State-of-charge inconsistency estimation for li-ion battery pack using electrochemical model

In order to maximize capacity utilization and guarantee safe operation of Li-ion battery pack, state-of-charge (SOC) inconsistency estimation is essential. And estimating cell electrochemical internal variables is also the requirement of next generation battery management system (BMS). However, it is challenging for the BMS in electric vehicles due to dynamic current conditions and limited computational resources. This paper proposes a novel approach to estimate SOC inconsistency for Li-ion battery pack with reference-plus-difference model where the reference model represents the overall performance of the battery pack. An electrochemical model is used as the reference model for its high-fidelity, low parameter sensitivity, and the capability of estimating cell electrochemical internal variables. Then SOC difference of each battery is achieved by bias correction technique using readily available measurements. The proposed approach is verified by experiments and simulations which show a satisfactory balance between estimation accuracy and computational burden. Based on the SOC inconsistency, battery equalization can be further implemented.

[1]  Bhaskar Saha,et al.  Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework , 2009, IEEE Transactions on Instrumentation and Measurement.

[2]  Stephen Yurkovich,et al.  Battery cell state-of-charge estimation using linear parameter varying system techniques , 2012 .

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

[4]  Jasim Ahmed,et al.  Algorithms for Advanced Battery-Management Systems , 2010, IEEE Control Systems.

[5]  Karen E. Thomas,et al.  Mathematical Modeling of Lithium Batteries , 2002 .

[6]  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.

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

[8]  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 .

[9]  Gregory L. Plett,et al.  Electrochemical state and internal variables estimation using a reduced-order physics-based model of a lithium-ion cell and an extended Kalman filter , 2015 .

[10]  Hongwen He,et al.  A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique , 2016 .

[11]  Hosam K. Fathy,et al.  Genetic parameter identification of the Doyle-Fuller-Newman model from experimental cycling of a LiFePO4 battery , 2011, Proceedings of the 2011 American Control Conference.

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

[13]  Hongwen He,et al.  State-of-Charge Estimation of the Lithium-Ion Battery Using an Adaptive Extended Kalman Filter Based on an Improved Thevenin Model , 2011, IEEE Transactions on Vehicular Technology.

[14]  Hongwen He,et al.  Model-based dynamic multi-parameter method for peak power estimation of lithium-ion batteries , 2012 .

[15]  Hosam K. Fathy,et al.  Battery State of Health and Charge Estimation Using Polynomial Chaos Theory , 2013 .

[16]  Xiaosong Hu,et al.  A comparative study of equivalent circuit models for Li-ion batteries , 2012 .

[17]  Jianqiu Li,et al.  Cell state-of-charge inconsistency estimation for LiFePO4 battery pack in hybrid electric vehicles using mean-difference model , 2013 .

[18]  Jianqiu Li,et al.  Lithium ion battery pack power fade fault identification based on Shannon entropy in electric vehicles , 2013 .

[19]  Miroslav Krstic,et al.  Battery State Estimation for a Single Particle Model With Electrolyte Dynamics , 2017, IEEE Transactions on Control Systems Technology.

[20]  Jiahao Li,et al.  Multicell state estimation using variation based sequential Monte Carlo filter for automotive battery packs , 2015 .