Performance Analysis of GRU, LSTM and Feedforward Neural Networks for the State of Charge Estimation of a Lithium-Ion Battery for e-Bike Applications-Reference-Cited by-同舟云学术

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Performance Analysis of GRU, LSTM and Feedforward Neural Networks for the State of Charge Estimation of a Lithium-Ion Battery for e-Bike Applications

Published:2023-12-16 Issue: Volume: Page:1-6
ISSN:
Container-title:2023 14th International Renewable Energy Congress (IREC)
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Author:

Loussif M.¹,El Khil S. Khojet²,Charaabi L.²,Sayahi S.³

Affiliation:

1. Universite Tunis El manar,Laboratoire des Systémes Electriques, IES Ecole Nationale d'lngénieurs de Tunis,Department of research and innovation ACTIA Engineering Services,Tunis,Tunisia

2. Université Tunis EL manar,Laboratoire des Systémes Electriques, LRIIEES Ecole Nationale d'Ingénieurs de Tunis,Tunis,Tunisia

3. ACTIA Engineering Services Technopôle El Ghazala - 1,Department of research and innovation,Ariana,Tunisia,2088

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10389178/10389179/10389327.pdf?arnumber=10389327

Reference11 articles.

1. E-Bike Motor Drive: A Review of Configurations and Capabilities

2. State of Charge Estimation for Lithium-Ion Batteries Using Model-Based and Data-Driven Methods: A Review

3. State-of-charge estimation of lithium-ion battery using an improved neural network model and extended Kalman filter

4. State of Charge Estimation for Lithium-Ion Battery Using Recurrent NARX Neural Network Model Based Lighting Search Algorithm

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