Battery state of charge estimation based on a combined model of Extended Kalman Filter and neural networks

• Published in: The 2011 International Joint Conference on Neural Networks pp. 2156 - 2163
• Main Authors: Zhihang Chen, Shiqi Qiu, Masrur, M. A., Murphey, Y. L.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-07-2011
• Subjects: Batteries; Battery charge measurement; battery SOC; Discharges; Estimation; Hysteresis; intelligent battery management; Kalman filtering; Kalman filters; neural networks; System-on-a-chip
• ISBN: 1424496357; 9781424496358
• ISSN: 2161-4393; 2161-4407
• DOI: 10.1109/IJCNN.2011.6033495

This paper presents our research in battery State of Charge (SOC) estimation for intelligent battery management. Our research focus is to investigate online dynamic SOC estimation using a combination of Kalman filtering and a neural network. First, we developed a method to model battery hysteresis effects using Extended Kalman Filter (EKF). Secondly, we designed a SOC estimation model, NN-EKF model, that incorporates the estimation made by the EKF into a neural network. The proposed methods have been evaluated using real data acquired from two different batteries, a lithium-ion battery U1-12XP and a NiMH battery with 1.2 V and 3.4 Ah. Our experiments show that our EKF method developed to model battery hysteresis based on separated charge and discharge Open Circuit Voltage (OCV) curves gave the top performances in estimating SOC when compared with other advanced methods. Secondly, the NN-EKF model for SOC estimation gave the best SOC estimation with and without temperature data.