Doctoral Thesis / Dissertation, 2014
208 Pages, Grade: magna cum laude
Chapter 1 Introduction
1.1. Historical Retrospect – The Advent of the Electric Vehicle in 1900
1.2. Electric Vehicles within the Context of Sustainability
1.3. Objective of Thesis and Methodical Approach
1.4. Present State of Knowledge
Chapter 2 The Zero-Emission Vehicle Fleet Simulation Model – ZEVS
2.1. Theory on the Prerequisites of Energy Demand Comparisons
2.2. Selected Electric Vehicle Technologies
2.2.1 The Battery Electric Vehicle – BEV
2.2.2 The Fuel Cell Electric Vehicle – FCV
2.3. Applied Driving Cycles
2.4. Modelling the Development of the Future Kerb Weight of Vehicles
2.4.1 Past Development and Future Simulation of Vehicle Masses
2.4.2 Estimated Future Improvements for Lithium-based Batteries
2.4.3 Methodology to Model the “Degressive Weight Spiral” of Future Electric Vehicles
2.4.4 The Simulated Mass Reduction Potential of Electric Vehicles from 2015 to 2050
2.5. Modelling the Power Demand for Heating and Cooling Electric Vehicles
2.5.1 Methodology for Modelling Ambient Temperature Type Years of 27 European Countries
2.5.2 The Simplified Thermodynamic Model of a Vehicle
2.5.3 The Additional HVAC Demand of BEVs
2.5.4 The Resulting Impact of HVAC Power Demand on Driving Range
2.6. Methodology for Calculating the Total Electric Vehicle Fleet of a Country
2.7. The Implications of Mobility Habits
2.7.1 Characteristic Distribution of the Energy Consumption of Short Distance Trips
2.7.2 Characteristic Distribution of the Energy Consumption of Medium and Long Distance Trips
2.8. The Resulting Consumption Curves Specific to Electric Vehicles
Chapter 3 The Energy System Simulation & Optimisation Model – URBS-EU
3.1. Model Genesis
3.2. Model Methodology
3.3. Model Input Data
Chapter 4 Integration & Evaluation Principals of Electric Vehicles
4.1. Theory on the Integration of Electric Vehicles in Energy Systems
4.1.1 The Merit Order of Power Plants
4.1.2 Uncontrolled Charging
4.1.3 Controlled Charging (Cost-Optimised)
4.2. Allocation and Evaluation Methods for Analysing Electric Vehicles
4.2.1 The Mix Method
4.2.2 The Delta Method
4.2.3 The Parallel Market Method
Chapter 5 The System of Reference – ICVs
5.1. The Technological Basis of ICVs
5.2. CO2 Emission Goals for ICVs until 2050
5.3. The Growing Link between the Transport and the Energy Sectors: EVs
Chapter 6 Simulation Results & Analyses
6.1. The Sustainability of Electric Vehicles Today (2015)
6.1.1 Energy Efficiency
6.1.2 CO2 Emissions
6.1.3 Energy Conversion Costs
6.1.4 Summary on the Sustainability of Electric Vehicles Today
6.2. The Sustainability of Electric Vehicles in the Future (2015 – 2050)
6.2.1 Energy Efficiency
6.2.2 CO2 Emissions
6.2.3 Energy Conversion Costs
6.2.4 The Economic Costs of CO2 Emission Reductions with Electric Vehicles
Chapter 7 Summary & Conclusion
A.1. Additional ZEVS Model Specifics
A.2. Additional URBS-EU Model Specifics
Abbreviations & Notations
The following dissertation evolved from my activities as a research associate at the Institute for Energy Economy and Application Technology at Technische Universität München. Numerous research projects in co-operation with industry and academic institutions, particularly the EASYBAT project partners, laid the technical and scientific basis for my work. Its financial endorsement was provided among others by industry project partners, the European Commission, the International Graduate School of Science and Engineering (IGSSE) and by the Institute of my alma mater itself. I am particularly grateful to all for their ongoing support, their resources and esteemed working environments allowing the successful completion of this thesis.
Throughout the past years, the continuous advice given by the heads of the Institute – Prof. Dr.-Ing. Ulrich Wagner and Prof. Dr. rer. nat. Thomas Hamacher – has been of great help in discussing and reviewing the developed methodologies, simulations results and interpretations in the context of the greater picture surrounding this thesis. I would like to express my great appreciation to them for having faith in me as a person and my scientific work as a research associate. A special thanks is also extended to Prof. Dr.-Ing. Markus Lienkamp for his ongoing support and the co-operation possibilities with his institute in the mechanical engineering department. I would also like to express my appreciation to Prof. Dr. sc. techn. Andreas Herkersdorf for assuming the examination’s chairmanship.
The framework and basis for distinguished scientific work necessitates a research environment which allows creativity, endurance and constant review and scrutiny by fellow peers. This and many inspiring moments I found given at our Institute, which is constituted by its students, fellow research associates and staff. I would like to offer a warm thank you to all of them, former and current, for their valued advice and contributions throughout the course of our collective scientific years. Many times together also including those outside of our professional work at the Institute have been rewarding and unforgettable and I am sure to miss them extensively.
Here in particular, I would like to mention and thank Dr. Philipp Kuhn and Dr. Hans Roth for their long-standing advice, support and friendship in all matters of professional and personal relevance. Their willingness to give their time and assistance so generously was invaluable to me and is highly appreciated.
Furthermore, I would especially like to extend my gratitude to my colleagues Mathias Huber, Johannes Dorfner, Patrick Wimmer and Thomas Zipperle for their continuous help, patience and hours of coding work, which was necessary for a smoothly and efficiently running energy systems model URBS-EU. In addition, I would like to acknowledge Mrs. Marianne Winkelmayer and Mr. Heinrich Kleeberger for their on-going help throughout the past years. Finally, I would like to extend a sincere thanks to Mrs. Astrid Merling for her positive spirit, long-time guidance and support in helping me successfully accomplish my commitments and research work.
And to my dear Clara: Thank you for always being there for me, particularly during the tough stages of all of my doings. Your strong moral support, qualified advice as a fellow engineer and highly structured approach has always been a boon to my work. Mille mercis!
Lastly, I would like to dedicate this work to my parents. Since my childhood, they have always stood by me and invested an immeasurable amount of strength and time in me. To both of you, my heartfelt thanks and deepest appreciation! Your never wavering faith and support is a true inspiration, which I will always treasure.
Munich, July 2014
Within the scope of this dissertation, a model for synthesising the future development of EV power consumption is presented and incorporated in a European energy system model. Three allocation methodologies – the mix, delta and parallel market methods – are presented and allow a differentiated evaluation of selected sustainability criteria of EVs (well-to-wheel efficiencies, CO2 emissions and energy costs). These are compared to values of ICVs from 2015 to 2050 in 27 European countries. Results conclude that the sustainability advantages of EVs in Europe fully come to effect from 2025 onwards.
Im Rahmen dieser Arbeit wird ein Modell für die Synthese des zukünftigen Stromverbrauchs von Elektrofahrzeugen vorgestellt und in einem europäischen Energiesystemmodell integriert. Drei Zuweisungsmethoden – die Mix-, Delta- und Parallelmarkt-Methode – ermöglichen eine differenzierte Bewertung von Elektro-fahrzeugen (Primärenergieeffizienz, CO2-Emissionen und Energiekosten). Diese werden mit den Werten von konv. Kfz von 2015 bis 2050 in 27 europäischen Ländern verglichen. Die Ergebnisse zeigen, dass die Nachhaltigkeitsvorteile von Elektrofahrzeugen in Europa ab 2025 voll zum Tragen kommen.
“Sustainability creates and maintains the conditions, under which humans and nature can exist in productive harmony, that permit fulfilling the social, economic and other requirements of present and future generations.”1
United States Environmental Protection Agency
Washington, D.C. 2012
Over a hundred years ago, at the turn of the last century, a techno-economic development rolled up an entire industry, which was dedicated to satisfying the need for individual travel. The ubiquitously found horse-and-carriage was to become technically and economically obsolete in comparison to the breakthrough development of coupling an engine to the axel of a carriage. The years around 1900 went down in history as a tipping point for individual travel – away from horse-powered carriages towards electrified and mechanised auto-mobility.
As an unforeseen side effect specifically in large urban centres, the technical evolution of the horse-and-carriage also proved to be a step towards cleaner, more sustainable mobility. This might be misconceived in today’s terms if the high traffic pollution in cities comes to mind. However, a closer examination of individual travel in 1900 shows why the horse-and-carriage provides an even greater contribution to urban pollution than automobiles.
The common mode of individual travel in those days involved the equipping of horses in conjunction with their required harnessing gear to the designated carriage, which accommodated the driver with the steering reins and other passengers (Figure 1-1). In today’s terms, the preparation of the horse-and-carriage for a trip entailed quite an elaborate procedure: It involved stabling, feeding and grooming the horses adequately (high additional expenditures of time and money) and yoking them into their harnessing position in front of the carriage. These labour and time intensive arrangements were often the part time of a number of employees, provided the owner was able to afford the whole financial extent of this mode of individual travel.
With increased population concentrations in ever larger American and European cities and their considerable numbers of people entering and exiting from rural outskirts on a daily basis, individual travel by horse-and-carriage became increasingly popular and also affordable. It was the first materialisation of mass mobility on an individual basis.
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Figure 1-1: The Common Mode of Individual Travel – The Horse-and-Carriage (Picture Source: Museum Victoria 12
In the Deutsche Museum’s quarterly magazine “Kultur & Technik” – Culture and Technology – B. Flessner portrays in edition 04/2007 13 the road conditions of 1900 New York: Alone the presence of 150 000 horses within the city was described as an economic burden, an affront to cleanliness and a terrible tax upon human life (Figure 1-2).
A portion of the dried dung was then carried by the wind through the streets and contributed to the spread of tuberculosis and tetanus. In addition, around 15 000 horses died per year not seldom in the middle of the street and lied there to rot until taken away much too late (Figure 1-3).
While automobiles were still a very young and unreliable invention at the beginning of the last century, the horse-and-carriage was at the height of its popularity prompting even German Kaiser Wilhelm II (1859-1941) to publicly proclaim: “I believe in the horse. The automobile is only a temporary phenomenon.”
However, history had proven him mistaken. The industry for individual mobility was undergoing a tipping point, which was fed by a consumer market yearning for a simpler, more affordable and faster means of travel – the automobile was to become the new archetype of unlimited individual mobility.
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Figure 1-2: First Signs of Major Traffic-Related Pollution in Cities – Piles of Horse Manure Clogging the Roads (Picture Source: George E. Waring) 14
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Figure 1-3: Further Indicators Marking the Unsustainable Impact of Individual Mobility in 1900 (Picture Source: Ephemeral New York 15 )
From 1896 to 1913, historic records show the growth rates of automobile fleets taking off exponentially in major western economies (Figure 1-4). This trend was driven by innovators, trendsetters and early technology adopters, who mainly intended to boast with their motor sport hobbies and impress the wealthy and chick of Paris, London, New York and Berlin.
Especially from 1913 onwards, due to assembly line mass production of automobiles, pioneered by the Ford Model T at the River Rouge factory site in Detroit, production costs decreased considerably and paved the way for the automobile into the mass market. Along with the automobile’s considerable speed and range advantage, its lower additional expenditures (running and service costs) and higher reliability, the horse-and-carriage increasingly became technically outdated and economically unviable.
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Figure 1-4: Historical Development of the First Automobile Fleet in Selected Countries (Raw Data Source: Laux and Historical Statistics16 )
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Figure 1-5: First Battery Switch Station for Automobiles in Paris in 1898 (Picture Source: La Nature17 )
Particularly interesting at that stage was the diversity of drive train types, competing for market share. For example, in the United States at the beginning of the automobile’s existence, the vehicle fleet was comprised of 40 % steam, 38 % electric and 22 % gasoline based automobiles 16. Evidently in those days, electric vehicles were highly common and a popular clean alternative to the noisy and greasy steam- and gasoline-powered automobiles. Even their limited range and long recharging times were technically circumvented for example by the French manufacturer Krieger with the invention and deployment of battery switch stations in Paris in 1898 (Figure 1-5).
These stations were primarily intended for taxi fleet companies, who were the first commercial adopters of numerous automobiles but quickly realised the technical limits of the electric drive train concerning the limited driving range and long recharging duration.
With the invention of the electric starter by Charles Kettering in 1912 the gasoline-powered automobile relieved itself from its last major disadvantage: the high injury risk and cumbersome turning of the crank shaft for starting the combustion engine of a gasoline-powered automobile. The advent of low gasoline prices and its ubiquitous availability furthered the market penetration of internal combustion engine vehicles. Their unlimited range and high technical availability was no match for steam and electric vehicles. Unable to compete technically and due to a lack of public demand, the production of electric vehicles finally ceased in 1931.
The electric drive train was thus placed in storage only re-appearing in the 1990s and again today after major cost reductions and technological advances of the capacity of lithium-based batteries make it more competitive with conventional vehicles. Furthermore, the verified impact of fuel-powered automobiles on the anthropological share of global warming and the concentration of their tail-pipe emissions in urban agglomerations have prompted policy makers, academia and the industry to adopt new technologies such as electric vehicles in their roadmap to higher sustainability for the transport sector.
However, more sustainability is not merely an improvement in environmental factors alone; it also involves a likewise economic and social betterment by the introduction of a new technology. Thus, the term “sustainability” mirrors the increasing complexity of finding a technical solution that provides a measurable improvement of the status quo without shifting the problem to other sections of society while masking its true impact. This aspect is important to understand, when evaluating the impact of electric vehicles and the goal of finding a more sustainable alternative for individual travel.
In comparison to the time span of modern human’s technical progress over the past centuries, the concept of sustainability is still in its early stages of development. In the mid-1970s to late-1980s the limits of boundless economic growth in industrialised nations became increasingly palpable to a broad public. Already in 1972 pioneering research was done in The Limits to Growth18, a report commissioned by the Club of Rome exploring how exponential growth interacts with finite resources. At that time, such unorthodox statements and change in mind-sets raised considerable public attention, creating a first awareness of the importance of conserving limited resources.
Furthermore, short term consequences such as severe air pollution and smog, forest dieback caused by acid rain, erosion of fertile top soil, expansion of the hole in the ozone layer, extinction of endangered species etc., encouraged political and public deliberation in industrialised countries. Due to the severe impact of these environmental problems, an urgency emerged, to rethink the way the economy dominated the development of industrialised countries. The primacy of sole economic expansion and progress was being challenged on a broad basis in every area of developed societies. It became ever more apparent that further unregulated economic growth would reach its toll on the environment and create degenerative ripple-effects on social structures of societies.
The past decades of the 1970s and 1980s brought the importance of environmental conservation to the forefront. Entrenched in the minds of leaders (the establishment) and disgruntled citizens, it resulted in the founding of important agencies and grass-root organisations alike, such as the Club of Rome (1968), US Environmental Protection Agency (1970), Greenpeace (1971), Sea Shepherd Conservation Society (1977), political Green Parties (such as “Die Grünen” in Germany in 1980) etc. By that time, ministries of environmental affairs were still uncharted political domains. Long term anthropogenic impacts on the environment, such as today’s observable climate change of global proportion, were also still a matter of mere speculation lacking any sound scientific proof. It soon became apparent that conserving the environment only on a short term basis was short lived. Perceived environmental problems and drawbacks were rooted far deeper and were barely understood as a complex function of time and socio-economic dependency. This next step of a higher-ranking and more holistic way of problem solving was still in its children’s shoes.
Only on the 20th of March 1987 the more holistic term of “sustainability” finally found itself in a definition published by the Brundtland Commission of the United Nations: “Sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs”19. For the first time in history the term “sustainability” gained broad public attention and evolved into a new scientific field of research.
Until today this statement and the underlying UN document laid the seed on the long and tedious road to conciliate economic growth with equal environmental and social advances. The virtue of economic growth, which at the same time preserves the environment and furthers solid social structures, was recognised as a necessity – the necessity to foster and cultivate a more sustainable development of modern day societies.
In 2005 the International Union for Conservation of Nature (IUCN) presented the overlapping circles model (Figure 1-6) to describe the challenge of encompassing every aspect of sustainability based on the confluence of the three major pillars: social, economic and environmental.
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Figure 1-6: Overlapping Circles Model of Sustainable Development by the IUCN 10
From left to right, Figure 1-6 shows the theory, the reality perceived today and the change over time needed to better balance the model and bring it in line again with theory. It is essential to note the importance of time at this stage, which makes clear that sustainability is a long term and on-going process. This process strives to fulfil the equal advancement of each of the three aspects of sustainable development. In broad terms, “sustainability” describes the necessity to “hold”, “maintain”, “support” and “endure”, derived from the Latin sustinere.
On their own the three major spheres encompass typical goals 10:
- pollution reduction and prevention
- nature conservation
- species diversity
- unlimited growth
- laissez-faire/free market enterprise
- profit maximisation
- equality in opportunity
- equality in basic living conditions
The overlapping circles model was further refined and specified in more detail ever since, of which many similar configurations are found in literature. Due to the historical dominance of the economic sphere, ways and means to quantify social and environmental criteria have been undertaken in economic (money) terms via positive and negative “externalities”. This allows the internalisation of external costs incurred in the social and environmental spheres due to economic activity. For example, the German government implements a 0.15 € “Ökosteuer” – a “green tax” – on every litre of gasoline or diesel bought by the end-consumer. Hence, a fracture of the negative externality incurred by the environment and society due to the tail-pipe emissions of combustion engines is intended to redress the injustices and imbalances thereof. Although this green tax is used for purposes other than implied (financing of retirement pensions), it has a certain corrective on the frequency of gasoline and diesel usage, which results in an increased motivation to save energy and promote resource efficiency.
On the other hand, certain other criteria may be penalised in exchange. The green tax for example may lead to a decrease in economic competitiveness or may even heighten the cost burden for financially stressed parts of society, thus undercutting gains in the social sphere. Effectively, this regulatory measure affects all three spheres of the overlapping circles model and stands as an example for “the change needed” as described by the IUCN in Figure 1-6.
Hence, furthering sustainable development on a holistic level – with equal gains in all three spheres – becomes a truly complex challenge. Conferred onto the introduction of electric vehicles, this is likewise the case in coming to a fair and just assessment of their future impact on the environment, society and economy in comparison to their conventional counterparts, internal combustion engine vehicles (ICVs). The term “sustainable mobility” is often used synonymously with electric vehicles to describe their promise of a cleaner, more efficient and yet still affordable way of transport.
In order to assess this common statement, the term “sustainable mobility” shall be specified more clearly and so provide a basis for the on-going assessment of electric vehicles from an energy-economic perspective. The three constituent parts of a more sustainable mobility – social compatibility, environmental and economic improvements – can be described according to the following goals:
- reduced noise levels
- decreased resource dependency
- higher diversification of primary energy sources
- reduced cumulated energy demand entailing production, usage and disposal
- higher well-to-wheel efficiency
- lower attributable emissions, specifically lower CO2 emissions
- reduced market barriers by way of policy adjustments
- lower running costs per km travelled
- lower total cost of ownership
- lower costs for society with the same or higher technical gain
- The requirement of a new vehicle type is to fulfil its social role as a means for the freedom of movement via individual mobility. This new technology should neither discriminate against a particular group of people, further social unrest nor incite negative human behaviour differently to the current technology in place today. Concerning electric vehicles, no studies are known which indicate the contrary in comparison to current ICVs.
The mentioned criteria are only a selection of a larger number of indicators, which in their entirety would justify the synonymic lingual use of “electric vehicles” and “sustainable mobility”. However, an exhaustive analysis of these criteria lies beyond the scope of this thesis and should rather be seen as a project of many over a longer period of time.
For this reason, the thesis focuses on three particular energy-economic indicators, which can be quantified and allocated to electric vehicles: energy conversion efficiencies, CO2 emissions and energy costs for mobility. Crucial factors influencing these indicators are the types of power plants generating their electricity requirements. By means of simulation tools, emulating electric vehicles and their interaction with power plants, an abstraction of reality is undertaken to analyse and interpret the future impact of a large scale introduction of electric vehicles. This thesis strives to provide a more detailed understanding on the ability of electric vehicles to fulfil the need for a more sustainable transport option, which accommodates the conflicting objectives of a cleaner and at the same time affordable individual mobility.
The objective of this thesis is to assess the claim that electric vehicles represent a more sustainable means for individual mobility in comparison to ICVs. Or in other words, is it just to place electric vehicles further at the confluence of the three constituent parts of social, environmental and economic development, as discussed in the previous subchapter. The taken approach is done by quantifying sustainability criteria from an energy-economic perspective and placing them into relation to the prevalent reference technology for mass individual mobility – internal combustion engine vehicles (ICVs). The analysis will focus mainly on well-to-wheel efficiencies, CO2 emissions and primary energy conversion costs of battery electric vehicles (BEVs), fuel cell vehicles (FCVs) and ICVs in 27 European countries over a time period of 35 years from 2015 to 2050. Various allocation methods exist in evaluating the energy-economic criteria of electric vehicles and thus play an important role in results interpretation and ultimately achieving the objective of this thesis. Essentially, in comparison to many previous published analyses, a differentiated and more holistic approach is chosen to assess if electric vehicles supplement a more sustainable development of the automobile sector.
The basis for fulfilling the objective of this thesis is provided by evaluations on selected sustainability criteria of electric vehicles. It is laid out in this dissertation by developing, adapting and combining two simulation models and applying three evaluation methodologies on the models’ combined results. This methodical approach is summarised in Figure 1-7. It is divided into three parts, each providing legwork to the overall goal of evaluating the sustainability of electric vehicles according to the selected energy-economic criteria. The three parts incorporate the simulation of the energy demand of electric vehicles, the simulation of the energy sector’s electricity production together with the additional power demand of electric vehicles and finally the results analysis, interpretation and comparison to ICVs. All three parts are separate working steps, independent in their execution of each other but when linked together and applied as a whole, provide new valuable insights on the topic of electric mobility.
The first part entails the ZEVS Model (green bubble fields in Figure 1-7), specifically suited for simulating zero emission vehicles – BEVs and FCVs. It emulates the nature of driving habits in general and in particular the specific parameters of electric vehicles, such as the plug-to-wheel demand, their electrical heating demand in winter, predicted market penetration rates, etc. Together with future technological advancements in light-weighting and battery technology development, the total power demand characteristic of electric vehicles and its future development from 2015 to 2050 is modelled on an hourly basis for 27 European countries.
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Figure 1-7: Methodical Overview for the Analysis of Selected Sustainability Criteria of Electric Vehicles in Europe from 2015 to 2050
The resulting consumptions curves of the ZEVS model are transferred to the URBS-EU model – the orange bubble fields and second part of Figure 1-7 – which then optimises the future power plant portfolio and its operation from 2015 to 2050. Here, input parameters such as investment and fuel costs, renewable energy influx, power transmission capacities, etc. feed the model, which finds a cost-minimal solution for the system as a whole. As a result, electricity generation costs, power plant CO2 emissions, primary energy conversion efficiencies and a number of other energy-economic criteria are calculated.
The third part of the methodical approach in Figure 1-7 consists of the allocation of the energy-economic criteria of the URBS-EU model to the consumption criteria of electric vehicles in the ZEVS model (purple bubble fields). Three allocation methodologies – the mix, delta and parallel market methods – are developed and applied for evaluating the sustainability of electric vehicles in comparison to their ICV counterparts. These analyses and interpretations include the future development of sustainability criteria from 2015 to 2050 in 27 European countries.
The structure of this dissertation is arranged into three sections:
In Chapters 2, 3 and 4 the models for simulating electric vehicles and the European power production sector are described along with the theory of their energy-economic integration and evaluation according to three allocation methods:
Chapter 2 focuses on the detailed description of the developed electric vehicle simulation model, ZEVS, which models the energy consumption curves of electric vehicles from 2015 to 2050 in 27 European countries.
Chapter 3 describes how the applied European energy system model, URBS-EU, operates methodically, which input parameters are used and how the power demand of electric vehicles is incorporated.
Chapter 4 explains how energy-economic synergies between the charging periods of electric vehicles and the power plant dispatch are optimally accommodated. Additionally allocation and evaluation methods for analysing selected sustainability criteria of electric vehicles are presented.
The second main section, Chapter 5, portrays the system of comparison, ICVs. It includes insight on estimations of how ICVs could technically develop in the future, in order to meet the CO2 emission reduction targets until 2050. Furthermore, the current environmental policies of the energy and transport sectors are described and prepared as a realistic scenario for simulation purposes in the URBS-EU model.
In the third and last main section, in Chapter 6, the URBS-EU simulation results on selected sustainability criteria of electric vehicles are interpreted and evaluated according to the allocation methods presented in the previous Chapter 4.
The main findings of this dissertation are summarised finally in Chapter 7 along with a concluding interpretation on the overall sustainability of selected energy-economic criteria of electric vehicles in Europe.
The questions surrounding the environmental and economic impact of a large scale introduction of electric vehicles is a topic which has already undergone several scientific assessments. Many previous studies and research projects can roughly be divided into two groups with differing viewpoints on the topic. Due to the professional background of the participants, the contents and applied methodologies of the studies of the one group tend to focus on the impact of electric vehicles as seen from the transport sector, while those of the other group place more weight on the perspective of the energy sector. Both perspectives are now summarised and discussed in relation to the objective of this thesis in the following paragraphs.
Studies Conducted by the First Group
Research project consortiums initiated and dominated by original equipment manufacturers (OEMs) generally approach the study with a focus on the vehicle. This approach is often highly factual, based on the efficiencies, emissions and costs of conventional vehicles in comparison to electric vehicles. This is done with actual data available nowadays and expert estimations on their technical potential in the future. These studies also refer to the energy sector when assessing the energy-economic criteria of electric vehicles, but due to a lack of knowledge and experience in this sector only simplified assumptions and basic deductions are made.
For example, the study on “A portfolio of power-trains for Europe: a fact-based analysis” prepared by McKinsey & Company 11 in co-operation with a number of companies and organisations mainly from the car manufacturing sector, did not involve the modelling of the European energy sector and the integration of electric vehicles with their charging specifics (controlled charging, fast charging, etc.). Instead, a singular allocation methodology was chosen, in which every major primary energy carrier – oil, gas, coal, nuclear and renewable – was assessed on their own in providing the required energy supply with all related efficiencies, emissions and costs. This allocation methodology is based on a simple balance sheet approach, in which supply and demand are matched according to the yearly required energy quantity of the vehicles. This is the simplest form to assess electric vehicles. However, it does not consider the specifics of the power production sector, such as the merit order of power plants, their yearly full load hours, the characteristics of other electricity consumers or the energy sector’s cap-and-trade system of CO2 emissions, etc.
In other studies such as the “Well-to-wheels Analysis of Future Automotive Fuels and Power Trains in the European Context” by the European Commission’s Joint Research Centre 12 the allocation of emissions and well-to-wheel efficiencies is based on the energy sector’s overall generation mix. Here, broad averages of the sector’s energy-economic indicators are taken and attributed to the impact of electric vehicles. Studies applying this methodology omit the effects on the energy system brought about by controlled charging or immediate fast charging. Mobility habits of electric vehicle users generally do not play a role when the mix allocation methodology is applied. Although these assessments are generally fairly accurate for a smaller number of electric vehicles (such as predicted for 2020), considerably larger fleet sizes of several million vehicles impact the energy system in such a way that power plant investments and their dispatch noticeably affect the sector’s overall mix.
Long-term assessments of the impact of electric vehicles are therefore not often found in this first group of studies revolving mainly around the electric vehicle, its direct competitor (the ICV) and the transport sector in general.
Studies Conducted by the Second Group
On the other hand, the second group of research studies are often initiated and concluded by utilities, research institutes of universities and other organisations with more theoretical scientific backgrounds. They generally focus on modelling the energy sector at a very high level of detail. The role of an electric vehicle in the energy sector is often modelled as an intelligent user of electricity with load-shifting potential. The simulation methodology is hence similar to that of storage facilities such as pumped hydro storages optimising power plant dispatch in relation to the load forecast of the consumers.
The study “OPTUM: Maximising the reduction of environmental impact by means of electric vehicles: Integrated analysis of vehicle use and the energy sector in Germany” conducted by the Öko-Institut in Germany 13 is an example of such a modelling approach. Here, the electricity demand of electric vehicles is a static and daily re-occurring value, which is indifferent to external influences such as very cold weather affecting particularly the heating requirements and thus higher electricity demand of electric vehicles in winter. A very similar approach is followed by the ifeu-Institut in its ‘Electric Mobility and Renewable Energies” study14. In general such studies often contain the following simplifications for modelling electric vehicles and their power demand from the grid:
- Range extension by means of fast charging, battery switching or using an ICE range extender is often mentioned but not modelled in detail and its impact assessed in combination with vehicles shifting their load to off-peak hours.
- Thus, the load-shifting potential of electric vehicles is often overestimated, insufficiently considering mobility habits which immediately require a means to recharge or extend the vehicle’s range.
- The future technical development potential of vehicles until 2050, such as mass reductions due to light-weighting, battery improvements, etc. is not considered for modelling their energy consumption.
- A focus is laid solely on the impact of electric vehicles either in Europe as a whole or in a specific country alone (with a lot of publications found on electric vehicles in Germany). A broad comparison of the impact of electric vehicles of several European countries and their advantages and disadvantages in this respect have not yet been seen in this context. No comparative details are available, which indicate which countries provide optimal conditions for the introduction of electric vehicle on the basis of low emissions and costs and high well-to-wheel efficiencies.
To date, only a study commissioned by the German Federal Ministry of Economics and Technology (BMWi) and concluded by the DLR in co-operation with Fraunhofer ISE, RWTH Aachen and FGH 15 shows first signs of a complementary approach. And yet the focus still lies strongly on the modelling of the German energy sector, while the integration of the power demand of electric vehicles is more a static value on a yearly basis. However, possible future advances in vehicle technology have been considered along with the additional energy demand for HVAC purposes.
The Novelties of this Thesis
Combining the simulation and modelling of both sectors concurrently and with equal weight seems to be a challenge, due to its high level of scope and complexity. Many previously made assumptions by research groups to circumvent this challenge have shown to be too general or have caused the impact of the technology to be shifted into another sector or area while remaining unnoticed. This dissertation is one of very few first attempts undertaking to manage this challenge in scope and complexity, while striving for a more holistic view of the whole energy conversion chain.
Complementing the available knowledge of the above mentioned research studies are a number of factors. This dissertation includes a closer screening of the plug-to-wheel demand of electric vehicles, emulating more realistic states of use at a higher degree of precision. Hence, the increase in energy demand due to heating and cooling on a dynamic hourly basis is new in comparison to other studies. Also, this additional demand from electric vehicles correlates more precisely with the seasonal specifics of the general load curve of a European country particularly on very cold and hot days. Furthermore, the non-shiftable energy demand of long distance trips of electric vehicle use is also incorporated and will increase peak demand of load curves when fast charging is applied. This results from the analysis and classification into trips with and without the demand to extend the range of the vehicle immediately within 24 hours.
The results analyses and interpretations of this thesis spanning a time frame from 2015 to 2050 do not intend to make a prediction of the future development of the energy and transport sector resulting from electric vehicles. The goal is rather to understand the long-term behaviour of the modelled system, which focuses on its overall trend in comparison to the current reference system (ICVs) and its modelled future development.
The implemented model versions of ZEVS and URBS-EU in this dissertation are, like every other kind of system models, an abstraction of the present state of knowledge. Both models use methods of simplification, inter- and extrapolation techniques and in seldom cases, when necessary, expert assumptions, particularly involving estimations of likely future developments. Due to the innate nature of models, both ZEVS and URBS-EU also are per se imperfect, unfinished and merely an attempt to merge theory with praxis. On their own and in combination they seek to increase the present state of knowledge for emulating future interactions between electric vehicle fleets and the European energy system. References to the models’ deficits and shortcomings will be made at appropriate places with suggestions for future improvement and continued research.
Evidently, there is a difference in terminology concerning the various degrees of describing future developments – predictions, forecasts, estimations or extrapolation of past developments. They are best illustrated by a simple example on a ball rolling down a hill onto an even plain: If at the top of the hill the ball is let go and rolls down, it is possible to predict its generally expected behaviour. The ball will roll down, continuously accelerating until it reaches the bottom of the hill. Once it rolls over the even plain, it starts to decelerate until it comes to a complete stand-still. The predicted outcome of the ball’s final state is common knowledge. As is the circumstance that the ball will not continue over the plain forever or reverse course back uphill or even leave the ground and remain in mid-air. It is this understanding of the behaviour of the ball’s state of motion which is of interest when developing and applying the simulation models ZEVS and URBS-EU. If it were intended to predict the exact location of the ball coming to rest, numerous factors would need to be calculated additionally. This would include most importantly the potential energy of the ball when let go at the top of the hill, its kinetic energy when reaching the bottom of the hill and its rolling and air resistances on the even plain. To decrease the margin of error in the final prediction of the location of the ball, more precise information is required on a multitude of influencing factors not mentioned. Similarly, if the goal were to predict the exact number of electric vehicles and their environmental impact in 2020 or 2050, much more complex energy and transport system models would be necessary. While a true prediction for 2020 would still be something realistically achievable, the prediction for 2050, however, is far from anything reliable and serious with the technical means and knowledge available today.
Nonetheless, the objective of this thesis and its methodical approach strive to deepen the understanding of the long-term trend of the electric vehicle impact in comparison to the energy-economic impact of the technology currently in place, ICVs.
Some few extracts found in the following main chapters and subchapters have been presented before in 2013 at two international conferences in Europe (International Conference on Clean Electrical Power – Renewable Energy Resources Impact (ICCEP) in Alghero, Italy and at the Conference for Future Automotive Technology – Focus Electromobility (CoFAT) in Garching bei München, Germany and published as 16 and 17. These were prepared in the course of the FP7 funded EASYBAT Research Project and incorporated in TUM’s final deliverable, the Technical Report titled “Analysis of the Renewable Energy Grid Integration Potential by Range Extension Technologies of EVs in Europe”18. Where necessary the few extracts of these publications have been extended and altered to meet the present state of knowledge and model development applied in this dissertation.
“The cost of one year of oil will get us off oil forever!”19
Shai Agassi, former CEO Better Place
Washington, D.C. 2008
The ZEVS model is a simulation tool developed and implemented in MATLAB, a high-level language for technical computing, generally used for analysing data, developing algorithms and creating models of technical applications. The name of the simulation model, ZEVS, was derived from the original term “ZEV – zero-emission vehicle”, originally coined by the California Air Resources Board (CARB) in the clean air legislation dating back to 1990 for referring to a vehicle’s tailpipe pollutants. Excluded from this definition is water in gaseous form, as emitted by FCVs or during the combustion of hydrogen in specifically adapted internal combustion engines of ICVs.
In this dissertation the technical application of MATLAB involves the modelling of energy consumption curves of zero-emission vehicle fleets consisting either of battery electric vehicles (BEVs) or fuel cell vehicles (FCVs). Figure 2-1 shows the flow chart of the complete ZEVS model. Here, each green bubble field marks specific mathematical formulae and process functions to emulate a certain aspect affecting the future power demand of electric vehicles. It begins with the initialisation of a selected electric vehicle type and its specific drive train. After modelling the future kerb weight development and HVAC power demand of the selected vehicle from 2015 to 2050, the ZEVS model simulates the plug-to-wheel demand for the weather specifics of a single vehicle in any of the 27 European countries. Thereafter, the plug-to-wheel demands are conveyed onto the whole electric vehicle fleet predicted for a certain year in the future of the relevant country. A final categorisation of the fleet’s power demand into shiftable and non-shiftable charging demand is performed.
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Figure 2-1: Flow Chart of the ZEVS Model
The individual functions of the ZEVS model are concurrently fed by input data (grey bubble fields), reflecting realistic industry wide vehicle averages and their projected future developments until 2050. As a result, the ZEVS model supplies 54 different consumption curves for any simulation year between 2015 and 2050, depicting the hour-sharp, shiftable and non-shiftable power demand of electric vehicles in 27 European countries.
The following subchapters now discuss the modelling setup of each green bubble field of the ZEVS model in more detail. For orientation purposes, the green-fielded extract from Figure 1-7 provides a simplified system overview of the ZEVS model and will be shown always at the beginning of each subchapter.
Before the ZEVS modelling process of an electric vehicle fleet is explained, this subchapter introduces the general theory behind energy demand comparisons between different vehicle types. This involves the definition of energy boundaries for different system levels as a prerequisite. What this entails is visualised in Figure 2-2, which defines four energy boundaries from a vehicle’s perspective. These boundaries are commonly used to quantify and compare the consumptions of various vehicle types on different system levels. By switching from one system level to the next, energy conversion processes are performed. This always includes a number of losses specific to a certain technical process.
The inner most circle of the figure constitutes the effective energy demand – German: “Nutzenergiebedarf” – and is considered the initial point for energy demand comparisons. It quantifies the amount of energy required to overcome physical laws resisting a vehicle to be brought into lateral and vertical motion, also commonly known as driving resistances. These rolling, air and acceleration resistances are common to all standardised driving cycles, while the inclination resistance is found rather in geographically relevant or situation-based driving cycles. Although the latter driving resistance is commonly excluded in energy demand comparisons, it can amount to a considerably large percentage of the total energy consumption in certain terrains.
The next system level comprises the energy demand of the complete vehicle as an entity on its own. It is defined as the final energy demand – German: “Endenergiebedarf” – and is more commonly referred to as the vehicle’s plug-to-wheel (BEVs) or tank-to-wheel demand (FCVs and ICVs). Here, the energy conversion process from the vehicle’s effective to its final energy demand can be understood as follows: In every instance the stationary vehicle is set in motion, it gains either kinetic energy by accelerating to higher speeds or potential energy by ascending to greater altitudes. In order to overcome the associated driving resistances in this process, energy in chemical or electrochemical form is taken from the vehicle’s storage and converted by the drive train into mechanical energy leading to propulsion. In the course of these energy conversion processes, losses accumulate mainly in form of dissipated heat.
These heat losses are highest at the internal combustion engine of the ICV and lowest at the electric motor of the BEV. The heat losses of the fuel cell stack of an FCV lie roughly in between the other two. In this context, it is important to understand that in case of BEVs the vehicles do not take part in the conversion process of the combustion of primary energy carriers or in other words, of converting thermal to electrical energy. In comparison to ICVs this is now shifted from the transport sector to the energy sector, from many small “decentralised” engines of ICVs to larger and more centralised power plants with higher efficiencies. FCVs on the other hand incorporate both systems. Their fuel, hydrogen, is produced by electrolysers supplied with power from the grid (similar to BEVs) or via steam reforming processes in industrial plants, while the conversion process of the fuel happens in small decentralised fuel cell stacks (similar to ICVs).
A further drive train feature can influence the total final energy demand additionally. When it is intended to slow down the described vehicle above or drive downhill without gaining speed, the high kinetic energy is reduced often in a short period of time by applying the vehicle’s brakes. Yet, vehicles with the technical possibility of recuperation (e.g., BEVs, FCVs and hybrid ICVs), are able to regain a large portion of the abundant kinetic energy when slowing down or driving downhill. In the example of electric drive trains, the electric motor can serve as a generator and charge the battery by the process of converting kinetic into electric energy and storing it in an electrochemically stable form of energy. In case of current ICVs, the abundant kinetic energy is dissipated as heat losses via the vehicle’s braking system. Vehicle ancillary power, such as for air conditioning, on-board electronics, charging losses, etc. are also included in the calculation of the final energy demand. The energy carriers often used for denoting the total amount of final energy required, are for example diesel/gasoline (ICVs), electricity (BEVs) or hydrogen (FCVs). They are of secondary origin, defining them as non-occurring in nature, while having already undergone several conversion processes from their original primary energy state.
illustration not visible in this excerpt
Figure 2-2: Energy Boundaries from a Vehicle's Perspective
This introduces the next system level of comparison, termed the primary energy demand – German: “Primärenergiebedarf”. It takes the next step of including the energy demand required for refining or combusting the original resource commodity found in nature and transporting it to the point, where vehicles are able to make use of it. Naturally occurring resources, which are used for conversion processes from primary to secondary energy carriers, are for example crude oil, natural gas, hard coal, solar irradiation, biomass, etc. The term well-to-wheel demand is also commonly used to describe the complete energy consumption in the use phase of a vehicle. It ranges from the point of origin of the natural resource via a multitude of conversion processes (e.g. refineries, power plants, reformers), via the transport of the secondary energy (e.g. pipelines, fuel trucks, electricity grid) to the place of its uptake and storage by the vehicle, to finally providing the service of mobility in the form of propulsion (lateral and vertical motion). The whole usage phase of the vehicle is quantified by this energy term.
The cumulated energy expenditure – German: “Kumulierter Energieaufwand” – finally summarises next to the usage phase of the vehicle also its production and ultimately its disposal phase (recycling) at the vehicle’s end of life. Equivalent terms and more commonly used are life cycle analyses or the cradle-to-grave energy expenditure.
A further prerequisite for a fair and equal comparison involves outlining all vehicle parameters not affecting the drive trains, which are identical or at least very similar. What is commonly referred to as conversion design engineering, generally involves the same model platforms, vehicle exteriors and cabin interiors. On technical terms this translates to the same parameters for example the drag co-efficient, cross sectional area, rolling resistance co-efficient, etc.
By going into more detail, the basis of comparison between the ICV, BEV and FCV therefore involves similar vehicle setups with single architecture platforms, solely accommodating alterations in the vehicles’ mechanical or electrical drive trains, their energy storage units (diesel fuel tank, lithium-ion battery, hydrogen tank) and its packaging thereof. Mercedes-Benz for example presented a variety of electric drive systems based on the principle of conversion design, its Concept BlueZERO in Figure 2-3 20. This incorporates a modular concept with three different drive train configurations (a straightforward BEV, a FCV and a BEV with an additional ICE as range extender).
The drive trains are all located in the sandwich floor and between the vehicle axles. The packaging of any of the three drive trains under analysis in this dissertation is similarly integrated into the same overall vehicle platform. Next to the different packaging designs, the three drive trains are associated with different volumes and masses. While the volume of the drive train mainly affects the usable interior space, its mass impacts the driving resistances considerably. Thus already the effective energy demands of the vehicles will be noticeably different, directly resulting from heavier or lighter drive trains. Due to the lack of comparability on the basis of the effective or final energy demand, all final results are based on the primary energy demand, assessing the overall holistic impact of the usage phase of these vehicle types with three different drive train technologies.
illustration not visible in this excerpt
Figure 2-3: Basis of Energy-economic Comparisons: Vehicles in Conversion Design (Picture Source: Daimler AG 20 )
The vehicle type comparisons in this dissertation focus solely on the usage phase of the vehicles, analysing their sustainability criteria on primary energy level. However, the production and disposal phases, forming part of a complete life cycle analysis, are a highly relevant area of sustainability analyses as well. A different picture on the outcome of vehicle type comparisons can be expected by including these other two phases. This results mainly from the fact that certain drive train components are highly energy and resource intensive in their production phase. For example the mining of rare earth elements for the production of magnets in certain electric motors and lithium for the battery involves a considerable energy expenditure and impact on nature and the living and working environment of humans. A more complete picture on the sustainability of vehicle type comparisons cannot do without this complex and extensive examination. However, due its very broad scope of research and due to this dissertation’s more in-depth focus on the vehicle’s usage phase, it has not formed part of the final sustainability analysis.
The technical specifications relevant for modelling an electric vehicle fleet in ZEVS are presented in this subchapter for the two designated electric vehicle types: the battery electric vehicle (BEV) and the fuel cell electric vehicle (FCV). Here, the principles of conversion design engineering are now applied for an average compact class vehicle in the C-segment, which represents the broad average of electric vehicles from the A-segment to the D-segment, all constituting a future fleet of electric vehicles. The third vehicle type, the internal combustion engine vehicle (ICV), also forms part of the sustainability analyses in this dissertation. Yet, because the ZEVS model only simulates the energy consumption curves of electric vehicle fleets, ICVs are presented later on in Chapter 5 as the benchmark technology for the sustainability comparisons.
The simulation of a battery electric vehicle in a driving cycle requires an abstraction of the technical complexity of the whole vehicle and in particular its electrical drive train. Figure 2-4 depicts its simplified setup on component-system level. It consists of the high voltage (HV) level commonly on 400 V basis, to which the low voltage (LV) level on 48 V basis is connected via a DC/DC converter. It is assumed that the 48 V level will be the industry standard for the low voltage level, even though this might not be the case already by 2015. Even further increases in the low voltage level or the high voltage level (e.g., to 800 V for higher efficiencies and driving speeds) are assumed possible if proven to be advantageous.
The HV electrical drive train is constituted of the lithium based battery, which via the power electronics with integrated inverters provides power for a common three phase electric motor (DC to AC). The electric heating device (e.g., heat pump, standard electric heater) draws a variable current from the battery in accordance with the comfort requirements of the cabin, which effectively are regulated via the power electronics.
The LV level, commonly found in today’s conventional ICE vehicles, is responsible for covering the ancillary power demand of electronics and electrical equipment such as the air conditioning unit, wind-screen wipers, window lifters, radio, satellite navigation system, lighting, etc. Average base load requirements of the LV system are estimated at 400 W. This includes future efficiency gains, which are equalised again by a larger number of electrical equipment fitted as a standard configuration and therefore also used more often by the vehicle’s occupants.
illustration not visible in this excerpt
Figure 2-4: Setup of the Drive Train of a Battery Electric Vehicle (BEV)
Furthermore, the charger of the battery, also incorporated in the power electronics, converts the standard three or single phase system of the electrical grid to direct current. Here, the power supply allowed by the socket and effectively the fuses of the electrical system, to which the BEV is connected, determines the charging rate (C-rate) of the battery. A one phase AC connection of most wall sockets accommodates charging power of up to 3.6 kW (230 V). This is generally the standard charging procedure in situations, where BEVs are charged as soon as they are connected to a standard wall socket (uncontrolled charging). However, controlled charging scenarios require a certain intelligence which optimises the charging demand of the vehicle in accordance with cost criteria of electricity production. This intelligence is located in the vehicle and reacts to certain electricity price signals sent via a smart meter (or wall box) to which the vehicle is connected. Higher charging power with three-phase 400 V AC charging of up to 11 kW (common in most households, e.g., to power the electrical stove and oven) is also possible here.
Although even higher charging power is possible with the described three phase AC system, the future standard for fast charging is assumed to be conducted via high-power DC charging. Particularly recharging a vehicle’s depleted batteries in a very short period of time, makes high-power DC charging one of three viable options for extending the range of a BEV. The following three range extension technologies – fast charging, battery switching and driving with a serial micro ICE – are now described in the following paragraphs:
1. DC fast charging technology
The modelling of a fast charging process is based on the expected international standard IEC 62196-3, which is maintained by the International Electrotechnical Commission (IEC). It fulfils the goal of an adequate and safe means of range extension by recharging the battery capacity to a state of charge (SOC) of above 80 % in under 10 minutes. Charging rates higher than 6C (six times the capacity of the vehicle’s battery, 1C = 1 h charging time) are applied. This allows the BEV to draw a direct current from the fast charge station with a power of up to 170 kW (850 V DC, 200 A) via a conductive charging system such as the Combo Connector proposed by Mennekes21. This connector version is based on the Mennekes Type 2 plug geometry (formally backed by the European Commission) in combination with two additional DC contacts. Particularly these high C-rates lead to increased energy conversion losses at the battery and at its power electronics. An additional energy demand of 15 % is estimated, covering all electrical charging losses and the necessary power demand to cool the battery concurrently and thus prevent rapid cell degradation. This value is based on measurements and technical experience by industry experts of the EASYBAT consortium 22.
2. Battery switch technology
Another possibility of range extension is integrating battery switch technology into the design of BEVs and covering Europe with a network of battery switch stations along highways and main traffic arteries of high vehicle densities. Battery switch stations are modelled as electricity consumers with a limited flexibility for load shifting. The flexibility arises as a result of reserve batteries held in storage at the switching stations, in order to accommodate a seamless switch of an incoming depleted battery with a fully charged outgoing battery. This advantage favours an energy system with a varying load demand over a 24 h period and an uncontrollable feed-in of renewable energy sources. However, due to the limited chances of a European-wide rollout (minor political backing, bankruptcy of battery switching pioneer Better Place), this thesis will only touch on this option of range extension occasionally in the following chapters not going into any greater detail. A detailed energy-economic analysis on the advantages of the battery switching technology as a means for range extension is found in TUM’s technical report on the research project EASYBAT18.
3. Serial micro ICE technology
The third option for extending a BEV’s driving range is the usage of an on-board micro ICE in serial configuration (no mechanical coupling to the axle). The ICE is treated as an add-on designed and configured to provide power of between 15-30 kW. Therefore its very compact dimensions barely affect cabin or trunk volume. Rather it is considered that the additional space is attained by reducing the BEVs original battery size slightly, while keeping the vehicle’s overall weight constant. The serial arrangement of the range extender and hence a decoupling from the mechanical drive train allows the ICV to run in the sweet spot of the speed-torque characteristic of the engine. Together with an empty battery as a power and energy buffer, this configuration facilitates highest possible ICE efficiencies for most driving conditions necessitating range extension. Once range extension becomes necessary on longer trips, BEVs with an additional micro ICE have the advantage that fuel stations are ubiquitous. It can be assumed that in the course of the transition from ICVs to BEVs more and more traditional filling stations will close down for economic reasons. Thus during the first decades BEVs with a small ICE may have the advantage of a higher geographic flexibility; while BEVs with fast charge technology will still highly rely on their range extension infrastructure to become available more commonly. Until then, drivers of BEVs with fast charge technology may still be dependent on a greater detour, in the instance when a range extension becomes necessary. However, over time and until 2050 this advantage may well recede. For this analysis, however, these described detours are considered negligible.
In this thesis the following assumptions have been made on the electric driving range of BEVs: An average range of 150 km in the NEDC for BEVs was selected, which represents a broad average of available BEVs in the A- to D-class vehicle segments. Furthermore, it is assumed that this average driving range will more or less remain a constant dimension for the construction and design of BEVs until 2050. Considering the fact that BEVs will inherently come with additional weight and investment costs in comparison to ICVs, a larger battery and thus a greater electric range proves counterproductive for the following reasons: less economic competitiveness due to higher total costs of ownership, reduced driving dynamics and an increased plug-to-wheel demand as a result of higher vehicle weight. It is therefore assumed that in future OEMs are hesitant to increase battery size and thus electric range higher than 150 km as an average over all vehicle classes, in particular when an adequate infrastructure to extend range (either by battery switching, fast charging or filling up at fuel stations) is in place. Due to the availability of range extension technologies, it is assumed that further improvements in battery technology (such as higher energy densities) lead to a reduction in battery system weight and thus a decreased plug-to-wheel demand rather than a further increase in electric range with all its adverse side-effects mentioned before.
The simulation of a FCV fleet in ZEVS also requires an abstraction of the whole vehicle and the specifics of its electrical drive train with a focus on the fuel cell stack and the hydrogen storage tank. The main source of power supplied for the propulsion of a FCV is provided by an electrochemical conversion process in the fuel cell stack. In this dissertation it is assumed that the polymer electrolyte membrane fuel cell (PEMFC) will remain the most suited and advanced fuel cell technology for vehicular power sources. All following descriptions and data specifics refer to this fuel cell type.
The following paragraphs explain in short the conversion process of chemical to electrical energy of the PEMFC (and vice-versa of electrolysis). These explanations help understand the differences between FCVs and BEVs in aspects of total vehicle energy demand, the efficiencies of the energy conversion process, HVAC requirements, driving range and the load shifting potential for supplying hydrogen to the whole FCV fleet. The complex energy conversion process in the PEMFC can be described by the chemical equation of the redox reaction of hydrogen and oxygen to water, involving the transfer of electrons and their immediate use by the electrical drive train of the FCV:
Here, the redox reaction’s stoichiometry is describing the ratio of the relative quantities of reactants and products with positive integers.
The overall setup of the drive train components in a FCV are depicted in Figure 2-5. It consists of the high voltage (HV) level mainly regulated by a small lithium-ion battery (approx. 1.4 kWh) and its power electronics, to which the low voltage (LV) level on 48 V basis is connected via a DC/DC converter. The fuel cell stack is operated most of the time in the operational sweet spot of the cells, a narrow band of highest efficiency at low and medium power supply (linear area of polarisation curve). Should the driving situation require a higher power demand (e.g. accelerating or driving uphill) the lithium-ion battery mainly steps in to compensate the power deficit. In cases of deceleration or driving downhill the fuel cell stack continues in its state of highest efficiency, while the generator recuperates the braking energy, which charges the small lithium-ion battery. The installed battery therefore functions more as a buffer for instances of high power fluctuations, rather than as a BEV’s battery, which acts more as a storage for large quantities of energy. Battery cell designs therefore differ considerably when designed for a FCV or BEV. The FCV’s storage tank of commonly 700 bar of compressed hydrogen is the actual source of high energy density. Hence, the described technical operation and design of the FCV drive train provides an optimised fuel economy, while at the same time the aging processes of the fuel cells are kept at a minimum. The set-up of the ancillary equipment is similar to that of a BEV. Only the electric heating device is smaller in dimension and power output. It activates itself in support of the waste heat of the fuel cells during cold starts or low ambient temperatures, whilst during all other times the waste heat generally suffices to warm the cabin interior.
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Figure 2-5: Setup of the Drive Train of a Fuel Cell Electric Vehicle
The maximum theoretical efficiency of the electrochemical conversion process, described by the redox reaction above, lies at . This considers the maximum amount of usable energy in the form of electricity (Gibb’s free reaction enthalpy) over the heating enthalpy of hydrogen. The implementation of a technical application requires a physical separation for the transfer of protons (H+) and electrons (e-) during the course of the redox reaction described before. Inevitably activation, resistance and diffusion losses incur within the fuel cells, mainly due to material and cell design limitations. These overpotentials are noticeable due to a drop in voltage from the theoretical ideal of (the reversible cell voltage according to Gibb’s free enthalpy). This drop in voltage is expressed by the voltage efficiency , the quotient of the terminal cell voltage over the reversible cell voltage:
Further operational efficiency losses result from a non-ideal reaction stoichiometry of , in which a few hydrogen reactants do not undergo the electrochemical separation process of protons and electrons via the cell. They are also mainly attributed to material and design limitations of the cells and are expressed by the current efficiency the quotient of used hydrogen reactants to the total amount of hydrogen fed into the fuel cell:
The measured value is also known as the hydrogen consumption of a FCV. It can be derived from Faraday’s laws of electrolysis, which is summarised in mathematical form as:
Here, Q is the total electric charge, is the number of electrons transferred per hydrogen molecule, the amount of moles of hydrogen used in the reaction with oxygen and the Faraday constant. The change in electric charge and thus the amount of moles (reactants) consumed over time leads to a constant current flow in a singular cell of:
Thus according to the number of cells aligned in series, the mole flow rate in per fuel cell stack is:
According to the redox reaction equation, this holds true for a hydrogen stoichiometry of . The relative quantity of the second reactant, oxygen, would consequently be half an oxygen molecule. However, the electrochemical conversion process of molecular hydrogen and oxygen to electricity is not ideal in practice. A stoichiometry of and of are more common. As mentioned before, the unused hydrogen reactants consequently lower the current efficiency of the process.
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