Your Battery Is A Blast!

Safeguarding Against Counterfeit Batteries with Authentication
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Francesco Marchiori · Mauro Conti

Table of Contents

1. Abstract
2. Citation
3. Usage
4. Flowchart
5. Datasets
6. Models

🧩 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 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.

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🗣️ Citation

Please, cite this work when reffering to EISthentication.

@inproceedings{10.1145/3576915.3623179,
  author = {Marchiori, Francesco and Conti, Mauro},
  title = {Your Battery Is a Blast! Safeguarding Against Counterfeit Batteries with Authentication},
  year = {2023},
  isbn = {9798400700507},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3576915.3623179},
  doi = {10.1145/3576915.3623179},
  pages = {105–119},
  numpages = {15},
  keywords = {lithium-ion batteries, identification, machine learning, authentication},
  location = {, Copenhagen, Denmark, },
  series = {CCS '23}
}

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⚙️ Usage

First, start by cloning the repository.

git clone https://github.com/Mhackiori/EISthentication.git
cd EISthentication

Then, install the required Python packages by running:

pip install -r requirements.txt

You now need to add the datasets in the repository. You can do this by downloading the zip file here and extracting it in this repository.

To replicate the results in our paper, you simply need to execute the Jupyter Notebook.

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🚥 Flowchart

In the next Figure, we summarize the functioning of the whole system by showing a flowchart of the different steps happening before the authentication response.

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📚 Datasets

Here is the list of the datasets used for EISthentication.

Name	Battery	Data
SiCWell [Ref]	Unknown (NMC)	Cycled with driving profiles (sWLTP, UDDS).
SNL [Ref]	• LGDBHE21865 (NMC) • ANR18650M1 (LFP) • NCR18650A (LCO) • LGDBHG21865 (NCA)	Cycled at same SOC, different temperatures.
Zhang et al. [Ref]	Eunicell LR2032 (LCO)	Three temperatures at nine different stages of cycling.

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🤖 Models

Here is the list of the models used for EISthentication and their hyperparameters tuned during Grid Search.

Models	Hyperparameters
AdaBoost (AB)	• Number of estimators
Decision Tree (DT)	• Criterion • Maximum Depth
Gaussian Naive Bayes (GNB)	• Variance Smoothing
Nearest Neighbors (KNN)	• Number of neighbors • Weight function
Neural Network (NN)	• Hidden layer sizes • Activation function • Solver
Quadratic Discriminant Analysis (QDA)	• Regularization Parameter
Random Forest (RF)	• Criterion • Number of estimators
Support Vector Machine (SVM)	• Kernel • Regularization parameter • Kernel coefficient

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