Lithium-Ion Battery State of Charge and Critical Surface Charge Estimation Using an Electrochemical Model-Based Extended Kalman Filter
By D. Domenico, Anna G. Stefanopoulou, G. Fiengo. 2010
377 citations recorded by the source corpus.
Subfields: Automotive Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/04835c86f9712121af28a5bd4aac136fe9750c9c
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
- Rui Xiong
- Fengchun Sun
- Miroslav Krstic
- Hongwen He
- Anahita Mirtabatabaei
- Federico Bribiesca Argomedo
- Reinhardt Klein
- Scott Jason Moura
In Electrical and Electronic Engineering
- Rui Xiong
- Fengchun Sun
- Miroslav Krstic
- Hongwen He
- Anahita Mirtabatabaei
- Federico Bribiesca Argomedo
- Reinhardt Klein
- Scott Jason Moura
Citations in the corpus
- Adaptive Partial Differential Equation Observer for Battery State-of-Charge/State-of-Health Estimation Via an Electrochemical Model (2014, result): “This step is mathematically motivated by the fact that the SPM states are weakly observable from voltage measurements, as has been previously noted in the literature [11].”
- Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles (2018, methodology): “The widely used electrochemical models for battery SoC estimation are the one-dimension (1D) model [32]–[34], the pseudo two-dimensional (P2D) model [35], the quasithree dimensional full order physical model [36] and the first principle model [31], among which the simplification of the P2D model, si”
- Battery State Estimation for a Single Particle Model With Electrolyte Dynamics (2017, methodology): “Second, local state observability does not hold globally [4].”