Estimation of Battery State-of-Charge using Feedforward Neural Networks
Author:
Affiliation:
1. Universiti Sains Malaysia (USM),School of Electrical and Electronic Engineering,Pulau Pinang,Malaysia
2. NFC Institute of Engineering & Technology (NFC IET),Department of Electrical Engineering,Multan,Pakistan
Funder
Universiti Sains Malaysia
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9795349/9795357/09795401.pdf?arnumber=9795401
Reference19 articles.
1. A Novel Open Circuit Voltage Based State of Charge Estimation for Lithium-Ion Battery by Multi-Innovation Kalman Filter
2. Battery State-of-Charge Approximation for Energy Harvesting Embedded Systems
3. State estimation for advanced battery management: Key challenges and future trends
4. Lithium-Ion Battery Pack State of Charge and State of Energy Estimation Algorithms Using a Hardware-in-the-Loop Validation
5. Probability based remaining capacity estimation using data-driven and neural network model
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1. The State of Charge Estimation of LiFePO4 Batteries Performance Using Feed Forward Neural Network Model;Applied Mechanics and Materials;2024-01-09
2. Comparison of Lithium-Ion Battery SoC Estimation Accuracy of LSTM Neural Network Trained with Experimental and Synthetic Datasets;Lecture Notes in Electrical Engineering;2024
3. Enhancing State of Charge Estimation in Ni-MH Batteries Through a Hybrid Approach Incorporating RRBF and K-Means;2023 IEEE Third International Conference on Signal, Control and Communication (SCC);2023-12-01
4. NARX Model Based Estimation of State of Charge of Lithium-Ion Batteries in Electric Vehicles under Varying Temperature Ranges;2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT);2023-07-06
5. State of Charge Estimation of Li-ion Batteries through Efficient Gated Recurrent Neural Networks using Engineered features;2022 IEEE 19th India Council International Conference (INDICON);2022-11-24
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