LAPSE:2018.1169
Published Article
LAPSE:2018.1169
Online Identification with Reliability Criterion and State of Charge Estimation Based on a Fuzzy Adaptive Extended Kalman Filter for Lithium-Ion Batteries
Zhongwei Deng, Lin Yang, Yishan Cai, Hao Deng
November 28, 2018
In the field of state of charge (SOC) estimation, the Kalman filter has been widely used for many years, although its performance strongly depends on the accuracy of the battery model as well as the noise covariance. The Kalman gain determines the confidence coefficient of the battery model by adjusting the weight of open circuit voltage (OCV) correction, and has a strong correlation with the measurement noise covariance (R). In this paper, the online identification method is applied to acquire the real model parameters under different operation conditions. A criterion based on the OCV error is proposed to evaluate the reliability of online parameters. Besides, the equivalent circuit model produces an intrinsic model error which is dependent on the load current, and the property that a high battery current or a large current change induces a large model error can be observed. Based on the above prior knowledge, a fuzzy model is established to compensate the model error through updating R. Combining the positive strategy (i.e., online identification) and negative strategy (i.e., fuzzy model), a more reliable and robust SOC estimation algorithm is proposed. The experiment results verify the proposed reliability criterion and SOC estimation method under various conditions for LiFePOâ‚„ batteries.
Keywords
battery management system, fuzzy adaptive extended Kalman filter, intrinsic model error, parameter reliability criterion, state of charge
Suggested Citation
Deng Z, Yang L, Cai Y, Deng H. Online Identification with Reliability Criterion and State of Charge Estimation Based on a Fuzzy Adaptive Extended Kalman Filter for Lithium-Ion Batteries. (2018). LAPSE:2018.1169
Author Affiliations
Deng Z: School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Yang L: School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Cai Y: School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Deng H: School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
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Journal Name
Energies
Volume
9
Issue
6
Article Number
E472
Year
2016
Publication Date
2016-06-21
Published Version
ISSN
1996-1073
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PII: en9060472, Publication Type: Journal Article
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LAPSE:2018.1169
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doi:10.3390/en9060472
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Nov 28, 2018
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Calvin Tsay
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