The battery system, as the core energy storage device of new energy vehicles, faces
increasing safety issues and threats. An accurate and robust fault diagnosis technique is …

To ensure the secure and healthy usage of lithium-ion batteries, it is necessary to accurately
estimate the state of charge (SOC) in battery management systems. The development of …

With the advances in computer science, deep learning (DL) has been developed for battery
management systems (BMSs) with artificial intelligence. State of charge (SOC) estimation of …

The widespread adoption of lithium-ion batteries has ushered in a transformative era across
industries, powering an array of devices from portable electronics to electric vehicles. This …

The development of new electrode materials plays an important role in enhancing the
performance of batteries. Machine learning can provide powerful support for discovering …

The multi-scale modeling of lithium-ion battery (LIB) is difficult and necessary due to its
complexity. However, it is difficult to capture the aging behavior of batteries, and the coupling …

Iron-chromium flow batteries (ICRFBs) have emerged as an ideal large-scale energy
storage device with broad application prospects in recent years. Enhancement of the Cr …

Accurate estimation of the State of Health (SOH) of lithium-ion batteries is crucial for
ensuring their safe and reliable operation. Data-driven methods have shown excellent …

It is challenging for common artificial intelligence (AI) frameworks to optimize layouts of array
structures, because AI is unable to recognize and adjust layouts. To address this challenge …

With the increasing demands for battery safety management, data-driven method becomes a
promising solution for highly accurate battery state of health (SOH) estimation. However, the …