Artificial Intelligence-Based Predictive Analytics for Battery Energy Storage Systems in Electric Vehicle Applications = Tehisintellektil põhinev ennustav andmeanalüüs akupõhiste energiasalvestussüsteemide jaoks elektrisõidukites
supervisor
statement of authorship
Rolando Antonio Gilbert Zequera ; [supervisor: Anton Rassõlkin, co-supervisor: Toomas Vaimann ; Tallinn University of Technology, School of Engineering, Department of Electrical Power Engineering and Mechatronics]
type of dissertation
doktoritöö
university/scientific institution
Tallinna Tehnikaülikool
location of publication
Tallinn
publisher
year of publication
pages
224 p. : ill
series
Tallinn University of Technology. Doctoral thesis = Tallinna Tehnikaülikool. Doktoritöö ; 61/2025
subject term
subject of form
ISSN
2585-6898
2585-6901 (PDF)
ISBN
978-9916-80-363-9 (PDF)
978-9916-80-362-2
notes
Autori publikatsioonide nimekiri leheküljel 8
Bibliogr. lk. 93-98
Kokkuvõte eesti keeles
Kättesaadav ka võrguteavikuna
Autori CV inglise ja eesti keeles, lk. 223-224
Thesis (Ph.D.) : Tallinn University of Technology, 2025
url
Open Access
Open Access
scientific publication
teaduspublikatsioon
classifier
TalTech department
language
inglise
- Overview of digital twin platforms for EV applications
- Overview of battery energy storage systems readiness for digital twin of electric vehicles
- Modeling battery energy storage systems based on remaining useful lifetime through regression algorithms and binary classifiers
- Clustering and outlier analysis for key performance indicators in battery energy storage systems applications
- Data science-based techniques for modelling and diagnostics of battery cells based on end-of-life criteria
- Health and charge indicators for battery energy storage systems in electric vehicles applications
- Deep Learning methodology for charging management applications in battery cells based on Neural Networks
- Kolmogorov-Arnold networks for algorithm design in battery energy storage system applications
- Charge Diagnostics and State Estimation of Battery Energy Storage Systems Through Transformer Models
- Meetod, seade, arvutiloetav andmekandja juhiste salvestamiseks ja tarkvaratoode akude energiasalvestussüsteemi (BESS) haldamiseks, kasutades Kolmogorov-Arnoldi võrke (KAN-e)
Zequera, R.A.G. Artificial Intelligence-Based Predictive Analytics for Battery Energy Storage Systems in Electric Vehicle Applications = Tehisintellektil põhinev ennustav andmeanalüüs akupõhiste energiasalvestussüsteemide jaoks elektrisõidukites. Tallinn : TalTech Press, 2025. 224 p. : ill. (Tallinn University of Technology. Doctoral thesis = Tallinna Tehnikaülikool. Doktoritöö ; 61/2025).