Vehicle-specific Power Distributions Research Articles

Impact of ambient temperature on light-duty gasoline vehicle fuel consumption under real-world driving conditions

The widening gap between real-world vehicle energy consumption and modeled predictions can be attributed to discrepancies between actual ambient temperatures and assumptions made in laboratory tests. This study collected a detailed, extensive dataset comprising 25,640,666 records of real-world vehicle operating (speed, acceleration, etc.) and fuel consumption data alongside 124,938 hourly meteorological profiles (temperature, relative humidity, etc.). High-resolution fuel consumption rates (FCRs) based on ambient temperature were developed, and adjustment factors were introduced based on ambient temperature and vehicle specific power (VSP) binning. Fuel consumption factors (FCFs) were compared across different temperatures by incorporating VSP distributions and the adjusted FCRs, revealing larger FCFs at extreme temperatures compared to moderate ones. Fuel consumption inventories, both with and without temperature adjustments, were evaluated. The results indicated a 6% underestimation of annual fuel consumption in Beijing when disregarding temperature adjustments. The variation was observed across months (in July and August, underestimations can reach 11%) and across VSP bins (larger impact in low VSP bins). The relationship between FCR and ambient temperature is similar to a quadratic curve, with the lowest consumption occurring at 10 °C–20 °C. The FCF adjustment factor does not vary across speed intervals in cold weather and remains stable at approximately 1.15 at −10 °C, but it drops from 1.25 to 1 as speed increases from 5 to 100 km/h in hot weather. This study underscores the importance of considering ambient temperature in vehicle energy consumption modeling and the necessity of temperature-adjusted approaches for accurate fuel consumption estimations.

Research on the CO2 Emission Characteristics of a Light-Vehicle Real Driving Emission Experiment Based on Vehicle-Specific Power Distribution

China implemented the China VI emission standard in 2020. The China VI emission standard has added requirements for the RDE (real-world driving emission) test. To evaluate vehicle CO2 emission for different vehicles, 10 conventional gasoline vehicles were tested under the RDE procedure using the PEMS (portable emission testing system) method. All vehicles tested meet the sixth emission regulation with a displacement of 1.4 L~2.0 L. Among the vehicles tested, the highest CO2 emission factor was 281 g/km and the lowest was 189 g/km, while the acceleration of RDE gets a wider distribution, varying from −2.5 m/s2 to 2.5 m/s2. The instantaneous mass emission rate could reach around 16 g/s. The amounts of total CO2 emission in the positive region and the negative region make up 82~89% and 11~18% of the overall CO2 emission during the entire RDE driving period, respectively. The same vehicle has a wide range of CO2 emission factors at different VSP (vehicle specific power) intervals. Different RDE test conditions can lead to large differences in CO2 emissions.

Impact of real-world driving characteristics on the actual fuel consumption of motorcycles and implications for traffic-related air pollution control in Vietnam

This study aims to identify the impact of real-world driving characteristics on the actual fuel consumption (FC) of motorcycles (MC) so that drivers can have better driving behavior to save fuel and reduce air emissions. A data logger installed directly on MC was utilized to gather real-world operation data of MC which includes instantaneous speed and fuel consumption rate (FRinst), second by second. The collected data were preprocessed, then fed to extract typical driving characteristic parameters that strongly impact the fuel consumption of MC. Among the extracted parameters, vehicle-specific power (VSP) and its distribution best reflect the impact of real-world driving characteristics on the FC rate of MC (R2 = 0.88). The VSP bin in the range [0, 1] has the highest FC, 3.6 L/100 km, while the actual FC corresponding to the full VSP distribution of MC in Hanoi is 3.01 L/100 km only. Among the running modes of MC, creeping mode is the most fuel-wasting one, up to 7.9 L/100 km. In steady running mode, FRinst in the high-speed range is higher than that in the low-speed range. Overall, the larger the average speed and cruising time proportion plus the smaller the proportions of stopping and creeping time, the lower the emissions and the better the fuel economy. Thus, smooth driving technique could save fuel and reduce air pollutant emissions.

A fuel consumption model for mixed traffic flow in multiple connected and autonomous scenarios

AbstractThe potential increase/decrease in energy consumption of traffic operation modes is a hot topic in the studies of connected and autonomous vehicles (CAVs), but there are few theoretical studies on the fuel consumption of human driving vehicles (HDVs) and CAV mixed platoon as a result of limited data. Based on the power distribution of light‐duty vehicles, this study developed a fuel consumption model for HDV and CAV mixed platoons by theoretical hypothesis considering the oscillation along with the platoon. Firstly, a vehicle‐specific power (VSP) distribution conforming to normal distribution is constructed with hypothesis‐based VSP standard deviation and constant VSP average value at each speed bin under different platoon arrangement conditions. Then, the fuel factors (mL/km) are calculated by weighted fuel rate (mL/s) according to the VSP distribution. Finally, a library of fuel consumption values for multiple penetration rates, platoon intensities, and speeds was established. In the case, at a speed of 30 km/h, the fuel consumption in the CAV group was reduced by 0.8–5%, 1.9–12.5%, 8.6–12%, and 12.4%, respectively, at the penetration rate of 20%, 50%, 80%, and 100%, compared to the HDV group. The fuel consumption reduction effect of CAV was negatively correlated with the speed.

The Construction of Xi’an Urban Bus Driving Cycle: A Case Study

At present, there are many methods to construct a vehicle driving cycle, such as the micro-trip-based method and the Markov chain method. Different methods have different advantages and disadvantages. To compare these methods, this paper uses the micro-trip-based method, the Markov chain method, and the method combining micro-trips and Markov chains to construct the representative driving cycle of a Xi’an urban bus based on the driving data of the Xi’an No.2 bus. Firstly, the driving data is collected and preprocessed. Then, representative driving cycles are constructed based on different methods. Finally, different driving cycles based on different methods are compared. By calculating and comparing characteristic parameters, velocity distributions, acceleration distributions, and vehicle-specific power distributions between different representative driving cycles, this paper shows the advantages and disadvantages of different construction methods.

An improved method for evaluating eco-driving behavior based-on speed-specific vehicle-specific power distributions

Eco-driving is the driving behavior that improves fuel efficiency and reduces emissions. It is important to develop a method to accurately evaluate driving behaviors to support eco-driving training. However, the lack of evaluation baseline in the previous studies leads to uncertainty in the eco-driving evaluation under different traffic conditions. This study develops a method for evaluating driving behaviors in relation to the vehicle trajectory data of 19,779 drivers based on speed-specific distributions of vehicle-specific power (VSP), and develops the speed-specific baseline of VSP distributions and the baseline of fuel rates from the drivers’ driving behaviors. The results show the following: (1) the speed-specific VSP distributions can evidently characterize the differences among individuals’ eco-driving behavior; (2) the proposed evaluation method based on the baseline is able to overcome the uncertainty of eco-driving evaluation under different traffic conditions. The findings are helpful in supporting eco-driving training.

Analysis of the Characteristics of Real-World Emission Factors and VSP Distributions—A Case Study in Beijing

Vehicle emissions intensity at a given travel speed is well known among the public since travel speed is the key parameter in both the traffic model and the emission model. Yet, several problems still remain in traditional approaches of measuring the emission intensity. To establish accurate and high-resolution emission factors, an established method of emission factors is proposed based on the real-time monitoring operation conditions data, which can reflect the effect of dynamic traffic changes on emissions. The speed-specific vehicle-specific power (VSP) distributions of different months, as well as those in different vehicles in Beijing were developed and compared. Statistical analyses such as Coefficient of Variation (CV) and Root Mean Square Error (RMSE) were used to quantify the differences in the VSP distribution. The results showed the significant correlation between the distribution of VSP, velocity, and operating patterns at time intervals within the annual range. Driving conditions in 2021 are more eco-friendly because of the improvement of digital development and driving habits. Furthermore, research on CO, HC, and NOx emission factor situations in different cycles revealed that the emission factors of NOx and HC are always underestimated in typical operating modes, while sometimes the emissions of CO are overvalued.

Heterogeneity Analysis of Operating Mode Distribution for Modeling Energy Consumption of Light-Duty Vehicles

The use of vehicle operating mode (OpMode) distribution is widely accepted for estimating energy consumption and emissions in the Motor Vehicle Emission Simulator (MOVES) model. However, the heterogeneity of driving behavior may lead to errors when using the default OpMode distribution. To improve the accuracy of energy consumption estimations, it is necessary to recognize the heterogeneity in OpMode distribution among different driving behaviors. With this aim, this paper designs a speed-specific indicator of energy efficiency reflecting driving behavior based on the speed-specific vehicle-specific power (VSP) distribution. The paper uses field data from 26,082 drivers recorded second by second during workdays. It also discusses the intra-heterogeneity and inter-heterogeneity of driving behavior based on unsupervised algorithm clustering. The findings of this paper are as follows. (1) The speed-specific VSP distribution clearly reflects the differences in energy efficiency of individuals’ driving behavior. (2) The energy efficiency indicator reflects the multidimensional inter-heterogeneity and intra-heterogeneity of driving behavior. (3) Drivers’ varied driving behavior causes heterogeneity in energy efficiency at different speeds, possibly causing an error of 6.34% in the emissions estimations. (4) Drivers of electric vehicles (EVs) and hybrid electric vehicles (HEVs) show more aggressive driving behaviors than drivers of conventional vehicles (CVs), which may cause an energy estimation error of over 6% for EVs and HEVs. Thus, the OpMode distribution of EVs, HEVs, and CVs should be modeled separately for on-road energy estimations.

Quantitative of instantaneous BC emissions based on vehicle specific power from real-world driving diesel trucks in China

In-depth exploration of the potential links between instantaneous black carbon (BC) emissions and driving parameters from real-world diesel trucks (DTs) is a key step toward development of a highly flexible vehicle emissions estimation system. In this study, we conducted real-world measurements on 22 DTs with mainstream types and emission standards, and obtained instantaneous data of BC emissions and vehicle driving. Since vehicle specific power (VSP) is an excellent surrogate for engine load, we characterize the instantaneous BC emissions and VSP distributions, and then establish links between VSP and fuel consumption, VSP and BC emission rates, VSP and BC emission factors (EFs), respectively. We find that BC emission rates of China V light-duty DTs installed with diesel particulate filter (DPF) are significantly lower (2 to 3 orders of magnitude) than those with China III and China IV. Frequent acceleration and deceleration of vehicles maybe the main reason leads to high BC emissions. The distribution of VSP is mainly concentrated in the ranges of −30 to 35 kW/t in the scope of this study. We find that VSP and BC EFs did not show a consistent pattern for all tested DTs, and BC EFs present obvious fluctuations with the VSP variation. The average fuel-based BC EFs vary by factors of 2.27–8.25 from the lowest to highest EFs. Through a fitting of the third-order polynomial function, we finally quantify and provide fitting formulas of BC EFs and VSP under more detailed categorization. Our results can provide important data support for accurate quantification of BC EFs, and even emission inventory calculations.

How do errors occur when developing speed correction factors for emission modeling?

• Detailed explanation of errors in speed correction factors (SCFs) • Quantifying the impact of vehicle specific power (VSP) distribution on SCFs. • Quantitative analysis of relationship between emission rate and SCFs. Speed correction factors (SCFs) reflect the impact of traffic conditions on vehicle emissions. However, uncertainty often occurs when calculating SCFs in existing emission models. Investigating how uncertainty occurs is helpful in improving accuracy when calculating SCFs. This study calculates SCFs of carbon dioxide (CO 2 ) of non-hybrid light-duty vehicles on urban restricted access roadways, and analyzes two important explanatory variables when calculating SCFs: speed-specific vehicle specific power (VSP) distributions and emission rates. The characteristics of how SCFs change in response to changes in VSP distributions and emission rates are discussed in detail. The results show that distortions in VSP distributions and emission rates significantly affect SCFs. A 1% change of the fraction in a VSP bin or a 1% change in the average emission rate in a VSP bin can cause 1.3–3.2% or 0.08–0.13% of changes in SCFs, respectively. These findings are helpful in reducing uncertainty when calculating SCFs.

Modeling the emissions of rural vehicles based on real-world driving cycles

Characterized by a high frequency of use, harsh working environments, poor maintenance, and low levels of emission controls, rural vehicles (RVs) are becoming an important source of air pollution. Our study used a portable emission measurement system (PEMS) to test the real-world emissions of 35 RVs on provincial, rural, and farm roads. The results show that high emission rates of carbon monoxide (CO) and hydrocarbons (HC) mainly occurred when accelerating at low speeds. However, high levels of nitrogen oxides (NOx) were emitted during high-speed acceleration. The particulate number (PN) of emissions was higher when the RVs were accelerating. According to the overall test results, the vehicle specific power (VSP) on the provincial road mostly ranged within (0, 3], accounting for 68.80% of the total. The VSP on rural and farm roads was concentrated within (0, 2] kW·ton−1, accounting for 67.09% and 76.64% of the total, respectively. We defined 14 bins based on the distribution of the VSP values and calculated the average emission rate of each bin. By comparing the average emission rate among the bins, we found that within Bins 1–7 (VSP < 0 kW·ton−1), CO, HC, and NOx emissions slowly increased as the VSP increased. In Bins 8–13 (VSP ≥ 0 kW·ton−1), the average emission rates of four pollutants increased as the VSP increased. However, all pollutants decreased in Bin 14 (VSP ≥ 6 kW·ton−1). We built a microscopic emission model according to the VSP distribution characteristics of RVs on different road types. We compared the measured and simulated emission factors and found that our emission model can greatly simulate the HC, NOx and PN emission factors of RVs.

Development of a Simplified Model of Speed-Specific Vehicle-Specific Power Distribution Based on Vehicle Weight for Fuel Consumption Estimates

The vehicle-specific power (VSP) distribution, as one of the fundamental inputs of VSP-based emission models such as the motor vehicle emission simulator model, is sensitive to vehicle weight. Developing field VSP distributions requires extensive vehicle type-specific trajectory data, which is expensive and time-consuming. On the other hand, estimating fuel consumption accurately by employing VSP distributions for various vehicle types is computationally highly complex. This study aims to develop a simplified model of speed-specific VSP distribution based on vehicle weight for fuel consumption. First, field speed-specific VSP distributions of eight types of vehicles are developed. Second, the Gaussian function is employed to fit the field speed-specific VSP distributions to “change” the discrete VSP distributions into continuous distributions to facilitate quantifying the relationship between VSP distributions and vehicle weights. Third, the relationship between VSP distributions and vehicle weights is quantified by employing polynomial functions. The results indicate the acceptable accuracy of the simplified model, with 93.8% of R2 of the Gaussian function being greater than 0.90. The error in estimating fuel consumption using the simplified model is acceptable. For vehicles weighing 1.5 t (1.5 metric tons), the average error is 6.3%. Besides the “hole filling” of VSP distributions of inaccessible vehicles, the simplified model will reduce the computational complexity of estimating fuel consumption by about 50%, which is beneficial for the realization of real-time online estimates of fuel consumption.

Development of Operating Mode Distribution Models for Light-Duty Vehicles on Unrestricted Access Roadways

The key correlating traffic variable for modeling vehicle emissions has evolved from average speed to vehicle-specific power (VSP), and recently to operating mode as defined in Motor Vehicle Emission Simulator (MOVES). The analysis of operating mode and its distribution, however, requires a large amount of data and is time consuming and challenging. This paper attempts to build models between the operating mode distributions and the common traffic variable—average speed—to facilitate the emission estimation. Focusing on light-duty vehicles and unrestricted access roadways, a floating car survey was conducted separately on arterials and collectors in Shaoshan, China. The trajectory data were processed to reveal the characteristics of operating mode distributions. A key finding is that, when the data points of the operating mode of idle are excluded, the VSP distributions of the remaining data points follow logistic distributions and the parameters can be linearly regressed with the average speed. Arterials and collectors feature different operating mode distributions even at the same average speed, and therefore different models were developed. The models were then applied to generate operating mode distributions, which were validated with the real-world data from the test bed and which, when compared with the default values generated by MOVES, fit the real-world condition better.

How Many Trajectories Are Needed to Develop Facility- and Speed-Specific Vehicle-Specific Power Distributions for Emission Estimation? Case Study in Beijing

Vehicle-specific power (VSP) distributions, or operating mode (OpMode) distributions, are one of the most important parameters in VSP-based emission models, such as the motor vehicle emission simulator (MOVES) model. The collection of second-by-second vehicle activity data is required to develop facility- and speed-specific (FaSS) VSP distributions. This then raises the problem of how many trajectories are needed to develop FaSS VSP distributions for emission estimation. This study attempts to investigate the adaptive sample size for developing robust VSP distributions for emission estimations for light-duty vehicles. First, vehicle activity data are divided into trajectories and categorized into different trajectory pools. Then, the uncertainty of FaSS VSP distribution caused by sample size is analyzed. Further, the relationship between VSP distribution sample size and emission factor uncertainty is discussed. The case study indicates that error in developing FaSS VSP distributions decreases significantly with increased sample size. In different speed bins, the sample size required to develop robust FaSS VSP distributions and estimate emission factors is significantly different. In detail, in each speed bin, for a 90% confidence level, 30 trajectories (1,800 s) are enough to develop robust FaSS VSP distributions for light-duty vehicles with the root mean square errors (RMSEs) lower than 2%, which means errors in calculating fuel consumption and greenhouse gas (GHG) emissions are lower than 5%. However, 35 trajectories (2,100 s) are needed to estimate emissions of carbon monoxide (CO), nitrogen oxide (NOX), and hydrocarbons (HC) with an estimation error lower than 5%.

Method for Evaluating Eco-Driving Behaviors Based on Vehicle Specific Power Distributions

Eco-driving is an effective way to reduce vehicle fuel consumption and exhaust emissions. Numerous studies have been conducted on eco-driving, however, there is still a lack of quick and accurate methods for evaluating eco-driving behaviors. This paper proposes a novel method to evaluate eco-driving behaviors based on vehicle specific power (VSP) distributions. First, the baseline speed-specific VSP distributions were derived based on second-by-second vehicle activity data of driving trajectories from 159 drivers on expressways in Beijing. Then, individual drivers’ speed-specific VSP distributions were developed for comparison with the baseline VSP distributions. A model was proposed to evaluate eco-driving behaviors based on the identified differences. Additionally, an eco-driving index (EDI) was designed to quantify the ecological level of driving behaviors for different speed ranges. The consistency of individual driving behaviors across different speed ranges was assessed. The minimum sample size and the appropriate speed bins required for reliable evaluation of individual eco-driving were also determined. The results showed that the differences between individual drivers’ VSP distributions and the baseline distributions could be used to identify eco-driving behaviors, and the eco-driving behaviors of individual drivers were consistent for different speed ranges. The minimum sample size for a reliable evaluation of individual eco-driving behaviors is 420 seconds. Data for speed bins above 70 km/h and below 10 km/h were not representative of the driving behavior and the driving behavior was especially consistent in the speed bins from 20 km/h to 40 km/h.

The “acceleration cliff”: An investigation of the possible error source of the VSP distributions generated by Wiedemann car-following model

Errors have been found in the speed-specific vehicle specific power (VSP) distributions generated by the Wiedemann car-following model. The impact of the existence of the regime structure on the distributions was investigated in this paper. The analysis was conducted based on massive field car-following trajectories and numerical simulation of the Wiedemann model. The field and simulated speed-specific VSP distributions and acceleration distributions were compared. The field and simulated accelerations were analyzed with the help of the Wiedemann Cartesian systems. The unrealistic “acceleration cliffs” were found along with the regime borders. It was indicated that the “acceleration cliff” was a source of the generated emission errors. The existence of the discrete regimes was responsible for the “acceleration cliff.” It was indicated that the “acceleration cliff” could not be eradicated by the parameter calibration, unless the logics of the regimes were modified. A data-driven model was designed based on the Wiedemann plane. The “acceleration cliffs” were successfully eliminated by implementing continuous accelerations. It was indicated that the Wiedemann model had the potential to generate realistic VSP distributions.

Analysis of Emission Effects Related to Drivers' Compliance Rates for Cooperative Vehicle-Infrastructure System at Signalized Intersections.

Unknown remaining time of signal phase at a signalized intersection generally results in extra accelerations and decelerations that increase variations of operating conditions and thus emissions. A cooperative vehicle-infrastructure system can reduce unnecessary speed changes by establishing communications between vehicles and the signal infrastructure. However, the environmental benefits largely depend on drivers’ compliance behaviors. To quantify the effects of drivers’ compliance rates on emissions, this study applied VISSIM 5.20 (Planung Transport Verkehr AG, Karlsruhe, Germany) to develop a simulation model for a signalized intersection, in which light duty vehicles were equipped with a cooperative vehicle-infrastructure system. A vehicle-specific power (VSP)-based model was used to estimate emissions. Based on simulation data, the effects of different compliance rates on VSP distributions, emission factors, and total emissions were analyzed. The results show the higher compliance rate decreases the proportion of VSP bin = 0, which means that the frequencies of braking and idling were lower and light duty vehicles ran more smoothly at the intersection if more light duty vehicles complied with the cooperative vehicle-infrastructure system, and emission factors for light duty vehicles decreased significantly as the compliance rate increased. The case study shows higher total emission reductions were observed with higher compliance rate for all of CO2, NOx, HC, and CO emissions. CO2 was reduced most significantly, decreased by 16% and 22% with compliance rates of 0.3 and 0.7, respectively.

Validation of the Rakha-Pasumarthy-Adjerid car-following model for vehicle fuel consumption and emission estimation applications

The Rakha-Pasumarthy-Adjerid (RPA) car-following model has been demonstrated to successfully replicate empirical driver car-following behavior. However, the validity of this model for fuel consumption and emission (FC/EM) estimation has yet to be studied. This paper attempts to address this research need by analyzing the applicability of the model for FC/EM estimation and comparing its performance to other state-of-practice car-following models; namely, the Gipps, Fritzsche and Wiedemann models. Naturalistic empirical data are employed to generate ground truth car-following events. The model-generated second-by-second Vehicle Specific Power (VSP) distributions for each car-following event are then compared to the empirical distributions. The study demonstrates that the generation of realistic VSP distributions is critical in producing accurate FC/EM estimates and that the RPA model outperforms the other three models in producing realistic vehicle trajectory VSP distributions and robust FC/EM estimates. This study also reveals that the acceleration behavior within a car-following model is one of the major contributors to producing realistic VSP distributions. The study further demonstrates that the use of trip-aggregated results may produce erroneous conclusions given that second-by-second errors may cancel each other out, and that lower VSP distribution errors occasionally result in greater bias in FC/EM estimates given the large deviation of the distribution at high VSP levels. Finally, the results of the study demonstrate the validity of the INTEGRATION micro-simulator, given that it employs the RPA car-following model, in generating realistic VSP distributions, and thus in estimating fuel consumption and emission levels.

Multi-objective calibration of traffic microsimulation models

Use of traffic microsimulation for emission estimation and traffic analysis has increased over the last decade, increasing the need for a more detailed calibration. This paper calibrates the Toronto Waterfront Area microsimulation network three times based on different objective functions. A genetic algorithm with a multi-criteria objective function is used to minimize the Root-Mean-Square of Errors between model estimates and field measurements for counts, speeds, and standard deviation of acceleration. The terms for speed and standard deviation of acceleration are then removed in turn from the objective function, and the model is recalibrated. The three calibrated models are compared according to their calibrated parameters, their simulated driving cycles and driving cycle parameters, their Vehicle-Specific Power distribution, and estimated emission factors. The comparison shows that aggressive driving decreases and emission factors are closer for the network that is calibrated to all three goodness-of-fit measures.

Emissions Characteristics for Heavy-Duty Diesel Trucks Under Different Loads Based on Vehicle-Specific Power

Both operating modes and emissions factors for heavy-duty diesel (HDD) trucks were analyzed under different loads to understand the effect of vehicle loads on emissions. Second-by-second speed data for different loads for HDD trucks were collected first. Then a method for calculating the vehicle-specific power (VSP) values and an emissions model for heavy-duty vehicles by using the VSP value were developed to evaluate the effect of different vehicle loads. The VSP distributions and emissions characteristics for fully loaded and unloaded trucks were analyzed and compared. The results illustrate that the fully loaded vehicles spent more time driving in steady modes and the time percentage of VSP values in the bin of 0 kW/ton for fully loaded trucks was lower than the percentage for unloaded trucks. However, the time percentage at the positive VSP value was significantly higher than the percentage for the unloaded trucks. The emissions factors of fully loaded trucks were significantly higher than those of unloaded trucks. Emissions factors were affected the most at speed intervals of 20 to 40 km/h, with emissions factors for carbon dioxide, carbon monoxide (CO), oxides of nitrogen (NOx), hydrocarbon, and particulate matter (PM) at 20.4%, 23.5%, 29.0%, 11.7%, and 9.4% higher, respectively, than those levels for unloaded vehicles. With an increase of travel speed, the impact of the load on emissions weakened. Vehicle loads had the greatest effect on emissions of NOx, followed by emissions of CO. PM emissions were the least affected by vehicle loads. The impact of vehicle loads on emissions was affected by different acceleration behaviors under different loads.