MLA-Machine Learning Approach for Dependable Battery Condition Monitoring in Electric Vehicles

The paper introduces smart battery monitoring to address the growing demand in the automotive market for efficient and reliable battery solutions. It uses a machine learning framework that runs a Gated Recurrent Unit (GRU) network with an attention mechanism grow the accuracy and lifespan of battery health predictions. The GRU model study real-time variables such as voltage, current, and temperature to identify physical patterns. The monitoring process help out the model focus on the most important data, leading to more accurate predictions. By giving more weight to key particulars that influence battery health and performance, the system reduces uncertainty improves the accuracy of battery testing. The system can also be adjusted for different tasks. By focusing on relevant data at each moment, the monitoring process enhances the model’s ability to track long- term changes in battery life. This leads to more accurate predictions of critical parameters like State of Charge (SOC), State of Health (SOH), and Remaining Useful Life (RUL). The mixture of the listening and GRU models discharge better by offering the advantages of traditional methods, reducing noise, and boosting the system’s power. Experimental results show that this approach surpasses traditional models in accuracy and reliability, supporting the development of high-quality systems. Stable electronic products, especially in the electric vehicle industry.