On the advantages of the new power-split infinitely variable transmission over conventional mechanical transmissions based on fuel consumption analysis

Journal of Cleaner Production xxx (xxxx) xxx

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On the advantages of the new power-split infinitely variable transmission over conventional mechanical transmissions based on fuel consumption analysis a, b _ Ender Ince , Mehmet A. Güler c, a, * a b c

Department of Mechanical Engineering, TOBB University of Economics and Technology, Ankara, 06560, Turkey R&D Department, TurkTraktor, Ankara, 06560, Turkey College of Engineering and Technology, American University of the Middle East, Kuwait

a r t i c l e i n f o

a b s t r a c t

Article history: Received 4 May 2019 Received in revised form 19 September 2019 Accepted 7 October 2019 Available online xxx

The use of power-split infinitely variable transmissions (PS-IVT) in off-road vehicles and, in particular, in agricultural machinery has recently increased considerably thanks to the high driving comfort they provide and the variable transmission ratios facilitating agricultural operations. Conventional mechanical transmissions are also still in use due to their high mechanical efficiency and cost-effective structure. Although the efficiency of the PS-IVT is known to be lower than that of the conventional transmission, it can reduce fuel consumption by allowing the control of internal combustion engine in an optimum way by allowing the continuous transmission ratio. This study is intended to obtain lower fuel consumption values by employing PS-IVT as compared to the conventional transmission. In order to determine the fuel consumption values to be obtained by two different types of transmission, GPS data of a real tractor at real time from the real user in real world conditions were collected, and the driving resistances for the considered vehicle were determined with the longitudinal vehicle dynamics model. Dynamometer test and theoretical calculations based on the kinematic model developed in our earlier study were carried out to determine the mechanical efficiencies of the conventional transmission and the PS-IVT respectively. Fuel consumption values obtained using different types of transmissions were compared in terms of mass by using a BSFC map of a four-cylinder common-rail turbo diesel engine. Fuel consumption simulations were performed in MATLAB environment, and two different power management strategies with eight different scenarios were evaluated. It is found that a reduction of 8.2% in fuel consumption compared with the conventional transmission can be achieved by the proper power management strategy for the PS-IVT. © 2019 Elsevier Ltd. All rights reserved.

Handling Editor: Giorgio Besagni Keywords: GPS trajectory Fuel consumption Power-split transmission Powertrain simulation

1. Introduction Until 2040, The Organization of Petroleum Exporting Countries (OPEC) considers that the sector where oil is the most powerful in the competition with alternative fuels, will be the transportation sector (OPEC, 2017). Furthermore, OPEC estimates that between 2016 and 2040, two out of every three barrels of oil will be consumed by the transportation sector. From these estimates, the importance of making the existing operations more efficient becomes apparent.

* Corresponding author. College of Engineering and Technology, American University of the Middle East, Kuwait. _ E-mail addresses: [email protected] (E. Ince), [email protected] (M.A. Güler).

Studies on reducing the fossil fuel consumption of vehicles are of great importance for preventing energy shortages and for reducing the adverse effects of fossil fuels on the environment. Most of the studies in the literature have focused on the efficiency and fuel consumption of on-road vehicles such as taxis (Nyhan et al., 2016; Luo et al., 2017; Kan et al., 2018), buses (Lajunen, 2014; Guo et al., 2015; Antonio et al., 2017; Wang and Rakha, 2016), and passenger vehicles (Saboohi and Farzaneh, 2009; lan et al., 2019). Karaog We briefly describe the mentioned fuel consumption studies for on road vehicles herein. Kan et al. (2018) proposed a new method for estimating the fuel consumption of the taxis in traffic and the pollution they produce. They claimed that their method estimates the consumption and pollution with an accuracy of over 90%.

https://doi.org/10.1016/j.jclepro.2019.118795 0959-6526/© 2019 Elsevier Ltd. All rights reserved.

_ Please cite this article as: Ince, E., Güler, M.A., On the advantages of the new power-split infinitely variable transmission over conventional mechanical transmissions based on fuel consumption analysis, Journal of Cleaner Production, https://doi.org/10.1016/j.jclepro.2019.118795

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Nyhan et al. (2016) analyzed the Global Positioning System (GPS) trajectory data from a taxi fleet to estimate air pollution emissions. According to their results, high-resolution spatiotemporal vehicle trajectories using GPS in large taxi fleets can be used to reveal highly localized areas of air pollution emissions in cities. Luo et al. (2017) analyzed the energy consumption and refinement of taxis and their spatial-temporal distribution in Shanghai. Their results stressed that spatially, energy consumption and emission present a distribution of the dual core cyclic structure in which the considered zone. Wang and Rakha (2016) aimed to improve bus fuel consumption modeling by addressing a control problem. They claimed that the optimum cruising speeds are between 40 and 50 km/h. Lajunen (2014) conducted an energy consumption analysis based on extensive simulations on different bus routes. They examined five different full size hybrid and electric city bus. According to their simulations, plug-in hybrid and electric city buses have the best potential to reduce energy consumption and emissions. Guo et al. (2015) tested two parallel hybrid and two diesel buses equipped with selective catalytic reduction systems under real-world conditions. Considered buses provided less fuel consumption in hybrid mode and lower NOx at engine output. Antonio et al. (2017) aimed to reduce emissions by using power-split transmission on city buses. According to their results, it is possible to reduce fuel consumption and emissions with an accurate power management logic. Saboohi and Farzaneh (2009) aimed to introduce an optimal eco-driving strategy that is based on the minimum fuel consumption. According to their results, optimal driving strategy based on coordinating the speed and gear ratio through engine load would lead to the minimization of fuel conlan et al. (2019) investigated the effect of different sumption. Karaog gear ratios on fuel consumption and emissions to be used for the torque coupling unit on a parallel hybrid powertrain. They claimed that the proper arrangement of gear ratios can be reduced the fuel consumption and the harmful emissions. Tractors are the main source of power for carrying out agricultural operations. Among the off-road mobile machinery, agricultural machinery (especially tractors) are considered as the main source of fuel consumption and emission of harmful gases  _ 2018; Ettl et al., 2018). Tractors burn a (Janulevi cius and Ciplien e, large amount of fuel during their operations and the harmful gases produced cause greenhouse effect (Hermanet al., 2014; Mantoam et al., 2016; Sørensen et al., 2014). According to the International Council on Clean Transportation (ICCT), off-road vehicles, most of which consist of tractors and construction equipment, are seen as a major cause of pollution in many countries. In the United States, off-road vehicles account for almost three quarters of fine particulate matter (PM2.5) and one quarter of nitrogen oxides (NOx) emitted from mobile sources. In Europe, off-road vehicles account for about a quarter of PM2.5 and more than 15% of NOx from mobile sources. More importantly ICCT reported that due to better control of emissions for road vehicles and the rapidly expanding off-road vehicle market, off-road vehicles will soon become the dominant source of air pollution in the world (Kubsh, 2017). Lee et al. (2016) stated that tractors are one of the most fuel consuming machines among off-road vehicles, and that 17% of the fossil fuel usage in agricultural operations in Korea were due to tractors. Moreover, due to the increase in the world population and as a consequence increasing food demand, the need for tractors is increasing day by day (Kubsh, 2017; Lee et al., 2016). The increase in oil prices and the environmentally threatening engine emissions increase bio-diesel fuel production in recent  _ 2018). However, although the use years (Janulevi cius and Ciplien e, of bio diesel fuels can reduce HC, CO and smoke emissions compared to fossil fuels, they often increase NOx emissions

(Elsanusi et al., 2017). Exhaust Gas Recirculation (EGR) and Selective Catalytic Reduction (SCR) systems, which are the latest techniques developed to reduce harmful NOx emissions, increase fuel consumption and urea solution consumption respectively  _ 2018; Bacenetti et al., 2018). Under these (Janulevicius and Ciplien e, circumstances, the importance of new technologies to increase the fuel efficiency of tractors and reduce harmful emissions without causing power loss is becoming more important. The studies mentioned proves that the global warming and energy shortage are two of the biggest global challenges in recent years. More plants and animals are needed to produce sufficient food for the growing world population, which causes more mechanization demand, more energy use, and consequently more greenhouse gas emissions, and all of these cause energy shortage in agriculture (Mantoam et al., 2016). In order to decrease the fuel consumption and hazardous emissions, in other words, to ensure sustainable agriculture, it is necessary to do research on the total efficiency of off-road vehicles such as tractors as well as on-road vehicles. Tractors are used not only for agricultural operations but also for transportation of crops, seeds, etc. (Hermanet al., 2014). In addition to the increased tractor demand and the dominant effect on fuel consumption, emission limitation laws have been applied for tractors in many countries. Increasing the total efficiency of tractors under these conditions is exceedingly important in terms of both economic and environmental factors. There are studies in the literature about the parameters that affect the fuel consumption of lan et al., 2019; Kolator and on-road and off-road vehicles (Karaog Białobrzewski, 2011; Park et al., 2010; Hermanet al., 2014; Mantoam et al., 2016). We now give a brief literature review on the studies related to off-road vehicles. Peça et al. (2010) studied the effect of engine speed and gear selection on the total efficiency of a tractor. They reported that the efficiency can be increased by 10e20% if the engine speed was decreased from 2200 to 1750 rpm. They also stated that ballast weights and tire pressure have an impact on efficiency. Park et al. (2010) suggested that fuel consumption can be reduced by an average of 69% in plow operations and 54% in cultivation operations by developing an eco-driving system that can assist the tractor user in selecting the appropriate gear and engine speed depending on the tractor’s working loads. Herman et al. (Hermanet al., 2014) suggested an energy consumption model for the agricultural activities. They evaluated the effects of soil type, tractor size, field size and machine load and they claim that the tractor size has the biggest impact on the fuel consumption. Mantoam et al. (2016) studied the energy demand and the green house gas emission in the life-cycle of tractors. They evaluated four different tractors which have distinct power levels and claimed that the more powerful tractors require less energy and emit less greenhouse gas per mass and power. Kolator and Białobrzewski (2011) developed a MATLAB Simulink model to determine the performance of a tractor in cultivation operations, and performed traction efficiency simulations. Lee et al. (2016) investigated the effect of tire pressure, tractor ballast weights, transmission ratios, engine speed, and working load on fuel consumption. They developed a tractor model consisting of 5 sub-models, namely engine, power transmission, fuel consumption, traction power, and compared the power requirement with experimental studies in the literature. They mentioned that tire pressure has a critical effect on fuel consumption and found out that the engine speed should be close to the speed at which the maximum torque is obtained, in order to reduce the fuel consumption per work hour and fuel consumption per tilled area. It has been claimed that all the parameters that were examined have an effect on fuel consumption and should be optimized to minimize consumption (Lee et al.,

_ Please cite this article as: Ince, E., Güler, M.A., On the advantages of the new power-split infinitely variable transmission over conventional mechanical transmissions based on fuel consumption analysis, Journal of Cleaner Production, https://doi.org/10.1016/j.jclepro.2019.118795

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2016). Power-split transmissions are frequently used because of the fuel economy they provide, especially in electrical hybrid vehicles (Huanxin et al., 2018; Z. et al., 2016; Hutchinson et al., 2014). However, power-split hydrostatic transmissions, are still being used due to the high torque requirements on off-road vehicles. Even if the transmitting the power from one point to another by a hydraulic way is not efficient due to losses, efficient use of internal combustion engines can even compensate for the lowest efficiency _ points of hydrostatic transmissions (Ince and Güler, 2019). Hydromechanical transmissions have been developed to increase the efficiency of hydrostatic transmissions. Hydro-mechanical transmissions are transmissions that combine a mechanical transmission with a hydrostatic unit. This special transmission system combining mechanical and hydraulic paths is called power split system. In order to split or merge the power, planetary gears are used in these systems. The structure in power-split IVT varies depending on where the split is made. There are two main types of structure which are named as input-coupled and output-coupled. If the power is split from the planetary gear set and then merged on driveline, the system is called output-coupled. Regarding inputcoupled systems the power is splitting from the output of the engine and then merged on the planetary system. In Fig. 1 a scheme of output and input coupled power splitting types is given. In this study, a novel input-coupled power-split IVT system is considered for the fuel consumption comparison between the conventional mechanical transmission and the power-split IVT. Detailed transmission layout and the kinematic characteristics of this novel _ design was given in our previous study (Ince and Güler, 2019). Overall, the existing studies in literature mostly consisted of investigating the parameters that would reduce the fuel consumption for on-road vehicles using the available duty cycles (e.g. New European Drive Cycle (NEDC)) or for off-road vehicles using the duty-cycles that are obtained by assuming an off-road operation cycle. To the best of authors knowledge, there is no study on the fuel consumption investigation using the data cycles collected at real time from the real user in real world conditions using portable GPS measurements of the velocity, acceleration and altitude for off-road vehicles considering transportation duty cycles. In this study, we investigate whether the novel power split infinitely variable transmission, which was originally developed in our earlier study, can reduce fuel consumption compared to a conventional mechanical transmission that is currently in use.

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transport and tillage operations. In this study, only the transportation task was considered for the fuel consumption simulation. The trajectory data is shown in Fig. 2. The GPS used in this study had a 10 Hz (0.1s) sensor. Four different sampling intervals (0.1, 0.5, 1 and 8 s) were used for fuel consumption calculations in the first 4250 s part of the entire trajectory. However, 8 s sampling interval was used for the entire trajectory due to the computational cost. Some studies in the literature show that this sampling interval provides adequate accuracy in calculations of average velocity and average acceleration (Luo et al., 2017; Tang et al., 2016; Kan et al., 2018). A total of 312 min of road data were collected from the transport operations performed by a single driver on two different days. The data comprises speed and altitude values collected by using the GPS on the vehicle. The highest acceleration, slope, and speed along the route were recorded as 0.52 m=s2 , 15.6
, and 38 km=h, respectively. The altitude and velocity values along the trajectory are shown in Fig. 3.

2.2. Vehicle model In this study, the torque and power demands on the wheels were calculated with the longitudinal vehicle dynamics model in order to meet the acceleration, speed, and slope values obtained via GPS. The longitudinal vehicle dynamics model examines the acceleration and deceleration performances of the vehicles, especially by examining issues such as driving resistances, power, and energy requirements. Since the vehicle discussed in this study does not have a suspension, and there is no expectation of performance related to road handling, the constraints of the lateral and vertical vehicle dynamics models are neglected.

2. Methodology 2.1. GPS trajectory The GPS trajectory data was collected from a tractor in a suburb of the city of Eskis¸ehir, Turkey which was engaged in agricultural

Fig. 2. GPS trajectory of the tractor.

Fig. 1. Types of power splitting.

_ Please cite this article as: Ince, E., Güler, M.A., On the advantages of the new power-split infinitely variable transmission over conventional mechanical transmissions based on fuel consumption analysis, Journal of Cleaner Production, https://doi.org/10.1016/j.jclepro.2019.118795

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expressed as follows:

r

Fig. 3. Altitude (a) and velocity (b) values along the trajectory.

In order to calculate the torque demand on the wheels, the driving resistances were determined. The driving resistances can be divided into two parts namely steady-state resistances and dynamic resistances. Steady-state resistances are the resistances that continuously affect the vehicle when the vehicle is traveling at a constant speed. Rolling resistance, aerodynamic drag resistance, and climbing resistance can be considered to be in this category. Dynamic resistances, occur when the vehicle is accelerating, and these resistances arise from the inertia of the vehicle. Steady-state resistances are also active when the vehicle is accelerating. Fig. 4 shows the resistance forces acting on the tractor and the trailer. The total driving resistance F is equal to Freq , which is the resistance required to maintain the rotational movement of the wheels, and can be expressed as follows:

F ¼ Freq ¼ FA þ FR;tractor þ FR;trailer þ FC þ FI ;

(1)

where FA is the aerodynamic drag resistance, FR;tractor is the total rolling resistance affecting the tractor, FR;trailer is the total rolling resistance affecting the trailer, FC is the climbing resistance for both the tractor and the trailer, and FI is the inertial resistance resulting from the mass of the tractor and the trailer. These resistances can be

FA ¼ Cd Af V 2avg ; 2

(2)

FR;tractor ¼ mtractor gkR1 ;

(3)

FR;trailer ¼ mtrailer gkR2 ;

(4)

FC ¼ ðmtractor þ mtrailer ÞgsinðaÞ;

(5)

FI ¼ ðmtractor þ mtrailer Þaavg ;

(6)

where r is the density of air, Cd is the dimensionless aerodynamic resistance coefficient associated with the shape of the vehicle, Af is the projection area of the tractor, m is the mass of the tractor or trailer, g is the gravitational acceleration, kR is the total rolling resistance coefficient for the wheels of the tractor or trailer, and a is the slope of the road obtained from the GPS data, and Vavg and aavg are the average velocity and average acceleration, respectively. The inertial resistance of the rotating components was neglected in the calculation of the driving resistances. Although, the inertial resistances of the rotating components are important for vehicles that have relatively low mass such as passenger cars, considering the mass of the tractor and the trailer, the inertial forces coming from the rotating components can be neglected (Taghavifar and Mardani, 2017; Heibing and Ersoy, 2011). The other assumptions that were made on the calculated driving resistances to determine the torque on the wheel were as follows: The effect of wind on aerodynamic drag was neglected, the road surface was dry and nondeformable, and the aerodynamic drag force on the trailer was neglected. The average velocity, Vavg , and the average acceleration, aavg , were calculated for each GPS sampling interval, and they can be written as follows:

Vi;avg ¼

Vi þ Viþ1 ; 2

ax;i;avg ¼

Viþ1  Vi ; tiþ1  ti

(7)

(8)

where ti and Vi are the time and velocity values at the sampling point, respectively. After calculating the average velocity from Eq. (7), and the average acceleration from Eq. (8), the required force for each

Fig. 4. A schematic tractor and trailer and driving resistances acting on them.

_ Please cite this article as: Ince, E., Güler, M.A., On the advantages of the new power-split infinitely variable transmission over conventional mechanical transmissions based on fuel consumption analysis, Journal of Cleaner Production, https://doi.org/10.1016/j.jclepro.2019.118795

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interval can be calculated using Eq. (1) to determine the torque and power demand at the traction wheels. Eventually, the wheel torque and wheel power can be expressed as follows:

Tw;i ¼ Freq rdyn ;

(9)

Pw;i ¼ Freq Vi;avg ;

(10)

where rdyn is the dynamic radius of the wheels. When calculating the torque and power values, it is assumed that the wheel radius is constant and that there is no slip on the wheels while traveling on the GPS trajectory. The torque and power at the traction wheels are shown in Fig. 5, and the input parameters used in the calculation are given in Table 1. Note that these calculated torque and power values are the values required on the wheels to ensure that the vehicle travels on the GPS trajectory. In order to determine the fuel consumption in this trajectory, the transmission ratios and mechanical efficiencies for each ratio are required.

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Table 1 Input parameters used in the calculation of the torque and the power demand at the wheels. Parameter

Value

Density of air, r [kg/m3] Aerodynamic drag coefficient, Cd Projection area, Af [m2] Tractor mass, mtractor ½kg Trailer mass, mtrailer ½kg Rolling resistance coefficient of the tractor tyres, kR1 (Wong, 2008) Rolling resistance coefficient of the trailer tyres, kR2 (Wong, 2008) Dynamic radius of the traction wheels, rdyn [m]

1.2 0.96 3.2 3500 8000 0.02 0.015 0.725

2.3. Mechanical efficiencies for conventional and infinitely variable transmissions In this study, the mechanical efficiency of conventional transmission was determined by conducting a dynamometer test, while the efficiency of IVT was determined through theoretical correlations. The conventional transmission used in the tractor, in which the GPS data was collected, was driven by connecting it to an electric motor as shown in Fig. 6. The electric motor was rotated at two different speeds, namely the speeds at which the maximum torque and the maximum power points of the ICE considered in this study were obtained, and the transmission was loaded by using the dynamometer with the half torque value of these points. The temperature of the transmission oil was kept constant at 90
C during the tests. The efficiency values for different gear ratios are calculated as follows:

Fig. 5. Torque (a) and power (b) at the traction wheels.

Fig. 6. Mechanical efficiency test bench.




hconventional ¼

TRightaxle









urightaxle þ TLeftaxle 

TInput



uInput






uleftaxle

 ; (11)

where T is the torque and u is the angular velocity. Fig. 7 shows the measured efficiency values in each gear for the maximum torque and maximum power values. When calculating the fuel consumption values, the arithmetic mean of the two measured efficiency values for each gear is taken. The total transmission ratios, tj , for the gears 1 to 12 were: 325, 219.4, 148.2, 100.8, 139.6, 93.4, 62.7, 42.75, 57, 39.7, 26.7, and 17.1, respectively. The efficiency of power-split IVT is influenced by much more parameters than a conventional mechanical transmission. The total efficiency of an IVT is affected by parameters such as power flow _ type (Ince and Güler, 2019; Volpe et al., 2009; Bottiglione and _ Mantriota, 2011; Mantriota, 2002), variator efficiency (Ince and Güler, 2019; Volpe et al., 2009; Bottiglione and Mantriota, 2011; Linares et al., 2010), transmission ratio of the planetary gear set that distributes or combines power, the ratio of the fixed gear (Volpe _ et al., 2009; Ince and Güler, 2019) and, most importantly, the rate _ of variator use (Ince and Güler, 2019; Volpe et al., 2009; Bottiglione and Mantriota, 2011; Mantriota, 2002; Linares et al., 2010; De Pinto and Mantriota, 2014). In order to be able to calculate the mechanical efficiency of the PS-IVT, it is necessary to determine how much of the power flows through the variator. The power flow equations of the PS-IVT, which is considered in this study, have been explained in our previous _ study (Ince and Güler, 2019), and in brief, they are expressed here for each stage as follows:

_ Please cite this article as: Ince, E., Güler, M.A., On the advantages of the new power-split infinitely variable transmission over conventional mechanical transmissions based on fuel consumption analysis, Journal of Cleaner Production, https://doi.org/10.1016/j.jclepro.2019.118795

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Fig. 7. Efficiency values of conventional transmission.





PIVU PIN PIVU PIN



I ¼



tIVU tIIVT  tFR ; tIIVT ðtFR  tIVU Þ 

II ¼

(12)



tIVU tIIIVT  tFR ; tIIIVT ðtFR  tIVU Þ

(13)

and



PIVU PIN

III





tIVU tIII IVT  tFR ¼ III ; tIVT ðtFR  tIVU Þ

(14)

where PIVU =PIN is the ratio of the hydrostatic variator usage, tIVU is the transmission ratio of the hydrostatic variator, tFR is the constant ratio of the planetary gear set merging the power, and tstage IVT is the transmission ratio of the PS-IVT at different stages. Using Eqs.12e14,the mechanical efficiencies can be expressed for the Type I, Type II, and Type III power flows as follows:

hIVT ¼ hM hIVU hIVT ¼ hIVU





PIVU PIN

PIVU PIN





þ hM

 h1 M

  PIVU  PIN

  PIVU þ h1 M PIN

(15)

(16)

and



hIVT ¼ hM 1 

PIVU PIN



þ hIVU



PIVU PIN

 (17)

where hM is the efficiency of the mechanical path and hIVU is the efficiency of the hydraulic path. The measured efficiency values of the mechanical transmission included the efficiency of the entire power train from the input shaft to the output shaft. Likewise for the PS-IVT, the total mechanical efficiency can be expressed by including the final drive as follows:

hIVT;total ¼ hIVT hFD ;

(18)

where, hFD is the efficiency of the final drive. The efficiency of the hydraulic path, hIVU , was assumed to be fixed under all working conditions and was taken as 0.87 (Manring, 2016). The efficiency of the mechanical path, hM , and the final drive, hFD , were also considered to be fixed under all working conditions and both of them were taken to be 0.96. The total reduction ratios, and therefore the total mechanical efficiencies of PS-IVT transmissions, vary with changes in the Willis _ transmission ratio of the planetary gear set (Ince and Güler, 2019). In this study, 8 different scenarios were evaluated with the Willis transmission ratios given in Table 2. The transmission ratio for the final drive unit was set at 28.4. 2.4. Internal combustion engine (ICE) model In order to determine the fuel consumption values corresponding to the torque and velocity required by the engine while the vehicle is moving on the virtual track, a brake specific fuel consumption (BSFC) map of the engine used in the tractor, in which the GPS data is collected, was created. The BSFC map is frequently used to evaluate the efficiency of combustion engines that burn fossil fuels and in return generate rotational power. In order to determine the BSFC map, the engine is loaded with a dynamometer at different speeds to determine the fuel mass flow rate for different torque values. In this study, BSFC test values at 160 different points of a four cylinder common-rail turbo diesel engine were taken and the map

Table 2 Willis transmission ratios for the different scenarios.

tPG1 tPG2 tPG3

Sce.1

Sce.2

Sce.3

Sce.4

Sce.5

Sce.6

Sce.7

Sce.8

0.5 0.75 0.75

0.75 0.75 0.75

0.75 0.5 0.75

0.5 0.75 0.5

0.5 0.5 0.5

0.5 0.5 0.75

0.75 0.5 0.5

0.75 0.75 0.5

_ Please cite this article as: Ince, E., Güler, M.A., On the advantages of the new power-split infinitely variable transmission over conventional mechanical transmissions based on fuel consumption analysis, Journal of Cleaner Production, https://doi.org/10.1016/j.jclepro.2019.118795

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shown in Fig. 8 was generated by using the fifth degree polynomial response surface (PRS) method. The dots in Fig. 8 show the values obtained from the tests and these values form the boundaries of the BSFC fit surface. The engine was tested at a speed range of 1200e2400 rpm and at a torque range of 100e575 Nm. Moreover the engine’s fuel consumption values were determined to be between 205 and 342 g/kWh for the mentioned speed and torque range. In the BSFC map there are torque values that are obtained from vehicle model which can fall outside the range of test conditions, for this values (below 100 Nm) extrapolation was performed via the fit function. To evaluate the accuracy of the map, the sum of squares due to error (SSE), the square of the correlation between the response values and the predicted response values (R2 ), adjusted (R2 ), and root mean squared error (RMSE) values were examined and they are given in Table .3. The fit equation composing the BSFC map was used for the calculation of fuel consumption, considering that the error values in the correlation equation obtained by the PRS method are acceptable. Note that, this study focuses on the fuel consumption advantage that can be achieved if PS-IVT transmission is used instead of mechanical transmission, rather than achieving a real fuel consumption value. We created a BSFC map using a real engine in our bench (engine is tested with the cooling pack, exhaust system and other auxiliary units) to minimize the effect of transient conditions. In the tests conducted, the wheel load is transferred to the engine as a load torque when calculating the fuel consumption. If the calculations were made taking into account the torque induced in the engine, engine friction, effects of accessories, turbo lag etc. factors could have caused miscalculations due to transient conditions. Nevertheless, it is clear that transient conditions such as gear shifting effect, neglected in this study, may alter the actual fuel consumption values. This is thought to increase fuel consumption, especially in the use of mechanical transmission. However, in order to take into account the effect of gear shifts on fuel consumption, factors such the gear shifting time and the response of the engine to these sudden changes in loads need to be known. Since it is hard to get this unknown transient parameters or they have to be assumed, transient conditions as well as gear shifting were neglected in this study.

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Table 3 Error values for BSFC map. SSE

R2

Adjusted R2

RMSE

268.5214

0.9978

0.9975

1.3703

2.5. Fuel consumption simulation In order to calculate the fuel consumption on the GPS trajectory, it is necessary to calculate how much energy is taken from the ICE. For this purpose, the torque and velocity of the engine values for each interval can be calculated as follows:

TE;i;j ¼

Tw;i

htrans;j tj

;

uE;i;j ¼ uw;i tj ; uw;i ¼

Vi;avg ; rdyn

(19)

(20)

(21)

where TE;i;j is the engine torque for each interval and each transmission ratio, tj is the transmission ratio, htrans;j is the mechanical efficiency for each corresponding ratio, uE;i;j is the angular speed of the engine for the corresponding interval and transmission ratio. Note that there are 12 different transmission ratios for the conventional mechanical transmission. Regarding PS-IVT, 1000 different ratios are calculated for each of the 3 stages. For both types of transmissions, the lowest fuel consumption with the same vehicle dynamics model was deemed to be obtained on the same track. Therefore, the problem is cast to an optimization problem and can be stated as: minimize ðFuel ConsumptionÞ TE ; uE subject to:

1200  uE;i;j  2400

Fig. 8. Brake specific fuel consumption map.

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  0  TE;i;j  TE;max uE;i If we replace the engine torque determined from Eq. (19) and the engine speed determined from Eq. (20) in the BSFC function, we get:

  fi;j ¼ bsfc TE;i;j ; uE;i;j

(22)

To select the transmission ratios that provide the lowest consumption values from Eq. (22) we use


 argmin fi;j ¼ jselect

(23)

Note that the unit of the fuel consumption values obtained using the BSFC function is fuel consumption mass per energy (g/kWh). In order to calculate the fuel consumption in terms of fuel mass, this equation must be multiplied by the energy value in each interval of the trajectory:


 Fi ¼ fi;jselect TE;i;jselect uE;i;jselect Dti

(24)

The lowest total fuel consumption for the entire trajectory can be calculated as follows:

FuelConsumption ¼

n X i¼0

Fi

(25)

In this study, a code was written in MATLAB to calculate the lowest fuel consumption of the PS-IVT and the conventional mechanical transmission. Fig. 9 shows the flowchart diagram of this code. When calculating the fuel consumption of the conventional transmission, it was assumed that the driver instantaneously selects the gear that provides the lowest fuel consumption in all intervals. The shifting losses were neglected for both transmission types. In automatic transmissions including variable transmissions, the control of the transmission ratio is carried out by means of transmission control units (TCU). TCUs require control strategies to determine the transmission ratio that ensures the lowest fuel consumption. Since transmissions in different architectures are managed with different strategies, it is not possible to determine a common strategy for all transmission architectures (Yildiz et al., 2016; Chen et al., 2018). The following two different power management strategies are discussed with regard to the calculation of fuel consumption by using PS-IVT:  First Power Management Strategy (FPMS): Calculate the engine torque and speed for each interval by considering the efficiency of the transmission ratios. To find the consumed fuel mass, multiply the BSFC values with the energy. Choose the best consumption value in each interval.  Second Power Management Strategy (SPMS): Calculate the engine torque and speed for each interval and for each possible

Fig. 9. The algorithm used to calculate the lowest fuel consumption of the PS-IVT and the conventional mechanical transmission.

_ Please cite this article as: Ince, E., Güler, M.A., On the advantages of the new power-split infinitely variable transmission over conventional mechanical transmissions based on fuel consumption analysis, Journal of Cleaner Production, https://doi.org/10.1016/j.jclepro.2019.118795

_ E. Ince, M.A. Güler / Journal of Cleaner Production xxx (xxxx) xxx

transmission ratio. Choose the best BSFC values at these points, and then multiply them with the energy divided by the efficiency value at that transmission ratio. The first strategy takes into account the transmission efficiency while trying to run the ICE at the most efficient point. With this strategy, even if a transmission ratio is determined to run the ICE at the most efficient point, the detected transmission ratio is neglected if the total efficiency is low. The second strategy, regardless of transmission efficiency, always tries to run ICE at its most efficient point in each interval. 3. Results and discussion When all of the 312-min GPS trajectories were evaluated, it was found that the conventional mechanical transmission had a total fuel consumption value of 9605.3 g for the 8 s sampling interval, with the assumption that the driver always selects the gear that provides the lowest fuel consumption. Since an ordinary driver cannot provide this condition continuously, the resulting fuel consumption value can be considered as an optimistic value. The same fuel consumption values were obtained in the calculations made with two different power management strategies for conventional transmission. The reason for this is that there are a few gear option which can provide the required torque and power value in each calculation interval, and the mechanical efficiency values of these gears are close to each other. The 9605.3 g fuel consumption value obtained by conventional mechanical transmission is considered as a reference in the evaluation of the fuel consumption achieved using the PS-IVT. Table 4 shows the fuel consumption values for 8 s sampling interval obtained by using PS-IVT transmission for 8 different scenarios and 2 different power management strategies. It also depicts percent difference from the fuel consumption of the reference transmission value. Note that a negative value in the percent difference means less fuel consumption compared to the reference

Table 4 Fuel consumption values (grams) obtained by using PS-IVT and % difference from the consumption value of the reference transmission (9605.3 g)a.

Sce.1 Sce.2 Sce.3 Sce.4 Sce.5 Sce.6 Sce.7 Sce.8 a

FPMS

Difference (%)

SPMS

Difference (%)

9093.2 8810.4 8985.2 9116.5 9084.5 8962.5 9130.2 9102.5

5.3 8.2 6.4 5.1 5.4 6.6 4.9 5.2

11081.2 10965.5 10989.6 10862.9 10828.8 11002.9 11074.6 10990.1

15.4 14.2 14.4 15.1 12.7 14.6 15.3 14.4

Table 5 Fuel consumption values (grams) obtained by using 0.1 s sampling interval and % difference from the consumption value of the reference transmission (2635.9 g)a. Conventional

Sce.1 Sce.2 Sce.3 Sce.4 Sce.5 Sce.6 Sce.7 Sce.8 a

FPMS

Difference (%)

SPMS

Difference (%)

2635.9

Reference

2635.9

Reference

2421.8 2409.9 2419 2431.7 2425.6 2417.5 2435.5 2425.9

8.1 8.6 8.2 7.7 8.0 8.3 7.6 8.0

2946.8 2940 2939.1 2936.6 2870.9 2941.2 2938.2 2942.2

value and a positive value means more consumption compared to the reference value. The lowest fuel consumption with PS-IVT is achieved with scenario 2 and using FPMS. This combination achieved 8.2% lower fuel consumption than the conventional mechanical transmission. We examined the variables affecting the efficiency of new PS-IVT in _ the previous study (Ince and Güler, 2019). One of the important findings in the previous study was that the efficiency values can be changed only by changing the Willis planetary ratio. Therefore, in this study, 8 different scenarios were examined in order to study different Willis planetary ratios. According to these results, it is seen that different power management strategies affect fuel consumption values more than the Willis planetary ratio. It is noteworthy that, all of the consumption values in FPMS are lower than the consumption value of the conventional mechanical transmission. On the other hand, all of the consumption values in SPMS are higher than the reference transmission. For SPMS and scenario 1, 15.4% more fuel consumption is calculated compared to the reference. In the calculations made with the SPMS, fuel

Table 6 Fuel consumption values (grams) obtained by using 0.5 s sampling interval and % difference from the consumption value of the reference transmission (2614.1 g)a. Conventional

Sce.1 Sce.2 Sce.3 Sce.4 Sce.5 Sce.6 Sce.7 Sce.8 a

11.8 11.5 11.5 11.4 8.9 11.6 11.5 11.6

The values are obtained using first 4250 s part of the trajectory.

FPMS

Difference (%)

SPMS

Difference (%)

2614.1

Reference

2614.1

Reference

2402.1 2391.5 2397.4 2413.8 2409.8 2395.8 2420.1 2409

8.1 8.5 8.3 7.7 7.8 8.3 7.4 7.8

2926.2 2937.1 2935.1 2932.1 2857.4 2937.5 2931.1 2938.4

11.9 12.4 12.3 12.2 9.3 12.4 12.1 12.4

The values are obtained using first 4250 s part of the trajectory.

Table 7 Fuel consumption values (grams) obtained by using 1 s sampling interval and % difference from the consumption value of the reference transmission (2612.5 g)a. Conventional

Sce.1 Sce.2 Sce.3 Sce.4 Sce.5 Sce.6 Sce.7 Sce.8 a

The values are obtained using the whole trajectory (18720 s).

9

FPMS

Difference (%)

SPMS

Difference (%)

2612.5

Reference

2612.5

Reference

2404.7 2392.8 2398.8 2415 2410.7 2397.3 2422.9 2411.3

8.0 8.4 8.2 7.6 7.7 8.2 7.3 7.7

2925.8 2935.4 2937 2930 2853.8 2939 2929.9 2940

12 12.4 12.4 12.2 9.2 12.5 12.1 12.5

The values are obtained using first 4250 s part of the trajectory.

Table 8 Fuel consumption values (grams) obtained by using 8 s sampling interval and % difference from the consumption value of the reference transmission (2551.8 g)a. Conventional

Sce.1 Sce.2 Sce.3 Sce.4 Sce.5 Sce.6 Sce.7 Sce.8 a

FPMS

Difference (%)

SPMS

Difference (%)

2551.8

Reference

2551.8

Reference

2405.4 2396.6 2402.1 2417.8 2411.4 2399 2425.6 2414.2

5.7 6.1 5.9 5.3 5.5 6 4.9 5.4

2926.3 2936.4 2938.4 2931.2 2856.9 2940.1 2932.1 2941.8

14.7 15.1 15.2 14.9 12 15.2 14.9 15.3

The values are obtained using first 4250 s part of the trajectory.

_ Please cite this article as: Ince, E., Güler, M.A., On the advantages of the new power-split infinitely variable transmission over conventional mechanical transmissions based on fuel consumption analysis, Journal of Cleaner Production, https://doi.org/10.1016/j.jclepro.2019.118795

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Table 9 Best consumption values for the different sampling intervals using first 4250 s part of the trajectory a. Sampling

Consumption (g)

Interval 0.1 s 0.5 s 1s 8s

FPMS (Sce.2) 2409.9 2391.5 2392.8 2396.6

a

SPMS (Sce.5) 2870.9 2857.4 2853.8 2856.9

Conventional 2635.9 2614.1 2612.5 2551.8

The values are obtained using first 4250 s part of the trajectory.

consumption is calculated by selecting the transmission ratio which gives the lowest BSFC value for each point. The lowest fuel consumption value is selected for each point by calculating the total fuel consumption value in all ratios that the PS-IVT system can provide for each calculation interval. In order to examine the effect of sampling interval on fuel consumption values, simulations were repeated at four different sampling intervals (0.1, 0.5, 1 and 8 s) during the first 4250 s part of the entire trajectory and the results are listed in Tables 5e8

respectively. PS-IVT Sce.2 enabled to get the best fuel consumption and PS-IVT Sce.7 led to the worst fuel consumption in FPMS using the entire trajectory. PS-IVT Sce.2 and Sce.7 also achieved the lowest and the highest fuel consumption respectively for all sampling intervals in the 4250 s part of the trajectory. For the results obtained using FPMS, the following discussions can be made. Although Sce.1 led to the worst fuel consumption value in the calculations made with SPMS on the whole trajectory, Sce.8 led to the worst fuel consumption value in the calculations made with 8 s sampling interval on the 4250 s part of the trajectory. As the sampling interval decreased to 0.1 s, Sce.1 caused the worst fuel consumption. The results obtained by using several different sampling intervals show that SPMS is more sensitive to the sampling interval in the calculation of the fuel consumption value. On the other hand, the GPS system used in this study had a position accuracy of 1e5 m. It has to be noted that sampling intervals below 1 s may lead to inaccuracies in fuel consumption calculations considering the speed of the vehicle (about 2.8 m/s) on the corresponding trajectory. Best consumption values for PS-IVT FPMS, PS-IVT SPMS, and conventional transmission at different sampling intervals are

Fig. 10. FPMS results using different sampling intervals.(a)0.1 s sampling interval, (b)0.5 s sampling interval, (c)1 s sampling interval, (d)8 s sampling interval.

_ Please cite this article as: Ince, E., Güler, M.A., On the advantages of the new power-split infinitely variable transmission over conventional mechanical transmissions based on fuel consumption analysis, Journal of Cleaner Production, https://doi.org/10.1016/j.jclepro.2019.118795

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summarized in Table 9. It should be noted that only considerable change in fuel consumption calculations is the case when the conventional transmission is used. Changing the sampling interval from 0.1 s to 8 s resulted in about 3.3% change in fuel consumption for this case. Figs. 10 and 11 show the fuel consumption values for different sampling intervals obtained by FPMS and SPMS respectively when 4250 s part of the entire trajectory is used in calculations. The results show that as the sampling interval becomes shorter, the fuel consumption values of the mechanical transmission is increased. This might be attirubuted to the fact that mechanical transmission does not have many gear ratios for sudden changes in loads occurring in short sampling intervals. However, this is not the case for PS-IVT. Since it has continuously variable transmission ratios that can provide a transmission ratio which has the best performance for lower fuel consumption, shortening of the sampling interval did not cause a significant change in fuel consumption values for PS-IVT. Guo et al. (2015) showed that although hybrid buses operate at

11

higher BSFC values, they provide lower total fuel consumption than diesel buses on the considered trajectory. Similarly, this study shows that even though the internal combustion engine can be used at the most efficient point with the IVT usage in SPMS, the total consumption value increases as the transmission efficiency at the corresponding point is low. Antonio et al. (2017) showed that hydro-mechanical transmission reduces fuel consumption or emission levels compared to conventional transmission when managed according to appropriate control strategies. Also, our results indicate that the variable efficiency of the PS-IVT transmission can affect fuel consumption considerably and that even if the engine is operated with lower BSFC values, the fuel consumption can be increased without an accurate power management strategy. Fig. 12 shows the lowest fuel consumption values for the conventional mechanical transmission selected by the MATLAB code on the BSFC contour map for the entire GPS trajectory and the positive power calculated points. Fig. 13 shows the lowest fuel consumption values for the PS-IVT

Fig. 11. SPMS results using different sampling intervals.(a)0.1 s sampling interval, (b)0.5 s sampling interval, (c)1 s sampling interval, (d)8 s sampling interval.

_ Please cite this article as: Ince, E., Güler, M.A., On the advantages of the new power-split infinitely variable transmission over conventional mechanical transmissions based on fuel consumption analysis, Journal of Cleaner Production, https://doi.org/10.1016/j.jclepro.2019.118795

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Fig. 12. Lowest fuel consumption values for the conventional mechanical transmission.

Fig. 13. Lowest fuel consumption values for the PS-IVT.

calculated by the MATLAB code with FPMS and Sce.2 on the BSFC contour map for the entire GPS trajectory and the positive power calculated points. The consumption values that are more than 342 g/kWh in Figs. 12, and Fig.13 corresponds to the extrapolation that are mentioned in the section 2.4. Fig. 14 shows the total transmission ratios selected for the conventional transmission and PS-IVT in a 500 s section from the GPS trajectory. As can be seen from Fig. 8 the fuel consumption can be reduced

by reducing the engine speed. This phenomena for different offroad operations was also shown by (Lee et al., 2016). When Figs. 12, Fig. 13, and Fig. 14 are examined, it seems that the engine speed can be reduced for almost all calculation points by using PSIVT. Although the engine is used much more efficiently at almost all calculation points, the fuel consumption value did not decrease significantly. This is due to the relatively low efficiency of the hydrostatic variator and the power recirculation phenomena occurring in the PS-IVT system. The efficiency of hydrostatic variators varies with pressure and

_ Please cite this article as: Ince, E., Güler, M.A., On the advantages of the new power-split infinitely variable transmission over conventional mechanical transmissions based on fuel consumption analysis, Journal of Cleaner Production, https://doi.org/10.1016/j.jclepro.2019.118795

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real user. This study can be extended with other operations such as plowing, cultivation, and etc. In general, the assumptions in fuel consumption calculations were made to allow the mechanical transmission to achieve lower fuel consumption values than it actually did. For example, It was assumed that the driver chooses the best possible gear when calculating the fuel consumption of the conventional transmission, which consumption values are considered as a reference for the PSIVT. Nevertheless, the variable efficiency of the considered PS-IVT can change the fuel consumption values obtained in this study in other duty cycles. Thus, further studies are needed for the justification of the fuel consumption value of the considered IVT system. 4. Conclusions

Fig. 14. Total transmission ratios for the PS-IVT and the conventional mechanical transmission.

Table 10 Fuel mass amounts in the entire trajectory for various values of HSU Efficiency. HSU

Fuel Mass

Difference

Efficiency (%)

[g]

(%)

81 83 85 87 89 91

8903.8 8871.4 8839.8 8810.4 8780.9 8747.3

1.06 0.69 0.34 Reference 0.33 0.72

speed in the hydraulic pump and motor. In this study, the efficiency of the hydrostatic variator was assumed to be fixed 0.87, however, the efficiency of the hydrostatic variator can exceed this point (Manring, 2016). Assuming the variator efficiency as a constant in the preliminary design stage was also deemed appropriate in other studies in the literature (Rossetti and Macor, 2019; Li et al., 2005; Volpe et al., 2009). For example, Manring (2016) proposed a method for generating efficiency maps of hydrostatic variators by fixing some variables as a constant which can change the total efficiency. Note that in order to determine the exact efficiency values of a hydrostatic variator, the variator should be tested with different torque and speed values. Then these tested values can be used for generating an efficiency map as mentioned in Section 2.4. Since the transmission considered in this study is a concept design, it is not possible to accurately determine the instantaneous pressures in the variator, hence, design or select a hydrostatic variator. In order to understand the effect of the variator’s efficiency in fuel consumption, we assigned different efficiency values to the hydro-static unit. The total fuel consumption values for different efficiencies of the hydrostatic variator and the percent change in the consumption values relative to the reference (best fuel consumption with Scenario 2 and FPMS) are listed in Table 10. When the results given in Table 10 are analyzed, varying the HSU efficiency between 81  91 % resulted in þ1:06 to  0:72 percent change in fuel mass. This can be explained by the fact that the in-house MATLAB code with FPMS selects transmission ratios in which the efficiency of the PS-IVT system is high while trying to use the engine at the most efficient point. In this study transportation of agricultural goods on rural roads has been evaluated. Note that, the duty cycle considered in this study represents a real operation which is currently conducted by a

In this study, fuel consumption analyses were carried out in MATLAB by using the GPS data collected at real time from the real tractor user in real world conditions to obtain minimum possible fuel consumption values. In order to drive the tractor on the predefined GPS track, the driving resistances, the power and torque demands to be provided by the internal combustion engine are calculated by using the longitudinal vehicle dynamics model. Conventional mechanical transmission efficiency data values are collected by using the in-house dynamometer while the PS-IVT theoretical efficiencies are calculated using the kinematic model _ developed in our earlier study (Ince and Güler, 2019) for eight different scenarios. The following conclusions can be drawn from the findings of our study:  While calculating fuel consumption values, two different power management strategies (FPMS and SPMS) were applied for both transmission types. No difference was observed for the different strategies in the fuel consumption values obtained by conventional transmission.  The fuel consumption values of IVT transmission calculated according to FPMS were lower than the conventional transmission’s consumption for all scenarios. Regarding the calculations made according to SPMS, higher fuel consumption values were obtained in all scenarios with the PS-IVT.  With its FPMS, PS-IVT achieved the lowest fuel consumption in scenario 2 and the highest fuel consumption in scenario 7, which were 8.2%, and 4.9% lower than the consumption of conventional transmission, respectively.  In this study, it was shown that the new PS-IVT can provide even lower fuel consumption than the conventional high-efficiency transmission if the correct power management strategy is used. Acknowledgements This study is supported by TUBITAK (Scientific and Research Council of Turkey) Innovation Funding Programmes Directorate €r R&D (TEYDEB) under Grant no. 1150702 and by TürkTrakto Department. References Antonio, R., Alarico, M., Alberto, B., 2017. Impact of control strategies on the emissions in a city bus equipped with power-split transmission. Transp. Res. D Transp. Environ. 50, 357e371. Bacenetti, J., Lovarelli, D., Facchinetti, D., Pessina, D., 2018. An environmental comparison of techniques to reduce pollutants emissions related to agricultural tractors. Biosyst. Eng. 171, 30e40. Bottiglione, F., Mantriota, G., 2011. Reversibility of power-split transmissions. J. Mech. Des. 133, 084503. Chen, S., Wu, C., Hung, Y., Chung, C., 2018. Optimal strategies of energy management integrated with transmission control for a hybrid electric vehicle using dynamic particle swarm optimization. Energy 160, 154e170.

_ Please cite this article as: Ince, E., Güler, M.A., On the advantages of the new power-split infinitely variable transmission over conventional mechanical transmissions based on fuel consumption analysis, Journal of Cleaner Production, https://doi.org/10.1016/j.jclepro.2019.118795

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_ Please cite this article as: Ince, E., Güler, M.A., On the advantages of the new power-split infinitely variable transmission over conventional mechanical transmissions based on fuel consumption analysis, Journal of Cleaner Production, https://doi.org/10.1016/j.jclepro.2019.118795