Deep Reinforcement Learning-Based Battery Management System for Electric Vehicles

Información del proyecto

DeepBMS

Identificador del acuerdo de subvención: 101064083

DOI

10.3030/101064083

Proyecto cerrado

Fecha de la firma de la CE 12 Julio 2022

Fecha de inicio 15 Marzo 2023

Fecha de finalización 14 Marzo 2025

Financiado con arreglo a

• Marie Skłodowska-Curie Actions (MSCA)

Coste total

Sin datos

Aportación de la UE

€ 230 774,40

Coordinado por

AALBORG UNIVERSITET
Denmark

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

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

Publicaciones

Conference proceedings (4)

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

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

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

Autores: Farshid Naseri, Anders Christian Solberg Jensen, Erik Schaltz
Publicado en: IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society, 2025
Editor: IEEE
DOI: 10.1109/IECON55916.2024.10905256

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

Autores: Farshid Naseri, Peyman Setoodeh, Erik Schaltz
Publicado en: 2024 IEEE International Conference on Industrial Technology (ICIT), 2024
Editor: IEEE
DOI: 10.1109/ICIT58233.2024.10540773

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

Autores: Farshid Naseri, Gautam Sethia, Erik Schaltz
Publicado en: IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society, 2025
Editor: IEEE
DOI: 10.1109/IECON55916.2024.10905346

Peer reviewed articles (1)

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

Autores: Farshid Naseri; Zahra Kazemi; Peter Gorm Larsen; Mohammad Mehdi Arefi; Erik Schaltz
Publicado en: Batteries, 2023, ISSN 2313-0105
Editor: MDPI
DOI: 10.3390/BATTERIES9070382