9 papers
System Identification of Lithium-Ion Battery Equivalent Circuit Models Using Ensemble Kalman Inversion
Farzaneh Barat, Sara Wilson, Huijeong Kim +1
System identification remains an intriguing challenge for lithium-ion batteries, as many models are nonlinear, exhibit multi-physics coupling, and involve a large number of paramet…
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…
Model Predictive Inferential Control of Neural State-Space Models for Autonomous Vehicle Motion Planning
Iman Askari, Ali Vaziri, Xuemin Tu +2
Model predictive control (MPC) has proven useful in enabling safe and optimal motion planning for autonomous vehicles. In this paper, we investigate how to achieve MPC-based motion…
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…
Optimal Power Management of Battery Energy Storage Systems via Ensemble Kalman Inversion
Amir Farakhor, Iman Askari, Di Wu +1
Optimal power management of battery energy storage systems (BESS) is crucial for their safe and efficient operation. Numerical optimization techniques are frequently utilized to so…
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)…