- Email: [email protected]
Building a cycle for Real Driving Emissions
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Energy Procedia 126 891–898 Energy Procedia 00(201709) (2017) 000–000 www.elsevier.com/locate/procedia
72nd Conference of the Italian Thermal Machines Engineering Association, ATI2017, 6-8 72nd Conference of the ItalianSeptember Thermal Machines Engineering Association, ATI2017, 6-8 2017, Lecce, Italy September 2017, Lecce, Italy
Building a cycle for Real Driving Emissions
Building a cycleSymposium for RealonDriving Emissions The 15th International District Heating and Cooling
Teresa Donateoaa*, Mattia Giovinazziaa Donateo Mattiathe Giovinazzi the Teresa feasibility of*,using heat demand-outdoor
AssessingDepartment of Engineering for Innovation, via per Monteroni, 73100 Lecce, Italy Department of Engineering Innovation, via perdistrict Monteroni, 73100 Lecce, demand Italy temperature function for a forlong-term heat forecast a a
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Abstract I. Andrić *, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre Abstract a IN+ Center for Innovation, Technology Policy Research - Instituto Técnico,type Av. Rovisco Pais 1, 1049-001within Lisbon,the Portugal The EU6d Emission Regulation requiresand Real Driving Emissions asSuperior an additional approval requirement 2017 b Veolia Recherche &the Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France The Emission Regulation requires Real Driving Emissions as an additional type approval requirement within the 2017as2020EU6d timeframe in order to take into account influence of the road profile, the ambient conditions and the traffic situation c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, Francesituation as 2020 timeframe in order take into account of the road profile,Monitoring the ambient conditions and the traffic well as the behavior of to the driver. The newthe testinfluence uses Portable Emissions System (PEMS) to measure on-board well as theThe behavior of the driver. The new usesaPortable Monitoring System (PEMS) to measurein on-board emissions. trip sequence shall consist of atest urban, rural andEmissions a motorway sections with specific requirements terms of emissions. trip sequence a urban, a rural and a motorway sections with specific in terms distance andThe average speed forshall eachconsist section.ofFor example, the overall trip duration shall be between 90 requirements and 120 minutes. Due of to distance and average speed for each section. For example, the overall trip duration shall be between 90 and 120 minutes. Due to these strong requirements, the execution of RDE measurements has to be preceded by an accurate planning of the route to reduce Abstract these strongrisk requirements, the execution of RDE measurements to be preceded by an accurate the routetoto build reducea test failure and, consequently, experimental costs. The aimhas of the present investigation is to planning present aof procedure test failure riskdriving and, consequently, costs. The aim of the with present investigation is to presentofatraffic procedure to build a cycle for heating real emissions thatexperimental minimizes the distance, is robust respect the uncertainties District networks are commonly addressed in the literature as one of thetomost effective solutions forconditions decreasingand the cycle for real driving emissions that minimizes the distance, is robust with respect to the uncertainties of traffic conditions satisfy the requirements of the regulations. The procedure has been applied to routes from and to the Department of Engineering greenhouse gas emissions from the building sector. These systems require high investments which are returned through the and heat satisfy the of athe regulations. The procedure has been applied policies, to routes and to theinDepartment of Engineering for Innovation. preliminary analysis of effect ofrenovation instantaneous speedfrom and acceleration on real drive emissions is sales. Duerequirements to Moreover, the changed climate conditions andthebuilding heat demand the future could decrease, for Innovation. Moreover, a preliminary analysis of the effect of instantaneous speed and acceleration on real drive emissions is presented. prolonging the investment return period. presented. The main scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand ©forecast. 2017 TheThe Authors. Published by Elsevier Ltd. districtPublished of Alvalade, locatedLtd. in Lisbon (Portugal), was used as a case study. The district is consisted of 665 © 2017 The The Authors. Elsevier nd © 2017 Authors. Published by by Ltd. committee of the 72nd Conference of the Italian Thermal Machines Engineering Peer-review under responsibility of Elsevier the scientific buildings that vary in both construction period and typology. weather scenarios (low, Thermal medium,Machines high) andEngineering three district Peer-review under responsibility of the scientific committee of theThree 72nd Conference of the Italian Peer-review under responsibility of the scientific committee of the 72 Conference of the Italian Thermal Machines Engineering Association. renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were Association Association. compared with results from a dynamic heat demand model, previously developed and validated by the authors. Keywords: real driving emissions, on-board measurements The results showed that when only weather change is considered, the margin of error could be acceptable for some applications Keywords: real driving emissions, on-board measurements
(the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). 1.The Introduction value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the 1.decrease Introduction in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and Over the past few years, European Commission pointed out that laboratory tests do per notdecade reflect(depending the amount renovation scenarios considered). On the other hand,has function intercept increased for 7.8-12.7% on of the Over theemissions past few emitted years, European Commission has pointed out laboratory tests do the not reflect the amount of exhaust during real driving particularly for nitrogen oxides (NO has been x). Europe coupled scenarios). The values suggested could beconditions, used to modify thethat function parameters for scenarios considered, and exhaust emitted driving conditions, particularly for nitrogen oxides (NO x). Europe has been improveemissions the accuracy of heat during demandreal estimations. © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * Corresponding author. Tel.: +39 0832 29 7754. Cooling. address:author. [email protected] * E-mail Corresponding Tel.: +39 0832 29 7754.
E-mail address: [email protected] Keywords: Heat demand; Forecast; Climate change 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review underThe responsibility of theby scientific of the 72 nd Conference of the Italian Thermal Machines Engineering 1876-6102 © 2017 Authors. Published Elsevier committee Ltd.
Association. Peer-review under responsibility of the scientific committee of the 72 nd Conference of the Italian Thermal Machines Engineering Association. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling.
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 72nd Conference of the Italian Thermal Machines Engineering Association 10.1016/j.egypro.2017.08.307
Teresa Donateo et al. / Energy Procedia 126 (201709) 891–898 T. Donateo et al./ Energy Procedia 00 (2017) 000–000
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using the New European Driving Cycle (NEDC) certification cycle since 1970, but it has been widely criticized, especially in the wake of the 2015 diesel emissions scandal, due to its low acceleration pattern, constant speed cruising and high number of idling events, which do not represent transient accelerations regimes [1]. This topic has been extensively addressed in literature where several methods were proposed to obtain realistic driving cycles (see for example [2]-[4]). In order to overcome these issues, starting from September 2017, the NEDC will be replaced by the new Worldwide harmonized Light vehicles Test Procedure (WLTP), and a Real Driving Emissions (RDE) test will be mandatory. RDE tests have some requirements that should be further analyzed, one of which is the trip selection. In March 2017, PSA Peugeot Citroën and NGOs published results of first real-world fuel economy test. The measurements were made under a protocol developed with the NGO Transport & Environment, on public roads near Paris (25.5 km urban, 39.7 km rural, and 31.1 km motorway) and under real-life driving conditions [5]. Except for this work, the literature on the development of specific tests for the RDE procedure is almost null even if the general topic of real driving cycles was the object of many investigations as already underlined. Since small and large car manufacturers have now to develop a route for their vehicles, the method described in this work could help them to do it, reducing development costs. Another key aspect is the robustness of the RDE driving cycle with respect to traffic conditions. In the present investigation, the robustness of the cycle has been performed experimentally by repeating the same route under different traffic conditions. The effects of driving conditions could be extensively evaluated using a traffic simulation software like SUMO (Simulation of Urban MObility), developed by the German Institute of Transportation (DLR).. Once imported the map from Openstreetmap and converted it into a network via a proprietary subprogram, the user could generate routes and traffic, either using automatic randomization or manually [6]. Sumo source code has been edited to define the number of vehicles generated in the map, the route length and to record the speed-time data of a target car. In fact, once generated the traffic, the user must define manually the route that the car follows in reality. These changes allow the user to predict the maximum number of cars (i.e. the traffic) for which trip requirements are met. Another software that can be used to simulate traffic is Automotive Simulation Models (ASM), developed by the German company dSpace GmbH. ASM traffic features a real-time-capable simulation model and a graphical user interface for defining the necessary components, such as road networks, traffic signs, vehicles, and sensors. Developers can simulate a test vehicle, urban and rural road networks, and unlimited number of fellow vehicles [7]. 2. The RDE cycle The main trip requirements for a RDE cycle are described in Commission Regulation (EU) 2016/427 that specifies the features of the route (speeds, distances, durations, etc.) and the ambient conditions (not considered in this work) while Regulation 2016/646 entails, among other things, requirements in terms of trip dynamics. 2.1. Commission Regulations (EU) 2016/427 Table 1 summarizes the requirements of the Regulation. The trip sequence shall consist of a urban section followed by a rural segment and a motorway part. Ambient temperature shall be between 0°C and 30°C, and altitude lower or equal to 700 m a.s.l.. Table 1. Specification of the RDE trip requirements (Regulation 2016/427)
Speed (V) Distance Minimum distance Average speed (VAVG) Number of stops Maximum Speed Total test time Elevation difference
Unit km/h
Urban V≤ 60
Rural 60
Motorway 90≤V≤145
% of the total distance km km/h
29-44
33±10
33±10
16 15≤VAVG≤40
16 -
16 -
s km/h min m
several>10 60 90 Between 90 and 120 100
Notes V>100 for at least 5 min in motorway
145 Between start and end point
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2.2. Commission Regulation (EU) 2016/646 This Regulation establishes the quantitative RDE requirements. After the calculation of the acceleration, ai, distance, di, speed, vi, and the product (v·a)i for each time-step i, all these values must be ranked in ascending order of the vehicle speed. All datasets are then divided into three bins, according to vehicle speed (refer to Sec. 2.1). For each bin the average vehicle speed, vk, shall be calculated. The next step is to calculate the 95th percentile of the product (v·apos) - where apos is the acceleration with a value greater than 0.1 m/s2 - and, consequently, the relative positive acceleration (RPA). There are restrictions about the values of RPA and the number of positive accelerations for each bin. Other requirements (i.e. correction of instantaneous vehicle altitude data) are not taken into account in this work because they were not of interest for the specific geographic zone. 3. Building a RDE cycle 3.1. Minimizing the distance The first goal of the proposed procedure was to identify the minimum distance that allowed the requirements of Commission Regulation (EU) 2016/427 to be satisfied. To this scope, an optimization has been performed using the input variables listed in Table 2 together with their range of variations and steps. Table 2. Inputs, outputs and result of the optimization
Average speed Min Max Step Best value Distance Min Max Step Best value
Unit
Urban
Rural
Motorway
km/h km/h km/h km/h
22 28 0.2 23.6
70 80 0.25 70
92 105 0.5 92.5
km km km km
16 32 0.5 24
16 40 0.5 21
16 40 0.5 18.5
According to the results of the optimization the minimum distance of the overall route is about 63.5km. This corresponds to a driving time of 91 minutes with 67% of time in urban cycle. 3.2. Building the route Different routes have been taken into account in this work (see Table 3): Route 1: a urban path proposed in a previous study of the authors [8][6]; Route 2: a route built empirically before the optimization described in the previous section; Route 3: a route that tries to reproduce the result of the optimization while using Route 1 for the urban path. Route 4: a route used for a preliminary analysis of the emissions. Route 1 and 2 start from and return to the Department of Engineering for Innovation (Lecce, Italy). They were identified and created in Openstreetmap. Route 1 was originally developed to test an electric car under real-world driving conditions well before the introduction of the new regulations [8]. Eight tests were conducted by Driver 1 with a small electric city car and the average speed was found to vary between 14.7 and 22.8 km/h [8]. Route 2 has a length of 80.1 km (24.2 km urban, 27.1 km rural and 28.8 km motorway). It starts from the Department of Engineering for Innovation (Lecce, Italy), continues through the city (speed limit 50 km/h), on a secondary state road (speed limit 90 km/h) and ends on a main state road (speed limit 110 km/h). Route 3 was chosen among those selected by the genetic algorithm. The three path lengths are 24.5, 25.7 and 22 km respectively; it is very similar to the first route built, except for the start and end points of the urban path (both in the city of Lecce) and the distance travelled in the other two paths. Two tests were conducted by Driver 2 with a small
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family car. One test has been discarded because of an excessive urban average speed. All the requirements of Regulation 2016/646 are met and the distance is minimized with respect to route 2, so this is the route could be used for future RDE measurements after a suitable analysis of its robustness to traffic conditions. Table 3. Routes and vehicles considered in the investigation Route ID
Vehicle
Curb mass (kg)
Length
1 2 3 4
City car Small family car Small family car SUV
900 1350 1350 2260
12 82 72.2 10
(km)
Route type
Driver
Repetitions
Urban Urban/rural/motorway Urban/rural/motorway Urban/rural
1 2 2 3
8 1 2 1
3.3. Acquisition and analysis of the driving cycle Once defined the trip, experimental tests were performed driving the vehicles with a USB GPS receiver. This was connected to a laptop which recorded time, longitude, latitude and instantaneous speed with a sampling rate of 1 Hz using a LabVIEW virtual instrument (VI). A few errors appeared in the collected raw data, such as extraneous or outlying data points and signal loss. Unaddressed, these errors significantly impact the reliability of source data and limit the effectiveness of traditional drive cycle analysis approaches and vehicle simulation software [9]. The GPS data filtration approach presented here consists of three distinct filters designed and arranged specifically for this work. The first filter replaces false negative-speed records that are the result of temporary GPS signal dropout with zeros; in the second step in the filtration process, any invalid data point, such as single-sample high-speed data spikes, are removed and replaced with valid data. Each speed value is processed through the filter and compared to the following one; if the difference between them is greater than a pre-set value, the filter replaces the source data point with speed information of the previous point. Lastly, the data were smoothed using a Savitzky-Golay FIR filter. Acceleration has been calculated from the speed data as follows: (1) v(i 1) v(i 1)
a(i)
3.6 [t (i 1) t (i 1)]
where a(i) is the acceleration [m/s2] and v(i) is the actual vehicle speed [km/h] in time-step i. The acceleration values vary between -2.75 and 1.82 m/s2, and the number of datasets with acceleration values greater than 0.1 m/s 2 are bigger than 150 in each speed bin. 4. Analysis of the results The preliminary data collected in the investigation were used to put in evidence some critical aspect in the definition of RDE cycles: the differences between real driving cycles vs laboratorial cycles (Routes 3), the effect of traffic (Route 1) and the necessity to minimize the distance and so the experimental costs (Route 2 vs Route 3) and. Route 4 was also used for a preliminary test with the PEMS equipment. 4.1. Real world vs laboratorial cycles Fig. 1 shows the acceleration-time and speed-time graphs of a RDE performed on the route 3. Fig. 2 shows the acceleration vs. time plots for two regulated cycles, NEDC and WLTC, and the RDE test performed on route 3. Note that the WLTC cycle considered is the WLTC3b, valid for the vehicles with maximum speed greater than 120 km/h and power-to-unladen-mass ratio greater than 34 W/kg (Regulation UN GTR 15).
Teresa Donateo et al. / Energy Procedia 126 (201709) 891–898 T. Donateo et al./ Energy Procedia 00 (2017) 000–000
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Fig. 1. Speed and acceleration vs. time for the RDE test on Route 3
The NEDC cycle consists of four repeated ECE-15 urban driving cycles (UDC) and one Extra-Urban driving cycle (EUDC), while the WLTC consists of four phases (low, medium, high and extra-high speed). Compared to NEDC, the WLTC class 3 is more realistic. It covers a wider range of engine conditions and is more representative of real driving. It has higher speeds (maximum and average speed with stops are 131.3 km/h and 46.5 km/h, respectively), steeper accelerations and decelerations and less idling time (13.44%) compared to the NEDC. More details of the test procedure can be found in the literature [10]. RDE tests are performed on-road, meaning that the vehicle performs a real-world driving cycle where acceleration covers a wider range of operation conditions, unlike under laboratorial driving cycles. The RDE cycle performed has higher accelerations at both lower and higher speeds compared to the WLTC; the former condition occurs when the driver slows down before proceeding at intersections or stops, the latter when overtaking another vehicle. Note also that decelerations are much steeper mostly in the urban path. All these features mostly depend on the driving style and require further investigation.
Fig. 2. Acceleration vs. speed for NEDC, WLTC and RDE (Route 3) driving cycles
Fig. 3 shows an estimation of the probability density function for speed and acceleration obtained in both WLTC3 cycle and RDE route. The relative likelihoods of speed samples in the range between 30 and 90 km/h in the RDE route is higher than in WLTC3, meaning that its average speed in the urban path is lower than the RDE one. Also, the accelerations are steeper in the RDE route, even if they have a similar probability. This implies less emissions, as further proof of the fact that laboratorial tests do not fully reflect real world driving style and emissions.
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Teresa Donateo et al. / Energy Procedia 126 (201709) 891–898 T. Donateo et al./ Energy Procedia 00 (2017) 000–000
Fig. 3. Probability density function for speed (a) and acceleration (b) of WLTC and RDE (Route 3) driving cycles
Fig. 4. Probability density function vs. speed and acceleration for the 8 tests performed on Route 1
Teresa Donateo et al. / Energy Procedia 126 (201709) 891–898 T. Donateo et al./ Energy Procedia 00 (2017) 000–000
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4.2. Effect of driving conditions The robustness with respect to traffic conditions was analyzed by considering the eight tests performed on route 1 whose average speed varies between 14.72 and 22.82 km/h. These tests were conducted in different working days with the same driver on the same route. Thus, their differences are due to different traffic conditions. Lower speed values were obtained in high traffic conditions, seen during rush hours (about 8 a.m. and 1 p.m.). On the one hand, long idling time during rush hours clearly affects the total emissions and should be avoided, but on the other hand the total stop time shall be at least 10% of the total urban path time, so define the urban path and test starting time is a crucial point in the definition of a RDE cycle. Fig. 4. shows the probability density function of speed and acceleration in the 8 tests of route 1. From a representation of this type, both the average speed and the time elapsed in each velocity range can be evaluated. For example, comparing tests 3 and 7, the average speed of test 3 is about half of the average speed of speed 7 (as a matter of fact, the distribution is shifted to the left); furthermore, the time elapsed in a speed range between 0 and 10 km/h is about twice in the former with respect to the latter. Since the original path’s length was 12 km, the urban part considered in this work consists of two laps; a sensitivity analysis was performed to verify that every 2combination without repetition meets the Regulation requirements. 4.3. Minimization of the distance Route 2 was empirically built before the software optimization process. The fuel consumption in the urban path was about 1.7 liters (which corresponds to 14.2 km/liter), so the next goal was to minimize the distance and costs. Route 3 was consequently built and followed at a different time (soon after lunch time) in order not to run into any traffic jam and to comply with the software optimization result. The average speed increased from 26.4 to 28.9 km/h, while the consumed fuel decreased by 23.5%. Also, the length of the rural and motorway paths was decreased accordingly to the trip requirements seen in Sec. 2.1, and the overall time decreased to 94 minutes. Further investigation is required to achieve better results. 4.4. Preliminary test of the PEMS
Fig. 5. Preliminary analysis of the effect of speed and acceleration on greenhouse and pollutant emissions on Route 4
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Teresa Donateo et al. / Energy Procedia 126 (201709) 891–898 T. Donateo et al./ Energy Procedia 00 (2017) 000–000
The PEMS used for emissions regulatory purposes integrate advanced gas analyzers, exhaust mass flow meters, weather station, Global Positioning System (GPS) and connection to the vehicle on-board diagnostics (OBD). PEMS provide a complete and very accurate real-time monitoring of the pollutants emitted by the engine (HC, CO, CO2, NOx) together with the associated engine, vehicle, and ambient parameters. Route 4 has been performed for a first test and has been followed only once. Its length is 10 km (69.5% urban, 30.5% rural), and the average speed was 23.8 km/h. The vehicle was cold-started and the ambient temperature and pressure were about 21 °C and 1010.8 mbar respectively. Fig. 5. shows the preliminary results of the correlation between emissions and instantaneous values of acceleration and speed based on the measurements performed on route 4. 5. Conclusions The present investigation addresses the problem of building suitable trips for real world emissions measurements with a methodology that minimize the route length and take into account the robustness with respect to traffic conditions while fulfilling the stringent requirements of the European Regulations. Several data of actual speed and acceleration were recorded, post-processed and compared with laboratory driving cycles (NEDC and WLTC) to put into evidence the higher spectrum of speed and acceleration in real world cases. Moreover, the effect of traffic conditions on the probability density functions of speed and acceleration have been considered Finally, a preliminary test has been performed with a device for portable emission measurements to map the instantaneous values of CO2, CO, NOx and THC as a function of speed and acceleration. A route that fulfills the requirements of Regulation 2016/646 and reduce the experimental cost by minimizing the overall distance was found. This route will be used for future RDE measurements at the Department of Engineering for Innovation after a suitable analysis of its robustness to traffic conditions, for example with the help of a traffic simulator. References [1]
Varella, R., Duarte, G., Baptista, P., Sousa, L. et al., Comparison of Data Analysis Methods for European Real Driving Emissions Regulation. SAE Technical Paper, 2017; 2017-01-0997; 1-14. [2] Ashtari A., Bibeau E., and Shahidinejad S., Using large driving record samples and a stochastic approach for real-world driving cycle construction: Winnipeg driving cycle. Transportation Science, 2014, 48-2, pp. 170–183. [3] Gong Q., Midlam-Mohler S., Marano V., Rizzoni G., An iterative markov chain approach for generating vehicle driving cycles. SAE International Journal of Engines, 2011, 4-1, pp. 1035 – 1045. [4] Nyberg P., Frisk E, Nielsen L., Driving Cycle Equivalence and Transformation. IEEE Transactions on Vehicular Technology, 2017, 66-3, pp. 1963 – 1974. [5] Le Borge G., Lanternier P., “Real World Fuel Consumption Measurements of Peugeot, Citroen and DS vehicles”, Geneva Motorshow, March 7, 2017. [6] Deutsches Zentrum für Luftund Raumfahrt (DLR), SUMO user documentation, Available at: http://sumo.dlr.de/wiki/Simulation_of_Urban_MObility_-_Wiki [Accessed: 2 May 2017]. [7] Abdelgawad, K., Henning, S, Biemelt, P., Gausemeier, S., Trächtler, A., Advanced Traffic Simulation Framework for Networked Driving Simulators, IFAC-PapersOnLine, Volume 49, Issue 11, 2016, Pages 101-108. [8] Donateo T., Pacella D., Laforgia D., “A Method for the Prediction of Future Driving Conditions and for the Energy Management Optimization of a Hybrid Electric Vehicle”, International Journal of Vehicle Design, Special Issue on: "Enabling Technologies for Sustainable Vehicle Electrification: Control, Optimisation and Diagnostics", Vol. 58, Nos. 2/3/4, 2012, ISSN (Online): 1741-5314-ISSN (Print): 0143-3369, Vol 58, No. 2-4, 2012, pp. 111-133 [9] Adam Duran, Matthew Earleywine, GPS Data Filtration Method for Drive Cycle Analysis Applications, SAE Technical Paper, 2012; 2012-01-0743; 1-2. [10] Ciuffo, B., Tutuianu, M., “The development of the World-wide Harmonized Test Procedure for Light Duty Vehicles ( WLTP ) and the pathway for its implementation into the EU legislation,” TRB 2015 Annual Meeting, 2015. [11] Council Regulation (EC) 715/2007 of 20 June 2007 on type approval of motor vehicles with respect to emissions from light passenger and commercial vehicles (Euro 5 and Euro 6) and on access to vehicle repair and maintenance information
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Energy Procedia 126 891–898 Energy Procedia 00(201709) (2017) 000–000 www.elsevier.com/locate/procedia
72nd Conference of the Italian Thermal Machines Engineering Association, ATI2017, 6-8 72nd Conference of the ItalianSeptember Thermal Machines Engineering Association, ATI2017, 6-8 2017, Lecce, Italy September 2017, Lecce, Italy
Building a cycle for Real Driving Emissions
Building a cycleSymposium for RealonDriving Emissions The 15th International District Heating and Cooling
Teresa Donateoaa*, Mattia Giovinazziaa Donateo Mattiathe Giovinazzi the Teresa feasibility of*,using heat demand-outdoor
AssessingDepartment of Engineering for Innovation, via per Monteroni, 73100 Lecce, Italy Department of Engineering Innovation, via perdistrict Monteroni, 73100 Lecce, demand Italy temperature function for a forlong-term heat forecast a a
a,b,c
a
a
b
c
c
Abstract I. Andrić *, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre Abstract a IN+ Center for Innovation, Technology Policy Research - Instituto Técnico,type Av. Rovisco Pais 1, 1049-001within Lisbon,the Portugal The EU6d Emission Regulation requiresand Real Driving Emissions asSuperior an additional approval requirement 2017 b Veolia Recherche &the Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France The Emission Regulation requires Real Driving Emissions as an additional type approval requirement within the 2017as2020EU6d timeframe in order to take into account influence of the road profile, the ambient conditions and the traffic situation c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, Francesituation as 2020 timeframe in order take into account of the road profile,Monitoring the ambient conditions and the traffic well as the behavior of to the driver. The newthe testinfluence uses Portable Emissions System (PEMS) to measure on-board well as theThe behavior of the driver. The new usesaPortable Monitoring System (PEMS) to measurein on-board emissions. trip sequence shall consist of atest urban, rural andEmissions a motorway sections with specific requirements terms of emissions. trip sequence a urban, a rural and a motorway sections with specific in terms distance andThe average speed forshall eachconsist section.ofFor example, the overall trip duration shall be between 90 requirements and 120 minutes. Due of to distance and average speed for each section. For example, the overall trip duration shall be between 90 and 120 minutes. Due to these strong requirements, the execution of RDE measurements has to be preceded by an accurate planning of the route to reduce Abstract these strongrisk requirements, the execution of RDE measurements to be preceded by an accurate the routetoto build reducea test failure and, consequently, experimental costs. The aimhas of the present investigation is to planning present aof procedure test failure riskdriving and, consequently, costs. The aim of the with present investigation is to presentofatraffic procedure to build a cycle for heating real emissions thatexperimental minimizes the distance, is robust respect the uncertainties District networks are commonly addressed in the literature as one of thetomost effective solutions forconditions decreasingand the cycle for real driving emissions that minimizes the distance, is robust with respect to the uncertainties of traffic conditions satisfy the requirements of the regulations. The procedure has been applied to routes from and to the Department of Engineering greenhouse gas emissions from the building sector. These systems require high investments which are returned through the and heat satisfy the of athe regulations. The procedure has been applied policies, to routes and to theinDepartment of Engineering for Innovation. preliminary analysis of effect ofrenovation instantaneous speedfrom and acceleration on real drive emissions is sales. Duerequirements to Moreover, the changed climate conditions andthebuilding heat demand the future could decrease, for Innovation. Moreover, a preliminary analysis of the effect of instantaneous speed and acceleration on real drive emissions is presented. prolonging the investment return period. presented. The main scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand ©forecast. 2017 TheThe Authors. Published by Elsevier Ltd. districtPublished of Alvalade, locatedLtd. in Lisbon (Portugal), was used as a case study. The district is consisted of 665 © 2017 The The Authors. Elsevier nd © 2017 Authors. Published by by Ltd. committee of the 72nd Conference of the Italian Thermal Machines Engineering Peer-review under responsibility of Elsevier the scientific buildings that vary in both construction period and typology. weather scenarios (low, Thermal medium,Machines high) andEngineering three district Peer-review under responsibility of the scientific committee of theThree 72nd Conference of the Italian Peer-review under responsibility of the scientific committee of the 72 Conference of the Italian Thermal Machines Engineering Association. renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were Association Association. compared with results from a dynamic heat demand model, previously developed and validated by the authors. Keywords: real driving emissions, on-board measurements The results showed that when only weather change is considered, the margin of error could be acceptable for some applications Keywords: real driving emissions, on-board measurements
(the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). 1.The Introduction value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the 1.decrease Introduction in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and Over the past few years, European Commission pointed out that laboratory tests do per notdecade reflect(depending the amount renovation scenarios considered). On the other hand,has function intercept increased for 7.8-12.7% on of the Over theemissions past few emitted years, European Commission has pointed out laboratory tests do the not reflect the amount of exhaust during real driving particularly for nitrogen oxides (NO has been x). Europe coupled scenarios). The values suggested could beconditions, used to modify thethat function parameters for scenarios considered, and exhaust emitted driving conditions, particularly for nitrogen oxides (NO x). Europe has been improveemissions the accuracy of heat during demandreal estimations. © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * Corresponding author. Tel.: +39 0832 29 7754. Cooling. address:author. [email protected] * E-mail Corresponding Tel.: +39 0832 29 7754.
E-mail address: [email protected] Keywords: Heat demand; Forecast; Climate change 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review underThe responsibility of theby scientific of the 72 nd Conference of the Italian Thermal Machines Engineering 1876-6102 © 2017 Authors. Published Elsevier committee Ltd.
Association. Peer-review under responsibility of the scientific committee of the 72 nd Conference of the Italian Thermal Machines Engineering Association. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling.
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 72nd Conference of the Italian Thermal Machines Engineering Association 10.1016/j.egypro.2017.08.307
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using the New European Driving Cycle (NEDC) certification cycle since 1970, but it has been widely criticized, especially in the wake of the 2015 diesel emissions scandal, due to its low acceleration pattern, constant speed cruising and high number of idling events, which do not represent transient accelerations regimes [1]. This topic has been extensively addressed in literature where several methods were proposed to obtain realistic driving cycles (see for example [2]-[4]). In order to overcome these issues, starting from September 2017, the NEDC will be replaced by the new Worldwide harmonized Light vehicles Test Procedure (WLTP), and a Real Driving Emissions (RDE) test will be mandatory. RDE tests have some requirements that should be further analyzed, one of which is the trip selection. In March 2017, PSA Peugeot Citroën and NGOs published results of first real-world fuel economy test. The measurements were made under a protocol developed with the NGO Transport & Environment, on public roads near Paris (25.5 km urban, 39.7 km rural, and 31.1 km motorway) and under real-life driving conditions [5]. Except for this work, the literature on the development of specific tests for the RDE procedure is almost null even if the general topic of real driving cycles was the object of many investigations as already underlined. Since small and large car manufacturers have now to develop a route for their vehicles, the method described in this work could help them to do it, reducing development costs. Another key aspect is the robustness of the RDE driving cycle with respect to traffic conditions. In the present investigation, the robustness of the cycle has been performed experimentally by repeating the same route under different traffic conditions. The effects of driving conditions could be extensively evaluated using a traffic simulation software like SUMO (Simulation of Urban MObility), developed by the German Institute of Transportation (DLR).. Once imported the map from Openstreetmap and converted it into a network via a proprietary subprogram, the user could generate routes and traffic, either using automatic randomization or manually [6]. Sumo source code has been edited to define the number of vehicles generated in the map, the route length and to record the speed-time data of a target car. In fact, once generated the traffic, the user must define manually the route that the car follows in reality. These changes allow the user to predict the maximum number of cars (i.e. the traffic) for which trip requirements are met. Another software that can be used to simulate traffic is Automotive Simulation Models (ASM), developed by the German company dSpace GmbH. ASM traffic features a real-time-capable simulation model and a graphical user interface for defining the necessary components, such as road networks, traffic signs, vehicles, and sensors. Developers can simulate a test vehicle, urban and rural road networks, and unlimited number of fellow vehicles [7]. 2. The RDE cycle The main trip requirements for a RDE cycle are described in Commission Regulation (EU) 2016/427 that specifies the features of the route (speeds, distances, durations, etc.) and the ambient conditions (not considered in this work) while Regulation 2016/646 entails, among other things, requirements in terms of trip dynamics. 2.1. Commission Regulations (EU) 2016/427 Table 1 summarizes the requirements of the Regulation. The trip sequence shall consist of a urban section followed by a rural segment and a motorway part. Ambient temperature shall be between 0°C and 30°C, and altitude lower or equal to 700 m a.s.l.. Table 1. Specification of the RDE trip requirements (Regulation 2016/427)
Speed (V) Distance Minimum distance Average speed (VAVG) Number of stops Maximum Speed Total test time Elevation difference
Unit km/h
Urban V≤ 60
Rural 60
Motorway 90≤V≤145
% of the total distance km km/h
29-44
33±10
33±10
16 15≤VAVG≤40
16 -
16 -
s km/h min m
several>10 60 90 Between 90 and 120 100
Notes V>100 for at least 5 min in motorway
145 Between start and end point
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2.2. Commission Regulation (EU) 2016/646 This Regulation establishes the quantitative RDE requirements. After the calculation of the acceleration, ai, distance, di, speed, vi, and the product (v·a)i for each time-step i, all these values must be ranked in ascending order of the vehicle speed. All datasets are then divided into three bins, according to vehicle speed (refer to Sec. 2.1). For each bin the average vehicle speed, vk, shall be calculated. The next step is to calculate the 95th percentile of the product (v·apos) - where apos is the acceleration with a value greater than 0.1 m/s2 - and, consequently, the relative positive acceleration (RPA). There are restrictions about the values of RPA and the number of positive accelerations for each bin. Other requirements (i.e. correction of instantaneous vehicle altitude data) are not taken into account in this work because they were not of interest for the specific geographic zone. 3. Building a RDE cycle 3.1. Minimizing the distance The first goal of the proposed procedure was to identify the minimum distance that allowed the requirements of Commission Regulation (EU) 2016/427 to be satisfied. To this scope, an optimization has been performed using the input variables listed in Table 2 together with their range of variations and steps. Table 2. Inputs, outputs and result of the optimization
Average speed Min Max Step Best value Distance Min Max Step Best value
Unit
Urban
Rural
Motorway
km/h km/h km/h km/h
22 28 0.2 23.6
70 80 0.25 70
92 105 0.5 92.5
km km km km
16 32 0.5 24
16 40 0.5 21
16 40 0.5 18.5
According to the results of the optimization the minimum distance of the overall route is about 63.5km. This corresponds to a driving time of 91 minutes with 67% of time in urban cycle. 3.2. Building the route Different routes have been taken into account in this work (see Table 3): Route 1: a urban path proposed in a previous study of the authors [8][6]; Route 2: a route built empirically before the optimization described in the previous section; Route 3: a route that tries to reproduce the result of the optimization while using Route 1 for the urban path. Route 4: a route used for a preliminary analysis of the emissions. Route 1 and 2 start from and return to the Department of Engineering for Innovation (Lecce, Italy). They were identified and created in Openstreetmap. Route 1 was originally developed to test an electric car under real-world driving conditions well before the introduction of the new regulations [8]. Eight tests were conducted by Driver 1 with a small electric city car and the average speed was found to vary between 14.7 and 22.8 km/h [8]. Route 2 has a length of 80.1 km (24.2 km urban, 27.1 km rural and 28.8 km motorway). It starts from the Department of Engineering for Innovation (Lecce, Italy), continues through the city (speed limit 50 km/h), on a secondary state road (speed limit 90 km/h) and ends on a main state road (speed limit 110 km/h). Route 3 was chosen among those selected by the genetic algorithm. The three path lengths are 24.5, 25.7 and 22 km respectively; it is very similar to the first route built, except for the start and end points of the urban path (both in the city of Lecce) and the distance travelled in the other two paths. Two tests were conducted by Driver 2 with a small
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family car. One test has been discarded because of an excessive urban average speed. All the requirements of Regulation 2016/646 are met and the distance is minimized with respect to route 2, so this is the route could be used for future RDE measurements after a suitable analysis of its robustness to traffic conditions. Table 3. Routes and vehicles considered in the investigation Route ID
Vehicle
Curb mass (kg)
Length
1 2 3 4
City car Small family car Small family car SUV
900 1350 1350 2260
12 82 72.2 10
(km)
Route type
Driver
Repetitions
Urban Urban/rural/motorway Urban/rural/motorway Urban/rural
1 2 2 3
8 1 2 1
3.3. Acquisition and analysis of the driving cycle Once defined the trip, experimental tests were performed driving the vehicles with a USB GPS receiver. This was connected to a laptop which recorded time, longitude, latitude and instantaneous speed with a sampling rate of 1 Hz using a LabVIEW virtual instrument (VI). A few errors appeared in the collected raw data, such as extraneous or outlying data points and signal loss. Unaddressed, these errors significantly impact the reliability of source data and limit the effectiveness of traditional drive cycle analysis approaches and vehicle simulation software [9]. The GPS data filtration approach presented here consists of three distinct filters designed and arranged specifically for this work. The first filter replaces false negative-speed records that are the result of temporary GPS signal dropout with zeros; in the second step in the filtration process, any invalid data point, such as single-sample high-speed data spikes, are removed and replaced with valid data. Each speed value is processed through the filter and compared to the following one; if the difference between them is greater than a pre-set value, the filter replaces the source data point with speed information of the previous point. Lastly, the data were smoothed using a Savitzky-Golay FIR filter. Acceleration has been calculated from the speed data as follows: (1) v(i 1) v(i 1)
a(i)
3.6 [t (i 1) t (i 1)]
where a(i) is the acceleration [m/s2] and v(i) is the actual vehicle speed [km/h] in time-step i. The acceleration values vary between -2.75 and 1.82 m/s2, and the number of datasets with acceleration values greater than 0.1 m/s 2 are bigger than 150 in each speed bin. 4. Analysis of the results The preliminary data collected in the investigation were used to put in evidence some critical aspect in the definition of RDE cycles: the differences between real driving cycles vs laboratorial cycles (Routes 3), the effect of traffic (Route 1) and the necessity to minimize the distance and so the experimental costs (Route 2 vs Route 3) and. Route 4 was also used for a preliminary test with the PEMS equipment. 4.1. Real world vs laboratorial cycles Fig. 1 shows the acceleration-time and speed-time graphs of a RDE performed on the route 3. Fig. 2 shows the acceleration vs. time plots for two regulated cycles, NEDC and WLTC, and the RDE test performed on route 3. Note that the WLTC cycle considered is the WLTC3b, valid for the vehicles with maximum speed greater than 120 km/h and power-to-unladen-mass ratio greater than 34 W/kg (Regulation UN GTR 15).
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Fig. 1. Speed and acceleration vs. time for the RDE test on Route 3
The NEDC cycle consists of four repeated ECE-15 urban driving cycles (UDC) and one Extra-Urban driving cycle (EUDC), while the WLTC consists of four phases (low, medium, high and extra-high speed). Compared to NEDC, the WLTC class 3 is more realistic. It covers a wider range of engine conditions and is more representative of real driving. It has higher speeds (maximum and average speed with stops are 131.3 km/h and 46.5 km/h, respectively), steeper accelerations and decelerations and less idling time (13.44%) compared to the NEDC. More details of the test procedure can be found in the literature [10]. RDE tests are performed on-road, meaning that the vehicle performs a real-world driving cycle where acceleration covers a wider range of operation conditions, unlike under laboratorial driving cycles. The RDE cycle performed has higher accelerations at both lower and higher speeds compared to the WLTC; the former condition occurs when the driver slows down before proceeding at intersections or stops, the latter when overtaking another vehicle. Note also that decelerations are much steeper mostly in the urban path. All these features mostly depend on the driving style and require further investigation.
Fig. 2. Acceleration vs. speed for NEDC, WLTC and RDE (Route 3) driving cycles
Fig. 3 shows an estimation of the probability density function for speed and acceleration obtained in both WLTC3 cycle and RDE route. The relative likelihoods of speed samples in the range between 30 and 90 km/h in the RDE route is higher than in WLTC3, meaning that its average speed in the urban path is lower than the RDE one. Also, the accelerations are steeper in the RDE route, even if they have a similar probability. This implies less emissions, as further proof of the fact that laboratorial tests do not fully reflect real world driving style and emissions.
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Fig. 3. Probability density function for speed (a) and acceleration (b) of WLTC and RDE (Route 3) driving cycles
Fig. 4. Probability density function vs. speed and acceleration for the 8 tests performed on Route 1
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4.2. Effect of driving conditions The robustness with respect to traffic conditions was analyzed by considering the eight tests performed on route 1 whose average speed varies between 14.72 and 22.82 km/h. These tests were conducted in different working days with the same driver on the same route. Thus, their differences are due to different traffic conditions. Lower speed values were obtained in high traffic conditions, seen during rush hours (about 8 a.m. and 1 p.m.). On the one hand, long idling time during rush hours clearly affects the total emissions and should be avoided, but on the other hand the total stop time shall be at least 10% of the total urban path time, so define the urban path and test starting time is a crucial point in the definition of a RDE cycle. Fig. 4. shows the probability density function of speed and acceleration in the 8 tests of route 1. From a representation of this type, both the average speed and the time elapsed in each velocity range can be evaluated. For example, comparing tests 3 and 7, the average speed of test 3 is about half of the average speed of speed 7 (as a matter of fact, the distribution is shifted to the left); furthermore, the time elapsed in a speed range between 0 and 10 km/h is about twice in the former with respect to the latter. Since the original path’s length was 12 km, the urban part considered in this work consists of two laps; a sensitivity analysis was performed to verify that every 2combination without repetition meets the Regulation requirements. 4.3. Minimization of the distance Route 2 was empirically built before the software optimization process. The fuel consumption in the urban path was about 1.7 liters (which corresponds to 14.2 km/liter), so the next goal was to minimize the distance and costs. Route 3 was consequently built and followed at a different time (soon after lunch time) in order not to run into any traffic jam and to comply with the software optimization result. The average speed increased from 26.4 to 28.9 km/h, while the consumed fuel decreased by 23.5%. Also, the length of the rural and motorway paths was decreased accordingly to the trip requirements seen in Sec. 2.1, and the overall time decreased to 94 minutes. Further investigation is required to achieve better results. 4.4. Preliminary test of the PEMS
Fig. 5. Preliminary analysis of the effect of speed and acceleration on greenhouse and pollutant emissions on Route 4
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The PEMS used for emissions regulatory purposes integrate advanced gas analyzers, exhaust mass flow meters, weather station, Global Positioning System (GPS) and connection to the vehicle on-board diagnostics (OBD). PEMS provide a complete and very accurate real-time monitoring of the pollutants emitted by the engine (HC, CO, CO2, NOx) together with the associated engine, vehicle, and ambient parameters. Route 4 has been performed for a first test and has been followed only once. Its length is 10 km (69.5% urban, 30.5% rural), and the average speed was 23.8 km/h. The vehicle was cold-started and the ambient temperature and pressure were about 21 °C and 1010.8 mbar respectively. Fig. 5. shows the preliminary results of the correlation between emissions and instantaneous values of acceleration and speed based on the measurements performed on route 4. 5. Conclusions The present investigation addresses the problem of building suitable trips for real world emissions measurements with a methodology that minimize the route length and take into account the robustness with respect to traffic conditions while fulfilling the stringent requirements of the European Regulations. Several data of actual speed and acceleration were recorded, post-processed and compared with laboratory driving cycles (NEDC and WLTC) to put into evidence the higher spectrum of speed and acceleration in real world cases. Moreover, the effect of traffic conditions on the probability density functions of speed and acceleration have been considered Finally, a preliminary test has been performed with a device for portable emission measurements to map the instantaneous values of CO2, CO, NOx and THC as a function of speed and acceleration. A route that fulfills the requirements of Regulation 2016/646 and reduce the experimental cost by minimizing the overall distance was found. This route will be used for future RDE measurements at the Department of Engineering for Innovation after a suitable analysis of its robustness to traffic conditions, for example with the help of a traffic simulator. References [1]
Varella, R., Duarte, G., Baptista, P., Sousa, L. et al., Comparison of Data Analysis Methods for European Real Driving Emissions Regulation. SAE Technical Paper, 2017; 2017-01-0997; 1-14. [2] Ashtari A., Bibeau E., and Shahidinejad S., Using large driving record samples and a stochastic approach for real-world driving cycle construction: Winnipeg driving cycle. Transportation Science, 2014, 48-2, pp. 170–183. [3] Gong Q., Midlam-Mohler S., Marano V., Rizzoni G., An iterative markov chain approach for generating vehicle driving cycles. SAE International Journal of Engines, 2011, 4-1, pp. 1035 – 1045. [4] Nyberg P., Frisk E, Nielsen L., Driving Cycle Equivalence and Transformation. IEEE Transactions on Vehicular Technology, 2017, 66-3, pp. 1963 – 1974. [5] Le Borge G., Lanternier P., “Real World Fuel Consumption Measurements of Peugeot, Citroen and DS vehicles”, Geneva Motorshow, March 7, 2017. [6] Deutsches Zentrum für Luftund Raumfahrt (DLR), SUMO user documentation, Available at: http://sumo.dlr.de/wiki/Simulation_of_Urban_MObility_-_Wiki [Accessed: 2 May 2017]. [7] Abdelgawad, K., Henning, S, Biemelt, P., Gausemeier, S., Trächtler, A., Advanced Traffic Simulation Framework for Networked Driving Simulators, IFAC-PapersOnLine, Volume 49, Issue 11, 2016, Pages 101-108. [8] Donateo T., Pacella D., Laforgia D., “A Method for the Prediction of Future Driving Conditions and for the Energy Management Optimization of a Hybrid Electric Vehicle”, International Journal of Vehicle Design, Special Issue on: "Enabling Technologies for Sustainable Vehicle Electrification: Control, Optimisation and Diagnostics", Vol. 58, Nos. 2/3/4, 2012, ISSN (Online): 1741-5314-ISSN (Print): 0143-3369, Vol 58, No. 2-4, 2012, pp. 111-133 [9] Adam Duran, Matthew Earleywine, GPS Data Filtration Method for Drive Cycle Analysis Applications, SAE Technical Paper, 2012; 2012-01-0743; 1-2. [10] Ciuffo, B., Tutuianu, M., “The development of the World-wide Harmonized Test Procedure for Light Duty Vehicles ( WLTP ) and the pathway for its implementation into the EU legislation,” TRB 2015 Annual Meeting, 2015. [11] Council Regulation (EC) 715/2007 of 20 June 2007 on type approval of motor vehicles with respect to emissions from light passenger and commercial vehicles (Euro 5 and Euro 6) and on access to vehicle repair and maintenance information