Li-Ion Battery Charging with a Buck-Boost Power Converter for a Solar Powered Battery Management System

This paper analyzes and simulates the Li-ion battery charging process for a solar powered battery management system. The battery is charged using a non-inverting synchronous buck-boost DC/DC power converter. The system operates in buck, buck-boost, or boost mode, according to the supply voltage conditions from the solar panels. Rapid changes in atmospheric conditions or sunlight incident angle cause supply voltage variations. This study develops an electrochemical-based equivalent circuit model for a Li-ion battery. A dynamic model for the battery charging process is then constructed based on the Li-ion battery electrochemical model and the buck-boost power converter dynamic model. The battery charging process forms a system with multiple interconnections. Characteristics, including battery charging system stability margins for each individual operating mode, are analyzed and discussed. Because of supply voltage variation, the system can switch between buck, buck-boost, and boost modes. The system is modeled as a Markov jump system to evaluate the mean square stability of the system. The MATLAB based Simulink piecewise linear electric circuit simulation tool is used to verify the battery charging model.

[1]  Christopher D. Rahn,et al.  Model-Based Electrochemical Estimation and Constraint Management for Pulse Operation of Lithium Ion Batteries , 2010, IEEE Transactions on Control Systems Technology.

[2]  Mohammad Farrokhi,et al.  State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF , 2010, IEEE Transactions on Industrial Electronics.

[3]  G.A. Rincon-Mora,et al.  A low voltage, dynamic, noninverting, synchronous buck-boost converter for portable applications , 2004, IEEE Transactions on Power Electronics.

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

[5]  Seongjun Lee,et al.  State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge , 2008 .

[6]  M. Verbrugge,et al.  Electrochemical characterization of high-power lithium ion batteries using triangular voltage and current excitation sources , 2007 .

[7]  Ziyad M. Salameh,et al.  A mathematical model for lead-acid batteries , 1992 .

[8]  Roger A. Dougal,et al.  Dynamic lithium-ion battery model for system simulation , 2002 .

[9]  Kandler A. Smith.  Electrochemical Control of Lithium-Ion Batteries , 2010 .

[10]  S. Gold,et al.  A PSPICE macromodel for lithium-ion batteries , 1997, The Twelfth Annual Battery Conference on Applications and Advances.

[11]  Giacomo Marangoni.  Battery management system for Li-Ion batteries in hybrid electric vehicles , 2010 .

[12]  Hongwen He,et al.  Online Estimation of Peak Power Capability of Li-Ion Batteries in Electric Vehicles by a Hardware-in-Loop Approach , 2012 .

[13]  Chaoyang Wang,et al.  Control oriented 1D electrochemical model of lithium ion battery , 2007 .

[14]  K.A. Smith,et al.  Electrochemical Control of Lithium-Ion Batteries [Applications of Control] , 2010, IEEE Control Systems.

[15]  M. Armand,et al.  Issues and challenges facing rechargeable lithium batteries , 2001, Nature.

[16]  Yongtao Yao,et al.  Analysis of Buck-Boost Converters for Fuel Cell Electric Vehicles , 2006, 2006 IEEE International Conference on Vehicular Electronics and Safety.

[17]  Ralph E. White,et al.  Review of Models for Predicting the Cycling Performance of Lithium Ion Batteries , 2006 .

[18]  R. D. De Doncker,et al.  Impedance-based simulation models of supercapacitors and Li-ion batteries for power electronic applications , 2003, IEEE Transactions on Industry Applications.

[19]  Jaw-Kuen Shiau,et al.  Circuit Simulation for Solar Power Maximum Power Point Tracking with Different Buck-Boost Converter Topologies , 2014 .

[20]  M. Armand,et al.  Building better batteries , 2008, Nature.

[21]  M. Doyle,et al.  Simulation and Optimization of the Dual Lithium Ion Insertion Cell , 1994 .

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

[23]  M. Ceraolo,et al.  New dynamical models of lead-acid batteries , 2000 .

[24]  Suleiman M. Sharkh,et al.  Estimation of State of Charge of Lithium-Ion Batteries Used in HEV Using Robust Extended Kalman Filtering , 2012 .

[25]  Min Chen,et al.  Accurate electrical battery model capable of predicting runtime and I-V performance , 2006, IEEE Transactions on Energy Conversion.

[26]  J. Newman,et al.  Porous‐electrode theory with battery applications , 1975 .

[27]  M. Doyle,et al.  Modeling of Galvanostatic Charge and Discharge of the Lithium/Polymer/Insertion Cell , 1993 .

[28]  R. S. Weissbach,et al.  A noninverting buck-boost converter with reduced components using a microcontroller , 2001, Proceedings. IEEE SoutheastCon 2001 (Cat. No.01CH37208).

[29]  Yanqing Shen,et al.  Adaptive online state-of-charge determination based on neuro-controller and neural network , 2010 .

[30]  Giovanni Fiengo,et al.  Lithium-ion battery state of charge estimation with a Kalman Filter based on a electrochemical model , 2008, 2008 IEEE International Conference on Control Applications.

[31]  M. Gaboriault,et al.  A high efficiency, noninverting, buck-boost DC-DC converter , 2004, Nineteenth Annual IEEE Applied Power Electronics Conference and Exposition, 2004. APEC '04..

[32]  Ralph E. White,et al.  Online Estimation of the State of Charge of a Lithium Ion Cell , 2006, ECS Transactions.