State-of-charge estimation of lithium-ion batteries based on gated recurrent unit with element-wise-attention gate
To enhance the precision of state-of-charge(SOC)estimation for lithium-ion batteries,we introduce a novel re-current neural network leveraging element attention gates.Different importance levels are assigned to each feature element of the input vector.The test results under different numbers of neurons and hidden layers are verified and analyzed.The opti-mal parameter settings determined are used to perform SOC estimation under different temperatures.Visualization analysis is conducted on the importance of SOC estimation tasks under different battery feature parameters.The SOC estimation accura-cy of the same dataset shows that the proposed network model has significantly improved accuracy in SOC estimation tasks.
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