Digital Twinning From Vehicle Usage Statistics for Customer-Centric Automotive Systems Engineering
Towards customer-centric automotive systems engineering, it is essential to incorporate physical models and vehicle usage behavior into decision support systems (DSSs). Such DSSs tend to apply digital twin concepts, where simulations are parameterized with fine-grained time-series data acquired from...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Open Journal of Intelligent Transportation Systems |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10342795/ |
| _version_ | 1797376292423729152 |
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| author | Kunxiong Ling |
| author_facet | Kunxiong Ling |
| author_sort | Kunxiong Ling |
| collection | DOAJ |
| description | Towards customer-centric automotive systems engineering, it is essential to incorporate physical models and vehicle usage behavior into decision support systems (DSSs). Such DSSs tend to apply digital twin concepts, where simulations are parameterized with fine-grained time-series data acquired from customer fleets. However, logging vast amounts of data from customer fleets is costly and raises privacy concerns. Alternatively, these time-series data can be aggregated into vehicle usage statistics. The feasibility of creating digital twins from these vehicle usage statistics and the corresponding DSSs for systems engineering is yet to be established. This paper aims to demonstrate this feasibility by proposing a DSS framework that integrates four key elements of digital twinning: aggregate usage statistics from customer fleets, logging data from testing fleets, physical models for vehicle simulation, and evaluation models to derive decision support metrics. The digital twinning involves a four-step process: pre-processing, profiling, simulation, and post-processing. Based on a real-world fleet of 57110 vehicles and four evaluation metrics, a proof of concept is conducted. Results show that the digital twin covers the evaluation metrics of 99% of the vehicles and reaches an average fleet twinning accuracy of 91.09%, which indicates the feasibility and plausibility of the proposed DSS framework. |
| first_indexed | 2024-03-08T19:36:27Z |
| format | Article |
| id | doaj.art-7d452ca025f641d78ccb61e2718a6e86 |
| institution | Directory Open Access Journal |
| issn | 2687-7813 |
| language | English |
| last_indexed | 2024-03-08T19:36:27Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Intelligent Transportation Systems |
| spelling | doaj.art-7d452ca025f641d78ccb61e2718a6e862023-12-26T00:12:49ZengIEEEIEEE Open Journal of Intelligent Transportation Systems2687-78132023-01-01496697810.1109/OJITS.2023.333943010342795Digital Twinning From Vehicle Usage Statistics for Customer-Centric Automotive Systems EngineeringKunxiong Ling0https://orcid.org/0000-0003-0203-301XIngolstadt School of Management, Catholic University of Eichstätt-Ingolstadt, Ingolstadt, GermanyTowards customer-centric automotive systems engineering, it is essential to incorporate physical models and vehicle usage behavior into decision support systems (DSSs). Such DSSs tend to apply digital twin concepts, where simulations are parameterized with fine-grained time-series data acquired from customer fleets. However, logging vast amounts of data from customer fleets is costly and raises privacy concerns. Alternatively, these time-series data can be aggregated into vehicle usage statistics. The feasibility of creating digital twins from these vehicle usage statistics and the corresponding DSSs for systems engineering is yet to be established. This paper aims to demonstrate this feasibility by proposing a DSS framework that integrates four key elements of digital twinning: aggregate usage statistics from customer fleets, logging data from testing fleets, physical models for vehicle simulation, and evaluation models to derive decision support metrics. The digital twinning involves a four-step process: pre-processing, profiling, simulation, and post-processing. Based on a real-world fleet of 57110 vehicles and four evaluation metrics, a proof of concept is conducted. Results show that the digital twin covers the evaluation metrics of 99% of the vehicles and reaches an average fleet twinning accuracy of 91.09%, which indicates the feasibility and plausibility of the proposed DSS framework.https://ieeexplore.ieee.org/document/10342795/Customer centricitydecision support systemdigital twinfleet datasystems engineering |
| spellingShingle | Kunxiong Ling Digital Twinning From Vehicle Usage Statistics for Customer-Centric Automotive Systems Engineering IEEE Open Journal of Intelligent Transportation Systems Customer centricity decision support system digital twin fleet data systems engineering |
| title | Digital Twinning From Vehicle Usage Statistics for Customer-Centric Automotive Systems Engineering |
| title_full | Digital Twinning From Vehicle Usage Statistics for Customer-Centric Automotive Systems Engineering |
| title_fullStr | Digital Twinning From Vehicle Usage Statistics for Customer-Centric Automotive Systems Engineering |
| title_full_unstemmed | Digital Twinning From Vehicle Usage Statistics for Customer-Centric Automotive Systems Engineering |
| title_short | Digital Twinning From Vehicle Usage Statistics for Customer-Centric Automotive Systems Engineering |
| title_sort | digital twinning from vehicle usage statistics for customer centric automotive systems engineering |
| topic | Customer centricity decision support system digital twin fleet data systems engineering |
| url | https://ieeexplore.ieee.org/document/10342795/ |
| work_keys_str_mv | AT kunxiongling digitaltwinningfromvehicleusagestatisticsforcustomercentricautomotivesystemsengineering |