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Li-ion battery internal short circuits are a major safety issue for electric vehicles, and can lead to serious consequences such as battery thermal runaway. An internal short can be caused by mechanical abuse, high temperature, overcharging, and lithium plating. The low impedance or hard internal short circuit is the most dangerous kind. The high internal current flow can lead to battery temperature increase, thermal runaway, and even explosion in a few seconds. Algorithms that can quickly detect such serious events with a high confidence level and which are robust to sensor noise are needed to ensure passenger safety. False positives are also undesirable as many thermal runaway mitigation techniques, such as activating pyrotechnic safety switches, would disable the vehicle. Conventional methods of battery internal short detection, including voltage and surface temperature based algorithms, work well for a single cell. However, these methods are difficult to apply in large scale battery packs with many parallel cells. In this study, we propose a new internal short detection method by using cell swelling information during the early stages of a battery heating caused by an internal short circuit. By measuring cell expansion force, higher confidence level detection can be achieved for an internal short circuit in an electric vehicle scale battery pack.
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Leveraging Structures in Fault Diagnosis for Lithium-Ion Battery Packs
Lithium-ion battery packs consist of a varying number of single cells, designed to meet specific application requirements for output voltage and capacity. Effective fault diagnosis in these battery packs is an essential prerequisite for ensuring their safe and reliable operation. To address this need, we introduce a novel model-based fault diagnosis approach. Our approach distinguishes itself by leveraging informative structural properties inherent in battery packs such as uniformity among the constituent cells, and sparsity of fault occurrences to enhance its fault diagnosis capabilities. The proposed approach formulates a moving horizon estimation (MHE) problem, incorporating such structural information to estimate different fault signals—specifically, internal short circuits, external short circuits, and voltage and current sensors faults. We conduct various simulations to evaluate the performance of the proposed approach under different fault types and magnitudes. The obtained results validate the proposed approach and promise effective fault diagnosis for battery packs.
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- Award ID(s):
- 1847651
- PAR ID:
- 10566968
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-6301-2
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Location:
- St. Louis, MO, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICPS59941.2024.10640051
