Battery Sentiment Analysis and Recommention System

- The increasing reliance on battery technology across industries such as renewable energy, electric vehicles, and portable electronics underscores the importance of robust analysis and optimization of battery systems. Battery-centric analysis focuses on understanding key parameters such as temperature, current, voltage, state of charge (SOC), and state of health (SOH) to enhance performance, safety, and longevity. These parameters are critical in identifying potential failures, improving efficiency, and predicting battery life cycles. This study employs advanced methodologies, including machine learning algorithms such as Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), Gradient Boosting, Neural Networks (NN), and Deep Neural Networks (DNN), to analyse battery performance data. By training and testing these models on extensive datasets, the research provides accurate predictions of battery behavior under diverse operating conditions. Key metrics, including accuracy, root mean square error (RMSE), and mean absolute error (MAE), are used to evaluate the effectiveness of these algorithms, ensuring reliable insights into battery health and operation. The integration of real-time monitoring systems with predictive analytics enhances the safety and efficiency of batteries, reducing risks such as overheating, overcharging, and premature failure. Additionally, this approach supports the development of sustainable energy solutions by identifying areas for material optimization and improving the design of energy storage systems. Key Words: Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), Gradient Boosting, Neural Networks (NN), and Deep Neural Networks (DNN).