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original research article

Development of Digital Twin Platform for Electric Vehicle Battery System

Putu Handre Kertha Utama 1 * , Irsyad Nashirul Haq 1 , 2 , Edi Leksono 1 , Muhammad Iqbal Juristian 1 , Ghulam Azka Alim 1 , Justin Pradipta 1

1Engineering Physics, Faculty of Industrial Technology, Institut Teknologi Bandung, Indonesia

2National Center for Sustainable Transportation Technology, Indonesia

*Email: irsyad.n@itb.ac.id
The battery system in electric vehicles needs proper monitoring and control to ensure reliable, efficient, and safe operation. Recent advancement in cyber-physical technology has brought the emerging digital twin concept. This concept opens a new possibility of real-time condition monitoring and fault diagnosis of the battery system. Although it sounds promising, the concept implementation still faces many challenges. One of the challenges is the availability of a platform to develop digital twins, which involves data pipelines and modeling tools. The data pipeline will include the acquisition, storing, and extract-transform-load (ETL) with high velocity, volume, value, variety, and veracity data, known as big data. The modeling tools must provide applications to build the high-fidelity model, one of the required elements of the digital twin. Based on those urgencies, this paper proposes a platform that facilitates a digital twinning of the battery system in an electric vehicle. The platform is built on the open-source framework CDAP, equipped with a data pipeline and modeling tools. It has run several performance tests with different computation resource configurations and workloads. Doubling the processing power can reduce 12% of computation time while increasing memory size by four times only reduces 10% of computation time. The result shows that the processing power affects the performance digital twin platform more than the memory size.

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