Prediction of electric vehicle battery state of health estimation using a hybrid deep learning mechanism

Lithium-ion batteries (LIBs) are widely employed, but fluctuations in temperature, overcharging, and overdischarging reduce their service lifetime. Battery health issues such as accelerated deterioration, loss of capacity, and thermal runaway can also endanger battery safety and functionality. This paper presents the integration of a Bidirectional Recurrent Neural Network and Long Short-Term Memory (biRNN-LSTM) network improve the prediction capability of Li-ion battery State of Health (SoH) with complex patterns identification and higher prediction accuracy. Compared to traditional feed-forward neural networks, RNNs are designed to learn temporal dependencies and perform sequence recognition on the original data. After this, LSTM modules improve this by being an example of the long-term time series information, which helps solve problems such as vanishing gradients. To highlight the effectiveness of the proposed method and compare it with the Deep Convolutional Neural Network and Long Short-Term Memory (DCNN-LSTM), Gate Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) from the literature to make accurate and reliable predictions, the Root Mean Square Error (RMSE), Maximum Accuracy Error (MAE), and Maximum Error (MAX) assessment metrics were used for performance evaluation. GRU needs 8000 iterations to identify SoH estimation errors because it is less capable of learning long-term dependencies. The proposed technique can detect errors after 7000 iterations since it performs exceptionally well in capturing fine-grained temporal dynamics.