Modelling fuel consumption and refuelling of autonomous vehicles

Zöldy, Máté; Zsombók, Imre

doi:10.1051/matecconf/201823500037

Cited by 15 publications

(15 citation statements)

References 10 publications

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“…where C f = fuel consumption in a farm, L; D 1 = distance between farm and receival site, km; D 2 = distance between receival site, fuel distribution point, and farm, km; FCR l = fuel consumption rate/loaded trip, L per 100 km; FCR e = fuel consumption rate/empty trip, L per 100 km; N = cargo vehicles operated by a farm during a year, number of vehicles; T = trips per vehicle, number of trips; R = fuel reserve, L. Transformation 3. In earlier studies, Zoldy and Zsombok [67], Nkakini et al [68], Zhou and Jin [69], and Huertas et al [70] attempted to incorporate various external and internal parameters, including vehicle driven, road related, usage related, and ambient parameters, that could have predictive effect on fuel consumption and refueling of the vehicle. To the best of the authors' knowledge, none of the studies have employed the EOQ model to measure the variations in fuel consumption, while the models which were used in the abovementioned studies cannot provide satisfactory predictions for vehicles operating under the conditions of underdeveloped and disrupted logistics chains.…”

Section: Methodsmentioning

confidence: 99%

Dynamic Optimization of Fuel and Logistics Costs as a Tool in Pursuing Economic Sustainability of a Farm

2019

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Improving the performance and economic sustainability of agricultural producers requires the integration of many dimensions, one of which is logistics. Establishing efficient and cost-effective transportation is a key element of establishing sustainable linkages along food supply chains between farmers, storage and transport companies, and consumers. In this regard, infrastructural constraints to sustainability in agricultural production exacerbate transportation costs and risks, and thus result in lower performance of agricultural producers. As fuel consumption is, first, the most significant cost in agricultural logistics and, second, particularly sensitive to disruptions of transport, loading, and storage infrastructure, management of fuel costs is crucial to assure profit margin of an agricultural enterprise. By transforming the standard economic order quantity (EOQ) model, the authors attempt to build an approach to the optimization of fuel costs. The analysis made in the cases of twelve large crop farms in three territories of Southern Russia allowed the consideration of: (1) fragmentation in storage infrastructure; (2) variations in fuel consumption depending on the vehicle load ratio; (3) the use of their own fleet of vehicles against the outsourcing of transport operations. The authors find that the tactics of optimization of fuel costs vary depending on the location of a farm in relation to grain storage facilities. Particularly, the farms located in areas of high concentration of storage facilities benefit from using their own fleet of vehicles, while those experiencing longer distances of transportation should outsource the performance of logistics operations to third parties. To overcome a site-specific nature, the transformed EOQ model should accommodate country-specific requirements, specifically, the level of fragmentation of transport and storage infrastructure, average distance of transportation from a farm to receival site, and average fuel consumption rates depending on the types of trucks commonly used by farmers. The key recommendation is that sustainability-aimed management of logistics costs should consider combining the operation of trucks by a farm with the outsourcing of transportation operations to address the fragmentation of transport and storage infrastructure.

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Section: Methodsmentioning

confidence: 99%

Dynamic Optimization of Fuel and Logistics Costs as a Tool in Pursuing Economic Sustainability of a Farm

2019

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“…A diploma thesis work provided the basis for estimating the value of investment costs [2]. The diploma project includes similar alternative drive system configurations based on the same measurement data.…”

Section: Capital Costsmentioning

confidence: 99%

“…1 Operational profile of a cruise ship on Basel-Amsterdam route [1] One of the most important components of operating costs is fuel consumption. It is generally true that efforts should be made to minimise fuel consumption because via this the increase of profit can be made [2]. In addition to the old traditional propulsion systems, several alternative solutions can be used better to suit the propulsion system to the operational profile.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2021

brod

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The unique feature of river transport is given by the rivers' flow and the changing crosssections of the riverbed. Most river vessels operate with a conventional propulsion system. Engine power should be scaled to the maximum expected power demand, which means that the engine is forced to run at medium or low load, almost always downstream and upstream for part of the operating time. In these cases, the engine's operating point is far from ideal, so there is an increase in specific fuel consumption. This is especially true for ships that are forced to stop and start frequently. An example for the circumstances mentioned above is the BKV-100 type ship operated at Budapest as a public transport vessel. A measurement on a schedule route revealed that with given environmental conditions (water level, river flow rate, etc.), the power installed in the vessel is almost double the maximum power demand measured. Due to this reason and to the differences in power demand of downstream and upstream, it may be technically worthwhile to replace the existing conventional propulsion system with an alternative propulsion system. Based on the measurements, in this paper the authors compare 4 alternative drive systems to the existing one with regards to fuel consumption and the investment costs. Beside this, a simple but effective fuel consumption estimation method and a simple cost-benefit analysis are also described.

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“…location, date, time, number of vehicles involved, road characteristics, presence of junctions, weather conditions), crashed vehicle characteristics (i.e. type, model, age, engine capacity, type of accident), [16] and description of casualties (i.e. severity, age, gender, type).…”

Section: Datamentioning

confidence: 99%

A Novel Approach in Evaluating the Impact of Vehicle Age on Road Safety

Török

2020

PROMET

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This study examines the correlation between road accident casualties and the age of the vehicle, assuming that the age of vehicles and the improvements in their safety designs are related. The study evaluates the impact of the interrelationship between road segment characteristics and road accident type on vehicle age at the time of the accident (AVC). To analyse the nested relationship between these variables, a multinomial logistic regression (MML) model has been developed. The result of the analysis also duly finds that vehicle age has an emphatic role in the occurrence of accidents.

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Modelling fuel consumption and refuelling of autonomous vehicles

Cited by 15 publications

References 10 publications

Dynamic Optimization of Fuel and Logistics Costs as a Tool in Pursuing Economic Sustainability of a Farm

Dynamic Optimization of Fuel and Logistics Costs as a Tool in Pursuing Economic Sustainability of a Farm

Untitled

A Novel Approach in Evaluating the Impact of Vehicle Age on Road Safety

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