Fast UD factorization-based RLS online parameter identification for model-based condition monitoring of lithium-ion batteries

This paper proposes a novel parameter identification method for model-based condition monitoring of lithium-ion batteries. A fast UD factorization-based recursive least square (FUDRLS) algorithm is developed for identifying time-varying electrical parameters of a battery model. The proposed algorithm can be used for online state of charge, state of health and state of power estimation for lithium-ion batteries. The proposed method is more numerically stable than conventional recursive least square (RLS)-based parameter estimation methods and faster than the existing UD RLS-based method. Moreover, a variable forgetting factor (VF) is included in the FUDRLS to optimize its performance. Due to its low complexity and numerical stability, the proposed method is suitable for the real-time embedded Battery Management System (BMS). Simulation and experimental results for a polymer lithium-ion battery are provided to validate the proposed method.

[1]  Real-time SOC and SOH estimation for EV Li-ion cell using online parameters identification , 2012, 2012 IEEE Energy Conversion Congress and Exposition (ECCE).

[2]  A. Kohli,et al.  Performance Evaluation of Adaptive Polynomial Filtering Algorithms for Time-varying Parameter Estimation Master of Engineering , 2022 .

[3]  M. A. Roscher,et al.  Reliable State Estimation of Multicell Lithium-Ion Battery Systems , 2011, IEEE Transactions on Energy Conversion.

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

[5]  T. R. Fortescue,et al.  Implementation of self-tuning regulators with variable forgetting factors , 1981, Autom..

[6]  Xidong Tang,et al.  Li-ion battery parameter estimation for state of charge , 2011, Proceedings of the 2011 American Control Conference.

[7]  G. Bierman.  Factorization methods for discrete sequential estimation , 1977 .

[8]  Y. Nishi.  Lithium ion secondary batteries; past 10 years and the future , 2001 .

[9]  Thomas Kailath,et al.  A parallel architecture for Kalman filter measurement update and parameter estimation , 1986, Autom..

[10]  Jian Li.  Discrete-Time Signals and Systems , 1999 .

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

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

[13]  Luigi Chisci,et al.  Parallel architectures for RLS with directional forgetting , 1987 .

[14]  Wei Qiao,et al.  Online SOC and SOH estimation for multicell lithium-ion batteries based on an adaptive hybrid battery model and sliding-mode observer , 2013, 2013 IEEE Energy Conversion Congress and Exposition.

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

[16]  Ming Jiang,et al.  Research on PNGV model parameter identification of LiFePO4 Li-ion battery based on FMRLS , 2011, 2011 6th IEEE Conference on Industrial Electronics and Applications.

[17]  T. M. Jahns,et al.  Implementation of online battery state-of-power and state-of-function estimation in electric vehicle applications , 2012, 2012 IEEE Energy Conversion Congress and Exposition (ECCE).

[18]  Nasir Ahmed,et al.  Discrete Time Signals and Systems , 1983 .

[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]  Gregory L. Plett,et al.  Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 1. Background , 2004 .

[21]  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 .

[22]  J. Vetter,et al.  OCV Hysteresis in Li-Ion Batteries including Two-Phase Transition Materials , 2011 .

[23]  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 .