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CORDIS - Resultados de investigaciones de la UE
CORDIS

NEXT GENERATION BATTERY MANAGEMENT SYSTEM BASED ON DATA RICH DIGITAL TWIN

CORDIS proporciona enlaces a los documentos públicos y las publicaciones de los proyectos de los programas marco HORIZONTE.

Los enlaces a los documentos y las publicaciones de los proyectos del Séptimo Programa Marco, así como los enlaces a algunos tipos de resultados específicos, como conjuntos de datos y «software», se obtienen dinámicamente de OpenAIRE .

Resultado final

AI model for heat monitoring (se abrirá en una nueva ventana)

A description of the proposed model architecture will be provided. Qualitative and quantitative comparison with SoA will also be reported.

New methods of resilient of data transmission (se abrirá en una nueva ventana)

A new approach to develop blockchain services in the form of Network Function Virtualizations that can improve both maintainability and deployability of IoT networks. In addition, it enables sophisticated analysis of IoT transactions, improves security, and increases privacy, thanks to the global network awareness.

Mining model from explanation of AI models (se abrirá en una nueva ventana)

The algorithm used to mine explanation from the model. A expert evaluation of the explanation is given as well.

Ontology of the battery domain integrating the BMS definition and the concept of failures and maintenance (se abrirá en una nueva ventana)

A descrption of the built ontlogy and the different retained battery failure. The different used semantic rule of the ontology are described.

AI model for SoX prediction (se abrirá en una nueva ventana)

A description of the architecture of the used model for predicting the SoX. A comparative results with the State of the Art wil be given.

Plan and Strategy for Dissemination and Communication (se abrirá en una nueva ventana)

This deliverable will establish the basis for the development of common dissemination & exploitation plan in the project. This deliverable is linked with the task T7.1. Updates at M18 and M39.

Technical report on battery sizing methodologies for optimising first and second lives (se abrirá en una nueva ventana)

"The ""battery first life"" configuration for current electric vehicle applications is our first point of interest. Second, the report outlines the methodology used to size ""battery second life."" Depending on the application type and energy management strategy, the to-be-developed sizing tools are meant to direct the designer in his decisions."

Benchmark service providers (se abrirá en una nueva ventana)

Various research will be done to compare data’s security and storage, sustainability, infrastructures, environment and architecture of the different cloud providers in order to identify the solution best suited to our requirements and needs.

Benchmark of multiphysics models of battery systems including cooling (se abrirá en una nueva ventana)

Report detailing existing multiphysics models of battery systems including cooling. Including aspect of cell modelling and how this is extended to sytem modelling including cooling system.

Training programme (se abrirá en una nueva ventana)

Report on monthly seminars that will be organized. A summer school will be organized in Tallinn (June 2025)

Use cases for automotive and stationary (se abrirá en una nueva ventana)

This deliverable will define and present the both use cases : auto & stationnary, which will be used for first life and second life applications of batteries.

Selected DT software for heat monitoring and battery ageing (se abrirá en una nueva ventana)

A report reviewing existing DT software will be delivered. It will also explain the choice of the selected DT software.

Demonstration of a Hardware Abstraction Layer featuring expanded set of inputs (se abrirá en una nueva ventana)

Hardware Abstraction framework on an embedded device capable of sensoring battery sensor signals and delivering an abstraction for upper layers resp. network transfer.

Simulated thermal images to train AI (se abrirá en una nueva ventana)

A simulator for generating synthetic thermal images will be developed. This will enable producing a dataset formed by simulated thermal images that will be in turn used for training AI models.

Publicaciones

OntoSoC: An ontology-based approach to battery pack SoC estimation (se abrirá en una nueva ventana)

Autores: Ala Eddine Hamouni, Franco Giustozzi, Ahmed Samet, Ali Ayadi, Slimane Arbaoui, Tedjani Mesbahi
Publicado en: Procedia Computer Science, Edición 225, 2024, ISSN 1877-0509
Editor: Elsevier BV
DOI: 10.1016/j.procs.2023.10.216

Toward Anomaly Representation in Lithium-Ion Batteries: An Ontology-Based Approach (se abrirá en una nueva ventana)

Autores: Marwa Zitouni, Franco Giustozzi, Ahmed Samet, Tedjani Mesbahi
Publicado en: Procedia Computer Science, Edición 246, 2025, ISSN 1877-0509
Editor: Elsevier BV
DOI: 10.1016/j.procs.2024.09.563

AI-enabled thermal monitoring of commercial (PHEV) Li-ion pouch cells with Feature-Adapted Unsupervised Anomaly Detection (se abrirá en una nueva ventana)

Autores: Abdelrahman Shabayek, Arunkumar Rathinam, Matthieu Ruthven, Djamila Aouada, Tazdin Amietszajew
Publicado en: Journal of Power Sources, Edición 629, 2025, ISSN 0378-7753
Editor: Elsevier BV
DOI: 10.1016/j.jpowsour.2024.235982

Data-driven strategy for state of health prediction and anomaly detection in lithium-ion batteries (se abrirá en una nueva ventana)

Autores: Slimane Arbaoui, Ahmed Samet, Ali Ayadi, Tedjani Mesbahi, Romuald Boné
Publicado en: Energy and AI, Edición 17, 2024, ISSN 2666-5468
Editor: Elsevier BV
DOI: 10.1016/j.egyai.2024.100413

Lecture Notes in Computer Science (se abrirá en una nueva ventana)

Autores: Amel Hidouri, Slimane Arbaoui, Ahmed Samet, Ali Ayadi, Tedjani Mesbahi, Romuald Boné, François de Bertrand de Beuvron
Publicado en: Lecture Notes in Computer Science, Computational Science – ICCS 2024, 2024, ISSN 0302-9743
Editor: Springer Verlag
DOI: 10.1007/978-3-031-63783-4_14

Dual-model approach for one-shot lithium-ion battery state of health sequence prediction (se abrirá en una nueva ventana)

Autores: Slimane Arbaoui, Ahmed Samet, Ali Ayadi, Tedjani Mesbahi, Romuald Boné
Publicado en: Array, Edición 24, 2024, ISSN 2590-0056
Editor: Elsevier BV
DOI: 10.1016/j.array.2024.100367

Parameter Estimation for a Generic Na-ion Battery Model Using The Curve Fitting Approach (se abrirá en una nueva ventana)

Autores: Lakhdar Mamouri, Thomas Pavot, Tedjani Mesbahi
Publicado en: 2024 IEEE Vehicle Power and Propulsion Conference (VPPC), 2025
Editor: IEEE
DOI: 10.1109/VPPC63154.2024.10755187

Hysteresis in Sodium-ion Batteries: Temperature and Relaxation Time Effects (se abrirá en una nueva ventana)

Autores: Sary Yehia, Lakhdar Mamouri, Nagham El Ghossein, Tedjani Mesbahi
Publicado en: 2024 IEEE Vehicle Power and Propulsion Conference (VPPC), 2025
Editor: IEEE
DOI: 10.1109/VPPC63154.2024.10755409

Design of an Alternative Hardware Abstraction Layer for Embedded Systems with Time-Controlled Hardware Access (se abrirá en una nueva ventana)

Autores: Gabriel Simmann, Vinay Veeranna, Reiner Kriesten
Publicado en: SAE Technical Paper Series, Edición 1, 2024
Editor: SAE International
DOI: 10.4271/2024-01-2989

Accurate Recommendation of EV Charging Stations Driven by Availability Status Prediction (se abrirá en una nueva ventana)

Autores: Meriem Manai, Bassem Sellami, Sadok Ben Yahia
Publicado en: Proceedings of the 19th International Conference on Software Technologies, 2024
Editor: SCITEPRESS - Science and Technology Publications
DOI: 10.5220/0012752600003753

Digital Battery Passport as an Enabler of Environmental Impact Assessment in Electric Vehicle Applications (se abrirá en una nueva ventana)

Autores: Cyrine Soufi, Tedjani Mesbahi, Ahmed Samet
Publicado en: 2023 IEEE Vehicle Power and Propulsion Conference (VPPC), 2024
Editor: IEEE
DOI: 10.1109/VPPC60535.2023.10403389

Lecture Notes in Computer Science (se abrirá en una nueva ventana)

Autores: Amel Hidouri, Slimane Arbaoui, Ahmed Samet, Ali Ayadi, Tedjani Mesbahi, Romuald Boné, François de Bertrand de Beuvron
Publicado en: Lecture Notes in Computer Science, Computational Science – ICCS 2024, 2024, ISSN 0302-9743
Editor: Springer Verlag
DOI: 10.1007/978-3-031-63783-4_14

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