Capturing Users’ Privacy Expectations To Design Better Smart Car Applications

Smart cars learn from gathered operating data to add value to the users’ driving experience and increase security. Thereby, not only users benefit from these data- driven services; various actors in the associated ecosystem are able to optimize their business models based on smart car related information. Continuous collection of data can defy users’ privacy expectations, which may lead to reluctant usage or even refusal to accept services offered by smart car providers. This paper investigates users’ privacy expectations using a vignette study, in which participants judge variations of smart car applications, differing with respect to factors such as data transmission and the type of information transferred. We expect to identify application dependent privacy expectations, that eventually yield insights on how to design smart car applications and associated business models that respect users’ privacy expectations

Potentials to Reduce the Energy Consumption of Electric Vehicles in Urban Traffic

By means of a parameter study using a detailed backwards facing model of the longitudinal vehicle dynamics, the design of the transmission ratio in battery electric vehicles (BEV) is analyzed for different driving cycles and it is shown that the electric consumption in urban operation can be significantly reduced by up-speeding the electric machine (EM) using a high 1st transmission ratio. But this potential currently remains unused in fixed-speed BEV due to various additional driving requirements of extra-urban driving with higher vehicle speeds. For this reason, multi-speed BEV are further investigated as a solution to the conflicting design objectives. An additional parameter study for multi-speed BEV with two transmission ratios shows further potentials for the reduction of electric consumption both in urban and extra-urban driving scenarios. Furthermore, the more complex "Two-Drive-Transmission" (TDT) concept is investigated as a multi-speed BEV powertrain with two downsized EMs instead of one high-power EM and it is compared with the other BEV variants using a comparative optimization approach. The TDT uses low-cost and energetically efficient shifting devices based on the technology of an automated manual transmission with simple dog clutches without friction surfaces, allowing shifting without interruption of traction force. Dynamic programming is applied as operational strategy for all simulations considering shifting losses to achieve a benchmarking of the potentials of fixed-speed and multi-speed BEV

Realfahrtbasierte Bewertung des ökologischen Potentials von Fahrzeugantriebskonzepten

Um die Auswirkungen des Klimawandels einzudämmen besteht das Bestreben, möglichst klimafreundliche Pkw-Antriebe zu gestalten, wozu die Bewertung der Treibhausgasbilanz von hoher Relevanz ist. Üblicherweise werden die zu vergleichenden Fahrzeuge verschiedener Antriebskonzepte auf Basis der Auslegung und der Verbrauchswerte von bestehenden Marktfahrzeugen charakterisiert, wodurch eine realitätsnahe Bewertung der aktuellen Umsetzung der Konzepte erreicht wird. Allerdings bleibt bei diesem Vorgehen unbekannt, wie die Antriebskonzepte in der Bewertung abschneiden würden, wenn ihre Auslegung und Betriebsweise zur Erreichung der minimal möglichen Treibhausgasbilanz, welche als ökologisches Potential definiert wird, optimiert werden. Um einen realfahrtbasierten und belastbaren Vergleich des Potentials zur Reduktion der THG-Emissionen zu ermöglichen, wird im Rahmen dieser Arbeit ein Optimierungsansatz umgesetzt. Für eine bestmögliche Vergleichbarkeit werden stets einheitliche Anforderungen, Nutzungsprofile und Randbedingungen zugrunde gelegt. Zur Abbildung der Fahrweise werden repräsentative Fahrzyklen für verschiedene Distanzbereiche verwendet. Die Nutzbarkeit der synthetischen Fahrzyklen wird durch einen neuen Validierungsprozess überprüft. Innerhalb der Optimierung wird eine Gütefunktional-basierte Betriebsstrategie eingesetzt, um das quasi-optimale Betriebsverhalten für jede Antriebsparametrierung zu ermitteln. Durch die Bestimmung der optimalen Auslegung und Betriebsweise jedes Antriebskonzepts entsteht eine einheitliche Bewertungsbasis, die den belastbaren Vergleich des ökologischen Potentials der Konzepte ermöglicht. Mit diesem Vorgehen wird das ökologische Potential verschiedener Antriebskonzepte in den Bezugsjahren 2020 und 2030 in Deutschland untersucht

Real-Driving-Based Comparison of the Eco-Impact of Powertrain Concepts using a Data-Driven Optimization Environment

In order to limit the effects of man-made climate change, the assessment of the ecological impact of different powertrain concepts is of increasing relevance and intensely studied. In this contribution we present a data-driven optimization environment that enables to identify the ecological potential of different concepts for different scenarios. The parametrization of each powertrain concept is dedicatedly optimized to minimize the ecological impact, which allows for an unbiased and reliable comparison on an uniform evaluation basis. To exploit the potential of each single powertrain parametrization, the operating strategy of the powertrain is adapted. Naturalistic driving profiles, including the speed, acceleration and road-slope information are depicted by multidimensional and representative driving cycles, allowing for an efficient search of the real-driving-optimal powertrain parametrizations within the optimization. In this study, we investigate long-range capable vehicles for a scenario in the reference year 2030 in Germany. Conventional vehicles, battery electric vehicles, fuel cell electric vehicles and plug-in hybrid electric vehicles are examined. Finally, the results are compared to an evaluation of the CO2 emissions according to the Worldwide harmonized Light vehicles Test Procedure (WLTP)

Special Issue on Future Powertrain Technologies: Editorial

Synthetic Driving Cycles have been used in numerous studies to describe a certain driving profile of relevance. An important purpose of synthetic cycles is to limit the necessary time on a test-rig or to reduce the computational effort within simulations, which is achieved by compressing a larger amount of gathered operating data from a certain vehicle or a vehicle fleet to a necessary minimum. Interestingly, despite the intensive use of the synthetic driving cycles, there is only limited literature on the validation of using synthetic driving cycles. Therefore, the scope of this work is to further investigate under which conditions synthetic driving cycles can be used to replace the entirety of the relevant operating data in the evaluation of a vehicle’s consumption. We apply a longitudinal vehicle simulation model to calculate the fuel and electric consumption of vehicles with different powertrain concepts on many generated synthetic driving cycles for different compression rates. We then compare that to the consumption if considering the original driving data. A legislative driving cycle (WLTC) as well as naturalistic driving data sets are used for the evaluation. The results show, that synthetic driving cycles allow for a compact representation of the original data sets but possible compression rates depend on the specific driving data. The presented two-step process can be extended to a generalized validation process for the use of synthetic driving cycles