Assessment of the impact of dynamic states of an internal combustion engine on its operational properties

Chłopek, Z.; Biedrzycki, J.; Lasocki, Jakub; Wojcik, P.

doi:10.17531/ein.2015.1.5

Cited by 22 publications

(30 citation statements)

References 12 publications

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“…One of the main descriptors of personal driving style is the choice of vehicle speed, which has direct implication on fuel consumption [15]. Not only the average value of speed, but also the way the speed changes is of a great importance [16]. For example, intensive acceleration and braking, causing higher fuel consumption [17,18], are often regarded as indicators of driving aggressiveness.…”

Section: Introductionmentioning

confidence: 99%

Environmental effects of driving style: impact on fuel consumption

Lasocki

Boguszewski

2019

E3S Web Conf.

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From an environmental point of view, the fuel consumption of vehicles with combustion engines is directly related to the depletion of non-renewable crude oil resources and pollutant emission. The aim of this paper is to evaluate the effect of driving style on fuel consumption of lightduty vehicles. The study considered five metrics used for quantitative description of driving style: Dynamic Performance Index (DPI), Aggressiveness Factor (AF), Vehicle Aggressivity (VA), Total Aggressivity (TA), based upon the previous works of other researchers, and a newly proposed metric named Driving Style Indicator (DSI). All metrics were applied to the results of chassis dynamometer tests of two light-duty vehicles with spark-ignition and compression-ignition combustion engines. The values of metrics were plotted against corresponding values of fuel consumption to create dependences. Their analysis revealed that AF metric has strong correlation with fuel consumption, but is mathematically complex and requires numerous input data. DSI metric has simple mathematical form and is based only on the speed profile of the vehicle, and yet is characterized by a strong correlation with fuel consumption. DSI metric was further employed to investigate the influence of driving style on greenhouse gas (GHG) emissions from the Well-to-Wheel (WtW) perspective.

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Section: Introductionmentioning

confidence: 99%

Environmental effects of driving style: impact on fuel consumption

Lasocki

Boguszewski

2019

E3S Web Conf.

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“…At Automotive Industry Institute (Przemysłowy Instytut MOToryzacji -PIMOT) in Warsaw, families of vehicle driving test cycles have been prepared, which simulate the conditions of motor car drives in street congestion, urban, extra-urban, and high-speed (motorway and fast road) traffic (Chłopek et al 2013a(Chłopek et al , 2013b(Chłopek et al , 2014(Chłopek et al , 2015. The PIMOT tests are treated as realizations of stochastic processes that characterize the conditions of vehicle drives (Chłopek et al 2013b).…”

Section: Introductionmentioning

confidence: 99%

Correlational Analysis of Pollutant Emission Intensity in Various Conditions of Operation of the Automotive Internal Combustion Engine

et al. 2019

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The aim of this article was to analyse the correlation of emission intensity of carbon monoxide, hydrocarbons, nitrogen oxides, and carbon dioxide in various conditions of operation of the automotive internal combustion engine. The operational properties of the engine were investigated on chassis dynamometer in driving test cycles simulating various real-world conditions of the vehicle drive: street congestions, urban traffic without congestions, extra-urban traffic, and high-speed traffic. The correlational dependence of the pollutant emission intensity on the non-negative effective power of the engine and the correlational interdependence between the emission intensity of individual pollutants were investigated. The coefficients of Pearson’s linear correlation, Spearman’s rank correlation, Kruskal’s gamma correlation, and Kendall’s tau correlation were calculated. It was found that the emission intensity of pollutants in the driving test cycles strongly depends on the dynamic states of operation of the engine. The time histories of the emission intensity of pollutants were strongly correlated with the non-negative effective power of the engine. There were only a few cases where this correlation can be assessed as weak. The time histories of the emission intensity of individual pollutants were also strongly correlated with each other, with only a few exemptions.

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“…Conventional methods require tests to be conducted in quasi-steady states. This assumes that changes to the engine parameters (such as increase/decrease in engine speed or torque) do not exceed previously assumed limits [14]- [17] in given time. However, such methods prove infeasible as was shown in [18], [19] that in real world, combustion engines rarely operate in steady states.…”

Section: Introductionmentioning

confidence: 99%

Heavy duty vehicle fuel consumption modeling using artificial neural networks

Wysocki

Deka

Elizondo

2019

2019 25th International Conference on Automation and Computing (ICAC)

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In this paper an artificial neural network (ANN) approach to modelling fuel consumption of heavy duty vehicles is presented. The proposed method uses easy accessible data collected via CAN bus of the truck. As a benchmark a conventional method, which is based on polynomial regression model, is used. The fuel consumption is measured in two different tests, performed by using a unique test bench to apply the load to the engine. Firstly, a transient state test was performed, in order to evaluate the polynomial regression and 25 ANN models with different parameters. Based on the results, the best ANN model was chosen. Then, validation test was conducted using real duty cycle loads for model comparison. The neural network model outperformed the conventional method and represents fuel consumption of the engine operating in transient states significantly better. The presented method can be applied in order to reduce fuel consumption in utility vehicles delivering accurate fuel economy model of truck engines, in particular in low engine speed and torque range.

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Assessment of the impact of dynamic states of an internal combustion engine on its operational properties

Cited by 22 publications

References 12 publications

Environmental effects of driving style: impact on fuel consumption

Environmental effects of driving style: impact on fuel consumption

Correlational Analysis of Pollutant Emission Intensity in Various Conditions of Operation of the Automotive Internal Combustion Engine

Heavy duty vehicle fuel consumption modeling using artificial neural networks

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