Cloud Based Digital Twin Development for High-Voltage Battery Systems in Commercial Vehicles

<div class="section abstract"><div class="htmlview paragraph">Over recent years, BorgWarner has intensified its efforts to explore and leverage trending technologies such as Artificial Intelligence (AI) and Machine Learning (ML) to enhance products and processes. This includes digital twin technology, which has potential use cases for system behavior analysis, product optimization and predictive maintenance.</div><div class="htmlview paragraph">This paper outlines the development process of a digital twin for a commercial vehicle battery, which serves as a demonstrator and learning platform for this technology. In order to assess the feasibility as well as hard- and software requirements, a cloud-based digital twin demonstrator was developed, integrating vehicle telemetry data with physics-based battery electric and thermal models, and an aging prediction algorithm. The key components are an Internet of Things (IoT) gateway, simulation models, data processing and ingestion pipelines, a machine learning algorithm for anomaly detection, and visualizations of telemetry and simulation data.</div><div class="htmlview paragraph">A custom dashboard developed during the work enables monitoring of the battery's state of charge (SOC), state of health (SOH), and temperatures in real-time, as well as offline analysis of historical data. The below work gives an overview of tools and methods used and describes challenges and the corresponding solutions in building up a digital twin of a vehicle component in its use phase.</div></div>