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

2025-01-8214

04/01/2025

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Event
WCX SAE World Congress Experience
Authors Abstract
Content
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.
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.
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.
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DOI
https://doi.org/10.4271/2025-01-8214
Pages
11
Citation
Bongards, A., Liu, X., Beemer, M., Gajowski, D. et al., "Cloud Based Digital Twin Development for High-Voltage Battery Systems in Commercial Vehicles," SAE Technical Paper 2025-01-8214, 2025, https://doi.org/10.4271/2025-01-8214.
Additional Details
Publisher
Published
Apr 01
Product Code
2025-01-8214
Content Type
Technical Paper
Language
English