Energy Efficiency of Heavy-Duty Vehicles in Mexico

Serrano-Guevara, Oscar S.; Huertas, José I.; Quirama, Luis F.; Mogro, Antonio E.

doi:10.3390/en16010459

Open AccessArticle

Energy Efficiency of Heavy-Duty Vehicles in Mexico

by

Oscar S. Serrano-Guevara

¹

,

José I. Huertas

^1,*,

Luis F. Quirama

² and

Antonio E. Mogro

¹

Energy and Climate Change Research Group, School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, NL, Mexico

²

Sustainable Mobility Unit, United Nations Environment Program, Nairobi 30552, Kenya

^*

Author to whom correspondence should be addressed.

Energies 2023, 16(1), 459; https://doi.org/10.3390/en16010459

Submission received: 14 November 2022 / Revised: 23 December 2022 / Accepted: 23 December 2022 / Published: 31 December 2022

(This article belongs to the Section B1: Energy and Climate Change)

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Abstract

:

The energy consumption of a large sample of vehicles (6955) operating during the last 3 years under everyday conditions across Mexico was monitored via OBD-based telematics systems. A life cycle statistical analysis of the obtained data showed that, on average, 54 t diesel vehicles used for long-distance freight transport consume 44.25 L/100 km and emit 1513 g CO₂e/km. When these vehicles are powered by natural gas, the energy consumption and the emissions of greenhouse gases (GHG) are increased by 23% and reduced by 0.8%, respectively. Using manufacturers’ data, these values reduce energy consumption by 16% and GHG emissions by 52% when they are electric. Similar observations were made for other vehicles sizes used for transporting goods and people.

Keywords:

heavy-duty vehicles; buses; trucks; fuel economy; telematics; energy efficiency

1. Introduction

There is an increasing interest in determining the nationwide real-world energy or fuel consumption of heavy-duty vehicles (HDVs) aiming at developing new policies to reduce energy consumption, emissions of greenhouse gases (GHG) and air pollutants. In this work, we consider HDV as those with a gross vehicular weight rate (GVWR, vehicle weight plus payload) greater than 3.5 t.

Worldwide, HDVs represent a small fraction of the total number of road vehicles. However, they represent approximately a quarter of their total Carbon Dioxide (CO₂) emissions [1]. This impact is strongly connected to their high dependence on the use of fossil fuels. Such is the case of Mexico in which 99% of HDV use diesel as fuel [2]. HDVs not only emit large quantities of GHG, such as CO₂, Methane (CH₄), and Nitrous Oxide (N₂O), but also emit high amounts of air pollutants, such as Carbon Monoxide (CO), Nitrogen Oxides (NOx), and Particulate Matter (PM). Their emissions are of great concern since the growth rate of the number of HDVs increases yearly [3].

To reduce the worldwide emissions of GHG through the decarbonization of the transport sector, it is of great importance to determine the current baseline of real-world energy or fuel consumption of HDVs [4]. It would allow tailoring: (i) energy efficiency regulations, (ii) decarbonization public policies, and (iii) the decision-making process on the most appropriate vehicle trademarks according to their energy efficiency, operational costs, deterioration rate, and useful lifetime. This baseline helps to define better future average fuel economy targets, which is one of the most important public policies used to decarbonize the transport sector around the world.

Governmental and non-governmental institutions worldwide require energy/fuel consumption baselines to assess their public policy to achieve the country’s GHG mitigation objectives. Currently, the United Nations Environment Program (UNEP) is implementing a project that aims to promote a global transition toward mobility without or with low GHG emissions that not only allows GHG mitigation but also helps to improve air quality. To achieve this general objective, they support cities and countries to develop and implement projects with the following three expected results: (i) cities/countries switching to more efficient vehicles, including electric mobility, (ii) cities/countries switching to more efficient and cleaner fuels (low-sulfur fuels), and (iii) cities/countries developing and implementing public policies that prioritize walking and cycling infrastructure [5].

1.1. Fuel Consumption Determination in HDVs

Using real-world fuel consumption data to evaluate HDV efficiency is not new since it is the most important tool for transport companies to reduce their operational costs. However, this information is kept private due to internal competition within the sector, and therefore, it cannot be used to compare the energy performance between different fleets nor be used by governmental authorities to take action in this sector. When determining the fuel/energy consumption of HDVs, two alternatives are evidenced in the literature (Table 1): Estimation or measurements.

1.1.1. Estimation of Fuel Consumption in HDVs

It consists of an energy balance analysis developed specifically to estimate the energy efficiency of HDV. Table A1 of Appendix A shows that in some instances, the fuel consumption of HDVs has been determined via simulation tools, such as VECTO [3,9], GEM [10], and Autonomie [11]. The first two were developed by governments from Europe and the United States, respectively. They are open source and are available for any user. The last one has been mainly used in scientific works. As input information, these models use the vehicle weight, the map of the engine-specific fuel consumption, the diagrams of torque and power of the engine used, the aerodynamic and rolling resistance coefficients of the vehicles, the energy consumption of auxiliary systems, and the typical driving cycle followed by the drivers of these vehicles. As outputs, these models report the estimation of fuel consumption and CO₂ emissions [9,12]. Results from these models have been compared with real on-road measurements, obtaining minimal differences. Results from these models have been used to generate regulations in countries, such as the US, Japan, China, and the European Union. However, the use of these tools to evaluate the real-world fuel consumption of vehicles is limited by the need for lab tests that evaluate the performance of the different components found in a vehicle. Furthermore, it provides an estimated value of fuel consumption instead of real-world fuel consumption.

1.1.2. Measurement of Fuel Consumption in HDVs

The fuel consumption in HDV can be monitored by:

Recording manually the whole trip’s fuel consumption. Transport companies and/or vehicle owners have developed platforms where fleet operators report the amount of fuel loaded and the distance traveled until the next fill-up. Results from this alternative can be biased by third parties’ interests involved in the fuel supply business.
Performing on-road tests using Portable Emissions Measurement Devices (PEMS); The instruments involved and the resources required to carry on the tests are highly costly, limiting the number of vehicles and the duration of the test to the resources available. To overcome these difficulties, and as an approximation, researchers select a few vehicles (~10), instrument them with a PEMS, in some cases simulate the load with water tanks, and made them reproduce a driving cycle that represents the driving pattern of the region of interest [13] or track the vehicle following a specific route [14]. At the same time, they measure their fuel consumption and tailpipe emissions under lab conditions using a chassis dynameter or by on-road tests. This methodology is repeatable and reproducible. It isolates the effect of external conditions, such as driving habits and road conditions. Thus, it is suitable to evaluate the energy performance of the vehicle’s technology. The representativeness of the driving cycle and of the sampled vehicles remains a subject of discussion. Furthermore, several studies have reported differences between the results obtained following this methodology and the fuel consumption observed in real life;
Monitoring the instant fuel consumption via the vehicle On-Board Diagnostic (OBD) port. It consists of reading the information from different sensors of the Engine Control Unit (ECU) via the OBD port, i.e., air flow, engine temperature, engine speed, intake air temperature, manifold absolute pressure, vehicle speed, and the driver demanded acceleration and torque or directly the fuel consumption. The data obtained is used to quantify the accumulated used fuel or the flow that is injected into the engine [15]. This method was previously validated by Pepper (2010) [16] and Quirama et al. (2020) [17], who obtained differences lower than 3% when compared to the standard gravimetric method specified by SAE [18]. OBD is an interface used by telematics service companies [19]. The low cost of this alternative makes it suitable for monitoring the fuel or energy consumption of a representative sample of vehicles for a long time (~1 year).

1.2. Previous Works Focused on the Determination of HDVs’ Fuel Consumption

Most of the studies published and listed in Table A1 of Appendix A have evaluated the fuel consumption or the emissions of vehicles under specific conditions of use. Some have focused on assessing the influencing factors on fuel consumption, others on the comparative evaluation of different technologies, i.e., emission standards, alternative fuels, etc.

1.2.1. Fuel Consumption in Buses

Ghaffarpasand et al. (2021) [20] evaluated several passenger buses in a medium-sized city in the Middle East. The diesel SFC ranged between 28.9 and 32.4 L/100 km. They concluded that although the Euro IV standard emissions technology did not show the lowest SFC (compared to the Euro II standard), they recommended this technology as the most appropriate alternative to renew the local fleet. This technology showed the most efficient results in emissions and SFC when these variables consider the number of transported passengers. Giraldo & Huertas (2019) [14], reported an average of 41 L/100 km for the diesel consumption of 15 transit buses operating in Mexico City (2700 masl) for eight months. These two values underline the disparity in the SFC reported for buses in the literature.

1.2.2. Fuel Consumption in Trucks

On the other hand, Quiros et al. (2016) [13] evaluated the performance of seven heavy-duty trucks under actual operating conditions and different types of routes in California. The results varied between 34.5 and 63 L/100 km for the diesel case. In this study, various technologies were compared, and it was identified that the most efficient technology in terms of CO₂ emissions and, consequently, of SFC was compressed natural gas (CNG), with an average reduction of 12% compared to diesel. However, Sandhu et al. (2021) [21] reported that the SFC of CNG trucks doubles the respective value of diesel. In China, Zhang et al. (2014) [22] reported greater SFC for CNG and liquified natural gas (LNG) vehicles (27 DEL/100 km) with respect to diesel vehicles (20 L/100 km) with different emission certifications (Euro II to Euro IV). They found that the newest vehicles are not necessarily the most efficient. Similarly, Lv et al. (2020) [23] reported 33 DEL/100 km for LNG vehicles, which is 60% higher than diesel vehicles (21 L/100 km). The authors did not attempt to explain the reason for those differences. In the previous results, the energy content of one sm³ of CNG is equivalent to the energy content of one diesel equivalent liter (DEL), i.e., ~1 sm³ = 1 DEL. This value varies depending on the natural gas composition.

1.2.3. The Influence of the after-Treatment System on Fuel Consumption

Sandhu et al. (2015) [24] reported the evaluation of several vocational trucks (trash collectors) with different emission certification standards, which included vehicles that have three after-treatment systems: Exhaust Gas Recirculation (EGR), Diesel Particulate Filter (DPF), and Selective Catalyst Reducer (SCR). Results showed a better SFC in the vehicles that included these three after-treatment systems than those with EGR only. The reported SFC for EGR vehicles was 54 L/100 km, while for the vehicles with the three systems was 42 L/100 km. In another work [25]., the same authors compared the same emission standard but with heavier vehicles (gross vehicle weight rate, GVWR > 50 t). The SFC results for only EGR vehicles were between 80 and 100 L/100 km, while new vehicles exhibited SFC = 70 L/100 km. It is important to emphasize that these studies were carried out with vocational vehicles. This fact explains the high values of SFC reported compared to the observed for long-distance vehicles.

1.2.4. Fuel Consumption as a Function of Vehicle Age

The works reported in Table A1 of Appendix A, through simulation models, show that newer vehicles have lower SFC than older ones. However, there is no strong evidence that it happens under real-world operating conditions.

1.2.5. The Influence of Topographic Conditions on Fuel Consumption

Huertas et al. (2022) [26] evaluated the SFC of 47 different HDVs in the variable topography (0–4000 masl) observed in Colombia, obtaining values between 50 to 80 L/100 km. In this study, they associated the SFC with its GVWR and concluded that neither altitude nor vehicle age influence SFC as long as they are properly mechanically maintained. They observed that one of the most influencing factors is the road grade.

1.2.6. Attempts to Obtain National Baselines of Fuel Consumption

Few studies have focused on obtaining a baseline of fuel consumption of a freight fleet at a national level. Through origin-destination surveys and vehicle counting with video cameras, Malik & Tiwari (2017) [27] determined the SFC of a representative sample (~5000 units) of HDVs and light-duty vehicles (LDVs) in India for a short time (<1 month). This study highlighted that the observed vehicle fleet was relatively new, i.e., 72% of the vehicles were up to 6 years old, and less than 5% were more than 10 years old. The obtained SFC were in the range between 23 and 30 L/100 km.

1.3. About This Work

The objective of this work is the measurement of the nationwide (Mexico in this case) real-world fuel consumption for Heavy-duty Vehicles based on the measurements of a large sample of vehicles under their daily driving operation for a long time (>1 year).

Previous sections indicate that the scope of this objective has not been attempted before. The achievement of this objective requires the instrumentation of a representative sample of vehicles and the observation of their fuel consumption for a long time (>1 year) to capture seasonal effects. The sample should include the most used trademarks and year models. Vehicles should be driven by different drivers and should run on the most important roads of the region under study. Vehicles should be operated by several companies transporting different loads. This task is highly costly and time-consuming.

In this work, we propose the monitoring of fuel consumption by reading it directly from the ECU through the OBD port connected to the cloud with a telematics system. These telematic systems were not originally developed to report fuel consumption. Furthermore, in most cases, they do not report fuel consumption. Therefore, adjustments to the available telematic systems are required, and extensive labor hours are required to download historical data on fuel consumption per vehicle.

In the process of pursuing the objective of this work, we contribute to new knowledge in the following way:

Establishment of the nationwide real-world energy consumption baseline of HDVs used for the transport of people and goods in Mexico;
Evaluation of the energy consumption and greenhouse gas emissions of vehicles with different energy sources (diesel, CNG, and electricity)

Relevance of the Contributions

Our results are meaningful for the scientific community, especially the ones involved in the decarbonization of the transport sector. Environment and energy governmental institutions and international organizations, such as the environmental program of the United Nations (UNEP), are looking for alternatives to decarbonize the transport sector as a priority. In aiming to achieve this objective, the first step is to determine the actual GHG emissions of this sector. The present work addresses those needs for the case of Mexico as a representative country of LATAM countries.

2. Methodology

Figure 1 summarizes the methodology followed to establish the real fuel/energy consumption of HDVs working under normal operating conditions in Mexico. We first describe the region of study. Then, we describe the method used to monitor the real fuel/energy consumption of a representative sample of vehicles for a long time (~3 years). Finally, we describe the statistical analysis developed to obtain average values of consumption per energy source, vehicle age, and technology.

2.1. Region of Study

Mexico, as in most Latin American countries, is characterized by long chains of high mountains. Its capital, Mexico City, is located at 2240 masl, while its second and third largest cities, Guadalajara and Monterrey, are located at 1500 and 500 masl, respectively. Mexico is located between 15.649 and 31.762 grades of latitude north, and thus it exhibits mild marked seasons. Temperatures range between 0 and 40 °C. Mexico possesses an extensive road network (Figure 1) with more than 330,000 km, where 49% correspond to highways, 34% to rural roads, and 11% to urban streets and avenues. The rest of the road network belongs to transition elements (links, returns, roundabouts) and braking ramps [28].

2.2. Fuel Characteristics

Table 1 lists the physic-chemical characteristics of diesel and CNG, which are the fuels available in Mexico for powering HDVs. This data is included as reference information that could be useful when comparing the results of fuel consumption in Mexico with other countries.

2.3. HDVs in Mexico

Mexico is one of the world’s major manufacturers and exporters of HDVs. According to the National Institute of Statistics and Geography (INEGI) and the Mexican Administrative Registry for the HDVs Industry (RAIAVP), in 2018 and 2019, approximately 200,000 vehicles per year were produced in Mexico. During the Covid-19 pandemic (2020 and 2021), that number decreased to 137,000 and 166,000, respectively. Most of this production (~85%) was exported, mainly to the United States (94%) and Canada (3%). Therefore, Mexican manufacturers comply with US regulations for HDV. However, Mexico has not yet considered a timeline for the establishment of fuel economy or CO₂ emissions standards for HDVs [29].

The Secretary of Communications and Transportation (SCT) keeps a record of the number of registered vehicles in Mexico. The SCT reported that in 2021 there were more than 650,000 HDVs (580,000 for freight and 86,000 for passengers), which represent 1.3% of the total vehicle fleet in Mexico. HDVs have shown an annual average growth rate of 8% in the last 10 years. The SCT reported that between 2016 and 2021, the HDVs registered in Mexico for freight transport were mostly Freightliner (45,031 units), Kenworth (44,449 units), and International (20,901 units). In the case of passenger transport, the leaders were Mercedes-Benz (7035 units), International (4044 units), and Scania (3266 units) (Figure 2a). It also reported that more than 69% of the HDV fleet is out of its useful life (>10 years, Figure 2b). Only 17% and 19%, for freight and passengers, respectively, consist of relatively new vehicles (<5 years).

The SCT also indicated that 99% of HDVs in Mexico are diesel-powered. The remaining percentage is made up of electric vehicles used for the urban transport of people and CNG powered used for both the long-distance transport of freight and the urban transport of people.

Road transport is essential for the Mexican economy. In 2020, approximately 500,000 HDV transported ~500 million tons (Figure 2d). As in most LATAM countries, people travel around the country mainly (96%) using buses [30]. In 2020, approximately 69,000 buses transported more than 1.5 million passengers [2]. During the last 10 years, road transport demand has increased annually between 2% and 3%. However, the COVID-19 pandemic in 2020 decreased by 7% for freight transportation and 39% for passenger transport (Figure 2c).

The Mexican government classifies the HDVs according to their weight and linear dimensions (NOM-012-SCT-2-2017). For the transport of passengers, the designation Van, B2, B3, and B4 correspond to van-type passenger transport vehicles (<19 passengers) or buses with two, three, and four axles, respectively. For the transport of cargo, this classification is based on the number of axles and wheels of the vehicles. The designations C2, C3, T2, and T3 correspond to rigid trucks with two and three axles and tractor-trailers with two and three axles in their towing unit plus its respective trailer with two axles (or sometimes up to three axles) [31]. By regulation, depending on the vehicle configuration, an axle can support up to 5.0–6.5 t with a single tire, depending on the type of road. This limitation is established with the aim of preventing the rapid deterioration of the roads.

2.4. Sample

Considering the composition of the Mexican fleet described in the previous section, we look after monitoring the operation of the main categories and trademarks of HDVs in Mexico (Table 2). According to the Andrew Fisher’s formula [32], for a population of 650,000 vehicles, 385 vehicles are required to have a representative sample with 95% confidence. In practice, we ended up using as many vehicles as possible with the time constraints and resources available and monitored 6955 vehicles. We also verified that by increasing the sample size, the results did not change. Next, we describe the sample of vehicles monitored.

2.4.1. Diesel Vehicles

A total of 6501 diesel HDVs were monitored from 2019 to the end of 2021, where 4411 belonged to freight transportation and 2090 to passengers’ transport (Table 2). In the case of freight transport, the dominant category was three-axle trucks (T3), and 99% of the vehicles sampled belong to this category. In total, 55% of vehicles were at most 5 years old, 38% between 6 and 10 years old, and the remaining 7% were more than 10 years old. A total of 80% of the sampled vehicles were Kenworth, 10% Volvo, 8% Freightliner, and 2% International.

For the case of HDVs for the passengers’ transportation, 85% of the sampled vehicles were two-axle buses (B2). 62% of the monitored buses were less than 5 years old, 14% were between 6 and 10 years old, and the remaining 24% were more than 10 years old. The dominant trademarks were Mercedes Benz (37%) and Volkswagen (27%). The remaining were Dina (17%), Ayco (5%), Irizar (5%), Hyundai (4%), and other manufacturers (5%). Finally, the most common engine manufacturers for these monitored HDVs were Cummins (61%), Mercedes Benz (13%), MAN (9%), Detroit (7%), Volvo (6%), and other manufacturers (4%).

2.4.2. CNG Vehicles

As previously stated, CNG vehicles are currently used in Mexico for both the long-distance transport of freight and the transport of people within the urban centers. According to official sources, such as the Mexican Oil Institution (PEMEX), Energy Regulation Commission (CRE), and Mexican Association for Vehicular Natural Gas (AMGNV), CNG is between 40% and 50% cheaper than diesel in Mexico [6,33]

We observed the CNG consumption of a fleet of 259 T3 vehicles used for the long-distance transport of freight during 2020 and 2021. Kenworth accounted for 94% of these vehicles; the remaining 6% were Freightliner. A total of 55% of this CNG fleet was 5 years old or less, and the remaining 45% of vehicles were between 6 and 10 years old. Kenworth and Freightliner vehicles used Cummins ISX 12G and 12N CNG engines [34,35,36].

In the case of the passenger transport sector, we observed a fleet of 195 vehicles powered by CNG. A total of 51% of these vehicles were Hyundai, model-year 2019. The remaining 49% were Dina model years 2018 and 2019.

2.4.3. Electric Vehicles

Fully 100% of the electric HDV in Mexico are used as part of the public transport services in Mexico City and Guadalajara, which are the largest Metropolitan Areas of Mexico. For comparative purposes, we included the energy consumption reported by the main world manufacturers of 30 electric buses (BYD, Yutong, Foton, Orten) and 29 electric trucks (BYD, Mitsubishi, Orten, Volvo, Kenworth) since we did not have access to observe the real-world energy consumption of these vehicles. We highlight that the values reported do not necessarily correspond to the energy performance of these vehicles when operating under real conditions in Mexico. Finally, we included the results of Dewesoft, (2022) [37], and Pineda, Jimenez, & Delgado, (2022) [38], who tested different types of electric buses under normal conditions in Mexico City.

2.5. Instrumentation and Data Gathering

In this section, we describe the process of obtaining the real-world fuel/energy consumption of HDVs in Mexico using the instrumentation described in Table 3.

Vehicle location (longitude and latitude) and speed were provided by a GPS installed in the vehicles. Measurements of altitude from GPS possess high uncertainties. Thus, this variable should be obtained from altimetry databases using location as input. In this study, altitude was obtained using the GPS Visualizer Digital Elevation Model (DEM) www.gpsvisualizer.com/elevation (accessed on 31 August 2021). This methodology was validated by Huertas et al. (2022) [26], who compared results from this tool with official values reported by government institutions.

The operating conditions (fuel consumption, speed) of the diesel-fueled vehicles were obtained via the vehicle’s engine computer unit (ECU), which reads the sensors that the manufacturers install on the engine to control its operation. The accumulated fuel consumption was reported at a low frequency (1 data every 1–20 min). Pavlovic et al. (2021) [40] verified that this methodology produces reliable data on fuel consumption. Alternatively, transport companies keep a record of the fuel consumed at the end of each trip, along with the distance traveled. The fuel consumption of CNG-powered vehicles was obtained through this alternative.

Vehicle weight has a strong influence on fuel consumption. Although this variable is frequently monitored to ensure that the circulating vehicles satisfy the local regulation in terms of maximum load transported, it is hard to have the payload associated with each trip. Currently, vehicles do not have sensors to monitor this variable, and frequently transport companies do not keep a record of this variable. Since payload can have a wide range of variation (0–35 t), in this study, we used the gross vehicle weight rate (GVWR) instead of the actual vehicle weight.

The use of OBD devices and sensors to monitor the location and operation of vehicles and gather such information in the cloud via the cellular network is known as telemetry or telematics systems. Currently, the continuous observation of the vehicles is carried out by third companies with the main objective of alerting fleet managers on eventualities in the driving conditions, unexpected location of the vehicles, and excessive fuel consumption. Through collaboration agreements with freight and passenger transport companies and telematics services companies (Métrica Movil through its servers GeoTab, Linkerweb, Global Track, and Traffilog), we observed the real-world fuel consumption of more than 101 vehicles from seven freight transport and four passenger transport companies carrying out more than 2500 trips during the last 3 years under normal conditions of operation in Mexico.

Additionally, we collected records of diesel consumption and traveled distance of almost three million trips (2.9 M) from 6400 diesel and 454 CNG vehicles. This sample of vehicles traveled more than 536 million km during their last 3 years of operation.

2.6. Data Quality Analysis

A data quality analysis was conducted to identify and disregard atypical values. Values of speed outside the range of 0–140 km/h were ignored. Similarly, sporadic values of location outside main roads were discarded. Then, values of SFC in L/100 km were calculated starting from data on fuel consumption and traveled distance. Values of SFC greater than the 99th percentile were discarded. We used the traditional vehicle energy balance model [17] to fill in the missing fuel consumption data, assuming constant acceleration between consecutive data points.

2.7. Determination of the SFC

The fuel consumption was expressed in two ways: from the vehicle perspective as SFC in L/100 km and from the logistics perspective as SFC * in L/100 km-t or for the case of buses in L/100 km-pas. Traditionally, SFC * is expressed as fuel consumption per kilometer and ton of payload transported or passenger transported. However, in this work, we express SFC * per ton of GVWR due to the lack of information regarding the payload per trip. For the case of buses, we preferred to express results per GVWR and assumed a load of 75 kg/passenger, considering that the vehicles’ passenger capacity is a subjective value. In some cases, manufacturers consider only sit passengers, and others include stand-up passengers.

Results were reported as a function of vehicle age, i.e., the difference between the year when the data was analyzed (2021) and the vehicle model year. Additionally, results were expressed as a function of vehicle technology.

Aiming to compare the fuel consumption of technologies that use different sources of energy, SFC was expressed in terms of diesel equivalent liters (DEL). The energy content of a diesel liter in Mexico is 37.7 MJ. The Mexican diesel has a density of 826 kg/m³ and an LHV of 45.61 MJ/kg [7].

Thus, the fuel consumption of CNG vehicles was expressed in terms of DEL. The typical composition of natural gas in northern Mexico [8] is 85 % Methane (CH₄), 3% Carbon dioxide (CO₂), 11% Ethane (C₂H₆), and 1% of Nitrogen (N₂). Therefore, this gas has a density of 0.844 kg/m³ at standard conditions and an LHV of 46.31 MJ/kg. Thus, 1.06 sm³ of natural gas is equivalent to 1 DEL [8].

Similarly, the energy consumption of electric vehicles was expressed in terms of DEL. In this case, 1 DEL corresponds to the average electric energy that the national system of electricity generation can produce with 37.7 MJ (10.5 kWh) of energy. Table 4 shows the distribution of fuels used in Mexico for electricity generation and the corresponding thermal efficiency [41]. Aiming at determining the equivalent energy consumption, an efficiency of 100% was considered for the case of renewable energies. Thus, a weighted average efficiency of 53% was obtained. In this way, it was obtained that 5.55 kWh of electric energy is equivalent to 1 DEL. Using this reasoning, the USEPA established that 7.41 kWh of electricity is comparable to 1 US liter of diesel in terms of its energy content [42].

2.8. GHG Emissions

Using the emission factors (Table 5) established by the local environmental authority (SEMARNAT), which are based on the emission factors established by the USEPA, the GHG emissions of these vehicles were obtained by type of fuel. CO₂, CH₄, and N₂O were considered the main GHG emitted by vehicles. The results were reported in terms of CO₂ equivalent (CO₂e) per kilometer traveled or kilometer ton transported, considering that CH₄ and N₂O have a global warming potential (GWP) equivalent to 28 and 265 times that of CO₂, respectively [43,44]. A life cycle analysis was carried out, which included emissions during fuel production (power source to fuel tank, WtT) and emissions during vehicle operation (from tank to wheel, TtW).

It is important to highlight that there is significant variability in the reported values of GWP and N₂O emission factors, especially in the case of the use of CNG. In this work, the values stipulated by the local environmental authority were used [46,47].

Table 5 shows that, in a life cycle analysis (WtW), when a vehicle is powered by diesel, regardless of local conditions and specific technology, it emits 3.42 kg CO₂e/DEL, and CNG and electric emissions are 19% and 42% lower using the energy sources available in Mexico.

3. Results

Below are the results obtained by monitoring the vehicles in their normal operation in Mexico in terms of SFC and SFC *. They were compared against those reported in other studies. We use the concept of DEL described previously to compare technologies that use different energy sources.

3.1. Diesel SFC

Figure 3a shows the SFC frequency distribution for T3 and B2 diesel-fueled vehicles, which exhibit a log-normal distribution. The behavior agrees with previous studies [26]. Thus, from now on, we will use median values instead of average values to describe the tendencies of SFC.

Figure 3a–c and Table 6 show that buses (B2) in Mexico consume a median value of 22.72 L/100 km while trucks (T3) consume 44.25 L/100 km. Considering that 99% of the HDVs operating in Mexico use diesel, the values reported for diesel vehicles correspond to the baseline or reference values of the actual energy consumption of HDVs in Mexico. We highlight that B2 and T3 are the main segments of the Mexican fleet within the buses (95%) and trucks (63%) categories, respectively.

3.1.1. Effect of Vehicle Age

As a first attempt to explore the effect of age, the results of fuel consumption were plotted as a function of vehicle age per category. Figure 3b,c show the boxplot of results of SFC for passenger and freight transport vehicles, respectively, according to their vehicle age. All technologies were included.

Results, as presented in Figure 3b,c cannot be used for evaluating the deterioration in energy efficiency with age, measured in terms of SFC, of the vehicles operating in Mexico, or of a given technology. These figures describe the SFC observed during the few years of a sample of the many vehicles running in Mexico as a function of the model year. There are many factors influencing fuel consumption, including vehicle technology, road conditions, driving habits, etc. Thus, the evaluation of the energy efficiency deterioration as a function of age requires, as a first step, isolating vehicle age from other factors influencing SFC. This analysis requires the observation of the SFC of the same individual vehicle for many years and repeats the analysis for a representative sample of the same technology. This analysis is being reported in a companion paper.

Furthermore, Figure 3b shows that in the case of buses (B2 and vans), it cannot be stated, statistically, that SFC or SFC * (not shown) increases with the vehicle age (R² < 0.21). However, it does show that the dispersion of SFC or SFC * (size of the box) increases with age, indicating the lack of reliability of old vehicles, at least in terms of SFC. Similarly, For the case of freight transport, trucks (T3) SFC and SFC * also show a negligible correlation with vehicle age (R² ~ 0.18). Thus, it is meaningless to state that T3 HDV deteriorate their SFC at a rate of 0.27 L/100 km age. We highlight that these results were obtained, including the whole sample of vehicles described in Section 2.4 Sample, and thus, it is affected by the particularities of the use of these vehicles in Mexico.

The no so clear reduction in SFC expected from new vehicles can be explained by considering the following facts:

Improvements in the energy performance of new technologies are counterbalanced by the inclusion of additional after-treatment systems that demand additional energy consumption;
As occurs with any new technology, drivers need additional training to adjust their driving habits to it, and this process could take months. Thus, the observed SFC of new vehicles does not necessarily correspond to their best performance;
Fleet administrators tend to disregard worst-performing vehicles regardless of vehicle age. Considering that the cost associated with fuel consumption represents 40–50% of the total operative cost, fleet administrators tend to disregard the vehicles that, under the frequent conditions of use (traveled routes, load percentage, usual drivers, etc.), exhibit high fuel consumption. Thus, the observed SFC of old vehicles corresponds to the vehicles that, under the local conditions, exhibit satisfactory SFC.

3.1.2. Effect of Vehicle Technology

We explored the effect of technology on the observed diesel consumption. We selected vehicles of recent model-year (<2 years old) and plotted their observed fuel consumption in terms of SFC * as a function of their vehicle technology (Figure 3d,e). In this study, a technology MM-E-LL is the combination of the vehicle manufacturer (MM), engine manufacturer (E), and engine size (LL engine displacement expressed in L). Manufacturers included were Kenworth, International, Freightliner, Volvo, Mercedes Benz, Scania, Volkswagen, Chevrolet, Dina, among others. Engines considered were Cummins (C), Detroit (D), International (I), Maxxforce (M), MAN (MA), Mercedes Benz (MB), MWM (MWM), Paccar (P), Scania (S), Volvo (V), and Volkswagen (VW).

Figure 3d shows an average value of 1.52 L/100 km-t for buses (B2). Statistical analyses indicate that there are no differences in the SFC * observed among the technologies included in the analysis, except for MB-4.8, MA-6.8-7, C-5.9, and I-7.6 which exhibit the smallest SFC * in their regular conditions of use in Mexico, under the B2 category. On the other side, technologies MB-1.2, D-9.3, and C-10.8 exhibit the largest SFC * under the same category. Similarly, for the case of cargo under the T3 category, the average SFC * was 0.82 L/100 km-t. Most of the technologies exhibit similar SFC * except for D-12.8 and S-12.7, which are the leading technologies under this category. On the other hand, technologies P-12.9, C-12.8, C-15, MB-7.2, V12.8, and C-11.9 exhibit SFC * above the average, under their regular conditions of use in Mexico under the T3 category.

3.2. CNG Consumption

As stated previously, during the last few years, a few companies have started to use CNG in their HDVs fleets. These actions have been driven by the CNG lower price compared to diesel and by the potential reductions in GHG emissions and air pollutants. This section compares the fuel consumption of CNG HDVs with diesel HDVs.

Table 6 shows that the medians of the CNG vehicle consumption in Mexico are 56.19 and 54.38 DEL/100 km for buses and trucks, respectively. These values are 147% and 23% higher than the median values obtained for diesel HDV in buses and trucks, respectively.

We explored these differences. Figure 4a,d compare the fuel consumption of HDV used for passenger and freight transport, respectively, by means of the SFC frequency distribution. These two figures confirm that during 2020–2021 recent models of CNG vehicles exhibited higher SFC than similar diesel vehicles in both sectors: passenger (147% higher) and freight (23% higher) transport.

Then, we explored the influence of vehicle age on CNG consumption. As in the case of diesel, Figure 4b,e show that data do not support any statistical variation of SFC with vehicle age. Figure 4a,d additionally show that the SFC exhibit a log-normal distribution, which agrees with previous studies [26,48] and therefore, we can conclude that the observed high CNG consumption is not due to an irregular behavior of the vehicles.

The high differences in SFC of CNG vehicles compared to diesel vehicles are an unexpected result. Figure 4c,f compare these results, expressed as SFC *, with the published in the literature for similar vehicles. The horizontal line in Figure 4d indicates the average value of the CNG SFC * reported in the literature (1.5 DEL/100 km-t), which is lower than the average SFC * = 3.75 DEL/100 km-t observed in Mexico for B2 buses. Similarly, the horizontal line in Figure 4d indicates the value obtained averaging the values reported in the literature for SFC of CNG-fueled T3 trucks (0.82 DEL/100 km-t), which is close to the average value observed in Mexico for similar vehicles (SFC * = 1.01 DEL/100 km-t). A statistical analysis of the data obtained confirms that the SFC * observed in Mexico for CNG buses is much higher than reported in the literature, while the CNG SFC * for T3 trucks is within the range of observed values.

The reason for this unexpected result is out of the scope of the present work. However, we highlight that the use of CNG in buses in Mexico started a few years ago (<5 years) and that our results correspond to the observed in a single bus company in a single city and in a single freight company operating in the northwest region of Mexico (Tijuana City). The fleet managers mentioned the following issues:

The city’s topography is not suitable for this type of vehicle due to its high variability. Thus, vehicles’ engines are subjected to accelerations and decelerations that do not allow them to operate in their optimal performance ranges;
Maintenance costs are three to four times higher than diesel vehicles;
The region of operation (northwest of Mexico) has faced natural gas shortage during summer.

3.3. Electricity Consumption of HDV

We compared (Figure 5) the energy efficiency observed in Mexico for diesel and CNG HDVs with the performance of electric vehicles using data reported by manufacturers. Additionally, the experimental results reported by the several authors listed in Table A1 were included and plotted in Figure 5. In this figure, the size of the symbols is proportional to the sample size. The numbers in parentheses indicate the reference in Table A1.

Figure 5a and Table 6 show that, under the local conditions of Mexico, the SFC (22.65 DEL/100 km) of electric HDVs under the category B2 (GVWR ~ 13–19 t, 19–41 pas), used as buses, could be similar to diesel buses under the same category. However, for Vans (GVWR ~ 5.5 t, 19 pas), their SFC could be higher (67%) to diesel (SFC = 9.33 L/100 km for diesel vs. SFC = 5.65 DEL/100 km for electric). Pineda, Jimenez, & Delgado (2022) [38] reported an average SFC = 0.91 kWh/km (SFC = 16.3 DEL/100 km, SFC * = 0.83 DEL/100 km-t, SFC⁺ = 0.41 DEL/100 km-pas) from 9 electric vehicles of GVWR = 19 t (~41 passengers) operating under normal conditions of use in Mexico City. Similarly, Dewesoft (2022) [37] reported an energy consumption of 0.33 kWh/km (SFC = 5.95 DEL/100 km, SFC * = 1.19 DEL/100 km-t, SFC⁺ = 0.27 DEL/100 km-pas) for a single BMW, 135 kW, 5 t, electric bus rated for 22 passengers, tested under controlled conditions on a Mexico City road. These apparently contradictory results underline the high influence of driving conditions on the energy consumption of the vehicles.

For the case of freight transport, Figure 5b shows that electric energy consumption grows linearly with GVWR up to ~10 t. This fact makes them appropriate for the last-mile distribution of goods where the payload is usually small. Few manufacturers report energy consumption for vehicles with the T3 configuration but with smaller GVWR (38–46.5 t) than listed in Table 6 (46.5–54.5 t). They reported an average value of SFC = 36.96 DEL/100 km, which is 16% lower than the SFC observed for diesel vehicles under the T3 category. Under these circumstances, a comparison in terms of SFC * is more appropriate. Figure 5d shows that, for the case of freight transport, the energy performance of electric vehicles, evaluated in terms of SFC *, is similar to diesel vehicles. Again, we highlight that the energy efficiency data for electric vehicles correspond to data supplied by manufacturers. Therefore, these conclusions must be confirmed with data on the actual electric consumption under the conditions in Mexico. We also recall that HDV are those with GVWR >3.5 t.

We expected that SFC per vehicle weight, that is SFC *, would exhibit a constant value. However, the reported results (Figure 5c,d) exhibit a significant variability indicating that additional influencing factors are relevant, especially in the case of buses. For the case of freight transport, Figure 5d shows that vehicle GVWR tends to reduce SFC *. This observation highlights the convenience, in terms of energy consumption, of consolidating payload in large vehicles instead of atomizing it in several small freight vehicles.

Figure 5c,d confirm that the values of SFC * for diesel HDVs observed in Mexico for the transport of people or goods are among the values reported in the literature for similar vehicles operating in other parts of the world. However, the CNG SFC * are higher than the reported in the literature.

3.4. GHG Emissions

Table 5 shows that, under the local conditions of the Mexican energy infrastructure, in a life cycle analysis (WtW), when a vehicle is powered by diesel, it could emit an average of 3.42 Kg CO₂e/DEL. CNG and electric vehicles could emit 19% and 42% less GHG, respectively.

However, when the vehicles’ fuel consumption is taken into consideration, for the case of freight transport, the GHG emission from T3 diesel vehicles, in a WtW analysis, is 1513 g CO₂e/km (Table 6, Figure 6c). The GHG emissions from CNG vehicles are less than 1% lower, and the emissions from electric vehicles could be 69% lower than the emissions from diesel vehicles. The result that the CO₂e emissions are relatively equal in CNG than in diesel vehicles is contrary to expectations. It is a direct consequence of the SFC of CNG vehicles operating in Mexico. Finally, when GVWR is considered, the GHG emissions from T3 diesel vehicles are, on average, 28.03 g CO₂e/km-t, and the GHG emissions from CNG and electric vehicles are 0.8% and 31% lower, respectively, than this value (Figure 6d).

For the case of passenger transport, Figure 6a and Table 6 show that the GHG emissions from CNG and electric buses are 100% higher and similar, respectively, than diesel. These figures correspond to 52, 104, and 54 g CO₂e/km-t emitted from diesel, CNG, and electric vehicles, respectively (Figure 6b).

4. Suggested Alternatives to Decarbonize the Transport Sector in Mexico

Today, it is well accepted that vehicle manufacturers must ensure the best performance of their vehicles under the real-world condition of use of their clients. Thus, we suggest to the Mexican authorities the use of these results as the base for regulations on the energy efficiency of vehicles with the objective of achieving reductions in emissions of GHG. Additionally, we suggest fleet managers use these results in the decisions making process of selecting the technologies to renew their fleet. We highlight that the observed SFC and SFC * reported in this study were influenced by additional factors to the technology of the vehicle. These results take into consideration the presence of after-treatment systems to control the emissions of air pollutants.

The strategies to speed up the decarbonization of the transport sector should go beyond the marginal improvements that could be obtained by improvements in vehicle technology. Savings larger than 20% are expected by the interested parties. Strategies should consider all factors- influencing energy consumption, such as driving habits, road conditions, transport efficiency, logistics, and final users. Based on the results obtained in the present work, the following alternatives to accelerate the decarbonization of the transport sector are suggested.

Promote the use of electric vehicles to transport people and goods in urban areas. In Mexico, as in the majority of the LATAM countries, most of the people move using private or public transport services. Buses move at low speeds, due mainly to traffic congestion, covering short distances (<50 km/route). Under these circumstances of low autonomies and low speed, electric buses have been shown to have a lower total cost of ownership (TCO), higher energy efficiency, and lower GHG emissions than diesel vehicles. Electric vehicles have the potential to reduce up to 52% of the emission of GHG in countries, such as Mexico, where electricity is obtained mainly (90%) from thermal power plants;
Establishment of a threshold limit of GHG emissions per kilometer traveled and a ton of payload transported to any vehicle fleets (fleets larger than five vehicles) equivalent to a 20% reduction of the current value (22.42 g CO₂e/km-t for T3) to be accomplished in the next 5 years. This alternative will force companies in the transport services to increase the energy efficiency of the transport service without limiting their economic growth. Transport providers will look after low-emission technologies, lighter vehicles, logistics improvements, better driving habits, fleet renewable, and any other complementary strategy to reduce energy consumption. For the case of passengers, the threshold value should be expressed per ton of GVWR, and a threshold value of 41.5 g CO₂e/km-t (15.16 g CO₂e/km-pas) is recommended for B2 buses;
Energy efficiency limits for HD vehicles addressed to manufacturers. Vehicle manufacturers should be responsible for the actual performance of their vehicles under the local conditions of use, which is far beyond complying with energy performance obtained in laboratory tests that, after all, can be manipulated. We suggest the use of 34.7 DEL/100 km for T3 vehicles and 17.9 DEL/100 km for B2 buses.

Previous strategies require further work, especially on how they can be followed up and reinforced.

5. Conclusions

This work focused on determining the fuel consumption of HDVs operating under the actual conditions in Mexico. The fuel consumption and distance traveled of a large sample of vehicles (6955) were monitored for ~3 years. This work included the main manufacturers of HDVs for both cargo and passenger transport.

Currently, most (99%) of the HDVs operating trucks in Mexico are diesel-powered. The results show that diesel B2 buses consume 22.7 L/100 km and emit 777 g CO₂e/km in a WtT analysis, while T3 trucks consume 44.25 L/100 km and emit 1513 g CO₂e/km. There was no statistical evidence that these values change with vehicle age, provided that vehicles are kept well maintained and they remain within their useful life (<10 years).

The use of natural gas in HDV in Mexico is negligible (<1%). Experiences using CNG in buses show excessive fuel consumption (56.19 DEL/100 km), which is 147% higher than the fuel consumption observed in similar diesel vehicles. Even though the CO₂e emissions in CNG vehicles (2760 g CO₂e/DEL) could be 20% lower than diesel, they are 100% (1550 g CO₂e/km) and 0.8% (1500 g CO₂e/km) higher in buses (B2) and lower in trucks (T3), respectively, when fuel consumption is taken into consideration.

As in the previous case, the use of electric HDV in Mexico is negligible (<1%). They are used only for passenger transport in the largest urban centers (Mexico City and Guadalajara). This work did not include real-world energy consumption for these vehicles. Based on manufacturers’ data, it was estimated that electric B2 buses operating in Mexico could consume 22.65 DEL/100 km and emit 446 g CO₂e/km, however under real operating conditions in Mexico City, some studies showed better energy performance: SFC = 16.3 DEL/100 km, SFC * = 0.83 DEL/100 km-t, SFC⁺ = 0.41 DEL/100 km-pas, and SFC = 5.95 DEL/100 km, SFC * = 1.19 DEL/100 km-t, SFC⁺ = 0.27 DEL/100 km-pas, for a B3 19 t bus and a 5 t electric bus, respectively.

Author Contributions

Conceptualization, O.S.S.-G. and J.I.H.; methodology, O.S.S.-G. and J.I.H.; software, O.S.S.-G. and J.I.H.; validation, O.S.S.-G., J.I.H., L.F.Q. and A.E.M.; formal analysis, O.S.S.-G. and J.I.H.; investigation, O.S.S.-G. and J.I.H.; resources, J.I.H. and L.F.Q.; data curation, O.S.S.-G., J.I.H. and A.E.M.; writing—original draft preparation, O.S.S.-G. and J.I.H.; writing—review and editing, O.S.S.-G. and J.I.H.; visualization, O.S.S.-G. and J.I.H.; supervision, J.I.H.; project administration, J.I.H. and L.F.Q. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Not applicable.

Acknowledgments

The authors thank the following organizations for their support in the development of this work: the National Council of Science and Technology of Mexico (CONACYT), the United Nations Environment Program (UNEP), the Global Climate Partnership Fund (GCPF), the Global Fuel Economy Initiative (GFEI), the Campus City Initiative of Tecnológico de Monterrey, Metrica Movil Corporation, Traxion Group, SENDA Group, Transportes Cuauhtémoc, MYM Express, and the Ibero-American Science and Technology Program (CYTED) within the framework of the Latin American Network for Research in Energy and Vehicles RELIEVE (Ref. 720RT0014).

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Recent studies that report measured fuel economy or specific fuel consumption of HDVs.

Source	Country	Objective		Model Year	Emission Standard	GVWR	Fuel	Method	Results
Delgado, Rodríguez, & Muncrief, (2017) [3]	European Union	To establish through simulation modeling the current efficiency baseline levels of European trucks and to estimate the potential for fuel consumption reduction through different technology packages in the 2020–2030-time frame.	T	2016	NA	40 t Tractor-Trailer (TT) 12t Rigid Truck (RT)	D	VECTO simulation model with Urban delivery (UD), regional delivery (RD) & long haul (LH) test cycles	TT: UD 28.20–58.81 L/100 km RD 25.00–46.38 L/100 km LH 23.56–36.96 L/100 km RT: UD 21.6 L/100 km RD 19.9 L/100 km LH 24.9 L/100 km
EPA, NHTSA, & DOT, (2016) [49]	US	To establish standards for medium- and heavy-duty vehicles that would improve fuel efficiency and cut carbon pollution to reduce the impacts of climate change.	T	2017	NA	14.9 t >14.9 t	D	Laboratory tests for engines and GEM model for the whole vehicle	TT: 71.8–75.2 L/100 km RT: 20.48–30.47 L/100 km
Delgado, Miller, Sharpe, & Muncrief, (2016) [50]	Brazil, China, EU, India, US	To estimate the potential for technology improvements to reduce the fuel consumption of key HDV segments in markets around the world.	T	2016	NA	40 t Tractor-Trailer (TT) 12T Rigid Truck (RT)	D	Autonomy simulation model with certified driving cycles	Brazil 39.8 L/100 km for TT and 23.7 L/100 km for RT China: 41.6 L/100 km for TT and 21.2 L/100 km for RT UE: 33.6 L/100 km for TT and 23.0 L/100 km for RT India; 54.8 L/100 km for TT and 24.9 L/100 km for RT US: 40.4 L/100 km for TT and 27.6 L/100 km for RT
ICCT, (2019) [51]	Japan	To update a new fuel economy standard for new on-road heavy-duty vehicles as part of the government’s ongoing effort to reduce the country’s petroleum usage and greenhouse gas (GHG) emissions.	T&B	2015	NA	40t (T) 18t (B)	D	Engine dynamometer and vehicle simulation model (does not specify the name of the tool)	TT: 35.21 L/100 km OT: 14.08 L/100 km TA: 14.88 L/100 km TB: 20.96 L/100 km GB: 16.47 L/100 km BA: 17.51 L/100 km
Ragon & Rodríguez, (2021) [52]	European Union	To understand how the industry currently performs compared to the targets set out by the European Commission	T	2018	NA	NA	D	Vecto Simulation Model	RT: 23. 9–31.1 L/100 km TT: 30.8–32.7 L/100 km
Malik & Tiwari (2017) [27]	India	To estimate freight transportation fleet fuel consumption	T	2017	BSI, BSII, and BSIII. **	NA	D (4921)	Origin and destination survey	23.81–29.5 L/100 km
Aroua, Lhomme, Redondo-Iglesias, & Verbelen, (2022) [53]	France	To evaluate fuel savings varying power train components	T	NA	NA	36.2 t	D (1)	Vecto Simulation Model	37.38 L/100 km
Quiros et al., (2016) [13]	California, US	Compare vehicles emissions in different routes and driving conditions (i.e., regional, urban, etc.)	T	2007–2014	EPA 2007, EPA 2013	27.0–37.0 t	D CNG, H (7)	On-road measurement with PEMS	Diesel: 34.5–63.6 L/100 km CNG *: 33–57 [DEL/100 km] Hybrid: 42–60 L/100 km
Sandhu et al., (2021) [21]	United States	Characterize duty cycle, fuel use, and tailpipe exhaust emissions rates of recent model year CNG front-loader and side-loader refuse trucks	T	2012–2013	NA	30.0 t	CNG * (6)	On-road measurement with SAE J1939 scan tool	123.78 [DEL/100 km]
Sandhu et al., (2015) [24]	United States	Determine the real-world OpMode based duty cycles and rates of fuel use and emissions for roll-off refuse trucks	T	2005–2012	EPA 2004, EPA 2010	30.0 t	D (6)	On-road measurement with SAE J1939 scan tool	53.5 L/100 km
(Sandhu et al., (2016) [25]	United States	Characterize the activity, fuel use, and emissions of 84 diesel side loader refuse trucks	T	2003–2012	EPA 2004, EPA 2010	24–30 t	D (6)	On-road with SAE J1939 scan tool	83.5 L/100 km
Huertas et al., (2022) [26]	Colombia	Evaluate actual km-by-km fuel consumption and external factors effects	T	2007–2020	EPA 1998, Euro IV	32–52 t	D (48)	On-road with SAE J1939 scan adapter	50–80 L/100 km
Díaz-Ramirez et al., (2017) [48]	Colombia	Determine influencing factors on fuel consumption in a heavy-duty truck fleet and evaluate the impact of eco-driving programs	T	2008 (1) 2012 (4)	EPA 1998	10.4–54.54 t	D(5)	On-road, telematics	3.11 [L/100 km-t]
Mane, Djordjevic, & Ghosh, (2021) [54]	Ireland	Determine critical factors influencing fuel consumption in HDVs	T	2010–2019	Euro V, Euro VI	30 t	D (22)	On-road, telematics	49.6 L/100 km
Zhang et al., (2014) [22]	China	Evaluate impacts on fuel consumption and CO₂ emissions from operating conditions	B	NA	Euro II to Euro V	15–18 t	D (62) CNG (9) LNG (2) H (2)	SEMTECH-DS PEMS	Diesel: 33 L/100 km CNG : 47.4 [DEL/100 km] LNG : 39.5 [DEL/100 km] Hybrid: 24.3 L/100 km
Quirama, Giraldo, Huertas, & Jaller, (2020) [17]	Mexico	Represent local driving patterns through driving cycle construction methodologies	B	2012–2014	EPA 2004	13.85 t	D (15)	OBD interface	40 L/100 km
Perrotta et al., (2019) [15]	London	Verify the accuracy of a fuel consumption estimation model comparting with real measurements	T	NA	NA	44 t	NA (1110)	On-road with SAE J1939 scan tool	26.50 L/100 km
Lv et al., (2020) [23]	China	Characterize emissions under real driving conditions	T	2016–2018	China IV China V	NA	LNG (2) D (4)	On-road PEMS	LNG * 30 L/100 km Diesel: 21 L/100 km
Gómez et al., (2021) [55]	Madrid	Analyze the pollutant emissions under real driving emissions	B	2016	Euro VI	24–25 t	D (1) CNG (1)	HORIMBA OBS-ONE PEMS	Diesel: 30 L/100 km LNG *: 40 L/100 km
Chikishev & Chainikov, (2022) [56]	Russia	Study the influence of external operating conditions on the fuel consumption of a bus operating on a regular city route.	B	NA	NA	NA	D (1)	On-road, telematics	46–68 L/100 km
Ghafarpasand et al., (2021) [20]	Middle east	Study the urban bus fleet in terms of driving behavior, emission performance, and emission estimation under real-world conditions	B	NA	Euro II, Euro III, Euro IV	16 t	D (20)	On-road PEMS	28.9–32.4 L/100 km
Pineda et al., (2022) [38]	Mexico	Model the electrification opportunities of two lines of the Bus Rapid Transit System (BRT) “Metrobus” of Mexico City.	B	NA	NA	15	E (9)	Telematics system	16.35 [DEL/100 km]
Dewesoft, (2022) [37]	Mexico	Evaluate the energy needed to operate electric minibuses on various routes in Mexico City.	B	NA	NA	5	E (1)	Telematics system	5.95 [DEL/100 km]

* CNG and LNG vehicles’ fuel consumption is expressed as diesel equivalent liter per 100 km. ** Bharat Stage Emission Standards: Emission standard for internal combustion engines in India.

References

IEA. The Future of Trucks—Implications for Energy and the Environment; International Energy Agency: Paris, France, 2017. [Google Scholar]
SCT. Estadística Básica del Autotransporte Federal. 2021. Available online: https://www.sct.gob.mx/transporte-y-medicina-preventiva/autotransporte-federal/estadistica/ (accessed on 31 July 2021).
Delgado, O.; Rodriguez, F.; Muncrief, R. Fuel Efficiency Technology in European Heavy-Duty Vehicles: Baseline and Potential for the 2020–2030 Timeframe. 2017. Available online: https://theicct.org/wp-content/uploads/2021/06/EU-HDV-Tech-Potential_ICCT-white-paper_14072017_vF.pdf (accessed on 18 February 2022).
Fontaras, G.; Zacharof, N.G.; Ciuffo, B. Fuel consumption and CO₂ emissions from passenger cars in Europe—Laboratory versus real-world emissions. Prog. Energy Combust. Sci. 2017, 60, 97–131. [Google Scholar] [CrossRef]
UNEP. What We Do? 2022. Available online: https://www.unep.org/explore-topics/transport/what-we-do/partnership-clean-fuels-and-vehicles/what-we-do (accessed on 30 November 2021).
AMGNV. Asociación Mexicana de Gas Natural Vehicular. 2022. Available online: https://amgnv.org/ (accessed on 20 March 2022).
CRE. NOM-016-CRE-2016. 2016. Available online: https://www.dof.gob.mx/nota_detalle.php?codigo=5450011&fecha=29/08/2016 (accessed on 14 December 2021).
SECRE. NORMA Oficial Mexicana NOM-001-SECRE-2010, Especificaciones del Gas Natural. 2010. Available online: https://www.dof.gob.mx/normasOficiales/3997/sener/sener.htm#:~:text=de%20emergencia%20severa)-,NORMA%20Oficial%20Mexicana%20NOM%2D001%2DSECRE%2D2010%2C%20Especificaciones,que%20dice%3A%20Estados%20Unidos%20Mexicanos. (accessed on 4 December 2022).
European Commission. Vehicle Energy Consumption calculation TOol—VECTO. 2021. Available online: https://ec.europa.eu/clima/eu-action/transport-emissions/road-transport-reducing-co2-emissions-vehicles/vehicle-energy-consumption-calculation-tool-vecto_es (accessed on 5 December 2021).
EPA. Greenhouse Gas Emissions Model (GEM) for Medium- and Heavy-Duty Vehicle Compliance. 2022. Available online: https://www.epa.gov/regulations-emissions-vehicles-and-engines/greenhouse-gas-emissions-model-gem-medium-and-heavy-duty (accessed on 25 July 2022).
Delgado, O.; Lutsey, N. Advanced Tractor-Trailer Efficiency Technology Potential in the 2020–2030 Timeframe; 2015. Available online: https://theicct.org/wp-content/uploads/2021/06/ICCT_ATTEST_20150420.pdf (accessed on 2 March 2022).
Franco, V.; Delgado, O.; Muncrief, R. Heavy-Duty Vehicle Fuel-Efficiency Simulation: A Comparison of US and EU Tools; International Council on Clean Transportation: Washington, DC, USA, 2015. [Google Scholar]
Quiros, D.C.; Thiruvengadam, A.; Pradhan, S.; Besch, M.; Thiruvengadam, P.; Demirgok, B.; Carder, D.; Oshinuga, A.; Huai, T.; Hu, S. Real-World Emissions from Modern Heavy-Duty Diesel, Natural Gas, and Hybrid Diesel Trucks Operating Along Major California Freight Corridors. Emiss. Control Sci. Technol. 2016, 2, 156–172. [Google Scholar] [CrossRef] [Green Version]
Giraldo, M.; Huertas, J.I. Real emissions, driving patterns and fuel consumption of in-use diesel buses operating at high altitude. Transp. Res. Part D Transp. Environ. 2019, 77, 21–36. [Google Scholar] [CrossRef]
Perrotta, F.; Parry, T.; Neves, L.C.; Buckland, T.; Benbow, E.; Mesgarpour, M. Verification of the HDM-4 fuel consumption model using a Big data approach: A UK case study. Transp. Res. Part D Transp. Environ. 2018, 67, 109–118. [Google Scholar] [CrossRef]
Pepper, G.T. Methods and System for Determining Consumption and Fuel Efficiency in Vehicles. U.S. Patent No. 7,774,130, 10 August 2010. [Google Scholar]
Quirama, L.F.; Giraldo, M.; Huertas, J.I.; Jaller, M. Driving cycles that reproduce driving patterns, energy consumptions and tailpipe emissions. Transp. Res. Part D Transp. Environ. 2020, 82, 102294. [Google Scholar] [CrossRef]
SAE. Fuel Consumption Test Procedure—Type II J1321_202010. 2020. Available online: https://www.sae.org/standards/content/j1321_202010 (accessed on 10 March 2022).
Kushiro, N.; Oniduka, Y.; Sakurai, Y. Initial Practice of Telematics-Based Prognostics for Commercial Vehicles: Analysis Tool for Building Faults Progress Model for Trucks on Telematics Data. Procedia Comput. Sci. 2017, 112, 2155–2164. [Google Scholar] [CrossRef]
Ghaffarpasand, O.; Talaie, M.R.; Ahmadikia, H.; Khozani, A.T.; Shalamzari, M.D.; Majidi, S. Real-world assessment of urban bus transport in a medium-sized city of the Middle East: Driving behavior, emission performance, and fuel consumption. Atmos. Pollut. Res. 2021, 12, 113–124. [Google Scholar] [CrossRef]
Sandhu, G.S.; Frey, H.C.; Bartelt-Hunt, S.; Jones, E. Real-world activity, fuel use, and emissions of heavy-duty compressed natural gas refuse trucks. Sci. Total Environ. 2021, 761. [Google Scholar] [CrossRef]
Zhang, S.; Wu, Y.; Liu, H.; Huang, R.; Yang, L.; Li, Z.; Fu, L.; Hao, J. Real-world fuel consumption and CO₂ emissions of urban public buses in Beijing. Appl. Energy 2014, 113, 1645–1655. [Google Scholar] [CrossRef]
Lv, L.; Ge, Y.; Ji, Z.; Tan, J.; Wang, X.; Hao, L.; Wang, Z.; Zhang, M.; Wang, C.; Liu, H. Regulated emission characteristics of in-use LNG and diesel semi-trailer towing vehicles under real driving conditions using PEMS. J. Environ. Sci. 2020, 88, 155–164. [Google Scholar] [CrossRef]
Sandhu, G.S.; Frey, C.H.; Bartelt-Hunt, S.; Jones, E. In-use activity, fuel use, and emissions of heavy-duty diesel roll-off refuse trucks. J. Air Waste Manag. Assoc. 2015, 65, 306–323. [Google Scholar] [CrossRef] [PubMed]
Sandhu, G.S.; Frey, H.C.; Bartelt-Hunt, S.; Jones, E. Real-world activity, fuel use, and emissions of diesel side-loader refuse trucks. Atmos. Environ. 2016, 129, 98–104. [Google Scholar] [CrossRef]
Huertas, J.I.; Serrano-Guevara, O.; Díaz-Ramirez, J.; Prato, D.; Tabares, L. Real vehicle fuel consumption in logistic corridors. Appl. Energy 2022, 314, 118921. [Google Scholar] [CrossRef]
Malik, L.; Tiwari, G. Assessment of interstate freight vehicle characteristics and impact of future emission and fuel economy standards on their emissions in India. Transp. Res. Part D Transp. Environ. 2017, 108, 121–133. [Google Scholar] [CrossRef]
INEGI; SCT; IMT. Red Nacional de Caminos Documento Técnico Descriptivo. 2015, Volume 1. Available online: https://www.snieg.mx/DocAcervoINN/documentacion/inf_nvo_acervo/SNIGMA/Red_Nacional_Caminos/documento_tecnico_descriptivo_rnc_2019.pdf (accessed on 20 March 2022).
White, B.; Kirsch, F.; Levin, S.; Skinner, I.; Norris, J.; Jones, L. Analysis of Fuel Economy and GHG Emission Reduction Measures from Heavy Duty Vehicles in Other Countries and of Options for the EU. 2017. Available online: https://ec.europa.eu/clima/sites/clima/files/transport/vehicles/heavy/docs/analysis_fuel_economy_hdv_en.pdf (accessed on 28 February 2022).
SCT. Estadística Básica del Autotransporte Federal. 2022. Available online: https://www.sct.gob.mx/fileadmin/DireccionesGrales/DGAF/EST_BASICA/EST_BASICA_2021/Estadística_Básica_del_Autotransporte_Federal_2021.pdf (accessed on 15 March 2022).
SCT. NORMA Oficial Mexicana NOM-012-SCT-2-2017, Sobre el peso y Dimensiones Máximas con los que Pueden Circular los Vehículos de Autotransporte que Transitan en las vías Generales de Comunicación de Jurisdicción Federal. 2017. Available online: https://www.dof.gob.mx/nota_detalle.php?codigo=5508944&fecha=26/12/2017#gsc.tab=0 (accessed on 30 November 2021).
Jung, S.H. Stratified Fisher’s exact test and its sample size calculation. Biom. J. 2014, 56, 129–140. [Google Scholar] [CrossRef] [Green Version]
CRE. Precios de Gasolinas y Diésel Reportados por los Permisionarios. 2022. Available online: https://www.cre.gob.mx/ConsultaPrecios/GasolinasyDiesel/GasolinasyDiesel.html (accessed on 20 March 2022).
Freightliner. The Road to Sustainability. 2019. Available online: https://freightlineradsaem.azureedge.net/content/dam/enterprise/documents/2912-vocational_natural_gas_trucks_-2019-05-31.pdf (accessed on 15 June 2022).
Freightliner. Natural Gas Fuel Your Future. 2021. Available online: https://freightlineradsaem.azureedge.net/content/dam/enterprise/documents/3642-new_cascadia_natural_gas_sell_-2019-06-13-1.pdf (accessed on 15 June 2022).
Kenworth. Kenworth T680 Next Generation Offers Cummins Westport ISX12N Near-Zero Emissions Natural Gas Engine. 2021. Available online: https://www.kenworth.com/about-us/news/kenworth-t680-next-generation-offers-cummins-westport-isx12n-near-zero-emissions-natural-gas-engine/ (accessed on 20 March 2022).
Dewesoft. Evaluación del Consumo de Energía en Minibús Eléctrico. 2022. Available online: https://dewesoft.com/es/case-studies/evaluacin-del-consumo-energetico-de-un-minubus-electrico (accessed on 21 December 2022).
Pineda, L.; Jimenez, C.; Delgado, O. Estrategia Para el Despliegue de Flota Eléctrica en el Sistema de Corredores de Transporte Público de Pasajeros de la Ciudad de México “ Metrobús ”: Líneas 3 y 4. 2022. Available online: https://theicct.org/wp-content/uploads/2022/03/hdv-latam-estrategia-despliegue-flota-electrica-sistema-corredores-transporte-publico-ciudad-mexico-metrobus-lineas-3-y-4.pdf (accessed on 30 April 2022).
Nasa, J.P.L. NASA Shuttle Radar Topography Mission Global 3 arc Second. 2013. Available online: https://lpdaac.usgs.gov/products/srtmgl3v003/ (accessed on 20 June 2021).
Pavlovic, J.; Fontaras, G.; Broekaert, S.; Ciuffo, B.; Ktistakis, M.A.; Grigoratos, T. How accurately can we measure vehicle fuel consumption in real world operation? Transp. Res. Part D Transp. Environ. 2021, 90, 102666. [Google Scholar] [CrossRef]
SENER. Programa de Desarrollo del Sistema Eléctrico Nacional. 2018. Available online: https://www.gob.mx/cms/uploads/attachment/file/331770/PRODESEN-2018-2032-definitiva.pdf (accessed on 15 April 2022).
Kelley Blue Book. What Is MPGe? Everything You Need to Know. 2021. Available online: https://www.kbb.com/car-advice/what-is-mpge/#:~:text=MPGe%20stands%20for%20miles%20per,electricity%20as%20its%20fuel%20source (accessed on 30 April 2022).
IPCC. Climate Change 2014: Synthesis Report. Contribution. In Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; IPCC: Geneva, Switzerland, 2014. [Google Scholar]
SEMARNAT; INECC. Inventario Nacional de Emisiones de Gases y Compuestos de Efecto Invernadero. 2019. Available online: https://www.gob.mx/inecc/acciones-y-programas/inventario-nacional-de-emisiones-de-gases-y-compuestos-de-efecto-invernadero (accessed on 30 March 2022).
DBEIS; DEFR. Greenhouse Gas Reporting: Conversion Factors 2021. 2022. Available online: https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2021 (accessed on 23 February 2022).
INECC. Factores de Emisión Para los Diferentes Tipos de Combustibles Fósiles que se Consumen en México (Volume 52). 2014. Available online: http://www.inecc.gob.mx/descargas/cclimatico/2014_inf_parc_tipos_comb_fosiles.pdf (accessed on 24 May 2022).
SEMARNAT. Inventario Nacional de Emisiones de Contaminantes Criterio (INEM). 2019. Available online: https://www.gob.mx/semarnat/acciones-y-programas/inventario-nacional-de-emisiones-de-contaminantes-criterio-inem (accessed on 15 March 2022).
Díaz-Ramirez, J.; Giraldo-Peralta, N.; Flórez-Ceron, D.; Rangel, V.; Mejía-Argueta, C.; Huertas, J.I.; Bernal, M. Eco-driving key factors that influence fuel consumption in heavy-truck fleets: A Colombian case. Transp. Res. Part D Transp. Environ. 2017, 56, 258–270. [Google Scholar] [CrossRef]
EPA; NHTSA; DOT. Greenhouse Gas Emissions and Fuel Efficiency Standards for Medium- and Heavy-Duty Engines and Vehicles—Phase 2. 2016. Available online: https://www.epa.gov/regulations-emissions-vehicles-and-engines/final-rule-phase-2-greenhouse-gas-emissions-standards (accessed on 14 November 2021).
Delgado, O.; Miller, J.; Sharpe, B.; Muncrief, R. Estimating the Fuel Efficiency Technology Potential of Heavy-Duty Trucks in Major Markets around the World. 2016. Available online: https://www.globalfueleconomy.org/media/404893/gfei-wp14.pdf (accessed on 18 February 2022).
ICCT. Second-Phase Fuel Economy Standards for On-Road Heavy-Duty Vehicles in Japan; International Council on Clean Transportation: Washington, DC, USA, 2019; Available online: https://theicct.org/wp-content/uploads/2021/06/Japan_HDV_FE_Phase_2_20190129.pdf (accessed on 30 November 2021).
Ragon, P.-L.; Rodríguez, F. No. 2021-35; CO₂ emissions from trucks in the EU: An analysis of the heavy-duty CO₂ standards baseline data. National Academies of Sciences, Engineering, and Medicine: Washington, DC, USA, 2021.
Aroua, A.; Lhomme, W.; Redondo-Iglesias, E.; Verbelen, F. Fuel saving potential of a long haul heavy duty vehicle equipped with an electrical variable transmission. Appl. Energy 2022, 307, 118264. [Google Scholar] [CrossRef]
Mane, A.; Djordjevic, B.; Ghosh, B. A data-driven framework for incentivising fuel-efficient driving behaviour in heavy-duty vehicles. Transp. Res. Part D Transp. Environ. 2021, 95, 102845. [Google Scholar] [CrossRef]
Gómez, A.; Fernández-Yáñez, P.; Soriano, J.A.; Sánchez-Rodríguez, L.; Mata, C.; García-Contreras, R.; Armas, O.; Cárdenas, M.D. Comparison of real driving emissions from Euro VI buses with diesel and compressed natural gas fuels. Fuel 2021, 289, 119836. [Google Scholar] [CrossRef]
Chikishev, E.; Chainikov, D. Assessment of External Factors Influence on the Route Fuel Consumption of a Diesel Bus Operating on a City Route. Transp. Res. Procedia 2022, 61, 354–360. [Google Scholar] [CrossRef]

Figure 1. Illustration of the general methodology followed in this work to obtain the real fuel/energy consumption of HDVs working under the normal conditions of use in Mexico.

Figure 2. Summary of HDVs’ market in Mexico (a) Vehicle sales by manufacturer and segment (freight and passengers), (b) Age distribution of HDVs, (c) historical evolution of the HDVs fleet, and (d) annual activity demand.

Figure 3. Diesel consumption of HDVs in Mexico. (a) Frequency distribution of SFC for B2 and T3 categories. For the case of passenger transport: (b) SFC vs. vehicle age, and (d) SFC * vs. technology considering recent models (<2 years old). For the case of freight transport: (c) SFC vs. vehicle age, and (e) SFC * vs. technology considering recent models (<2 years old). Boxes correspond to 25 and 75 percentiles. Vertical lines to the 5 and 95% percentile and dots are outlier data. Horizontal lines correspond to the median value. Discontinued red lines correspond to the tendency of the median values.

Figure 4. Comparison of diesel and CNG consumption of HDV operating under normal conditions of use in Mexico. For the segment of HDV used for passenger transport: (a) SFC frequency distribution, (b) SFC as a function of vehicle age and (c) SFC * as a function of the vehicle model year and technology. For the segment of HDV used for freight transport: (d) SFC frequency distribution, (e) SFC as a function of vehicle age, and (f) SFC * as a function of the vehicle model year and technology.

Figure 5. Comparison of real-world energy consumption of vehicles for the transport of passengers (a,c) and goods (b,d) powered by diesel, CNG, and electricity, expressed in terms of diesel equivalent liters (DEL) per traveled kilometer (a,b) and per kilometer-ton transported (c,d).

Figure 6. Comparison of the WtW GHG emissions expressed in terms of CO₂e. Frequency distribution of CO₂e expressed in terms of (a) g/km and (b) g/km-t for the case of passenger transport. Frequency distribution of CO₂e expressed in terms of (c) g/km and (d) g/km-t for the case of freight transport.

Table 1. Description of the fuels available in Mexico for powering HDVs.

Parameter	Units	Diesel	CNG
Natural state	-	Liquid	Gas
Cetane number	-	45 (min)	-
Research octane number	-	-	130
Lower heating value	MJ/kg	45.61	46.31
Density	kg/m³	826	0.844
Sulfur content	ppm	15–500 (max)	150 (max)

Source: [6,7,8].

Table 2. Description of the Sample of HDVs included in this study.

Use	Fuel	Category	Manufacturer	Age	Sample Size
Freight	Diesel	C2	International	0–5 years	12
			Isuzu	0–5 years	6
			Isuzu	5–10 years	9
			Kenworth	0–5 years	33
		C3	Kenworth	0–5 years	7
		C3	International	0–5 years	9
		T3	Chevrolet	more than 10 years	1
			Freightliner	0–5 years	274
				5–10 years	48
				more than 10 years	42
			International	0–5 years	19
				5–10 years	50
				more than 10 years	1
			Kenworth	0–5 years	2398
				5–10 years	828
				more than 10 years	228
			Scania	0–5 years	1
			Volvo	0–5 years	10
				5–10 years	364
				more than 10 years	71
	CNG	T3	Freightliner	0–5 years	1
				5–10 years	7
			Kenworth	0–5 years	187
				5–10 years	64
	Electric *	C2, C3, and T3	BYD	Brand new vehicles	18
			Mitsubishi		1
			Orten		6
			Mercedes Benz		1
			Kenworth		1
			Volvo		2
Passenger	Diesel	B2	Ayco	0–5 years	59
				5–10 years	44
				more than 10 years	9
			Chevrolet	more than 10 years	1
			Dina	0–5 years	203
				5–10 years	81
				more than 10 years	15
			International	0–5 years	42
			International	5–10 years	1
			Irizar	0–5 years	20
				5–10 years	12
				more than 10 years	78
			Marco polo	0–5 years	1
				5–10 years	1
				more than 10 years	11
			MCI	5–10 years	1
			MCI	more than 10 years	6
			Mercedes Benz	0–5 years	175
				5–10 years	142
				more than 10 years	253
			Oisa	0–5 years	10
			Scania	0–5 years	1
			Vissta	5–10 years	1
			Volkswagen	0–5 years	578
			Volkswagen	5–10 years	25
		B3	Marco polo	0–5 years	37
		V	Mercedes Benz	0–5 years	188
				5–10 years	43
				more than 10 years	34
			Volkswagen	0–5 years	18
	CNG	B2	Dina	0–5 years	95
	CNG	B2	Hyundai	0–5 years	100
	Electric *	V, B2, and B3	BYD	Brand new vehicles	20
			Mercedes Benz		1
			Orten		1
			Goldstone		1
			Lion		1
			Foton		2
			Zhongton		1
			King Long		1
			Avevai		1
			Karsan Jest BMW		1
			Yutong		2

Table 3. Instruments and methods used to monitor the real-world fuel/energy consumption of HDVs in Mexico.

Variable	Instrument/Method	Features	Sampling Frequency
Position, speed	GPS	Position: ~2 m Speed: ~0.05 m/s	1–20 s
Fuel consumption	OBD	Through injection time	1–20 min
Fuel consumption	Manual record of the fuel needed to refill the vehicle tank	N/A	After each trip
Altitude	Digital Elevation Model	Based on (NASA JPL, 2013 [39]) Accuracy: ~90 m	1–20 s
Weight	Reported by manufacturer	N/A	N/A

Table 4. Thermal efficiency and emission factors of the electric power generation in Mexico for the year 2018 [41].

Fuel	Generation *	Efficiency	WtT Emitted GHG
Fuel	%	%	g CO₂e/DEL
Coal	7	39	4636.03
Natural gas	56	46	2090.12
Fuel oil	10	30	2640.51
Geothermal	10	100 **	0
Solar		100 **	0
Wind		100 **	0
Hydraulic	7	100 **	0
Nuclear	3	34	0
Other	7	N/D	N/D

* Maximum total capacity of 12.1 GW. Total generation of 329,162 GWh. ** efficiency for calculating the equivalence of liters of diesel.

Table 5. Emission factors for the operation of diesel, CNG, and electric vehicles.

Energy Source	WtT	TtW			WtW
	WtT	CO₂	CH₄	N₂O	WtW
	kg CO₂e/DEL	kg/DEL	kg/DEL	kg/DEL	kg CO₂e/DEL
Diesel	0.64 *	2.73	0.0001	0.0001	3.42
CNG	0.45 *	2.18	0.0035	0.0001	2.76
Electric	1.97	0.00	0.0000	0.0000	1.97

* For the case of production, distribution, and transportation of diesel and CNG emission factors, the values reported by the United Kingdom were used [45].

Table 6. Real-world SFC of HDVs in Mexico per vehicle category.

Segment	Category	Number of Axles	Number of Wheels	GVWR [t]	Vehicle	% in Segment Fleet *	Diesel				CNG				Electric
Segment	Category	Number of Axles	Number of Wheels	GVWR [t]	Vehicle	% in Segment Fleet *	SFC [DEL/100 km]	SFC * [DEL/100 km-t]	CO₂ (W2W) [g/km]	CO₂ (W2W) [g/km-t]	SFC [DEL/100 km]	SFC * [DEL/100 km-t]	CO₂ (W2W) [g/km]	CO₂ (W2W) [g/km-t]	SFC [DEL/100 km]	SFC * [DEL/100 km-t]	CO₂ (W2W) [g/km]	CO₂ (W2W) [g/km-t]
Passengers	Van **	2	4	6.5		5.0	9.33	1.70	319	58.01	N/D	N/D	N/D	N/D	5.65	0.86	111	16.99
	B-2	2	6	14–19		95.0	22.72	1.52	777	51.92	56.19	3.75	1551	103	22.65	2.72	446	53.55
	B-3	3	8–10	17–27.5		~0.0	38.48	2.26	1316	77.46	N/D	N/D	N/D	N/D	30.29	2.09	597	41.08
Freight	C-2	2	According to the trailer configuration	19		20.3	23.02	1.21	787	41.43	N/D	N/D	N/D	N/D	16.97	1.50	334	29.60
	C-3	3		24–27.5		15.7	29.39	1.07	1005	36.55	N/D	N/D	N/D	N/D	21.88	0.84	431	16.58
	T-3	3		46.5–54.5		63.2	44.25	0.82	1513	28.03	54.38	1.01	1501	27.79	36.96	0.99	728	19.46

* Refers to the portion in the segment of HDVs. ** Van category was added to this table by authors due to their considerable use in passenger transport. W2W: Well to Wheel.

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Serrano-Guevara, O.S.; Huertas, J.I.; Quirama, L.F.; Mogro, A.E. Energy Efficiency of Heavy-Duty Vehicles in Mexico. Energies 2023, 16, 459. https://doi.org/10.3390/en16010459

AMA Style

Serrano-Guevara OS, Huertas JI, Quirama LF, Mogro AE. Energy Efficiency of Heavy-Duty Vehicles in Mexico. Energies. 2023; 16(1):459. https://doi.org/10.3390/en16010459

Chicago/Turabian Style

Serrano-Guevara, Oscar S., José I. Huertas, Luis F. Quirama, and Antonio E. Mogro. 2023. "Energy Efficiency of Heavy-Duty Vehicles in Mexico" Energies 16, no. 1: 459. https://doi.org/10.3390/en16010459

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Energy Efficiency of Heavy-Duty Vehicles in Mexico

Abstract

1. Introduction

1.1. Fuel Consumption Determination in HDVs

1.1.1. Estimation of Fuel Consumption in HDVs

1.1.2. Measurement of Fuel Consumption in HDVs

1.2. Previous Works Focused on the Determination of HDVs’ Fuel Consumption

1.2.1. Fuel Consumption in Buses

1.2.2. Fuel Consumption in Trucks

1.2.3. The Influence of the after-Treatment System on Fuel Consumption

1.2.4. Fuel Consumption as a Function of Vehicle Age

1.2.5. The Influence of Topographic Conditions on Fuel Consumption

1.2.6. Attempts to Obtain National Baselines of Fuel Consumption

1.3. About This Work

Relevance of the Contributions

2. Methodology

2.1. Region of Study

2.2. Fuel Characteristics

2.3. HDVs in Mexico

2.4. Sample

2.4.1. Diesel Vehicles

2.4.2. CNG Vehicles

2.4.3. Electric Vehicles

2.5. Instrumentation and Data Gathering

2.6. Data Quality Analysis

2.7. Determination of the SFC

2.8. GHG Emissions

3. Results

3.1. Diesel SFC

3.1.1. Effect of Vehicle Age

3.1.2. Effect of Vehicle Technology

3.2. CNG Consumption

3.3. Electricity Consumption of HDV

3.4. GHG Emissions

4. Suggested Alternatives to Decarbonize the Transport Sector in Mexico

5. Conclusions

Author Contributions

Funding

Data Availability Statement

Acknowledgments

Conflicts of Interest

Appendix A

References

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