This chapter explains data that is used for this thesis project and the scenarios that will be used in the mathematical model to get the insight.
4.1. Data
This part explains the data needed and its value as the input for the mathematical model. The source and of the data also explained. The data for this project can be seen in the Table (3). This thesis project will compare two different vehicle types; those are Isuzu NPR as diesel truck and a defender vehicle and Navistar-eStar as electric truck and as a challenger vehicle for vehicle replacement model.
Table 3. Input Data for The Project
Data name Data Information and source
diesel truck Isuzu NPR
electric truck
Navistar-eStar
diesel truck purchasing price ($) 50,000 based on Isuzucv (2017) electric truck purchasing price
without its battery($) 129,000
based on USDepartementOfEnergy (2017) diesel truck maximum capacity (kg) 3,000 based on Isuzucv (2017)
electric truck maximum capacity
(kg) 2,300
based on USDepartementOfEnergy (2017) maximum age of vehicle (years) 10 based on Feng and Figliozzi (2012) Discount rate in Netherlands 0.01 the discount rate in the Netherlands
(Mecometer.com, 2017)
vehicle speed (km/h) 50
the assumption made for the BSS system based on chapter 2 and generated based on the Netherlands's driving regulation and average speed of truck on the report made by Walkowich (2017)
working hours/day 9.33
approximation of daily working hours based on the driving regulations in Europe (RoadSafetyAuthority, 2017)
the salvage value of battery ($) 357 the approximation of salvaged value battery based on the battery's weight (scrapsalesusa.com, 2017)
30
service level 0.95 the assumption made for the BSS system based on
chapter 2
effective swapping time (hours) 0.083
the assumption of effective swapping time based on the swapping information that BSS needs three until ten minutes to get electric vehicle can have a fully charged battery (Liu,2012; Avci, Girotra, and Netessine,2015)
Maximum utilization at swapping
station 0.67 the assumption made for the BSS system based on
chapter 2 battery switching station
investment($) 500,000 investment planning cost for battery switching station based on Tesla (Electrek, Electrek.co, 2017) annualized battery switching station
investment ($) 33,972 Based on the calculation from the formula of annualized investment cost in chapter 2 maintenance price for station/year
($/year) 3,397 assumption for maintenance cost of BSS is 10%
from annualized BSS investment
waiting price ($/hr) 17.2 waiting price is based on the salary of truck driver per hours in Netherlands (salaryexpert.com, 2017)
energy price for diesel/km ($/km) 0.3
Based on the gasoline needed by diesel truck, for this case is Isuzu NPR, to power truck and gasoline price in Netherlands. Isuzu NPR can work 5.55km for 1 liter gasoline (fuelly.com, 2017) and the average gasoline price in Netherlands for $1.7/liter for this year (globalpetrolprices.com, 2017)
electricity price($/kWh) 0.25 electric price for charging ($/kWh) based on Netherlands standards (ev-box.com, 2017) electricity/km for e-Star (kWh/km) 0.5 electricity rate is based on the e-Start standard
(Feng & Figliozzi, 2012)
battery price ($) 20,000 approximation of battery cost based on the current battery cost information (electreck.co, 2017)
battery maximum age (years) 7
battery maximum age for an electric vehicle is based on the Tesla's waranty of electric vehicle's battery (cleantechnica.com, 2017)
battery power (kWh) 80
battery power is differed based on the electric vehicle's characteristic, and for this case, the battery power is based on the e-Star's battery
characteristics (USDepartementOfEnergy, 2017) battery holding price ($/year) 2,000 the assumption made that battery holding cost if
10% of its purchasing cost
max distance range(km) 322 assumption from chapter 2
emission/km for diesel truck (kg
CO2eq/km) 0.5
based on the Netherlands standards for well to wheel emission type
(1cm170greenlogistics.wixsite.com, 2017)
31 emission/km for e-truck (kg
CO2eq/km) 0.17
Based on the Netherlands standards for well to wheel emission type while considering the energy resource to generate electricity. The standard emission can change based on the energy resource used. Right now, Netherlands use 35% of renewable energy resource to generate electricity
(1cm170greenlogistics.wixsite.com, 2017) emission price ($/kg CO2 eq) 0.025 emission tax price based on the carbon tax in
Europe (carbontax.org, 2017)
4.1. Scenarios
Several scenarios are generated to understand the effect of several parameters in the EV adoption. The scenarios can be differentiated into two sections. Sections one is focusing the relation of parameters in the EV adoption in the company that has low daily demands, which is 40,000 kg with planning period of 16 years. The second section is focusing the effect of parameters in the EV adoption in the company that has high daily demand, which is 200,000 kg with planning period of 16 years with the starting year of year 0. The scenarios are all run for a company that has emission reduction targets.
The scenario plan for this thesis project can be seen in the Figure (6). The experiment is done in two section, which are in low-daily demand and high-daily demand. To reduce experiment time, for the section two, which has high daily demand input, this section has four emission reduction strategies but those are described only in percentage emission reduction. The schema of this scenario plan is shown in the Figure (6).This scenario plan is used to see the difference of describing the emission in percentage and numerical target, to understand the pattern of EV emission adoption for each emission reduction strategy, and its difference.
Figure 6. Scenario Plan
The emission reduction target strategy is explained in the Table (4). The emission reduction target can be explained based on the percentage of emission reduction want to be achieved or based on the number of maximum allowable emission generated (kg CO2eq) like what is explained at equation (39).
Table 4. Emission Reduction Target Stratecy
Strategy 1 Strategy 2 Strategy 3 Strategy 4
32
To get an insight on the factors that affect EV adoption, the factors that are considered can affect the decision of the company to adopt EV are changed in the several scenarios. Those factors are vehicle capacity, demand change, oil price, a limited driving distance of electric truck, emission of the electric truck, carbon tax price, and battery price. The schema for this experiment plan is shown in Figure (7). Nevertheless, to save time, these scenarios are only run in the low-daily demand with emission reduction strategy one described in percentage and the high-daily demand with emission reduction strategy one described in percentage. The other aim of this experiment part is to see the effect of high daily demand to EV’s adoption compares to a system that has low daily demand.
Figure 7. Scenario Plan to Investigate Factors That Affect EV Adoption