Estimation of Discharge Energy of Lithium-ion Battery for Different Temperatures by FOX-Bidirectional Recurrent Neural Network Method

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Abstract
In this study, the discharge energy of the lithium-ion battery was estimated by using the FOX- Bidirectional Recurrent Neural Network (Bi-RNN) method for the Dynamic Stress Test (DST) driving cycle method and different temperatures. For lithium-based batteries, discharge energy estimation is critical for long-term use, while problems such as overheating are major problems. For this reason, in this study, the discharge energy of lithium-ion batteries under different temperature conditions was estimated using bidirectional-based deep learning methods. In addition, the hyperparameter values of the BiRNN method were determined with FOX optimization, and the FOX-BiRNN method was proposed. The discharge energy estimations of FOX-BiRNN, BiRNN, Bidirectional Gated Recurrent Unit (Bi-GRU), and Bidirectional Long-short term (Bi-LSTM) methods were compared. The obtained estimation results were compared using the most commonly used battery parameter estimation metrics in the literature for performance comparison. The estimation success of the proposed method was presented using many comparison metrics and graphics. The FOX-BiRNN method was the most successful method for discharge energy estimation by obtaining values of %99.4186 at 0 0C according to the R2 metric, %99.6080 at 25 0C according to the R2 metric, and %99.4148 at 45 0C according to the R2 metric.

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