A Computer Tool for Modelling CO2 Emissions in Driving Cycles for Spark Ignition Engines Powered by Biofuels

Tucki, Karol

doi:10.3390/en14051400

Cited by 9 publications

(8 citation statements)

References 144 publications

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“…The authors of the manuscript in published scientific papers have analysed a wide range of different approval procedures carried out on vehicles with compression ignition and spark ignition engines, taking into account different fuel mixtures and equipment with assistance systems, e.g. start-stop system [55,56]. For the purpose of achieving the set research goals, proprietary computational tools created in Open-Modelica, Scilab, Matlab packages using artificial intelligence methods were used.…”

Section: Discussionmentioning

confidence: 99%

Analysis of the Possibility of Using Neural Networks to Monitor the Technical Efficiency of Diesel Engines During Operation

Tucki,

Orynycz,

Świć

et al. 2023

Adv. Sci. Technol. Res. J.

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The aim of the research was to analyse the possibility of using neural networks to determine the parameters of the chemical composition of exhaust gases as a function of engine performance parameters obtained from the on-board diagnostics system such as crankshaft speed and engine load index. The subject of the study was a Fiat Panda car equipped with a 1.3 Multijet diesel engine and powered by pure diesel. The tests used the MAHA MET 6.3 exhaust gas analyser and the on-board diagnostics system OBD II. The obtained values of NO x ,O 2 ,CO 2 and PM measured behind the DPF were analysed. For the purpose of building a neural network model, preliminary studies were carried out in non-urban traffic (high-speed route). On the basis of the data obtained, processes of learning neural network structures with approximate properties with backward propagation of errors were carried out. Subsequently, tests were performed on the operational parameters of the vehicle and the chemical composition of exhaust gases in urban traffic. Analysis of the obtained values of the average parameters obtained during the measurement and on the basis of the prepared neural models allows determining the relative differences at the level of not more than 10 percent.

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Section: Discussionmentioning

confidence: 99%

Analysis of the Possibility of Using Neural Networks to Monitor the Technical Efficiency of Diesel Engines During Operation

Tucki,

Orynycz,

Świć

et al. 2023

Adv. Sci. Technol. Res. J.

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“…Unlike other models, emission factors are calculated as a result of the kinematics of vehicle movement in the modelled area. This approach to calculating emissions is contained in three steps [78,79]:…”

Section: Macroscopic Emission Modelsmentioning

confidence: 99%

Vehicle Emission Models and Traffic Simulators: A Review

Mądziel¹

2023

Preprint

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Accurate estimation of vehicle emissions is essential and can be helpful in the decision-making process. Thanks to calculation methods, it is possible to accurately estimate the emissions that result from driving a car and to determine their impact on, for example, the health of pedestrians who are on the pavement near an arterial road. Recent years have seen an increase in the use and importance of emission models, as well as vehicle traffic simulation models. Increasingly, emission models are being combined with traffic simulation models, as it is not practical to actually measure the on-road emissions of an entire fleet of vehicles over a given stretch of road. This paper provides an overview of the selected work on the topic of emission modelling and vehicle traffic simulation models. The models are distinguished according to their respective scales of accuracy, i.e., micro, meso, and macro. Selected work combining emission and traffic simulation models is also presented, as well as development trends in the field. In particular, the paper also highlights the fact of proper calibration of vehicle traffic simulation models in order to obtain the most reliable vehicle emissions results from computational models. The review of the works carried out may be helpful for future projects concerning the use of simulation tools for transport and environmental decision-makers in the area of arterial roads.

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“…In addition, the world-harmonized light-duty vehicle test cycle (WLTC) was developed employing the actual driving behavior data collected through six regions, including Amer-ica, Japan, India, Korea, the EU, and Switzerland. However, it is lacking Chinese data acquisition [49,51]. With different levels of congestion, WLTC driving condition better reflects the characteristics of fast and slow vehicle speeds, but the idling ratio and average speed of the two main operating conditions under WLTC condition are quite different from the actual operating conditions in China [51].…”

Section: Introductionmentioning

confidence: 99%

A State of Charge Estimation Approach for Lithium-Ion Batteries Based on the Optimized Metabolic EGM(1,1) Algorithm

Sun

Wang²,

Gao

et al. 2022

Batteries

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The accurate estimation of the state of charge (SOC) for lithium-ion batteries’ performance prediction and durability evaluation is of paramount importance, which is significant to ensure reliability and stability for electric vehicles. The SOC estimation approaches based on big data collection and offline adjustment could result in imprecision for SOC estimation under various driving conditions at different temperatures. In the traditional GM(1,1), the initialization condition and the identifying parameter could not be changed as soon as they are confirmed. Aiming at the requirements of battery SOC estimation with non-linear characteristics of a dynamic battery system, the paper presents a method of battery state estimation based on Metabolic Even GM(1,1) to expand battery state data and introduce temperature factors in the estimation process to make SOC estimation more accurate. The latest information data used in the optimized rolling model is introduced through the data cycle updating. The experimental results show that the optimized MEGM(1,1) effectively considers the influence of initial data, and has higher accuracy than the traditional GM(1,1) model in the application of data expansion. Furthermore, it could effectively solve the problem of incomplete battery information and battery capacity fluctuation, and the dynamic performance is satisfactory to meet the requirements of fast convergence. The SOC estimation based on the presented strategy for power batteries at different temperatures could reach the goal of the overall error within 1% under CLTC conditions with well robustness and accuracy.

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A Computer Tool for Modelling CO2 Emissions in Driving Cycles for Spark Ignition Engines Powered by Biofuels

Cited by 9 publications

References 144 publications

Analysis of the Possibility of Using Neural Networks to Monitor the Technical Efficiency of Diesel Engines During Operation

Analysis of the Possibility of Using Neural Networks to Monitor the Technical Efficiency of Diesel Engines During Operation

Vehicle Emission Models and Traffic Simulators: A Review

A State of Charge Estimation Approach for Lithium-Ion Batteries Based on the Optimized Metabolic EGM(1,1) Algorithm

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