6 papers
Accelerating Bayesian Optimization for Nonlinear State-Space System Identification with Application to Lithium-Ion Batteries
Hao Tu, Jackson Fogelquist, Iman Askari +4
This paper studies system identification for nonlinear state-space models, a problem that arises across many fields yet remains challenging in practice. Focusing on maximum likelih…
Machine Learning-Driven Prediction of Lithium-Ion Battery Power Capability for eVTOL Aircraft
Hao Tu, Yebin Wang, Shaoshuai Mou +1
Electric vertical take-off and landing (eVTOL) aircraft have emerged as a promising solution to transform urban transportation. They present a few technical challenges for battery…
Motion Planning for Autonomous Vehicles: When Model Predictive Control Meets Ensemble Kalman Smoothing
Iman Askari, Yebin Wang, Vedeng M. Deshpande +1
Safe and efficient motion planning is of fundamental importance for autonomous vehicles. This paper investigates motion planning based on nonlinear model predictive control (NMPC)…
Optimal Power Management for Large-Scale Battery Energy Storage Systems via Bayesian Inference
Amir Farakhor, Iman Askari, Di Wu +2
Large-scale battery energy storage systems (BESS) have found ever-increasing use across industry and society to accelerate clean energy transition and improve energy supply reliabi…
Efficient Fault Diagnosis in Lithium-Ion Battery Packs: A Structural Approach with Moving Horizon Estimation
Amir Farakhor, Di Wu, Yebin Wang +1
Safe and reliable operation of lithium-ion battery packs depends on effective fault diagnosis. However, model-based approaches often encounter two major challenges: high computatio…
Remaining Discharge Energy Prediction for Lithium-Ion Batteries Over Broad Current Ranges: A Machine Learning Approach
Hao Tu, Manashita Borah, Scott Moura +2
Lithium-ion batteries have found their way into myriad sectors of industry to drive electrification, decarbonization, and sustainability. A crucial aspect in ensuring their safe an…