20 citations · 24 across the 14 of their papers we have counts for
15 papers
Recursive Parameter Identification of Nonlinear Stochastic State-Space Models via Sequentialized Ensemble Kalman Inversion
Farzaneh Barat, Rachel Carter, Sara Wilson +1
Recursive parameter identification in nonlinear stochastic state-space models is challenging because unknown parameters affect the measurements through latent-state dynamics. This…
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…
A Compression Perspective on Simplicity Bias
Tom Marty, Eric Elmoznino, Leo Gagnon +5
Deep neural networks exhibit a simplicity bias, a well-documented tendency to favor simple functions over complex ones. In this work, we cast new light on this phenomenon through t…
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)…
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…
Economic Optimal Power Management of Second-Life Battery Energy Storage Systems
Amir Farakhor, Di Wu, Pingen Chen +3
Second-life battery energy storage systems (SL-BESS) are an economical means of long-duration grid energy storage. They utilize retired battery packs from electric vehicles to stor…
System Identification for Lithium-Ion Batteries with Nonlinear Coupled Electro-Thermal Dynamics via Bayesian Optimization
Hao Tu, Xinfan Lin, Yebin Wang +1
Essential to various practical applications of lithium-ion batteries is the availability of accurate equivalent circuit models. This paper presents a new coupled electro-thermal mo…
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…
Integrated Optimal Fast Charging and Active Thermal Management of Lithium-Ion Batteries in Extreme Ambient Temperatures
Zehui Lu, Hao Tu, Huazhen Fang +2
This paper presents an integrated control strategy for optimal fast charging and active thermal management of Lithium-ion batteries in extreme ambient temperatures, striking a bala…
Scalable Optimal Power Management for Large-Scale Battery Energy Storage Systems
Amir Farakhor, Di Wu, Yebin Wang +1
Large-scale battery energy storage systems (BESS) are helping transition the world towards sustainability with their broad use, among others, in electrified transportation, power g…
Application-Oriented Co-Design of Motors and Motions for a 6DOF Robot Manipulator
Adrian Stein, Yebin Wang, Yusuke Sakamoto +2
This work investigates an application-driven co-design problem where the motion and motors of a six degrees of freedom robotic manipulator are optimized simultaneously, and the app…
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…