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Each sample vehicle was fitted with a set of OBD and GPS loggers for a period of approximately two weeks. Vehicles were driven by their owners, as they normally would be during this period. The only requirement was that the OBD sockets in the vehicles were operational. This was tested by briefly inserting the data logger into the OBDII port of the sample vehicle, starting the vehicle and letting it idle for a few minutes and then downloading the reports from the data logger onto a computer. The report indicated whether the data logger was compatible with the specific vehicle.

No special instructions were given to the drivers in order to avoid changes in driving behaviour. In some cases, however, it was necessary to ask the drivers to plug the GPS unit into and out of the cigarette lighter power socket at the beginning and end of trips as the power socket of some vehicles remained on when the ignition was off. If the power socket remained on while the vehicle was parked, the battery in the GPS data logger would discharge. The equipment required no other human interaction from the volunteers.

The GPS units recorded position and time at one-second intervals. The OBD units recorded time and speed at one-second intervals and engine-operating parameters at five-second intervals. The OBD data loggers also logged information about each trip including start time, duration and distance. For the purposes of this study a trip was defined as the period between when an engine was started and when it was turned off.

Data processing

Engine-operating data from the OBD data logger and location data from the GPS logger were downloaded after each sampling period using the manufacturers’ software. The data were then manually copied into Microsoft Excel files where they were formatted and screened for missing values and outliers (caused for example by the GPS losing contact with the satellite when passing under a bridge or entering an underground parking). After the entire set of vehicles had been sampled, individual Excel files were combined into a single Microsoft Access^® database. The data flow from the survey to the database is shown in Figure 3.8.

GPS data points were allocated to road types by matching their coordinates to the closest road element in the Johannesburg GIS (Geographical Information System) road database.

The OBD and GPS data were correlated to each other based on the date, time and unique vehicle identifier within the Access^® database. The data compilation is represented in Figure 3.9.

During the survey, engine speed was logged in revolutions per minute and the engine load was logged as a percentage of maximum engine load for any given engine speed. The maximum engine load is defined in the OBDII standard (ISO 15031/SAE J1979) as the percentage of maximum volumetric efficiency for petrol vehicles and the percentage of maximum fuel flow rate for diesel vehicles. This is equivalent to the percentage of the maximum torque curve of an engine. The measured rpm and engine load values were

converted to mean effective pressure and mean piston speed using the torque curves and engine strokes for the vehicles in the sample (obtained from Car Magazine). This was done so that the data were in the correct units and dimensions to be used in the fuel consumption and emissions simulation model.

Figure 3.8: Data flow of vehicle performance survey data.

Figure 3.9: Merging of the GPS, GIS and OBD datasets within the Access^® database.

Two problems arose during the survey. The OBD data loggers had time drift that resulted in time loss (or gain depending on the specific data logger) in the order of three seconds per day and the GPS sensors were not accurate enough to differentiate between roads that are close to and running parallel to each other. The time drift was compensated for by correlating the speed from the OBD data logger to the speed measured by the GPS. This was visually done by comparing the OBD and GPS speed profiles, one trip at a time, and adding a correction factor to the time from the OBD data. The second problem was rare and only occurred when there were service roads next to a freeway or at intersections. This problem was addressed by considering the data points before and after the unresolved condition occurred.

3.4.6. Analysis

Data from the survey were analysed to determine (i) how driving conditions influence engine operation and (ii) vehicle usage profiles. This was done by aggregating the data using five dimensions: day of the week (either week or weekend), period of the day, road type, vehicle fuel type and engine capacity class.

Driving conditions and travel behaviour

Driving conditions are determined by travel behaviour and number of vehicles using the road network at the same time. Travel behaviour was determined by considering the distribution of distance travelled and time spent travelling by the sample of vehicles by time of day, day of the week and road type. Driving conditions were determined by calculating average vehicle speeds, acceleration and number of stops by time of day, day of the week and road type. Hourly intervals were used for analysis of driving conditions and travel behaviour. For development of local engine-operating patterns, driving conditions and travel behaviour were aggregated into longer time intervals of several hours e.g.

morning or evening rush hours.

Relationships between the parameters were explored using Microsoft Excel pivot tables linked to the Access^® database of compiled data (summarised in Figure 3.9).

Survey data were used also to determine vehicle kilometres travelled per year for vehicles of different fuel types and capacity classes. Travel behaviour was determined by calculating average distances travelled per year for each fuel type and capacity class using

trip data collected during the survey. Once again, a combination of Microsoft Excel pivot tables linked to the Access^® database was used.

Developing local engine-operating patterns

Measured engine speeds and engine loads from the survey were binned into speed and load intervals to produce engine-operating patterns for each vehicle, day of week, period of day and road type. The binning process was the same as described in Section 3.3.4 for the development of the base engine-operating patterns for the fuel consumption and emissions simulation model. The resulting patterns were then aggregated further by fuel types and capacity classes. This ensured an equal weighting of each vehicle in the sample irrespective of the number of hours each vehicle was monitored.

Aggregate engine-operating patterns were produced for six intervals of the day: 06:30 – 09:00 (morning commute), 09:00 – 12:00 (mid morning), 12:00 – 14:00 (lunch time), 14:00 – 16:00 (mid afternoon), 16:00 – 18:30 (evening commute period) and other (all other periods i.e. 18:30 – 06:30) instead of using one hour intervals used to determine the driving conditions. This was necessary because the lower sampling frequency of the engine-operating parameters (every 5 seconds) compared to vehicle speed used to determine overall driving conditions (every second), and the separation of the engine operation data into vehicle fuel types and capacity classes reduced the quantity of data available for each grouping of dimensions.

The dimensions and intervals used to aggregate the engine-operating patterns are summarised in Table 3.5. There are 216 possible combinations of engine-operating patterns from the table i.e. 3 road types × 6 periods of the day × 2 days of the week × 2 fuel

× 3 capacity classes.

Table 3.5: Dimensions and intervals used to aggregate the engine-operating patterns.

Operating environment dimensions Vehicle dimensions Road types Periods of day Period description Day of week Fuel Capacity class (ℓ)

Freeway

3.4.7. Limitations

The number of vehicles sampled limits the certainty of the conclusions drawn from the survey. In the case of diesel vehicles only one < 1.4 ℓ capacity class vehicle was sampled and two of each 1.4 – 2.0 ℓ and > 2.0 ℓ capacity class were sampled, which is too few to make any reliable conclusions about these vehicles. As the main objective of this study is to demonstrate a process rather than produce a complete vehicle emissions inventory, the sample is viewed as sufficient to demonstrate typical engine operation for diesel vehicles, and to indicate their differences from petrol engines. The small sample does not reduce the accuracy of the emissions simulation model but influences how representative the sampled data are with respect to actual driving behaviour.

The equipment was convenient to use due to its size and ease of installing and removing it from a vehicle. Extracting and processing the data, however, was more complicated. For each trip, the data from the OBD logger was manually copied and formatted in Microsoft Excel before it could be imported into a database and coupled to the GPS data. This was a limitation in the manufacturer’s software.

The memory capacity of the OBD and GPS data loggers limited the survey period to approximately 25 hours of driving. This allows for about two and a half weeks of normal vehicle use before the equipment needed to be removed to have the data extracted.

Unfortunately, due to the design of the equipment, if this period is exceeded, there is data loss. The OBD overwrites the oldest data, whereas the GPS logger protects existing data and rejects new data. This was avoided by sampling each vehicle for only two weeks.