A Novel Battery State of Charge Estimation Method Based on a Super-Twisting Sliding Mode Observer

Abstract

A novel method for Li-ion battery state of charge (SOC) estimation based on a super-twisting sliding mode observer (STSMO) is proposed in this paper. To design the STSMO, the state equation of a second-order RC equivalent circuit model (SRCECM) is derived to represent the dynamic behaviors of the Li-ion battery, and the model parameters are determined by the pulse current discharge approach. The convergence of the STSMO is proven by Lyapunov stability theory. The experiments under three different discharge profiles are conducted on the Li-ion battery. Through comparisons with a conventional sliding mode observer (CSMO) and adaptive extended Kalman filter (AEKF), the superiority of the proposed observer for SOC estimation is validated.

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

“…In [148], the dual SMO was proposed considering the capacity fading effect for SOC estimation. To improve the efficiency, accuracy, and robustness of the SMO, some variants, such as the adaptive gain SMO (AGSMO) [149], adaptive switching gain SMO (ASGSMO) [150], super-twisting SMO (STSMO) [151], fractional order SMO (FOSMO) [152], and Fuzzy SMO (FSMO) [153], have been reported in the literature. Table 13 shows the comparison of the SMO-based SOC estimation methods.…”

“…Kim et al 2008 [140] ME ≤ ± 3% Ning et al 2016 [143] ME ≤ ± 2% Ma et al 2016 [144] ME ≤ ± 3% Xia et al 2017 [145] ≤± 0.86% Chen et al 2013 [150] Unspecified Zhong et al 2017 [153] ≤± 1% Huangfu et al 2018 [151] ≤± 2% Chen et al 2018 [148] ≤± 1%…”

State-Of-Charge Estimation for Lithium-Ion Battery Using Improved DUKF Based on State-Parameter Separation

“…The IAUKF-UKF proposed in this paper ranked the two filters to turn the parameter modification on only when the voltage prediction converges to the observed value. In order to verify the reliability of the proposed algorithm, different initial values of SoC (the reference is [1]) are set in this section. The convergence performance of AUKF-UKF and IAUKF-UKF are compared under DST test.…”

“…Accurate estimation of SoC can not only be used as an important basis for drivers to judge the endurance of batteries, but also for achieving equalization control, overcharge and discharge protection, overheat protection and other functions in battery management system. However, as an artificially defined variable, SoC can only be calculated by voltage, current and temperature of batteries [1]. Based on this, the methods for SoC estimation in recent years can be divided into those based on characterization parameters and definitions, data-driven and battery modeling theory [2].…”

Online State of Charge and State of Health Estimation for a Lithium-Ion Battery Based on a Data–Model Fusion Method

“…Amongst others, the ECMs have a better trade-off between accuracy and complexity and thus are favorable candidates for application in micro-controller units. Generally, ECMs are used to simulate the dynamics of an LIB, while the states of interest are estimated in real time with various observers, such as the Luenberger observer [20], the extended Kalman filter (EKF) [21][22][23], the square root cubature Kalman filter [24], the unscented Kalman filter (UKF) [25], the sliding mode observer (SMO) [26], the particle filter (PF) [27], and the nonlinear observer [28]. For these methods, the ECMs are calibrated offline and the model parameters are assumed to be fixed during operation.…”