A comparative study of observer design techniques for state of charge estimation in electric vehicles

A well-known Luenburger observer and two sliding mode observers (SMO) for battery state of charge (SOC) estimation based on an improved battery equivalent circuit are presented. The comparison of their SOC estimation results is discussed. The testing data under different discharge current profiles generated by DUALFOIL battery simulation program are used to extract the circuit model parameters and verify the effectiveness of the observers for the SOC estimation. It shows that two sliding mode based SOC observers have robust tracking performance and provide more accurate SOC estimation in electric vehicle driving conditions.

[1]  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.

[2]  Il-Song Kim,et al.  Nonlinear State of Charge Estimator for Hybrid Electric Vehicle Battery , 2008, IEEE Transactions on Power Electronics.

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

[4]  D. Luenberger.  Observers for multivariable systems , 1966 .

[5]  Vadim I. Utkin,et al.  Sliding Modes and their Application in Variable Structure Systems , 1978 .

[6]  Il-Song Kim,et al.  The novel state of charge estimation method for lithium battery using sliding mode observer , 2006 .

[7]  M. Doyle,et al.  Simulation and Optimization of the Dual Lithium Ion Insertion Cell , 1994 .

[8]  G. Plett.  Kalman-Filter SOC Estimation for LiPB HEV Cells , 2002 .

[9]  M. Doyle,et al.  Modeling of Galvanostatic Charge and Discharge of the Lithium/Polymer/Insertion Cell , 1993 .

[10]  J. Newman,et al.  Thermal Modeling of the Lithium/Polymer Battery .1. Discharge Behavior of a Single-Cell , 1995 .

[11]  S. Spurgeon,et al.  On the development of discontinuous observers , 1994 .

[12]  Ching Chuen Chan,et al.  Adaptive neuro-fuzzy modeling of battery residual capacity for electric vehicles , 2002, IEEE Trans. Ind. Electron..

[13]  Gregory L. Plett,et al.  Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 1: Introduction and state estimation , 2006 .

[14]  Jean-Marie Tarascon,et al.  Performance of Bellcore's plastic rechargeable Li-ion batteries , 1996 .

[15]  C. Moo,et al.  Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries , 2009 .

[16]  K. T. Chau,et al.  A new battery available capacity indicator for electric vehicles using neural network , 2002 .

[17]  S. Rodrigues,et al.  A review of state-of-charge indication of batteries by means of a.c. impedance measurements , 2000 .

[18]  S.M.T. Bathaee,et al.  Predicting state of charge of lead-acid batteries for hybrid electric vehicles by extended Kalman filter , 2008 .

[19]  Vadim I. Utkin,et al.  Sliding Modes in Control and Optimization , 1992, Communications and Control Engineering Series.

[20]  Myoungho Sunwoo,et al.  State-of-charge estimation of lead-acid batteries using an adaptive extended Kalman filter , 2009 .

[21]  Gregory L. Plett,et al.  Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 2: Simultaneous state and parameter estimation , 2006 .