Kalman Filters versus Neural Networks in Battery State-of-Charge Estimation: A Comparative Study

Ala A. Hussein^*

Department of Electrical Engineering, UAE University, Al Ain, UAE.

DOI: 10.4236/ijmnta.2014.35022                

Abstract

Battery management systems (BMS) must estimate the state-of-charge (SOC) of the battery accurately to prolong its lifetime and ensure a reliable operation. Since batteries have a wide range of applications, the SOC estimation requirements and methods vary from an application to another. This paper compares two SOC estimation methods, namely extended Kalman filters (EKF) and artificial neural networks (ANN). EKF is a nonlinear optimal estimator that is used to estimate the inner state of a nonlinear dynamic system using a state-space model. On the other hand, ANN is a mathematical model that consists of interconnected artificial neurons inspired by biological neural networks and is used to predict the output of a dynamic system based on some historical data of that system. A pulse-discharge test was performed on a commercial lithium-ion (Li-ion) battery cell in order to collect data to evaluate those methods. Results are presented and compared.

Keywords

Artificial Neural Network (ANN), Battery, Extended Kalman Filter (EKF), State-of-Charge (SOC)

Conflicts of Interest

The authors declare no conflicts of interest.

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