IoT battery management system in electric vehicle based on LR parameter estimation and ORMeshNet gateway topology

Abstract

Over the last few years, electric cars (EVs) have grown in popularity. The battery management system (BMS) is critical to the long-term viability and smooth operation of an electric vehicle. The management of electric vehicles' batteries daily may help them operate better. All battery-related data is monitored and transmitted to the cloud in real time for monitoring via the Internet of Things, which is completely automated. Open loop approaches for predicting SOC, SOH, and SOP parameters suffer from a decrease in reliability when current sensor uncertainties and the rarity of relaxation status are addressed. Also, keep in mind that IoT network nodes are generally delay-tolerant, and message delivery latency still has a substantial impact on monitoring an electric vehicle’s battery system. (EV). The research team created an Internet of Things BMS based on LR parameter estimation and an ORMeshNet gateway topology to address this issue. Before any other systems can be monitored or diagnosed with the BMS or any other system, techniques are first created based on an LR to estimate SOC and SOH accurately and efficiently. This method achieves a higher rate of convergence as well as a higher level of fault tolerance than other estimation methods. The updated parameters and estimated states are used for the SOP estimator to provide more accurate peak power estimates while fulfilling operational constraints of the battery current, voltage, and SOC. Thereafter, the estimated results are transferred via IoT platform that comprises of OTH-AJS node selection followed with LND-BES optimal routing based MeshNet gateway protocol to transfer the data for monitoring. The proposed approach yields a throughput of 88.97%, a PDR of 87.98%, and a Goodput of 83.98%. Experimental results show enormous improvement in estimating the parameters with better throughput, PDR, and goodput value as compared to existing methods.