Life-Cycle Oriented Decision Support for the Planning of Fleets with Alternative Powertrain Vehicles

Mennenga, Mark; Thiede, Sebastian; Herrmann, Christoph

doi:10.1007/978-981-4451-48-2_12

Cited by 1 publication

(1 citation statement)

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“…As the complex physical relationships in automotive systems are hard to be integrated into pure data analytical models, model-based DSSs have been widely used in the automotive industry. Mennenga et al [34] applied DSSs for lifetime-based fleet planing for electrified vehicles. Kloör et al [35] repurposed batteries for electric vehicles using a model-based DSS.…”

Section: Related Workmentioning

confidence: 99%

Digital Twinning From Vehicle Usage Statistics for Customer-Centric Automotive Systems Engineering

Ling

2023

IEEE Open J. Intell. Transp. Syst.

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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.

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Section: Related Workmentioning

confidence: 99%

Digital Twinning From Vehicle Usage Statistics for Customer-Centric Automotive Systems Engineering

Ling

2023

IEEE Open J. Intell. Transp. Syst.

View full text Add to dashboard Cite

show abstract

Life-Cycle Oriented Decision Support for the Planning of Fleets with Alternative Powertrain Vehicles

Cited by 1 publication

References 30 publications

Digital Twinning From Vehicle Usage Statistics for Customer-Centric Automotive Systems Engineering

Digital Twinning From Vehicle Usage Statistics for Customer-Centric Automotive Systems Engineering

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