Applications of artificial neural network based battery management systems: A literature review
Lithium-ion batteries have gained significant prominence in various industries due to their
high energy density compared to other battery technologies. This has led to their …
high energy density compared to other battery technologies. This has led to their …
[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 …
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 …
Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling
Designing and deployment of state-of-the-art electric vehicles (EVs) in terms of low cost and
high driving range with appropriate reliability and security are identified as the key towards …
high driving range with appropriate reliability and security are identified as the key towards …
An overview of data-driven battery health estimation technology for battery management system
Battery degradation, caused by multiple coupled degradation mechanisms, severely affects
the safety and sustainability of a battery management system (BMS). The battery state of …
the safety and sustainability of a battery management system (BMS). The battery state of …
[HTML][HTML] A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries
As widely used for secondary energy storage, lithium-ion batteries have become the core
component of the power supply system and accurate remaining useful life prediction is the …
component of the power supply system and accurate remaining useful life prediction is the …
[HTML][HTML] Two-stage aging trajectory prediction of LFP lithium-ion battery based on transfer learning with the cycle life prediction
With the wide application of the LFP lithium-ion batteries, more attention is paid to the battery
life and future aging behaviors as the safety and performance of the battery are guaranteed …
life and future aging behaviors as the safety and performance of the battery are guaranteed …
[HTML][HTML] A critical review of improved deep convolutional neural network for multi-timescale state prediction of lithium-ion batteries
Lithium-ion batteries are widely used as effective energy storage and have become the main
component of power supply systems. Accurate battery state prediction is key to ensuring …
component of power supply systems. Accurate battery state prediction is key to ensuring …
Bayesian deep-learning for RUL prediction: An active learning perspective
Deep learning (DL) has been intensively exploited for remaining useful life (RUL) prediction
in the recent decade. Although with high precision and flexibility, DL methods need sufficient …
in the recent decade. Although with high precision and flexibility, DL methods need sufficient …