Content uploaded by Benedikt Kirpes
Author content
All content in this area was uploaded by Benedikt Kirpes on Oct 29, 2019
Content may be subject to copyright.
E
ner
gy
Informatic
s
Kirpes et al. Energy Informatics (2019) 2:15
https://doi.org/10.1186/s42162-019-0072-4
RESEARCH Open Access
E-Mobility Systems Architecture: a
model-based framework for managing
complexity and interoperability
Benedikt Kirpes1*† , Philipp Danner2†, Robert Basmadjian3, Hermann de Meer3and Christian Becker1
*Correspondence:
benedikt.kirpes@uni-mannheim.de
†Benedikt Kirpes and Philipp Danner
contributed equally to this work.
1University of Mannheim, Schloss,
68131 Mannheim, Germany
Full list of author information is
available at the end of the article
Abstract
The future of e-mobility will consist of a large number of connected electric vehicles,
smart charging stations and information systems at the intersection of electricity and
mobility sector. When engineering and integrating the multitude of systems into even
more complex systems-of-systems for e-mobility, interoperability and complexity
handling are vital. Model-based system architectures support the engineering process
of information systems with the concepts of abstraction, reduction and separation of
concerns. In this paper, we contribute to the research body, by extracting requirements
for managing complexity and interoperability of these systems. Further, a comparative
analysis of the state-of-the-art in existing architecture models and frameworks for
e-mobility is conducted. Based on the identified gaps in existing research, we propose
the E-Mobility Systems Architecture (EMSA) Model, a three-dimensional systems
architecture model for the e-mobility sector. Its structure originates from the
well-established Smart Grid Architecture Model. We further allocate all relevant entities
from the e-mobility sector to the EMSA dimensions, including a harmonized role
model, functional reference architecture, component and systems allocation, as well as
a mapping of data standards and communication protocols. The model then is
validated qualitatively and quantitatively against the requirements with a case study
approach. Our evaluation shows that the EMSA Model fulfills all requirements
regarding the management of complexity and ensuring interoperability. From the case
study, we further identify gaps in current data model standardization for e-mobility.
Keywords: E-Mobility, Electric Vehicles, Systems Architecture, Information Systems,
Interoperability, Complex Systems, Model-Based Systems Engineering, Smart Grid
Architecture Model (SGAM)
Introduction
With the advent of the “Energy Transition”, the limitations of the power grid in its tra-
ditional form are becoming increasingly apparent. New requirements arise due to high
amounts of power feed-in from renewable energy sources and changed consumption pat-
terns, e.g. from self-consumption optimization and from Electric Vehicle (EV) charging
processes. The resulting volatility requires fundamental changes of the electricity infras-
tructure to ensure future stability of the power grid. Some of the most important changes
include a finer monitoring granularity in the grid and increased automation of energy
management using Information and Communications Technology (ICT). In this respect,
© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made.
Kirpes et al. Energy Informatics (2019) 2:15 Page 2 of 31
ICT has been playing a prominent role in enabling an intelligent energy management
which helps in provisioning a more efficient system for generation, transmission, distri-
bution, and consumption. Energy Informatics is playing an eminent part in cross-linking
energy systems with the utilization of ICT. The Smart Grid Architecture Model (SGAM)
was proposed and has soon become the de-facto reference model for the engineering and
analysis of intelligent energy management systems with a special focus on power grid
systems.
On the quest for realizing this energy transition and hence operating the energy sys-
tem more efficiently, e-mobility is envisioned to play a fundamental role (Kossahl et al.
2012). The three former separated sectors of Electricity, Individual Mobility and ICT are
merging together in future e-mobility systems. In this context, e-mobility denotes the
application of electric propulsion in the daily transport sector (e.g. for people and goods).
This, however, leads to novel challenges, e.g. EVs entailing an unknown demand on low-
voltage grids with potential high volatility. But it also enables synergies, for example by
utilizing EVs as energy storage units or for demand-side management approaches. Thus,
digitization and synergies among all sectors, not only including the energy domain (elec-
tricity, heat and mobility) but also traffic and public health, are especially important when
it comes to super-ordinate coordination, like in smart cites (Neureiter et al. 2014).
Smart charging comprises concepts, enabled by ICT, which reduce the impact of EV
charging processes on the grid from an energetic and economic perspective (Schmidt and
Busse 2013). Besides grid-related topics, innovative business aspects such as machine-to-
machine payments and transaction processing on a blockchain-based Information System
(IS), require computational capability and connectivity deployed in the vehicles and in
the charging infrastructure (Kirpes and Becker 2018). In order to enable such concepts,
interoperable systems are required, consisting of hardware, software, processes, data,
procedures and humans (ISO Central Secretary 2015).
In the fast developing field of e-mobility, harmonization of the interaction between
the various systems and stakeholders, e.g. car manufactures, grid operators, energy sup-
pliers, Charging Station (CS) operators, e-mobility service providers and EV users, is
essential. In addition, new business models have to be supported by providing a map of
allocated highly relevant standards, e.g. ISO 15118 and IEC 61851. Model-based systems
engineering and model-driven architectures may support in handling and reducing the
increasing complexity of such interoperable systems in e-mobility. A similar approach is
already utilized in the electricity sector with the well-established SGAM (Dänekas et al.
2014; Neureiter et al. 2014; Neureiter et al. 2016). It supports energy domain and sys-
tem engineering experts in their joint tasks to efficiently design and validate complex
and interoperable smart grid systems. The SGAM constitutes a role model for the appli-
cation of systems architecture models, also for other sectors. In this paper, we focus on
system modelling, analysis and engineering for e-mobility and therefore consider follow-
ing research question: “How does a systems architecture model need to be designed in
order to ensure interoperability and manage complexity of e-mobility systems and their
engineering”
The research methodology for answering this research question is based on the itera-
tive six-step Design Science Research (DSR) process as proposed by Peffers et al. (2008),
which is visualised in Fig. 1. Further, we adhere to the general DSR guidelines defined
in Hevner et al. (2004). The remainder of this work is structured according to the DSR
Kirpes et al. Energy Informatics (2019) 2:15 Page 3 of 31
Fig. 1 Design Science Research process used for developing the EMSA framework (Peffers et al. 2008)
process as follows: First, we identify and motivate the problem in “Complexity and inter-
operability of e-mobility systems” section. Based on the findings from literature, these two
major requirements are split into six relevant objectives of a solution. We then discuss
the SGAM as a role model for tackling these challenges in the energy sector and moti-
vate on the need for a systems architecture model for e-mobility. In “State-of-the-Art:
analysis of existing e-mobility systems architecture models” section, we conduct a com-
prehensive analysis of related work on existing system architecture approaches. The
results indicate, that all of the existing approaches have a slightly different focus, tackle
different issues, and do not fully satisfy the relevant requirements of complexity and inter-
operability. Based on the findings and identified gaps in research, we contribute to the
research body by (1) deriving design principles for a more comprehensive artifact and (2)
defining the three-dimensional EMSA in “The e-mobility systems architechture (EMSA)
model” section. All relevant entities are mapped to the respective layers of the model,
providing a reference and guideline for adoption and implementation of the framework.
Subsequently, in “Demonstration of the EMSA in the ELECTRIFIC case study”section,
we show, how the EMSA can be used to solve the identified problem in a suitable con-
text. Based on the case study in the ELECTRIFIC research project (Electrific Consortium
2019), a pragmatic observational evaluation of the artifact regarding fulfillment of the
identified requirements is conducted. In the last section, the EMSA concept is concluded
and we provide an outlook into future work. As typical for DSR, in this paper we pro-
vide a novel synthetic knowledge contribution to the body of research as an adaption
of existing knowledge (systems architecture model) to a new problem (complexity and
interoperability of e-mobility systems). Based on informal feedback and evaluation from
external experts (domain and system engineering experts, who applied the framework in
the context of the research project), multiple iteration cycles were conducted to design
and develop our artifact. The final findings and results, specifically the proposed EMSA
framework are communicated within this paper.
Complexity and interoperability of e-mobility systems
In this section, we first clarify and provide the definitions and terminology of complex-
ity and interoperability considered in this paper. For each of the two concepts, we then
extract requirements from literature. Finally, we provide the example of SGAM as a role
model of the energy sector.
Complexity requirements of e-mobility systems
Complexity of systems and their engineering is a well-known challenge in the energy
IS research domain (Benbya and Mckelvey 2006). The topic of complexity itself is quite
Kirpes et al. Energy Informatics (2019) 2:15 Page 4 of 31
complex and no common view or consistent definition of system complexity among var-
ious research disciplines exist. Some of the major challenges are the growing technical
heterogeneity, changing requirements, human factors and multidisciplinary aspects. Two
essential aspects need to be considered: 1) technical or technological complexity, and 2)
organizational complexity (Lemberger and Morel 2012; Courtney et al. 2008).
The complexity of systems engineering originates in the need for expertise from differ-
ent disciplines, mainly the application domain (here e-mobility) and the field of systems
engineering. Experts from both fields would be the main users and beneficiaries of a
better methodology. The first critical issue in the engineering process of systems for e-
mobility is the definition of requirements, which typically reflect complex organizational
problems and their transfer to a solution system (Benbya and Mckelvey 2006). Further
challenges arise from the complexity of the different steps within the systems engineering
process: design and modelling of the system architecture and its development.
In this paper, we refer to the terminology concerning systems architecture and related
concepts as defined by ISO 42010: “The architecture of a system constitutes what is
essential about that system considered in relation to its environment” (ISO et al. 2011).
The system architecture is a critical element when engineering and analyzing ISs for e-
mobility, including their sub-systems, also referred to as system-of-systems. As a first step
in modelling a system, the system under study needs to be abstracted at a high level which
leads to the generation of a conceptual architecture model. We extract the following main
requirement and sub-requirements for a potential solution regarding complexity handling
and reduction.
Requirement 1: Provide means to manage and reduce e-mobility systems
complexity
•
Req1.1 : Abstraction and conceptualization
Abstraction and conceptualization reduce complexity. They are typically enabled by
model-based concepts and characterized by the level of abstraction. Equally
important are granularity, hierarchical organization and interrelation of the concepts,
systems and sub-systems (Lemberger and Morel 2012; Ranganathan and Campbell
2007). The different models can be further simplified by defining additional
assumptions and limitations (Holub 2016).
•
Req1.2 : Separation of concerns
An architecture view expresses the architecture of the system-of-interest in
accordance with an architecture viewpoint”
(ISO et al. 2011). Architecture
viewpoints of a system are an essential concept to handle complexity by separating
the concerns between and within the viewpoints. This can be achieved by
considering different dimensions, layers or tiers, as well as de-coupling and
simplifying each of them (Holub 2016; Lemberger and Morel 2012).
•
Req1.3 : Re-usability of components
Components on different layers (sub-systems, concepts, roles, services) should be
re-usable in order to reduce complexity (Lemberger and Morel 2012). Re-usability
can be ensured by harmonization (e.g. due to role models or technical homogeneity)
and standardization (e.g. by utilizing standardized notations such as Unified
Modeling Language (UML) and Systems Modeling Language (SysML)).
Kirpes et al. Energy Informatics (2019) 2:15 Page 5 of 31
•
Req1.4 : Complexity reduction of the engineering process
The complexity of the overall engineering process can be reduced by simplifying each
of its phases: requirements, design, development, verification and validation (King
and Simon 2015; Honour 2006; Benbya and Mckelvey 2006). A visual representation
of the views on the model, supports the engineering process (Uslar et al. 2019).
Guidance through the whole process and documentation of the tool provide support
with application and execution.
Interoperability requirements of e-mobility systems
In order to successfully integrate EVs with existing and upcoming smart grid, mobility
and energy systems, interoperability and standards play a vital role (Brown et al. 2010).
The IEEE defines interoperability as “the ability of two or more systems or components
to exchange information and to use the information that has been exchanged” (Geraci et
al. 1991). Consequently, interoperable systems are able to provide and support services
between each other. However, this typically requires common standards. Connectivity
and compatibility are weaker states than interoperability. If one system is dominant and
does not depend on open standards, a second system adapted to work with this first sys-
tem, is only compatible and not interoperable. According to the GridWise Architecture
Council (GWAC) interoperability stack (GWAC 2008), interoperability can be defined
on three levels: organizational (pragmatics), informational (semantics), and technical
(syntax). We extract following main requirement and sub-requirements for a potential
solution regarding interoperability.
Requirement 2: Provide means to analyze and ensure e-mobility systems
interoperability
•
Req2.1:
Analysis and assessment of interoperability
Interoperability should be analyzed and ensured on all three levels (organizational,
informational and technical) of a system within its environment and between its
sub-systems (GWAC 2008). Due to the standardization aspect of this requirement
(Brown et al. 2010), it is closely connected to
Req1.3 (Re-usability of components)
.
The analysis of interoperability is important for new, but also for existing systems
and sub-systems.
•
Req2.2:
Identification of standardization gaps
A comprehensive solution approach for handling interoperability of system
architectures should also enable the identification of gaps in standardization. Based
on the interoperability analysis as defined in
Req2.1
it should become clear, whether
an identified interoperability issue is either a shortcoming of the current system, or a
gap in the overall standardization.
Various solution approaches exist to potentially tackle these requirements for e-
mobility systems, e.g. Architecture Description Languages, Model-based systems engi-
neering (Lopes et al. 2011) in combination with a Domain-Specific Language (DSL)
(Neureiter et al. 2016) or the Model-Driven Architecture (MDA) defined by the Object
Management Group (Object Management Group 2014). In this paper, we focus on sys-
tems architecture models which constitute a suitable solution for the identified problems
and are highly desirable as a common frame for reference (Uslar and Engel 2015).
Kirpes et al. Energy Informatics (2019) 2:15 Page 6 of 31
The Smart Grid Architecture Model (SGAM)
Regarding the handling of complexity and ensuring interoperability of cyber-physical sys-
tems with systems architecture models, the electricity sector acts as a role model for
e-mobility. Following a standardization request of the European Commission in 2012, the
SGAM has been proposed as a reference architecture model for smart grid systems (CEN-
CENELEC-ETSI Smart Grid Coordination Group 2012). This framework can be used to
design, engineer, visualize and validate smart grid architectures and to analyze smart grid
use cases and systems regarding interoperability and standardization gaps in a structured
way. The SGAM has a strong European focus and has been applied in several European
R&D projects (European Commission 2019) such as FP7 DISCERN, FP7 ELECTRA, FP7
EcoGrid EU, H2020 SmartNet and H2020 Flexiciency. Further, many research institutions
such as RWTH Aachen, OFFIS Oldenburg, TU Delft or KTH Stockholm, consider the
SGAM as part of their energy systems research and education. An exhaustive overview
about the usage of SGAM in research is given in Uslar et al. (2019). In industry, it is
adopted and used for practical applications regarding visualization, validation and config-
uration of smart grids by multiple organizations, e.g. Siemens Infrastructure and Cities,
Accenture Consulting, City of Mannheim or the German Energy Agency (dena).
The SGAM consists of three dimensions (see Fig. 2): interoperability layers,zones
and domains. The five smart grid interoperability layers are extracted from the GWAC
interoperability stack (GWAC 2008).
The business layer represents the business viewpoint on the information exchange in
a smart grid, e.g. regulatory and economic (market) structures, business models, busi-
Fig. 2 Smart Grid Architecture Model (from CEN-CENELEC-ETSI Smart Grid Coordination Group (2012))
Kirpes et al. Energy Informatics (2019) 2:15 Page 7 of 31
ness processes and business cases of stakeholders. The function layer describes system
use cases, functions and services including their relationships from an architectural
viewpoint, independent from actors and physical implementation of applications, sys-
tems and components. Both, business and function layer implement the organizational
(pragmatics) level from the GWAC stack, including economic/regulatory policy, busi-
ness objectives and business procedures. The informational (semantics) level, including
business context and semantic understanding, is mapped to the information layer of the
SGAM. It describes the information flow and the corresponding information objects that
are exchanged between functions, services and components and their underlying canon-
ical data models, representing the common semantics for an interoperable information
exchange. The communication layer describes communication protocols and technolo-
gies for the interoperable exchange of information between components in the context of
the underlying function and related information objects or data models. The component
layer represents the physical components in the smart grid. These include systems and
devices, power system equipment (typically located in the process and field zone), net-
work infrastructure and any kind of computational hardware. The communication and
the component layers implement the GWAC technical (syntax) level, including aspects
such as syntactic interoperability, network interoperability and basic connectivity.
The zones (process, field, station, operation, enterprise, and market) reflect the hier-
archy within power systems management and the domains (generation, transmission,
distribution, distributed energy resources, and customer premises) represent all steps in
the energy conversion chain. Further details about the considered zones and fields are
givenin“The E-Mobility Systems Architecture (EMSA) model” section of this paper.
Within the energy sector, the SGAM is highly relevant, both for research and industry. It
supports the main users, systems engineering and domain experts in better fulfilling their
tasks such as visualizing, modelling, designing, engineering and analyzing complex and
interoperable smart grid systems. systems architecture models like the SGAM are com-
prehensive and efficient concepts to tackle complexity and interoperability requirements
of such systems. The e-mobility sector faces similar challenges of increasing complex-
ity on all layers and the need for interoperability. Therefore, we limit potential solution
approaches to systems architecture models for e-mobility by considering the identified
requirements.
State-of-the-Art: analysis of existing e-mobility systems architecture models
In this section, we provide a comparison of related work on systems architecture
models and frameworks in the e-mobility sector (Table 1). We analyze and dis-
cuss the state-of-the-art regarding the scope of the respective approach, its struc-
ture, allocation of entities to the dimensions, applicability for pure e-mobility or
sector-coupled systems and whether their usefulness has been demonstrated and
evaluated. Further, the fulfillment of both requirements, complexity reduction and
interoperability analysis, are shortly assessed. Based on this analysis, we identify gaps
in the existing research body and in the next section, derive design principles for
our artifact.
A promising approach to tackle the complexity of sector-coupled systems (e.g. for smart
cities), is the Generic Smart City Architecture Model (GSCAM) proposed by Neureiter
et al. (2014). This framework focuses on the multiple sectors of a smart city. It can be
Kirpes et al. Energy Informatics (2019) 2:15 Page 8 of 31
Table 1 Overview about architecture models and frameworks for e-mobility available, () partly available (see description), ✗not/insufficiently available
Name Basis Scope Structure Allocation Standalone Sector
coupling
Complexity
Red. Req1
Interoperability
Anal. Req2
Evaluation
EV Smart Charging Report (for
SGAM) (CEN-CENELEC 2015)
SGAM Smart Charging of EVs 3 dim. (layers,
domains, zones)
AC, SC, RU,
EP
(Case Study)
E-Mobility Architecture Model
(EMAM) (Uslar and Gottschalk 2015)
SGAM Electric Cars 3 dim. (layers,
domains, zones)
✗()AC,SC,RU()✗
E-Mobility Information System Archi-
tecture (EM-ISA) (Schuh et al. 2013)
SGAM, ISA E-Mobility IS 3 dim. (layers,
domains, zones)
()✗AC, SC, RU ()✗
Smart City Infrastructure Architec-
ture Model (SCIAM) (DKE/DIN/VDE
2014)
SGAM Smart Cities (Mobility,
Energy)
3 dim. (layers,
domains, zones)
✗()()AC,SC✗✗
Interoperability Reference Architec-
ture for E-Mobility (Brand et al. 2015)
Enterprise Architecture EV-Grid-Integration 1 dim. (layers) ()✗AC, SC, RU ()(Inter-views)
AC - abstraction and conceptualization (model-based) , SC - separation of concerns (viewpoints), RU - re-usability (standardization and harmonization), EP - reduce complexity of engineering process (guidance, documentation)
Kirpes et al. Energy Informatics (2019) 2:15 Page 9 of 31
used to consider multiple application domain cubes parallel to the SGAM, in order to
handle complexity and interoperability on a larger scale and broader context. We con-
sider GSCAM as an extension of SGAM, which could be used to model and depict sector
coupling of systems (column Sector coupling in Table 1).
E-mobility in the SGAM
The SGAM has initially not been designed for an extensive applicationwith e-mobility use
cases and systems. Consequently, the e-mobility process chain does not perfectly fit to the
SGAM’s grid-specific domains, which are designed to only represent the energy conver-
sion chain. E-mobility additionally requires an interconnection of multiple domains such
as charging infrastructure (e.g. electrical and hydrogen CSs), (mobile) EVs and especially
human machine interfaces for the users with mobility-focused use cases.
In a technical report of CEN-CENELEC about EV smart charging (CEN-CENELEC
2015), first steps have been taken to map parts of the e-mobility sector onto the SGAM.
This approach considers specific battery charging-related systems (e.g. smart charging
with relevant systems, standards and protocols) as scope, while e-mobility in general is
not tackled adequately. Considering the EV as enabler of e-mobility (= power grid as
enabler of electricity in SGAM) and the EV user as consumer of e-mobility (= consump-
tion devices in SGAM), the EV user interactions are only modelled through its respective
EV in SGAM. As another example, CSs have been allocated multiple times in SGAM, once
in the Distributed Electrical Resource (DER) and once in the Customer Premise domain,
which might lead to confusion. Due to the view on mainly energy-specific domains, the
structure is also not ideal for e-mobility. The majority of use cases and systems of the e-
mobility ecosystem are located in the distribution, DER and customer premises domains.
From the e-mobility point-of-view, this leads to a reduction of details in the separation of
systems and actors to the corresponding domains.
The SGAM is applicable for standalone usage but could also be used for sector-coupled
systems (e.g. within the GSCAM (Neureiter et al. 2014)). The relevant requirements
for interoperability and complexity are sufficiently fulfilled. The smart charging use
cases have been evaluated with a case study focusing on grid-related topics (e.g. volt-
age control), demand-side flexibility management and different charging actions (e.g.
uncontrolled or demand-response charging).
The SGAM is well established for the usage in smart grid domains and some archi-
tecture models for e-mobility exist, which adhere to the fundamental SGAM structure
by utilizing a three-dimensional approach with layers, domains and zones. Typically,
these models have a very strong focus on grid integration and consider e-mobility as a
sub-discipline of electricity. According to Uslar and Gottschalk (2015), the challenge for
SGAM-related frameworks is the definition of a useful and appropriate sector-specific
structure mainly on the zones and domains dimension.
EMAM
The EMAM is an SGAM-based approach, which has been introduced as work-in-
progress by Uslar and Trefke (2014); Uslar and Gottschalk (2015). The three-dimensional
EMAM has its scope mainly focused on electric cars and is intended to be used stan-
dalone. The interoperability layer dimension is the same as in SGAM, while zones (in-car,
plug, cable, pole, pcc, grid) and domains (distribution, DER, building, home Energy
Kirpes et al. Energy Informatics (2019) 2:15 Page 10 of 31
Management System (EMS)/human machine interface, EV battery) have been adjusted to
better fit the conductive charging process of battery electric cars.
Definition and description of domains and zones are missing or are very elementary.
Specifically, the zones dimension is lacking the hierarchical organizational granularity and
data aggregation aspects which are used in the SGAM zones. The proposed zones more
describe the actual process chain of charging an EV, thus would better fit on the domain
axis. In the EMAM, Sector coupling can only be modelled for the electricity sector,
since the zones dimension is incompatible with the GSCAM approach. Interoperability
is addressed on business (harmonization), information and communication layers (stan-
dardization), but no allocation of standards to the model is included. Standards and roles
are described but not mapped and allocated to the model dimensions. This makes the
EMAM unsuitable for interoperability assessment. Complexity handling is similar to the
SGAM, but much more concise with guidance and documentation completely missing.
Further, no evaluation has been conducted.
EM-ISA
Schuh et al. (2013) also identify the need for interoperability and propose the domain-
specific EM-ISA. It is extracted from a collection of generic IS architectures and mapped
onto an adaptation of the SGAM. It provides guidelines for the architecture design of IS
supporting different business models in e-mobility. The EM-ISA changes the naming of
the information layer to data layer and component layer to element layer,consideringthe
user as actor and hence violating the separation of concerns.
For the zones, process and field have been removed, the domains are distinguished in
immobile/infrastructure (CS, parking, IT back-end), mobile/user of infrastructure (EV,
user, user device). This makes it incompatible to the GSCAM and only usable standalone.
For harmonization purposes, meta models (e.g. for functions) are included, but an alloca-
tion of the entities to the EM-ISA structure has been done only partially. Interoperability
is not considered in more detail, no standards are mentioned. Also no evaluation has been
conducted.
SCIAM
Another approach is the SCIAM which has been shortly introduced as a draft by the
German standardization road map smart city (DKE/DIN/VDE 2014). Its focus is not
purely on e-mobility but the model considers e-mobility as part of electricity and mobil-
ity sector in a smart city. The architecture model is intended to be used standalone,
but e-mobility is only considered at the intersection between mobility and electricity.
The SCIAM itself considers sector coupling, but is not compatible with the GSCAM
framework. No further information on this architecture model has been provided.
Interoperability reference architecture for e-mobility
The interoperability reference architecture for e-mobility (Brand et al. 2015)isintended
to support the integration of EVs into the electricity system. Its one-dimensional struc-
ture consists of four layers (business, business services, application, and infrastructure).
However, it supports complexity and interoperability handling only on conceptual level
without providing any specific standards. As it is not based on SGAM, compatibility with
GSCAM is not given. Relevant entities have been allocated to the respective layers. It can
Kirpes et al. Energy Informatics (2019) 2:15 Page 11 of 31
be used standalone and acts as a reference architecture for interoperable e-mobility, but
is not directly intended for the engineering of complex systems as it provides no tools
or guidelines. The validity of the reference architecture has been evaluated with expert
interviews.
Analysis results
As shown (with an overview in Table 1), most of the state-of-the-art approaches lack the
scope of a comprehensive e-mobility ecosystem, that not solely focuses on charging via a
conductive connection. Only the technical report on smart charging of EVs in the SGAM
includes an adequate allocation of existing standards. All models can be used standalone,
but sector-coupling is not their focus and only possible with some of the concepts to some
extent. Most of the approaches are not comprehensive, missing guidance for application
and an evaluation. The main requirements regarding complexity reduction and interoper-
ability assessment are not satisfactorily met and thus, a new systems architecture model
based on the advantages of the state-of-the-art needs to be introduced.
The E-Mobility Systems Architecture (EMSA) model
In this section, we describe the final artifact design (Basic Design Principles for the EMSA
Model) and its definition (Development of the EMSA Model). Multiple iterations and
feedback cycles were conducted, considering informal feedback from discussions with
system engineering and domain experts, who applied the model and framework during
the three-year ELECTRIFIC project for multiple systems.
Basic design principles for the EMSA model
From the analyzed systems architecture models, we extract principles for the design of
our artifact. These constitute the guidelines for the development of the e-mobility sys-
tems architecture model. The first three design principles are directly extracted from the
related work analysis. Design principles four and five are more generic and stem from the
basic principles for the domain-specific SGAM.
Design principle 1: Scope and applicability The systems architecture model is
intended to be comprehensive and cover the complete scope of the e-mobility sector. It
should be applicable for standalone usage, considering use cases and systems, which are
not related to any other sectors. At the same time, it should also be applicable for systems
that affect multiple sectors (e.g. compatible with the GSCAM approach).
Design principle 2: Multi-dimensional structure The systems architecture model is
intended to provide an appropriate number of useful sector-specific dimensions. For a
model, which is based on the SGAM and compatible with the GSCAM, the ideal structure
also consists of three dimensions: interoperability layers, domains and zones. Contiguity
(either geographical, hierarchical or logical) of all dimensions along each axis is essen-
tial. Only limited aspects of the domain value chain should be changed, providing a clear
domain abstraction (Uslar and Gottschalk 2015).
Design principle 3: Allocation, localization and consistency The fundamental idea of
the systems architecture model is to provide an appropriate allocation of all e-mobility
Kirpes et al. Energy Informatics (2019) 2:15 Page 12 of 31
entities to its structure. Across all dimensions, the appropriate location for each entity
should be identified and specified. By adhering to this principle, all entities and their
relations can be represented in a clear systematic and comprehensive view. Further, a
consistent mapping is essential in order to be able to identify gaps in specifications or
inconsistencies in the system (CEN-CENELEC-ETSI Smart Grid Coordination Group
2012).
Design principle 4: Universality and flexibility The systems architecture model is
intended to represent e-mobility architectures in a common and neutral view. It should
be technology-agnostic and not give any preferences to existing architectures. Obtaining
flexibility on all layers supports alternative use cases, system designs and implementa-
tions. It further supports future advancements and enables concepts like scalability and
extensibility (CEN-CENELEC-ETSI Smart Grid Coordination Group 2012).
Design principle 5: Extensibility and scalability The systems architecture model can
be extended with additional entities or even by adding new domains and zones, when
the sector evolves (compatibility with the SGAM needs to be maintained). The systems
architecture model can be scaled up to a top-level view of the whole e-mobility ecosystem
or scaled down to a specific and very detailed subset of use cases, functions and systems
(CEN-CENELEC-ETSI Smart Grid Coordination Group 2012).
The EMSA is developed considering the above mentioned five design principles. In
order to apply some of the SGAM framework’s tools, it is recommended to only alter
aspects of the domain dimension (Uslar and Trefke 2014). This principle is also in line
with the GSCAM approach (Neureiter et al. 2014). Subsequently, the EMSA is defined in a
way, that is compatible with the GSCAM but also usable as standalone architecture model.
Cross-cutting issues, that cannot be allocated explicitly to one domain or one zone like
telecommunication systems and security, need to be represented separately and are not
considered in this work (CEN-CENELEC-ETSI Smart Grid Coordination Group 2012).
Development of the EMSA model
In the following, the structure of the EMSA Model including all relevant domains along
the e-mobility process chain will be discussed. For the EMSA scope, e-mobility is not
limited to battery electric vehicles, but also includes all kind of other vehicles with an
electric drive train, for example electric tram or hydrogen trucks with fuel cells. The
dimensions of the EMSA Model are visualized in Fig. 3. In order to obtain a maxi-
mum level of compatibility to GSCAM and SGAM, both the number of layers (five -
business, function, information, communication, component) and zones (six - process,
field, station, operation, enterprise, market) in the EMSA Model are kept the same as in
SGAM. The definition of the zones is adapted to be more appropriate for the context
of e-mobility.
Definition of domains
Similar to the energy supply chain in SGAM, the whole e-mobility process chain is
represented on the domain axis and split into different domains. Inspired by Schuh et al.
(2013), the proposed domains are also classified as immobile (Energy Conversion, Energy
Kirpes et al. Energy Informatics (2019) 2:15 Page 13 of 31
Business
Function
Information
Communication
Component Process
Field
Station
Operation
Enterprise
Market
Energy
Conversion
Energy Transfer
from/to EV
Electric
Vehicle
EV User
Premises
Immobile Mobile
Fig. 3 Visualization of the E-Mobility Systems Architecture (EMSA) model
Transfer from/to EV) and mobile (Electric Vehicle, EV User Premises). Next, we provide
a definition for each domain by giving specific examples:
•Energy Conversion includes energy sources and the energy conversion chain. This
contains the electricity system with all levels including generation, transmission grid,
distribution grid and local power generation like photo-voltaic systems. It can also
represent energy from other sources that is later transformed into electrical energy,
like hydrogen fuels that may be generated locally or transported via a piping system.
•Energy Transfer from/to EV includes the necessary infrastructure for transferring
the energy to the EV and vice versa. As example, CSs, catenary wires for trains or
hydrogen fuel stations can be listed. In addition, the CS management system and all
kind of entities required for the process of getting energy to/from the EV, like
vehicle-to-vehicle, vehicle-to-grid, grid-to-vehicle, vehicle-to-home or
home-to-vehicle, are included.
•Electric Vehicle includes the entities to perform the electric driving process. This
includes e-bikes, e-scooters, e-cars, e-buses and e-railway. In addition, all
components and systems, that are part of the moving EV, like the battery, Battery
Management System (BMS) or monitoring systems as well as EV (fleet) management
systems are part of this domain.
•EV User Premises includes interfaces for the end users like mobile devices, personal
computers or (RFID) charging cards. This could be an interface for the purpose of
managing the EV (e.g. smartphone app for EV preconditioning) or
searching/booking/reserving CSs or vehicles, e.g. train and car-sharing. In addition,
Kirpes et al. Energy Informatics (2019) 2:15 Page 14 of 31
intelligent route planning, navigation and all kind of e-mobility services for end users
are located in this domain.
Definition of zones
The zones represent the hierarchical levels of e-mobility management and use the concept
of aggregation and functional separation. Concerning the aggregation, one can distin-
guish between data aggregation (e.g. data concentrated from field to station zone) and
spatial aggregation (from distinct location at field and station to wider area at oper-
ation, enterprise and market). Functional separation is given, inter alia, by the spatial
aggregation, as local functions, like in-car communication or protection equipment in
the CS or in the grid, are mainly implemented in the field and station zones. The same
applies for more global functions like monitoring or billing, which are located in the zones
operation, enterprise and market. We therefore define the zones of the EMSA Model sim-
ilar to the well-defined zones in SGAM in order to also ensure compatibility with the
GSCAM framework. However, the definition has been adjusted to better fit the e-mobility
sector:
•Process includes the physical or chemical transformation of energy (electricity,
hydrogen fuel, etc.), the information flow in all domains, and all directly involved
physical equipment. This can be entities of the power grid, CSs, EVs, end user devices
or any kind of sensors and actuators which are directly associated with the e-mobility
process.
•Field includes equipment to protect, control, monitor and support the process of
e-mobility such as (1) protection relays at a CS, power grid or in the EV, (2) metering
devices and any kind of intelligent electronic devices which acquire, process and use
related data like the RFID authentication method.
•Station represents the areal aggregation for the field zone, e.g. for data
concentration, functional aggregation or local sensor systems. An aggregation level
could be a charging spot with multiple CSs or the internal communication and
control system of an EV (e.g. in-car Ethernet, FlexRay or CAN bus).
•Operation hosts management entities in the respective domain for the processing of
aggregated data, e.g. Local or Grid Energy Management System, EV Management
System, CS Management System, Human Machine Interface Devices for input from
the user or data provision services.
•Enterprise includes commercial and organizational processes, services and
infrastructures for enterprises (utilities, service providers, energy traders, etc.), such
as asset management, logistics, work force management, staff training, customer
relation management, billing and procurement.
•Market reflects the market operations possible along the e-mobility chain, e.g.
charging service networks, e-mobility provider services, EV sharing, energy trading,
as well as (user) data trading platforms.
EMSA interoperability layers
One methodology for complexity handling is the separation of concerns, which can be
realized by considering different architecture viewpoints such as business, functional
Kirpes et al. Energy Informatics (2019) 2:15 Page 15 of 31
or communication architecture, here implemented as layers. On each layer, various
standardization and harmonization means are implemented to ensure interoperability
and enhance re-usability. Further, all relevant entities are appropriately allocated to the
EMSA dimensions (layers, domains, zones).
EMSA business layer On the business layer, different economic and legal aspects of the
business architecture can be modelled, e.g. business cases, business services, business
processes, business models and regulatory constraints. Harmonization and abstraction
are the major constituents to handle complexity on this layer. Besides standardized nota-
tion languages such as UML, a harmonized business actor role model is essential. The
most important business actor roles in the domain of e-mobility, compiled from different
standards (Open Charge Alliance 2018; Nationaal Kennisplatform Laadinfrastructuur et
al. 2019;ISOCentralSecretary2016), are shown and allocated to the EMSA domains (not
considering zones) in Fig. 4.
The EV user can be differentiated into private EV Owner and EV Fleet User (e.g. a taxi
driver). The latter one is using an EV that is owned by the EV Fleet Operator. The Equip-
ment Provider sells or leases equipment (e.g. EV or battery) to EV Owner and EV Fleet
Fig. 4 Harmonized Business Actors in EMSA. Compiled from Open Charge Alliance (2018); Nationaal
Kennisplatform Laadinfrastructuur et al. (2019); ISO Central Secretary (2016)
Kirpes et al. Energy Informatics (2019) 2:15 Page 16 of 31
Operator (e.g. public bus transport). In the energy conversion and transfer domains, three
major roles exist. The Power Grid Operator (typically the Distribution System Operator
(DSO) or Transmission System Operator (TSO)) provides the grid infrastructure either
directly to the EV fleet operator (e.g. catenary for trains) or to the CS operator. Both
actors are provided with electricity from the Energy Supplier. The CS operator provides
charging infrastructure to the EV user and enters into a bilateral agreement with the E-
Mobility Service Provider. The E-Mobility Service Provider offers services to the EV user
and also handles the billing process. Payment transaction settlement is done via roaming
by a Clearing House.
EMSA function layer The function layer describes the functional architecture and ele-
ments of the system. It connects business cases with their physical implementation by
an abstraction of interconnected functions. The interactions of the functions indicates
required information exchange between them. Depending on the level of abstraction, the
functions can be described, grouped and clustered differently. In Fig. 5, the most relevant
high-level function groups of e-mobility sector (extracted from CEN-CENELEC (2015))
are allocated to the EMSA function layer. The functional architecture can be detailed, e.g.
by utilizing UML activity or sequence diagrams.
EMSA component layer The component layer is the basis for the upper four layers.
In Fig. 6, the component layer of the EMSA Model and the most relevant systems and
Fig. 5 EMSA function layer with grouped functions from e-mobility sector
Kirpes et al. Energy Informatics (2019) 2:15 Page 17 of 31
Fig. 6 EMSA component layer with relevant systems for battery e-mobility
hardware/software components for battery-electric mobility are shown. The components
for non-battery e-mobility are excluded for reasons of clarity and comprehensibility. To
comply with the case study in the validation section, here the focus is limited to battery-
electric mobility.
EMSA information layer The information layer is closely linked to the communication
layer. The focus of the information layer is on the three aspects of data management,
integration concepts and the required information exchange interfaces.Standardized
information flow and data models between services are important for homogeneous con-
nected sub-systems, ultimately leading to interoperability of the whole complex system-
of-systems.InFig.7, the most relevant standards and protocols for the e-mobility sector,
in specific for battery-electric mobility, are categorized. The protocols are extracted from
ElaadNL (2017); Rodríguez et al. (2015); Schmutzler et al. (2013). This allocation helps in
identifying gaps in standardization.
Various standards from IEC, ISO, ETSI, ITU, IEEE and SAE can be mapped to the dif-
ferent zones and domains. In the area of grid management, the IEEE 2030.5 (Adoption
of Smart Energy Profile), the IEC 61850 family and OpenADR (IEC 62746-10-1) can be
mentioned. For the information exchange between the Grid Operator and the CS oper-
ator, OSCP (Portela et al. 2015) or the Flexibility Reward Scheme (Danner et al. 2018)
deliver solution to adjust the EV charging processes to the needs of the power grid.
Information exchange protocols related to energy markets are for example OASIS EMIX,
IEC 62325, EN 62325-301/351 (Entso-e MADES). The information exchange between
car and CS could be handled with IEC 15118 or IEC 61851-1. The information exchange
Kirpes et al. Energy Informatics (2019) 2:15 Page 18 of 31
Fig. 7 EMSA information layer with standards. Compiled from ElaadNL (2017); Rodríguez et al. (2015);
Schmutzler et al. (2013)
and control from the CS management system or an Energy Management System to
the CS is usually done with OCPP. To handle information exchange between the CS
operator and the E-Mobility Service Provider, OCPI or OCHPdirect could be used within
a direct communication or OCHP, OICP or eMIP using an E-Mobility Clearing House as
mediator.
EMSA communication layer The main objective of the communication layer is to visu-
alize the communication infrastructure (protocols, technology) and identify gaps in the
existing communication standardization, or to show lack of standards implementation
in the respective system. Therefore, we allocated existing and commonly used proto-
cols, extracted from CEN-CENELEC (2015); ElaadNL (2017); Rodríguez et al. (2015), to
the corresponding domains and zones. The result is shown in Fig. 8and indicates no
major gap for communication standards. In high level zones (operation, enterprise and
market, as well as, for the communication with end user devices), usually Web Services,
HTTP over SSL and TCP/IP are used. For energy markets the IEC 61968-100 and for
general market places EN82325-450/451 can be mentioned. Communication in the field
of metering and grid management usually uses domain-specific protocols, e.g. the IEC
62056 xDLMS/COSEM for smart meter communication in general and CLC/prTS 50568-
5 for smart meter data exchange communication are relevant, for their communication
with higher zones usually ISO 9506 (MMS) from the IEC61850 standard family comes
into play. Concerning communication from EV to CS either Pulse Width Modulation
Kirpes et al. Energy Informatics (2019) 2:15 Page 19 of 31
Fig. 8 EMSA communication layer with protocols. Compiled from CEN-CENELEC (2015); ElaadNL (2017);
Rodríguez et al. (2015)
(PWM) signals according to IEC61851-1 (SAE J1772) or the newer communication stan-
dard ISO/IEC 15118-2 XML (EXI) TCP/IP can be utilized. CHAdeMO uses two CAN
buses for communication to the EV. Charging spots can use ETSI TS 101 556 ASN.1 and
future standards for OCPP/OCHP. For in-car communication, typically the ISO 11898
CAN bus or more recently the former industry standard FlexRay (now ISO 17458-1 to
17458-5) or Ethernet (ISO/DIS 8802/3) are used for sending information between the
different components.
MDA approach for the EMSA model
In the smart grid domain, MDA as a sub-discipline of Model-Driven Engineering is a suit-
able approach for handling systems complexity in combination with the SGAM (Uslar et
al. 2019; Dänekas et al. 2014). We apply this methodology and re-use this mapping to pro-
pose a similar approach for our EMSA Model (represented in Fig. 9). The MDA concept is
intended to foster separation of concerns by separating the business and functional archi-
tecture from a specific technological implementation (Object Management Group 2014).
Mapped to the EMSA Model, the Computational Independent Model is defined in the
System Analysis Phase on business and function layer. The Platform Independent Model
is specified during the System Architecture Phase on component, information and com-
munication layer. In the last phase, the Design and Implementation Phase,thePlatform
Independent Model is transformed into a Platform Specific Model and implemented as
Platform Specific Implementation.
Kirpes et al. Energy Informatics (2019) 2:15 Page 20 of 31
Fig. 9 MDA engineering process mapped to EMSA Model, based on Dänekas et al. (2014); Uslar et al. (2019)
Demonstration of the EMSA in the ELECTRIFIC case study
The EMSA Model, presented in this paper, has been applied within the context of the
EU Horizon2020 research project ELECTRIFIC. The main objective of the project is to
seamlessly integrate EVs into the daily life by taking into account the competing goals
of different stakeholders (Kirpes et al. 2017; Eider et al. 2017). Among the considered
stakeholders are EV user, DSO, EV fleet operator and CS operator. To this end, the main
objective of the (1) DSO is to increase the intake of renewable energy as well as ensure
certain quality of the provided power, (2) EV fleet operator is to manage its fleet of EVs
in terms of optimal charging with respect to costs, percentage of renewable energy, grid-
and battery-friendliness (e.g to preserve the health of the battery), (3) CS operator is to
increase its net profit and (4) EV user is to execute his/her trip plan in the most optimal
and efficient manner.
Within the ELECTRIFIC project, in general, we adhere to the granularity and meta
model as proposed by Neureiter (2013) for the engineering process in SGAM. This leads
to a level of abstraction for ELECTRIFIC as shown in Fig. 10. The following concepts were
used:
•Business layer: Business Actors, (Business) Goals, Business Cases
•Function layer: High Level Use Cases (UCs), Primary UCs (=Functions), Function
Interrelation
•Information layer: Information Objects, Information Flow
•Communication layer: Communication Protocols and Technologies
•Component layer: Systems, Devices
System modelling on interoperability layers
A pivotal use case for the ELECTRIFIC system, is the ability to generate and execute a
travel plan for the EV user based on his goals. All High Level UCs defined in ELECTRIFIC
are the result of iterative discussions and workshops where system engineers and domain
experts from different stakeholders have been involved.
Kirpes et al. Energy Informatics (2019) 2:15 Page 21 of 31
Fig. 10 Level of abstraction adopted from SGAM CEN-CENELEC-ETSI Smart Grid Coordination Group (2014),
as applied in the ELECTRIFIC case study
Figure 11 gives a generic overview about this High Level UC on the business layer by
illustrating the main business actor, the EV user, its two major goals (improve e-mobility
experience and reduce costs from e-mobility), as well as three relevant business cases:
reduce range anxiety, improve EV usage behavior, and reduce EV charging costs. This High
Level UC is focused only on private EV usage, not for commercial users. Due to space
considerations and to keep the use case as simple as possible, we don’t consider those
other actors and their use cases in this paper.
Fig. 11 Business analysis for the EV user
Kirpes et al. Energy Informatics (2019) 2:15 Page 22 of 31
In addition to the business perspective, the corresponding High Level UC can be mod-
elled and visualized with its functional architecture. It gets further decomposed into
following three Primary UCs (also called functions) on the function layer (see Fig. 12):
(1) retrieval of EV user travel preferences and planning inputs,(2)travel plan creation
and optimization and (3) travel plan execution. The realization of the three Primary UCs
necessitates functional interrelation and the interaction of the following components on
the function layer: ELECTRIFIC App (App), Route-planning service, Charging Station
Operator system (CS operator system), Energy-information service, and Battery-health
service. To this end, the EV user enters his/her travel preferences into the App. The latter
retrieves further travel planning inputs from Route-planning service, CS operator system,
Energy-information service and Battery-health service (Primary UC: retrieval of EV user
travel preferences and planning inputs). Based on those inputs, the App creates the corre-
sponding travel plans (Primary UC: travel plan creation and optimization). The EV user
then reviews the proposed travel plans and can either (1) select a plan for execution, or (2)
adjust the input and re-plan another trip or (3) stop interaction with the App. Upon the
selection of a travel plan by the EV user, the App performs different tasks (e.g. showing
of CS on the map with percentage of renewable energy and navigation) to ensure the exe-
cution of the selected plan, and sends back an acknowledgment to the EV user (Primary
UC: travel plan execution).
Figure 12 shows an aggregated representation of all EMSA layers (except business layer):
the relevant components (component layer), exchanged information (information
layer) as well as the communication protocols (communication layer) for this system.
Fig. 12 Mapping of considered High Level UC to the component, information and communication layers of
EMSA Model
Kirpes et al. Energy Informatics (2019) 2:15 Page 23 of 31
Further details on the information connection (dashed line, colored in blue and labeled
with ICT) are provided in Table 2.
Analysis of data models and protocols
The first exchange of information (ICT 1) takes place between the App and Battery-health
service for providing the EV user with a list of battery health recommendations before
the start of any new trip (Table 2). To achieve this, the App sends data about the last
trip’s driving, parking and charging information to the Battery-health service. The data
models for both, request and response between those two components, are proprietary
Table 2 Overview of the exchanged information between different components, data models and
communication protocols for the high-level UC
ID Information Object Data Model Protocols
ICT 1 From App to Battery-health (Request) Proprietary HTTP over SSL
Last trip’s data for
•Parking: SoC & ambient temp
•Driving: SoC, discharge rate & ambient temp
•Charging: SoC, charging rate & ambient temp
From Battery-health to App (Response) TCP/IP
List of recommendations
•Textual description
•Rationale
•Weighing factor for priorities
ICT 2 From App to Route-planning (Request) Decimal Degrees HTTP over SSL
•GPS coordinates of the source and destination
From Route-planning to App (Response) Proprietary TCP/IP
•List of GPS coordinates needed for navigation
•Traffic Information
ICT 3 From App to Energy-information (Request) ISO 3166-1 ISO 8601 HTTP over SSL
•Country code
•Start and end times
From Energy-information to App (Response) Proprietary TCP/IP
•Percent of renewables
•Energy market price
ICT 4 From App to CS operator system (Request) Proprietary HTTP over SSL
•GPS coordinates of a middle point on the trip
•Radius value for Charging Stations filtering
From CS operator system to App (Response) TCP/IP
List of Charging Stations
•GPS coordinates
•Charging Station ID
ICT 5 From Battery-health to Sensors (Request) ISO 3779 ISO 4030 HTTP over SSL
•Vehicle Identification Number
From Sensors to Battery-health (Response) Proprietary TCP/IP
•State of Charge
•Remaining range
ICT 6 From CS operator sys. to CS manag. sys. (Request) ISO 9834-8 HTTP over SSL
•Charging Station ID
From CS manag. sys. to CS operator sys. (Response) Proprietary TCP/IP
List of static information
•Connector ID, type and standard
•Maximum capacity
ICT 7 From CS manag. sys. to CS Controller (Request) ISO 9834-8 HTTP over SSL
•Charging Station ID
•Connector ID
From CS Controller to CS manag. sys. (Response) OCPP TCP/IP
•Status information (available)
Kirpes et al. Energy Informatics (2019) 2:15 Page 24 of 31
and data exchange happens using HTTP over SSL communication protocol. To extract
the last trip’s information which is relevant to driving, parking and charging, information
exchange (ICT 5) happens between the Battery-health service and Sensors and Con-
trollers. The former sends the Vehicle Identification Number (which is based on ISO 3779
and ISO 4030 standards) to the latter and receives as a response the (Battery) State of
Charge (SoC) and remaining range information directly fetched from the EV. The data
model of the response is proprietary and two-way communication happens using HTTP
over SSL protocol. Here, the Renault Z.E. Services API has been used, which is modelled
and utilized as a Sensors and Controllers component.
The second exchange of information (ICT 2) happens between App and Route-planning
service component, where the EV user specifies his/her travel plan by indicating the
source and destination of the planned trip. For this purpose, the App sends the GPS coor-
dinates (in the form of Decimal Degrees) to the Route-planning service. It then receives a
list of GPS coordinates necessary for the navigation of the considered trip, as well as traf-
fic related information. The data model of the response is proprietary (based on Mapbox)
and two-way communication happens using HTTP over SSL protocol.
After receiving the complete list of GPS coordinates from Route-planning service, the
App requires to interact with other components. This is necessary for providing the EV
user with additional information relevant to the planned trip. To this end, the App sends
the GPS coordinates of the location in the middle of the trip as well as a radius value to the
CS operator system (ICT 4). The CS operator system identifies all relevant CSs within the
range specified. The data models for both, request and response between those two com-
ponents, are proprietary and exchange happens using HTTP over SSL communication
protocol. In order to show the greenness (renewable share) of CSs along the planned trip,
the App sends a request to the Energy-information service (ICT 3) by specifying the geo-
graphical region (in terms of country code) and the required time period. The data models
are based on ISO 3166-1 and ISO 8601 respectively. As a response, the App receives the
percentage of the renewable energy (currently on country level) as well as the market
energy price. The data model of the response is proprietary and two-way communication
happens using HTTP over SSL protocol.
The EV user can select a CS (e.g. based on cheapest or greenness factors) on the planned
route. Consequently, the App provides various information (e.g. number of connectors,
type of the connectors, status of a connector, etc.) about that specific CS. To achieve
this, information need to be exchanged between CS operator system and CS management
system (ICT 6) and CS management system and CS controller (ICT 7). Static information
of a specific CS can be fetch from CS management system once the CS operator system
sends a unique ID (specified by ISO 9834-8 standard) to the latter. The data model of
the response is proprietary and two-way communication happens using HTTP over SSL
protocol. Dynamic information (e.g. status information) for a specific connector of a CS
can be fetch from the CS Controller once the CS management system sends a unique ID
(specified by ISO 9834-8 standard) of the CS and the corresponding connector. The data
model of the response is based on OCPP using TCP/IP.
Summary of the interoperability analysis
From Table 2, on the one hand it can be noticed that for this system most of the informa-
tion objects sent as a request are based on standard data models. The only information
Kirpes et al. Energy Informatics (2019) 2:15 Page 25 of 31
object, which does not follow any standard data model, is ICT 1 (request). Hence, we
assess that there is a need for a standardized data model regarding battery health rec-
ommendations. On the other hand, except for ICT 7 (e.g. OCPP), all responses are
proprietary, although certain parts of the information objects follow standardized data
models like unique identifiers. Despite the fact that a de-facto standard for the exchange
of GPS coordinates (e.g. GPX) exists, it is not yet widely adopted. Instead, proprietary
data models are preferred. This is the case for the route-planning service (Mapbox) of this
system. Consequently, this leads to the conclusion that there is indeed (1) a lack of adop-
tion for standardized data models (e.g. GPX) in the system and (2) a need for data model
standards, which hampers the provision of an interoperable system.
Evaluation of the EMSA model
The evaluation of the final EMSA Model, regarding the identified requirements, is mainly
performed qualitatively, realized with an observational case study approach. To this end,
we first compare our model with SGAM and then evaluate the fulfillment of the require-
ments for our framework. We only include the final evaluation, earlier validation on
former iterations and feedback cycles with external experts are not considered for this
paper.
Evaluative comparison with SGAM
For a first evaluation of our EMSA Model, we compare it with SGAM by placing all impor-
tant components of e-mobility into both models’ dimensions (Fig. 6for EMSA Model
and Fig. 13 for SGAM): domains and zones. The allocation of the systems in SGAM is
aligned with the technical report of the E-Mobility Coordination Group (M/468) and the
CEN-CENELEC-ETSI Smart Grid Coordination Group (M/490) (CEN-CENELEC 2015).
Fig. 13 SGAM component layer for e-mobility based on CEN-CENELEC (2015)
Kirpes et al. Energy Informatics (2019) 2:15 Page 26 of 31
In SGAM, mainly the three domains of Distribution,DER and Customer Premises are
utilized. As the focus in SGAM is on the electricity process chain, EVs and CSs are placed
in the field zone instead of the process zone. In addition, the EVs and CSs are placed
in both domains of DER and Customer Premises, to differentiate between varying grid
connection points. In the case of our EMSA Model, both EVs and CSs are handled as
separate domains which gives the advantage of allocating components to domains in a
clearer fashion, thus giving the possibility to identify gaps even more adequately.
The Grid Management System combines grid balancing and congestion management
services. In SGAM the focus is on the grid’s point-of-view, whereas in the EMSA Model,
the focus is on e-mobility. Here again, the e-mobility process chain (domain division) is
better represented in EMSA and the allocation of components to the domains is more
clear from e-mobility point-of-view.
In order to quantitatively evaluate the domain separation of our EMSA Model, we
placed all ten High Level UCs of the ELECTRIFIC case study into the SGAM as well as
into our EMSA Model (Fig. 14). Domain experts of e-mobility and smart grids performed
an allocation of the UCs to the domains and zones. As result, a heat map of fields usage
frequency of the dimensions has been created.
The focus of the ELECTRIFIC case study is quite grid-related (e.g. smart charging on
distribution grid with power quality and grid congestion constraints (Alyousef et al. 2018),
dynamic prices for grid-friendly charging). But it also includes battery health recommen-
dations, EV fleet usage and advanced charging services like reservation, dynamic prices
and green charging (e.g. renewable share of the energy mix). Concerning the domains in
SGAM, all these use cases can therefore be found in the Distribution and DER domain.
Some use cases also include the Customer Premises. In the EMSA Model, the use cases are
more evenly spread along the available domains, although, there is a tendency to Energy
Conversion,Energy Transfer from/to EV and EV User Premises domains.
Concerning the distribution of the High Level UCs over the zones, the difference is
quite small. This is due to the fact that the EMSA Model reuses the same six zones from
SGAM, which have been adapted to fit the e-mobility point of view. The average usage of
Fig. 14 Heat map of domain and zone distribution of the ELECTRIFIC case study in the SGAM and EMSA Model
Kirpes et al. Energy Informatics (2019) 2:15 Page 27 of 31
a certain domain-zone field in SGAM is 2.1 and in the EMSA Model 2.75, which indicates
a better fitting domain separation of the EMSA Model for the use cases in the case study
of ELECTRIFIC. In order to generalize this statement, additional use cases (e.g. public
charging stations, wallbox and home charging) would need to be allocated and compared
in both models in the future.
Validation of the EMSA regarding complexity and interoperability requirements
In this section, the fulfillment of Requirement 1 (Provide means to manage and reduce
e-mobility systems complexity) and Requirement 2 (Provide means to analyze and ensure
e-mobility systems interoperability) regarding their sub-requirements (=objectives) is dis-
cussed and evaluated. We provide reference to the relevant design principles and evaluate
(1) the implementation of each objective in the EMSA and (2) the demonstration within
the case study.
Req1.1: Abstraction and conceptualization The relevant design principles that con-
tribute to the fulfillment of this requirement by the EMSA are Principle 2 (Multi-
Dimensional Structure) and Principle 3 (Allocation, Localization and Consistency).
Abstraction and conceptualization are enabled and implemented in the EMSA by uti-
lization of a model-based approach over multiple layers. Further, the level of abstraction
can be varied and hierarchical systems with different granularity of sub-systems are con-
sidered. The consistent allocation of entities in the EMSA additionally supports these
concepts as shown in the comparison with SGAM. In the case study, it has been demon-
strated that the means for modelling the systems are sufficient to reduce the complexity
and successfully engineer the desired system. The proposed MDA approach may fur-
ther enhance the abstraction and conceptualization on different layers. This needs to be
evaluated in future work.
Req1.2: Separation of concerns The relevant design principle that contributes to the
fulfillment of this requirement by the EMSA is Principle 2 (Multi-Dimensional Structure).
Separation of concerns is implemented and enacted by the EMSA through integration of
different architecture viewpoints. This is mainly achieved with the five interoperability
layers, which allow for different perspectives on the architecture of an e-mobility system.
In the case study, it has been demonstrated that the separation of concerns in the EMSA
is appropriate. When working with the various domain experts from different business
units, reduction of the complexity by only considering one or two layers of the system
proved to be very useful. The system was successfully engineered, appropriately reflecting
the needs of all stakeholders.
Req1.3: Re-usability of components The relevant design principles that contribute to
the fulfillment of this requirement by the EMSA are Principle 1 (Scope and Applicabil-
ity), Principle 3 (Allocation, Localization and Consistency) and Principle 4 (Universality
and Flexibility). Re-usability of components is implemented and ensured by applying
standardization and harmonization means on all layers. Using standards such as ISO
15118 or IEC 61851 and mapping them to the appropriate location in the EMSA, facil-
itates allocation and subsequently supports the re-usability of components and systems.
In the case study, it has been demonstrated that re-usability of services and sub-systems
Kirpes et al. Energy Informatics (2019) 2:15 Page 28 of 31
is an important feature to reduce complexity of a system. Many of the ELECTRIFIC ser-
vices and components are used by multiple systems, their alignment is ensured by the
EMSA Model.
Req1.4: Complexity reduction of the engineering process The relevant design prin-
ciples that contribute to the partial fulfillment of this requirement by the EMSA are
Principle 2 (Multi-Dimensional Structure) and Principle 3 (Allocation, Localization and
Consistency). Complexity reduction of the engineering process is implemented by the
EMSA framework with the inclusion of a very brief guidance and documentation. In
future work, this needs to be further enhanced by providing a sophisticated and compre-
hensive engineering process with guidance. In the case study, it has been demonstrated
that the EMSA methodology indeed reduces complexity of the engineering process, espe-
cially for the domain experts and the system engineers. This could probably be further
improved by applying the suggested MDA approach, which works well with the SGAM.
Req2.1: Analysis and assessment of interoperability The relevant design principles
that contribute to the fulfillment of this requirement by the EMSA are Principle 2 (Multi-
Dimensional Structure) and Principle 3 (Allocation, Localization and Consistency). The
interoperability assessment of systems is implemented by providing an allocation of com-
munication and information standards on the respective layers. In the case study, it has
been demonstrated that the EMSA has successfully been used to analyze the interoper-
ability of the ELECTRIFIC system considered for that respective use case. By conducting
multiple iterations of the assessment, potential interoperability issues of this system have
been eliminated during the project.
Req2.2: Identification of standardization gaps The relevant design principles that
contribute to the fulfillment of this requirement by the EMSA are Principle 2 (Multi-
Dimensional Structure) and Principle 3 (Allocation, Localization and Consistency). Simi-
lar to Req2.1, the identification of gaps in standardization is implemented by allocating all
existing relevant standards to the respective layers (information and communication). In
the case study, it has been demonstrated that the EMSA Model can be utilized to identify
such gaps on different layers. If for an exchanged information object between two com-
ponents, a proprietary data model is used this is either 1) an interoperability issue within
the system or 2) a gap in standardization. This can be analyzed with the EMSA. Future
created standards need to be mapped to the respective domain/zone on the appropriate
layer.
With the demonstration in the ELECTRIFIC case study and its evaluation, we show
that most of the requirements are completely fulfilled. Due to the limitations of a journal
paper, Req1.4 (Complexity reduction of the engineering process) is only partially fulfilled.
Provision of further guidance on the engineering process and additional documentation
is out of scope for this research work, but will be considered in future work.
Conclusion and outlook
A suitable design and engineering process for ISs is required to align use cases and busi-
ness objectives with the functional and technological requirements of a system. This
is especially important for sector-coupled applications like e-mobility, which combines
Kirpes et al. Energy Informatics (2019) 2:15 Page 29 of 31
aspects from the energy and the mobility sector. Typically, the complexity of system
architectures is reduced through abstraction, conceptualization and other means. For
the energy sector, SGAM has been proposed as a systems architecture model to facil-
itate the design and development of ISs in the context of power systems. Inspired by
SGAM, several independent approaches with a similar purpose in the context of smart
cities or e-mobility have been proposed. We showed, that none of the existing approaches
satisfies all of the requirements adequately. Subsequently, we designed and developed
the E-Mobility Systems Architecture (EMSA) model with multiple iterations and in this
paper, introduced the final model which is based on the extracted design principles.
The EMSA is suitable for application with systems of the whole e-mobility sector. A
case study is introduced, which has been conducted during the ELECTRIFIC project to
demonstrate utility of the EMSA Model and in order to assess and validate it against
the above-mentioned requirements. We find, that our systems architecture model ful-
fills all requirements sufficiently. Further, we provide some guidance on the usage of the
EMSA Model for research-oriented as well as for practical applications. It supports the
users of the model, namely domain and system engineering experts, typically from indus-
try in their complex tasks of analyzing existing and building new interoperable systems
for e-mobility. However, the description and validation of our model is limited to battery
electric vehicles.
The evaluation of the domains and zones definitions compared to SGAM serves as
an outlook for future research. Further research activities could be conducted, e.g. an
enhanced quantitative comparison of the related approaches with the EMSA Model,
by considering a larger and harmonized set of e-mobility related use cases. Due to the
dynamically evolving e-mobility ecosystem, standardization activities are still ongoing.
Future released standards, such as IEC 61980, IEC 63110 or IEC 63119 need to be mapped
to the respective location. In future work, the EMSA Model might be extended to a com-
plete framework including an engineering process, more guidance and documentation
and a reference architecture for e-mobility information systems.
Abbreviations
BMS: Battery Management System; CIM: Computational Independent Model; CS: Charging Station; DER: Distributed
Electrical Resource; DSL: Domain-Specific Language; DSO: Distribution System Operator; DSR: Design Science Research;
EM-ISA: E-Mobility Information System Architecture; EMAM: E-Mobility Architecture Model; EMS: Energy Management
System; EMSA: E-Mobility Systems Architecture; EV: Electric Vehicle; GSCAM: Generic Smart City Architecture Model;
GWAC: GridWise Architecture Council; ICT: Information and Communications Technology; IS: Information System; MDA:
Model-Driven Architecture; PIM: Platform Independent Model; PSI: Platform Specific Implementation; PSM: Platform
Specific Model; PWM: Pulse Width Modulation; SCIAM: Smart City Infrastructure Architecture Model; SGAM: Smart Grid
Architecture Model; SoC: (Battery) State of Charge; SysML: Systems Modeling Language; TSO: Transmission System
Operator; UC: Use Case; UML: Unified Modeling Language
Acknowledgements
We would like to thank all ELECTRIFIC project partners for the fruitful discussions and their valuable feedback during
design, development and evaluation of the EMSA model.
Authors’ contributions
BK did main research on related work and wrote the corresponding sections. PD and BK were mainly involved in creating
the EMSA Model, especially the layers, zones and domains definition. RB detailed the High Level UC of the case study
done within the ELECTRIFIC project. The comparison of SGAM and EMSA qualitatively on component and quantitatively
on function layer was mainly performed by PD with the help of BK. The validation section was done by BK. HdM and CB
contributed in conception and revision of the EMSA Model and the whole paper. All authors have read and approved the
final manuscript.
Funding
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under
grant agreement No. 713864 (ELECTRIFIC).
Kirpes et al. Energy Informatics (2019) 2:15 Page 30 of 31
Availability of data and materials
The detailed description of the use cases which have been used and/or analyzed within the case study are available from
the corresponding author on reasonable request and will also be published on the project’s website (https://electrific.eu/).
Competing interests
The authors declare that they have no competing interests.
Author details
1University of Mannheim, Schloss, 68131 Mannheim, Germany. 2Bayernwerk AG, Lilienthalstraße 7, 93049 Regensburg,
Germany. 3University of Passau, Innstraße 41, 94032 Passau, Germany.
Received: 22 March 2019 Accepted: 18 July 2019
References
Alyousef A, Danner D, Kupzog F, Meer HD (2018) Enhancing power quality in electrical distribution systems using a smart
charging architecture. Energy Inform 1(1):28
Benbya H, Mckelvey B (2006) Toward a complexity theory of information systems development. Inf Technol People
19(1):12–34
Brand A, Iacob M-E, van Sinderen MJ (2015) Interoperability Architecture for Electric Mobility. In: van Sinderen M,
Chapurlat V (eds). Enterprise Interoperability. Springer, Berlin, Heidelberg. pp 126–140
Brown S, Pyke D, Steenhof P (2010) Electric vehicles: The role and importance of standards in an emerging market. Energy
Policy 38(7):3797–3806
CEN-CENELEC (2015) Report About Smart Charging of Electric Vehicles in Relation to Smart Grid E-Mobility Smart
Charging. ftp://ftp.cen.eu/EN/EuropeanStandardization/HotTopics/ElectricVehicles/SmartChargingReport.pdf.
Accessed 31 July 2019
CEN-CENELEC-ETSI Smart Grid Coordination Group (2012) Smart Grid Reference Architecture. https://ec.europa.eu/
energy/sites/ener/files/documents/xpert_group1_reference_architecture.pdf. Accessed 31 July 2019
CEN-CENELEC-ETSI Smart Grid Coordination Group (2014) SG-CG/M490/K_SGAM Usage and Examples. ftp://ftp.cencenelec.
eu/EN/EuropeanStandardization/HotTopics/SmartGrids/SGCG_Methodology_SGAMUserManual.pdf. Accessed 31
July 2019
Courtney J, Merali Y, Paradice D, Wynn E (2008) On the Study of Complexity in Information Systems. Int J Inform Technol
Syst Approach 1(1):37–48
Dänekas C., Neureiter C, Rohjans S, Uslar M, Engel D (2014) Towards a Model-Driven-Architecture Process for Smart Grid
Projects. In: Benghozi P, Krob D, Lonjon A, Panetto H (eds). Digital Enterprise Design & Management. Springer, Cham.
pp 47–58
Danner P, Duschl W, Danner D, Alyousef A, de Meer H (2018) Flexibility Reward Scheme for Grid-Friendly Electric Vehicle
Charging in the Distribution Power Grid. In: Proceedings of the 3th Workshop on Electric Vehicle Systems, Data, and
Applications of EV-Sys ’18. ACM, New York. pp 1–6
DKE/DIN/VDE (2014) The German Standardization Roadmap Smart City. Technical report, DIN e.V. and DKE Deutsche
Kommission Elektrotechnik Elektronik Informationstechnik im DIN und VDE
Eider M, Stolba M, Sellner D, Berl A, Basmadjian R, de Meer H, Klingert S, Schulze T, Kutzner F, Kacperski C (2017) Seamless
Electromobility. In: Proceedings of the Eighth International Conference on Future Energy Systems - e-Energy ’17.
pp 316–321. http://dl.acm.org/citation.cfm?doid=3077839.3078461. Accessed 31 July 2019
ElaadNL (2017) EV Related Protocol Study v1.1. Technical report, ElaadNL
Electrific Consortium (2019) Electrific Project. https://electrific.eu/. Accessed 31 July 2019
European Commission (2019) Database of European projects. https://cordis.europa.eu/projects/en. Accessed 31 July 2019
Geraci A, Katki F, McMonegal L, Meyer B, Lane J, Wilson P, Radatz J, Yee M, Porteous H, Springsteel F (1991) IEEE standard
computer dictionary: Compilation of IEEE standard computer glossaries. IEEE Press, Piscataway
GWAC (2008) Gridwise Interoperability Context-Setting Framework. https://www.gridwiseac.org/pdfs/
interopframework_v1_1.pdf
Hevner AR, March ST, Park J, Ram S (2004) Design Science in Information Systems Research. MIS Q 28(1):75–105
Holub I (2016) Methodology for Measuring the Complexity of Enterprise Information Systems. J Syst Integr 7(3):34–53
Honour E (2006) Systems Engineering and Complexity. In: Proceedings of the Conference on Systems Engineering
Research. INCOSE, San Diego
ISO Central Secretary (2015) Systems and software engineering – System life cycle processes, ISO/IEC/IEEE 15288:2015,
Geneva
ISO Central Secretary (2016) Road vehicles – Vehicle-to-Grid Communication Interface – Part 2: Network and application
protocol requirements, ISO/DIS 15118-2, Geneva
ISO, IEC, IEEE (2011) International Standard ISO/IEC/IEEE 42010:2011 Systems and software engineering - Architecture
Description. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6146379&isnumber=6146378
King JL, Simon CP (2015) Complications with Complexity in Requirements. ACM Trans Manag Inf Syst 5(3):1–12
Kirpes B, Becker C (2018) Processing Electric Vehicle Charging Transactions in a Blockchain-based Information System. In:
24th Americas Conference on Information Systems (AMCIS 2018). Association for Information Systems (AIS), New
Orleans. pp 1–5
Kirpes B, Klingert S, Basmadjian R, de Meer H, Eider M, Perez Ortega M (2017) EV Charging Coordination to secure Power
Grid Stability. In: 1st E-Mobility Power System Integration Symposium (Berlin). Association for Information Systems,
Berlin
Kossahl J, Busse S, Kolbe LM (2012) The Evolvement of Energy Informatics in the Information Systems Community - A
literature Analysis and Research Agenda. In: 20th European Conference on Information Systems, ECIS 2012,
Barcelona, Spain, June 10-13, 2012. p 172. http://aisel.aisnet.org/ecis2012/172. Accessed 31 July 2019
Kirpes et al. Energy Informatics (2019) 2:15 Page 31 of 31
Nationaal Kennisplatform Laadinfrastructuur, Robert and Philips, Simon and Zwirello, Chris and Schilling, Simon and
Warnier, Olger (2019) OCPI 2.2-RC1. https://github.com/ocpi
Lemberger PP, Morel M (2012) Managing Complexity of Information Systems: The Value of Simplicity. Wiley, Hoboken
Lopes AJ, Lezama R, Pineda R (2011) Model Based Systems Engineering for Smart Grids as Systems of Systems. In:
Procedia Comput Sci Vol. 6. pp 441–450
Neureiter C (2013) Introduction to the “SGAM Toolbox”. Tech. Rep.
Neureiter C, Engel D, Uslar M (2016) Domain Specific and Model Based Systems Engineering in the Smart Grid as
Prerequesite for Security by Design. Electronics 5(2):24
Neureiter C, Rohjans S, Engel D, Dänekas C, Uslar M (2014) Addressing the Complexity of Distributed Smart City Systems
by Utilization of Model Driven Engineering Concepts. In: VDE-Kongress 2014. VDE, Frankfurt/Main. pp 1–6
Object Management Group (2014) MDA Guide rev. 2.0. Technical Report June, Object Management Group. http://www.
omg.org/cgi-bin/doc?ormsc/14- 06-01
Open Charge Alliance (2018) OCPP 2.0 Public Draft document. https://www.openchargealliance.org
Peffers K, Tuunanen T, Rothenberger MA, Chatterjee S (2008) A Design Science Research Methodology for Information
Systems Research. J Manag Inform Syst 24(3):45–77
Portela CM, Klapwijk P, Verheijen L, de Boer H, Slootweg H, van Eekelen M (2015) OSCP - An open protocol for smart
charging of electric vehicles. CIRED
Ranganathan A, Campbell R (2007) What is the Complexity of a Distributed Computing System?. Complexity 12(6):37–45
Rodríguez R, Madina C, Zabala E (2015) EV Integration in Smart Grids Through Interoperability Solutions. EVS28 Int Electric
Veh Symp Exhibition:1–12
Schmidt J, Busse S (2013) The Value of IS to Ensure the Security of Energy Supply - The Case of Electric Vehicle Charging.
In: 19th AMCIS Proceedings. Association for Information Systems, Chicago, Illinois. pp 1–9
Schmutzler J, Andersen CA, Wietfeld C (2013) Evaluation of OCPP and IEC 61850 for smart charging electric vehicles.
World Electric Veh J 6(4):863–874
Schuh G, Fluhr J, Birkmeier M, Sund M (2013) Information system architecture for the interaction of electric vehicles with
the power grid. In: 2013 10th IEEE International Conference on Networking, Sensing and Control (ICNSC). IEEE, Evry,
France. pp 821–825. http://ieeexplore.ieee.org/document/6548844/
Uslar M, Engel D (2015) Towards Generic Domain Reference Designation: How to learn from Smart Grid Interoperability.
In: D-A-CH Energieinformatik. KIT
Uslar M, Gottschalk M (2015) Extending the SGAM for Electric Vehicles Vol. 95. pp 219–226
Uslar M, Rohjans S, Neureiter C, Pröstl Andrén F, Velasquez J, Steinbrink C, Efthymiou V, Migliavacca G, Horsmanheimo S,
Brunner H, Strasser T (2019) Applying the Smart Grid Architecture Model for Designing and Validating
System-of-Systems in the Power and Energy Domain: A European Perspective. Energies 12(2):258
Uslar M, Trefke J (2014) Applying the Smart Grid Architecture Model SGAM to the EV Domain. In: Proceedings of the 28th
EnviroInfo 2014 Conference. BIS-Verlag, Oldenburg. pp 821–826
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Submit your manuscript to a
journal and benefi t from:
7 Convenient online submission
7 Rigorous peer review
7 Immediate publication on acceptance
7 Open access: articles freely available online
7 High visibility within the fi eld
7 Retaining the copyright to your article
Submit your next manuscript at 7 springeropen.com
