Comparison of Nonlinear Filtering Methods for Estimating the State of Charge of Li4Ti5O12Lithium-Ion Battery

Jianping Gao,Hongwen He

doi:10.1155/2015/485216

Abstract

Accurate state of charge (SoC) estimation is of great significance for the lithium-ion battery to ensure its safety operation and to prevent it from overcharging or overdischarging. To achieve reliable SoC estimation for Li4Ti5O12lithium-ion battery cell, three filtering methods have been compared and evaluated. A main contribution of this study is that a general three-step model-based battery SoC estimation scheme has been proposed. It includes the processes of battery data measurement, parametric modeling, and model-based SoC estimation. With the proposed general scheme, multiple types of model-based SoC estimators have been developed and evaluated for battery management system application. The detailed comparisons on three advanced adaptive filter techniques, which include extend Kalman filter, unscented Kalman filter, and adaptive extend Kalman filter (AEKF), have been implemented with a Li4Ti5O12lithium-ion battery. The experimental results indicate that the proposed model-based SoC estimation approach with AEKF algorithm, which uses the covariance matching technique, performs well with good accuracy and robustness; the mean absolute error of the SoC estimation is within 1% especially with big SoC initial error.

Highlights

To address the two urgent tasks nowadays of protecting the environment and achieving energy sustainability, it is of a strategic significance on a global scale to replace the oil-dependent vehicles with electric vehicles
We will discuss whether the adaptive extend Kalman filter (AEKF)-based state of charge (SoC) estimation can achieve accurate SoC estimation with the erroneous initial SoC
We can observe that the SoC estimation errors are less than 1%

Summary

Introduction

Lithium-ion batteries are currently considered to be the development trends of traction batteries and have been widely used in plugin hybrid electric vehicles (PHEVs) due to its high power and energy density, its high voltage, being pollution-free, having no memory effect, its long cycle life, and its low self-discharge [1,2,3]. It is difficult to accurately estimate SoC, because SoC is an inner state of each battery cell which cannot be directly measured and is greatly influenced by many factors, including ambient temperature, discharging current, and battery aging [4, 5]. The battery SoC has to be estimated with specific estimation techniques according to measured battery parameters, such as voltage, current, and temperature

Results

Discussion

Conclusion