Development of an Intelligent Charger with a Battery Diagnosis Function Using Online Impedance Spectroscopy

Battery diagnosis is vital to battery-based applications because it ensures system reliability by avoiding battery failure. This paper presents a novel intelligent battery charger with an online diagnosis function to circumvent interruptions in system operation. The charger operates in normal charging and diagnosing modes. The diagnosis function is performed with the impedance spectroscopy technique, which is achieved by injecting a sinusoidal voltage excitation signal to the battery terminals without the need for additional hardware. The impedance spectrum of the battery is calculated based on voltage excitation and current response with the aid of an embedded digital lock in amplifier in a digital signal processor. The measured impedance data are utilized in the application of the complex nonlinear least squares method to extract the battery parameters of the equivalent circuit. These parameters are then compared with the reference values to reach a diagnosis. A prototype of the proposed charger is applied to four valve-regulated lead–acid batteries to measure AC impedance. The results are discussed.

[1]  Sang-Kyoo Han,et al.  Low Cost High Power Density Photovoltaic Power Conditioning System with an Energy Storage System , 2012 .

[2]  Albert Moser,et al.  Optimal Allocation and Capacity of Energy Storage Systems in a Future European Power System with 100% Renewable Energy Generation , 2014 .

[3]  Christian Fleischer,et al.  On-line adaptive battery impedance parameter and state estimation considering physical principles in reduced order equivalent circuit battery models: Part 1. Requirements, critical review of methods and modeling , 2014 .

[4]  Jong-Hak Lee,et al.  Novel State-of-Charge Estimation Method for Lithium Polymer Batteries Using Electrochemical Impedance Spectroscopy , 2011 .

[5]  Rik W. De Doncker,et al.  Impedance measurements on lead–acid batteries for state-of-charge, state-of-health and cranking capability prognosis in electric and hybrid electric vehicles , 2005 .

[6]  Seongjun Lee,et al.  Complementary Cooperation Algorithm Based on DEKF Combined With Pattern Recognition for SOC/Capacity Estimation and SOH Prediction , 2012, IEEE Transactions on Power Electronics.

[7]  D. Sauer,et al.  Influence of relaxation time on the lifetime of commercial lithium-ion cells , 2012 .

[8]  W. Waag,et al.  SECONDARY BATTERIES – LEAD– ACID SYSTEMS | State-of-Charge/Health , 2009 .

[9]  C. V. Suryanarayana,et al.  Computer program for nonlinear least square analysis of impedance and admittance data , 1986 .

[10]  Joseph M. Lasker,et al.  Digital Lock-In Detection for Discriminating Multiple Modulation Frequencies With High Accuracy and Computational Efficiency , 2008, IEEE Transactions on Instrumentation and Measurement.

[11]  Fu-Cheng Wang,et al.  Low power proton exchange membrane fuel cell system identification and adaptive control , 2007 .

[12]  Yuanyuan Xie,et al.  Mathematical modeling of the electrochemical impedance spectroscopy in lithium ion battery cycling , 2014 .

[13]  Woojin Choi,et al.  Development of a method to estimate the lifespan of proton exchange membrane fuel cell using electrochemical impedance spectroscopy , 2010 .

[14]  Hongsup Lim,et al.  Factors that affect cycle-life and possible degradation mechanisms of a Li-ion cell based on LiCoO2 , 2002 .

[15]  Hiroaki Ishikawa,et al.  State of Charge Dependency of Graphitized-Carbon-Based Reactions in a Lithium-ion Secondary Cell Studied by Electrochemical Impedance Spectroscopy , 2014 .

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

[17]  Hua Wang,et al.  Modelling and control of hybrid UPS system with backup PEM fuel cell/battery , 2012 .

[18]  Michael A. Danzer,et al.  Electrochemical parameter identification—An efficient method for fuel cell impedance characterisation , 2008 .

[19]  Huei Peng,et al.  On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression , 2013 .

[20]  Woojin Choi,et al.  Development of the low-cost impedance spectroscopy system for modeling the electrochemical power sources , 2007, 2007 7th Internatonal Conference on Power Electronics.

[21]  Tingshu Hu,et al.  Control design for robust tracking and smooth transition in power systems with battery/supercapacitor hybrid energy storage devices ☆ , 2014 .

[22]  M. A. Habib,et al.  The effect of temperature on capacity and power in cycled lithium ion batteries , 2005 .

[23]  B. Boukamp.  A Nonlinear Least Squares Fit procedure for analysis of immittance data of electrochemical systems , 1986 .

[24]  Hee-Jun Kim,et al.  Development of an Advanced Hybrid Energy Storage System for Hybrid Electric Vehicles , 2009 .

[25]  F. Huet.  A review of impedance measurements for determination of the state-of-charge or state-of-health of secondary batteries , 1998 .

[26]  Faouzi Ben Ammar,et al.  Causal tree analysis of depth degradation of the lead acid battery , 2013 .

[27]  Ricardo M. Souto,et al.  Characterisation of localised corrosion processes using scanning electrochemical impedance microscopy , 2014 .

[28]  Heung-Geun Kim,et al.  Bi-Directional Multi-Level Converter for an Energy Storage System , 2014 .

[29]  Mohd Wazir Mustafa,et al.  Energy storage systems for renewable energy power sector integration and mitigation of intermittency , 2014 .

[30]  P. Ruetschi.  Aging mechanisms and service life of lead–acid batteries , 2004 .

[31]  Hongjie Jia,et al.  A statistical model to determine the capacity of battery–supercapacitor hybrid energy storage system in autonomous microgrid , 2014 .

[32]  Hyung-Min Ryu.  Highly Efficient High-Voltage MOSFET Converter with Bidirectional Power Flow Legs , 2014 .

[33]  D. Sauer,et al.  Characterization of high-power lithium-ion batteries by electrochemical impedance spectroscopy. II: Modelling , 2011 .