Algorithms for Advanced Battery-Management Systems

Lithium-ion (Li-ion) batteries are ubiquitous sources of energy for portable electronic devices. Compared to alternative battery technologies, Li-ion batteries provide one of the best energy-to-weight ratios, exhibit no memory effect, and have low self-discharge when not in use. These beneficial properties, as well as decreasing costs, have established Li-ion batteries as a leading candidate for the next generation of automotive and aerospace applications. In the automotive sector, increasing demand for hybrid electric vehicles (HEVs), plug-in HEVs (PHEVs), and EVs has pushed manufacturers to the limits of contemporary automotive battery technology. This limitation is gradually forcing consideration of alternative battery technologies, such as Li-ion batteries, as a replacement for existing leadacid and nickel-metal-hydride batteries. Unfortunately, this replacement is a challenging task since automotive applications demand large amounts of energy and power and must operate safely, reliably, and durably at these scales. The article presents a detailed description and model of a Li-ion battery. It begins the section "Intercalation-Based Batteries" by providing an intuitive explanation of the fundamentals behind storing energy in a Li-ion battery. In the sections "Modeling Approach" and "Li-Ion Battery Model," it present equations that describe a Li-ion cell's dynamic behavior. This modeling is based on using electrochemical principles to develop a physics-based model in contrast to equivalent circuit models. A goal of this article is to present the electrochemical model from a controls perspective.

[1]  John Newman,et al.  Cyclable Lithium and Capacity Loss in Li-Ion Cells , 2005 .

[2]  Ralph E. White,et al.  Reduction of Model Order Based on Proper Orthogonal Decomposition for Lithium-Ion Battery Simulations , 2009 .

[3]  M. D. Rooij,et al.  Electrochemical Methods: Fundamentals and Applications , 2003 .

[4]  V. Subramanian,et al.  Mathematical Model Reformulation for Lithium-Ion Battery Simulations: Galvanostatic Boundary Conditions , 2009 .

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

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

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

[8]  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 .

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

[10]  M. Verbrugge,et al.  Electrochemical and Thermal Characterization of Battery Modules Commensurate with Electric Vehicle Integration , 2002 .

[11]  Fang Fang,et al.  A Modular Battery Management System for HEVs , 2002 .

[12]  Chester G. Motloch,et al.  High-Power Battery Testing Procedures and Analytical Methodologies for HEV's , 2002 .

[13]  M. Krstić.  Boundary Control of PDEs: A Course on Backstepping Designs , 2008 .

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

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

[16]  Chaoyang Wang,et al.  Power and thermal characterization of a lithium-ion battery pack for hybrid-electric vehicles , 2006 .

[17]  Tony Markel,et al.  Battery Requirements for Plug-In Hybrid Electric Vehicles -- Analysis and Rationale , 2007 .

[18]  V. Subramanian,et al.  Efficient Macro-Micro Scale Coupled Modeling of Batteries , 2005 .

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

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

[21]  Richard T. Haasch,et al.  Diagnosis of power fade mechanisms in high-power lithium-ion cells☆ , 2003 .

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

[23]  Marc Doyle,et al.  Mathematical Modeling of the Lithium Deposition Overcharge Reaction in Lithium‐Ion Batteries Using Carbon‐Based Negative Electrodes , 1999 .

[24]  Georg Brasseur,et al.  Modeling of high power automotive batteries by the use of an automated test system , 2003, IEEE Trans. Instrum. Meas..

[25]  Daniel H. Doughty,et al.  FreedomCAR Electrical Energy Storage System Abuse Test Manual for Electric and Hybrid Electric Vehicle Applications , 2006 .

[26]  Kevin L. Gering,et al.  Effect of cathode composition on impedance rise in high-power lithium-ion cells: Long-term aging results , 2006 .

[27]  J. Newman,et al.  Double‐Layer Capacitance in a Dual Lithium Ion Insertion Cell , 1999 .

[28]  Yuang-Shung Lee,et al.  Intelligent control battery equalization for series connected lithium-ion battery strings , 2005, IEEE Trans. Ind. Electron..

[29]  M. Krstić,et al.  Boundary Control of PDEs , 2008 .

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

[31]  Chester G. Motloch,et al.  PNGV Battery Testing Procedures And Analytical Methodologies For HEV's , 2002 .

[32]  Gan Ning,et al.  Cycle Life Modeling of Lithium-Ion Batteries , 2004 .

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