Abstract
This paper presents a novel approach to monitoring Electric Vehicle (EV) charging ecosystems, crucial for assessing their condition parameters. Leveraging sensor-derived data as a primary information source, this study focuses on the development of predictive models for battery and charging station conditions, particularly concerning voltage levels and associated variables. The core contribution lies in the design and implementation of a multisensory monitoring system tailored explicitly for Battery Management Systems (BMS). Unlike existing proposals, this system is founded upon a set of fundamental requirements that distinguish its design and functionality. Notably, it introduces a variance-based detection scheme for assessing the State of Health (SOH), enabling the prediction of battery degradation and facilitating fault detection within EV systems.
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