Tabaco

In this section, models used to determine fuel consumption and emission factors for different vehicle types, driving conditions, vehicle operation and driving styles are discussed. Models are broadly classified and selected examples are studied in detail.

2.5.1. Classification of emissions simulation models

Two types of emissions simulation models are considered by building on definitions in Section 1.7. The first model is based on correlating vehicle kinematics to fuel consumption and emission factors. The second model is based on engine-operating parameters and emissions maps. The two types of models differ in complexity, how they characterise emission factors and their purpose. Depending on their purpose, they can be divided further into aggregate and instantaneous models.

Models based on kinematics use driving cycles, driving patterns, average vehicle speed, or combinations of speed and acceleration to characterise emission factors. Kinematics models provide aggregated emission factors for emissions inventories based on descriptions of driving conditions. These models do not directly account for engine loads due to road gradient, auxiliary equipment use and driving styles (e.g. drivers gear changing habits); these factors are typically compensated for by using correction factors produced from engine operation models or calibrated using supplementary dynamometer tests.

Kinematic models generally use emission factors determined using the constant volume sampling method of measuring emissions, but in some cases are also determined from instantaneous emissions measurements. In cases where emissions are characterised by vehicle speed, it is also possible to use emission factors derived from infrared remote sensing of vehicle exhaust fumes.

Models based on fuel consumption and emissions maps rely on derived engine-operating parameters to estimate fuel consumption and emissions for any instant. These models provide fuel consumption and emission factors for a comprehensive range of engine loads.

Road gradients, auxiliary equipment use and driving styles can be accounted for by

conversion into additional engine loads. Emissions maps are often used to simulate fuel consumption and emissions in network models. Network models, such as NEMO (Network Emissions Model) (Rexeis and Hausberger, 2005), simulate the path and speeds of a vehicle travelling though a road network and typically estimate fuel consumption and emissions on a second-by-second time basis. They are useful in studying impacts of traffic management techniques such as traffic light synchronisation (Tate et al., 2005). Emissions and fuel maps are determined from instantaneous measurements making these models data intensive.

Engine maps are sometimes criticised for not compensating for transient effects (for example, effects of engine acceleration) on fuel consumption and emissions (Barth et al., 2000). The ability for emissions maps to account for transient effects depends on circumstances under which measurements are made. Measurements taken while an engine is following a standard simulated cycle take into account transient effects of a generalised typical driving cycle. Such cycles are, however, unlikely to take into account real-world driving behaviours of stop-start traffic congestion, or aggressive acceleration behaviours quite common in South African driving conditions. These driving habits result in higher fuel consumption and emissions. They can be taken into account in models only by monitoring real-world driving patterns.

Both kinematic and engine-operating parameter models can be either aggregate or instantaneous. Aggregate models provide fuel consumption and emissions for broadly described conditions such as the morning commute, whereas instantaneous models provide fuel consumption and emissions continuously at short intervals throughout a journey.

Aggregate models are useful for calculating total emissions for a city, country or region.

Instantaneous models are useful to study emissions for an intersection or a specific section of road, or in comparing various scenarios of traffic interventions.

Three models are discussed below. An aggregate kinematic model, an instantaneous kinematic model and a model based on fuel consumption and emissions maps. Other models with similar structure and operating principles to the three examples given are briefly mentioned.

2.5.2. TÜV method

The TÜV method was developed for the first version of the HBEFA (Handbook of Emission factors) (Hassel et al., 1994). The method uses instantaneous emissions measurements taken during simulation of real-world driving cycles grouped into matrices of vehicle speed and (speed × acceleration). Table 2.1 provides an example of a speed and (speed × acceleration) matrix used in the TÜV method.

Fuel consumption and emission factors for any vehicle capacity class and emissions regulation and driving cycle can be estimated using a frequency matrix of the cycle (such as Figure 2.9) multiplied by appropriate fuel consumption and emissions matrices developed from dynamometer tests (Table 2.1).

Table 2.1: An example of a speed and (speed × acceleration) matrix used in the TÜV method.

Vehicle: open loop catalytic converter, capacity class: 1.4 – 2.0 ℓ Parameter: CO (g h^-1)

Figure 2.9: An example of a frequency plot used with emissions matrices to estimate fuel consumption and emissions for a driving cycle.

The fuel consumption and emissions are calculated by summing the product of values in the emissions matrix with corresponding values in the frequency matrix. This is represented in Equation 1: consumption factor for the speed interval (i) and the (speed × acceleration) interval (j) in the emissions matrices; Pi,j is the proportion (%) of time spent in the speed interval (i) and the (speed × acceleration) interval (j) for the cycle; and n and m are the number of speed and (speed × acceleration) intervals respectively.

The TÜV method is a descriptive model because it does not consider physical parameters such as vehicle size and mass, and road gradient that cause emissions, but describes the

correlation between kinematics and emissions. The method does not account for engine loads due to road gradient, auxiliary equipment and gear change strategies. These are compensated for by using supplementary dynamometer tests and by applying correction factors to base emissions results.

An advantage of this model is that only speed profiles are needed to estimate emissions for a model run, while data intensive inventories of vehicle fleet characteristics are not needed.

2.5.3. HBEFA method

Instantaneous emission factors from the TÜV method used in version 1.1 and 1.2 of the HBEFA were found to be unsuitable for vehicles of Euro-2 and higher emissions regulations due to large variances in resulting instantaneous emissions (de Haan and Keller, 2004b). This is because of the wide variety of different emissions control mechanisms used in newer vehicles.

An alternative method, which characterises fuel consumption and emissions according to driving patterns, and which represents driving situations and their corresponding frequency plots, defined in Section 1.7, was developed by de Haan and Keller (2004b). Fuel consumption and emission factors for a newly measured driving pattern are determined by finding the linear combination, which produces the closest match to the new pattern, using up to three existing patterns of known fuel consumption and emission factors out of an available set of 12 patterns defined by the EMPA real-world driving cycles.

The matching procedure involves finding the combination of predefined patterns with the same average speed and (speed × acceleration) values of the new pattern and which results in the smallest sum of differences squared value between the frequency plot of the combination of predefined patterns and the new pattern. The formula for calculating the sum of the difference squared is represented in Equation 2:

∑

where SDSl,m is the sum of the differences squared for patterns l (the new pattern) and m (the combination of predefined patterns); Tis the proportion of time spent in the interval (j,k); and n and p are the number of speed and (speed × acceleration) intervals in the frequency plots respectively.

Emission factors (EF) of the new pattern are estimated by adding the corresponding emission factors of the matching combination of driving patterns as in Equation 3:

EFnew pattern = X×EFpattern 1+Y×EFpattern 2+Z×EFpattern 3 (3)

where EFnew pattern is the emission factors for the new pattern; EFpattern 1, EFpattern 2, EFpattern 3

are emission factors for individual patterns in the combination of predefined patterns; and X, Y and Z are proportions of predefined patterns in the combination.

The advantages of this model are the same as for the TÜV method in that besides the fuel type and emissions regulations, properties of vehicles do not need to be known. A disadvantage of the method is that it is not able to account for road gradient, auxiliary equipment and driving style.

2.5.4. CMEM

The Comprehensive Modal Emissions Model (CMEM) (Barth et al., 2000) was developed at the College of Engineering-Centre for Environmental Research and Technology (CECERT) at the University of California-Riverside in collaboration with the Lawrence Berkeley National Laboratory at the University of Michigan under the National Cooperative Highway Research Program. CMEM is an instantaneous emissions model. It is deterministic in that it uses physical properties of vehicles and operating environments to estimate fuel consumption and emission factors. CMEM has been used in microscopic (at the scale of single streets) simulation of fuel consumption and emission factors from traffic (Tate et al., 2005).

The structure of the model is shown in Figure 2.10. The model depends on a power factor or fuel rate. Emission factors for any fuel rate, engine speed and air fuel ratio are determined during a calibration procedure using multiple dynamometer simulations for different combinations of vehicle type, engine technologies, exhaust controls and driving modes.

For a given set of operating variables (speed, acceleration, road gradient and driving mode) and vehicle properties, the model calculates the power demand and engine speed (from vehicle speed and a gearshift schedule) and relates this to a fuel rate. The emissions can then be determined by finding the fuel rate that corresponds to the vehicle type, engine technologies, exhaust controls and driving modes.

Figure 2.10: Structure of the Comprehensive Modal Emissions Model (CMEM) (Barth et al. 2000).

Advantages of the model are that it can take account of loads other than kinematic loads. In addition, the model differentiates between emissions out of the engine and emissions out of the exhaust. This allows the model to account for different combinations of engine technologies and exhaust after-treatment controls. Disadvantages of the model are that it requires detailed information about vehicles (including emissions control equipment) comprising the overall fleet, operating environments and driving cycles that are being simulated, which makes the model data intensive.

2.5.5. Other models

VESIM was developed by General Motors during the 1970s for estimating fuel consumption. The model was modified by request of California Air Resources Board (CARB) to include emission factors. The modified model was called VSIME and more a recent version of the model developed is referred to as VEHSIME (TRB, 2000). VESIM (Vehicle Emissions Simulation Model) uses emissions maps and vehicle characteristics to estimate emissions from any given driving cycle.

PHEM (Passenger car and Heavy duty vehicle Emissions Model) (Zallinger et al., 2005) is an instantaneous emissions model developed as part of the ARTEMIS project (Andre et al., 2006; Joumard et al., 2007) and uses engine speed and engine power emissions maps. The emissions maps were developed from dynamometer simulations of the CADC (Common ARTEMIS Driving Cycles) driving cycles simulated on a dynamometer. The

model is similar to the CMEM model but used a different set of driving cycles to calibrate the model.

The EMPA instantaneous emissions model is based on engine-operating state using engine speed and load (Ajtay et al., 2005). This model is similar to the PHEM model.

The US Department of Energy has developed a fuel consumption and emissions model called ADVISOR (Johnson et al. 2000) which simulates fuel consumption and emissions based on the path of engine operation through engine fuel consumption and emissions maps due to any given driving cycle. This model was specifically designed to consider hybrid vehicle performance.

Other instantaneous emissions models simulate fuel consumption and emissions based on temperature, pressure and chemical reactions occurring in the combustion chamber during any one cycle of engine revolution. These models typically simulate emissions in a time scales of the order of one ms. The NASA equilibrium program is a good example of such a model (Heywood, 1988; Gordon and McBride, 1994).

2.5.6. Summary of emissions simulation models

Emission simulation models that use only kinematics are not able to account directly for road gradient, auxiliary equipment use and gear change schemes. They use empirical correction factors to account for such loads. Models that use fuel consumption and emissions maps to estimate fuel consumption and emissions, are able to account for non-kinematic loads but require data for individual vehicles and for operating environments, and are thus data intensive.

From considering the above models, it is proposed here that the ideal model for developing emission factors for emissions inventories should be able to:

• account accurately for all loads on an engine due to real-world driving i.e. account for road gradient, auxiliary equipment use and driving styles;

• represent all driving conditions experienced during real-world driving;

• account for transient engine operation effects;

• balance data requirements with flexibility and accuracy; and

• consider availability of data and simplify data collection processes.