Simple Spec-Based Modeling of Lithium-Ion Batteries

被引:10
作者
Kazhamiaka, Fiodar [1 ]
Keshav, Srinivasan [1 ]
Rosenberg, Catherine [2 ]
Pettinger, Karl-Heinz [3 ]
机构
[1] Cheriton Sch Comp Sci, Waterloo, ON N2L 3G1, Canada
[2] Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON N2L3G1, Canada
[3] Landshut Univ Appl Sci, Fac Mech Engn, D-84036 Landshut, Germany
关键词
Lithium-ion battery; Storage; Modeling; Simulink; EQUIVALENT-CIRCUIT; HYBRID;
D O I
10.1109/TEC.2018.2838441
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Lithium-ion battery models that estimate their energy content after a series of charge and discharge operations are essential in the optimal design, analysis, and operation of battery-based systems. We focus on the class of battery models that can be calibrated entirely from the batteries manufacturer-provided specifications (spec). Such models are simple to calibrate and are therefore widely used in practice. The best-known model in this category was proposed by Tremblay et al. in 2007. This model, however, has several shortcomings, including low fidelity at high C-rates, and the fact that it does not model the battery management system. We propose an alternative, called the Power-based Integrated model that is also completely spec-based, yet has much higher fidelity. We perform two types of validation, the first one uses the voltage profiles in the spec while the other is based on laboratory experiments. Both validations confirm that our model, which we have publicly released as a Simulink system block, has a mean absolute voltage error of less than 0.1 V across a wide range of C-rates.
引用
收藏
页码:1757 / 1765
页数:9
相关论文
共 21 条
[1]  
A123 Systems, 2009, High Power Lithium Ion APR18650M1A
[2]  
[Anonymous], 2009, World Electric Vehicle Journal
[3]  
[Anonymous], 2016, P 7 INT C FUT EN SYS
[4]   Recurrent Neural Network-Based Modeling and Simulation of Lead-Acid Batteries Charge-Discharge [J].
Capizzi, Giacomo ;
Bonanno, Francesco ;
Tina, Giuseppe M. .
IEEE TRANSACTIONS ON ENERGY CONVERSION, 2011, 26 (02) :435-443
[5]  
Changhao Piao, 2010, 2010 International Conference on Optics, Photonics and Energy Engineering (OPEE 2010), P115, DOI 10.1109/OPEE.2010.5508184
[6]   Accurate electrical battery model capable of predicting, runtime and I-V performance [J].
Chen, Min ;
Rincon-Mora, Gabriel A. .
IEEE TRANSACTIONS ON ENERGY CONVERSION, 2006, 21 (02) :504-511
[7]  
Eddahech A., 2011, 2011 8th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED 2011), P645, DOI 10.1109/DEMPED.2011.6063692
[8]   Time-Domain Parameter Extraction Method for Thevenin-Equivalent Circuit Battery Models [J].
Hentunen, Ari ;
Lehmuspelto, Teemu ;
Suomela, Jussi .
IEEE TRANSACTIONS ON ENERGY CONVERSION, 2014, 29 (03) :558-566
[9]  
Huria T., 2012, 2012 IEEE International Electric Vehicle Conference (IEVC), DOI 10.1109/IEVC.2012.6183271
[10]  
Jackey R., 2013, Battery Model Parameter Estimation Using a Layered Technique: An Example Using a Lithium Iron Phosphate Cell