Lithium Battery SOC Estimation Based on Multi-Head Attention Mechanism and GRU Algorithm

Authors

  • Xueguang Li
  • Menchita F. Dumlao

DOI:

https://doi.org/10.54097/ajst.v7i1.10997

Keywords:

Machine Learning, Lithium battery, Attention Mechanisms, State of Charge, GRU.

Abstract

 Pure electric vehicles have been widely used due to their non-pollution, low noise, high energy conversion efficiency and other advantages. SOC (State of Charge) is a crucial indicator for lithium batteries and pure electric vehicles. SOC cannot be directly measured. This article designs a new network structure. It is the GRU-Attention network structure. The stacked GRU algorithm in GRU-Attention network extracts the temporal characteristics of lithium battery test data, and the stacked multi-head self-attention network extracts the global information. The GRU-Attention network can avoid long-term dependency and gradient disappearance problems. The proposed network utilizes Stacked FFN as the dense layer. This article will test the network designed in the public data set at the University of Maryland. Simultaneously, this article compares the effects of different BatchSize on the performance of the algorithm. The network training process converges more effectively with a smaller BatchSize. Both too large and too small BatchSize have a negative impact on the generalization performance of the network. The extraction of the time-order character, however, may be hampered if the timestamp is too small. At the same time, the paper also compares the GRU-Attention network horizontally with the GRU and Attention networks. Eventually, the GRU-Attention network proposed in this article could better meet the estimate of the lithium battery SOC.

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Published

11-08-2023

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Articles

How to Cite

Lithium Battery SOC Estimation Based on Multi-Head Attention Mechanism and GRU Algorithm. (2023). Academic Journal of Science and Technology, 7(1), 90-98. https://doi.org/10.54097/ajst.v7i1.10997

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