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Business model quantification framework for the core participants of the EV charging marketGoncearuc, Andrei; Sapountzoglou, Nikolaos; De Cauwer, Cedric; Messagie, Maarten;Coosemans, Thierry; Crispeels, ThomasPublished in:34th International Electric Vehicle Symposium and Exhibition (EVS34) Nanjing, Jiangsu, June 25-28, 2021

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Citation for published version (APA):Goncearuc, A., Sapountzoglou, N., De Cauwer, C., Messagie, M., Coosemans, T., & Crispeels, T. (Accepted/Inpress). Business model quantification framework for the core participants of the EV charging market. In 34thInternational Electric Vehicle Symposium and Exhibition (EVS34) Nanjing, Jiangsu, June 25-28, 2021 (pp. 1-12)

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34th International Electric Vehicle Symposium and Exhibition 1

34th International Electric Vehicle Symposium and Exhibition (EVS34)

Nanjing, Jiangsu, June 25-28, 2021

Business model quantification framework for the core

participants of the EV charging market

Andrei Goncearuc1,a, Nikolaos Sapountzoglou1, Cedric De Cauwer1, Maarten Messagie1, Thierry

Coosemans1, Thomas Crispeels2

1 ETEC Departement & MOBI Research Group, Vrije Universiteit Brussel (VUB), Pleinlaan 2, Brussel,

2BUTO Department & MOBI Research Group, Vrije Universiteit Brussel, Pleinlaan 2, Brussel

a [email protected]

Summary

This paper presents a quantification framework for the business models of the core participants of the electric

vehicles charging market, defining the factors that directly influence their revenues and costs and providing two

sets of earnings before interest and taxes (EBIT) formulas: precise and approximated. The precise formulas

would be useful for business analytics of the current participants of EV charging market, while the

approximated could be applied by the new entrants, to make reliable predictions based on the benchmark data.

Finally, this research applies the defined framework on EBIT scenario of an archetypical charge point operator,

based on the real–life data.

Keywords: business model, charging, EV (electric vehicle), market, market development

1 Introduction

The electric vehicles (EV) market shows an immerse numerical growth over the last decade. A recent study of

the International Energy Agency (IEA) shows that the global EV stock has risen substantially from the total

number of 17,000 units in 2010 to more than 10 million vehicles worldwide in 2020. Obviously, the EV market

is tightly linked with the EV charging market, which, in its turn, shows an impressive development as well. In

2020, the total global number of publicly accessible electric vehicle charge points (EVCPs) had reached 1.3

million units, among which almost 400,000 were fast chargers [1].

However, the evolution of the EV charging market is not only characterized by a numerical growth. The busi-

ness models of the participants of the EV market evolve as well, trying to find the most optimal and viable

solutions. The existing literature on business modelling contains different qualitative frameworks and ap-

proaches with various elements and structures [2][3][4]. However, it is noticeable that these approaches have a

common part, being the quantitative part, which describes a company’s revenues and costs. Thus, the quantita-

tive part of every business model evaluates its profitability and even viability.

Since the EV charging market is relatively new, there is a clear shortage of studies focusing on the quantitative

business models of its participants. The current paper covers this research gap by presenting a business model

quantification framework for the core participants of the EV charging business ecosystem.


1.1 Business modelling approaches

As it was already mentioned, various qualitative business modelling approaches have the common quantitative

part. Table 1 gives an overview of quantitative elements of the three most cited business modelling approaches.

Table 1: Review of the quantitative parts of the most cited qualitative business modelling approaches [2][3][4]

Business modelling

approach

Business Model Canvas

by Osterwalder &

Pigneur [2]

Integrative business

modelling framework by

Richardson [3]

Business model

definition and innovation

framework by Teece [4]

Quantitative part Revenue Streams

Cost Structure

Value Capture Mechanism to capture

value

As it is shown on Table 1, all the quantitative parts of the aforementioned business modelling approaches look

quite similar, trying to capture the value through two main elements, namely revenues and costs, which are, at

the same time, the only elements for the earnings before interest and taxes (EBIT) calculation. Thus, it makes

the EBIT of the company a relatively good indicator of the viability and profitability of the business model on

the short term. The long–term viability of the business model requires the return on investment (ROI) and other

factors investigation and falls out of scope of this research, being a wide research topic on itself.

1.2 EV charging business ecosystem

As it was previously mentioned, the current paper proposes a framework for the quantification of the business

models of the core participants of EV charging business ecosystem. The concept of the business ecosystem was

introduced Moore [5] and can be defined as a set of interrelated entities (individuals and/or organizations) that

interact with each other aiming at creating benefit (or other value). According to Moore, every business

ecosystem comprises three layers of participants depending on their level of involvement. The inner layer of the

business ecosystem is its core, which includes only the entities directly participating into the core business of

the ecosystem, being the direct suppliers and distributors. The mid layer is the extended enterprise, including

not only the core business participants, but also their customers, customers of customers, standard bodies,

indirect suppliers and suppliers of complementary products and services. Finally, the top layer comprises the

whole business ecosystem. It includes both core business and extended enterprise layers, along with all the

other stakeholders, such as policy makers, trade associations, labour unions, investors etc.

The current research focuses only on the core participants of the EV charging business ecosystem, directly

related to the EV charging. According to De Cauwer et al. [6] the structure of the total EV charging value chain

can be divided in four groups of stakeholders. The involvement of these stakeholders into the EV charging

business can be translated into the terms of a business ecosystem as it is visualized on Fig. 1:

Figure 1: EV charging Business Ecosystem [5][6]


Thus, the core of EV charging business ecosystem includes three types of entities [7][8]:

• Equipment Manufacturers (EM): entities producing EVCPs and other charging equipment.

• Charge Point Operators (CPO): entities responsible for management and maintenance of EVCPs.

• Mobile Service Providers (MSP): entities providing access to the EV charging for EV drivers.

2 Methodology for quantification of the business models of the core

participants of EV charging business ecosystem

The following methodology for the quantification of the business models of the core participants of the EV

charging business ecosystem is based on the EBIT concept. The EBIT of a company can be calculated as a

difference between its revenues and costs before taking in consideration interests and taxes [9].

(1)

Considering the fact that various countries and even provinces have their own different taxing systems, the

EBIT concept becomes a relatively convenient way to quantify the business model on a global perspective. The

elements participating into the revenues and cost calculations of the key participants of the EV charging market

are based on the article of Madina et al. [10], validated and/or complemented by the means of the semi-

structured interviews with the key European EV charging market players and findings from other open sources

and publications.

The current section includes the detailed descriptions of the formulas defining the costs, revenues and derived

EBIT of the three core participants of EV charging infrastructure. Although the calculation of EBIT for EMs is

quite straightforward, this is not the case for CPOs and MSPs. The calculation of EBIT for the last two,

depends on multiple factors such as electricity costs, and the location and utilization rate of EVCPs among

others. In order to make use of the precise formulas set, the user has to own the full and detailed data about

every charging session on every controlled EVCP. The approximate set of formulas, on the other hand, allows

for the application of aggregate and average values of the factors, which simplifies data collection and

calculation, making, however, the results less precise. Thus, dependent on the capabilities and preferences of

the user, either a detailed or a simplified version of the framework can be used.

2.1 Equipment Manufacturer

2.1.1 Costs EM

The costs of EV charging EM can be defined by the following formula:

(2)

C Manufacturing : costs related to manufacturing of EVCPs (excluding human resources (HR) costs)

C Amortization : amortization EV charging systems production plant and other machinery

C HR : costs related to HR

C R&D : costs related to research and development activities

C Other : other costs not included in the previous categories

Furthermore, the total costs of manufacturing of EVCPs (C Manufacturing) can be defined as follows:

(3)

𝒚: type of EVCP, from 1 to Z (e.g. 𝒚1: slow AC charging system for residential use; 𝒚2: faster AC charging

system for business locations; 𝒚3: ultra-fast DC charging system for on-the-road charging).

Cy: cost of production of unit 𝒚 (not including the HR costs)

Ny: number of produced units type 𝒚

Thus, the total costs of EV charging equipment manufacturer can be redefined as follows:


(3)

2.1.2 Revenues EM

The total revenues streams of the EV charging EM are, quite obviously, generally dependent on the EVCPs

sales:

(4)

CS Sales : total revenues from the sales of different types of EVCPs

OR EM : other revenues of EV charging equipment manufacturers, not related to sales of EV charging systems.

The total revenues from charging systems sales are dependent on the number of items sold and their price. Thus,

the CS Sales can be defined by the following formula:

(5)

𝐏𝒚: price of EVCP of type 𝒚.

𝐍𝒚: number of EVCPs of type 𝒚 sold.

Thus, the redefined revenue formula of an EV charging systems EM is:

(6)

2.1.3 EBIT EM

Earnings before interest and taxes can be then formulated as the difference between EMs’ revenues and costs:

(7)

(8)

2.2 Charge Point Operator

2.2.1 Costs CPO

The costs of a CPO depend on multiple factors and can be generally defined by the following formula:

(9)

C Infrastructure: includes all the costs related to the infrastructure of EVCPs owned by CPO

C Electricity: cost of electricity bill which is supplied through the EVCPs owned by CPO.

C MP: costs for assessing the marketplace, mainly including the interoperability costs paid by the market

participants to the Roaming Service Providers.

CPO costs related to infrastructure can be defined by the following equation:

(10)

C Depreciation 𝒚: costs of depreciation of owned EVCP type 𝒚

CM&M 𝒚: costs related to the management and maintenance of an EVCP type 𝒚 owned/operated by the CPO

N𝒚: number of EVCPs of type 𝒚

The depreciation cost (C Depreciation) is defined through the methodology chosen by the company itself, based on

the initial investment and expected lifetime of the asset. However, the basic formula for linear depreciation

looks as follows:

(11)

𝐏𝒚: initial investment, being the price of EVCP type 𝒚

𝐈𝒚: initial investment, being the installation costs of EVCP type 𝒚

Ly: defined useful lifetime of EVCP type 𝒚

Sy: salvage value of EVCP of type 𝒚 after the end of its defined useful lifetime (Ly)


According to CREG [11], electricity costs can be divided into variable (per kWh) and fixed. The variable cost

component typically includes energy costs (energy production costs and suppliers’ mark-up), network costs (of

both transmission and distribution) and variable taxes. The fixed component includes taxes charged on yearly

basis [12]. Thus, the electricity costs (C Electricity) of a CPO could be defined by the following formula:

(12)

k: index number of EVCP (ranging from 1 to N) participating into the CPO network

i: index number of charging session (ranging from 1 to n) per EVCP number k

Ei: number of kWh charged during the charging session i at the EVCP number k

Cn: network costs per kWh, which include the transmission and distribution costs

Ces: energy cost per kWh, charged by the energy supplier

Cvt: variable energy taxes paid per kWh

Cft: fixed energy taxes paid on yearly basis

o Approximation: C Electricity

Even though the components of the electricity costs of EV charging paid by a CPO show certain fluctuations

depending on daytime, location of the EVCP, peak loads and other factors, in general, these fluctuations do not

show any significant deviation from the average cost per kWh, which depends on the total yearly energy

consumption of the company [13]. Thus, the equation (12) can be aggregated in the following manner:

(13)

Ce: average electricity cost per kWh

MCy: maximum capacity of kWhs, EVCP type y is able to transmit in 1 year time (24/7 availability)

URy: actual usage rate (in %) of the total maximum capacity (MC) of the EVCP type y

Ny: number of the EVCPs type y

The total CPO costs previously defined in equation (9) can be redefined in the following manner:

(14)

o Approximation: Costs CPO

Furthermore, the total costs defined in the precise equation (14) can be redefined by making use of the

approximated electricity costs calculation defined in the equation (13) in the following manner:

(15)

2.2.2 Revenues CPO

Charge point operators’ revenues depend, in general, on the charging activities on the managed network of

EVCPs:

(16)

TF CPO: total fee received from the charging activities on the CPO charge points network.

OR CPO: other revenues generated by side activities not directly related to the EV charging (e.g. software

subscription fees).

The total fee received from the charging activities (TFCPO) depends, in its turn, on several other factors, defined

by the following formula:

(17)

k: index number of charging point (ranging from 1 to N) participating into the CPO network

𝒊: index number of charging session (ranging from 1 to n) per charging point k


CFCPO 𝒊: CPO charging fee per kWh during the charging session 𝒊 at the charge point k

𝐄𝒊: number of kWh charged during the charging session 𝒊 at the charge point k

Thus, the total CPO revenues defined by the equation (16) can be redefined as follows:

(18)

o Approximation: Revenues CPO

Considering the approximation of energy costs calculation provided in equation (13), the revenues of the CPO

in total and its Ei component in particular can be approximated in the following manner:

(19)

CFCPO y: CPO charging fee, dependent on the type of charging system y

2.2.3 EBITCPO

As it is already mentioned, EBIT of a CPO can be calculated as a difference between its revenues (equation

(18)) and its costs (equation (14)) and defined as follows:

(20)

o Approximation: EBIT CPO

Considering the approximations of CPO’s revenues (equation (19)) and costs (equation (15)), the CPO’s EBIT

can be approximated as follows:

(21)

2.3 Mobile Service Provider (MSP)

2.3.1 Costs MSP

The costs of MSPs can be defined by the following formula:

(22)

TFCPO: total fee paid by MSP to the partner CPOs (see equation (17)) for their customers’ charging

CIT: costs related to management and maintenance of IT platforms and mobile apps (not including the HR costs)

C Other: other MSP costs not included in the previous categories.

Thus, considering the previously defined TFCPO formula, the costs of an MSP can be redefined as follows:

(23)

o Approximation: Costs MSP

The costs formula of an MSP can be approximated by making use of the formulas used in the approximation of

CPO revenues (see equation (19)). Thus, the approximated costs formula of an MSP looks as follows:

(24)

2.3.2 Revenues MSP

The MSPs currently existing in the market follow two different pricing strategies, having direct impact on their

revenue calculation formulas. Therefore, the current research divides the framework for the calculation of

MSPs’ revenues in two parts, namely revenues generated via variable and fixed pricing strategies.


2.3.2.1 Variable price strategy

The charging fee per kWh, that the MSP uses to bill its customer EV users, is variable and dependent on the

initial charging price (CP) determined by the CPO, managing the EVCP, while the MSP adds a fixed mark-up

(MU). Furthermore, the CPO charging price fluctuates, being influenced by different factors (e.g. energy price

at a given moment, EVCP location, etc.), causing the fluctuation of the total charging fees paid by final EV user.

On the one hand, the advantage of this pricing strategy is quite obvious, the MSP transfers the risk of additional

expenses to their customer EV users, maintaining a fixed mark-up. On the other hand, the current approach

increases the degree of uncertainty between the customers, decreasing the customer loyalty. Thus, the revenues

of an MSP following the variable price strategy can be defined by the following equation:

(25)

Var CFMSP: total variable charging fees paid by the EV users

SF: total subscription fees paid by the EV users on yearly (or monthly) basis.

ORMSP: other MSP revenues not related to the EV charging activities

Furthermore, typically, MSPs offer various subscriptions reducing their customers’ fees per kWh in exchange

for a fixed periodical fee. The formula defining the MSP revenues from these subscription fees looks as follows:

(26)

𝒔: number of subscription type (e.g. type 1 – gold; type 2 – silver etc.) ranging from type 1 to type M

SP: subscription price of subscription type 𝒔

NC: number of customers which have purchased the subscription type 𝒔

Thus, the mark-up (MU) of an MSP following the variable pricing strategy is fixed on a predefined value,

while the final price for charging paid by the customer EV user is fluctuating along with the CPO charging fee.

The total variable charging fees (Var CFMSP) of an MSP can be defined by the following formula:

(27)

k: index number of EVCP (from 1 to N) participating into the networks of partner CPOs

𝒊: index number of charging session (ranging from 1 to n) per charging point k

CFCPO: charging fee (of the CPO) per kWh during the charging session 𝒊 at the charge point k

MU: MSP mark-up per kWh on the charging fee (CF) of the CPO for charging session 𝒊 at EVCP k.

𝐄𝒊: number of kWh charged during the charging session 𝒊 at the charge point k

Thus, the redefinition of MSP revenue equation (25) following the variable price strategy looks as follows:

(28)

o Approximation: Variable Revenues MSP

The revenue formula of an MSP using variable price strategy can be approximated by making use of the

formulas used in the approximation of CPO revenues (see equation (19)) as follows:

(29)

2.3.2.2 Fixed price strategy

The charging fee per kWh, that the MSP uses to bill its customers (EV users), is fixed by the MSP and

independent from the fluctuations of the CPO charging price. Obviously, an MSP can follow this strategy only

by adjusting its mark-up to the fluctuations of the CPO charging price. Thus, this strategy increases the degree

of uncertainty about the company revenues, and the degree of customer loyalty. It also increases the number of

new customers, as the risk of higher expenses is born by the company. The revenues of an MSP following the

fixed price strategy can be defined by the following formula:

(30)


Total Fix CFMSP: total fixed charging fees paid by the EV users

SF: total subscription fees paid by the EV users on yearly (or monthly) basis.

ORMSP: other MSP revenues not related to the EV charging activities

It can be noticed that the only difference of the aforementioned formula (equation (28)) with the formula

defining the revenues of the MSP following the variable pricing strategy (equation (30)) lies in the total

charging fee, which has become fixed. Thus, the total charging fee of an MSP following the fixed price strategy

can be defined as follows:

(31)

𝒔: number of subscription type (e.g. type 1 – gold; type 2 – silver etc.) ranging from type 1 to type M

Fix CFMSP: charging fee per kWh fixed by MSP dependent on the subscription type 𝒔

Es: total number of kWh charged by the customer EV users having MSP subscription type 𝒔

The subscription fees (SF) and the other revenue (ORMSP) categories remain similar with the MSPs following

the variable price strategy. Thus, the redefined revenue formula for an MSP (defined in equation (30))

following the fixed price strategy looks as follows:

(32)

2.3.3 EBIT MSP

Considering the fact that the EBIT of a company can be calculated as the difference between its revenues and

costs, the formula of the EBIT of an MSP can be defined in two different ways, depending on the price strategy

it follows. Thus, the EBIT calculation of an MSP following either variable or fixed price strategy uses the

respective revenue formula, while the costs formula remains valid for both.

Thus, the EBIT of an MSP following the variable price strategy looks as follows:

(33)

While the EBIT of an MSP following the fixed price strategy can be defined in the following manner:

(34)

o Approximation: EBITMSP

The aforementioned formulas for the calculation of EBIT of MSPs can be approximated by replacing their

elements related to the CPO charging fee with their approximation from equation (19). Thus, the

approximations of the MSPs EBITs following variable and fixed pricing strategies are given by:

(35)

(36)


3 Application of the defined framework: EBITCPO calculation in

approximation of the real – life use case

The current application serves as a showcase for the calculation mechanism defined in the previous section. A

representative example of all the other formulas for every described entity type of the approximated calculation

of EBIT of an average CPO is provided below. The values of the different parameters presented in Table 2,

have been gathered from chargers in the Vrije Univeristeit Brussel (VUB) campus and from partner companies

such as Powerdale, New Motion, Fastned and EVBox.

Table 2: Aggregated numerical values for the parameters participating into the CPO EBIT calculation

Considering the given assumptions, the CPO gets the following results:

Table 3: Results of the CPO EBIT calculation

Revenues CPO 9 468 000 €

Costs CPO 8 727 800 €

C Depreciation 2 660 000 €

CM&M 2 800 000 €

C Electricity 2 152 800 €

CHR + CMP + C Other 1 115 000 €

EBIT CPO 740 200 €

However, the presented results can serve only as a snapshot of the situation faced by this simulated CPO at a

certain moment. For this reason, it would be useful to simulate the influence on EBIT caused by the changes of

the relevant variables in order to define the power of their influence and define the most critical ones.

# Parameter Symbol Value EVCP type 1

slow AC

charging system

for residential use

EVCP type 2

faster AC

charging system

for business

locations

EVCP type 3

ultra-fast DC

charging system

for on-the-road

charging 1. EV charge point

(EVCP) power

levels

y kW 7.4 11 50

2. Prices Py € 1 000 1 500 20 000

3. Installation costs Iy € 1 000 1 500 3 000

4. Charging fees CFy €/kW 0.30 0.35 0.60 5. Maximum yearly

charging capacity

MCy kWh/year 64000 96000 438000

6. Usage Rate URy % 3

7. Useful lifetime Ly years 10 8. Salvage value Sy % 5 9. HR cost CHR € 1 000 000

10. Cost for accessing

the marketplace CMP € 15 000

11. Number of EVCPs Ny Units 1 000 12. Miscellaneous costs C Other € 100 000

13. Electricity costs CElectricity €/kWh 0.12 14. Management and

maintenance costs

CM&M € 10% of the initial investment in EVCP (P+I)


The aforementioned figures show the percentual influences of increases of the relevant variables on EBIT of

the simulated CPO.

The increase in price and/or installation costs, represented in Fig. 2, has, obviously, a negative influence on the

CPO EBIT. Even though, the initial investment is, in general, considered as capital expenses (CAPEX) and

cannot be a part of EBIT, the negative influence is due to the rise of depreciation costs, that are, among other,

dependent on the amount of initial investment.

Figure 3 presents the increase in number of EVCPs, fixing their initial prices and installation costs. As it can be

observed, in general, the increase in number of chargers shows a relatively positive influence on the CPO’s

EBIT. However, considering the current usage rate of the chargers, the increase of the number of EVCPs type 1

(with the lowest power level of 7,4 kW) shows a slight negative influence. The problem lies in the currently

applicable low usage rate of the chargers, due to the relatively low total number of EVs on the roads. Thus, the

revenues per kWh are not able to cover the expenses. Furthermore, the EBIT rise due to the increase in number

of EVCPs of other types is also related to their utilization rate and if the number of EVCPs would be

significantly higher than the number of EVs, the effect would be the opposite. Thus, the increase in number of

EVCPs would only make sense in the situation where the number of EVs is expected to grow as well.

Figure 4 shows the potential influence in charging fees and electricity price on EBIT. Obviously, the increase in

electricity price causes the reduction of EBIT, as it means the increase in costs, while the increase of charging

fees shows a positive effect. However, it is important to underline that the current simulation does not consider

Figure 2: % Influence of increase of initial

investment (Py, Iy) on EBIT Figure 3: % Influence of increase in number of

EVCPs on EBIT

Figure 5: % Influence of increase in usage rate of

EVCPs of every type on EBIT Figure 4: % Influence of increase in charging fees of

EVCPs of every type and electricity costs on EBIT


the customers’ willingness to pay. Therefore, after a certain point the increase in price would show a negative

effect, due to the loss of customers’ loyalty.

Finally, Fig. 5 shows the effect of increase of usage rate of the chargers on EBIT. As it can be observed, this

parameter has the strongest effect, but is partially dependent on external factors. The crucial factor for the

increase of the usage rate of EVCPs is the increase of number of EVs on the roads. If the number of EVs would

grow high enough, the usage rate would reach its maximum, rising the CPO’s EBIT and motivating the

company to increase the number of EVCPs in its network.

4 Conclusions

The current paper has defined a quantitative business modelling framework for the core participants of the

electrical vehicle (EV) charging business ecosystem. This framework can serve as a good modelling and

analytical tool both for new (or potential) market entrants and for its current participants.

The paper includes two sets of formulas, the precise and approximated one. The precise formula set is mainly

useful for the current participants of the market, as they already have an existing database with values of all the

necessary parameters. These equations can be used as a business analysis tool, allowing these companies to

make more precise and profitable steps in their business development.

The approximated formula set, on the contrary, can be a handy tool for the new EV charging market entrants.

Due to the lack of internal and precise data about every charging session on a high number of charge points,

these companies might not be able to use the precise formula set. However, simulation of their potential costs,

revenues and EBIT will be possible with the approximated formula set.

Finally, it is important to mention that as the EV charging market is currently developing relatively fast, so do

the business models of its participants. This evolution can influence all the aspects of their business models,

including pricing strategies and all the variables participating in this business model quantification framework.

Therefore, it becomes impossible to generalize and apply these defined equations in every case and a

readjustment is deemed necessary on a per site basis.

References

[1] IEA, Global EV Outlook 2021, https://www.iea.org/reports/global-ev-outlook-2021/trends-and-developments-in-

electric-vehicle-markets#abstract, accessed on 2021-04-07

[2] Osterwalder, A., & Pigneur, Y. Business model generation: a handbook for visionaries, game changers, and challeng-

ers. ISBN 978-0-470-87641-1, John Wiley & Sons, 2010

[3] Richardson, J. The business model: an integrative framework for strategy execution. Strategic change, 17(5‐6), 133-

144, 2008

[4] Teece, D. J. Business models, business strategy and innovation. Long range planning, 43(2-3), 172-194, 2010

[5] J.F. Moore, The Death of Competition: Leadership & Strategy in the Age of Business Ecosystems, New York, Harper

Business, ISBN 978-0-887-30850-5, 1996

(SGEMS). European Commission, 2017

[6] C. De Cauwer, et. al., Towards EU-wide Interoperability of charging infrastructure for electric vehicles-The Belgian

Case. Symposium EVS31, 2018

[7] J.C. Pandazis et. al. Memorandum of Understanding (MoU) fostering seamless and valuable EV customer experience in

Europe. SUB-GROUP TO FOSTER THE CREATION OF AN ELECTROMOBILITY MARKET OF SERVICES

[8] R. Ferwerda et. al. Advancing E-Roaming in Europe: Towards a Single “Language” for the European Charging

Infrastructure. World Electr. Veh. J. 2018, 9, 50. https://doi.org/10.3390/wevj9040050

[9] Z. Bodie, A. Kane, A. Marcus, Essentials of Investments, ISBN 978-0-072-51077-5, McGraw Hill, 2004

[10] C. Madina et. al., Methodology for assessing electric vehicle charging infrastructure business models, Energy Policy,

ISSN 0301-4215, 89(2016), 284-293

[11] CREG. Hoe is de energieprijs opgebouwd? Retrieved from https://www.creg.be/nl/consumenten/prijzen-en-

tarieven/hoe-de-energieprijs-opgebouwd , assessed on 2021-03-12

[12] VREG. Netwerktarieven en heffingen van toepassing op de professionele elektriciteitsklanten aangesloten op het

laagspanningsnet zonder piekmeting. Retrieved from

https://www.iea.org/reports/global-ev-outlook-2021/trends-and-developments-in-electric-vehicle-markets#abstract

https://www.iea.org/reports/global-ev-outlook-2021/trends-and-developments-in-electric-vehicle-markets#abstract

https://doi.org/10.3390/wevj9040050

https://www.creg.be/nl/consumenten/prijzen-en-tarieven/hoe-de-energieprijs-opgebouwd

https://www.creg.be/nl/consumenten/prijzen-en-tarieven/hoe-de-energieprijs-opgebouwd


https://www.vreg.be/sites/default/files/uploads/distributienettarieven/2020/Elektriciteit/kleine_bedrijven_elek_01.04.2020_

t.e.m._31.12.2020_excl._btw.pdf , assessed on 2021-02-18

[13] VLAIO. Energiescans in kmo’s: 2017-2018. EINDRAPPORT, DEEL 1. Agentschap Innoveren & Ondernemen.

Retrieved from: https://www.vlaio.be/nl/begeleiding-advies/duurzaam-ondernemen/energieadvies/energieadvies assessed

on 2020-12-19

Authors

Andrei Goncearuc has obtained a Master of Science degree in International Business and works as a

PhD Researcher at MOBI (Mobility, Logistics & Automotive Technology) Research Team of Vrije

Universiteit Brussel. MOBI is one of Belgium’s leading research centers for electromobility, socio-

economic evaluations for Andrei’s research area includes business aspects of EV charging, along with

innovative business modelling approaches for the emerging technologies in the EV charging

infrastructure (e.g. V2G).

Nikolaos Sapountzoglou received his Diploma in Electrical and Computer Engineering from the

Aristotle University of Thessaloniki (AUTH) in 2015, specializing in Electrical Energy. He obtained his

Ph.D. in 2019 from the Grenoble Electrical Engineering laboratory (G2Elab) of Université Grenoble

Alpes (UGA) as part of the Marie Sklodowska-Curie ITN Incite. His PhD research focused on fault

diagnosis in LV distribution grids with distributed generation. As of 2020, he is working on vehicle-to-

grid projects at Vrije Universiteit Brussel (VUB).

Dr. Cedric De Cauwer obtained his Master’s Degree in Engineering at the Vrije Universiteit Brussel in

2011, with a specialization in vehicle and transport technology. He immediately joined the MOBI re-

search group to work on electric and hybrid vehicle technology. Since 2013, Cedric’s PhD research was

funded by an IWT scholarship and focused on the prediction of energy consumption and driving range

of electric vehicles, and energy-efficient routing. He obtained his PhD in 2017, and has since continued

to apply his expertise in national and international projects. He is currently focused on the integration of

mobility solutions (EVs, autonomous vehicles) into the electricity grid (charging infrastructure, V2G).

Thierry Coosemans obtained his PhD in Engineering Sciences from Ghent University in 2006. After

several years in the industry, he became a member of the MOBI research team at the VUB, where he

works now as the co-director of the EVERGi team on sustainable energy communities. He is currently

involved in the scientific support for the Green Energy Park Zellik, and had an active role in Flanders

Make and the Living Labs EV Flanders. On a European level, Thierry was and is involved in the H2020

and FP7 projects SafeDrive, OPERA4FEV, SuperLIB, Smart EV-VC, Batteries20202, GO4SEM (coord),

FIVEVB, ELIPTIC, MOBILITY4EU, FUTURE-RADAR, OBELICS, INTERCONNECT, ENSEMBLE,

REDIFUEL, CEVOLVER, and RENAISSANCE, which he coordinates. His main research interests are

the development of CO2-neutral Sustainable Local Energy Systems, electric and hybrid propulsion

systems, and the performances of automated EV fleets, including in a V2G perspective. Thierry

Coosemans is an active member of EARPA, EGVIA, the Bridge Initiative and Flux50.

Maarten Messagie manages R&D&I activities at the Vrije Universiteit Brussel (VUB) in order to

support the transition towards a sustainable energy system. He works in the research center MOBI of the

VUB in which he co-leads the research unit EVERGi. The interdisciplinary research unit EVERGi

focusses with +25 dedicated researchers on sustainable multi-energy systems including the integration

of electric (and automated) vehicles, with local energy communities and thermal grids. EVERGi

developed and operates together with the ‘Green Energy Park’ a research co-creation platform and living

lab, demonstrating real-life applications of crucial elements in the energy transition.

Thomas Crispeels is Assistant Professor at the Vrije Universiteit Brussel at the department of Business

Technology and Operations (BUTO). His research is situated in the field of Technology & Innovation,

with a special focus on technology transfer and collaborative R&D in high technology industries such as

the biotechnology and smart logistics industries. Thomas teaches several courses on technology

entrepreneurship and the business economics of high-technology industries.

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https://www.vlaio.be/nl/begeleiding-advies/duurzaam-ondernemen/energieadvies/energieadvies

vrije universiteit brussel business model quantification … · 34th international electric vehicle...

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