Stochastic Synthesis of Representative and Multidimensional Driving Cycles

Eßer, Arved; Zeller, Martin; Foulard, Stéphane; Rinderknecht, Stephan

doi:10.4271/2018-01-0095

Cited by 30 publications

(18 citation statements)

References 39 publications

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“…Instead, it is necessary to generate driving cycles with different trip distances that are representative for a given driving profile. Hence, the approach introduced in Reference [16] is applied to synthesize driving cycles of different trip-distances with the property that all cycles represent the respective overall driving profile. Details about the cycle synthesis algorithm are not a focus of this work but are presented in Reference [16].…”

Section: Data Basis For the Evaluation Of Vehicle's Consumptionmentioning

confidence: 99%

“…. Database of driving data used for this study as an input for the cycle synthesis procedure [16]. In (a), the considered tracks from the region around the cities of Darmstadt and Frankfurt in Germany are illustrated.…”

Section: Data Basis For the Evaluation Of Vehicle's Consumptionmentioning

confidence: 99%

“…Q passenger = nλA body (15) . m water,passenger = 8.3n·10 −6 kg s (16) where n is the number of passengers inside the vehicle, λ is the human metabolic rate and A body is the area of the human body. Additionally, the water mass induced by the passengers is calculated as a constant flow rate such that the total water mass inside the cabin can be calculated with respect to the recirculating air, the fresh air from outside and the dehumidification by the air conditioning system.…”

Section: Secondary Consumption Modelmentioning

confidence: 99%

“…Since the cycles are synthesized stochastically, the results vary according to a certain probability distribution and can thus vary in their quality. Therefore, for each defined distance, 10,000 cycles are synthesized, using the approach presented in Reference [16] and the best are chosen for further evaluation. This approach is shown in Figure 3, where the error distributions of all cycles for each trip distance are shown on the left and the cycles with the lowest overall error (regarding the criteria set θ) are shown on the right.…”

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confidence: 99%

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The Sensitivity in Consumption of Different Vehicle Drivetrain Concepts Under Varying Operating Conditions: A Simulative Data Driven Approach

et al. 2019

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As an important aspect of today's efforts to reduce greenhouse gas emissions, the energy demand of passenger cars is a subject of research. Different drivetrain concepts like plug-in hybrid electric vehicles (PHEV) and battery electric vehicles (BEV) are introduced into the market in addition to conventional internal combustion engine vehicles (ICEV) to address this issue. However, the consumption highly depends on individual usage profiles and external operating conditions, especially when considering secondary energy demands like heating, ventilation and air conditioning (HVAC). The approach presented in this work aims to estimate vehicle consumptions based on real world driving profiles and weather data under consideration of secondary demands. For this purpose, a primary and a secondary consumption model are developed that interact with each other to estimate realistic vehicle consumptions for different drivetrain concepts. The models are parametrized by referring to state of the art contributions and the results are made plausible by comparison to literature. The sensitivities of the consumptions are then analysed as a function of trip distance and ambient temperature to assess the influence of the operating conditions on the consumption. The results show that especially in the case of the BEV and PHEV, the trip distance and the ambient temperature are a first-order influencing factor on the total vehicle energy demand. Thus, it is not sufficient to evaluate new vehicle concepts solely on one-dimensional driving cycles to assess their energy demand. Instead, the external conditions must be taken into account for a proper assessment of the vehicle's real world consumption.

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Section: Data Basis For the Evaluation Of Vehicle's Consumptionmentioning

confidence: 99%

Section: Data Basis For the Evaluation Of Vehicle's Consumptionmentioning

confidence: 99%

Section: Secondary Consumption Modelmentioning

confidence: 99%

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confidence: 99%

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The Sensitivity in Consumption of Different Vehicle Drivetrain Concepts Under Varying Operating Conditions: A Simulative Data Driven Approach

et al. 2019

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“…This experiment type could not be fully replaced with mathematical modelling or computer simulation [7][8][9]. During such experiments (apart from fuel consumption and movement characteristics) it is possible to determine required engine operation parameters and based on that, to choose optimal engine control parameters.…”

Section: Introductionmentioning

confidence: 99%

Investigations of diesel engine in the road test

Grytsyuk

Vrublevskyi

2018

Diagnostyka

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This article describes the details of the method designed to combustion engine with compression ignition (CI) during road tests. The method requires simultaneous measurements for the crankshaft rotation frequency, fuel pressure in the injector, pressure in the combustion chamber, air pressure and temperature in the intake system. More, this method utilizes possibilities of the GPS navigation -all the received data is being synchronized with navigation data and saved to the database. Fuel consumption was calculated using the original method based on processing the pressure data from inside the cylinder alongside with the fuel injection parameters, previously received from the injection system and engine tests in the laboratory. Obtained characteristics allow to perform detailed analysis for the engine single work cycle in the transient processes, which are the . Measurements results received with help of the created experimental gear were used to determine CI engine parameters. Those parameters may help in simulation of the engine work inside the vehicle, and could be used in engine parameters optimization tasks.

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