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 …

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 …

The prediction ability of all traditional machine learning models is limited to a few batteries.
When the RUL of more batteries needs to be predicted, the prediction performance of …

Lithium-ion batteries have been generally used in industrial applications. In order to ensure
the safety of the power system and reduce the operation cost, it is particularly important to …

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 …

Batteries are prevalent energy providers for modern systems. They can also be regarded as
storage units for renewable and sustainable energy. Failures of batteries can bring huge …

Fault diagnosis and prognosis are some of the most crucial functionalities in complex and
safety-critical engineering systems, and particularly fault diagnosis, has been a subject of …

Lithium-ion batteries are currently being widely used. Accurately predicting their remaining
useful life (RUL) is essential for battery management systems (BMS) and rationally planning …

Estimating the capacity of lithium-ion cells employed in electric vehicles is challenging
because of the complex vehicle conditions and inconsistent cell decay. This paper proposes …

In this study, we developed multiple hybrid machine-learning models to address parameter
optimization limitations and enhance the spatial prediction of landslide susceptibility models …