Assessing Lifespan of Lithium-Ion Rechargeable Batteries through Hybrid CNN-LSTM-DNN Method

Lithiumion batteries are crucial to the functioning of contemporary society, since they are used in a wide variety of applications from powering electric cars to storing renewable energy. Yet, there is still a significant problem in making these batteries safe and long-lasting. Using an unique CNN-LSTM-DNN architecture, we offer a new approach for predicting the RUL of lithium-ion batteries in this research. Our method combines the strengths of CNNs for image analysis, LSTMs for sequence data, and DNNs for feature learning. By combining these models, we are able to outperform existing state-of-the-art approaches to estimating battery RUL. An extensive lithium-ion battery dataset from the Center for Innovative Product Lifecycle Management is used to assess the efficacy of our approach; the findings show a considerable decrease in identification mistakes and an increase in RUL identification performance. This research provides the path for a more accurate and efficient early warning system to be created, which will be crucial in preventing dangerous and short-lived lithium-ion battery failures in the future.