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

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. The digital twin technology has potential use cases on system behavior analysis, product optimization and predictive maintenance. As the high-voltage battery is the most valuable component in any electric vehicle understanding the mechanisms of battery aging and failure, along with their dependencies, is crucial for improving performance, battery lifespan, and residual value. A digital twin not only aids in comprehending the impacts of user behavior on the battery's state of health but also facilitates further fault diagnosis and analysis. This paper outlines the development process of a digital twin for a commercial vehicle battery. A cloud-based digital twin demonstrator was developed, which integrates vehicle telemetry data with physics-based battery electric and thermal models, and an aging prediction algorithm. The focus is on developing an Internet of Things (IoT) gateway, the simulation models, data processing and ingestion pipelines. Another key element of the digital twin is the visualization of telemetry and simulation data. A custom dashboard 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. Additionally, a machine learning algorithm for anomaly detection was deployed, providing a glance at potential features and future developments.