Optimized deep learning-based remaining useful life estimation for lithium-ion batteries

The right primary secret of making a safe, reliable and cost-effective maintenance of electric cars, renewable energy storage and mobile electronic appliances is the correct Remaining Useful Life (RUL) determination of a lithium-ion battery. The power to learn long-range correlations between time and non-linear degradation trends may not necessarily be possessed by traditional machine learning algorithms and a simple recurrent neural network, which are present in battery aging data. The recommendation to this limitation, as will be developed in this paper is the optimization of the deep learning-based model in RUL prediction, which is implied by the comparison of the performance on various models such as LSTM, GRU, CNN-LSTM, XGBoost and Transformer networks. The error analysis of the experiment validates that the model built on the basis of Transformers always has the best results due to the lowest error in prediction (MAE = 2.4 and RMSE = 2.8), and the most consistent dynamics of Loss on validation. CNN-LSTM model that came second was competitive but with slightly higher number of high values of error as compared to GRU and LSTM which had moderate prediction accuracy. XGBoost was the worst performing because it does not have high sequential dependency modelling capability. The reason is that the model Transformer has been capable of shining its self-attention mechanism that will assist to uncover the long-term patterns of health degradation without the need to repeat the structure limitations. In general, the suggested optimized Transformer-based RUL estimation model causes the prediction to be more accurate, stable and cost-efficient and, therefore, leads to the creation of the smart battery health control system, which can be applied in the real-time, industrial and vehicle environment.