TY - GEN
T1 - Improved State of Charge Estimation of a Lithium-Ion Battery Output
T2 - 17th International Conference on Interdisciplinarity in Engineering, INTER-ENG 2023
AU - Belmahdi, Brahim
AU - Madhiarasan, Manoharan
AU - Herbazi, Rachid
AU - Louzazni, Mohamed
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - 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%.
AB - 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%.
KW - Battery storage
KW - convolutional neural networks
KW - lithium-ion batteries
KW - state of charge
UR - http://www.scopus.com/inward/record.url?scp=85190413279&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-54674-7_8
DO - 10.1007/978-3-031-54674-7_8
M3 - Article in proceedings
AN - SCOPUS:85190413279
SN - 9783031546730
T3 - Lecture Notes in Networks and Systems
SP - 117
EP - 131
BT - The 17th International Conference Interdisciplinarity in Engineering - Inter-Eng 2023 Conference Proceedings - Volume 3
A2 - Moldovan, Liviu
A2 - Gligor, Adrian
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 5 October 2023 through 6 October 2023
ER -