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Article

Statistical Validation Framework for Automotive Vehicle Simulations Using Uncertainty Learning

Insitute of Automotive Technology, Technical Universiy of Munich, Boltzmannstr. 15, 85748 Garching, Germany
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Academic Editor: Omar Hegazy
Appl. Sci. 2021, 11(5), 1983; https://doi.org/10.3390/app11051983
Received: 19 January 2021 / Revised: 9 February 2021 / Accepted: 12 February 2021 / Published: 24 February 2021
The modelling and simulation process in the automotive domain is transforming. Increasing system complexity and variant diversity, especially in new electric powertrain systems, lead to complex, modular simulations that depend on virtual vehicle development, testing and approval. Consequently, the emerging key requirements for automotive validation involve a precise reliability quantification across a large application domain. Validation is unable to meet these requirements because its results provide little information, uncertainties are neglected, the model reliability cannot be easily extrapolated and the resulting application domain is small. In order to address these insufficiencies, this paper develops a statistical validation framework for dynamic systems with changing parameter configurations, thus enabling a flexible validation of complex total vehicle simulations including powertrain modelling. It uses non-deterministic models to consider input uncertainties, applies uncertainty learning to predict inherent model uncertainties and enables precise reliability quantification of arbitrary system parameter configurations to form a large application domain. The paper explains the framework with real-world data from a prototype electric vehicle on a dynamometer, validates it with additional tests and compares it to conventional validation methods. It is published as an open-source document. With the validation information from the framework and the knowledge deduced from the real-world problem, the paper solves its key requirements and offers recommendations on how to efficiently revise models with the framework’s validation results. View Full-Text
Keywords: modelling and simulation; electric powertrain; automotive vehicle; validation; verification; uncertainty quantification; statistics; extrapolation modelling and simulation; electric powertrain; automotive vehicle; validation; verification; uncertainty quantification; statistics; extrapolation
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MDPI and ACS Style

Danquah, B.; Riedmaier, S.; Meral, Y.; Lienkamp, M. Statistical Validation Framework for Automotive Vehicle Simulations Using Uncertainty Learning. Appl. Sci. 2021, 11, 1983. https://doi.org/10.3390/app11051983

AMA Style

Danquah B, Riedmaier S, Meral Y, Lienkamp M. Statistical Validation Framework for Automotive Vehicle Simulations Using Uncertainty Learning. Applied Sciences. 2021; 11(5):1983. https://doi.org/10.3390/app11051983

Chicago/Turabian Style

Danquah, Benedikt, Stefan Riedmaier, Yasin Meral, and Markus Lienkamp. 2021. "Statistical Validation Framework for Automotive Vehicle Simulations Using Uncertainty Learning" Applied Sciences 11, no. 5: 1983. https://doi.org/10.3390/app11051983

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