Using on-board logging devices to study the longterm ...

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We have evaluated the long-term impact of an eco-driving training course by monitoring ... To obtain data in a long-term driving monitoring experiment without.
Using on-board logging devices to study the long-term impact of an eco-driving course

Beusen Barta,*, Broekx Stevena, Denys Tobiasa, Beckx Caroliena,b, Degraeuwe Barta , Gijsbers Maartena, Scheepers Kristofa, Govaerts Leena, Torfs Rudia, Int Panis Luca, b

a

Flemish Institute of Technological Research (VITO), Boeretang 200, 2400 Mol, Belgium

b

Transportation Research Institute, Hasselt University, Wetenschapspark 5 bus 6, 3590

Diepenbeek, Belgium

*Corresponding author:

Bart Beusen: Flemish Institute of Technological Research (VITO) Address: Boeretang 200, 2400 Mol (Belgium) e-mail address: [email protected] Tel. +32 14 30 82 79 Fax +32 14 32 11 85

Abstract We have evaluated the long-term impact of an eco-driving training course by monitoring driving behavior and fuel consumption for several months before and after the course. Passenger cars were equipped with an on-board logging device that logged the position and speed of the vehicle by means of a GPS tracking system as well as real time electronic engine data extracted from the Controller Area Network (CAN). The CAN data included information on mileage, number of revolutions per minute, position of the accelerator pedal and instantaneous fuel consumption. Data gathered over a period of 8 to 10 months for 10 different drivers during real-life conditions enabled an individual drive style analysis. The average fuel consumption for all drivers 4 months after the course was reduced by 5.8%. Most drivers showed an immediate improvement in fuel consumption which was steady over time. On the other hand some drivers tend to fall back into their original driving habits for one or more aspects of driving behavior that were treated in the eco-driving course.

Keywords: Fuel-efficient driving, Eco-driving, on-board logging device, Controller Area Network

1

Introduction

CO2 emissions from vehicles in general and particularly from road transport are an important fraction of total greenhouse gas emissions in most developed countries and are often projected to rise further in the future. Among the policy options to reduce CO2 emissions from transport that have been studied eco-driving has always featured high among the choices with a great reduction potential. Reducing fuel consumption significantly by teaching drivers how to change their driving behavior is potentially a very cost-efficient manner to reduce energy use and emissions (IEA, 2005). A lot of studies report the short term impact of eco-driving on fuel consumption. ECMT/IEA (2005) decided on an average estimate of 5% reduction for all OECD regions based on an expert analysis of available literature. Few studies report on the long-term impacts of fuel-efficient driving courses. Wahlberg (2007) monitored fuel consumption reduction in busses and recorded 2% fuel savings during a 12 month period after training. Zarkadoula et al. (2007) mention fuel savings on busses of 4.35% during a post training monitoring period of 2 months. Both studies report on the fact that, after a certain time period, drivers (partially) slip back into a less environmentally friendly driving habit resulting in less fuel savings than originally attributed to the eco-driving course. The aim of this paper is to present the results of a long-term passenger car monitoring campaign that recorded driving patterns and fuel consumption from people participating in an eco-driving course. A panel of drivers was followed up during 8 to 10 months to analyze the impact on fuel consumption and on different driving parameters.

2

Methodology

To analyze the impact of an eco-driving course on fuel consumption and driving behavior on the long run, we developed a data logging device to monitor people’s driving behavior accurately. In this section we will first describe this data collection device and then discuss the set-up of the panel survey and the eco-driving course.

2.1

On-board data collection device

Tracking the amount of fuel bought by participants over a long period of time introduces unwanted effects into an eco-driving experiment. First the burden on the participants to record the details of every fuel purchase is high resulting in drop outs. Secondly bias may be introduced by directly confronting participants with their fuel use long before the ecodriving course. To obtain data in a long-term driving monitoring experiment without affecting the burden on its participants or introducing bias, we have used an on-board device to monitor and log the necessary driving parameters. The on-board device is equipped with a memory card, a GPRS-modem, a GPS tracking system and is connected to the Controller Area Network (CAN) of the vehicle. This configuration allows monitoring and logging of 2 types of data: the position and speed of the vehicle by means of the GPS tracking system and electronic engine data extracted from the CAN-bus, which includes data on mileage, number of revolutions per minute (RPM), position of the accelerator pedal, gear selection, instantaneous fuel consumption and engine coolant temperature. The logging device is small (approximately 10 by 10 cm) and was installed out of sight of the driver. Based on the decoding of the CAN messages, the device was programmed to log the required CAN parameters for a particular car. The logging device did not interfere with the engine management system. Data were read from the CAN, stored on the internal memory card of the on-board logger and transmitted to a central server via the GPRS-modem on a daily basis. Drivers could consult their recorded positions on a website, but not the data on their driving behavior or fuel consumption. By no means was any information on their driving behavior fed back to the drivers during the project, in order not to influence their driving behavior during the monitoring period. Drivers were requested to add additional information on a trip-by-trip basis concerning travel motives, the number of passengers and the actual driver of the vehicle. Especially this last feature is important in this study as mostly multiple drivers could use the same vehicle and the actual driver for a particular trip was not always the same person that participated in the eco-driving course. Based on the trip declarations of actual drivers this effect was filtered out. An analysis of fuel consumption and driving behaviour as a function of trip motives and locations will be presented in a forthcoming paper.

2.2

Experimental design

2.2.1 Participants and vehicle selection Participants for the experiment were solicited both internally at VITO and externally using an announcement in a car magazine (‘autogids’). In order to be eligible for the monitoring campaign, the candidates had to be the owner of the vehicle, the vehicle had to be of a model year later than 2001 (older vehicles generally do not have a CAN-bus) and had to be at least 2 years old at the start of the experiment (because of warranty issues). In total, 30 vehicles were equipped with the on board logging device as described earlier. All of these vehicles were registered in Belgium and predominantly used in the northern or Flemish speaking part of the country. The information available on the CAN-bus differs between vehicles from different manufacturers, or even between different models from the same manufacturer. As information on data protocols is not publicly available, signals had to be analyzed using a case by case testing approach to find out how different parameters are transmitted. Not all parameters were available for all cars to analyze because they were not found within the data stream or were simply not present on the CAN. Fuel consumption, the critical parameter within this trial, was registered for 19 vehicles. Throttle and RPM were registered for 19 and 21 vehicles respectively. Further, for some cars an insufficient amount of data was logged or too little trips were declared by the driver (for 2 months before and after the course at least 100 km per month had to be declared by the driver taking the course). 2 drivers did not take the driving course. Due to all these issues, of the 30 cars equipped with the monitoring device, only 10 could be used for further analysis in this paper. This sample size is too small to learn lessons on the general impact of eco-driving. However, as results will show, the sample size is large enough to indicate significant differences between individual drivers and how drivers respond in time to an eco-driving course. Details on the 10 vehicles used for this analysis are shown in Table 1. (Insert Table 1 here) 2.2.2 Eco-driving course Approximately halfway the project, the participants were given a 4 hour course on fuel efficient driving (see Table 1 for the exact dates). This course consisted of a drive with a test

vehicle prior to the course, a session on the rules of fuel-efficient driving and a second drive with the same test vehicle and with guidance of an instructor The main rules of fuel efficient driving (or e-positive driving (www.e-positief.be) as it was called), can be summarized as follows: 1. Shift up as soon as possible (Shift up between 2000 and 2500 revolutions/minute) 2. At steady speeds use the highest gear possible and drive with low engine RPM 3. Try to maintain a steady speed by anticipating traffic flow 4. Decelerate smoothly by releasing the accelerator in time while leaving the car in gear (this is called “coasting”). Further, some additional driving style instructions were provided at the course: 5. Shut down the engine for longer stops, e.g. before a level crossing or when you pick somebody up. 6. Do not drive faster than 120 km.h-1 (which is the legal speed limit on motorways in Belgium) Vehicle data were logged to study the impact of this training course on fuel consumption and to check how well the different rules stated above were observed by the participants. 2.3

Data analysis

Over a 10 month period we collected 116.355 km and 2.026 hours of data for these 10 private vehicles. Based on this we calculated 10 relevant driving parameters. The selection of these parameters was performed based on their relevance towards eco-driving. Table 2 presents an overview of the selected parameters, their units and the corresponding abbreviations used in this paper. Two trip related parameters, the total distance and the average speed, were also analysed to evaluate whether the travel pattern had changed significantly over time. All the parameters were calculated on a weekly basis. (Insert Table 2 here) 3 3.1

Results General effect of the eco-driving course

To get an overall picture of the effect of the course a 3-way ANOVA was performed. The three factors included in this statistical analysis are the driver (1-10), the course (before or after the course) and the week. Table 3 shows the p-values of the effects and interactions of the three factors for the different parameters. From this analysis of the dataset as a whole, we can conclude that: -

Neither week nor course have a significant effect on the total weekly distance and the weekly average speed. This means that the drivers did not change their travel patterns significantly. Differences can therefore be attributed to changes in driving patterns rather than changes in travel patterns.

-

The course has a significant effect on most driving parameters, except for average shifting point and time idling.

-

The course has a significant effect on the fuel consumption of the population as a whole.

-

The driver-course interaction has a significant effect on most behavioral parameters except for the time idling. This indicates that the size of the effect of the course depends on the driver.

-

The week does not have a significant effect on fuel consumption. However the interaction driver-week has a significant effect which indicates that the change of the effect over time also depends on the driver.

(Insert Table 3 here) Previous work from Vlassenroot et al. (2006) suggests that overall statistics for the population as a whole provide insufficient information to assess the impact of Intelligent Speed Adaptation on driving behaviour. Opposite individual impacts were averaged out if only statistics for the total population were considered. The fact that within this experiment the interactions of the factor course with the factor driver are very significant for most driving parameters, suggests that the impact of the eco-driving course differs greatly between drivers. Analyses on the impact of the eco-driving course on fuel consumption and driving parameters are therefore performed per driver in the next section. 3.2

Individual effect of the eco-driving course

Table 4 presents the results from a t-test on the selected parameters to analyze the differences before and after the eco-driving course for each driver. The effects, values and confidence intervals of the effects are listed. The change in fuel consumption is expressed as a relative change (%) of the average fuel consumption (l/100km) compared with the observation before the course. (Insert Table 4 here)

From Table 4 we can see that the trip parameters (average distance and average speed) did not change significantly between the trips before and after the course, indicating that, on average, the trips before the course were similar to the trips after the course. This confirms the result from the 3-way ANOVA. Average fuel consumption over all drivers decreased by approximately 6%. Per driver, these changes varied between -12% and +3%. Fuel consumption decreased significantly after the course for 7 out of 10 drivers. The differences in fuel consumption are also reflected in a changing driving behaviour. Parameters showing significant improvement for at least 7 out of 10 drivers are average shifting point, distance coasting and distance driven with low rpm. The shifting point decreased on average for all drivers with 97 rpm, with extreme values around 200 to 500 rpm. The percentage of distance coasting increased on average with 1.16% and the percentage of time driven at low rpm increases on average with 12.2%. Drivers 2 and 4 who did not achieve fuel savings after the course, also show significant improvement in the time driven at low rpm. The average shifting point did not change significantly for these two drivers indicating the importance of this parameter in achieving fuel savings. The percentage of time during heavy acceleration or deceleration decreases by 0.26% and 0.22% respectively. These changes may seem small. However these kind of events occur less frequently compared to coasting. The significant impact demonstrates that people did change their acceleration and deceleration behaviour in most cases. No significant impacts were observed in the time idling. In general the time idling decreases but this impact is significant in only 2 cases. The amount of longer stops justifying engine shutdowns is probably very limited. The fact that idling decreases is also not only due to

increased shutdown of engines during long stops. People showing increased coasting distance will also spent less time standing still at crossroads and traffic lights. The percentage of distance driven at speeds higher than 120 km/h decreases on average with 6.3 %. This decrease was significant for 3 drivers only. 3.3

Individual effects over time

We analyzed the different parameters on a weekly basis to check for a possible learning or fading effect. Figures 1 and 2 show the impact for the individual drivers over time for the parameters fuel consumption and shifting point. Regression lines were fitted before and after the course to indicate trends over time. Table 5 shows the number of drivers for which the slopes of these regression lines before and after the course decreased or increased significantly (p 0)

Percentage time heavy

%

Time_acc

% time driven at accelerations > 1.5m.s2

3

%

Time_dec

% time driven at decelerations > 2.5m.s2

3&4

%

Time_idl

% time standing still (v < 3 km.h-1) with the

5

acceleration Percentage time heavy deceleration Percentage time idling

engine running (idling) Percentage distance in

%

Time_rpm

optimal rpm

% distance covered with engine speed

2&3

between 1100 and 1700 rpm (optimal engine speed for steady speeds)

Percentage distance at more than 120 km.h

-1

%

Dist_120

% distance covered at speeds higher than -1

120 km.h

6

Table 3: Significance -values of the effects and 2-factor interactions of a 3-way ANOVA. Parameter

course

driver

week

course*driver course*week driver*week

Tot_dist

***

***

Avg_speed

***

**

Avg_fc

***

Avg_sp

***

**

***

*** ***

***

***

***

**

***

Dist_coast

**

***

Time_acc

***

***

Time_dec

***

***

Time_rpm

***

***

Dist_120

***

***

**

**

**

Time_idl *

* = p