Towards in-situ detection of nascent short circuits and accurate estimation of state of short in Lithium-Ion Batteries
| Autor: | Sagar Bharathraj, Anshul Kaushik, Younghun Sung, Shashishekar P. Adiga, K. Subramanya Mayya, Myeongjae Lee |
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| Rok vydání: | 2022 |
| Předmět: |
Battery (electricity)
Renewable Energy Sustainability and the Environment Computer science Accurate estimation Energy Engineering and Power Technology Early detection chemistry.chemical_element Reduced order Ion chemistry Electronic engineering Lithium State (computer science) Electrical and Electronic Engineering Physical and Theoretical Chemistry Short circuit |
| Zdroj: | Journal of Power Sources. 520:230830 |
| ISSN: | 0378-7753 |
| Popis: | Early detection of internal short circuits (ISC) in Lithium-Ion Batteries (LIBs) is crucial for avoiding potential catastrophes. State-of-the art health monitoring methods fall short in terms of their ability to detect early-stage short circuits and in terms of ease of implementation. We report a unique internal-short circuit detection method, capable of detecting early-stage short circuits. A set of electrochemically curated pulse current probes, activated at predetermined states of charge (SOC), accurately determine the short-induced leakage current by comparing with an on-board physics based electrochemical-thermal reduced order model, that considers characteristic non-linear behaviour of LIBs. These specially designed diagnostic probes act as a ‘treadmill’ test, which detect soft short and estimates the soft short resistance accurately. Importantly, we demonstrate its ability to detect, estimate both internal short and ageing-related battery degradation, even when both are present. Proof-of-concept experiments on commercial batteries show our method's ability to detect soft short (up to 200Ω) and ageing extents (>90%) with >98% accuracy. Anchored in underlying electrochemical processes of the battery, we provide a detailed analysis on how and why this method is uniquely positioned as an accurate, practically implementable health and safety monitoring algorithm on a battery management system. |
| Databáze: | OpenAIRE |
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