On-line estimation of lithium-ion battery impedance parameters using a novel varied-parameters approach

In this paper a novel technique for on-line estimation of impedance parameters of a battery equivalent circuit model (ECM) is presented. The ECM is often used as a basis of battery management algorithms to calculate the state of charge or the maximal available power. Especially the latter requires that the parameters of the ECM describe precisely the battery impedance. Unfortunately, the battery impedance changes over the battery lifetime due to aging. Therefore, the parameters of the ECM must be updated continuously. In this paper such an update is achieved using a novel approach that in addition allows considering current dependency of the battery impedance. The basic idea is that the change of the battery voltage under load is predicted using the ECM with different parameter sets during a short time period. The predicted voltage is then compared to the sensor data and the best parameter set is selected. It is then used as a basis in the next adaption step. A necessary strategy for building parameter sets is implemented for a fast and efficient convergence which results in an accurate parameter estimation. The algorithm runs on a standard 16 bit 80 MHz microcontroller at only 16% processor load.

[1]  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 .

[2]  Dirk Uwe Sauer,et al.  Impedance based battery monitoring , 2008 .

[3]  Kai Ding,et al.  Battery-Management System (BMS) and SOC Development for Electrical Vehicles , 2011, IEEE Transactions on Vehicular Technology.

[4]  S. Haykin.  Kalman Filtering and Neural Networks , 2001 .

[5]  T. M. Jahns,et al.  Improved nonlinear model for electrode voltage-current relationship for more consistent online battery system identification , 2011, 2011 IEEE Energy Conversion Congress and Exposition.

[6]  Ganesan Nagasubramanian,et al.  Accelerated calendar and pulse life analysis of lithium-ion cells , 2003 .

[7]  Xiaosong Hu,et al.  Recursive calibration for a lithium iron phosphate battery for electric vehicles using extended Kalman filtering , 2011 .

[8]  M. Broussely,et al.  Main aging mechanisms in Li ion batteries , 2005 .

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

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

[11]  Dirk Uwe Sauer,et al.  Development of a lifetime prediction model for lithium-ion batteries based on extended accelerated aging test data , 2012 .

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

[13]  Xiaosong Hu,et al.  Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for elec , 2011 .

[14]  S. Rael,et al.  State Estimation of a Lithium-Ion Battery Through Kalman Filter , 2007, 2007 IEEE Power Electronics Specialists Conference.

[15]  Bin Jia,et al.  On-line battery state of charge estimation using Gauss-Hermite quadrature filter , 2012, 2012 Twenty-Seventh Annual IEEE Applied Power Electronics Conference and Exposition (APEC).

[16]  Mark W. Verbrugge,et al.  Adaptive, multi-parameter battery state estimator with optimized time-weighting factors , 2007 .

[17]  I. Bloom,et al.  Performance degradation of high-power lithium-ion cells—Electrochemistry of harvested electrodes , 2007 .

[18]  Jonghoon Kim,et al.  State-of-Charge Estimation and State-of-Health Prediction of a Li-Ion Degraded Battery Based on an EKF Combined With a Per-Unit System , 2011, IEEE Transactions on Vehicular Technology.

[19]  D. A. Noren,et al.  Clarifying the Butler–Volmer equation and related approximations for calculating activation losses in solid oxide fuel cell models , 2005 .

[20]  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 .

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

[22]  Mark W. Verbrugge,et al.  Multi-parameter battery state estimator based on the adaptive and direct solution of the governing differential equations , 2011 .

[23]  Qiao Yan Chen,et al.  Battery SOC Estimation Based on Multi-Model Adaptive Kalman Filter , 2011 .

[24]  Bo-Hyung Cho,et al.  Li-Ion Battery SOC Estimation Method based on the Reduced Order Extended Kalman Filtering , 2006 .

[25]  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 .

[26]  M. Safari,et al.  Aging of a Commercial Graphite/LiFePO4 Cell , 2011 .

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

[28]  Dirk Uwe Sauer,et al.  Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application , 2013 .