Reducing CO 2 emissions of conventional fuel cars by vehicle photovoltaic roofs

Lodi, Chiara; Seitsonen, Antti; Paffumi, Elena; Gennaro, Michele De; Huld, Thomas; Malfettani, Stefano

doi:10.1016/j.trd.2018.01.020

Cited by 46 publications

(24 citation statements)

References 14 publications

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“…All these devices impose higher mechanical or electrical loads, increased alternator operation, and consequently additional FC. The total power requirements of the European vehicle certification test are estimated to be 350 W, while over the real-world driving are estimated to be 750 W [40,41]. During the RWD tests in this study the alternator current is monitored and the total power calculated.…”

Section: Impact Of Driver Factorsmentioning

confidence: 99%

Understanding the origins and variability of the fuel consumption gap: lessons learned from laboratory tests and a real-driving campaign

et al. 2020

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Background: Divergence in fuel consumption (FC) between the type-approval tests and real-world driving trips, known also as the FC gap, is a well-known issue and Europe is preparing the field for tackling it. The present study focuses on the monitoring of the FC of a single vehicle throughout 1 year with 20 different drivers and almost 14,000 km driven with the aim to analyze and quantify the true intrinsic variability in the FC gap coming from environmental and traffic conditions and driving factors. In addition, the regression model has been developed to evaluate the importance of these different factors on the FC gap's variability. Results:The 1-year FC gap measured in this study was 29% while driver's averages were in the range from 16 to 106%. The regression model developed had R 2 equal to 90.4 meaning that more than 90% of the FC gap's variance can be explained with this model and factors measured in this study. The results of the model showed that among all factors analyzed the highest contribution in the FC gap's variance is coming from the average vehicle speed (16.6%), followed by the road grade (13.4%), and trip distance (10.1%). Indeed, the highest FC gaps are measured when the average vehicle speeds were below 20 km/h, the average distance-weighted road grades above 1%, and the trip distances below 5 km. In addition, the impact of driver factors is not negligible (25%) and the highest FC gap is measured for the trips where average positive acceleration was higher than 0.7 m/s 2 (indicating aggressive driving) and the electric power demand higher than 800 W. Conclusions:The future lifetime on-board fuel consumption reporting is a crucial instrument that will allow the monitoring of the evolution of the FC gap and ensuring that it does not increase over time. The analysis presented in this study is a basis for setting up a more detailed and refined prediction model, which could assist the European Commission in closely monitoring the gap and the underlying factors generating it.

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Section: Impact Of Driver Factorsmentioning

confidence: 99%

Understanding the origins and variability of the fuel consumption gap: lessons learned from laboratory tests and a real-driving campaign

et al. 2020

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“…This goal is determined in the strategy called European 20/20/20 objectives. The strategy was established in 2007 and has three targets: a 20% increase in energy efficiency, a 20% CO 2 emissions reduction and 20% renewable sources usage by 2020 [9,10].…”

Section: Introductionmentioning

confidence: 99%

Impact of the Electric Mobility Implementation on the Greenhouse Gases Production in Central European Countries

et al. 2019

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The preference and the development of electromobility are included among the priorities of transport policies in many European countries. This article deals with the issue of electric vehicle operation from the point of view of the environmental impact of electric power production, specifically the energy effectiveness of its production by utilizing primary power production sources. Differences in the effectiveness of the conversion of mixed forms of energy into electricity and their share in the process directly affect the final level of greenhouse gases (GHG) production, and thus the ecological footprint of electric vehicle operations. The specification of energy consumption and GHG production is based on the principles of the EN 16 258: 2012 standard, which considers legislative-regulated power plant effectiveness values, statistical values of GHG emissions from electricity production, and real energy consumption values of an electric vehicle fleet. The calculation takes into account the share of primary sources and the efficiency of electricity production and effectiveness of electricity distribution in each of the evaluated countries. The specific research study is performed by comparing measured parameters for individual countries chosen from the Central Europe region. The results of the study show that the quantification of the positive environmental consequences of increasing electromobility varies greatly in different countries, which means full-scale deployment of electromobility does necessarily deliver the sustainability of transport that was expected from it.

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“…In the European Union, it is responsible for approx. 20% of the total emissions of this gas [20,11]. Passenger cars have the largest share in this emission.…”

Section: Introductionmentioning

confidence: 99%

“…The use of safety assistance driving systems in a car also affects fuel consumption [28]. There are also other technical solutions aimed at lowering fuel consumption, such as the use of photovoltaic panels on the car roof proposed by the authors of the work [11]. The introduction of technical improvements resulting in more fuel efficient cars does not always contribute to the overall reduction of fuel.…”

Section: Introductionmentioning

confidence: 99%

Modeling of fuel consumption using artificial neural networks

Witaszek¹

2020

Diagnostyka

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The article presents a model of operational fuel consumption by a passenger car from the B segment, powered by a spark ignition engine. The model was developed using artificial neural networks simulated in the Stuttgart Neural Network Simulator (SNNS) package. The data for the model was obtained from longterm operational tests, during which data from the engine control unit were recorded via the OBDII diagnostic interface. The model is based on neural networks with two hidden layers, the size of which was selected using an original iterative algorithm. During the structure selection process, a total of 576 different networks were tested. The analysis of the obtained test errors made it possible to select the optimal structure of the 6-19-17-1 model. The network input values were: vehicle speed and acceleration, road slope, throttle opening degree, selected gear number and engine speed. The networks were trained using the efficient RPROP method. A correctly trained network, based on the set parameters, was able to forecast the instantaneous fuel consumption. These forecasts showed a high correlation with the measured values. Average fuel consumption calculated on their basis was close to the real value, which was calculated on the basis of two consecutive fuelings of the vehicle.

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Reducing CO 2 emissions of conventional fuel cars by vehicle photovoltaic roofs

Cited by 46 publications

References 14 publications

Understanding the origins and variability of the fuel consumption gap: lessons learned from laboratory tests and a real-driving campaign

Understanding the origins and variability of the fuel consumption gap: lessons learned from laboratory tests and a real-driving campaign

Impact of the Electric Mobility Implementation on the Greenhouse Gases Production in Central European Countries

Modeling of fuel consumption using artificial neural networks

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