Estimating the State-of-Charge of Lithium-Ion Battery Using an H-Infinity Observer Based on Electrochemical Impedance Model

Abstract

The lithium-ion batteries in the electric vehicles are nonlinear systems with complex electrochemical dynamics, and estimation of battery state-of-charge (SOC) is affected by factors such as environmental temperature and battery current. Considering the above problems, the accurate estimation of battery SOC has always been a difficult and the critical issue of battery management system (BMS). In this paper, the constant phase element (CPE) is introduced to the traditional time domain circuit model by analyzing the electrochemical impedance spectra of lithium-ion batteries. Accordingly, an equivalent circuit model based on electrochemical impedance is constructed by using fractional order theory, which has specific physical significant, leading to the improved estimation accuracy to represent battery voltage. Moreover, the polarization resistance in the model is replaced by Butler-Volmer (BV) equation, which can solve the problem caused by large current and temperature variation during the actual operation of electric vehicles. Next, based on the model, an H∞ observer is designed for battery SOC estimation, and the proposed SOC observer is tested by real-time experimental data of battery. The efficiency of the proposed model and observer are validated by some simulations and experiment tests.INDEX TERMS Lithium-ion batteries, Butler-Volmer equation, state of charge estimation, electrochemical impedance model, fractional calculus, H∞ observer.

State of Charge Estimation of Lithium-Ion Batteries Using LSTM and NARX Neural Networks

“…The estimation results and error are shown in Fig. (12)(13)(14)(15). Due to external interference and temperature variation, the real rate of SOC presents a nonlinear and unstable trend.…”

“…The ampere hour integration method uses the definition of SOC to estimate the SOC. However, an accurate value of the initial SOC is difficult to obtain, and the degradation of 2017 1 battery performance affects the accurate estimation of SOC [12]. Physical model methods include the Kalman filter [13,14], sliding mode observer [15,16], and particle filter [17,18], among other methods.…”

Augmented system model-based online collaborative determination of lead–acid battery states for energy management of vehicles

“…whereÃ,B,C,D,F, andG are the coefficient matrices, which are listed in (23). For system (21), the current values of the system and input can be taken as x(0) and u(0), respectively.…”

“…In addition to these circuit model methods, the methods using electrochemical characteristics for battery state estimation have also been studied. In Chen et al 23 an observer based on the enhanced single-particle model is constructed. This model uses the growth of the solid electrolyte interphase layer to estimate the SOC and aging-sensitive transport parameters.…”

An Enhanced Lithium-Ion Battery Model for Estimating the State of Charge and Degraded Capacity Using an Optimized Extended Kalman Filter

“…Researchers have proposed and employed several models for lithium-ion batteries that emulate the operational behavior. In general, the models fall into two types; the first uses the electrochemical characteristics of the lithium-ion cell [9,38]. [19,23] This type uses equations to describe the electrochemical reactions in lithium-ion batteries, such as intercalations, diffusions, and migrations.…”