Deep Reinforcement Learning-Based Battery Management System for Electric Vehicles

Informations projet

DeepBMS

N° de convention de subvention: 101064083

DOI

10.3030/101064083

Projet clôturé

Date de signature de la CE 12 Juillet 2022

Date de début 15 Mars 2023

Date de fin 14 Mars 2025

Financé au titre de

• Marie Skłodowska-Curie Actions (MSCA)

Coût total

Aucune donnée

Contribution de l’UE

€ 230 774,40

Coordonné par

AALBORG UNIVERSITET
Denmark

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Livrables

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

Publications

Conference proceedings (4)

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

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

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

Auteurs: Farshid Naseri, Anders Christian Solberg Jensen, Erik Schaltz
Publié dans: IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society, 2025
Éditeur: IEEE
DOI: 10.1109/IECON55916.2024.10905256

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

Auteurs: Farshid Naseri, Peyman Setoodeh, Erik Schaltz
Publié dans: 2024 IEEE International Conference on Industrial Technology (ICIT), 2024
Éditeur: IEEE
DOI: 10.1109/ICIT58233.2024.10540773

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

Auteurs: Farshid Naseri, Gautam Sethia, Erik Schaltz
Publié dans: IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society, 2025
Éditeur: IEEE
DOI: 10.1109/IECON55916.2024.10905346

Peer reviewed articles (1)

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

Auteurs: Farshid Naseri; Zahra Kazemi; Peter Gorm Larsen; Mohammad Mehdi Arefi; Erik Schaltz
Publié dans: Batteries, 2023, ISSN 2313-0105
Éditeur: MDPI
DOI: 10.3390/BATTERIES9070382