Improving EV Charging System Reliability by Detecting Aborting Causes using Algorithmic Techniques

The increased prevalence of EV has given rise to an augmented demand for EV charging infrastructure. However, the dependability of charging systems can be compromised by various factors, including power grid complications, communication difficulties, issues with components, or EV and supply station incompatibility. This can result in charging interruptions, which can be an inconvenience for EV owners and could deter the adoption of EVs. In the context of advancing EV charging infrastructure, this research endeavors to pinpoint the pivotal parameters influencing the performance of the EV charging module. To achieve this objective, an experimental study is conducted, involving the collection of data in diverse scenarios. Subsequently, a comprehensive analysis is undertaken employing the RIPPER Algorithm to generate IF-THEN rules with sensitive and non-sensitive signals. Additionally, this study utilizes feature importance metrics to identify and present the top sensitive parameters, contributing to enhancing the understanding of critical factors in EV charging systems. This approach facilitates advancements in their reliability and efficiency.