Battery state of the charge estimation using Kalman filtering
By Mehrdad Mastali, J. Vazquez‐Arenas, Roydon Andrew Fraser, Michael W. Fowler, S. Afshar, M. Stevens. 2013
179 citations recorded by the source corpus.
Subfields: Automotive Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/abe882b2d053eeeda82288d53079c90b6697a506
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
- Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries (2019, methodology): “[128] to confirm the Table 4 Summary of the LKF estimation algorithm [65,66].”
- 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.”