Battery-Management System (BMS) and SOC Development for Electrical Vehicles

Battery monitoring is vital for most electric vehicles (EVs), because the safety, operation, and even the life of the passenger depends on the battery system. This attribute is exactly the major function of the battery-management system (BMS)-to check and control the status of battery within their specified safe operating conditions. In this paper, a typical BMS block diagram has been proposed using various functional blocks. The state of charge (SOC) estimation has been implemented using Coulomb counting and open-circuit voltage methods, thereby eliminating the limitation of the stand-alone Coulomb counting method. By modeling the battery with SOC as one of the state variables, SOC can be estimated, which is further corrected by the Kalman filtering method. The battery parameters from experimental results are integrated in the model, and simulation results are validated by experiment.

[1]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[2]  Allen J. Bard,et al.  Electrochemical Methods: Fundamentals and Applications , 1980 .

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

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

[5]  Syed Islam,et al.  A battery management system for stand alone photovoltaic energy systems , 1999, Conference Record of the 1999 IEEE Industry Applications Conference. Thirty-Forth IAS Annual Meeting (Cat. No.99CH36370).

[6]  Luca Solero,et al.  Nonconventional on-board charger for electric vehicle propulsion batteries , 2001, IEEE Trans. Veh. Technol..

[7]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[8]  Ka Wai Eric Cheng,et al.  Phase-shift controlled DC-DC convertor with bi-directional power flow , 2001 .

[9]  G. Plett Kalman-Filter SOC Estimation for LiPB HEV Cells , 2002 .

[10]  Alberto Bellini,et al.  Battery choice and management for new-generation electric vehicles , 2005, IEEE Transactions on Industrial Electronics.

[11]  Andrew Mills,et al.  Simulation of passive thermal management system for lithium-ion battery packs , 2005 .

[12]  Peng Rong,et al.  Battery-aware power management based on Markovian decision processes , 2002, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

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

[14]  Sun,et al.  Model based SOC estimation for high-power Li-ion battery packs used on FCHVS , 2007 .

[15]  Antoni Szumanowski,et al.  Battery Management System Based on Battery Nonlinear Dynamics Modeling , 2008, IEEE Transactions on Vehicular Technology.

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

[17]  David A. Stone,et al.  New Battery Model and State-of-Health Determination Through Subspace Parameter Estimation and State-Observer Techniques , 2009, IEEE Transactions on Vehicular Technology.

[18]  H. Akagi,et al.  State-of-Charge (SOC)-Balancing Control of a Battery Energy Storage System Based on a Cascade PWM Converter , 2009, IEEE Transactions on Power Electronics.

[19]  Ali Emadi,et al.  Advanced Integrated Bidirectional AC/DC and DC/DC Converter for Plug-In Hybrid Electric Vehicles , 2009, IEEE Transactions on Vehicular Technology.

[20]  Hao Hu,et al.  Hardware-in-the-Loop Simulation of Pure Electric Vehicle Control System , 2009, 2009 International Asia Conference on Informatics in Control, Automation and Robotics.