Tutorial


Tutorial 1Battery System Design Using Modeling and Simulation
Date/Time 24th May, 2021 Monday / 09:00 - 12:00 hrs
SpeakerJavier Gazzarri, Principal Application Engineer - Math Works

Abstract

Modern lithium-ion battery systems have safety, performance, and durability requirements that demand a battery management system (BMS) that ensures operation within voltage, current, and temperature limits. In addition, accurate state of charge (SOC) estimation is of vital importance to mitigate range anxiety and for effective charging planning. The BMS provides battery pack sensing, monitoring, protection, and control infrastructure to ensure that the system works according to specification in terms of performance and longevity. Typical BMS algorithms include cell balancing (passive or active), current limit calculation, supervisory logic, state of charge (SOC) estimation, and state of health (SOH) diagnosis.

The effective implementation of a battery management system relies on an accurate characterization of the unit battery cell. Specifically, we need to know its charge and discharge curve profiles, internal resistances, time constants, degradation rate, and its temperature and aging dependencies. These physical characteristics constitute the cell’s unique electrochemical fingerprint and must be found using a set of characterization experiments, designed to cover the entire operating range of the battery. The first part of this tutorial will show how to design and use characterization experiments to construct an accurate equivalent circuit for a specific battery cell, with parameters depending on environmental and operating conditions, as well as age.

Once the unit cell has been characterized, the battery engineer needs to design a battery module by connecting unit cells in series and parallel to satisfy the DC bus voltage level and capacity requirements of the application. At this stage, it is important to determine an optimal cell layout and to design a method for heat extraction to ensure that battery pack operates within acceptable temperature ranges irrespective of environmental conditions. In addition, cells in series must be balanced to maintain their SoC as close as possible to each other, thus maximizing energy utilization. During the second part of this event, we will discuss ways to design a battery module using physical-network-based software tools.

Subsequently, we will describe advanced state estimation techniques such as Kalman filtering to determine SOC. State estimation is superior in applications where simple coulomb counting or OCV estimation are not adequate, such as with chemistries that exhibit a flat discharge profile. A fundamental aspect of battery system design is understanding cell aging. Battery cells degrade over time with increasing internal resistance and progressive capacity fade. These phenomena are quantified and consolidated in what is known as state-of-health (SOH). In this tutorial we will discuss different monitoring and diagnosis techniques to determine SOH.

Duration
2 hours

Outline

    o Battery characterization

    o Battery pack modeling including thermal effects

    o Battery State of Charge estimation

    o Aging due to cycling and its temperature dependence

    o Degradation diagnosis


Biography



Javier Gazzarri jgazzarr@mathworks.com is a Principal Application Engineer at MathWorks in Novi, Michigan, USA, focusing on the use of physical modeling tools as an integral part of Model Based Design. Much of his work gravitates around battery modeling, from cell-level to system-level, parameter estimation for model correlation, battery management system design, balancing, aging, and stateof-charge estimation. Before joining MathWorks, Javier worked on fuel cell modeling at the National Research Council of Canada in Vancouver, British Columbia. He has a Bachelor’s degree in Mechanical Engineering from the University of Buenos Aires (Argentina), a MASc degree (Inverse Methods), and a PhD degree (Solid Oxide Fuel Cells) both from the University of British Columbia (Canada).

Relevant publications:

1. R. Ahmed, J. Gazzarri, S. Onori, S. Habibi, S. et al., "Model-Based Parameter Identification of Healthy and Aged Li-ion Batteries for Electric Vehicle Applications," SAE Int. J. Alt. Power. 4(2) 2015,

2. J. Gazzarri, N. Shrivastava, R. Jackey and C. Borghesani, Battery Pack Modeling, Simulation, and Deployment on a Multicore Real Time Target, SAE Int. J. Aerosp. 7(2) 2014

3. T. Huria, M. Ceraolo, J. Gazzarri, R. Jackey, Simplified Extended Kalman Filter Observer for SOC Estimation of Commercial Power-Oriented LFP Lithium Battery Cells, SAE 2013-01-1544

4. R. Jackey, M. Saginaw, P. Sanghvi, J. Gazzarri, T. Huria and M. Ceraolo, Battery Model Parameter Estimation Using a Layered Technique: An Example Using a Lithium Iron Phosphate Cell, SAE 2013-01-1547

5. T. Huria, M. Ceraolo, J. Gazzarri, R. Jackey, High Fidelity Electrical Model with Thermal Dependence for Characterization and Simulation of High Power Lithium Battery Cells, 978-1- 4673-1561-6/12 2012 IEEE

6. J. Liu, J. Gazzarri, M. Eikerling, Model-Based Ex Situ Diagnostics of Water Fluxes in Catalyst Layers of Polymer Electrolyte Fuel Cells, Fuel Cells, 13 (2), 134-142, (2013)

7. H. Li, J. Gazzarri, et al., PEM fuel cell cathode contamination in the presence of cobalt ion, Electrochimica Acta 55 (20), 5823-5830 (2010)

8. J. Gazzarri et al., Estimation of Local Relative Humidity in Cathode Catalyst Layers of PEMFC, Electrochem. Sol. State Lett. 13 (6), B58-B62, (2010)

9. Gary S. Schajer and Javier I. Gazzarri, Surface Profile Measurement, Independent of Relative Motions. US Patent 7,003,894 (2006)