activity
20242026
collaborators

6 papers

cs.LG2026

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…

eess.SY2025

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…

eess.SY2025

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…

eess.SY2025

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…

eess.SY2024

Improving Low-Fidelity Models of Li-ion Batteries via Hybrid Sparse Identification of Nonlinear Dynamics

Samuel Filgueira da Silva, Mehmet Fatih Ozkan, Faissal El Idrissi +2

Accurate modeling of lithium ion (li-ion) batteries is essential for enhancing the safety, and efficiency of electric vehicles and renewable energy systems. This paper presents a d…

eess.SY2024

Parameter Identification for Electrochemical Models of Lithium-Ion Batteries Using Bayesian Optimization

Jianzong Pi, Samuel Filgueira da Silva, Mehmet Fatih Ozkan +2

Efficient parameter identification of electrochemical models is crucial for accurate monitoring and control of lithium-ion cells. This process becomes challenging when applied to c…