Enhancing Electric Vehicle Battery Management With the Integration of IoT and AI

Abstract

The performance of electric vehicles (EVs) is influenced by various factors such as battery life, cell voltage, health, safety, and charging/discharging speeds. Battery management is a critical task in EVs, ensuring the effective functioning of the battery. This study proposes an enhanced monitoring system for battery state-of-charge (SOC) using internet of things (IoT) and artificial intelligence (AI). The primary focus is on addressing a significant concern for researchers to ensure the safety of vehicle by accurately estimating SOC, monitoring, and promptly identifying breakdowns in the rechargeable batteries of electric vehicles. The voltage obtained from the Photovoltaic (PV) system is enhanced through the Boost integrated fly back rectifier energy DC-DC (BIFRED) converter, controlled by a cascaded adaptive neuro-fuzzy inference system (ANFIS) controller. The battery's SOC is monitored using recurrent neural networks (RNN), and the data is stored in the IoT system.