Deep Reinforcement Learning-Based Battery Management System for Electric Vehicles

Informazioni relative al progetto

DeepBMS

ID dell’accordo di sovvenzione: 101064083

DOI

10.3030/101064083

Progetto chiuso

Data della firma CE 12 Luglio 2022

Data di avvio 15 Marzo 2023

Data di completamento 14 Marzo 2025

Finanziato da

• Marie Skłodowska-Curie Actions (MSCA)

Costo totale

Nessun dato

Contributo UE

€ 230 774,40

Coordinato da

AALBORG UNIVERSITET
Denmark

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Documents, reports (1)

Communication, Dissemination & Outreach Plan

The plan describes the planned measures to maximize the impact of the project, including the dissemination and exploitation measures that are planned, and the target group(s) addressed. Regarding communication measures and public engagement strategy, the aim is to inform and reach out to society and show the activities performed, and the use and the benefits the project will have for citizens.

Data Management Plan (1)

Data Management Plan

The Data Management Plan describes the data management life cycle for all data sets that will be collected processed or generated by the action It is a document describing what data will be collected processed or generated and following what methodology and standards whether and how this data will be shared andor made open and how it will be curated and preserved

Pubblicazioni

Conference proceedings (4)

An Effective Hybrid Approach for Detection of False Data Injection Attacks in Connected Battery Systems with Noisy Measurements

Autori: Farshid Naseri, Zahra Kazemi, Nima Tashakor, Anders Christian Solberg Jensen, Corneliu Barbu, Erik Schaltz
Pubblicato in: IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society, 2025
Editore: IEEE
DOI: 10.1109/IECON55916.2024.10905587

Robust Battery State-of-Charge Estimation in Presence of Model Uncertainties Using KalmanNet

Autori: Farshid Naseri, Anders Christian Solberg Jensen, Erik Schaltz
Pubblicato in: IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society, 2025
Editore: IEEE
DOI: 10.1109/IECON55916.2024.10905256

Online Tuning of Extended Kalman Filter Using Reinforcement Learning for Improved Battery State-of-Charge Estimation

Autori: Farshid Naseri, Peyman Setoodeh, Erik Schaltz
Pubblicato in: 2024 IEEE International Conference on Industrial Technology (ICIT), 2024
Editore: IEEE
DOI: 10.1109/ICIT58233.2024.10540773

Electrical Characterization and Performance Review of a New High-Power Lithium Iron Phosphate Cell

Autori: Farshid Naseri, Gautam Sethia, Erik Schaltz
Pubblicato in: IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society, 2025
Editore: IEEE
DOI: 10.1109/IECON55916.2024.10905346

Peer reviewed articles (1)

Cyber-Physical Cloud Battery Management Systems: Review of Security Aspects

Autori: Farshid Naseri; Zahra Kazemi; Peter Gorm Larsen; Mohammad Mehdi Arefi; Erik Schaltz
Pubblicato in: Batteries, 2023, ISSN 2313-0105
Editore: MDPI
DOI: 10.3390/BATTERIES9070382