NEXT GENERATION BATTERY MANAGEMENT SYSTEM BASED ON DATA RICH DIGITAL TWIN

Projektinformationen

ENERGETIC

ID Finanzhilfevereinbarung: 101103667

DOI

10.3030/101103667

EK-Unterschriftsdatum 2 Mai 2023

Startdatum 1 Juni 2023

Enddatum 31 August 2026

Finanziert unter

• Climate, Energy and Mobility

Gesamtkosten

€ 4 170 167,50

EU-Beitrag

€ 4 170 167,50

Koordiniert durch

INSTITUT NATIONAL DES SCIENCES APPLIQUEES, STRASBOURG
France

CORDIS bietet Links zu öffentlichen Ergebnissen und Veröffentlichungen von HORIZONT-Projekten.

Links zu Ergebnissen und Veröffentlichungen von RP7-Projekten sowie Links zu einigen Typen spezifischer Ergebnisse wie Datensätzen und Software werden dynamisch von OpenAIRE abgerufen.

Leistungen

Other (5)

AI model for heat monitoring

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

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

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

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

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.

Documents, reports (7)

Plan and Strategy for Dissemination and Communication

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

"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

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

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

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

Use cases for automotive and stationary

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

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

Demonstrators, pilots, prototypes (1)

Demonstration of a Hardware Abstraction Layer featuring expanded set of inputs

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

Data sets, microdata, etc (1)

Simulated thermal images to train AI

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.

Veröffentlichungen

Peer reviewed articles (6)

OntoSoC: An ontology-based approach to battery pack SoC estimation

Autoren: Ala Eddine Hamouni, Franco Giustozzi, Ahmed Samet, Ali Ayadi, Slimane Arbaoui, Tedjani Mesbahi
Veröffentlicht in: Procedia Computer Science, Ausgabe 225, 2024, ISSN 1877-0509
Herausgeber: Elsevier BV
DOI: 10.1016/j.procs.2023.10.216

Toward Anomaly Representation in Lithium-Ion Batteries: An Ontology-Based Approach

Autoren: Marwa Zitouni, Franco Giustozzi, Ahmed Samet, Tedjani Mesbahi
Veröffentlicht in: Procedia Computer Science, Ausgabe 246, 2025, ISSN 1877-0509
Herausgeber: 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

Autoren: Abdelrahman Shabayek, Arunkumar Rathinam, Matthieu Ruthven, Djamila Aouada, Tazdin Amietszajew
Veröffentlicht in: Journal of Power Sources, Ausgabe 629, 2025, ISSN 0378-7753
Herausgeber: Elsevier BV
DOI: 10.1016/j.jpowsour.2024.235982

Data-driven strategy for state of health prediction and anomaly detection in lithium-ion batteries

Autoren: Slimane Arbaoui, Ahmed Samet, Ali Ayadi, Tedjani Mesbahi, Romuald Boné
Veröffentlicht in: Energy and AI, Ausgabe 17, 2024, ISSN 2666-5468
Herausgeber: Elsevier BV
DOI: 10.1016/j.egyai.2024.100413

Lecture Notes in Computer Science

Autoren: Amel Hidouri, Slimane Arbaoui, Ahmed Samet, Ali Ayadi, Tedjani Mesbahi, Romuald Boné, François de Bertrand de Beuvron
Veröffentlicht in: Lecture Notes in Computer Science, Computational Science – ICCS 2024, 2024, ISSN 0302-9743
Herausgeber: 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

Autoren: Slimane Arbaoui, Ahmed Samet, Ali Ayadi, Tedjani Mesbahi, Romuald Boné
Veröffentlicht in: Array, Ausgabe 24, 2024, ISSN 2590-0056
Herausgeber: Elsevier BV
DOI: 10.1016/j.array.2024.100367

Conference proceedings (5)

Parameter Estimation for a Generic Na-ion Battery Model Using The Curve Fitting Approach

Autoren: Lakhdar Mamouri, Thomas Pavot, Tedjani Mesbahi
Veröffentlicht in: 2024 IEEE Vehicle Power and Propulsion Conference (VPPC), 2025
Herausgeber: IEEE
DOI: 10.1109/VPPC63154.2024.10755187

Hysteresis in Sodium-ion Batteries: Temperature and Relaxation Time Effects

Autoren: Sary Yehia, Lakhdar Mamouri, Nagham El Ghossein, Tedjani Mesbahi
Veröffentlicht in: 2024 IEEE Vehicle Power and Propulsion Conference (VPPC), 2025
Herausgeber: IEEE
DOI: 10.1109/VPPC63154.2024.10755409

Design of an Alternative Hardware Abstraction Layer for Embedded Systems with Time-Controlled Hardware Access

Autoren: Gabriel Simmann, Vinay Veeranna, Reiner Kriesten
Veröffentlicht in: SAE Technical Paper Series, Ausgabe 1, 2024
Herausgeber: SAE International
DOI: 10.4271/2024-01-2989

Accurate Recommendation of EV Charging Stations Driven by Availability Status Prediction

Autoren: Meriem Manai, Bassem Sellami, Sadok Ben Yahia
Veröffentlicht in: Proceedings of the 19th International Conference on Software Technologies, 2024
Herausgeber: SCITEPRESS - Science and Technology Publications
DOI: 10.5220/0012752600003753

Digital Battery Passport as an Enabler of Environmental Impact Assessment in Electric Vehicle Applications

Autoren: Cyrine Soufi, Tedjani Mesbahi, Ahmed Samet
Veröffentlicht in: 2023 IEEE Vehicle Power and Propulsion Conference (VPPC), 2024
Herausgeber: IEEE
DOI: 10.1109/VPPC60535.2023.10403389

Book chapters (1)

Lecture Notes in Computer Science

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