Towards safer and smarter design for lithium-ion-battery-powered electric vehicles: A comprehensive review on control strategy architecture of battery management …

B Ashok, C Kannan, B Mason, SD Ashok… - Energies, 2022 - mdpi.com
As the battery provides the entire propulsion power in electric vehicles (EVs), the utmost
importance should be ascribed to the battery management system (BMS) which controls all …

Machine learning-based lithium-ion battery capacity estimation exploiting multi-channel charging profiles

Y Choi, S Ryu, K Park, H Kim - Ieee Access, 2019 - ieeexplore.ieee.org
Prognostics and health management is a promising methodology to cope with the risks of
failure in advance and has been implemented in many well-known applications including …

Deep learning networks for capacity estimation for monitoring SOH of Li‐ion batteries for electric vehicles

K Kaur, A Garg, X Cui, S Singh… - International Journal of …, 2021 - Wiley Online Library
Data‐driven modeling using measurable battery signals tends to provide robust battery
capacity estimation without delving deep into electrochemical phenomenon inside the …

Batteries state of health estimation via efficient neural networks with multiple channel charging profiles

N Khan, FUM Ullah, A Ullah, MY Lee, SW Baik - Ieee Access, 2020 - ieeexplore.ieee.org
The prognostics and health management (PHM) plays the main role to handle the risk of
failure before its occurrence. Next, it has a broad spectrum of applications including utility …

Massively distributed bayesian analysis of electric aircraft battery degradation

A Bills, L Fredericks, V Sulzer… - ACS Energy …, 2023 - ACS Publications
Electric vertical takeoff and landing (EVTOL) aircraft have high power and energy
requirements that must be understood throughout their batteries' life. Using a fast …

AI‐Driven Digital Twin Model for Reliable Lithium‐Ion Battery Discharge Capacity Predictions

P Nair, V Vakharia, M Shah, Y Kumar… - … Journal of Intelligent …, 2024 - Wiley Online Library
The present study proposes a novel method for predicting the discharge capabilities of
lithium‐ion (Li‐ion) batteries using a digital twin model in practice. By combining cutting …

Universal battery performance and degradation model for electric aircraft

A Bills, S Sripad, WL Fredericks, M Guttenberg… - arxiv preprint arxiv …, 2020 - arxiv.org
Development of Urban Air Mobility (UAM) concepts has been primarily focused on electric
vertical takeoff and landing aircraft (eVTOLs), small aircraft which can land and takeoff …

[HTML][HTML] Ageing-aware battery discharge prediction with deep learning

L Biggio, T Bendinelli, C Kulkarni, O Fink - Applied Energy, 2023 - Elsevier
Electrochemical batteries are ubiquitous devices in our society. When employed in mission-
critical applications, the ability to precisely predict their end-of-discharge under highly …

Dynaformer: A deep learning model for ageing-aware battery discharge prediction

L Biggio, T Bendinelli, C Kulkarni, O Fink - arxiv preprint arxiv:2206.02555, 2022 - arxiv.org
Electrochemical batteries are ubiquitous devices in our society. When they are employed in
mission-critical applications, the ability to precisely predict the end of discharge under highly …

A rest-time-based prognostic model for remaining useful life prediction of lithium-ion battery

L Deng, W Shen, H Wang, S Wang - Neural Computing and Applications, 2021 - Springer
This paper proposes a novel empirical model for the remaining useful life prediction of
lithium-ion battery. The proposed model is capable of modeling both the global degradation …