Your Battery Is a Blast! Safeguarding Against Counterfeit Batteries with Authentication
Francesco Marchiori, Mauro Conti

TL;DR
This paper introduces two machine learning-based methods, DCAuth and EISthentication, for authenticating lithium-ion batteries using internal characteristics, enhancing safety by detecting counterfeit cells without external devices.
Contribution
The paper presents novel battery authentication techniques that are resilient, scalable, and do not require external hardware, improving upon existing methods.
Findings
High authentication accuracy (up to 0.99 for architecture, 0.96 for models)
Effective detection of counterfeit batteries in diverse datasets
Methods are scalable and adaptable to various battery types
Abstract
Lithium-ion (Li-ion) batteries are the primary power source in various applications due to their high energy and power density. Their market was estimated to be up to 48 billion U.S. dollars in 2022. However, the widespread adoption of Li-ion batteries has resulted in counterfeit cell production, which can pose safety hazards to users. Counterfeit cells can cause explosions or fires, and their prevalence in the market makes it difficult for users to detect fake cells. Indeed, current battery authentication methods can be susceptible to advanced counterfeiting techniques and are often not adaptable to various cells and systems. In this paper, we improve the state of the art on battery authentication by proposing two novel methodologies, DCAuth and EISthentication, which leverage the internal characteristics of each cell through Machine Learning models. Our methods automatically…
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Taxonomy
TopicsAdvanced Battery Technologies Research · Recycling and Waste Management Techniques
