An in-depth understanding of battery degradation and aging in-Operando not only plays a
vital role in the design of battery managing systems but also helps to ensure safe use and …

Abstract Machine learning has emerged as a transformative force throughout the entire
engineering life cycle of electrochemical batteries. Its applications encompass a wide array …

With the widespread application of energy storage systems, health monitoring of lithium-ion
batteries (LIBs) has become important. Transfer learning (TL) provides new ideas and …

Accurately estimating the state of health (SOH) of lithium-ion batteries in real-world
scenarios, especially for electric vehicles (EVs) is challenging due to dynamic operating …

This paper proposes a capacity trajectory prediction framework for the onboard batteries
equipped in electric buses. The framework is a sequence-to-sequence (Seq2Seq) structure …

This paper proposes a domain generalization-based method for state-of-health (SOH)
estimation of lithium-ion batteries. First, a combination of convolutional neural network and …

Diverse electrochemical characteristics and complex operational conditions of the lithium-
ion battery cause multi-domain discrepancies in practical applications, which poses huge …

Accurately estimating lithium-ion battery's state of health (SOH) can effectively improve the
safety and economics of energy systems, which is an unsolved challenge. Electrochemical …

Implementation of advanced intelligent deep learning techniques for Electric Vehicles (EVs)
energy consumption analysis is obstructed by two main subjects. First, the problem of finding …

The state of health (SOH) estimation of lithium-ion batteries is crucial for the operational
reliability and safety of electric vehicles. However, traditional data-driven methods face …