Battery Management Systems in Electric and Hybrid Vehicles

The battery management system (BMS) is a critical component of electric and hybrid electric vehicles. The purpose of the BMS is to guarantee safe and reliable battery operation. To maintain the safety and reliability of the battery, state monitoring and evaluation, charge control, and cell balancing are functionalities that have been implemented in BMS. As an electrochemical product, a battery acts differently under different operational and environmental conditions. The uncertainty of a battery’s performance poses a challenge to the implementation of these functions. This paper addresses concerns for current BMSs. State evaluation of a battery, including state of charge, state of health, and state of life, is a critical task for a BMS. Through reviewing the latest methodologies for the state evaluation of batteries, the future challenges for BMSs are presented and possible solutions are proposed as well.

[1]  A. Emadi,et al.  Battery balancing methods: A comprehensive review , 2008, 2008 IEEE Vehicle Power and Propulsion Conference.

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

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

[4]  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 .

[5]  Bo-Suk Yang,et al.  Intelligent prognostics for battery health monitoring based on sample entropy , 2011, Expert Syst. Appl..

[6]  Yangsheng Xu,et al.  Battery state-of-charge estimation based on H∞ filter for hybrid electric vehicle , 2008, 2008 10th International Conference on Control, Automation, Robotics and Vision.

[7]  Ahmad Pesaran,et al.  Battery thermal models for hybrid vehicle simulations , 2002 .

[8]  A. Jossen,et al.  Reliable battery operation — a challenge for the battery management system , 1999 .

[9]  Eberhard Meissner,et al.  Battery Monitoring and Electrical Energy Management , 2003 .

[10]  Terry Hansen,et al.  Support vector based battery state of charge estimator , 2005 .

[11]  Bhaskar Saha,et al.  Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework , 2009, IEEE Transactions on Instrumentation and Measurement.

[12]  Gyo-Bum Chung,et al.  SOC estimation of LiPB batteries using Extended Kalman Filter based on high accuracy electrical model , 2011, 8th International Conference on Power Electronics - ECCE Asia.

[13]  Michael Osterman,et al.  Prognostics of lithium-ion batteries based on DempsterShafer theory and the Bayesian Monte Carlo me , 2011 .

[14]  L.-A. Dessaint,et al.  A Generic Battery Model for the Dynamic Simulation of Hybrid Electric Vehicles , 2007, 2007 IEEE Vehicle Power and Propulsion Conference.

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

[16]  Ralph E. White,et al.  Analysis of capacity fade in a lithium ion battery , 2005 .

[17]  R. Spotnitz Simulation of capacity fade in lithium-ion batteries , 2003 .

[18]  Claus Daniel,et al.  Materials and processing for lithium-ion batteries , 2008 .

[19]  Liu Qiao,et al.  Automotive battery management systems , 2008, 2008 IEEE AUTOTESTCON.

[20]  Kuei-Hsiang Chao,et al.  State-of-health estimator based-on extension theory with a learning mechanism for lead-acid batteries , 2011, Expert Syst. Appl..

[21]  B. Scrosati,et al.  Lithium batteries: Status, prospects and future , 2010 .

[22]  Masatoshi Uno,et al.  Influence of High-Frequency Charge–Discharge Cycling Induced by Cell Voltage Equalizers on the Life Performance of Lithium-Ion Cells , 2011, IEEE Transactions on Vehicular Technology.

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

[24]  Davide Andrea,et al.  Battery Management Systems for Large Lithium Ion Battery Packs , 2010 .

[25]  P.E. Pascoe,et al.  Standby power system VRLA battery reserve life estimation scheme , 2005, IEEE Transactions on Energy Conversion.

[26]  Michael E. Tipping The Relevance Vector Machine , 1999, NIPS.

[27]  J. D. Kozlowski Electrochemical cell prognostics using online impedance measurements and model-based data fusion techniques , 2003, 2003 IEEE Aerospace Conference Proceedings (Cat. No.03TH8652).

[28]  Ralph E. White,et al.  Capacity fade analysis of a lithium ion cell , 2008 .

[29]  Rudi Kaiser,et al.  Optimized battery-management system to improve storage lifetime in renewable energy systems , 2007 .

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

[31]  Jay Lee,et al.  A review on prognostics and health monitoring of Li-ion battery , 2011 .

[32]  Mo-Yuen Chow,et al.  Comprehensive dynamic battery modeling for PHEV applications , 2010, IEEE PES General Meeting.