  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.
