Logo Logo Logo Logo Logo
  • Expertise
    • Automated Driving
    • Advanced Testing
      • Zertifizierte NVH Prüfstände
      • Forschungsprüfstände
      • ADAS/AD Verifikation & Validierung
      • Test Equipment
    • Digital Operation
    • Efficiency & Comfort
      • Human Factors Research & Engineering Services
    • Efficient Development
      • System Simulation Governance
    • EXPLORE Data Context Hub
    • Safety and Security
  • Kooperation
    • K2 Digital Mobility
    • Living Innovation Lab
    • Geförderte Projekte
    • Auftragsforschung
    • Projekte
      • Automated Driving
      • Advanced Testing
      • Data Security
      • Digital Operation
      • Efficient Development
      • Energy Efficiency
      • Passenger Comfort
      • Vehicle Safety
      • Shift2Rail
      • ALP.Lab
    • Partner Netzwerk
  • Karriere
    • VIRTUAL VEHICLE als Arbeitgeber
    • Offene Stellen
    • Studierende
    • Open Call – PhD Funding Program
    • Gender Equality Plan
  • Neuigkeiten
    • News
    • Veranstaltungen
    • Graz Symposium VIRTUAL VEHICLE
    • VIRTUAL VEHICLE Blog
    • Virtual Vehicle Magazin
  • Über Uns
  • English
  • Deutsch
  • Expertise
    • Automated Driving
    • Advanced Testing
      • Zertifizierte NVH Prüfstände
      • Forschungsprüfstände
      • ADAS/AD Verifikation & Validierung
      • Test Equipment
    • Digital Operation
    • Efficiency & Comfort
      • Human Factors Research & Engineering Services
    • Efficient Development
      • System Simulation Governance
    • EXPLORE Data Context Hub
    • Safety and Security
  • Kooperation
    • K2 Digital Mobility
    • Living Innovation Lab
    • Geförderte Projekte
    • Auftragsforschung
    • Projekte
      • Automated Driving
      • Advanced Testing
      • Data Security
      • Digital Operation
      • Efficient Development
      • Energy Efficiency
      • Passenger Comfort
      • Vehicle Safety
      • Shift2Rail
      • ALP.Lab
    • Partner Netzwerk
  • Karriere
    • VIRTUAL VEHICLE als Arbeitgeber
    • Offene Stellen
    • Studierende
    • Open Call – PhD Funding Program
    • Gender Equality Plan
  • Neuigkeiten
    • News
    • Veranstaltungen
    • Graz Symposium VIRTUAL VEHICLE
    • VIRTUAL VEHICLE Blog
    • Virtual Vehicle Magazin
  • Über Uns
  • English
  • Deutsch
Bachelorarbeit Master Thesis Masterarbeit

State Estimation for Batteries using Machine-Learning on Embedded Device

13. März 2023
by Barbara Cappello

Do you want to find out more about the Master Thesis?

Click the link for more details:

E_141_State Estimation for Batteries using Machine-Learning on Embedded Device

Department: Electrics/Electronics and Software

Master Thesis Ref.Nr. E_141

Did this thesis catch your attention? We are looking forward to hearing from you.

Apply
Previous post
Copyright and Analytics

Impressum | Rechtliche Hinweise | Datenschutzerklärung | Verhaltenskodex | Webmail