LAPSE

LAPSE:2019.0408
Published Article
LAPSE:2019.0408
Improved Battery Parameter Estimation Method Considering Operating Scenarios for HEV/EV Applications
Jufeng Yang, Bing Xia, Yunlong Shang, Wenxin Huang, Chris Mi
March 15, 2019
This paper presents an improved battery parameter estimation method based on typical operating scenarios in hybrid electric vehicles and pure electric vehicles. Compared with the conventional estimation methods, the proposed method takes both the constant-current charging and the dynamic driving scenarios into account, and two separate sets of model parameters are estimated through different parts of the pulse-rest test. The model parameters for the constant-charging scenario are estimated from the data in the pulse-charging periods, while the model parameters for the dynamic driving scenario are estimated from the data in the rest periods, and the length of the fitted dataset is determined by the spectrum analysis of the load current. In addition, the unsaturated phenomenon caused by the long-term resistor-capacitor (RC) network is analyzed, and the initial voltage expressions of the RC networks in the fitting functions are improved to ensure a higher model fidelity. Simulation and experiment results validated the feasibility of the developed estimation method.
Keywords
equivalent circuit modeling, lithium-ion battery, operating scenario, parameter estimation
Suggested Citation
Yang J, Xia B, Shang Y, Huang W, Mi C. Improved Battery Parameter Estimation Method Considering Operating Scenarios for HEV/EV Applications. (2019). LAPSE:2019.0408
Author Affiliations
Yang J: Department of Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; Department of Electrical and Computer Engineering, San Diego State University, San Diego, CA 92182, USA
Xia B: Department of Electrical and Computer Engineering, San Diego State University, San Diego, CA 92182, USA; Department of Electrical and Computer Engineering, University of California San Diego, San Diego, CA 92093, USA
Shang Y: Department of Electrical and Computer Engineering, San Diego State University, San Diego, CA 92182, USA; School of Control Science and Engineering, Shandong University, Jinan 250061, China
Huang W: Department of Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Mi C: Department of Electrical and Computer Engineering, San Diego State University, San Diego, CA 92182, USA [ORCID]
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Journal Name
Energies
Volume
10
Issue
1
Article Number
E5
Year
2016
Publication Date
2016-12-22
Published Version
ISSN
1996-1073
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Original Submission
Other Meta
PII: en10010005, Publication Type: Journal Article
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LAPSE:2019.0408
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doi:10.3390/en10010005
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Mar 15, 2019
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CC BY 4.0
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[v1] (Original Submission)
Mar 15, 2019
 
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Mar 15, 2019
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http://psecommunity.org/LAPSE:2019.0408
 
Original Submitter
Calvin Tsay
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