Adaptive State of Charge Estimation for Li-Ion Batteries Based on an Unscented Kalman Filter with an Enhanced Battery Model

Accurate estimation of the state of charge (SOC) of batteries is one of the key problems in a battery management system. This paper proposes an adaptive SOC estimation method based on unscented Kalman filter algorithms for lithium (Li)-ion batteries. First, an enhanced battery model is proposed to include the impacts due to different discharge rates and temperatures. An adaptive joint estimation of the battery SOC and battery internal resistance is then presented to enhance system robustness with battery aging. The SOC estimation algorithm has been developed and verified through experiments on different types of Li-ion batteries. The results indicate that the proposed method provides an accurate SOC estimation and is computationally efficient, making it suitable for embedded system implementation.

[1]  Yuang-Shung Lee,et al.  Soft Computing for Battery State-of-Charge (BSOC) Estimation in Battery String Systems , 2008, IEEE Transactions on Industrial Electronics.

[2]  Caisheng Wang,et al.  A joint model and SOC estimation method for lithium battery based on the sigma point KF , 2012, 2012 IEEE Transportation Electrification Conference and Expo (ITEC).

[3]  Liang-Rui Chen,et al.  Improving Phase-Locked Battery Charger Speed by Using Resistance-Compensated Technique , 2009, IEEE Trans. Ind. Electron..

[4]  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 .

[5]  Hongjie Wu,et al.  State of Charge Estimation Using the Extended Kalman Filter for Battery Management Systems Based on the ARX Battery Model , 2013 .

[6]  Xiaohong Su,et al.  Prognostics of Lithium-Ion Batteries Based on Battery Performance Analysis and Flexible Support Vector Regression , 2014 .

[7]  M Knauff,et al.  A new battery model for use with an extended Kalman filter state of charge estimator , 2010, Proceedings of the 2010 American Control Conference.

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

[9]  Bizhong Xia,et al.  Comparison Study on Two Model-Based Adaptive Algorithms for SOC Estimation of Lithium-Ion Batteries in Electric Vehicles , 2014 .

[10]  Mohammad Farrokhi,et al.  Online State-of-Health Estimation of VRLA Batteries Using State of Charge , 2013, IEEE Transactions on Industrial Electronics.

[11]  Rudolph van der Merwe,et al.  Sigma-point kalman filters for probabilistic inference in dynamic state-space models , 2004 .

[12]  Rolf Findeisen,et al.  State estimation of a reduced electrochemical model of a lithium-ion battery , 2010, Proceedings of the 2010 American Control Conference.

[13]  Wei He,et al.  State of charge estimation for electric vehicle batteries using unscented kalman filtering , 2013, Microelectron. Reliab..

[14]  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.

[15]  Jie Xu,et al.  Battery Model Parameters Estimation with the Sigma Point Kalman Filter , 2009, 2009 International Conference on Artificial Intelligence and Computational Intelligence.

[16]  Yiran Hu,et al.  Battery state of charge estimation in automotive applications using LPV techniques , 2010, Proceedings of the 2010 American Control Conference.

[17]  H.F. Durrant-Whyte,et al.  A new approach for filtering nonlinear systems , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[18]  Sheldon S. Williamson,et al.  Power-Electronics-Based Solutions for Plug-in Hybrid Electric Vehicle Energy Storage and Management Systems , 2010, IEEE Transactions on Industrial Electronics.

[19]  Michael Osterman,et al.  Prognostics of lithium-ion batteries based on DempsterShafer theory and the Bayesian Monte Carlo me , 2011 .

[20]  Guangjun Liu,et al.  Estimation of Battery State of Charge With $H_{\infty}$ Observer: Applied to a Robot for Inspecting Power Transmission Lines , 2012, IEEE Transactions on Industrial Electronics.

[21]  Chaoyang Wang,et al.  Control oriented 1D electrochemical model of lithium ion battery , 2007 .

[22]  S. Rael,et al.  State Estimation of a Lithium-Ion Battery Through Kalman Filter , 2007, 2007 IEEE Power Electronics Specialists Conference.

[23]  T. Kim,et al.  A Hybrid Battery Model Capable of Capturing Dynamic Circuit Characteristics and Nonlinear Capacity Effects , 2011, IEEE Transactions on Energy Conversion.

[24]  David A. Stone,et al.  Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles , 2005, IEEE Transactions on Vehicular Technology.

[25]  Le Yi Wang,et al.  Enhanced Identification of Battery Models for Real-Time Battery Management , 2011, IEEE Transactions on Sustainable Energy.

[26]  Yakup S. Ozkazanç,et al.  A new online state-of-charge estimation and monitoring system for sealed lead-acid batteries in Telecommunication power supplies , 2005, IEEE Transactions on Industrial Electronics.

[27]  Chunbo Zhu,et al.  State-of-Charge Determination From EMF Voltage Estimation: Using Impedance, Terminal Voltage, and Current for Lead-Acid and Lithium-Ion Batteries , 2007, IEEE Transactions on Industrial Electronics.

[28]  Michael Pecht,et al.  Battery Management Systems in Electric and Hybrid Vehicles , 2011 .

[29]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[30]  Carl E. Rasmussen,et al.  Model based learning of sigma points in unscented Kalman filtering , 2010, 2010 IEEE International Workshop on Machine Learning for Signal Processing.

[31]  Le Yi Wang,et al.  Integrated System Identification and State-of-Charge Estimation of Battery Systems , 2013, IEEE Transactions on Energy Conversion.

[32]  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.

[33]  Ivo Barbi,et al.  Three-Phase Push–Pull DC–DC Converter: Analysis, Design, and Experimentation , 2012, IEEE Transactions on Industrial Electronics.

[34]  Chi Ma,et al.  A Battery-Aware Scheme for Routing in Wireless Ad Hoc Networks , 2011, IEEE Transactions on Vehicular Technology.

[35]  A. Cruden,et al.  An Improved Lead–Acid Battery Pack Model for Use in Power Simulations of Electric Vehicles , 2012, IEEE Transactions on Energy Conversion.