LAPSE:2019.0525
Published Article
LAPSE:2019.0525
Model-Based Stochastic Fault Detection and Diagnosis of Lithium-Ion Batteries
April 15, 2019
The Lithium-ion battery (Li-ion) has become the dominant energy storage solution in many applications, such as hybrid electric and electric vehicles, due to its higher energy density and longer life cycle. For these applications, the battery should perform reliably and pose no safety threats. However, the performance of Li-ion batteries can be affected by abnormal thermal behaviors, defined as faults. It is essential to develop a reliable thermal management system to accurately predict and monitor thermal behavior of a Li-ion battery. Using the first-principle models of batteries, this work presents a stochastic fault detection and diagnosis (FDD) algorithm to identify two particular faults in Li-ion battery cells, using easily measured quantities such as temperatures. In addition, models used for FDD are typically derived from the underlying physical phenomena. To make a model tractable and useful, it is common to make simplifications during the development of the model, which may consequently introduce a mismatch between models and battery cells. Further, FDD algorithms can be affected by uncertainty, which may originate from either intrinsic time varying phenomena or model calibration with noisy data. A two-step FDD algorithm is developed in this work to correct a model of Li-ion battery cells and to identify faulty operations in a normal operating condition. An iterative optimization problem is proposed to correct the model by incorporating the errors between the measured quantities and model predictions, which is followed by an optimization-based FDD to provide a probabilistic description of the occurrence of possible faults, while taking the uncertainty into account. The two-step stochastic FDD algorithm is shown to be efficient in terms of the fault detection rate for both individual and simultaneous faults in Li-ion batteries, as compared to Monte Carlo (MC) simulations.
Keywords
fault detection and classification, lithium-ion battery, Optimization, polynomial chaos expansion, thermal management, uncertainty analysis
Suggested Citation
Son J, Du Y. Model-Based Stochastic Fault Detection and Diagnosis of Lithium-Ion Batteries. (2019). LAPSE:2019.0525
Author Affiliations
Son J: Department of Chemical & Biomolecular Engineering, Clarkson University, Potsdam, NY 13676, USA [ORCID]
Du Y: Department of Chemical & Biomolecular Engineering, Clarkson University, Potsdam, NY 13676, USA [ORCID]
[Login] to see author email addresses.
Journal Name
Processes
Volume
7
Issue
1
Article Number
E38
Year
2019
Publication Date
2019-01-13
Published Version
ISSN
2227-9717
Version Comments
Original Submission
Other Meta
PII: pr7010038, Publication Type: Journal Article
Record Map
Published Article

LAPSE:2019.0525
This Record
External Link

doi:10.3390/pr7010038
Publisher Version
Download
Files
[Download 1v1.pdf] (5.9 MB)
Apr 15, 2019
Main Article
License
CC BY 4.0
Meta
Record Statistics
Record Views
621
Version History
[v1] (Original Submission)
Apr 15, 2019
 
Verified by curator on
Apr 15, 2019
This Version Number
v1
Citations
Most Recent
This Version
URL Here
https://psecommunity.org/LAPSE:2019.0525
 
Original Submitter
Calvin Tsay
Links to Related Works
Directly Related to This Work
Publisher Version