State of Charge estimation of Li-ion battery in EVs based on second-order sliding mode observer

An accurate State of Charge (SoC) estimation method is one of the most significant and difficult techniques to promote the commercialization of electric vehicles. A novel approach based on second-order sliding mode observer for battery state of charge (SOC) estimation has been proposed. The Thevenin equivalent circuit model is selected to model the li-ion battery and cooperative particle swarm optimization parameter identification technique is then utilized to estimate the parametersof the battery model. The performances of the algorithm are validated through some experiments and simulations. Validation results show that the proposed SOC estimation algorithm can achieve an acceptable accuracy within the error less than 2%.

[1]  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).

[2]  Arie Levant,et al.  Higher-order sliding modes, differentiation and output-feedback control , 2003 .

[3]  Leonid M. Fridman,et al.  Second-order sliding-mode observer for mechanical systems , 2005, IEEE Transactions on Automatic Control.

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

[5]  Dean Patterson,et al.  Use of lithium-ion batteries in electric vehicles , 2000 .

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

[7]  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 .

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

[9]  Seongjun Lee,et al.  Discrimination of Li-ion batteries based on Hamming network using discharging–charging voltage pattern recognition for improved state-of-charge estimation , 2011 .

[10]  Frank C. Walsh,et al.  Energy and Battery Management of a Plug-In Series Hybrid Electric Vehicle Using Fuzzy Logic , 2011, IEEE Transactions on Vehicular Technology.

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

[12]  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 .

[13]  Jaime A. Moreno,et al.  A Lyapunov approach to second-order sliding mode controllers and observers , 2008, 2008 47th IEEE Conference on Decision and Control.

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

[15]  Kai Zhao,et al.  Evaluation on State of Charge Estimation of Batteries With Adaptive Extended Kalman Filter by Experiment Approach , 2013, IEEE Transactions on Vehicular Technology.

[16]  张飞,et al.  Battery State Estimation Using Unscented Kalman Filter , 2009 .