**PROJECT OBJECTIVES**

</th> </tr> </table>

EVERLASTING is focussing on model based battery management systems (BMS) for
Li-ion batteries. New or improved BMS features will be developed by performing
intensive research activities in the field of physical testing, simulations,
modelling and validation on battery cell and pack level to improve the
reliability, lifetime, performance and safety of Li-ion batteries when being
used in electric vehicles.

Today, batteries are not yet the ideal energy container they were promised to
be. They are expensive, fragile and potentially dangerous. Moreover the
current electric vehicle cannot compete yet with traditional vehicles when it
comes to driving range and flexibility. EVERLASTING intends to bring Li-ion
batteries closer to this ideal by focusing on the following technology areas:

  * Predicting the behaviour of battery systems in all circumstances and over their full lifetime. This enables accurate dimensioning and choice of the correct battery type, leading to lower cost. It also facilitates the development of a powerful battery management system during all stages of its evolution from idea to fully tested product. 

  

  * Sensing signals beyond the standard parameters of current, voltage and temperature. This multi-sensing approach, over early, mid and late stage time domains, provides more varied and in-depth data on the status of the battery facilitating a pro-active and effective management of the batteries, preventing issues rather than mitigating them. 

  

  * Monitoring the status of the battery by interpreting the rich sensor data. By intelligently combining this information with road, vehicle and driver data we intend to offer accurate higher-level driver feedback. This induces a bigger trust and hence a lower range anxiety. 

  

  * Managing the battery in a proactive way, based on a correct assessment of its status. Efficient thermal management and load management results in increased reliability and safety and leads to lower overall cost through an increased lifetime. 

  

  * Defining a standard BMS architecture and interfaces and gathering the necessary support in the market. This allows an industry of standard BMS components to flourish which will result in lower cost. 

  

  
<table>  
<tr>  
<th>

**2 DATA MANAGEMENT P**

</th>  
<th>

**LAN**

</th> </tr>  
<tr>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

**2.1 INTRODUCTION**

</td>  
<td>

</td> </tr> </table>

A new element in Horizon 2020 is the use of Data Management Plans. A **Data
Management Plan** (DMP) describes the data management life cycle for all
datasets to be collected, processed or generated by a research project. The
purpose of the Data Management Plan (DMP) is to provide an analysis of the
main elements of the data management policy that will be used by the
applicants with regard to all the datasets that will be generated by the
project.

Special attention will be given to “ **Open Access** ” to scientific
information and “ **Open Research Data** ”. Open access (OA) refers to the
practice of providing online access to scientific information that is free of
charge to the end-user and reusable without restrictions. In the context of
research and innovation, 'scientific information' can mean: peer-reviewed
scientific research articles (published in scholarly journals) or research
data (underlying data from publications, curated data and/or raw data).

Why give open access to publications and data in Horizon 2020?

Firstly, for the benefits of society in general. Modern research builds on
extensive scientific dialogue and advances by improving earlier work. The
Europe 2020 strategy for a smart, sustainable and inclusive economy underlines
the central role of knowledge and innovation in generating growth. Broader
access to scientific publications and data therefore helps to:

  * build on previous research results (improved quality of results) 

  

  * encourage collaboration and avoid duplication of effort (greater efficiency) 

  

  * speed up innovation (faster progress to market means faster growth) 

  

  * involve citizens and society (improved transparency of the scientific process). 

  

Secondly, also the EVERLASTING partners benefit from giving open access to
publications and research data by increasing the visibility of the project
results, leading to more citations for the research partners and increased
collaboration potential for setting up new projects or for the quicker
adoption and valorisation of the built up knowledge.

**Deliverable D8.2 “Data Management Plan”** describes the handling of research
data that will be collected, processed or generated by the EVERLASTING project
according to the guidelines for data management in the H2020 Online Manual.
This deliverable will evolve during the lifetime of the project in order to
describe the status of the project's reflections on data management.

EVERLASTING Deliverable D8.2 “Data Management Plan” must be considered as a “
**living document** ” and will be updated when important updates are
available: new datasets, updates on existing datasets, changes in consortium
policies (e.g. on exploitation of results and patenting) or other external
reasons (e.g. changes in consortium members and suggestions from advisory
board).

The DMP will be updated as a minimum in time with the periodic
evaluation/assessment of the project. This update will be in parallel with the
update of **D8.1 “Dissemination and exploitation plan”** . Obviously, there is
a close link to D8.1 “Dissemination and exploitation plan” since the generated
data within the EVERLASTING project is input for future exploitation and
dissemination activities. Some of the generated datasets will be used as
“underlying data” for the dissemination of EVERLASTING project results and the
Data Management Plan will describe how these datasets will be shared as “Open
Data”. By sharing data, we further improve the quality level and impact of the
dissemination activities within EVERLASTING.

##  2.2 SCOPE OF DATA MANAGEMENT PLAN VERSION 2.0

The DMP is not a fixed document, but evolves during the lifespan of the
project.

The scope of this **second version of the EVERLASTING DMP** contains the
updated status of reflection within the consortium about the overall types of
data that have been or will be collected or produced during the project and on
how and what data will be made open available to external stakeholders such as
research institutes and companies. In this version of the EVERLASTING DMP,
more detailed datasets and their specific handling will be described. The
first open datasets have been uploaded in the **4TU.Centre for Research Data**
**repository** (see 2.5.3 Data Inventory).

Existing guidance material will be used to structure this work such as the use
of **DMPOnline**

( _https://dmponline.dcc.ac.uk_ )  , which has been developed by the Digital
Curation Centre to help writing data management plans. **OpenAIRE** also
provides a range of resources, FAQs, webinars and support pages. OpenAIRE can
also be contacted via the local representatives in all EU countries: the
**National Open Access Desks** or NOADs. More information can be found on
_www.openaire.eu_ .

The **Horizon 2020 FAIR DMP template,** which is available on DMPOnline, has
been designed to be applicable to any Horizon 2020 project that produces,
collects or processes research data. A single DMP should be developed to cover
its overall approach. However, where there are specific issues for individual
datasets (e.g. regarding openness), these should be described in the specific
dataset. In general terms, research data should be 'FAIR' i.e. findable,
accessible, interoperable and re-usable.

##  2.3 OPEN ACCESS SCIENTIFIC PUBLICATIONS

In this section, the intended scientific dissemination will be described
briefly since **“underlying data”** of scientific publications forms the core
part of the “open data research pilot”.

The scientific dissemination will be implemented through:

  * The publishing of the generated results in open access peer-reviewed scientific journals 

  

  * The presentation of project results in scientific conferences and events 

  

Partners will publish scientific articles with Green or Gold-standard open
access to share results generated from the project. TU/e will ensure that
rules in the Consortium Agreement are respected concerning scientific
publications before their submission to journals. Prior to any disclosure
(conference, publications, defence of PhD theses or Masters) the protection of
the project progress must be secured. The project will generate research data
in a wide range of levels of detail from simulation and lab results to
demonstrator validation. Most data will be associated with results that may
have a potential for commercial or industrial protection and therefore cannot
be made accessible for verification and reuse in general due to intellectual
property protection measures. However, relevant data necessary for the
verification of results published in scientific journals can be made
accessible on a case by case basis. The decision concerning the publication of
data will be made by the Management Board, as the decision-making body of the
consortium. Research data of public interest such as those underlying
scientific publications will be made available via open access data
repositories while hyperlinks to these datasets will be placed on the
EVERLASTING website.

Below you can find the list of planned scientific publications, as mentioned
in the project proposal, which will generate “underlying data”. Besides these
publications some deliverables will also generate “open data” like D2.3
“Report containing aging test profiles and test results” (Type: ORDP & Due
Date: M42). For the most recent details on our dissemination activities, we
refer to the **M18 update of D8.1 “Dissemination and exploitation plan”** .  
  
<table>  
<tr>  
<th>

Topic

</th>  
<th>

Dissemination method

</th>  
<th>

Partners

</th> </tr>  
<tr>  
<td>

WP1: Improved simulation and modelling tools

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Dissemination of research results with respect to tools and methods achieving
a relevant prediction of pack behaviour, with coupled controls, including
validation (Siemens)

</td>  
<td>

Conference presentation

</td>  
<td>

Siemens PLM

</td> </tr>  
<tr>  
<td>

Presentation of (pre-)industrialized new models, tools and features allowing
the modelling and prediction of battery pack behaviour and BMS coupling

</td>  
<td>

Siemens user conference (including major carmakers, suppliers, OEMs,

battery suppliers…)

</td>  
<td>

Siemens PLM

</td> </tr>  
<tr>  
<td>

Demonstrating multi-level battery modelling, simulation, reduction and control
coupling applied to automotive transportation

</td>  
<td>

Simulation@Siemens (Siemens divisions)

</td>  
<td>

Siemens PLM

</td> </tr>  
<tr>  
<td>

Modelling order reduction techniques

</td>  
<td>

Conference

presentation,

Journal paper (Open Access)

</td>  
<td>

TUM, TU/e

</td> </tr>  
<tr>  
<td>

WP2: Increased reliability

</td>  
<td>

</td> </tr>  
<tr>  
<td>

The use of self-learning algorithms for estimation of SoH

</td>  
<td>

Conference

presentation, Journal paper (Open Access)

</td>  
<td>

VITO,

ALGOLiON

</td> </tr>  
<tr>  
<td>

Test procedures for BMS testing

</td>  
<td>

Conference presentation, Standard proposal

</td>  
<td>

TÜV SÜD, VITO

</td> </tr>  
<tr>  
<td>

WP3: Extended vehicle range

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Challenges of parametrization of physico-chemical battery model and comparison
to standard electro chemical battery models

</td>  
<td>

Conference presentation

</td>  
<td>

RWTH

</td> </tr>  
<tr>  
<td>

Deduction of minimum parameter set for physico-chemical model and adaption of
model for application on embedded hardware

</td>  
<td>

Journal paper (Open Access)

</td>  
<td>

RWTH

</td> </tr>  
<tr>  
<td>

Maximizing operation range of Li-Ion batteries to maximize energy output
without losing safety

</td>  
<td>

Conference paper, Journal Paper (Open

Access)

</td>  
<td>

RWTH

</td> </tr>  
<tr>  
<td>

Embedded BMS hardware requirements towards execution of extended battery
models

</td>  
<td>

Conference paper

</td>  
<td>

RWTH

</td> </tr>  
<tr>  
<td>

Costs for more complex BMS hardware, enabling the extraction of more energy
vs. higher capacity of battery cells

</td>  
<td>

Conference paper

</td>  
<td>

RWTH

</td> </tr>  
<tr>  
<td>

Development of common BMS software towards integration of more complex battery
models

</td>  
<td>

Conference paper

</td>  
<td>

RWTH

</td> </tr>  
<tr>  
<td>

Advanced BMS Models and Model Adaptation

</td>  
<td>

University courses and seminars

</td>  
<td>

RWTH

</td> </tr>  
<tr>  
<td>

Full Physico-Chemical Parameterization of the Battery Cell

</td>  
<td>

Journal paper (Open Access)

</td>  
<td>

RWTH

</td> </tr>  
<tr>  
<td>

Aging behaviour of the Battery Cell

</td>  
<td>

Journal paper (Open Access)

</td>  
<td>

RWTH

</td> </tr>  
<tr>  
<td>

Methodology for onBoard estimation of lithium loss and anode and cathode
degradation

</td>  
<td>

Journal paper (Open Access)

</td>  
<td>

RWTH,

ALGOLiON

</td> </tr>  
<tr>  
<td>

Drive power prediction of electric busses

</td>  
<td>

Conference

presentation and journal paper (Open Access)

</td>  
<td>

TU/e

</td> </tr>  
<tr>  
<td>

Online real-time vehicle parameter estimation

</td>  
<td>

Conference

presentation and journal paper (Open Access)

</td>  
<td>

TU/e

</td> </tr>  
<tr>  
<td>

Energy Management of Electrified Auxiliaries of electric busses

</td>  
<td>

Conference

presentation and journal paper (Open Access)

</td>  
<td>

TU/e

</td> </tr>  
<tr>  
<td>

Range extension and optimization: tradeoff between travel-time and energy
consumption

</td>  
<td>

Conference

presentation and Journal paper (Open Access)

</td>  
<td>

TU/e

</td> </tr> </table>

WP4: Safer batteries

Battery and cell safety and failure modes, early degradation Conference CEA
mechanisms of materials leading to cell thermal runaway presentation,

Journal paper (Open

Access)

Safety monitoring of Li-ion batteries: signal processing for battery
Conference CEA,

safety applications, multi-sensing safety algorithms presentation, ALGOLiON

journal paper (Open Access),

demonstration at trade exhibitions, trade

journal article

Multi-scale, multi-physics modelling of safety hazards in Li-ion Conference
CEA

batteries presentation,

Journal paper (Open Access)

WP5: Longer battery life

Active and passive thermal management of Li-ion batteries Conference CEA, VITO

presentation,

Journal paper (Open

Access)

Battery reconfiguration Conference VITO

presentation,

Journal paper (Open

Access)

Influence of cell-to-cell parameter variations on cell balancing Conference
TUM

presentation,

Journal paper (Open

Access)

Optimal utilization of dissipative cell balancing Conference TUM

presentation,

Journal paper (Open

Access)

Optimal utilization of non-dissipative cell balancing Conference TUM

presentation,

Journal paper (Open Access)

WP6: Standardized architecture

BMS architecture Standard proposal LION

Smart

Requirements and architecture concept of a highly modular Journal paper (Open
TUM

prototyping hardware platform. Access)

Implementation of a selection of the developed technologies in WP2- Journal
paper (Open LION

W5 according to their readiness levels on standardized BMS Access) Smart WP7:
Demonstrator

Battery pack demonstrator, integrated in electric vehicle Presentation to
VOLTIA

scientific community and general public

##  2.4 DATA REPOSITORIES

The EVERLASTING project will collect or generate a lot of different types of
datasets. Some of these datasets will be only for internal use between the
EVERLASTING consortium partners and will be stored in EVERLASTING
repositories, while other datasets will be made publicly accessible in open
access repositories.

**2.4.1 DATA REPOSITORIES - DATA OWNER**

The main data type collected in the EVERLASTING project is **BMS related
measurement/testing/simulation data** of which one of the partners is the
owner. If the data is considered to be privacy related, it is the
responsibility of the data owner to inform the impacted person(s) and provide
the necessary terms and agreements.

The data owner will store its own data on **data owner servers** following
internal data management procedures during the research project. Most partners
have a dedicated Data Protection Officer (DPO) and some partners have a
Research Data Management team (RDM).

###  2.4.2 DATA REPOSITORIES - EVERLASTING

To facilitate the exchange of information and data between the EVERLASTING
partners, the project coordinator has set-up 2 dedicated sites:

**EVERLASTING SharePoint site** to exchange all general information and
documents like MB and GA meeting minutes, WP related documents, such as the
Grant Agreement and the Consortium Agreement, deliverables and publications
(for more information see D9.1 “Project collaborative platform”).

**EVERLASTING Secure FTP site** to facilitate the exchange of research data
between the EVERLASTING partners.

**2.4.3 DATA REPOSITORIES - OPEN ACCESS**

A data repository is a digital archive collecting and displaying datasets and
their metadata. A lot of data repositories also accept publications, and allow
linking between publications and their underlying data. During the first 6
months, the EVERLASTING partners have been studying the different options
where to store the data that can be made openly accessible. An overview of
repositories can be found at **Re3data** (  _www.re3data.org_ )  .

The EVERLASTING partners prefer to use an open access data repository which is
co-developed by one of the own partners and the choice was made to use the “
**4TU.Centre for Research Data** ” **repository** as **main data repository
for EVERLASTING “Open Data”** .

The 4 Technical Universities in the Netherlands have created this research
data repository  ( _http://data.4tu.nl_ )  and are further developing this.
TU/e has an own RDM group and can help the EVERLASTING partners in using this
repository. It is possible to deposit EVERLASTING data as open data or deposit
it with embargo when it contains confidential data. The **TU/e RDM team** can
help in offering guidelines for sustainable data formats and metadata
standards, as well as support for dealing with sensitive data and licensing.

As complementary solution for storing open access publications and open
research data, the EVERLASTING partners can use the “Zenodo” repository (
_www.zenodo.org)_ . Zenodo repository is provided by OpenAIRE and hosted by
CERN. Zenodo is a catch-all repository that enables researchers, scientists,
EU projects and institutions to:

  * Share research results in a wide variety of formats including text, spreadsheets, audio, video, and images across all fields of science. 

  

  * Display their research results and get credited by making the research results citable and integrating them into existing reporting lines to funding agencies like the European Commission. 

  

  * Easily access and reuse shared research results. 

  

  * Integrate their research outputs with the OpenAIRE portal. 

  

##  2.5 DATA SUMMARY

**2.5.1 INTRODUCTION**

EVERLASTING is focussing on model based battery management systems (BMS) for
Li-ion batteries.

New or improved BMS features will be developed by performing intensive
research activities in the field of physical testing, simulations, modelling
and validation on battery cell and pack level to improve the reliability,
lifetime, performance and safety of Li-ion batteries when being used in
electric vehicles. The first 3 years of the EVERLASTING project, the research
activities will be mainly performed in the different labs of the research
partners. In a later phase, also real-life validation activities will be
performed on the electric van from VOLTIA and the electric bus from VDL ETS.

The main data type collected in the EVERLASTING project is **BMS related
measurement/testing/simulation data** . Some of the research data (such as
lifetime and safety test data) obtained in the project will be publicly shared
via the Open Research Data Pilot. In this chapter, the reader can find the
first available open datasets and can also get an idea of the different
datasets to be expected the remaining period from the EVERLASTING project.
Before elaborating on the datasets in the data inventory we first describe the
FAIR Data principles.

###  2.5.2 FAIR DATA

The datasets will be made available following the **FAIR Data principles**
which means findable, accessible, interoperable and re-usable.

####  Making data findable, including provisions for metadata [fair data]

_Outline the discoverability of data (metadata provision)_

All data underlying journal articles will be archived and made available via
the data repository of **4TU.Centre for Research Data** ( _http://data.4tu.nl_
)  . It is a data repository for technical and scientific research data,
mainly from The Netherlands. It offers data archiving services also to
researchers at non-Dutch institutions and to H2020 projects such as the
EVERLASTING project.

4TU.Centre for Research Data makes data openly available with a user license
which is comparable to a CC BY-NC license.

**Discoverability** of the data in 4TU.Centre for Research Data is achieved
by:

  * Adding bibliographic (descriptive) metadata to each dataset (according to the Datacite metadata standard); 

  

  * Allowing the metadata to be harvested by Google and portals such as Narcis 

  

( _http://www.narcis.nl_ )  ;

  * Presenting metadata in the open linked data RDF format; 

  

  * Providing a DOI to each dataset; 

  

  * Linking the data set to the accompanying publication. 

  

Recognisability or visibility of the data as originating from the EVERLASTING
project will be enhanced by creating a special collection of these data in
4TU.Centre for Research Data.

_Outline the identifiability of data and refer to standard identification
mechanism. Do you make use of persistent and unique identifiers such as
Digital Object Identifiers?_

Each data set deposited in 4TU.Centre for Research Data is assigned a **DOI**
so that it is uniquely identifiable and can be cited in scholarly articles.

####  Making data openly accessible [fair data]

_Specify which data will be made openly available? If some data is kept closed
provide rationale for doing so._

Partners will publish scientific articles with Green or Gold-standard open
access to share results generated from the project. TU/e will ensure that
rules in the Consortium Agreement are respected concerning scientific
publications before their submission to journals. Prior to any disclosure
(conference, publications, defence of PhD theses or Masters) the protection of
the project progress must be secured. The project will generate research data
in a wide range of levels of detail from simulation and lab results to
demonstrator validation. Most data will be associated with results that may
have a potential for commercial or industrial protection and therefore cannot
be made accessible for verification and reuse in general due to intellectual
property protection measures. However, relevant data necessary for the
verification of results published in scientific journals can be made
accessible on a case by case basis. The decision concerning the publication of
data will be made by the Management Board, as the decision-making body of the
consortium.

_Specify how the data will be made available_

The data will be made openly available via the data archive of 4TU.Centre for
Research Data  ( _http://data.4tu.nl_ )  .

_Specify where the data and associated metadata, documentation and code are
deposited_

As part of the dataset, a data guide will be deposited with data specific
information (parameters and/or variables used, column headings, codes/symbols
used, etc.) and with information on the provenance of the data. When software
is needed to reuse the data, the code will also be deposited together with the
data itself.

####  Making data interoperable [fair data]

_Assess the interoperability of your data. Specify what data and metadata
vocabularies, standards or methodologies you will follow to facilitate
interoperability._

_Specify whether you will be using standard vocabulary for all data types
present in your data set, to allow inter-disciplinary interoperability? If
not, will you provide mapping to more commonly used ontologies?_

These questions do not yet apply to the project. To make the data
interchangeable between the project partners and future users of the data, we
are developing a metadata scheme.

Related to standard vocabulary for data types, we can refer to our white
papers (see D8.1 for more details) in which we will explain the definitions of
some data types such as SOC and SOH.

####  Increase data re-use (through clarifying licenses) [fair data]

_Specify how the data will be licenced to permit the widest reuse possible_

Data sets deposited in 4TU.Centre for Research Data will have a user license
comparable to a CC BY-NC license. 4TU.Centre for Research Data is in the
process of offering data depositors a choice of different Creative Commons
user licenses. When this is realized a CC BY license will be selected.

_Specify when the data will be made available for re-use. If applicable,
specify why and for what period a data embargo is needed_

Data will be made available for re-use immediately upon publication of the
accompanying article or after a certain embargo period. This is on a case-by-
case basis.

_Specify the length of time for which the data will remain re-usable_

4TU.Centre for Research Data archives data for a minimum of 15 years. Data
from the EVERLASTING project are provided in a format that is recommended by
4TU.Centre for Research Data for long term preservation and re-use. 4TU.Centre
for Research Data has received a Data Seal of Approval which guarantees that
the research data deposited here will continue to be able to be found and
shared in the future.

_Describe costs and potential value of long term preservation_

The costs of archiving data in 4TU.Centre for Research Data depends on the
size of the data. If the data is deposited on behalf of one of the partners of
4TU.Centre for Research Data (the 4 special Dutch universities, including
Eindhoven University of Technology) and doesn’t exceed 100 Gb per year, then
4TU.Centre for Research Data can absorb these costs. Above 100 Gb per year,
the costs will be € 3,60 per Gb per 15 years. Archiving data from non-partners
is free till 10 Gb per year. Above 10 Gb, the costs are € 4,50 per Gb per 15
year.

**2.5.3 DATA INVENTORY**

The data inventory is structured alphabetically per partner. This version of
the DMP plan will elaborate on all expected datasets more in detail on topics
such as description, formats, metadata and whether the data will be
shared/made open access or not. In this section, based on the current
insights, the reader can get an view on the datasets to be expected from the
EVERLASTING project.

The first open datasets, from TU/e and TUM, are available in the 4TU.Centre
for Research Data repository.

####  ALGOLiON

Raw Data: cell and small pack measurements for current, voltage, temperature
and single frequency impedance data.

Raw Data Format: CSV time series.

Derived Parameters: electrochemical based parameters to diagnose the state of
safety and predict the development of safety hazards.

Derived Parameters Format: time domain normalized values.

Analysis of Derived Parameters: statistical analysis of hazard parameters. The
goal is to obtain data that will be helpful in identifying how the signature
of a healthy battery differs from the signature of a damaged battery. The work
will collect sufficient data to ascertain that the signatures are consistent
across cells, various conditions of cells and charge-discharge cycles. A large
statistical base will be used to quantify the extent to which signatures are
repeatable across these platforms.

Modelling: trace the development of internal short circuits to determine
empirical time constant for changes in cell resistance and state of safety;
projected time to threshold for thermal runaway.

####  CEA  
  
<table>  
<tr>  
<th>

</th>  
<th>

**Data Description**

</th>  
<th>

Access

</th> </tr>  
<tr>  
<td>

WP4-T4.2: Multi-sensing strategy

</td> </tr>  
<tr>  
<td>

Meta Data

</td>  
<td>

  * Type of sensors and specifications (Temperature, constraint gauge, IR, voltage, current). 

  

  * Sensor implementation (Number of sensors, location…) 

  

  * Cell ID 

  

  * Further experiment information if needed 

  

</td>  
<td>

EVERLASTING CONSORTIUM

</td> </tr>  
<tr>  
<td>

Data

</td>  
<td>

• Raw data coming from the multi-sensing experiments Format : CSV

</td>  
<td>

EVERLASTING CONSORTIUM

</td> </tr>  
<tr>  
<td>

Data

</td>  
<td>

• Post treated data coming from the multi-sensing experiments Format : CSV

</td>  
<td>

CEA

</td> </tr>  
<tr>  
<td>

WP4-T4.3: Safety Testing Campaign

</td> </tr>  
<tr>  
<td>

Meta Data

</td>  
<td>

• Conditions of tests, Cell ID…

</td>  
<td>

OPEN ACCESS

</td> </tr>  
<tr>  
<td>

Data

</td>  
<td>

• Raw data coming from the Safety Testing Campaign Format : CSV

</td>  
<td>

OPEN ACCESS

</td> </tr>  
<tr>  
<td>

Data

</td>  
<td>

• Post treated data coming from the Safety Testing Campaign Format : CSV

</td>  
<td>

EVERLASTING CONSORTIUM

</td> </tr>  
<tr>  
<td>

WP4-T4.4: Post Mortem Analysis

</td> </tr>  
<tr>  
<td>

Meta Data

</td>  
<td>

  * Protocols 

  

  * Pictures 

  

  * Cell Size, weight… 

  

</td>  
<td>

OPEN ACCESS

</td> </tr>  
<tr>  
<td>

Data

</td>  
<td>

• Characterization Raw data Format : tbd

</td>  
<td>

OPEN ACCESS

</td> </tr>  
<tr>  
<td>

Data

</td>  
<td>

• Post treated data Format : tbd

</td>  
<td>

EVERLASTING CONSORTIUM

</td> </tr>  
<tr>  
<td>

WP4-T4.5: Thermal and electrical modelling

</td> </tr>  
<tr>  
<td>

Meta Data

</td>  
<td>

  * Physical models (set of equations chosen to describe the model) 

  

  * Software version 

  

</td>  
<td>

OPEN ACCESS

</td> </tr>  
<tr>  
<td>

Data

</td>  
<td>

• Model implementation (full access software) Format : COMSOL software

</td>  
<td>

CEA

</td> </tr>  
<tr>  
<td>

Data

DEMONSTRAT

OR

SIMPLIFIED

CASE

</td>  
<td>

  * Data Input: 

    * Simple Geometry 

  

    * Mesh (Finite element) 

  

    * State of the art material physical specifications and models Format : COMSOL Application 

  

  

  * Data Output: 

    * Simulation Results Format: CSV 

  

  

</td>  
<td>

OPEN ACCESS

</td> </tr>  
<tr>  
<td>

Data & Meta

Data

DEMONSTRAT

OR

EVERLASTING

</td>  
<td>

• Data Output :

\- Simulation results Format : CSV

</td>  
<td>

OPEN ACCESS

</td> </tr>  
<tr>  
<td>

CASE

</td>  
<td>

  * Data and Meta Data input generated by EVERLASTING partners: 

    * Material specifications and models deduced from experimental characterization 

  

    * Experimental safety tests measurements (voltage and temperature time records) Format: CSV 

  

  

  * COMSOL Application 

  

</td>  
<td>

EVERLASTING CONSORTIUM

</td> </tr>  
<tr>  
<td>

WP4-T4.6: Safety warning and prediction algorithms

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Input Data

</td>  
<td>

• Database from multi-sensing measurements: exploitation Format: CSV

</td>  
<td>

EVERLASTING

CONSORTIUM

(Common data with ALGOLiON)

</td> </tr>  
<tr>  
<td>

Output Meta

Data

</td>  
<td>

• Detection & Learning algorithms Format: Matlab Code

</td>  
<td>

EVERLASTING CONSORTIUM:

(Share knowledge with ALGOLiON)

</td> </tr>  
<tr>  
<td>

Output Data

</td>  
<td>

• Results: statistics, figures, precision and robustness of the algorithms
Format : tbd

</td>  
<td>

OPEN ACCESS

</td> </tr>  
<tr>  
<td>

WP5: Longer battery life

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Data

</td>  
<td>

  * Thermal and hydraulic simulations results. 

  

Detailed comparison with experimental performances Format : tbd

  * Experimental tests : 

  

Detailed tests results in terms of thermal performance Format : tbd

</td>  
<td>

EVERLASTING CONSORTIUM

</td> </tr>  
<tr>  
<td>

Data

</td>  
<td>

• Experimental tests:

General evaluation of 2 cooling solutions

</td>  
<td>

OPEN ACCESS

</td> </tr> </table>

####  LION Smart  
  
<table>  
<tr>  
<th>

**WP**

</th>  
<th>

**Data**

</th>  
<th>

**Format**

</th>  
<th>

**Metadata**

</th>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<td>

WP6

</td>  
<td>

Reduced order electrochemical cells simulation models

</td>  
<td>

Matlab/Simulink

</td>  
<td>

Documentation simulation models

</td>  
<td>

of

</td>  
<td>

the

</td> </tr>  
<tr>  
<td>

WP6

</td>  
<td>

Cells internal states and parameter estimation procedures and models

</td>  
<td>

Matlab/Simulink

</td>  
<td>

Documentation simulation models

</td>  
<td>

of

</td>  
<td>

the

</td> </tr>  
<tr>  
<td>

WP6

</td>  
<td>

BMS simulation models

</td>  
<td>

Matlab/AmeSIM

</td>  
<td>

Documentation simulation models

</td>  
<td>

of

</td>  
<td>

the

</td> </tr> </table>

####  RWTH Aachen  
  
<table>  
<tr>  
<th>

</th>  
<th>

**Electrical cell**

**measurements**

</th>  
<th>

**Simulations with physicochemical model**

</th>  
<th>

**Post mortem laboratory data**

</th> </tr>  
<tr>  
<td>

Data

</td>  
<td>

  * Voltage 

  

  * Current 

  

  * Temperature 

  

  * (Complex impedance data) 

  

</td>  
<td>

  * Internal Cell Potentials 

  

  * Currents 

  

  * Temperatures 

  

</td>  
<td>

  * Sizes 

  

  * Weights 

  

  * Microscopic Pictures 

  

  * ICP Data 

  

</td> </tr>  
<tr>  
<td>

Format

</td>  
<td>

CSV time series

</td>  
<td>

Binary file with Matlab parsing script

</td>  
<td>

Text Files, jpeg, Excel

</td> </tr>  
<tr>  
<td>

Metadata

</td>  
<td>

  * Measurement Program 

  

  * Cell ID 

  

  * Time 

  

  * Further measurement conditions (e.g. ageing test the 

  

cell is part of)

</td>  
<td>

  * Simulation revision 

  

  * Parameter set (Stripped XML 

  

File)

</td>  
<td>

  * Protocol of cell opening 

  

  * Cell ID 

  

</td> </tr> </table>

####  Siemens

Some of the simulation results in WP1 (data produced by the simulators, not
the models and simulator themselves) may be considered as open: if the data
used for parameterizing the models is public or if the partners give their
approval for making simulation results public.

_**TU/e** _

####  _Current data set_  
  
<table>  
<tr>  
<th>

**Journal Publications**

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<td>

Lead partner

</td>  
<td>

W

P

</td>  
<td>

Status

</td>  
<td>

Open access

</td>  
<td>

RDM

</td>  
<td>

Year

</td>  
<td>

Authors

</td>  
<td>

Title

</td>  
<td>

Journal

</td> </tr>  
<tr>  
<td>

TUE

</td>  
<td>

1

</td>  
<td>

Accepted

</td>  
<td>

Green

</td>  
<td>

Data DOI:  _https://doi.org/10.41_

_21/uuid:0d19258e8fe2-44b7-b390cfc205669528_

</td>  
<td>

2018

</td>  
<td>

N. Jin, D. Danilov, P.M.J. Van den Hof, M.C.F.

Donkers

</td>  
<td>

Parameter Estimation of an

Electrochemistry-based

Lithium-ion battery model using a Two-Step Procedure and

Sensitivity Analysis

</td>  
<td>

Int J Energy Research

</td> </tr> </table>

The data set contains the Matlab simulation files needed to reproduce the
results of the paper. The data set is open accessible and available in
4TU.Centre for Research Data repository under following link:
_https://data.4tu.nl/repository/uuid:0d19258e-8fe2-44b7-b390-cfc205669528_

####  _Expected data sets_

Following data sets are expected in the next period. All data sets will be
open, unless confidential information was used to generate them.  
  
<table>  
<tr>  
<th>

**WP**

</th>  
<th>

**Data**

</th>  
<th>

**Format**

</th>  
<th>

**Metadata**

</th> </tr>  
<tr>  
<td>

WP1

</td>  
<td>

Implemented battery simulation models Implementation of parameter estimation
procedures

</td>  
<td>

Matlab

</td>  
<td>

Documentation of the simulation models

</td> </tr>  
<tr>  
<td>

WP3

</td>  
<td>

Simulation models implementing range estimation techniques

</td>  
<td>

Matlab and AmeSIM

</td>  
<td>

Documentation of the simulation models

</td> </tr> </table>

#####  TUM  
  
<table>  
<tr>  
<th>

**Journal Publications**

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<td>

Lead partner

</td>  
<td>

W

P

</td>  
<td>

Status

</td>  
<td>

Open access

</td>  
<td>

RDM

</td>  
<td>

Year

</td>  
<td>

Authors

</td>  
<td>

Title

</td>  
<td>

Journal

</td> </tr>  
<tr>  
<td>

TUM

</td>  
<td>

1

</td>  
<td>

Published

</td>  
<td>

Gold

</td>  
<td>

Data DOI:  _https://doi.org/10.41_
_21/uuid:c10a6b3fefe9-41ce-99f64093df68c653_

</td>  
<td>

2017

</td>  
<td>

J. Sturm, F.B. Spingler, B. Rieger, A. Rheinfeld, A. Jossen

</td>  
<td>

Non-Destructive Detection of

Local Aging in Lithium-Ion

Pouch Cells by Multi-Directional

Laser Scanning

</td>  
<td>

Journal of the

Electrochemica l Society

</td> </tr> </table>  
<table>  
<tr>  
<th>

**Journal Publications**

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<td>

Lead partner

</td>  
<td>

WP

</td>  
<td>

Status

</td>  
<td>

Open access

</td>  
<td>

RDM

</td>  
<td>

Year

</td>  
<td>

Authors

</td>  
<td>

Title

</td>  
<td>

Journal

</td> </tr>  
<tr>  
<td>

TUM

</td>  
<td>

5

</td>  
<td>

review

</td>  
<td>

Yes

</td>  
<td>

Reserved DOI:

_https://doi.org/10.4_

_121/uuid:65ed2b759cb7-469d-b55af4599e5b2126_

</td>  
<td>

</td>  
<td>

Ilya Zilberman,

Alexander Rheinfeld,

Andreas Jossen

</td>  
<td>

Uncertainties in Entropy due to Temperature Path Dependent Voltage

Hysteresis in Li-Ion Cells

</td>  
<td>

Journal of

Power

Sources

</td> </tr> </table>  
<table>  
<tr>  
<th>

**Task**

</th>  
<th>

**Author**

</th>  
<th>

**Publication**

</th>  
<th>

**Existing dataset used**

</th>  
<th>

**Dataset generated**

</th> </tr>  
<tr>  
<td>

T5.3

</td>  
<td>

Ilya

Zilberman

</td>  
<td>

Statistical analysis of self-discharge in lithium-ion cells

</td>  
<td>

</td>  
<td>

• 1.1 Check Up and EIS Measurement of 48 LG MJ1 cells: Distribution of the
capacity and of the impedance

</td> </tr>  
<tr>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

• 1.2 OCV measurement of 48 LG MJ1 cells

</td> </tr>  
<tr>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

• 1.3 Self-discharge measurement of 24 LG MJ1 cells (SOC, T)

</td> </tr>  
<tr>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

• 1.4 Correlation analysis between single cell parameters

</td> </tr>  
<tr>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

• 1.5 Simulation of the influence of different self-discharge rates on the
cell voltage within the battery pack

</td> </tr>  
<tr>  
<td>

T5.3

</td>  
<td>

Ilya

Zilberman

</td>  
<td>

Influence of cell-to-

cell variations, thermal conditions and balancing on the scalability of large
lithium-ion battery packs

</td>  
<td>

1.1, 1.2, 1.3,

1.5

</td>  
<td>

Thermal characterization

  * 2.1 Heat capacity measurement of the LG MJ1 cell 

  

  * 2.2 Heat generation measurement over the whole SOC range for 0.2C, 0.5C and 1C charge and discharge 

  

Electrical characterization

</td> </tr>  
<tr>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

• 2.3 EIS and pulse measurements (SOC, T)

</td> </tr>  
<tr>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

• 2.4 Simulation of electrical long-term and short-term behaviour of a battery
pack with different balancing circuits

</td> </tr>  
<tr>  
<td>

T5.3

</td>  
<td>

Ilya

Zilberman

</td>  
<td>

Analysis of balancing effort during the ageing of the battery module

</td>  
<td>

1.5, 2.4 •

•

</td>  
<td>

3.1 Module aging data with controlled temperature gradient

3.2 Balancing effort during the aging of the module

</td> </tr>  
<tr>  
<td>

T5.4

</td>  
<td>

Sebastian Ludwig

</td>  
<td>

Multi-objective nondissipative balancing

</td>  
<td>

2.4 •

</td>  
<td>

4.1 Temperature distribution of a battery module during a driving profile with
applied multi-objective non-dissipative balancing

</td> </tr>  
<tr>  
<td>

T1.1

</td>  
<td>

Johannes Sturm

</td>  
<td>

Multi-dimensional

simulation of internal short

circuits

</td>  
<td>

ECM parameters  •

(RWTH)

•

</td>  
<td>

4.2 Progression of the temperature within the cell during an internal short
circuit

4.3 Progression of the voltage during an internal short circuit

</td> </tr>  
<tr>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

•

</td>  
<td>

4.4 Influence of different cooling conditions for a state of the battery for
an internal short circuit

</td> </tr>  
<tr>  
<td>

T1.2

</td>  
<td>

Johannes Sturm

</td>  
<td>

Internal cell state estimation via electro chemical

based EKF

</td>  
<td>

ECM parameters  •

(RWTH)

•

</td>  
<td>

5.1 Different approximation methods for solid state diffusion

5.2 Estimation of the anode potential with Extended Kalman Filter (EKF) during
CCCV charge

</td> </tr> </table>

#####  TUV SUD

TUV SUD performs electrical cell measurements and cycling tests (WP2) and
abuse tests (WP4). These tests are recorded in great detail (15 Gb from 5 Cell
tests) because it might be useful also for other partners at a further stage
in the project.  
  
<table>  
<tr>  
<th>

</th>  
<th>

**Electrical cell measurements and cycling (WP2)**

</th>  
<th>

**Abuse Testing on Cells and Packs (WP4)**

</th> </tr>  
<tr>  
<td>

Data

</td>  
<td>

  * Voltage 

  

  * Current 

  

  * Temperature 

  

  * Impedance data 

  

</td>  
<td>

  * Voltage 

  

  * Current 

  

  * Temperature 

  

  * Videos 

  

  * Photos 

  

</td> </tr>  
<tr>  
<td>

Format

</td>  
<td>

  * CSV 

  

  * Open source video and picture codecs 

  

</td>  
<td>

  * CSV 

  

  * Open source video and picture codecs 

  

</td> </tr>  
<tr>  
<td>

Metadata

</td>  
<td>

Test report containing:

  * Measurement Program 

  

  * Cell ID 

  

  * Time 

  

  * Further experiment information if needed 

  

</td>  
<td>

Test report containing:

  * Measurement Program 

  

  * Test Set-up 

  

  * Cell/Pack ID 

  

  * Time 

  

  * Further experiment information if needed 

  

</td> </tr> </table>

#####  VITO  
  
<table>  
<tr>  
<th>

1\. Data Summary

</th> </tr>  
<tr>  
<td>

What is the purpose of the data collection/generation and its relation to the
objectives of the project?

</td>  
<td>

The tests consist of ageing commercial Li-ion cells within several stress
conditions (temperature, current, …). The results will allow to better
understand how these cells age when exposed to these conditions which can be
similar to the conditions that the cells will undergo in an electric vehicle.
Additionally these tests will be used to build an ageing model that can
estimate the state of health of a battery and also predict its lifetime.

</td> </tr>  
<tr>  
<td>

What types and formats of data will the project generate/collect?

</td>  
<td>

The ageing tests will generate measurements collected at cell level. The data
will contain time, current, voltage, impedance and temperature values.

</td> </tr>  
<tr>  
<td>

Will you re-use any existing data and how?

</td>  
<td>

We might use some data collected from previous EU projects.

</td> </tr>  
<tr>  
<td>

What is the origin of the data?

</td>  
<td>

The data will be generated from experimental measurements.

</td> </tr>  
<tr>  
<td>

What is the expected size of the data?

</td>  
<td>

Some gigabits

</td> </tr>  
<tr>  
<td>

To whom might it be useful

('data utility')?

</td>  
<td>

Researchers working on Li-ion battery ageing; Li-ion battery user in
automotive applications

</td> </tr>  
<tr>  
<td>

2\. FAIR data

</td> </tr>  
<tr>  
<td>

Are the data produced and/or used in the project discoverable with metadata,
identifiable and locatable by means of a standard identification mechanism
(e.g. persistent and unique identifiers such as Digital Object

Identifiers)?

</td>  
<td>

We can use metadata according to standards.

</td> </tr>  
<tr>  
<td>

What naming conventions do you follow?

</td>  
<td>

A naming agreed within the consortium.

</td> </tr>  
<tr>  
<td>

Will search keywords be provided that optimize possibilities for reuse?

</td>  
<td>

yes

</td> </tr>  
<tr>  
<td>

Do you provide clear version numbers?

</td>  
<td>

yes

</td> </tr>  
<tr>  
<td>

What metadata will be created? In case metadata standards do not exist in your
discipline, please outline what type of metadata will be created and how.

</td>  
<td>

An excel file with an overview of all the tests is usually created which makes
easier to follow up the tests and to identify the test conditions and results.

</td> </tr> </table>

#####  VOLTIA & VDL ETS

As demonstrator partners, VOLTIA and VDL ETS, can support the other
participants by supplying real vehicle data of their existing e-fleet to help
verifying and optimising algorithms and models. In the final year, VOLTIA and
VDL ETS also play a role in the real life validation of the EVERLASTING BMS
features in their respective electric vehicles.  
  
<table>  
<tr>  
<th>

**Task**

</th>  
<th>

**Author**

</th>  
<th>

**Existing dataset used**

</th>  
<th>

**Dataset generated**

</th> </tr>  
<tr>  
<td>

T3.1 T3.3

</td>  
<td>

VOLTIA

</td>  
<td>

Driving data from operation of electric utility vans. The data consists of:
GPS coordinates, driving range, battery SOC, maximal, current and average
speed, voltage, current, energy flow, temperature on cell and pack level.
Monitored vehicles have been driven more than 1 million km cumulatively.

</td>  
<td>

Driving data from operation of electric utility vans.

Measurement of data during specially designed driving profiles: voltage,
current and SOC will be measured with high data sampling rate (5Hz).

</td> </tr>  
<tr>  
<td>

T5.1

</td>  
<td>

VOLTIA

</td>  
<td>

\-

</td>  
<td>

Experimental validation of prototype of passive cooling system of battery pack

</td> </tr>  
<tr>  
<td>

T7.5

</td>  
<td>

VOLTIA, TÜV SÜD

</td>  
<td>

\-

</td>  
<td>

Measurement of electrical, thermal and functional parameters of demonstrator
battery pack.

</td> </tr> </table>  
<table>  
<tr>  
<th>

**3**

</th>  
<th>

**CONCLUSIONS**

</th> </tr> </table>

EVERLASTING Deliverable D8.2 “Data Management Plan” must be considered as a “
**living document** ” and will be updated when important updates are
available: new datasets, updates on existing datasets, changes in consortium
policies (e.g. on exploitation of results, patenting, …) or other external
reasons (e.g. changes in consortium members, suggestions from advisory board,
…).

####  The “Data Management Plan” will be updated as a minimum in time with the
periodic evaluation/assessment of the project and in parallel with the update
of D8.1

**“Dissemination and exploitation plan”** .

The scope of this **second version of the EVERLASTING DMP** contains the
updated status of reflection within the consortium about the overall types of
data that have been or will be collected or produced during the project and on
how and what data will be made open available to external stakeholders such as
research institutes and companies. The first open datasets have been uploaded
in the **4TU.Centre for Research Data** **repository** (see 2.5.3 Data
Inventory).