A Computer Tool for Modelling CO2 Emissions in Driving Cycles for Spark Ignition Engines Powered by Biofuels
Abstract
:1. Introduction
2. Materials and Methods
2.1. Building a Quantitative Model
2.2. Theoretical Assumptions of the Driving Test Simulator
2.3. Driving Test Generator
2.4. Simulator
- Drive tests generator (text files)—responsible for loading files with data that control the selected driving test process from a text file created with the use of the “Gearshift calculation tool” application. It is also responsible for converting the read data to other formats compatible with OpenModelica v1.16.2. The following parameters are then relayed to the following calculation modules of the simulation: engine speed, engine torque, vehicle speed;
- Model of specific consumption (neural)—this block calculates the instantaneous values of petrol 95 mass flow and relays this parameter to the next block, based on the quantities which characterize the engine operating parameters: engine speed, engine torque and the prepared neural network structure;
- Calculations of fuel and CO2 mass flows—this block is responsible for calculating the streams of the tested biofuels which are necessary to power the engine in the driving test. This is achieved using the petrol 95 mass flow parameter and the fuel calorific value characteristic for the fuel in question calculated in the previous block. This block also calculates the CO2 emission stream with the use of the carbon mass content property and the instantaneous fuel stream;
- Calculation of driving test parameters—on the basis of the driving test parameters, this block calculates the distance covered by the vehicle during the test, the power generated by the engine and the mechanical energy generated during the test.
3. Results
- the results of the simulation work for the processed EPA test data, which are learning models for the neural network;
- the results of the driving test simulator for the prepared drive tests (the “Gearshift Calculation Tool” application) in the form of vehicle speed graphs, distance travelled, engine speed, engine torque, engine power and mechanical energy used during the test;
- the simulation results for the stream and final fuel consumption;
- the simulation results for the stream and CO2 emissions for selected driving tests and selected fuels for the 2018 Toyota Camry LE vehicle;
- the results of fuel consumption and carbon dioxide emissivity per 1 km of the distance travelled by the vehicle in the tests and per 1 kWh of the mechanical energy generated in the test.
3.1. Simulation Work Results for the Processed EPA Test Data
3.2. Simulation Work Results for the Driving Tests Performed
3.3. Simulation Results for the Stream and Final Fuel Consumption for the Selected Driving Tests and Fuels
3.4. The Results of the Simulation of Carbon Dioxide Flux and Emissions for Selected Driving Tests and Fuels
4. Discussion
5. Conclusions
- Neural network structures characterized by approximation (regression) properties were used to build a model enabling the determination of instantaneous fuel consumption values as a function of engine rotational speed and torque produced by the engine. The process of learning these network structures used data from actual driving tests performed on a selected vehicle on a chassis dynamometer published by the EPA. After selecting the neural network structure that obtained the smallest value of relative error with respect to the data from real measurements, the verification of the obtained neural model was carried out using the verification data of real tests included in the EPA publication.
- Based on the operational parameters analyzed with the use of the “Gearshift Calculation Tool” application, the results of the optimization process of the neural network structures and the properties of the biofuels in question, a driving test simulator was developed in the OpenModelica v1.16.2 program. Scilab 6.1.0 numerical software was then used to build the neural model.
- The developed simulation tool used neural networks, whose learning processes used the Levenberg–Marquardt algorithm. An optimization process was carried out for various investigated network structures differing in the number of input parameters and the number of neurons in the hidden layer. The relative error between the model and actual data did not exceed 1%.
- Twelve driving tests were analyzed in this study. These tests differed from one another in terms of the duration, speeds achieved by the vehicle and allowances for the use of any additional equipment in the vehicle (e.g., A/C).
- When analyzing the consumption parameter of a given fuel per one kilometer driven in the test, the best results were achieved for CNG fuel, for which the minimum value was reached at 32 g/km for the US highway driving test, while the maximum value was obtained in the Random Cycle High test (×95) (52.0 g/km). The highest fuel consumption per one kilometer in the test was observed in the case of methanol in the Random Cycle High (×95) (129.4 g/km).
- When considering the emissions of carbon dioxide per kilometer in the test, the highest values were recorded for petrol 95, where the minimum value was reached at 116 g/km for the US highway driving test, and the maximum value was obtained at a Random Cycle High (×95) (187 g/km). For CNG, the minimum value was reached for the US highway (87.7 g/km).
- When analyzing the parameter of mass consumption of a given fuel per unit of mechanical energy produced (1 kilowatt hour) in the case of petrol 95, the minimum value was achieved at 486 g/kWh for the US 06 driving test, while the maximum value was obtained for the US SC03 (1630 g/kWh). The highest consumption was recorded for US SC03, also for DME (2507 g/kWh), ethanol (2667 g/kWh) and methanol (3573 g/kWh).
- For the parameter of carbon dioxide emission per unit of mechanical energy produced (1 kilowatt hour), the maximum values were obtained for US SC03 CNG (3906 g/kWh), LPG (4599 g/kWh), DME (4790 g/kWh), methanol (4907 g/kWh), ethanol (5095 g/kWh) and petrol 95 (5182 g/kWh).
- The developed computer tool could be the basis for the development of a method of identifying selected aspects of operating conditions and assessing the energy efficiency of vehicles with spark ignition engines powered by fuels and biofuels.
- The research method described in the manuscript aims to obtain a simulation model to calculate instantaneous fuel consumption as a function of engine speed and engine torque produced. This method allows the simulation of vehicle operations under different load conditions and will potentially allow the calculation of fuel consumption and carbon emissions. This method can be used for many popular vehicle models in a given market. In the case of estimating carbon dioxide emissions for real facilities where vehicles move, e.g., road tunnels and large parking lots, a very large number of simulations of individual vehicles in real traffic can be used in a single simulation. The use of such simulations will allow for the more precise selection of ventilation systems for such objects, which will prevent the increase in carbon dioxide content in the air.
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Parameter | Description | Unit |
---|---|---|
Engine rotational speed for the given gear number | min−1 | |
Vehicle speed for the given gear number | km/h | |
Input signals for the neuron | ||
Weight values of neurons in individual layers | ||
Polarity values of neurons in individual layers | ||
Given learning values | ||
Values of network responses in the learning process | ||
Mass of fuel consumed in the ith real road test carried out by EPA (tests: US 06, US highway, FTP-75) | kg | |
Mass of fuel consumed in the ith road test from the developed simulation (tests: US 06, US highway, FTP-75) | kg | |
Measured value of the engine rotational speed | min−1 | |
Instantaneous values of the fuel stream for petrol 95 | kg/s | |
The torque produced by the motor | N·m | |
Calorific value for i fuel | J/kg | |
Mass fraction of ith fuel in the mixture | kg/kg | |
Calorific value for petrol 95 | J/kg | |
Calorific value for other fuel | J/kg | |
Mass fraction of carbon in ith fuel | kg/kg | |
Mass fraction of ith fuel in the mixture | kg/kg |
Parameter | Description | Unit |
---|---|---|
Vehicle (MY, Make, Model) | 2018 Toyota Camry LE | - |
Equivalent test mass | 1644 | kg |
Rated power (declared) | 151 | kW |
Rated engine speed (declared) | 7000 | min−1 |
Idling engine speed (declared) | 800 | min−1 |
Max vehicle speed(declared) | 240 | km/h |
Number of gears | 8 | - |
Ratio n/v_1, gear 1 | 120.93 | h/(km·min) |
Ratio n/v_2, gear 2 | 69.75 | h/(km·min) |
Ratio n/v_3, gear 3 | 44.92 | h/(km·min) |
Ratio n/v_4, gear 4 | 33.54 | h/(km·min) |
Ratio n/v_5, gear 5 | 28.10 | h/(km·min) |
Ratio n/v_6, gear 6 | 23.04 | h/(km·min) |
Ratio n/v_7, gear 7 | 18.61 | h/(km·min) |
Ratio n/v_8, gear 8 | 15.50 | h/(km·min) |
Target Coeff f0 | 113.82 | N |
Target Coeff f1 | 0.5442 | N/(km/h) |
Target Coeff f2 | 0.02811 | N/(km/h) 2 |
Parameter | Petrol 95 | Ethanol | Methanol | DME | CNG | LPG |
---|---|---|---|---|---|---|
Calorific [MJ/kg] | 43.5 | 26.7 | 19.93 | 28.4 | 50.0 | 46.3 |
Carbon [%] | 86.4 | 52.1 | 37.5 | 52.1 | 74.9 | 81.7 |
Hydrogen [%] | 13.6 | 13.1 | 12.6 | 13.1 | 25.1 | 18.3 |
Oxygen [%] | 0.0 | 34.7 | 49.9 | 34.7 | 0.0 | 0.0 |
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Tucki, K. A Computer Tool for Modelling CO2 Emissions in Driving Cycles for Spark Ignition Engines Powered by Biofuels. Energies 2021, 14, 1400. https://doi.org/10.3390/en14051400
Tucki K. A Computer Tool for Modelling CO2 Emissions in Driving Cycles for Spark Ignition Engines Powered by Biofuels. Energies. 2021; 14(5):1400. https://doi.org/10.3390/en14051400
Chicago/Turabian StyleTucki, Karol. 2021. "A Computer Tool for Modelling CO2 Emissions in Driving Cycles for Spark Ignition Engines Powered by Biofuels" Energies 14, no. 5: 1400. https://doi.org/10.3390/en14051400