Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries

By D. Andre, Christian Appel, T. Soczka-Guth, Dirk Uwe Sauer. 2013

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A review on lithium-ion battery ageing mechanisms and estimations for automotive applications (2013, background): “Parameters identi cation can be directly made from measurements or from more complex approaches through equivalent circuit models [101, 102, 103].”
Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries (2019, methodology): “The computation of the Jacobian matrix is required during estimation through the EKF algorithm which conversely effects the accuracy of the estimated SOC [187].”
A Study of Cell-to-Cell Interactions and Degradation in Parallel Strings: Implications for the Battery Management System (2016, background): “BoL (SoH 1⁄4 100%) represents the state when the battery is new, and EoL (SoH 1⁄4 0%) is defined as the condition when the battery cannot meet the performance specification for the particular application for which it was designed [13].”
State-of-Charge Estimation for Lithium-Ion Batteries Using a Kalman Filter Based on Local Linearization (2015, methodology): “The Extended Kalman filter (EKF) [7–10], unscented Kalman filter (UKF) [5,11], which are algorithms developed based on the Kalman filter, better solve the state estimation problems for the nonlinear LIB [12], and can also obtain ideal estimation results.”