Automated feature extraction and selection for data-driven models of rapid battery capacity fade and end of life
S Greenbank, D Howey - IEEE Transactions on Industrial …, 2021 - ieeexplore.ieee.org
Lithium-ion cells may experience rapid degradation in later life, especially with more
extreme usage protocols. The onset of rapid degradation is called the “knee point,” and …
extreme usage protocols. The onset of rapid degradation is called the “knee point,” and …
[HTML][HTML] Capacity and Internal Resistance of lithium-ion batteries: Full degradation curve prediction from Voltage response at constant Current at discharge
The use of minimal information from battery cycling data for various battery life prognostics is
in high demand with many current solutions requiring full in-cycle data recording across 50 …
in high demand with many current solutions requiring full in-cycle data recording across 50 …
[HTML][HTML] Forecasting battery capacity and power degradation with multi-task learning
Lithium-ion batteries degrade due to usage and exposure to environmental conditions,
which affects their capability to store energy and supply power. Accurately predicting the …
which affects their capability to store energy and supply power. Accurately predicting the …
Model-free reconstruction of capacity degradation trajectory of lithium-ion batteries using early cycle data
Early degradation prediction of lithium-ion batteries is important to guarantee safe
operations and avoid unexpected failure in manufacturing and diagnosis processes …
operations and avoid unexpected failure in manufacturing and diagnosis processes …
A generalizable, data-driven online approach to forecast capacity degradation trajectory of lithium batteries
Estimating battery degradation is vital not only to monitor battery's state-of-health but also to
accelerate research on new battery chemistries. Herein, we present a data-driven approach …
accelerate research on new battery chemistries. Herein, we present a data-driven approach …
[HTML][HTML] Data driven analysis of lithium-ion battery internal resistance towards reliable state of health prediction
Accurately predicting the lifetime of lithium-ion batteries in the early stage is critical for faster
battery production, tuning the production line, and predictive maintenance of energy storage …
battery production, tuning the production line, and predictive maintenance of energy storage …
[HTML][HTML] Identification and machine learning prediction of knee-point and knee-onset in capacity degradation curves of lithium-ion cells
P Fermín-Cueto, E McTurk, M Allerhand… - Energy and AI, 2020 - Elsevier
High-performance batteries greatly benefit from accurate, early predictions of future capacity
loss, to advance the management of the battery and sustain desirable application-specific …
loss, to advance the management of the battery and sustain desirable application-specific …
A novel smart feature selection strategy of lithium-ion battery degradation modelling for electric vehicles based on modern machine learning algorithms
Lithium-ion batteries are a key storage technology for electric vehicles and renewable
energy applications. However, the complex degrading behaviour of batteries impacts their …
energy applications. However, the complex degrading behaviour of batteries impacts their …
Remaining useful life prediction of lithium-ion battery via a sequence decomposition and deep learning integrated approach
The remaining useful life (RUL) prediction of Lithium-ion batteries (LIBs) is of great
importance to the health management of electric vehicles and hybrid electric vehicles …
importance to the health management of electric vehicles and hybrid electric vehicles …