Data based modelling of driveability for internal combustion engines

Didcock, Nico; Jakubek, Stefan; Hametner, Christoph; Keuth, Nikolaus

doi:10.1080/0305215x.2017.1417397

Cited by 2 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, in terms of non-linear phenomena like friction, linearized or look-up table approaches are deployed [18, p. 31]. Didcock et al present a completely different approach by utilizing a conic (data) hull algorithm for drivability modelling [45].…”

Section: B Sensitivity Analysis and Modelling Approachesmentioning

confidence: 99%

Methods for Virtual Validation of Automotive Powertrain Systems in Terms of Vehicle Drivability—A Systematic Literature Review

2023

View full text Add to dashboard Cite

For the last two decades, an extensive transition in automotive X-in-the-loop activities from isolated electronic control units to real-time related, geographically distributed validation tasks has occurred. Benefits are strengthening frontloading, enabling concurrent engineering and reducing prototypes and testing efforts. As a downside, comprehensive system understanding and adequate simulation models must be provided. New technological trends like software-over-the-air-updates denote a continuous validation process even after the start of production. The present review focuses on the virtual validation of vehicle longitudinal dynamics. This exemplary field of application receives more and more attention as electrification of the vehicle powertrain accelerates, and this property directly influences the vehicle DNA. A systematic review process based on the PRISMA workflow has been conducted, focusing on drivabilityrelated powertrain applications. The investigation reveals the following trends: First, increasing complexity of virtualisation methods and models for validation activities influenced by vehicle-to-everything and geographically distributed development. Second, missing standards for virtual validation and proof of representativeness for combined real-virtual testing. In addition, many studies only contemplate the advantages of hardware-in-the-loop-driven development, disregarding crucial limitations and risks for such approaches. In conclusion, there is no longer the question of whether to validate virtually but how to comprehensible realise virtual validation.

show abstract

Section: B Sensitivity Analysis and Modelling Approachesmentioning

confidence: 99%

Methods for Virtual Validation of Automotive Powertrain Systems in Terms of Vehicle Drivability—A Systematic Literature Review

2023

View full text Add to dashboard Cite

show abstract

“…Note that the inclusion of other types of constraint is also possible in the presented approach. For example, more complex data hulls of measured feasible inputs can be necessary to assess the physical feasibility of decision variables as in Didcock et al (2018) and can therefore pose additional constraints on the optimization.…”

Section: Optimizationmentioning

confidence: 99%

Risk-averse real driving emissions optimization considering stochastic influences

Wasserburger

Hametner

Didcock

2019

Engineering Optimization

View full text Add to dashboard Cite

Optimization of vehicle powertrains is usually based on specific drive cycles and is performed on testbeds under reproducible conditions. However, in real-world operation, energy consumption and emissions differ significantly from the values obtained in testbed environments, which also implies breaching legislative thresholds. Therefore, in order to close the gap between testbed and real world, it is necessary to take random effects, like varying road and ambient conditions or various traffic situations, into account during the engine calibration process. In this article a stochastic optimization approach based on risk measures, that quantify the prevalent uncertainties, is presented. Rather than optimizing a deterministic value for one specific scenario described by a drive cycle, the distribution of possible outcomes is shaped in a way that it reflects the risk aversion and preferences of the decision maker. Simulation results show that incorporating randomness in the optimization process yields substantially more robust and reliable results.

show abstract

Data based modelling of driveability for internal combustion engines

Cited by 2 publications

References 12 publications

Methods for Virtual Validation of Automotive Powertrain Systems in Terms of Vehicle Drivability—A Systematic Literature Review

Methods for Virtual Validation of Automotive Powertrain Systems in Terms of Vehicle Drivability—A Systematic Literature Review

Risk-averse real driving emissions optimization considering stochastic influences

Contact Info

Product

Resources

About