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Impact of Acceleration Aggressiveness on Fuel Consumption Using Comprehensive Power Based Fuel Consumption Model
... Il consumo energetico aumenta evidentemente con la velocità e l'accelerazione sia per gli ICEVs [24][25] che per gli EVs [26], anche se gli EVs, in virtù delle caratteristiche di rendimento che li contraddistinguono in condizioni di traffico lento, stop e ripartenze, si dimostrano essere più efficienti in condizioni di guida urbane, considerando sia i risultati di test di laboratorio che dati empirici [27][28] [29]. Inoltre, l'utilizzo di valori medi di consumo per la modellazione della fase di uso, come viene tipicamente fatto in studi LCA degli EVs, è un'approssimazione che non permette di cogliere il reale consumo energetico degli stessi con differenti cicli di guida [30]. ...
... Energy consumption and emissions during the use phase of a vehicle, in general, depend on several variables that ZHOU et al. (2016) [23] classified into six categories related to different aspects: travel characteristics (time, duration), vehicle characteristics (engine, load conditions, speed and acceleration), environment (temperature, humidity and wind), road network (slope, roughness of the asphalt, radii of curvature), traffic (flows and signalling) and driving behaviour of the driver. [24] [25] and for EVs [26], Per quanto riguarda gli ICEVs, i fattori di emissioni per la fase WTT sono stati calcolati sulla base del database Ecoinvent [13] e dei poteri calorifici caratteristici dei diversi carburanti, mentre per la fase TTW sono stati utilizzati i fattori di emissioni del database italiano degli inquinanti da trasporto [32], che si basa sull'inventario degli inquinanti EMEP/EEA [33] ed è coerente con le linee guida IPCC [34]. I fattori relativi al consumo di carburante e i coefficienti di emissione sono sintetizzati in Tab. 4. I fattori relativi al consumo di elettricità utilizzati ai fini dello studio, riportati in Tab. 5, sono stati assunti sulla base di quanto indicato in precedenti studi di letteratura [30][35] [36], che simulano condizioni di guida sia urbana che extraurbana e presentano valori stimati coerenti con quanto ottenuto da cicli di guida standard NEDC e WLTP [37][38] [39], definiti per riprodurre condizioni di guida reale ai fini dell'omologazione dei veicoli (il ciclo NEDC, che prevedeva una fase urbana ed una extraurbana per 11 km totali di percorrenza ad una velocità media di 34 km/h con picchi di 120 km/h, è ormai stato sostituito dal WLTP, che prevede quattro diverse fasi di guida urbana ed extraurbana per una percorrenza di 23,25 km ad una velocità media di 46,5 km/h con punte di 131 km/h). ...
La presente ricerca è finalizzata a definire un modello per valutare le emissioni di gas climalteranti di un'intera flotta veicolare, in ottica di ciclo di vita, in funzione dei risultati di specifiche simulazioni di traffico. Sulla base del calcolo dell'impatto di ciclo di vita delle diverse categorie di veicoli circolanti e sulla base del flusso veicolare sulla rete stradale, sono state calcolate le prestazioni ambientali dell'intera rete di trasporto privato della città metropolitana di Roma Capitale in termini di anidride carbonica equivalente (CO2eq). Relativamente all'impatto ambientale del ciclo di vita, i veicoli completamente elettrici sono risultati essere quelli caratterizzati dai valori più elevati di emissione per quanto riguarda costruzione, manutenzione e smaltimento, sia rispetto ai veicoli a combustione interna che rispetto ai veicoli ibridi. Ciononostante, una penetrazione al 100% di veicoli elettrici nella flotta circolante può potenzialmente generare una riduzione di GHG a livello di rete di trasporto dell'ordine del 39%.
... It is well known that fuel consumption increases while acceleration is due to the need for more power. In addition, some studies show that acceleration level is quite effective on fuel consumption [36,37]. It is highlighted in another study that the same acceleration level at different speeds has a dissimilar effect on CO 2 or fuel economy [38]. ...
This study describes the methodology of creating Semi - Artificial City Cycle (SACC), which can be used for emission tests of road vehicles; sustainability of hybrid electric vehicle (HEV); or emission inventories of the city. This methodology uses the half-hour frequency traffic data of Istanbul to calculate the average speeds and travel distances of urban, rural and highway segments. Then, calculated average data convert into instantaneous time—velocity distribution by random time—speed values, which are appropriate for acceleration/deceleration of real-world driving. In addition, in this study, the obtained artificial cycles and the regulation cycle are modelled in the AVL Cruise software to compare, and the driving dynamics of the city are examined. The SACC has different acceleration/deceleration distribution, average driving speeds, trips and travel times than regulation tests. However, according to the simulation results, the same fuel consumption and CO 2 emission factors are obtained with the regulation test, except for the highway segment.
... [31] classified these variables into six broad categories, i.e., those related to travel characteristics (travel distance and times), to the vehicle (engine, vehicle loading, speed, and acceleration), to ambience (temperature, humidity, and wind), to the road network (roadway grade, surface roughness, and horizontal curvature), to traffic (traffic flow and signaling), and to the driver (driver behaviour and aggressiveness). In general, energy consumption is shown as increasing with speed and acceleration, both for ICEVs [32,33] and EVs [34], even if EVs seem to be more efficient in urban driving conditions, both considering laboratory driving tests and real-world vehicle activity patterns [35][36][37]. Moreover, the use of a global average value of energy consumption in [Wh/km]-as most of the LCA literature model the use phase of EVs-is considered an approximation that does not allow one to capture the real consumption of EVs among different driving cycles [38]. ...
This paper presents a model to evaluate the life cycle greenhouse gases (GHG) emissions, expressed in terms of carbon dioxide equivalent (CO2eq), of a generic fleet composition as a function of the traffic simulation results. First we evaluated the complete life cycle of each category of the vehicles currently circulating; next, by defining a general linear equation, the traffic environmental performances of a real road network (city of Rome) were evaluated using a traffic simulation approach. Finally, the proposed methodology was applied to evaluate the GHG emission of a 100% penetration of battery electric vehicles (BEVs) and various electric and conventional vehicles composition scenarios. In terms of life cycle impacts, BEVs are the vehicles with the highest GHG emissions at the vehicle level (construction + maintenance + end-of-life processes) that are, on average, 20% higher than internal combustion engine vehicles, and 6.5% higher than hybrid electric vehicles (HEVs). Nevertheless, a 100% BEVs penetration scenario generates a reduction of the environmental impact at the mobility system level of about 65%.
... Zhou et al. (2016) classified these variables into six broad categories, i.e. those related to travel characteristics (travel distance and times), to the vehicle (engine, vehicle loading, speed and acceleration), to the ambient (temperature, humidity and wind), to the road network (roadway grade, surface roughness, horizontal curvature), to the traffic (traffic flow and signaling) and to the driver (driver behavior and aggressiveness). In general, energy consumption is shown as increasing with speed and acceleration, both for ICEVs (Bakhit et al., 2015;Ahn et al., 2002) and EVs (Galvin, 2017), even if EVs seem to be more efficient in urban driving conditions, both considering laboratory driving test and real-world vehicle activity patterns (Qi et al., 2018;Wu et al., 2015;Karabasoglu and Michalek, 2013). Moreover, the use of a global average value of energy consumption in [Wh/km]as most of the LCA literature model the use phase of EVsis considered an approximation that does not allow for the capturing of real consumption of EVs among different driving cycles (Fiori et al., 2016). ...
This paper presents the results of a Carbon Footprint (CF) study of Autonomous Vehicles (AVs) and their environmental impact on the transportation network. By assuming that fully AVs are battery electric vehicles (BEVs) with connectivity, light detection and ranging sensors, this study measures the environmental impact at the urban mobility level. The AV complete life cycle impact was firstly evaluated. Next, by comparing the current situation with a future hypothetical scenario (100% AVs penetration), the positive environmental effect of the adoption of AVs on a real road network (city of Rome) is shown. For this scope, a traffic simulation-based approach was used to investigate the effects of AVs on the network congestion.
The results show that the full AVs penetration scenario leads to an improvement in the network performances in terms of travel time and average speed. The Total Time Spent (TTS) decreases (−35% for intra-urban roads and −21% for highways), and the average network speed increases (48% for intra-urban road and 37% for highways). Moreover, the final amount of Vehicle Kilometer Traveled (VKT) shows an 8% increase on longer extraurban routes, due to the higher capacity impact of AVs on highways, with a consequent load reduction for intra-urban shortcutting routes. In terms of life cycle impacts, AVs are characterized by the highest Greenhouse Gases (GHG) emissions related to construction, maintenance and end-of-life processes (on average 35% compared to internal combustion engine vehicles, 22% compared to hybrid electric vehicles and 5% compared to battery
electric vehicles). Nevertheless, a 100% AVs penetration scenario generates a reduction of the environmental impact at the mobility system level of about 60%.
... In order to assist aggressive drivers driving in an economical way, the effects of driving style on fuel saving potentials based on real-world cycles has been investigated [14], and it has demonstrated that acceleration is one of the main factors of impacting on fuel consumption and emission and that [9] the regenerative braking limitation mainly causes great fuel economy variation of HEVs. For example, aggressive behavior with large acceleration will lead to high fuel consumption [15]. Based on this, instantaneous acceleration has been used to model the increments of fuel consumption with quadratic and exponential functions [16]. ...
The performance of energy management systems in hybrid electric vehicles (HEVs) is highly related to drivers' driving style. This paper proposes a driving style-oriented adaptive equivalent consumption minimization strategy (AECMS-style) in order to improve fuel economy for HEVs. For this purpose, firstly, a statistical pattern recognition approach is proposed to classify drivers into six groups from moderate to aggressive using kernel density estimation and entropy theory. Then, the effects of driving style on EMS are discussed by analyzing the performance of equivalent consumption minimization strategy (ECMS). Based on the comprehensive analysis, we design a new optimal equivalent factor adjustment rule for the AECMS-style and also redesign the braking strategy of motors at driving charging mode for different driving styles. Finally, five drivers with typical driving styles participate in experiments to show the effectiveness of our proposed method. Experimental results demonstrate that the AECMS-style can improve the fuel economy and charging sustainability of HEVs, compared with ECMS.
... While studies on ICE vehicles show energy consumption increasing with speed and acceleration (Ahn et al., 2002;Bakhit et al., 2015), there are currently very few studies relating driver-related factors to energy consumption in e-vehicles. Karabasoglu and Michalek (2013) compare journey energy consumption of one e-vehicle with that of an ICE and several hybrid vehicles, on laboratory driving test cycles based on city and highway driving. ...
The number of electric vehicles in service throughout the world has increased from a few thousand in 2009 to some 740,000 in December 2014. These vehicles are often seen as a means of reducing climate and health damaging emissions, and their development is directly supported by some countries and endorsed by the EU. Aside from questions of rebound effects, embedded emissions and cleanness of electricity generation, there are unanswered questions about the energy performance of such cars under a range of driving conditions, and the results of existing studies are not easily interpretable by policymakers and drivers. This study uses the results of extensive dynamometer tests on eight commonly sold electric vehicles. It develops a multivariate model, with regression coefficients around 0.97, to map power demand and energy consumption for all likely combinations of speed and acceleration, producing accessible, easily interpretable displays. While electric vehicles are frequently marketed on the basis of their high acceleration, an important finding is that episodes of modest to high acceleration severely compromise their range and energy efficiency, regardless of speed. This also raises questions as to how well such vehicles perform in the erratic driving conditions of urban traffic.
... It is well established that speed and acceleration have significant impacts on vehicle fuel consumption. Bakhit et al. [24] include speed and acceleration in their comprehensive 'power-based' model of energy consumption in vehicles to estimate fuel consumption. Berry [25] uses both modelling and actual driving behavior monitoring, to investigate relationships between fuel consumption and what they call the 'driving aggressiveness factor'. ...
The “rebound effect” occurs when reductions in energy consumption following energy efficiency increases are lower than engineering estimates. In cars this happens when drivers increase their distance travelled or average speed, as a behavioural response to cheaper travel. Rebound effects due to increased distance travelled have been extensively studied, but only one existing study attempts to quantify rebound effects due to increased average speed. This paper builds on that study, using a much larger empirical base and offering more generalised and more widely applicable mathematical modelling. It uses data from 30 Formula 1 Grand Prix time trial sessions of 10 vehicles doing 3 trials each, in 2014 and 2015. The heavily regulated Formula 1 regime, with its precisely measured data, provides a highly controlled framework for developing mathematics of average speed rebounds. The study thereby shows how speed and distance rebounds can be coherently combined in road vehicle travel to produce total rebound figures. It then shows how even small increases in average speed can nullify all the energy savings that are expected from energy efficiency increases. It also raises critical questions on the adequacy of proposed new road vehicle fuel efficiency testing procedures.
Questions related to the manner in which drivers respond to different instructions by examining detailed distance-time histories which provide information on the stop and start maneuvers, speed, acceleration and braking characteristics are addressed. A test car was instrumented with a fuel meter and fifth wheel to obtain information on fuel consumption and distance-time histories.
Existing automobile fuel consumption and emission models suffer from two major drawbacks; they produce a bang–bang control through the use of a linear power model and the calibration of model parameters is not possible using publicly available data thus necessitating in-laboratory or field data collection. This paper develops two fuel consumption models that overcome these two limitations. Specifically, the models do not produce a bang–bang control and are calibrated using US Environmental Protection Agency city and highway fuel economy ratings in addition to publicly available vehicle and roadway pavement parameters. The models are demonstrated to estimate vehicle fuel consumption rates consistent with in-field measurements. In addition the models estimate CO2 emissions that are highly correlated with field measurements.Highlights► The research develops two simple vehicle fuel consumption models. ► The models are calibrated using the city and highway fuel economy ratings. ► The models estimate fuel consumption rates consistent with in-field measurements. ► The proposed model estimates CO2 emissions within a 2% error range. ► The proposed model can be easily integrated within a traffic simulation framework.
The paper presents a simple vehicle dynamics model for estimating maximum vehicle acceleration levels based on a vehicle's tractive effort and aerodynamic, rolling, and grade resistance forces. In addition, typical model input parameters for different vehicle, pavement, and tire characteristics are presented. The model parameters are calibrated/validated against field data that were collected along the Smart Road test facility at Virginia Tech utilizing a truck and trailer for 10 weight-to-power configurations, ranging from 85 kg/kW to 169 kg/kW (140 lb/hp to 280 lb/hp). The model was found to predict vehicle speeds at the conclusion of the travel along the section to within 5 km/h (3.1 mi/hr) of field measurements, thus demonstrating the validity and applicability of the model.
Driver Response to Different Driving Instructions Effect on Speed, Acceleration and Fuel Consumption
- References Chang
- R Herman
References
Chang, M. F., and R. Herman, (1979) "Driver Response to Different Driving Instructions Effect on Speed,
Acceleration and Fuel Consumption," General Motors Research Laboratories, September 1979.
Gasoline Consumption in Urban Traffic
- M F Chang
- L Evans
- R Herman
- P Wasielewski
Chang, M. F., L. Evans, R. Herman and P. Wasielewski, (1977) "Gasoline Consumption in Urban Traffic",
Transportation Research Record, Washington, D.C., No.599.
HDM-4 Fuel Consumption Modelling
- I D Greenwood
- C R Bennett
Greenwood, I.D. and Bennett, C.R. (1995a). "HDM-4 Fuel Consumption Modelling. Preliminary Draft Report -Task 2020-3," International Study of Highway Development and Management Tools, University of Birmingham.
Quantitative Effects of Acceleration Rate on Fuel Consumption
- R Jones
Jones, R. (1980) "Quantitative Effects of Acceleration Rate on Fuel Consumption", US Environmental
Protection Agency.
Research on Fuel Conservation for Cars
- M H Watson
- I B Baker
Watson, M.H., and I. B. Baker, (1980) "Research on Fuel Conservation for Cars", Transport and Road Research
Laboratory, England.