SAE PT-176-2016 Progress in Modeling and Simulation of Batteries (To Purchase Call 1-800-854-7179 USA Canada or 303-397-7956 Worldwide).pdf

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1、Progress in Modeling and Simulation of BatteriesOther SAE books of interest: Lithium Ion Batteries in Electric Vehicles Ahmad Pesaran (Product Code: PT-175) Electric and Hybrid Electric Vehicles Ronald K. Jurgen (Product Code: PT-143.SET) Electric and Hybrid Electric Vehicles - Batteries Ronald K. J

2、urgen (Product Code: PT-143/2) Battery Reference Book T.R. Compton (Product Code: R-167) Future Automotive Fuels and Energy Bruce Morey (Product Code: T-128) For more information or to order a book, contact: SAE INTERNATIONAL 400 Commonwealth Drive Warrendale, PA 15096-0001, USA;Phone: +1.877.606.73

3、23 (U.S. and Canada only) or +1.724.776.4970 (outside U.S. and Canada) Fax: +1.724.776.0790 Email: CustomerServicesae.org Website: books.sae.orgProgress in Modeling and Simulation of Batteries Edited by John A. Turner Warrendale, Pennsylvania, USA Copyright 2016 SAE International eISBN: 978-0-7680-8

4、366-8Copyright 2016 SAE International. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, distributed, or transmitted, in any form or by any means without the prior written permission of SAE International. For permission and licensing requests, contact

5、SAE Permissions, 400 Commonwealth Drive, Warrendale, PA 15096-0001 USA; e-mail: copyrightsae.org; phone: 724-772-4028; fax: 724-772- 9765. Library of Congress Catalog Number 2015957339 SAE Order Number PT-176 http:/dx.doi.org/10.4271/pt-176 Information contained in this work has been obtained by SAE

6、 International from sources believed to be reliable. However, neither SAE International nor its authors guarantee the accuracy or completeness of any information published herein and neither SAE International nor its authors shall be responsible for any errors, omissions, or damages arising out of u

7、se of this information. This work is published with the understanding that SAE International and its authors are supplying information, but are not attempting to render engineering or other professional services. If such services are required, the assistance of an appropriate professional should be

8、sought. ISBN-Print 978-0-7680-8282-1 ISBN-PDF 978-0-7680-8366-8 ISBN-epub 978-0-7680-8368-2 ISBN-prc 978-0-7680-8367-5 To purchase bulk quantities, please contact SAE Customer Service e-mail: CustomerServicesae.org phone: +1.877.606.7323 (inside USA and Canada) +1.724.776.4970 (outside USA) fax: +1.

9、724.776.0790 Visit the SAE Bookstore at books.sae.org 400 Commonwealth Drive Warrendale, PA 15096-0001 USA E-mail: CustomerServicesae.org Phone: +1.877.606.7323 (inside USA and Canada)+1.724.776.4970 (outside USA) Fax: +1.724.776.0790v Table of Contents Introduction . 1 1. Characterizing Thermal Beh

10、avior of an Air-Cooled Lithium-Ion Battery System for HEV Applications Using FEA Approach (2013-01-1520) . 3Kim F. Yeow and Ho Teng, AVL Powertrain Engineering Inc. 2. AutoLion: A Thermally Coupled Simulation Tool for Automotive Li-Ion Batteries (2013-01-1522) 13Jim Kalupson, Gang Luo and Christian

11、E. Shaffer, EC Power 3. Simplified Extended Kalman Filter Observer for SOC Estimation of Commercial Power-Oriented LFP Lithium Battery Cells (2013-01-1544). 19Tarun Huria and Massimo Ceraolo, Universit di PisaJavier Gazzarri and Robyn Jackey, MathWorks 4. A Complete Li-Ion Battery Simulation Model (

12、2014-01-1842). 29Xiao Hu and Scott Stanton, ANSYS Inc. 5. Comparison of Optimization Techniques for Lithium-Ion Battery Model Parameter Estimation (2014-01-1851) 37Adam Ing, Ramin Masoudi, and John McPhee, University of WaterlooThanh-Son Dao, Maplesoft 6. Physics-Based Models, Sensitivity Analysis,

13、and Optimization of Automotive Batteries (2014-01-1865) 47Joydeep Banerjee and John McPhee, Univ. of WaterlooPaul Goossens and Thanh-Son Dao, Maplesoft 7. Three-Dimensional Electrochemical Analysis of a Graphite/LiFePO4 Li-Ion Cell to Improve Its Durability (2015-01-1182). 59Mehrdad Mastali Majdabad

14、i Kohneh and Ehsan Samadani, University of WaterlooSiamak Farhad, University of Akron 8. Experimental Measurements of Thermal Characteristics of LiFePO4 Battery (2015-01-1189) 67Satyam Panchal, Scott Mathewson, Roydon Fraser, Richard Culham, and Michael Fowler, University of Waterloo9. Will Your Bat

15、tery Survive a World With Fast Chargers? (2015-01-1196) . 79Jeremy S. Neubauer and Eric Wood, National Renewable Energy Laboratory About the Editor 891 Introduction Even as the price of transportation fuels fluctuates, interest in hybrid and fully electric powered vehicles continues to grow, driven

16、by environmental, economic, and national security motives. Research and development efforts spanning universities, industry, and research institutions strive for ever higher energy and power densities, lower cost, and improved safety, all of which will further accelerate interest and adoption. It is

17、 also increasingly recognized that modeling and simulation can play a significant role in these efforts, working in conjunction with both theory and experiment, as it has in other fields such as aircraft design, vehicle crash safety, vehicle aerodynamics, and nuclear weapons. Indeed, in some of thes

18、e fields, particularly where experiments are difficult, expensive, or prohibited, modeling and simulation has become the foundation on which progress is built - at times leading theory and/or experiments. Although as a community we have not reached that level of predictive capability in modeling and

19、 simulation of batteries, significant progress has been made over the last few years. In this volume we present nine examples of this progress. Note that several of the included works focus on thermal behavior, and that we have included one experimental study of thermal characteristics due to its po

20、tential use in validating the simulation capabilities. Studies presented here range from fast-running approaches potentially useful in battery management system design and analysis to moderately high-fidelity 3D capabilities, and include the work of universities, industry and research institutions.

21、This is a fast-moving field, and progress is on-going, with more accurate models and more capable simulation tools under constant development. As a result, this collection represents a snapshot of capability and directions, and we look forward to the next advances in modeling and simulation capabili

22、ty. Some examples include tighter nonlinear coupling of physical phenomena, increased integration of sub-grid micro- and meso-scale simulations, and more integrated sensitivity and uncertainty analysis. In the meantime, we hope that this collection provides useful and compelling evidence of the prog

23、ress in modeling and simulation of batteries. John A. Turner Computational Engineering Knoxville, Tennessee National Center for Computational Engineering; Chattanooga, Tennessee3 ABSTRACT Thermal behavior of a Lithium-ion (Li-ion) battery module under a user-defined cycle corresponding to hybrid ele

24、ctrical vehicle (HEV) applications is analyzed. The module is stacked with 12 high-power 8Ah pouch Li-ion battery cells connected in series electrically. The cells are cooled indirectly with air through aluminum cooling plate sandwiched between each pair of cells. The cooling plate has extended cool

25、ing surfaces exposed in the cooling air flow channel. Thermal behavior of the battery system under a user specified electrical-load cycle for the target hybrid vehicle is characterized with the equivalent continuous load profile using a 3D finite element analysis (FEA) model for battery cooling. Ana

26、lysis results are compared with measurements. Good agreement is observed between the simulated and measured cell temperatures. Improvement of the cooling system design is also studied with assistance of the battery cooling analyses. The results of this study demonstrate that the 3D FEA battery cooli

27、ng model developed in this study can reasonably characterize thermal behavior of the Li-ion battery systems under the dynamic discharge-charge cycle. The correlated FEA model can be used to evaluate and/or optimize the cooling system designs for the battery systems with indirect air cooling. INTRODU

28、CTION The battery packs for HEV applications usually consist of multiple identical battery modules connected in series to achieve the required pack voltage. Within each of the battery modules, the cells are connected in a certain series-parallel configurations to achieve the required capacity and vo

29、ltage. In an ideal battery pack design, all the modules in the pack would be thermally symmetric, i.e., have identical cooling conditions. As such the cooling system of a battery module is the basic cooling system for a battery pack. The maximum cell temperatures and the maximum differential cell te

30、mperatures for the cells in the battery pack are crucial to the performance of the battery system. In operations under cold ambient, the coolest cell within the pack determines the maximum pack power. On the other hand, in operations under elevated ambient temperatures, the hottest cell within the p

31、ack limits the maximum allowed current for safe pack utilizations. The state of charge (SOC) for the HEV battery pack is typically controlled in a SOC range from 0.35 to 0.65 (i.e., SOC = 0.3) with SOC = 0.5 being a reference point for the SOC balance. Because of its narrow usable SOC range, a HEV b

32、attery pack experiences high discharge/charge pulse currents in pack utilizations. These high-pulse and high- frequency discharge/charge activities generate considerable amount of heat within the battery cells, resulting in high cell temperatures if the cell heat cannot be dissipated into the coolin

33、g medium rapidly. The maximum cell temperatures and the maximum differential cell temperatures are crucial factors to the performance, safety and durability of a battery system. Thus, these temperatures must be monitored and controlled by the battery thermal management system. A good battery thermal

34、 management system design lies on good understanding of the thermal behavior of the cells in the battery pack. Thermal modeling of the cells and modules can play an important role in understanding the thermal behavior of the battery cells under specified pack duties. This paper discusses the use of

35、3D FEA model in analyzing the thermal Characterizing Thermal Behavior of an Air-Cooled Lithium-Ion Battery System for Hybrid Electrical Vehicle Applications Using Finite Element Analysis Approach 2013-01-1520 Published 04/08/2013 Kim F. Yeow and Ho Teng AVL Powertrain Engineering Inc. Copyright 2013

36、 SAE International doi:10.4271/2013-01-15204 behavior of a battery system with indirect air cooling under a thermal load condition simulating HEV applications. Improvement of the battery cooling system design with assistance of the battery thermal analysis will also be discussed. BATTERY SYSTEM DESC

37、RIPTION The battery pack under study consists of 8 identical modules with each stacked with twelve 8Ah Li-ion pouch cells connected in series, giving a 96S1P pack configuration. Specifications of the cells are given in Table 1. If the pack cooling air flow is assumed to be equally distributed among

38、all modules, i.e., the 8 modules are thermally symmetric, then only a representative module needs to be studied. Figure 1 illustrates the FEA model of the battery module under study. Each pair of cells within the module is cooled via a 2-mm thick aluminum cooling plate sandwiched between them. The a

39、luminum cooling plate has extended surfaces (hereafter refers to as cooling fins) that are in the air flow channel. Such a cooling system may be defined as an indirect air cooling system. The side surface of the cell that is not in contact with the cooling plate is in contact with an elastomeric the

40、rmal pad. The module frame is made of plastic, which is not modeled because heat transfer through it is negligible in comparison to that through the cooling fins. The battery cell temperatures can be controlled by the air temperature and/or the heat transfer coefficient (HTC) on the cooling fins by

41、varying the air flow in the cooling channel. Figure 1. Battery module with indirect air cooling. Table 1. Battery cell specification. BATTERY SYSTEM MODELING As shown in the FEA model in Figure 1, the module has six cooling units. Figure 2 shows a representative cooling unit in the module. In modeli

42、ng a battery cell in the cooling unit, the multi-layer structure of the battery cell 1,2 is simplified to a single equivalent battery layer which has a positive current collector and a negative current collector, a pair of electrodes (anode and cathode) and a separator. This single-layer battery cel

43、l is characterized with composite local thermal and electrical properties equivalent to those for the multiple- layered battery structure 3. The composite local volumetric heat capacity are expressed as (1) where the subscript i indicates the properties for the component i. The composite local therm

44、al conductivities for the series and parallel connections are given respectively as (2) (3) In Eqs. (2) and (3), L iand k iare the thickness and the thermal conductivity for the component i respectively. Similarly, the composite local electrical conductivities can also be expressed by Eqs.(2) and (3

45、) in characterizing the electrical field of the cell. In this study, the thermal behavior of the cells in the battery system under a given electrical load is characterized with energy balance on a unit cell volume as (4) where , C pand k are the local density, heat capacity and thermal conductivity

46、of the cell medium described in Eqs.(1), (2), (3), t is the time, T is the temperature and q is the rate of5 local heat generation due largely to the cell ohmic heat resulting from the cell internal resistance and the cell discharge/charge currents 3. Figure 2. Basic cooling unit of the battery modu

47、le: (a) exploded view; (b) assembled view with a single cell. THERMAL ANALYSIS OF BATTERY SYSTEM Battery Thermal Load Characterization Figure 3 shows the simulated transient currents for the battery pack under a user specified cycle for a target hybrid vehicle. Because of the 96S1P pack configuratio

48、n, the cell current is equal to the pack current. As shown in Figure 1, the components in the system lead to a large thermal inertia that mitigates the rises in the cell temperatures in the system. Like other electrical systems, a battery system can also tolerate electrical overloading in a short du

49、ration. In this study, the battery thermal load is evaluated on an I 2 basis as (5) where I is a root-mean-square (rms) average current over a window time (i.e., the I 2 current) and i is the transient current. Because is a moving window, I = I (t) is a transient current in a thermal load sense, with the thermal history up to being considered 4. The influence of the battery thermal history on the battery temperature depends on the levels of the disturbance to the battery internal energy by the heat generated by the dynamic current pulses. The shorter the wi