Factors That Affect EV Adoption in High and Low Daily Demand

This section only explains the factors that affect the EV adoption as a distribution vehicle for the company. Based on the analysis result of the scenarios, it can be concluded that the decision of EV adoptions for the company that wants to invest in its battery switching stations will depend on the oil price, weight demand, emission reduction targets, limited distance range of electric truck, EV’s emission, and battery price.

To increase EV adoption, oil price needs to have high increase due to overcoming the a high investment of BSS. It also shows that the weight demand can affect the decision of EV adoption.

The higher the demand, the more sensitive the system will be. It can be seen that the company that has higher demand will need less increase of oil price and emission tax price compares to the company with lower demand. It makes sense since the more demand the company has, the more vehicle they need and the more distance it will travel. Therefore the change of oil price and emission tax price will give more cost difference for the company that has longer travel distance.

The sensitivity of high demand in EV’s adoption due to oil price and emission tax price can be seen from Figure (16) and Figure (17).

Figure 16. Relation between Percentages of EV Adoption with Oil Price

14.29% 14.29% 14.29% 33.33%

9.15% 9.15% 9.15% 22.93%

14.49% 84.23% 100% 100%

6.58% 38.5% 47.5% 52.85%

$ 0 . 3 / K M $ 1 . 9 / K M $ 2 / K M $ 6 . 8 / K M

RELATION BETWEEN PERCETANGE EV ADOPTION WITH OIL PRICE EV adoption on low demand (%) emission reduction for low demand (%)

EV adoption on high demand (%) emission reduction for high demand (%)

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Figure 17. Relation between Percentages of EV Adoption with Emission Tax Price

Figure (16) shows that only when oil price reaches $6.8/km, which is 22 times of current oil price in the Netherlands, the system that has low daily demand increases EV adoption until it is 33.33%

EV and the system that has high daily demand will have 100% EV. While for high demand system EV adoption starts to increase when oil price reaches $1.9/km, which is 6.3 times of current oil price, and EV adoption reaches 100% when oil price is $2/km. This increase of EV adoption when oil price increase is due to the high energy cost that company needs to pay if company insist to use diesel trucks. This high diesel trucks’s energy cost overcome the high investment cost of BSS and makes the system choose to adopt EV to reduce energy cost. Based on this, it can be seen that there is a high difference of the oil price increase to make EV adoption change in low and high demand. The less increases needed for the oil price to increase EV adoption in high demand setting is understandable since the energy cost is the second highest cost fraction from the total cost, based on the Figure (15). Therefore the change of energy cost might give more effect compare to other cost factors.

Figure (17) explains that the increase of emission tax price will increase the adoption of EV. When tax price reaches $24/kg of CO2eq, which is 960 times of current emission tax price, the system that has low daily demand will start to adopt EV until it reaches 46.67%, while the high daily demand will shift all of its diesel vehicles to EV. The high daily demand system for this case will start to increase adopting EV when emission tax price is $6/kg CO2eq, which is 240 times of current emission tax price. The high emission cost if the company insists to use diesel trucks can overcome the high investment cost of BSS, which makes EV become better option. This result shows that to increase EV adoption, the carbon tax price needs to increase very high. Based on this, although the increase carbon tax price can increase EV adoption, the increase carbon tax price might not be a good strategy to increase EV adoption due to the effect of emission tax price will only be felt when emission price tax increase very high and this high increase is highly impossible for next decade.

Based on these two result of the increase in oil price and carbon emission tax, it can be seen that oil prince need less increase compare to carbon emission tax although both of the costs are the function of distance travelled. This is understandable since energy cost is the second highest emission fraction of the total cost, based on the Figure (17). Therefore the change of energy cost might give more effect compare to other cost factors.

14.29% 14.29% 14.29% 46.67%

9.15% 9.15% 9.15% 22.93%

14.49% 84.34% 100% 100%

6.58% 38.5% 47.5% 52.85%

$ 0 . 0 2 5 / K G C O 2 E Q $ 6 / K G C O 2 E Q $ 6 . 1 / K G C O 2 E Q $ 2 4 / K G C O 2 E Q

RELATION BETWEEN PERCETANGE EV ADOPTION WITH EMISSION TAX PRICE

EV adoption on low demand (%) emission reduction for low demand (%) EV adoption on high demand (%) emission reduction for high demand (%)

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The emission targets are the most powerful force for the company to increase EV adoption and reduce emission. Nevertheless, the company needs to commit fully to this target and provide a high budget allocation to invest BSS. The most effective factor to improve the adoption is the increase of range distance of electric truck because this factor can overcome the high investment cost for BSS directly. The effect of distance range of electric truck to EV adoption at the low daily demand and high daily demand system is explained in Figure (18).

Figure 18.Relation between Percentage of EV Adoption with Limited Range Distance

Figure (18) shows that when the limited distance range of electric truck to deliver demand in the Netherland is high enough to make EV does not need to visit BSS and makes the company does not need to build BSS will result in high EV’s adoption. When the company does not need to build BSS and provide extra battery for EV and have waiting cost at BSS, the company with low daily demand can adopt EV until it reaches 88.23% of its total vehicle, while the company with high daily demand can shift all of its diesel truck to EV. This result also indicated that electric vehicle is economically interesting to be adopted in the company. Nevertheless, its limited distance range and the needs of the company to provide its charging infrastructure eliminated its adoption potential.

This analysis is strengthened by the result of EV adoption when the company does not need to build its BSS, which means the battery switching stations are already provided. The result regarding EV adoption and emission reduction for this scenario is shown in Figure (19).

Figure 19. EV Adoption and BSS Investment

14.29% 88.23%

9.15% 42.46%

14.49% 100%

6.58% 52.36%

3 2 2 K M 4 7 0 K M

RELATION BETWEEN PERCETANGE EV ADOPTION WITH LIMITED RANGE DISTANCE

EV adoption on low demand (%) emission reduction for low demand (%) EV adoption on high demand (%) emission reduction for high demand (%)

14.29% 88.23%

9.15% 42.46%

14.49% 100%

6.58 52.36%

B S S I N V E S T M E N T N E E D E D N O B S S I N V E S T M E N T N E E D E D

RELATION BETWEEN PERCETANGE EV ADOPTION WITH BSS INVESTMENT

EV adoption on low demand (%) emission reduction for low demand (%) EV adoption on high demand (%) emission reduction for high demand (%)

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The EV adoption percentage of this scenario is same with the scenario that has EV with increase limited range distance until it reaches 470 km. The difference is there is waiting cost at EV adoption scenario that does not require the company to build its BSS. Nevertheless, the waiting cost only has 0.05% fraction of the total cost if the BSS with the same system of this project is built. Based on this result, it is clear that the support from government to build EV’s charging infrastructure is important to increase EV’s adoption for the company. Else, the logistics companies might try to collaborate to build BSS together.

EV’s emission indicates that in the system with the company wants to invest in its BSS, the reduction of EV’s emission will reduce the adoption of EV in the company. This is because the company will only focus to fulfill its emission reduction target. It means if the electric truck has lower EV’s emission, the company will only need to buy less EV’s truck. This action is due to the system want to avoid to build more BSS, because the more electric truck in the system will lead to more BSS needed to build. This condition is shown in Figure (20).

Figure 20. Relation between Percentage of EV Adoption with EV's Emission

7.14% 14.29% 14.29%

6.08% 9.15% 9.15%11.59% 13% 14.49%

7.19% 6.19% 6.58%

0 . 0 7 K G C O 2 E Q / K M 0 . 1 5 K G C O 2 E Q / K M 0 . 1 7 K G C O 2 E Q / K M

EV ADOPTION BETWEEN PERCETANGE EV ADOPTION WITH EV'S EMISSION

EV adoption on low demand (%) emission reduction for low demand (%) EV adoption on high demand (%) emission reduction for high demand (%)

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Right now, the Netherland uses 35% renewable energy sources to generate electricity, and this condition leads electric truck to omit 0.17 kg CO2eq/km (1CM170GreenLogistics, 2017). When the Netherlands uses more renewable energy resources to generate electricity, the electric truck emission will reduce. Figure (14) shows the comparison of EV’s adoption when the electric truck has an emission of 0.17 kg CO2eq/km and when it has an emission of 0.15 kg CO2eq/km and 0.07 kg CO2eq/km. Both of systems that have low daily demand and high daily demand has EV’s adoption reduction when electric vehicle omits less emission.

Based on the Figure (21) that shows timely plan and prediction of battery price, the scenario based on that figure is generated. The battery cost reduces until it reaches $125/kWh in year 5 and $100/kWh in year 8 of the planning horizon. The result from this scenario shows that the reduction of battery price until it reaches $100/kWh that leads to lower battery purchase cost and holding cost, which is assumed to be 10% of battery purchase price, can not increase EV adoption due to high investment cost of BSS. Nevertheless, when the company does not need to provide its BSS, the reduction of battery price in this scenario can effectively increase EV adoption since it can cut down the BSS investment and battery safety stock at BSS costs. This result is described in Figure (21). When the company does not need to build and manage BSS, the company can adopt EV until it reaches 100% for both low and high demand.

Figure 21. Battery Price Reduction and EV Adoption

This result shows that battery price reduction until it reaches $100/kWh should attract the company to adopt EV more effectively than the increasing of limited driving distance that is shown in Figure (21), but only when the company does not need to build and manage its BSS. From Figure (21), it can be seen the increase of limited driving distance in encourages company with low daily demand only until it reaches 88.23%, not 100%.