4 papers
Conformalized Transfer Learning for Li-ion Battery State of Health Forecasting under Manufacturing and Usage Variability
Samuel Filgueira da Silva, Mehmet Fatih Ozkan, Faissal El Idrissi +1
Accurate forecasting of state-of-health (SOH) is essential for ensuring safe and reliable operation of lithium-ion cells. However, existing models calibrated on laboratory tests at…
Augmented Physics-Based Li-ion Battery Model via Adaptive Ensemble Sparse Learning and Conformal Prediction
Samuel Filgueira da Silva, Mehmet Fatih Ozkan, Faissal El Idrissi +1
Accurate electrochemical models are essential for the safe and efficient operation of lithium-ion batteries in real-world applications such as electrified vehicles and grid storage…
Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning
Mehmet Fatih Ozkan, Samuel Filgueira da Silva, Faissal El Idrissi +2
Accurate parameter estimation in electrochemical battery models is essential for monitoring and assessing the performance of lithium-ion batteries (LiBs). This paper presents a nov…
Real-Time Optimal Design of Experiment for Parameter Identification of Li-Ion Cell Electrochemical Model
Ian Mikesell, Samuel Filgueira da Silva, Mehmet Fatih Ozkan +3
Accurately identifying the parameters of electrochemical models of li-ion battery (LiB) cells is a critical task for enhancing the fidelity and predictive ability. Traditional para…