Research on parameter identification and state of charge estimation of improved equivalent circuit model of Li‐ion battery based on temperature effects for battery thermal management

Abstract

The performance and parameters of Li-ion battery are greatly affected by tem

Low Temperature, Current Dependent Battery State Estimation Using Interacting Multiple Model Strategy

“…The behavior of the battery changes considerably at low temperatures [2] and to account for that, model parameters are adjusted for different temperatures. Huo et al [3] developed a temperature dependent 2 nd order ECM and used it to improve SoC estimation using an EKF. Guo et al [4] combined a temperature adjusted 2 nd order ECM with a dual extended Kalman filter and showed that their method greatly improves SoC estimation compared to an EKF without temperature compensation.…”

Machine learning‐based model for lithium‐ion batteries in BMS of electric/hybrid electric aircraft

“…For parameters identification of the battery ECM (R 0 , R 1 , C 1 , R 2 , C 2 ), the HPPC test was implemented on the cell with SOC of 90% to 10% with SOC interval of 10; and SOC is calculated by Coulomb counting technique. Once the charge and discharge pulses are applied to the cell, the abrupt change in the terminal voltage is used for R 0 calculation, while other parameters (R 1 , C 1 , R 2 , C 2 ) are estimated by the remaining dynamic voltage change 41 . The identified model parameters of a fresh LIB cell in various temperatures and SOCs conditions are plotted in Figure 5.…”

A combined robust approach based on auto‐regressive long short‐term memory network and moving horizon estimation for state‐of‐charge estimation of lithium‐ion batteries

“…And the parameters used to describe the model can be identified offline by current profile test conveniently. 22,23 In engineering application, ECMs are often used for lithium battery system in combination with some filter-based methods, such as Kalman filter (KF), extended Kalman filter (EKF), 24 unscented Kalman filter (UKF), 25,26 Particle Filter (PF) 27 , and moving horizon estimation (MHE) 28 and its variation. 29 Huo et al, put forward a novel approach based on an improved ECM that considers the influence of ambient temperatures and battery surface temperature (BST) on battery parameters.…”

“…29 Huo et al, put forward a novel approach based on an improved ECM that considers the influence of ambient temperatures and battery surface temperature (BST) on battery parameters. 23 Combining the UKF and least support vector machines (LSVM), Meng et al, constructed a new SOC estimation concept, which can deal with the observation noise and system noise naturally and automatically. 25 However, the application of EKF is not very friendly in the estimation of batteries characterized by strong coupling and nonlinearity as the higher-order terms are ignored when Taylor expansion is performed on the nonlinear ECM.…”