Driving Range Estimation Using AMRIT for Prognostics Model of Electric Vehicles

State of charge (SoC) and discharging estimations are evolving techniques in electric vehicles. These two factors influence the thermal model and driving-range estimation logic efficiency and accuracy. Even though many research ideas have been presented with various data science principles and deep learning algorithms, sustainable embedded EV product realization has been the biggest challenge in the automotive industry during the last decade. In this study, Alternative Machine Reasoning and Integrative Techniques (AMRIT) were adapted to estimate battery discharge behavior and driving range estimation. The remaining driving range is a crucial metric as defined in the Indian regulation standard (AIS-040). Python simulations has been presented to compare the energy consumption, thermal model of the battery, and discharge pattern to deliver a consistent driving range in the dashboard for driver assistance.