# Your Battery Is a Blast! Safeguarding Against Counterfeit Batteries with Authentication

arXiv: https://arxiv.org/abs/2309.03607 · HTML: https://mlsanity.com/paper/2309.03607/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 82 references that are in the map.
- First posted on arXiv 7 Sep 2023.

## Builds on (most cited first, 9 of 9)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning](https://mlsanity.com/paper/1609.06570/) (2016)
- [Explainable Machine Learning for Scientific Insights and Discoveries](https://mlsanity.com/paper/1905.08883/) (2019)
- [All You Need is "Love": Evading Hate-speech Detection](https://mlsanity.com/paper/1808.09115/) (2018)
- ["Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice](https://mlsanity.com/paper/2212.14315/) (2022)
- [An Efficient Adversarial Attack for Tree Ensembles](https://mlsanity.com/paper/2010.11598/) (2020)
- [I-SiamIDS: an improved Siam-IDS for handling class imbalance in network-based intrusion detection systems](https://mlsanity.com/paper/2009.10940/) (2020)
- [Your Attack Is Too DUMB: Formalizing Attacker Scenarios for Adversarial Transferability](https://mlsanity.com/paper/2306.15363/) (2023)

## Built on by (most cited first, 1 of 1)

- [Profiling Electric Vehicles via Early Charging Voltage Patterns](https://mlsanity.com/paper/2506.07714/) (2025)

## Abstract (arXiv)

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 authenticate lithium-ion battery models and architectures using data from their regular usage without the need for any external device. They are also resilient to the most common and critical counterfeit practices and can scale to several batteries and devices. To evaluate the effectiveness of our proposed methodologies, we analyze time-series data from a total of 20 datasets that we have processed to extract meaningful features for our analysis. Our methods achieve high accuracy in battery authentication for both architectures (up to 0.99) and models (up to 0.96). Moreover, our methods offer comparable identification performances. By using our proposed methodologies, manufacturers can ensure that devices only use legitimate batteries, guaranteeing the operational state of any system and safety measures for the users.