Fuzzy modelling for the state-of-charge estimation of lead-acid batteries

Abstract This paper introduces a novel fuzzy model based structure for the characterisation of discharge processes in lead-acid batteries. This structure is based on a fuzzy model that characterises the relationship between the battery open-circuit voltage (Voc), the state of charge (SoC), and the discharge current. The model is identified and validated using experimental data that is obtained from an experimental system designed to test battery banks with several charge/discharge profiles. For model identification purposes, two standard experimental tests are implemented; one of these tests is used to identify the Voc–SoC curve, while the other helps to identify additional parameters of the model. The estimation of SoC is performed using an Extended Kalman Filter (EKF) with a state transition equation that is based on the proposed fuzzy model. Performance of the proposed estimation framework is compared with other parametric approaches that are inspired on electrical equivalents; e.g., Thevenin, Plett, and Copetti.

[1]  Long Xu,et al.  Kalman filtering state of charge estimation for battery management system based on a stochastic fuzzy neural network battery model , 2012 .

[2]  M. Barak,et al.  Power Sources 4 , 1974 .

[4]  Seongjun Lee,et al.  State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge , 2008 .

[5]  Gregory L. Plett,et al.  Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and identification , 2004 .

[6]  Amir Vasebi,et al.  A novel combined battery model for state-of-charge estimation in lead-acid batteries based on extended Kalman filter for hybrid electric vehicle applications , 2007 .

[7]  C. Fennie,et al.  Fuzzy logic modelling of state-of-charge and available capacity of nickel/metal hydride batteries , 2004 .

[8]  Tingshu Hu,et al.  Simple Analytical Method for Determining Parameters of Discharging Batteries , 2011, IEEE Transactions on Energy Conversion.

[9]  Mark Sumner,et al.  Optimal management of stationary lithium-ion battery system in electricity distribution grids , 2013 .

[10]  Krishna R. Pattipati,et al.  System Identification and Estimation Framework for Pivotal Automotive Battery Management System Characteristics , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[11]  Zonghai Chen,et al.  A new model for State-of-Charge (SOC) estimation for high-power Li-ion batteries , 2013 .

[12]  Dan Boneh,et al.  On genetic algorithms , 1995, COLT '95.

[13]  Andrew G. Glen,et al.  APPL , 2001 .

[14]  Jorge F. Silva,et al.  Particle-Filtering-Based Prognosis Framework for Energy Storage Devices With a Statistical Characterization of State-of-Health Regeneration Phenomena , 2013, IEEE Transactions on Instrumentation and Measurement.

[15]  Rodrigo Palma-Behnke,et al.  A Microgrid Energy Management System Based on the Rolling Horizon Strategy , 2013, IEEE Transactions on Smart Grid.

[16]  Souradip Malkhandi,et al.  Fuzzy logic-based learning system and estimation of state-of-charge of lead-acid battery , 2006, Eng. Appl. Artif. Intell..

[17]  Jonghoon Kim,et al.  State-of-Charge Estimation and State-of-Health Prediction of a Li-Ion Degraded Battery Based on an EKF Combined With a Per-Unit System , 2011, IEEE Transactions on Vehicular Technology.

[18]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[19]  Henrik Schiøler,et al.  American Control Conference (ACC), 2012 , 2012 .

[20]  Suleiman Abu-Sharkh,et al.  Rapid test and non-linear model characterisation of solid-state lithium-ion batteries , 2004 .

[21]  Karl Johan Åström,et al.  Computer-Controlled Systems: Theory and Design , 1984 .

[22]  Robert Babuska,et al.  Fuzzy Modeling for Control , 1998 .

[23]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[24]  Khadija El Kadri Benkara,et al.  Impedance Observer for a Li-Ion Battery Using Kalman Filter , 2009, IEEE Transactions on Vehicular Technology.

[25]  Shriram Santhanagopalan,et al.  State of charge estimation using an unscented filter for high power lithium ion cells , 2010 .

[26]  Vivek Agarwal,et al.  Development and Validation of a Battery Model Useful for Discharging and Charging Power Control and Lifetime Estimation , 2010, IEEE Transactions on Energy Conversion.

[27]  Pritpal Singh,et al.  Design and implementation of a fuzzy logic-based state-of-charge meter for Li-ion batteries used in portable defibrillators , 2006 .

[28]  Yuang-Shung Lee,et al.  A Merged Fuzzy Neural Network and Its Applications in Battery State-of-Charge Estimation , 2007, IEEE Transactions on Energy Conversion.

[29]  Andreas Jossen,et al.  Methods for state-of-charge determination and their applications , 2001 .

[30]  Gautam Biswas,et al.  Annual Conference of the Prognostics and Health Management Society , 2011 Deriving Bayesian Classifiers from Flight Data to Enhance Aircraft Diagnosis , 2011 .

[31]  A. Salkind,et al.  Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology , 1999 .

[32]  F. Chenlo,et al.  Lead/acid batteries for photovoltaic applications. Test results and modeling , 1994 .

[33]  Mohammad Farrokhi,et al.  State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF , 2010, IEEE Transactions on Industrial Electronics.

[34]  E. Lorenzo,et al.  A general battery model for PV system simulation , 1993 .

[35]  Hongwen He,et al.  Model-based dynamic multi-parameter method for peak power estimation of lithium-ion batteries , 2012 .

[36]  Xibo Yuan,et al.  IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society , 2013 .