A novel on-board state-of-charge estimation method for aged Li-ion batteries based on model adaptive extended Kalman filter
By S. Sepasi, R. Ghorbani, Bor Yann Liaw. 2014
224 citations recorded by the source corpus.
Subfields: Automotive Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/f46655f6f1df5c59fefa142f2add06db3ff7067a
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): “In order to further improve the accuracy of the LIB state estimation, some related technologies, such as observer theory [13–15], adaptive algorithms [16,17], bio-inspired algorithms [1,18] have been combined with the Kalman filter algorithm and obtained the expected effects.”
- Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries (2019, background): “The assumption of fixed measurement and process noise covariance in EKF estimation reduces the overall performance of estimation [188].”