Improved State of Charge Estimation of a Lithium-Ion Battery Output: Application to Conventional Neural Network

Brahim Belmahdi*, Manoharan Madhiarasan, Rachid Herbazi, Mohamed Louzazni

*Corresponding author af dette arbejde

Publikation: Bidrag til bog/antologi/rapport/proceedingKonferencebidrag i proceedingsForskningpeer review

Abstract

The safety and reliability of battery storage systems are essential for the widespread adoption of electrified transportation and new energy generation. One of the crucial parameters for the safe management and effective control of batteries is the state of charge (SOC). In recent years, there has been a great deal of interest in machine-learning-based SOC estimation methods for lithium-ion batteries. However, a common issue with these models is that they frequently exhibit unstable estimation performances, which makes it challenging to use them in real-world scenarios. To address this problem, a framework based on convolutional neural networks (CNNs) uses measurements of the voltage, current, and temperature while the battery is charging to directly estimate SOC. The CNN is trained using randomized data. To increase accuracy, training data was enhanced with noise and error that included multiple layers and neurons. Additionally, the algorithm was examined for various temperature distributions, which would be common for many applications. With the aid of statistical indicator metrics, the proposed model’s accuracy and generalizability are demonstrated in the experiments using data gathered under various working conditions. The experiment’s findings show that the proposed model’s maximum error is less than 1.9%.

OriginalsprogEngelsk
TitelThe 17th International Conference Interdisciplinarity in Engineering - Inter-Eng 2023 Conference Proceedings - Volume 3
RedaktørerLiviu Moldovan, Adrian Gligor
Antal sider15
ForlagSpringer Science and Business Media Deutschland GmbH
Publikationsdato2024
Sider117-131
ISBN (Trykt)9783031546730
DOI
StatusUdgivet - 2024
Udgivet eksterntJa
Begivenhed17th International Conference on Interdisciplinarity in Engineering, INTER-ENG 2023 - Targu Mures, Rumænien
Varighed: 5 okt. 20236 okt. 2023

Konference

Konference17th International Conference on Interdisciplinarity in Engineering, INTER-ENG 2023
Land/OmrådeRumænien
ByTargu Mures
Periode05/10/202306/10/2023
NavnLecture Notes in Networks and Systems
Vol/bind929 LNNS
ISSN2367-3370

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