Incremental State-of-Charge determination of a Lithium-ion battery based on Capacity update using Particle Filtering framework

被引:3
作者
Chouhan, Shreyansh [1 ]
Guha, Arijit [1 ]
机构
[1] Natl Inst Technol Rourkela, Dept Elect Engn, Rourkela, Odisha, India
来源
2023 IEEE TRANSPORTATION ELECTRIFICATION CONFERENCE & EXPO, ITEC | 2023年
关键词
Lithium-ion Battery; Particle Filter (PF); Incremental State-of-Charge (SOC); Battery Health; PROGNOSTICS; TUTORIAL;
D O I
10.1109/ITEC55900.2023.10186967
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
State-of-Charge (SOC) is considered as one of the key components of a Battery Management System (BMS), which provides an indication of the remaining charge in a battery. Accurate SOC measurement facilitates longer battery life and mitigates any possible catastrophic battery failure. However, accurate estimation of the battery SOC continues to be a challenging task. There are several topologies to find the SOC of a battery but none of them incorporates the aging factor of a battery due to repeated usage. Considering this fact, the proposed methodology considers the degrading capacity impacts on the incremental SOC of a battery. Incremental SOC is defined as the difference in SOC levels between two successive time intervals. The proposed model is developed based on Particle filtering (PF) framework which eliminates the need of extensive data. In the proposed approach, the incremental SOC has been considered as an indirect indication of the battery health. For validation purpose, battery data from NASA PCoE has been used. The proposed approach revealed that when battery capacity degradation is considered, a correction of 8.73% (absolute) in incremental SOC is observed, and prediction of incremental SOC with relative error of 1.23% in reference to the improved incremental SOC was achieved.
引用
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页数:6
相关论文
共 22 条
[11]   Battery Remaining Useful Life Prediction with Inheritance Particle Filtering [J].
Li, Lin ;
Saldivar, Alfredo Alan Flores ;
Bai, Yun ;
Li, Yun .
ENERGIES, 2019, 12 (14)
[12]   Resampling Methods for Particle Filtering [J].
Li, Tiancheng ;
Bolic, Miodrag ;
Djuric, Petar M. .
IEEE SIGNAL PROCESSING MAGAZINE, 2015, 32 (03) :70-86
[13]   A State of Health Estimation Method for Lithium-Ion Batteries Based on Improved Particle Filter Considering Capacity Regeneration [J].
Pan, Haipeng ;
Chen, Chengte ;
Gu, Minming .
ENERGIES, 2021, 14 (16)
[14]  
Saha B, 2007, NASA AMES Progn Data Repos
[15]  
Saha B, 2009, ANN C PHM SOC, V1, DOI DOI 10.1109/AERO.2009.4839676
[16]   Prognostics of lithium-ion batteries based on different dimensional state equations in the particle filtering method [J].
Su, Xiaohong ;
Wang, Shuai ;
Pecht, Michael ;
Ma, Peijun ;
Zhao, Lingling .
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL, 2017, 39 (10) :1537-1546
[17]   Particle filtering without tears: A primer for beginners [J].
Tulsyan, Aditya ;
Gopaluni, R. Bhushan ;
Khare, Swanand R. .
COMPUTERS & CHEMICAL ENGINEERING, 2016, 95 :130-145
[18]  
Wang W, 2016, 2016 IEEE CHINESE GUIDANCE, NAVIGATION AND CONTROL CONFERENCE (CGNCC), P346, DOI 10.1109/CGNCC.2016.7828809
[19]   Electrochemical impedance spectroscopy based estimation of the state of charge of lithium-ion batteries [J].
Westerhoff, U. ;
Kroker, T. ;
Kurbach, K. ;
Kurrat, M. .
JOURNAL OF ENERGY STORAGE, 2016, 8 :244-256
[20]   Battery Management Systems in Electric and Hybrid Vehicles [J].
Xing, Yinjiao ;
Ma, Eden W. M. ;
Tsui, Kwok L. ;
Pecht, Michael .
ENERGIES, 2011, 4 (11) :1840-1857