An online SOC and capacity estimation method for aged lithium-ion battery pack considering cell inconsistency

Abstract For lithium-ion battery packs, especially aged lithium-ion batteries, the inconsistencies in State-of-Charge (SOC), model parameter and capacity between cells cannot be ignored. In order to accurately estimate the SOC and capacity of each cell in the lithium-ion battery pack online, a "Special and Difference (S&D)" model, i.e. a serial-connected battery pack model, is established based on a second-order equivalent circuit model as cell model. The multi-time scale extended Kalman filter algorithm is proposed based on “S&D” model to estimate the SOC, model parameter and capacity of each cell in the battery pack. The proposed algorithm involves three time dimensions: a short time scale which contains special cell's SOC and model parameter estimation, a middle time scale which contains the remaining cells’ SOC and model parameter estimation, and a long time scale which contains all cells’ capacity estimation. The multi-time scale extended Kalman filter algorithm for aged battery pack is verified under two dynamic conditions. The results show that the SOC estimation error of each cell in the battery pack is within 5% in the whole testing period and it is within 3% when the later capacity estimation process keeps stable. In addition, the number of the cells with maximum and minimum capacity can be accurately identified after the middle stage of the capacity estimation process, which is significant for the consistency management of the battery pack.

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

[2]  Gregory L. Plett,et al.  Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation , 2004 .

[3]  Zechang Sun,et al.  ANFIS (adaptive neuro-fuzzy inference system) based online SOC (State of Charge) correction considering cell divergence for the EV (electric vehicle) traction batteries , 2015 .

[4]  J. Tarascon,et al.  Comparison of Modeling Predictions with Experimental Data from Plastic Lithium Ion Cells , 1996 .

[5]  Jianqiu Li,et al.  Enhancing the estimation accuracy in low state-of-charge area: A novel onboard battery model through surface state of charge determination , 2014 .

[6]  Jianqiu Li,et al.  Investigating the error sources of the online state of charge estimation methods for lithium-ion batteries in electric vehicles , 2018 .

[7]  Chenglin Liao,et al.  Research on Improved EKF Algorithm Applied on Estimate EV Battery SOC , 2010, 2010 Asia-Pacific Power and Energy Engineering Conference.

[8]  A. Stefanopoulou,et al.  Lithium-Ion Battery State of Charge and Critical Surface Charge Estimation Using an Electrochemical Model-Based Extended Kalman Filter , 2010 .

[9]  Shengbo Eben Li,et al.  Advanced Machine Learning Approach for Lithium-Ion Battery State Estimation in Electric Vehicles , 2016, IEEE Transactions on Transportation Electrification.

[10]  Bor Yann Liaw,et al.  On state-of-charge determination for lithium-ion batteries , 2017 .

[11]  Valerie H. Johnson,et al.  Battery performance models in ADVISOR , 2002 .

[12]  Xiaosong Hu,et al.  State estimation for advanced battery management: Key challenges and future trends , 2019, Renewable and Sustainable Energy Reviews.

[13]  Hongwen He,et al.  A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles , 2014 .

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

[15]  Michael S. Mazzola,et al.  Accurate battery pack modeling for automotive applications , 2013 .

[16]  Matthieu Dubarry,et al.  Origins and accommodation of cell variations in Li‐ion battery pack modeling , 2010 .

[17]  Abraham Mansouri,et al.  A circuit-based approach for electro-thermal modeling of lithium-ion batteries , 2016, 2016 32nd Thermal Measurement, Modeling & Management Symposium (SEMI-THERM).

[18]  Delphine Riu,et al.  A review on lithium-ion battery ageing mechanisms and estimations for automotive applications , 2013 .

[19]  David A. Stone,et al.  New Battery Model and State-of-Health Determination Through Subspace Parameter Estimation and State-Observer Techniques , 2009, IEEE Transactions on Vehicular Technology.

[20]  Rui Xiong,et al.  A data-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles , 2014 .

[21]  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 .

[22]  Guangzhong Dong,et al.  A method for state of energy estimation of lithium-ion batteries based on neural network model , 2015 .

[23]  Hao Yuan,et al.  Co-Estimation of State of Charge and State of Health for Lithium-Ion Batteries Based on Fractional-Order Calculus , 2018, IEEE Transactions on Vehicular Technology.

[24]  Taedong Goh,et al.  Capacity estimation algorithm with a second-order differential voltage curve for Li-ion batteries with NMC cathodes , 2017 .

[25]  Huei Peng,et al.  On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression , 2013 .

[26]  Hongwen He,et al.  Online estimation of model parameters and state-of-charge of LiFePO4 batteries in electric vehicles , 2012 .

[27]  Huajing Fang,et al.  An integrated unscented kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity prediction , 2015, Reliab. Eng. Syst. Saf..

[28]  Stephen Yurkovich,et al.  Electro-thermal battery model identification for automotive applications , 2011 .

[29]  Weijun Gu,et al.  Online cell SOC estimation of Li-ion battery packs using a dual time-scale Kalman filtering for EV applications , 2012 .

[30]  Yifeng Guo,et al.  SoC Estimation of Lithium Battery Based on Improved BP Neural Network , 2017 .

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

[32]  Jae Sik Chung,et al.  A Multiscale Framework with Extended Kalman Filter for Lithium-Ion Battery SOC and Capacity Estimation , 2010 .