the use of lithium-ion batteries to ensure the safe and stable operation, monitor the service status, and make efficient use of the discharge performance of lithium-ion batteries. 9,10 State-of-charge (SOC) is a key parameter in BMS; it is equivalent to the fuel gauge of vehicles, 11 providing the driver with real-time status of the vehicle and a better driving experience, 12 and it also provides the basis for the calculation of other state parameters of electric vehicles. 13 As the key and difficult point of electric vehicle development, 14,15 SOC estimation has important research significance.…”