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Shen, W.X., Chau, K.T., Chan, C.C. and Lo, E.W.C. (2005) Neural Network-Based Residual Capacity Indicator for Nickel-Metal Hydride Batteries in Electric Vehicles. IEEE Transactions on Vehicular Technology, 54, 1705-1712.
http://dx.doi.org/10.1109/TVT.2005.853448

has been cited by the following article:

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

  AUTHORS: Ala A. Hussein

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

  JOURNAL NAME: International Journal of Modern Nonlinear Theory and Application, Vol.3 No.5, November 7, 2014

  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.