Lithium-Ion Battery State Estimation, Health Monitoring and Life Prediction
This research community focuses on the computational modeling and algorithmic monitoring of lithium-ion batteries, specifically to estimate their charge level, assess their health status, and predict their remaining useful life for electric vehicles and energy storage systems.
The work centers on developing robust estimation algorithms for state-of-charge (SOC) and state-of-health (SOH), using methods such as Kalman filtering, neural networks, and deep learning. A significant portion of the literature addresses battery degradation mechanisms and remaining useful life (RUL) prediction, often applying machine learning techniques to voltage and current data. The applications are primarily electric vehicles and grid-scale energy storage, with some focus on battery management systems (BMS) and thermal monitoring. The community also investigates electrochemical impedance spectroscopy and physics-informed models to improve the accuracy of these estimations under varying operating conditions.
Lithium research accounts for the largest share of this community, representing 10.8% of all tracked lithium research and comprising 4,383 papers. Lead research follows with a 3.5% share of its respective element’s literature, contributing 1,089 papers.
The community comprises 9,418 papers, with the highest publication volume in the Journal of Energy Storage, Journal of Power Sources, and Energy.
Recent work continues to refine state-of-health estimation using advanced architectures like transformers and physics-informed neural networks, with specific applications to second-life batteries and electric aircraft.