Advancing battery state of charge estimation in electric vehicles through deep learning: A comprehensive study using real-world driving data
Accurately estimating the State of Charge (SOC) in Electric Vehicles (EVs) is critical for battery management and operational efficiency. This paper presents a Deep Learning (DL) approach to address this challenge, utilizing Feed-Forward Neural Networks (FFNN) to estimate SOC in real-world EV scenar...
Saved in:
Main Authors: | Mohd Herwan, Sulaiman, Zuriani, Mustaffa, Saifudin, Razali, Mohd Razali, Daud |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier B.V.
2024
|
Subjects: | |
Online Access: | http://umpir.ump.edu.my/id/eprint/42347/1/Advancing%20battery%20state%20of%20charge%20estimation%20in%20electric%20vehicles.pdf http://umpir.ump.edu.my/id/eprint/42347/ https://doi.org/10.1016/j.cles.2024.100131 https://doi.org/10.1016/j.cles.2024.100131 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Enhancing battery state of charge estimation through hybrid integration of barnacles mating optimizer with deep learning
by: Zuriani, Mustaffa, et al. -
Using the evolutionary mating algorithm for optimizing deep learning parameters for battery state of charge estimation of electric vehicle
by: Mohd Herwan, Sulaiman, et al.
Published: (2023) -
Battery state of charge estimation for electric vehicle using Kolmogorov-Arnold networks
by: Mohd Herwan, Sulaiman, et al.
Published: (2024) -
State of charge estimation for electric vehicles using random forest
by: Mohd Herwan, Sulaiman, et al.
Published: (2024) -
Metaheuristic approach for optimizing neural networks parameters in battery state of charge estimation
by: Zuriani, Mustaffa, et al.
Published: (2023)