E-Book, Englisch, 126 Seiten
Zerfaß Learning Rates of Electric Vehicles
1. Auflage 2017
ISBN: 978-3-96067-677-5
Verlag: Diplomica Verlag
Format: PDF
Kopierschutz: 0 - No protection
E-Book, Englisch, 126 Seiten
ISBN: 978-3-96067-677-5
Verlag: Diplomica Verlag
Format: PDF
Kopierschutz: 0 - No protection
Governments of many countries consider the electrification of individual passenger transport as a suitable strategy to decrease oil dependency and reduce transport-related carbon dioxide (CO2) and air pollutant emissions. However, battery-electric vehicles (BEVs) and plug-in hybrid-electric vehicles (PHEVs) have been more expensive than their conventional counterparts and suffer from relatively short electric driving ranges, which still hampers the market potential of these vehicles. Despite persisting shortfalls, mechanisms such as technological learning and economics of scale promise to improve the techno-economic performance of BEVs and PHEVs in the short- to mid-term.
Here, the author seeks to obtain insight into the techno-economic prospects of BEVs and PHEVs by: (i) establishing experience curves and (ii) quantifying user costs and the costs of mitigating carbon dioxide and air pollutant emissions in a time-series analysis. The analysis captures the situation in Germany between 2010 and 2016.
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Chapter 2: Methods:
2.1 Definitions:
Throughout this thesis, we use the terms ‘electric vehicle’ and ‘battery-electric vehicle’ (BEV) synonymously for passenger cars that are exclusively propelled by one or multiple electric engines, drawing their propulsion energy solely from an electric energy storage system such as a battery.
Plug-in hybrid electric vehicles (PHEVs) are defined as passenger cars that: (i) are equipped with an internal combustion engine (ICE) and one or multiple electric engines, (ii) draw their propulsion energy from combustible fuels and/or electricity, and (iii) can be charged from an external electricity source (UNECE, 2015). We do not distinguish between parallel PHEVs in which the internal combustion engine and the electric engine are both connected to a transmission and can thus propel the vehicle in parallel and series PHEVs (also referred to as range-extender vehicles) in which the electric engine propels the vehicle, whereas the internal combustion engine functions as an electricity generator to charge the battery. Our choice is justified by the limited number of PHEV models offered on the market in each individual year of our analysis. We acknowledge that not distinguishing between parallel and series PHEVs introduces uncertainty into our analysis because parallel PHEVs equipped with a small, thus less costly, battery and a full-size internal combustion engine are lumped together with series PHEVs equipped with a comparatively large, thus costly, battery and a rather small internal combustion engine. However, the database contains only around 8.5% (5 out of 59) vehicles with range extender, meaning that PHEVs take the major part in the analysis.
Throughout this thesis, we refer to ‘conventional vehicles’ (CVs) as passenger cars propelled exclusively by an internal combustion engine that draws its energy from combustible fuels such as gasoline or diesel.
2.2 Data collection:
In our analysis, we include BEVs, PHEVs, and comparable CVs sold in Germany in the period between 2010 and 2016. Having used an extended web search we start out by identifying all models of BEVs, PHEVs, and their respective conventional counterparts that are produced in series and offered for sale on the German market in each individual year of our analysis. Regarding BEVs, we only include vehicles for which the given price includes the traction battery. For example, the manufacturer Renault sells its BEVs without traction battery and instead charges a monthly leasing fee for the battery. Vehicles with lease battery are excluded from our data collection because their prices are not comparable to the battery-including prices stated for other BEVs. Moreover, we exclude from our analysis BEVs and PHEVs that are: (i) produced in limited series (i.e., less than 1,000 vehicles per year) and (ii) intended for racing rather than passenger transport (e.g., Porsche 918 Spyder; McLaren P1).
For each vehicle included in our analysis, we collect data on the sales price1 [EUR], maintenance costs [EUR], fixed costs related to insurance and vehicle registration in Germany [EUR], engine power [kW], if applicable, the capacity of the traction battery [kWh], certified distance specific energy consumption [kWh/km; l/100km], CO2 emissions [gCO2/100km] and the certified emissions standard as published by the miscellaneous manufacturers on their web pages, in their product brochures, or as published by third parties like newspaper articles or web pages (see Tables 16 and 17 in Appendix A). If there is no price for a certain vehicle model in a given year available, the price of the previous year has been assumed. This assumption can be made because manufacturers tend to announce price changes on their web pages; relevant information becomes also available via third parties like newspapers. Equivalent conventional vehicles are chosen, as far as feasible, to match the year of production, vehicle category, manufacturer name, model, type, size, as well as engine power of BEV and PHEV models in each year. We generally chose CVs with a manual transmission. This choice takes into account that not all consumers who purchase a BEV or PHEV may have chosen otherwise for a CV with automatic transmission but instead might have preferred a less costly, but otherwise equivalent, CV with a manual transmission. Following this argument, we consider the calculated price and cost differentials between BEVs/PHEVs and CVs to reflect somewhat a ‘worst-case scenario’. Moreover, electric cars don’t have a transmission that could increase energy consumption; by contrast automatic transmissions on conventional cars might unduly increase the fuel consumption.
Information on the value added tax in Germany is obtained from Statista (2017); the yearly inflation rate in Germany is obtained by Eurostat (2017; see Table 8 in Appendix A). More precisely, the specific inflation rates for motor cars are taken into consideration.
For our experience curve analysis, we furthermore assume a yearly global production of electric cars based on the number of yearly new registrations obtained from ZSW […] with BEVs and PHEVs ascertained together as they both contain a traction battery being accountable for large portion of the production costs.
For the calculation of use-phase cost and the costs of mitigating CO2 and air pollutant emissions, we collect data on:
- the yearly costs of vehicle maintenance and fix costs […].
- the generic prices of fuel (diesel and gasoline) and electricity that is kept constant for all years of our analysis […].
- the life time and yearly mileage of BEVs, PHEVs, and CVs […].
- the real-world electricity consumption of BEVs, as well as the real-word CO2 emissions at the tailpipe of PHEVs and CVs […].
- the CO2 and NOX emissions caused by the electricity production […].
- the CO2 emissions of battery production […].
- the real-word NOX and particle number emissions of PHEVs and CVs […].
The fix costs provided by ADAC contain an indemnity insurance with a contribution rate of 50% for the region group 6, regarding the car classification as well as a fully comprehensive cover with 500 EUR co-payment with a contribution rate of 50% for the region group 6, considering standard charges without any additional discounts. The fix costs also contain vehicle tax, however, tax exemptions for electric vehicles are considered. Further, a fixed rate of 200 € per year for, e.g., parking prices, general inspections, exhaust analyses, and minor accessories is added.
The maintenance costs contain costs for oil changes, inspections, wear and tear repairs, tires and for vehicles older than three years and additional fixed rate staggered by car classifications.
The life time of all vehicles contained in this analysis is set to 6 years because the ADAC (2017) data about the maintenance costs, which would increase with vehicle age, are based on a similar life time. The assumption of a relatively short life time of 6 years may also be justified in view of the uncertain durability of electric batteries and the likely need to replace batteries if longer life times are assumed (for a discussion, see Helmers and Weiss, 2017). In addition, the German ministry of finance also sets a period of six years for the depreciation of commercially used cars. We acknowledge that vehicles may have a substantially longer life time than 6 years; the assumption of a comparatively short lifespan leads to a higher influence of the vehicle purchasing price on the user costs in comparison to the other variables such as fuel costs.
We abstain from considering the results of road-side remote-sensing measurements. Remote sensing has been frequently applied to capture the emissions behavior of a large number of vehicles under real-world conditions (e.g., Carslaw et al., 2011; Chen and Borken-Kleefeld, 2014, 2016) Yet, measurements (i) have been focused on pre-Euro 6 vehicles, (ii) only capture a snap shot of the emissions performance of each vehicle at a specific location and under specific driving conditions, (iii) and require making assumptions about the instantaneous fuel use to be used for determining distance-specific emission factors [g/km] (Franco et al., 2013). The results of remote-sensing measurements may not in all instances accurately capture the average emissions behavior of vehicles. We differentiate between gasoline and diesel vehicles as well as vehicles certified according to the Euro 5 and 6 emission limits as NOX and particle number emissions differ between these vehicle categories. The Euro 5 standard is assumed to apply to all vehicles until 2015; the Euro 6 standard applies to all vehicles sold in the years 2015 and 2016 (Euro 6 has been mandatory for new vehicles since 2015 (EC, 2007)). It is noteworthy that the proportionality between energy consumption and NOX as well as particle number emissions is not as obvious as the proportionality between fuel consumption and CO2 emissions because former emissions are a function of various emissions control technologies.
