[HTML][HTML] The development of machine learning-based remaining useful life prediction for lithium-ion batteries
X Li, D Yu, VS Byg, SD Ioan - Journal of Energy Chemistry, 2023 - Elsevier
Lithium-ion batteries are the most widely used energy storage devices, for which the
accurate prediction of the remaining useful life (RUL) is crucial to their reliable operation and …
accurate prediction of the remaining useful life (RUL) is crucial to their reliable operation and …
Aging mechanisms, prognostics and management for lithium-ion batteries: Recent advances
In the rapidly evolving landscape of energy storage, lithium-ion batteries stand at the
forefront, powering a vast array of devices from mobile phones to electric vehicles and …
forefront, powering a vast array of devices from mobile phones to electric vehicles and …
Improved singular filtering-Gaussian process regression-long short-term memory model for whole-life-cycle remaining capacity estimation of lithium-ion batteries …
For the development of low-temperature power systems in aviation, the transport synergistic
carrier optimization of lithium-ions and electrons is conducted to improve the low …
carrier optimization of lithium-ions and electrons is conducted to improve the low …
Improved anti-noise adaptive long short-term memory neural network modeling for the robust remaining useful life prediction of lithium-ion batteries
Safety assurance is essential for lithium-ion batteries in power supply fields, and the
remaining useful life (RUL) prediction serves as one of the fundamental criteria for the …
remaining useful life (RUL) prediction serves as one of the fundamental criteria for the …
A deep feature learning method for remaining useful life prediction of drilling pumps
J Guo, JL Wan, Y Yang, L Dai, A Tang, B Huang… - Energy, 2023 - Elsevier
Abstract Remaining Useful Life (RUL) prediction of drilling pumps, pivotal components in
fossil energy production, is essential for efficient maintenance and safe operation of such …
fossil energy production, is essential for efficient maintenance and safe operation of such …
Lithium-ion battery capacity and remaining useful life prediction using board learning system and long short-term memory neural network
S Zhao, C Zhang, Y Wang - Journal of Energy Storage, 2022 - Elsevier
In order for lithium-ion batteries to function reliably and safely, accurate capacity and
remaining useful life (RUL) predictions are essential, but challenging. Some current deep …
remaining useful life (RUL) predictions are essential, but challenging. Some current deep …
An improved CNN-LSTM model-based state-of-health estimation approach for lithium-ion batteries
Abstract Accurate SOH (State of Health) estimation is one of the key technologies to ensure
the safe operation of lithium-ion batteries. When predicting SOH, efficient data feature …
the safe operation of lithium-ion batteries. When predicting SOH, efficient data feature …
A data-model interactive remaining useful life prediction approach of lithium-ion batteries based on PF-BiGRU-TSAM
Accurate remaining useful life (RUL) prediction of lithium-ion batteries is critical for energy
supply systems. In conventional data-driven RUL prediction approaches, the battery's …
supply systems. In conventional data-driven RUL prediction approaches, the battery's …
A hybrid neural network model with attention mechanism for state of health estimation of lithium-ion batteries
A Tang, Y Jiang, Q Yu, Z Zhang - Journal of Energy Storage, 2023 - Elsevier
Reliable state of health (SOH) estimation is significant for safe operation of lithium-ion
batteries (LIBs). However, due to the strong nonlinearity of battery degradation and complex …
batteries (LIBs). However, due to the strong nonlinearity of battery degradation and complex …
A variational local weighted deep sub-domain adaptation network for remaining useful life prediction facing cross-domain condition
Most supervised learning-based approaches follow the assumptions that offline data and
online data must obey a similar distribution, which is difficult to satisfy in realistic remaining …
online data must obey a similar distribution, which is difficult to satisfy in realistic remaining …