SOC Estimation of Multiple Lithium-Ion Battery Cells in a Module Using a Nonlinear State Observer and Online Parameter Estimation

In recent years, electric vehicles (EVs), hybrid electric vehicles (HEVs), and plug-in electric vehicles (PEVs) have become very popular. Therefore, the use of secondary batteries exponentially increased in EV systems. Battery fuel gauges determine the amount of charge inside the battery, and how much farther the vehicle can drive itself under specific operating conditions. It is very important to provide accurate state-of-charge (SOC) information of the battery module to the driver, since inaccurate fuel gauges will not be tolerated. In this paper, a model-based approach is proposed to estimate the SOCs of multiple lithium-ion (Li-ion) battery cells, connected in a module in series, by using a nonlinear state observer (NSO) and an online parameter identification algorithm. A simple method of estimating the impedance and SOC of each cell in a module is also presented in this paper, by employing a ratio vector with respect to the reference value. A battery model based on an autoregressive model with exogenous input (ARX) was used with recursive least squares (RLS) for parameter identification, in an effort to guarantee reliable estimation results under various operating conditions. The validity and feasibility of the proposed algorithm were verified by an experimental setup of six Li-ion battery cells connected in a module in series. It was found that, when compared with a simple linear state observer (LSO), an NSO can further reduce the SOC error by 1%.

[1]  Christian Fleischer,et al.  Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles , 2014 .

[2]  Hicham Chaoui,et al.  Aging prediction and state of charge estimation of a LiFePO 4 battery using input time-delayed neural networks , 2017 .

[3]  H. Razik,et al.  Estimation of the SOC and the SOH of li-ion batteries, by combining impedance measurements with the fuzzy logic inference , 2010, IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society.

[4]  Alexander Medvedev,et al.  An observer for systems with nonlinear output map , 2003, Autom..

[5]  Michael A. Roscher,et al.  Error detection for PHEV, BEV and stationary battery systems , 2013 .

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

[7]  Amir Vasebi,et al.  A novel combined battery model for state-of-charge estimation in lead-acid batteries based on extended Kalman filter for hybrid electric vehicle applications , 2007 .

[8]  M. Fowler,et al.  Thermal and electrical performance assessments of lithium-ion battery modules for an electric vehicle under actual drive cycles , 2018, Electric Power Systems Research.

[9]  Roydon Andrew Fraser,et al.  Experimental Measurements of Thermal Characteristics of LiFePO 4 Battery , 2015 .

[10]  Bor Yann Liaw,et al.  Battery management and life prediction , 2007 .

[11]  M. Ouyang,et al.  State-of-charge inconsistency estimation of lithium-ion battery pack using mean-difference model and extended Kalman filter , 2018 .

[12]  Andreas Rauh,et al.  Nonlinear state observers and extended Kalman filters for battery systems , 2013, Int. J. Appl. Math. Comput. Sci..

[13]  Wei Sun,et al.  State of charge estimation of lithium-ion batteries using optimized Levenberg-Marquardt wavelet neural network , 2018, Energy.

[14]  A. Salkind,et al.  Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology , 1999 .

[15]  T. Weigert,et al.  State-of-charge prediction of batteries and battery–supercapacitor hybrids using artificial neural networks , 2011 .

[16]  Mohamed Becherif,et al.  Experimental validation for Li-ion battery modeling using Extended Kalman Filters , 2017 .

[17]  Zhang Peng,et al.  State of Charge Estimation for Li-ion Battery Based on Extended Kalman Filter , 2017 .

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

[19]  Jesús Fraile-Ardanuy,et al.  Ambient Systems , Networks and Technologies ( ANT 2018 ) Using Dynamic Neural Networks for Battery State of Charge Estimation in Electric Vehicles , 2018 .

[20]  Zheng Chen,et al.  State of Charge Estimation of Lithium-Ion Batteries in Electric Drive Vehicles Using Extended Kalman Filtering , 2013, IEEE Transactions on Vehicular Technology.

[21]  Bijender Kumar,et al.  FPGA-based design of advanced BMS implementing SoC/SoH estimators , 2018, Microelectron. Reliab..

[22]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .