Adaptive Partial Differential Equation Observer for Battery State-of-Charge/State-of-Health Estimation Via an Electrochemical Model
By Scott Jason Moura, Nalin A. Chaturvedi, Miroslav Krstic. 2014
253 citations recorded by the source corpus.
Subfields: Automotive Engineering, Control and Systems Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/76d3e83057d5b868ee6fbfffc1c62ee1d6eed405
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
- Zhihao Yu
- Ruituo Huai
- Lin-jing Xiao
- Miroslav Krstic
- Anahita Mirtabatabaei
- Federico Bribiesca Argomedo
- Reinhardt Klein
- Scott Jason Moura
In Control and Systems Engineering
- Scott Jason Moura
- Zhihao Yu
- Ruituo Huai
- Lin-jing Xiao
- Anahita Mirtabatabaei
- Federico Bribiesca Argomedo
- Reinhardt Klein
- Miroslav Krstic
In Electrical and Electronic Engineering
- Zhihao Yu
- Ruituo Huai
- Lin-jing Xiao
- Miroslav Krstic
- Anahita Mirtabatabaei
- Federico Bribiesca Argomedo
- Reinhardt Klein
- Scott Jason Moura
Citations in the corpus
- Co-Estimation of State of Charge and State of Health for Lithium-Ion Batteries Based on Fractional-Order Calculus (2018, unknown): “and adaptiveness [12].”
- Battery State Estimation for a Single Particle Model With Electrolyte Dynamics (2017, methodology): “Remark 1 (SPMe Versus SPM Comparison): Note the voltage expression (32) is identical to the SPM voltage expression [15], but the SPMe adds the last two terms.”
- 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.”