Lithium Polymer Battery State-of-Charge Estimation Based on Adaptive Unscented Kalman Filter and Support Vector Machine

By Jinhao Meng, Guangzhao Luo, Fei Gao. IEEE transactions on power electronics, 2016

297 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Advanced Machine Learning Approach for Lithium-Ion Battery State Estimation in Electric Vehicles (2016, unknown): “2512237 and exacerbate battery safety/durability problems [8]–[10].”
Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries (2018, methodology): “like artificial neural network (ANN) [21], extreme learning machines [22], and SVMs [23], [24].”
Overview of Lithium-Ion battery modeling methods for state-of-charge estimation in electrical vehicles (2018, methodology): “In order to show the performance of the four typical modeling methods in more detail, the combined model [13], two RC ECM [10], SPM [14], and SVM battery models [15] are compared in terms of accuracy of voltage response and execution time in this section.”
An Overview and Comparison of Online Implementable SOC Estimation Methods for Lithium-Ion Battery (2018, background): “Because of the good performance in SOC estimation, nonlinear filters (such as, extended Kalman filter (EKF) [10], [11], unscented Kalman filter (UKF) [6], [12], [13], central difference Kalman filter (CDKF) [14]–[16], square root unscented Kalman filter (SR-UKF) [17], [18], square root central diffe”