State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF

By Mohammad Charkhgard, Mohammad Farrokhi. IEEE transactions on industrial electronics (1982. Print), 2010

742 citations recorded by the source corpus.

Subfields: Automotive Engineering, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/77ba59732ea50aca7373cf0be984e954d391b645

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

In Control and Systems Engineering

Citations in the corpus

Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art (2020, methodology): “A radial basis function neural network with an extended Kalman filter (EKF) was used in [23], [24] for online estimation of the SOC of a Li-ion battery cell and a lead-acid battery.”
Estimation of Battery State of Health Using Probabilistic Neural Network (2013, methodology): “Digital Object Identifier 10.1109/TII.2012.2222650 current, and SOC [9], modeling the hysteresis voltage versus the SOC during charging and discharging with the extended Kalman filter [10], measuring the charging voltage and current using artificial neural networks and the extended Kalman filter…”
Remaining Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Using Particle Filter and Support Vector Regression (2018, methodology): “For instance, Charkhgard and Farrokhi [25] presented a battery state-space model based on a radial basis function (RBF) NN, then the EKF was employed to estimate battery SOC based on the proposed battery model.”
A review on lithium-ion battery ageing mechanisms and estimations for automotive applications (2013, positive): “Arti cial Neural Networks (ANN) or Neural Networks (NN) are mainly used for battery's SOC prediction [136] but this method can also measure the battery SOH [137, 138], as it was done for leadacid battery [139, 140].”
Adaptive prognosis of lithium-ion batteries based on the combination of particle filters and radial basis function neural networks (2017, methodology): “For example, in [13] an extended Kalman filter (EKF) is coupled to a NN trained off-line to approximate the relationship between the observable battery terminal voltage at the present temporal step and the voltage, the current and the SOC at previous steps, in order to be able to estimate the…”
Battery State Estimation for a Single Particle Model With Electrolyte Dynamics (2017, background): “Over the past decade, a variety of ECM-based algorithms have been developed, including linear parameter varying observers [7], sliding-mode observers [8], polynomial chaos [9], neural networks [10], unscented Kalman filters (KFs) [11], adaptive KFs [12], and particle filters [13].”