A Hybrid Deep Neural Network and Self-Attention Mechanism for Accurate Estimation of Battery SoC and SoH

In order to assess the State of Charge (SOC) and State of Health (SOH) of batteries, this study proposes a hybrid machine learning model that combines Deep Neural Networks (DNNs) with self-attention mechanisms. Self-attention processes enhance the model's ability to identify and concentrate on significant aspects of the input data through the use of dynamic significance weights. This enables the model to make use of intricate linkages and long-range dependencies, which are crucial for deciphering SOC and SOH patterns. DNNs' hierarchical feature extraction capabilities enhance the selfattention process. DNNs improve the forecast accuracy of SOC and SOH values by transforming high-dimensional input information into complicated non-linear correlations through their layer-wise structure. Supervised learning methods are used to train the hybrid model. The difference between expected and actual SOC and SOH values is measured by task-specific loss functions, such as mean squared error (MSE). Early stopping strategies keep an eye on validation performance to avoid overfitting, and the Adam optimizer is used for adaptive learning rate training and effective training. To get performance, model parameters including learning rates, dropout rates, and the number of attention heads are adjusted. Scatter plots, which display the distribution and accuracy of forecasts among various data points, are used to visualize SOC and SOH predictions. The scatter plots demonstrate the model's precision and dependability in identifying significant patterns and connections. The model's performance is verified by performance metrics such as mean absolute error (MAE) and root mean squared error (RMSE). The suggested hybrid model's increased accuracy and stability are confirmed by baseline model comparison study. Because researchers can evaluate how SOC and SOH predicted values compare to actual values, scatter plot display further improves interpretability. In addition to predicting battery health, the modular architecture can analyze various datasets and applications. TensorFlow and PyTorch, two Python-based platforms, are used to implement this hybrid strategy. Utilizing high-end processing hardware such as GPUs and TPUs allows computation and scalability, while packages such as Hugging Face Transformers facilitate the speed at which self-attention mechanisms can be constructed. This hybrid approach's versatility makes it applicable to real-world systems, particularly those where precise and intelligible forecasts are essential, such energy storage, electric vehicle battery control, and industrial control. An effective and scalable way to comprehend battery health is to combine self-attention mechanisms with DNNs for SOC and SOH prediction. High predicted accuracy and dependability are achieved by the hybrid model's usage of feature prioritization and hierarchical learning. The usefulness of this technique is further demonstrated by the use of scatter plots to visualize predictions, which can offer helpful insights to battery monitoring and control systems. This study opens the door for future advancements in artificial intelligence applications to energy systems and beyond by confirming that machine learning algorithms may be used to carry out intricate forecasting tasks.