State of Charge Estimation for Lithium-Ion Battery Based on Nonlinear Observer: An H∞ Method

Abstract

This work is focused on the state of charge (SOC) estimation of a lithium-ion battery based on a nonlinear observer. First, the second-order resistor-capacitor (RC) model of the battery pack is introduced by utilizing the physical behavior of the battery. Then, for the nonlinear function of the RC model, a one-sided Lipschitz condition is proposed to ensure that the nonlinear function can play a positive role in the observer design. After that, a nonlinear observer design criterion is presented based on the H ∞ method, which is formulated as linear matrix inequalities (LMIs). Compared with existing nonlinear observer-based SOC estimation methods, the proposed observer design criterion does not depend on any estimates of the unknown variables. Consequently, the convergence of the proposed nonlinear observer is guaranteed for any operating conditions. Finally, both the static and dynamic experimental cases are given to show the efficiency of the proposed nonlinear observer by comparing with the classic extended Kalman filter (EKF).

The equivalent circuit battery model parameter sensitivity analysis for lithium‐ion batteries by Monte Carlo simulation

“…The OCV can also be obtained by HPPC test. This is a standardized testing process; the specific process can be seen in Lee et al and Zhu et al The battery model parameter identification results are displayed in Table .…”

“…For the battery SOC estimation method, extensive efforts have been made. These methods for the battery SOC estimation method can be roughly separated by the conventional method, the model‐based estimation method, and the machine learning algorithm . The coulomb counting (CC) method and the open circuit voltage (OCV) method are the conventional methods.…”

“…The OCV method takes a long time. The CC method cannot overcome the initial error and the accumulated error; the model‐based methods include the Kalman filter (KF) method, the H‐infinity method, the Luenberger filter method, the particle filter (PF), and variations of the above methods . The common feature of the model‐based methods is that they all need to design a filter according to the battery model.…”

Estimating the Lifetime of Wireless Sensor Network Nodes through the Use of Embedded Analytical Battery Models

“…There are several studies dealing with the problem of estimating the SoC in batteries for different types of applications (e.g., electric vehicles) [18][19][20][21]. Nevertheless, the applicability of the reported results within the WSN context is difficult, as both the battery capacities and discharge profiles considered in these works are very different from those found in sensor nodes.…”

Cascaded Fractional Kalman Filtering for State and Current Estimation of Large-Scale Lithium-Ion Battery Packs

“…Being that important, there are various approaches to determine the SOC of a single battery cell [1,2]. Many applications nowadays are based on models to consider the underlying physical dynamics and to be able to deal with measurement errors [2,3]. These models are then used as a basis for state estimation.…”