Improved extended Kalman filter for state of charge estimation of battery pack
By S. Sepasi, R. Ghorbani, Bor Yann Liaw. 2014
178 citations recorded by the source corpus.
Subfields: Automotive Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/b92d79d9eaf1d3a6a13a435cd24ddb879456444a
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
- Moh Yamani Idna Idris
- Zhihao Yu
- Saad Mekhilef
- Prashant Shrivastava
- Ruituo Huai
- Tey Kok Soon
- Lin-jing Xiao
In Electrical and Electronic Engineering
- Moh Yamani Idna Idris
- Zhihao Yu
- Saad Mekhilef
- Prashant Shrivastava
- Ruituo Huai
- Tey Kok Soon
- Lin-jing Xiao
- Yousef A. Mahmoud
Citations in the corpus
- State-of-Charge Estimation for Lithium-Ion Batteries Using a Kalman Filter Based on Local Linearization (2015, methodology): “The Extended Kalman filter (EKF) [7–10], unscented Kalman filter (UKF) [5,11], which are algorithms developed based on the Kalman filter, better solve the state estimation problems for the nonlinear LIB [12], and can also obtain ideal estimation results.”
- Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries (2019, methodology): “EKF works on the principle of linearisation of the nonlinear function [69] that uses partial derivatives and first-order Taylor series expansion.”