Electric Vehicle Battery State of Charge Estimation With an Ensemble Algorithm Using Central Difference Kalman Filter (CDKF) and Non-Linear Autoregressive With Exogenous Input (NARX)

Electric Vehicle Battery State of Charge Estimation With an Ensemble Algorithm Using Central Difference Kalman Filter (CDKF) and Non-Linear Autoregressive With Exogenous Input (NARX)

Conventional model-based probabilistic inference methods require increasingly complex models to improve Electric Vehicle (EV) battery State of Charge (SOC) estimation. Deep learning methods gained popularity in recent years with their model free estimations. However, practical constraints such as in...

Full description

Bibliographic Details
Main Authors: Ayodeji S. Ogundana, Pranaya K. Terala, Migara Y. Amarasinghe, Xuanchen Xiang, Simon Y. Foo
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
State of charge (SOC)
battery management system (BMS)
bidirectional LSTM (BiLSTM)
central difference Kalman filter (CDKF)
drive cycles
gated recurrent unit (GRU)
Online Access:https://ieeexplore.ieee.org/document/10453581/

Internet

https://ieeexplore.ieee.org/document/10453581/

Similar Items