A method for state-of-charge estimation of Li-ion batteries based on multi-model switching strategy

The accurate state-of-charge (SOC) estimation and real-time performance are critical evaluation indexes for Li-ion battery management systems (BMS). High accuracy algorithms often take long program execution time (PET) in the resource-constrained embedded application systems, which will undoubtedly lead to the decrease of the time slots of other processes, thereby reduce the overall performance of BMS. Considering the resource optimization and the computational load balance, this paper proposes a multi-model switching SOC estimation method for Li-ion batteries. Four typical battery models are employed to build a close-loop SOC estimation system. The extended Kalman filter (EKF) method is employed to eliminate the effect of the current noise and improve the accuracy of SOC. The experiments under dynamic current conditions are conducted to verify the accuracy and real-time performance of the proposed method. The experimental results indicate that accurate estimation results and reasonable PET can be obtained by the proposed method.

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

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

[3]  Zonghai Chen,et al.  A new model for State-of-Charge (SOC) estimation for high-power Li-ion batteries , 2013 .

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

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

[6]  Hongwen He,et al.  Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles , 2012 .

[7]  Seongjun Lee,et al.  Discrimination of Li-ion batteries based on Hamming network using discharging–charging voltage pattern recognition for improved state-of-charge estimation , 2011 .

[8]  Zonghai Chen,et al.  A novel temperature-compensated model for power Li-ion batteries with dual-particle-filter state of charge estimation , 2014 .

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

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

[11]  Dirk Uwe Sauer,et al.  Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries , 2013 .

[12]  Zonghai Chen,et al.  A method for state of energy estimation of lithium-ion batteries at dynamic currents and temperatures , 2014 .

[13]  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 .

[14]  Zonghai Chen,et al.  A method for the estimation of the battery pack state of charge based on in-pack cells uniformity analysis , 2014 .

[15]  Hongwen He,et al.  Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach , 2011 .

[16]  D. W. Malone,et al.  An introduction to the application of interpretive structural modeling , 1975, Proceedings of the IEEE.

[17]  Yih-Fang Huang,et al.  Asymptotically convergent modified recursive least-squares with data-dependent updating and forgetting factor , 1985, 1985 24th IEEE Conference on Decision and Control.

[18]  Á.G. Miranda,et al.  Integrated modeling for the cyclic behavior of high power Li-ion batteries under extended operating conditions , 2013 .

[19]  Mehrdad Mastali,et al.  Battery state of the charge estimation using Kalman filtering , 2013 .

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

[21]  Lennart Ljung,et al.  The Extended Kalman Filter as a Parameter Estimator for Linear Systems , 1979 .

[22]  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 .

[23]  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 .

[24]  B. Scrosati,et al.  Lithium batteries: Status, prospects and future , 2010 .

[25]  Chenbin Zhang,et al.  A method for joint estimation of state-of-charge and available energy of LiFePO4 batteries , 2014 .

[26]  Wei He,et al.  State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures , 2014 .

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

[28]  Ottorino Veneri,et al.  Experimental analysis on the performance of lithium based batteries for road full electric and hybrid vehicles , 2014 .

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

[30]  Zhang Chenbin,et al.  Method to calibrate and estimate Li-ion battery state of charge based on charging method , 2014 .