Results

From the analysis of the collected data from the bus operator, bus manufacturers, experimental measurements and EcoGest results it was possible to calculate the evolution of fuel consumption and greenhouse gases emissions with slope[16] in a normal operation condition and in acceleration and deceleration for different vehicle gross weights.

Fig. 4 shows the cumulative distribution of slope for the entire HF network.

image188

Fig. 4. Cumulative distribution of slope for the entire HF network.

Based on this distribution classes of slope were defined, they are presented in Table 2.

Fig. 5 presents for different weights, the evolution of C02 emissions, during rolling (C02 emissions during acceleration from stops and idle in stops are not included in rolling), along slope variation.

From these results is possible to conclude that C02 emissions for negative slopes is not very diverse for each weight. While for positive slopes those emissions are quite higher for the weightier vehicles, particularly in hilly conditions.

Figs. 6 and 7 present the evolution of C02 emissions in acceleration and deceleration from stops, in opposition to gross vehicle weight.

Class of Slope

Slopes

-5

Lower than -12%

-4

[-8%;-12%]

-3

[-5%;-8%]

-2

[-2%;-5%]

-1

[0%;-2%]

1

[0%;2%]

2

[2%;5%]

3

[5%;8%]

4

[8%;12%]

5

Higher than 12%

Table 2. Slope classification.

In Figs. 5-7 the most significant results are presented.

Подпись: -5 -3 -1 1 3 5 Slope Class E

JC

О

Є

Jt

<0

c

о

‘to

<0

E

0)

О

U

* 11.5 ton

» 8 ton

■ 10 ton

« 12 ton

♦ 16 ton

* Poly. {11.5 ton)

‘"“■’"Poly. (8 ton)

•“•Poly. (10 ton)

Poly. (12 ton)

—~Poly. (16 ton)

Fig. 5. Rolling C02 emissions versus class of slope for different gross vehicle’s weights.

Results

Fig. 6. CO2 emissions in acceleration from stop versus gross vehicle weight.

image192

These C02 emissions evolutions were then used in TRANSTEP in order to determine disaggregated emissions for all the fleet. The global results are presented in Fig. 8.

After characterizing the actual C02 emissions, it was possible to begin the sensitive analysis, in order to propose measures for improving energy efficiency and therefore reduce the emission of greenhouse gases.

These measures essentially focus on three levels of possible action the network, the vehicle and the motorist.

Therefore, the motorist effect on C02 emissions was one of the evaluated factors. In order to do it, the velocity profile measured during the experimental data collection was modified. The accelerations were softened according to a factor[17]. In Fig. 9, the C02 emissions evolution according to the acceleration factor for two HF network lines, line 21 and line 24, is presented. Line 21 is quite hilly and line 24 has soft slopes, that is why soften accelerations implies higher C02 emissions reduction in line 24. In line 21 the driving behaviour can not be submitted to significant changes. The C02 emissions reduction related to each acceleration factor are presented in Table 3.

image193

Acceleration Factor

I ♦ Line 21 Я Line 24 “—Poly. (Line 21) —Poly. (Line 24)

Fig. 9. CO2 emissions versus acceleration factor.

Table 3. CO2 emissions reduction for different acceleration factors.

Acceleration Factor

Line

1.3

1.2

1.1

1

0.9

0.8

0.7

0.5

21

C02 emissions (kg/100km)

a)

a)

a)

199.2

197.4

195.7

195.3

200.7

Emissions reduction (%)

a)

a)

a)

-0.9%

-1.7%

-1.9%

0.8%

24

C02 emissions (kg/100km)

145.0

140.6

135.6

131.4

125.8

123.1

120.4

115.6

Emissions reduction (%)

10.3%

7.0%

3.2%

-4.3%

-6.4%

-8.4%

-12.1%

a) For line 21 was not considered acceleration factors above 1, given that in almost all cases load is already maximum.

Despite the fact softer accelerations represent lower C02 emissions, it also implies longer travel time, as presented in Fig. 10, nevertheless, this extended travel time is not significant for line 24, because it only adds about five minutes to normal travel time, what can be easily compensated with less time in terminal bus stops (which also leads to less time on idle in terminal bus stops).

image194

Fig. 10. Travel time versus acceleration factor.

It was also evaluated the effect of vehicle’s gross weight reduction, minimization of idle time at stops and improvement of traffic conditions. Table 4 shows, for the case study, the energetic achievements and greenhouse gases emissions reduction associated to these measures.

Table 4. Annual energetic achievements and greenhouse gases emissions reduction associated to each measure.

Annual Final Energy £ Rc

Savings and C02 Emissions tduction

Measures

Diesel (m3)

Primary Energy (toe)

C02 Emissions (ton)

Emissions Reduction (%)

1 – Vehicles curb weight reduction

76.3

66.4

204.5

1.65%

2 – Reduction of idle time

94.2

82.3

252.5

2.03%

3 – Ecodriving

76.3

66.6

204.5

1.65%

4 – Better traffic conditions

55.7

48.7

149.3

1.20%

Consumption Reduction

302.5

264.1

C02 Emissions Reduction

810.7

6.53%

For reducing the vehicle’s gross weight it was suggested a plan for the fleet renewal were the vehicles should be 10% lighter[18], [5-6]. In three years it would be possible to achieve a 3% weight reduction on the fleet, by renewing three vehicles. This way the annual C02 emissions could be reduced in 1.65%. This measure also reduces fleet age which also has advantages in what concerns pollutant emissions.

Regarding idle time at terminal stops it was noted that it was responsible for 3% of the annual fuel consumption and C02 emissions. For solving this problem it was suggested that during motorist’s training, they should be taught in order to achieve idle of top 60 seconds. This measure can accomplish a 2% reduction of annual C02 emissions.

In what concerns Ecodriving it is possible to achieve almost 2% reduction of annual C02 emissions. This measure also involves motorist training in order to reduce speedy driving.

Better traffic conditions, implies reducing traffic and light stops. This measure is perhaps the most difficult one when it comes to implementation. Although in the case study presented it is possible to achieve a reduction of 50% of traffic stops just by controlling parking stops and by giving priority to buses in some urban streets. This measure can achieve 1.2% reduction on annual C02 emissions.

All these measures promote lower pollutant and greenhouse gases emissions, lower fuel consumptions and lower operational and maintenance costs.

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