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...

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...

Profit optimized machine learning aided electricity storage; Comparison between privately owned and neighborhood shared: An auto-machine learning solution

Batteries are an essential tool for energy transition. They provide the ability to reduce imbalances by absorbing the forecast errors for consumers and renewable energy sources. This has the added benefit that congestions occurring due to supply and demand mismatches would be prevented. Furthermore, they allow generating a stable power...

Predicting state of health and remaining useful lifetime of lithium-ion batteries for eVTOLs using data-driven machine learning