Improved multi-head bi-directional long and short-term memory temporal convolutional network for lithium-ion batteries state of charge estimation in energy storage systems.

Li, Yang; Wang, Shunli; Liu, Donglei; Cui, Yixiu; Fernandez, Carlos; Blaabjerg, Frede

Abstract

Lithium-ion batteries with their high voltage, large capacity, high discharge rate, no memory effect, and green environmental protection advantages are widely used in communication, power stations, backup power, and other energy storage fields. Accurate estimation of the state of charge (SOC) of lithium-ion batteries is a key prerequisite to ensure the safe, reliable, and efficient operation of battery systems. To address this core challenge, this paper innovatively proposes a composite model combining the Kepler optimization algorithm, temporal convolutional network (TCN), bi-directional long and short-term memory network (BiLSTM), and multi-head attention mechanism (MHA). Kepler optimization algorithm was used to search the optimal hyperparameters in the TCN-BiLSTM structure so that the model could adjust the structural parameters and extract the input features accurately under different working conditions and temperatures. The multi-head self-attention mechanism is introduced to assign different weights to the feature outputs extracted by time convolution according to the different importance of information to improve the adaptability of the model. Finally, the proposed model is tested and compared with other models under different temperatures and working conditions.

Citation

LI, Y., WANG, S., LIU, D., CUI, Y., FERNANDEZ, C. and BLAABJERG, F. 2024. Improved multi-head bi-directional long and short-term memory temporal convolutional network for lithium-ion batteries state of charge estimation in energy storage systems. In Proceedings of the 25th IEEE (Institute of Electrical and Electronics Engineers) China conference on system simulation technology and its application 2024 (CCSSTA 2024), 21-23 July 2024, Tianjin, China. Piscataway: IEEE [online], pages 581-586. Available from: https://doi.org/10.1109/CCSSTA62096.2024.10691761

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 25th IEEE (Institute of Electrical and Electronics Engineers) China conference on system simulation technology and its application 2024 (CCSSTA 2024)
• Start Date: Jul 21, 2024
• End Date: Jul 23, 2024
• Acceptance Date: Jun 30, 2024
• Online Publication Date: Jul 23, 2024
• Publication Date: Dec 31, 2024
• Deposit Date: Oct 3, 2024
• Publicly Available Date: Oct 3, 2024
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Pages: 581-586
• DOI: https://doi.org/10.1109/ccssta62096.2024.10691761
• Keywords: State of charge (SOH); Temporal convolutional network (TCN); Bi-directional long and short-term memory (BiLSTM); Multi-head attention mechanism; Kepler optimization algorithm
• Public URL: https://rgu-repository.worktribe.com/output/2509818