A holistic framework for acquisition, processing and evaluation of vehicle fleet test data

Wittmann, Michael; Lohrer, Jurgen; Betz, Johannes; Jager, Benedikt; Kugler, Maria; Klöppel, Markus; Waclaw, Adam; Hann, Moritz; Lienkamp, Markus

doi:10.1109/itsc.2017.8317637

Cited by 19 publications

(11 citation statements)

References 18 publications

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“…The sampling rate of the GPS loggers was three seconds per point and the positioning accuracy was four meters. GPS points was generated by the smart phones, recording the spatial-temporal information in real time (i.e., longitude, latitude, and timestamps) and uploading that information to the PostgreSQL database service with the GiST (combine R-Tree) index for data management [29,30]. This index is suitable for optimizing nearest-neighbor searching and enhancing computing efficiency.…”

Section: Data Collection and Processingmentioning

confidence: 99%

Identifying the Daily Activity Spaces of Older Adults Living in a High-Density Urban Area: A Study Using the Smartphone-Based Global Positioning System Trajectory in Shanghai

Yin

et al. 2021

Sustainability

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The characteristics of the built environment and the configuration of public facilities can affect the health and well-being of older adults. Recognizing the range of daily activities and understanding the utilization of public facilities among older adults has become essential in planning age-friendly communities. However, traditional methods are unable to provide large-scale objective measures of older adults’ travel behaviors. To address this issue, we used the smartphone-based global positioning system (GPS) trajectory to explore the activity spaces of 76 older adults in a high-density urban community in Shanghai for 102 consecutive days. We found that activity spaces are centered around older adults’ living communities, with 46.3% within a 1.5 km distance. The older adults’ daily activities are within a 15 min walking distance, and accessibility is the most important factor when making a travel choice to parks and public facilities. We also found that the travel range and spatial distribution of points of interest are different between age and gender groups. In addition, we found that using a concave hull with Alpha shape algorithm is more applicable and robust than the traditional convex hull algorithm. This is a unique case study in a high-density urban area with objective measures for assessing the activity spaces of older adults, thus providing empirical evidence for promoting healthy aging in cities.

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Section: Data Collection and Processingmentioning

confidence: 99%

Identifying the Daily Activity Spaces of Older Adults Living in a High-Density Urban Area: A Study Using the Smartphone-Based Global Positioning System Trajectory in Shanghai

Yin

et al. 2021

Sustainability

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show abstract

“…The presented method is shown based on a real driving data record according to the framework shown by Wittmann et al [15]. Here, an existing database is used, which has been entered with different vehicles, from small cars to luxury class, in different contexts.…”

Section: Used Databasementioning

confidence: 99%

“…Therefore, the duration of an idle state will not be derived from the real driving data in this paper. The duration of the driving state S 12 is randomly chosen between one and 90 s. This value corresponds to the maximum circulation time of a traffic light and is considered useful here [15]. For the results in this paper, an equal distribution for values between 1 to 90 s is used.…”

Section: Sequences Of Idlementioning

confidence: 99%

Compressed Driving Cycles Using Markov Chains for Vehicle Powertrain Design

Zähringer

Kalt

Lienkamp

2020

WEVJ

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In recent years, topics of digitalisation, urbanisation and sustainability are shaping future developments in the automotive industry and pose new challenges to the individual areas of vehicle development. In the design and simulation of multiple components in the vehicle, especially in powertrain, generically generated driving cycles play an important role since they reflect representative user behavior. Nowadays, driving cycles are mainly associated with the worldwide fuel consumption and emission tests, but they are nevertheless used in many fields of vehicle development. A design based on the currently used consumption and emission cycles proves to be unsuitable, especially for electric vehicles. This is reflected in the current discussion about the large discrepancy between the driving ranges achieved when using emission cycles and those under real driving conditions. In order to minimize the energy consumption of a vehicle, the main requirement is a good efficiency of the electric machine. Here, not the maximum efficiency of the machine is decisive, but the averaged overall system efficiency during real driving behavior. The electric machine must therefore be designed for the driving cycle. To optimize the use of electric machines in the future, the actual power requirements of future vehicle models must first be determined. In the course of this paper, a compressed vehicle class specific driving cycle will be created based on real driving data using Markov chains, which can be used for powertrain dimensioning.

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“…The driving data analyzed in this paper were recorded by the Institute of Automotive Technology at the Technical University of Munich [16]. The data originate from different test series in the private, business, and taxi sector and was, therefore, collected using different vehicles.…”

Section: Real Driving Datamentioning

confidence: 99%

Requirements for Electric Machine Design based on Operating Points from Real Driving Data in Cities

Kalt

Brenner

Lienkamp

2019

WEVJ

Self Cite

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Increasing environmental awareness leads to the necessity for more efficient powertrains in the future. However, the development of new vehicle concepts generates a trend towards ever shorter development cycles. Therefore, new concepts must be tested and validated at an early stage in order to meet the increasing time pressure. This requires the determination of real driving data in fleet tests in order to generate realistic driving cycles, which correspond as closely as possible to the actual driving behavior of the applications use case. Within the scope of this paper, real driving data are analyzed and used to create a representative driving cycle. The resulting driving cycle based on real driving characteristics is then used to investigate the impact of application-based design for powertrains on the design of electric machines, by illustrating the difference between synthetic operating points and real driving data.

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A holistic framework for acquisition, processing and evaluation of vehicle fleet test data

Cited by 19 publications

References 18 publications

Identifying the Daily Activity Spaces of Older Adults Living in a High-Density Urban Area: A Study Using the Smartphone-Based Global Positioning System Trajectory in Shanghai

Identifying the Daily Activity Spaces of Older Adults Living in a High-Density Urban Area: A Study Using the Smartphone-Based Global Positioning System Trajectory in Shanghai

Compressed Driving Cycles Using Markov Chains for Vehicle Powertrain Design

Requirements for Electric Machine Design based on Operating Points from Real Driving Data in Cities

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