An IOT-based Battery Surveillance System For E-Vehicles

Abstract

Battery surveillance is critical for the majority of battery-powered vehicles, for the benefit of the lead-acid battery's safety , functioning, and even to extend its life. Due to the development of EVs and HEVs, battery technology has made tremendous progress in recent years. However, the estimate of the state of charge (SOC) remains a battery engineering challenge. The remaining load ratio to the maximum load battery capacity is defined as the SOC. In terms of battery safety and maintenance, the SOC estimate is of prime importance. Artificial intelligence, notably machine learning-based systems, has recently been used to estimate battery state, both as part of adaptive systems and as stand-alone systems. The use of data-driven algorithms to estimate battery conditions with high precision is a potential approach. The purpose of this study is to offer a novel and highly accurate approach for predicting the state of charge (SOC) of a Li-ion battery cell that requires little conceptualization and modeling work. The battery aging process can be slowed down by properly treating the battery, including restricting frequent charge and deep drain cycles. This study presents an analysis based on IoT with an ultimate wireless battery surveillance system (WBSS) to determine the relationship between journey distance and discharge cycle. The proposed system's methodology has been tested and found to be effective.