=Paper=
{{Paper
|id=Vol-3760/paper5
|storemode=property
|title=Battery manufacturing knowledge infrastructure requirements for multicriteria optimization based decision support in design of simulation
|pdfUrl=https://ceur-ws.org/Vol-3760/paper5.pdf
|volume=Vol-3760
|authors=Martin Thomas Horsch,Dmytro Romanov,Eirik Valseth,Salim Belouettar,Luis Eduardo Córdova López,Johanna Glutting,Mathijs A. Janssen,Peter Klein,Andreas Linhart,Michael A. Seaton,Elin D. Sødahl,Noel Vizcaino,Stephan Werth,Simon Stephan,Ilian T. Todorov,Silvia Chiacchiera,Fadi Al Machot
|dblpUrl=https://dblp.org/rec/conf/semats/HorschRVBCGJ0LS24
}}
==Battery manufacturing knowledge infrastructure requirements for multicriteria optimization based decision support in design of simulation==
https://ceur-ws.org/Vol-3760/paper5.pdf
Battery manufacturing knowledge infrastructure
requirements for multicriteria optimization based decision
support in design of simulation
Martin Thomas Horsch1,2,* , Dmytro Romanov1 , Eirik Valseth1,3 , Salim Belouettar4 , Luis
Eduardo Córdova López1 , Johanna Glutting5 , Mathijs A. Janssen1 , Peter Klein6 ,
Andreas Linhart7 , Michael A. Seaton2 , Elin D. Sødahl1 , Noel Vizcaino2 , Stephan Werth5 ,
Simon Stephan8 , Ilian T. Todorov2 , Silvia Chiacchiera2,* and Fadi Al Machot1
1
Norwegian University of Life Sciences, Faculty of Science and Technology, Postboks 5003, 1432 Ås, Norway
2
UK Research and Innovation, STFC Daresbury Laboratory, Computational Chemistry Group, Daresbury WA4 4AD, UK
3
Simula Research Laboratory AS, Kristian Augusts gate 23, 0164 Oslo, Norway
4
Luxembourg Institute of Science and Technology, Avenue des Hauts-Fourneaux, 5, 4362 Esch-sur-Alzette, Luxembourg
5
Kaiserslautern University of Applied Sciences, Faculty of Applied Engineering Sciences, 67659 Kaiserslautern, Germany
6
Fraunhofer Institute for Industrial Mathematics, Fraunhoferplatz 1, 67663 Kaiserslautern, Germany
7
VANEVO GmbH, Johann-Hinrich-Engelbart-Weg 2, 26131 Oldenburg, Germany
8
RPTU Kaiserslautern, Department of Mechanical and Process Engineering, Postfach 3049, 67653 Kaiserslautern, Germany
Abstract
This position paper reports on the requirements analysis within the project Battery Cell Assembly Twin (BatCAT),
which develops a digital twin for battery manufacturing. The focus is on the aspects of this work that are at the
intersection between semantic web technology and materials science and engineering, specifically, the co-design
of the architecture of the semantic interoperability layer and the decision support system (DSS). First, visions and
ideas are provided on how the architecture will look, and what technology and previous work it will be based on.
Key elements to this include, on the side of the semantic technology, the Meta Object Facility (MOF) with the
OntoCommons ecosystem as a meta-metamodel (MOF M3 level), a system of ontologies with OWL EL or RL
expressivity as a metamodel (MOF M2 level), and a MOF M1-level model based on OO-LD. On the side of the DSS,
answer set programming will be combined with multicriteria optimization (MCO), such that MCO can be applied
to model parameterization and design of simulation to make best use of computational resources and data.
Keywords
decision support system, design of simulation, digital twin, multicriteria optimization, requirements analysis
1. Introduction
BatCAT is the project that realizes the Battery2030+ manufacturability programme [1, pp. 70–80] by
developing a digital twin platform and data space for manufacturing of vanadium-based redox-flow
batteries as well as Li-ion and Na-ion coin cells. BatCAT combines two approaches to decision support:
First, logical reasoning by answer set programming [2, 3] (ASP), which can increase the efficiency
of neural-network surrogate modelling while also ensuring its interpretability; second, multicriteria
optimization [4, 5] (MCO), integrating surrogate models into model parameterization, interoperating
with the MolMod database [6]. This line of work can build on business decision support systems (BDSS)
from previous projects [4, 7]. Model accuracy and reliability will be documented through epistemic
SeMatS 2024: The 1st International Workshop on Semantic Materials Science co-located with the 20th International Conference on
Semantic Systems (SEMANTiCS), September 17-19, Amsterdam, The Netherlands.
*
Corresponding authors: Martin Thomas Horsch and Silvia Chiacchiera.
$ martin.thomas.horsch@nmbu.no (M. Horsch); silvia.chiacchiera@stfc.ac.uk (S. Chiacchiera)
0000-0002-9464-6739 (M. Horsch); 0000-0002-4077-8306 (D. Romanov); 0000-0001-6940-4191 (E. Valseth);
0000-0002-2986-2902 (S. Belouettar); 0000-0002-1378-7100 (L. Córdova López); 0000-0003-0743-4904 (Mathijs A. Janssen);
0000-0002-5468-8889 (P. Klein); 0000-0002-4708-573X (Michael A. Seaton); 0000-0001-8877-9044 (Elin D. Sødahl);
0000-0002-6327-6747 (S. Werth); 0000-0002-4578-3569 (S. Stephan); 0000-0001-7275-1784 (Ilian T. Todorov);
0000-0003-0422-7870 (S. Chiacchiera); 0000-0002-1239-9261 (F. Al Machot)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
metadata [8]. Physics-based modelling in BatCAT includes molecular dynamics and Monte Carlo
simulation based on classical mechanical pair potentials, using the DL_POLY and ms2 codes; mesoscopic
DPD simulations will be carried out using DL_MESO, employing an nDPD potential [9]. The molecular
and mesoscopic simulation results will feed into continuum simulations, equivalent-circuit models, and
population balance models. Surrogate models, e.g., representing results from continuum simulation [10],
will include cellular neural networks (with the potential for exploitation by on-chip deployment [11]).
For use in production, it is necessary to make the models XAIR, i.e., explainable-AI-ready [12].
The present work summarizes reflections from the requirements analysis [13] and initial steps of
work done within the project, with a focus on the architecture and functional requirements for the
data space, semantic artefacts, and multicriteria optimization. For this purpose, we analyse pre-existing
lines of work that will be combined within BatCAT in view of insights from the requirements analysis.
Specifically, we consider here the conditions and options for deploying MCO in modelling and simulation
for design of simulation (DoS). This is taken to include both model parameterization and surrogate
model development guided by MCO, with the potential of addressing further aspects of simulation
workflow design and deployment if the requirements analysis indicates it to be appropriate.
2. Requirements procurement and analysis methodology
An agile requirements analysis was conducted based on interviews with team members and external
stakeholders. For this purpose, fifteen 30-minute interviews were conducted. Subsequently, the discussed
content was evaluated in combination with the BatCAT project work plan and the relevant policy
papers; the latter include the Battery2030+ Roadmap [1] and the Flow Batteries Europe Manifesto [14].
Following common practice in agile requirements analysis [16], the requirements obtained from
these sources were formulated as (low-level) user stories, grouped into (high-level) epics. More user
stories were procured from team members in a dedicated session at a project workshop. Both the user
stories and the epics have the format: “As , I intend to do in order to progress toward
.” ISO 6515:2018 defines an epic as a “major collection of related feature sets
broken down into individual features or user stories and implemented in parts over a longer period of
time” [17]. Here, in the case of a user story, the overarching objective is the relevant epic, whereas in
the case of an epic, the overarching objective represents a goal at a longer time scale or an even higher
degree of abstraction. The role label used in the expression above is a persona, corresponding to a type
of stakeholders. Following ISO 6515:2018, a persona is a “model of a user with defined characteristics,
based on research”, while a user story is a “simple narrative illustrating a user requirement from the
perspective of a persona” [17]. Accordingly, the personas are not real individuals [16] – they are a
means of structuring the requirements [18]. Consequently, multiple people’s input can contribute to the
same persona, and different requirements formulated by the same individual can be categorized under
different personas. The following nine personas were defined for this purpose based on our assessment
of key roles related to the BatCAT architecture: (1) AI: Administrator – internal; (2) DI: Digital twin
technology user – internal; (3) EI: Experimentalist – internal; (4) MI: Manufacturing use-case owner –
internal; (5) SI: Simulation researcher – internal; (6) CE: Customer – external; (7) DE: Developer of a
related platform – external; (8) EE: Experimentalist – external; (9) PE: Policy expert – external.
Beside grouping user stories into epics and mapping them to personas, the user stories expressing
functional requirements are also attributed to a design target, i.e., a component of the BatCAT archi-
tecture. (Epics and non-functional requirements are not assigned a design target.) The twelve design
targets are shown as ellipses in Fig. 1. The components most relevant to the topic discussed in the
present work are the semantic interoperability layer (SIL) and the decision support system (DSS). In case
of the semantic interoperability layer, in addition to the user stories, requirements were also collected in
the format of competency questions [19, 20, 21], following standard ontology engineering practices (e.g.,
the LOT methodology [22]). Moreover, a questionnaire was sent to project participants and external
stakeholders in order to identify the most relevant use case scenarios and problems to which MCO (and
the DSS in general) should be applied: In this instance, 19 responses were received and evaluated [13].
Figure 1: Architecture of the system developed by BatCAT, including a data space and a digital twin platform,
applied to two manufacturing use cases. For the present requirements analysis, the following twelve design
targets were defined: (1) DSS: Decision support system; (2) KBC: Knowledge base – central; (3) KBP: Knowledge
base – peripheral; (4) RTE: Real-time environment; (5) SIL: Semantic interoperability layer; (6) DKMS: Data and
knowledge management software (LinkAhead [15]); (7) MLP: Machine learning predictors; (8) PBM: Physics-based
modelling; (9) EXP: Experimental characterization and electronic lab notebook; (10) PIL: Pilots; (11) LDS: Large
data storage; (12) DTV: Digital twin visualization and front end.
The complete output from the knowledge infrastructure requirements analysis (excluding any sen-
sitive information) will be made openly accessible through CORDIS in due course as part of BatCAT
deliverable 4.1, “Data landscape and infrastructure related requirements.”
3. Aspects of the requirements analysis
3.1. Multicriteria optimization
From the responses to the questionnaire, the potential MCO problems most frequently selected as
relevant were “Slurry Formulation Optimization” and “Design of Simulation (DoS)” both leading at
each 45% affirmative answers. Following closely are “Electrode Material Selection”, “Coating Thickness
and Uniformity”, “Electrolyte Composition”, and “Design of Experiment (DoE)” each at 40%.
DoS is prioritized by respondents mapped to the following personas: Manufacturing use-case
owner (MI), experimentalist (EI), digital twin technology user (DI), simulation researcher (SI), and
developer of a related platform (DE). The high priority from diverse personas suggests that simulation
is a central component that integrates various aspects of the battery design process. Similarly, the
personas highlighting DoE include manufacturing use-case owner (MI), experimentalist (EI), digital
twin technology user (DI), simulation researcher (SI), and policy expert (PE). This broad interest reflects
the key role of experimental characterization, but also concerns about the resources required to generate
all the experimental data that will be needed in order to parameterize and validate models [13].
Figure 2: Architecture of the semantic interoperability layer (SIL) in view of the requirements analysis, following
the meta object facility (MOF) convention for the four hierarchy levels [28].
3.2. Explainable-AI-readiness
As regards explainable-AI-readiness [12], we follow the definition given by the XAIR principles working
group of the Knowledge Graph Alliance [23].1 Accordingly, data and models are XAIR to the degree
that their documentation and annotation is both consistent with and conducive to best practices
in making use of interpretable learning techniques. Therein, interpretable learning includes both
deduction (by logical reasoning) and induction (machine learning), and any combination of them or
other kind of acquisition of knowledge from knowledge, insofar as some support through explanation or
grounding can be given for it. Epistemic metadata [8] are the annotation through which the explanation,
grounding, and knowledge status of data and models can be documented. Currently, the concept of
interpretability in machine learning lacks standard metrics and a universally agreed-upon definition,
complicating the development and evaluation of models that are expected to be interpretable [24]. Types
of interpretability include transparency, which involves a direct understanding of a model’s workings,
and post-hoc explanations that provide insights after predictions are made, e.g., through techniques like
feature importance. Organizations including IEEE have developed standards for ethical AI, emphasizing
transparency and explainability, with the IEEE’s Ethically Aligned Design guidelines highlighting the
importance of designing AI systems that are understandable to users and stakeholders [25]. Widespread
misconceptions (criticized, e.g., by Lipton [24]) include an overemphasis on accuracy over interpretability
and the notion that linear models are always more interpretable than complex models.
4. Analysis and consequences for the architecture
4.1. Semantic architecture
It is planned that the digital twin platform will use BPMN 2.0 (business model process and notation [26])
for some of the required deployable workflows; possibly, e.g., for integrating elements such as surrogate
models into the Pareto front computation for the MCO module. Such an enterprise architecture would
be able to build on the previous work by Kavka et al. [27] in the COMPOSELECTOR project (H2020
GA no. 721105). In view of this, the meta object facility [28] (MOF) will be used as a formalism
for designing and specifying the architecture of the semantic interoperability layer (cf. Fig. 2); both
BPMN and MOF are standards devised by the Object Management Group and therefore designed to
be technically interoperable. Leveraging MOF, we can establish a unified semantic model [29] that
encapsulates diverse aspects of battery design, including materials, processes, and performance metrics.
This model can serve as a foundation for interoperable tools and systems, promoting efficient data
exchange and collaboration among stakeholders. Additionally, a semantic architecture based on MOF
can support advanced query capabilities, allowing users to retrieve and analyse data across domains.
Core elements to the M2-level metamodel are the battery domain ontologies BattINFO and BVCO [7],
ontologies for representing content from BPMN [30, 31, 32], and mid-level ontologies for epistemic meta-
1
See also https://www.kg-alliance.org/kga-wg-xai-24-4/.
data [8], presently undergoing refactoring as MSO-EM: Ontologies for modelling, simulation, optimization
and epistemic metadata [23].2 These ontologies will connect to the OntoCommons ecosystem [33]
(OCES) and rely on various mechanisms for alignment with other semantic artefacts, including strong
alignment (RDFS/OWL), weak alignment using SKOS [34], and bridge concepts [33].
4.2. MCO in model parameterization
Deploying semantics can streamline optimization by providing a common understanding of terms
and concepts, e.g., across simulation methods. Promising approaches to be explored for this purpose
include CWA ModGra [12, 35, 36] and the ongoing CWA 17815 revision process.3 MCO-based model
parameterization in BatCAT can build on substantial previous work, from which there is a well-
established methodology [5, 37, 38, 39, 40]. However, to apply these methods in concrete scenarios,
multiple prerequisites must be met: First, the model class must be characterized, i.e., it must be known
how the model behaves as a function of the model parameters. Second, the quantities for which the model
behaviour is known must be those for which experimental data are available (or other data used instead
of experiments, e.g., ab initio calculations). Third, a model (re-)parameterization must be admissible:
It does not make sense to optimize one model if there are other models or elements of the simulation
workflow that rely on a specific pre-existing model parameterization. This requires a comprehensive
understanding of the simulation workflows and their logic. Additionally, characterizing the model class
requires systematically exploring the model parameter space and constructing a surrogate model. It is
the first of these tasks, sampling model properties as a function of model parameters over the whole
relevant parameter space, that can be computationally demanding and that could benefit greatly from
design of simulation; the accuracy of the surrogate model can be a metric for the information gained.
4.3. MCO in design of simulation
MCO-based design of simulation (DoS) and experiment (DoE) are foreseen in the BatCAT architecture,
and will be applied both to the VRFB and the Li-/Na-ion use cases. The approach to DoS and DoE is in
principle the same, except that there usually is more flexibility at varying simulation parameters than
experimental parameters; in the case of DoE, there will be additional non-trivial constraints, which
might be accounted for through the answer-set (logical) programming component of the decision
support system. In design of simulation, MCO consists in selecting simulation parameters to optimize
the use of data and computational resources. This includes selecting the most relevant variables,
designing simulations that provide the most informative data, and configuring simulations to accurately
reflect real-world conditions. The MCO-based DoS can be used when characterizing a model class
(cf. Section 4.2); however, it is not limited to this. Its potential scope extends at least to all the simulation
workflows that explore some parameter space and from which surrogate models are to be developed.
MCO requires well-characterized parameter and objective spaces, and a multicriteria cost function
that maps parameters to objectives. To apply MCO to DoS, one must have a clear picture of the
parameter space that must be explored. Unimportant technical parameters of the solver are naturally
excluded, but depending on the scenario, model and solver parameters can be included. In general,
therefore, the explored simulation parameters x = (𝑥1 , . . . , 𝑥𝑘 ) from some 𝑋 ⊆ R𝑘 can include:
(1) Physical properties of the simulated system, such as thermodynamic or mechanical boundary
conditions; (2) model parameters; (3) solver parameters. However, it is not necessary to include
all three types of parameters. The immediate purpose of each simulation is to compute 𝑔sim (x) =
z = (𝑧1 , . . . , 𝑧ℓ ) ∈ 𝑍 ⊆ Rℓ , i.e., the quantities that are obtained from the simulation. These are
physically/technically meaningful quantities. DoS does not target optimizing a single simulation, but
the overall knowledge gained from a simulation series or programme; in our framework, the goal is to
2
The system of ontologies can be accessed through the persistent URL https://www.purl.org/mso-em. The development is
done on a public github repository, for the time being: https://github.com/martinhorsch/mso-em. This will soon be moved to
a new BatCAT organization site on github. There will then be a pointer to the new repository at the URL of the present one.
3
https://www.cencenelec.eu/news-and-events/news/2024/workshop/2024-04-22-nano/
create a surrogate model 𝑔corr : 𝑋 → 𝑍 that correlates the simulation data, 𝑔corr (x) ≈ 𝑔sim (x), can
stand in for the physics-based model, while being much more tractable numerically.
To achieve this goal, we wish to obtain maximum knowledge on how 𝑔sim behaves over 𝑋 by making
efficient use of a given amount of computational resources. The objective space for the DoS problem,
by convention and without loss of generality defined over minimization objectives, can therefore be
expressed in terms of metrics for remaining uncertainty or lack of knowledge (or negative information
on 𝑔sim , negative success at sampling 𝑋, or negative performance of 𝑔corr ). We can here simplify this
by assuming that the number 𝑚 of simulations to be arranged upon invoking the DoS is predetermined,
and that the choice of simulation parameters is restricted (as a boundary condition) such that the
expected computational resource requirements are constant. The parameter space for the MCO problem
is therefore 𝑋 𝑚 , i.e., the 𝑚𝑘-dimensional space from which simulation parameters for 𝑚 simulations
would be selected. The objective space 𝑌 ⊆ R𝑛 is defined over 𝑛 uncertainty metrics. Solving the
MCO-problem for this kind of DoS requires a cost function 𝑓 : 𝑋 𝑚 → 𝑌 , expressing the expectation of
how well the simulation parameter space 𝑋 will be sampled upon conducting 𝑚 additional simulations
while choosing the respective parameter values; for this, another surrogate model 𝑓corr is needed, which
approximates 𝑓 . On this basis, the Pareto front is computed over the 𝑚𝑘-dimensional parameter space
𝑋 𝑚 and the 𝑛-dimensional objective space 𝑌 , using the multicriteria cost function 𝑓corr : 𝑋 𝑚 → 𝑌 .
5. Conclusion
For the multicriteria optimization and its use for design of simulation to play together with the whole
architecture of a data-driven and knowledge-based digital twin project, it is necessary, but not enough
to rely on ontology-based semantic interoperability: Different levels of representation and actionable
workflows need to interoperate technically. In the case of the BatCAT project, it appears that the Object
Management Group’s standards provide a suitable framework, comprising four hierarchy levels of
semantics from MOF together with workflow orchestration based on BPMN. It remains a major challenge
to formulate the simulation problems, including the interaction between physics-based simulation and
data-driven surrogate models, such that multicriteria optimization really can be used to optimize the
selection of simulation parameters. Realizing this in practice is beyond what we can report through this
position paper. However, there is substantial previous work as a basis for it, to which the requirements
analysis in BatCAT, as summarized here, adds one more building block that was needed.
Acknowledgments
The project Battery Cell Assembly Twin (BatCAT) has received funding from the EU’s Horizon Europe
research and innovation programme under GA no. 101137725.
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