A multi-step fast charging-based battery capacity estimation framework of real-world electric vehicles

Accurately evaluating battery degradation is not only crucial for ensuring the safe and reliable operation of electric vehicles (EVs) but also fundamental for their intelligent management and maximum utilization. However, the non-linearity, non-measurability, and multi-stress coupled operating conditions have posed significant challenges for...

Cloud-based battery failure prediction and early warning using multi-source signals and machine learning

The swift advancement of electric vehicle technology has led to increased requirements for ensuring the safety of batteries. Various models for predicting battery life and aging have been introduced to facilitate the appropriate utilization of batteries. Timely prediction and alert systems for identifying potential battery failure due to...

Prognostics for Lithium-ion batteries for electric Vertical Take-off and Landing aircraft using data-driven machine learning

The health management of batteries is a key enabler for the adoption of Electric Vertical Take-off and Landing vehicles (eVTOLs). Currently, few studies consider the health management of eVTOL batteries. One distinct characteristic of batteries for eVTOLs is that the discharge rates are significantly larger during take-off and landing, compared...