A Multiscale Framework with Extended Kalman Filter for Lithium-Ion Battery SOC and Capacity Estimation

By Chao Hu, Byeng Dong Youn, Jaesik Chung, Randy Ortanez. 2010

471 citations recorded by the source corpus.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/8cd543bbff05fbecff22a32168a6a37dd409d3fb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Xiaosong Hu
2. Michael G. Pecht
3. P. Kollmeyer
4. Ali Emadi
5. Lei Zhang
6. Changfu Zou
7. Zonghai Chen
8. Zhen Hu

In Electrical and Electronic Engineering

1. Xiaosong Hu
2. P. Kollmeyer
3. Ali Emadi
4. Michael G. Pecht
5. Zhen Hu
6. Changfu Zou
7. Lei Zhang
8. Zonghai Chen

Citations in the corpus

• Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries (2018, background): “Luenberger observer [6], [10], adaptive observer [6], [11], sliding mode observer [6], [12], [13], and Kalman filters (KFs) [14], [15].”
• Remaining useful life prediction of lithium-ion battery with unscented particle filter technique (2013, methodology): “proposed a multiscale framework with the EKF for the battery SOC and capacity estimation [17], and the battery SOH can be monitored through the battery capacity estimation.”