Battery Digital Twin for Electric Vehicle Deployed on Cloud

<div class="section abstract"><div class="htmlview paragraph">A BDT (Battery digital Twin) is a virtual representation of a vehicle's physical battery system, combining electrochemical and machine learning models to provide insights into key battery parameters like State of Charge (SOC), State of Health (SOH), Internal Resistance (IR), and Remaining Useful Life (RUL). This BDT model is calibrated using cell testing throughout its degradation process up to 80% SOH, alongside vehicle data for accurate predictions under diverse conditions. By continuously monitoring the battery under various operating scenarios, the BDT aids in effective battery management, identifying cells that degrade more quickly and the likely causes of this degradation. Current and temperature profiles offer insights into battery usage patterns. The BDT aggregates fleet-wide parameters and analyzes individual cell performance, providing critical information on SOC, SOH, IR, RUL, and voltage. Additionally, the BDT includes prognostic capabilities to alert users of potential issues like thermal runaway and other performance failures. It details faults related to current, voltage, and temperature over specified durations. Outputs are accessible through an interactive user interface, allowing users to explore battery performance over time. Validated against actual cell testing data, this cloud-based model updates in real time using field data from electric vehicles, thereby reducing battery-related issues and vehicle downtime.</div></div>