6 papers
CAR-EnKF: A Covariance-Adaptive and Recalibrated Ensemble Kalman Filter Framework
Shida Jiang, Shengyu Tao, Zihe Liu +1
The ensemble Kalman filter (EnKF) is widely used for nonlinear and high-dimensional state estimation because it replaces complex covariance propagation with simple ensemble statist…
Model-Agnostic Energy Throughput Control for Range and Lifetime Extension of Electric Vehicles via Cell-Level Inverters
Shida Jiang, Shengyu Tao, Vincent Molina +2
A conventional electric vehicle (EV) powertrain relies on a centralized high-voltage DC-AC inverter, thereby limiting cell-level control and potentially reducing overall driving ra…
Mitigating Overconfidence in Nonlinear Kalman Filters via Covariance Recalibration
Shida Jiang, Junzhe Shi, Scott Moura
The Kalman filter (KF) is an optimal linear state estimator for linear systems, and numerous extensions, including the extended Kalman filter (EKF), unscented Kalman filter (UKF),…
Design Guidelines for Nonlinear Kalman Filters via Covariance Compensation
Shida Jiang, Jaewoong Lee, Shengyu Tao +1
Nonlinear extensions of the Kalman filter (KF), such as the extended Kalman filter (EKF) and the unscented Kalman filter (UKF), are indispensable for state estimation in complex dy…
An Adaptive Estimation Approach based on Fisher Information to Overcome the Challenges of LFP Battery SOC Estimation
Junzhe Shi, Shida Jiang, Shengyu Tao +3
Robust and Real-time State of Charge (SOC) estimation is essential for Lithium Iron Phosphate (LFP) batteries, which are widely used in electric vehicles (EVs) and energy storage s…
Relax, Estimate, and Track: a Simple Battery State-of-charge and State-of-health Estimation Method
Shida Jiang, Junzhe Shi, Scott Moura
Battery management is a critical component of ubiquitous battery-powered energy systems, in which battery state-of-charge (SOC) and state-of-health (SOH) estimations are of crucial…