Paper Contents
Abstract
The rise of electric mobility has accelerated the deployment of lithium-ion batteries and consequently intensified end-of-life concerns. Although batteries removed from vehicles lose their automotive suitability, a significant portion of their capacity remains functional for less demanding applications. However, unstructured evaluation practices often lead to unsafe reuse or premature disposal. This paper introduces an IoT-assisted health assessment framework combined with machine learning to determine the residual life of used electric vehicle batteries. Sensor readings collected through an embedded platform are analysed using a supervised classification model that predicts battery state of health (SoH) and generates reuse or recycling recommendations. Results indicate that the system improves decision accuracy, increases circular usage of batteries and contributes to sustainability in electric transportation ecosystems.
Copyright
Copyright © 2025 V. KRUPA PRASAD. This is an open access article distributed under the Creative Commons Attribution License.
