A Novel Real-Time State-of-Health and State-of-Charge Co-Estimation Method for LiFePO4 Battery*

The state of charge (SOC) and state of health (SOH) are two of the most important parameters of Li-ion batteries in industrial production and in practical applications. The real-time estimation for these two parameters is crucial to realize a safe and reliable battery application. However, this is a great problem for LiFePO4 batteries due to the large constant potential plateau in the charge/discharge process. Here we propose a combined SOC and SOH co-estimation method based on the experimental test under the simulating electric vehicle working condition. A first-order resistance-capacitance equivalent circuit is used to model the battery cell, and three parameter values, ohmic resistance (R s ), parallel resistance (R p ) and parallel capacity (C p ), are identified from a real-time experimental test. Finally we find that R p and C p could be utilized to make a judgement on the SOH. More importantly, the linear relationship between C p and the SOC is established to make the estimation of the SOC for the first time.

[1]  Peter Loyson,et al.  The testing of batteries linked to supercapacitors with electrochemical impedance spectroscopy: A comparison between Li-ion and valve regulated lead acid batteries , 2013 .

[2]  Jiahao Li,et al.  Sequential Monte Carlo filter for state estimation of LiFePO4 batteries based on an online updated model , 2014 .

[3]  E.W.C. Lo,et al.  The available capacity computation model based on artificial neural network for lead–acid batteries in electric vehicles , 2000 .

[4]  IL-Song Kim,et al.  A Technique for Estimating the State of Health of Lithium Batteries Through a Dual-Sliding-Mode Observer , 2010, IEEE Transactions on Power Electronics.

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

[6]  Yan Li,et al.  A Novel Battery State-of-Health Estimation Method for Hybrid Electric Vehicles , 2015, IEEE/ASME Transactions on Mechatronics.

[7]  Bo-Hyung Cho,et al.  Li-Ion Battery SOC Estimation Method based on the Reduced Order Extended Kalman Filtering , 2006 .

[8]  D. Sauer,et al.  Dynamic electric behavior and open-circuit-voltage modeling of LiFePO4-based lithium ion secondary batteries , 2011 .

[9]  Jean-Michel Vinassa,et al.  Behavior and state-of-health monitoring of Li-ion batteries using impedance spectroscopy and recurrent neural networks , 2012 .

[10]  Michael Buchholz,et al.  State-of-health monitoring of lithium-ion batteries in electric vehicles by on-board internal resistance estimation , 2011 .

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

[12]  Vojtech Svoboda,et al.  Capacity loss in rechargeable lithium cells during cycle life testing: The importance of determining state-of-charge , 2007 .

[13]  H. Akagi,et al.  State-of-Charge (SOC)-Balancing Control of a Battery Energy Storage System Based on a Cascade PWM Converter , 2009, IEEE Transactions on Power Electronics.

[14]  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 .

[15]  Yi-Hwa Liu,et al.  Search for an Optimal Rapid-Charging Pattern for Li-Ion Batteries Using the Taguchi Approach , 2010, IEEE Transactions on Industrial Electronics.

[16]  R. Spotnitz.  Simulation of capacity fade in lithium-ion batteries , 2003 .

[17]  Phl Peter Notten,et al.  REVIEW ARTICLE: State-of-the-art of battery state-of-charge determination , 2005 .

[18]  P. Regtien,et al.  Modeling Battery Behavior for Accurate State-of-Charge Indication , 2006 .

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

[20]  Matthieu Dubarry,et al.  From single cell model to battery pack simulation for Li-ion batteries , 2009 .

[21]  K. T. Chau,et al.  A new battery capacity indicator for lithium-ion battery powered electric vehicles using adaptive neuro-fuzzy inference system , 2004 .

[22]  Cher Ming Tan,et al.  A practical framework of electrical based online state-of-charge estimation of lithium ion batteries , 2014 .

[23]  Binyu Xiong,et al.  Extended Kalman filter method for state of charge estimation of vanadium redox flow battery using thermal-dependent electrical model , 2014 .

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

[25]  Iryna Snihir,et al.  Battery open-circuit voltage estimation by a method of statistical analysis , 2006 .

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

[27]  Jun Xu,et al.  Online battery state of health estimation based on Genetic Algorithm for electric and hybrid vehicle applications , 2013 .