• Battery Management and Safety


Estimation and control of the states of the battery (including temperature, state of charge, and state of health) are essential to extract the maximum usable energy and highest available power while maintaining safe operation in hybrid and electric vehicle systems. The role of the Battery Management System (BMS) is to collect the current voltage and temperature sensor measurements from the battery cells to manage the power flows and provide the functional safety, prognostic, and diagnostic information needed to ensure safe and efficient operation. This course reviews the modeling techniques, concepts, and algorithms used in advanced battery management systems.


Learning Objectives


Upon completion of this online course, participants will be able to:

  • Summarize the basic components and functionality of the Battery Management System

  • Design and model battery systems

  • Choose the appropriate model complexity for a given application

  • Parameterize equivalent circuit battery models using experimental I,V,T data

  • Understand Battery Management System (BMS) components and function

  • Discuss the factors that influence battery performance and required protection schemes

  • Apply the state of the art in battery modeling and controls research



  • Overview Parts 1-3
    (61 min)

  • Equivalent Circuit Models for the Lithium lon Battery
    (25 min)

  • 1+1D Electrochemical Model
    (25 min)

  • Battery Thermal Modeling
    (20 min)

  • Data Collection and Model Parameterization
    (23 min)

  • Vehicle Energy Management Functions
    (12 min)

  • State of Charge (SOC) Estimation
    (12 min)

  • Battery Cell Balancing
    (21 min)

  • Battery Charging Standards and Algorithms
    (27 min)

  • Power Limits, Cold Temperature Performance
    (34 min)

  • Lithium lon Battery Safety Issues
    (26 min)

  • Battery Aging
    (34 min)




Jason Siegel
Jason Siegel
Assistant Research Scientist

Dr. Jason Siegel is an Assistant Research Scientist in the Department of Mechanical Engineering at the University of Michigan. His research interests include modeling and control of Lithium-Ion batteries. Dr. Siegel uses neutron imaging to better understand how Lithium is distributed throughout the electrode in high power applications. He received his Ph.D. in 2010, MS in 2006 and BS in Electrical Engineering in 2004 at the University of Michigan.