A Comparative Analysis of Lithium Ion Battery Input Profiles for Remaining Useful Life Prediction by Cascade Forward Neural Network

The Remaining Useful Life (RUL) of a battery is very important factor to allow for efficient working of all associated systems. In this paper, a Multi-Battery Input Profile (MBIP) based Cascade Forward Neural Network (CFNN) model is proposed to predict the RUL of Lithium-ion battery. The proposed model was trained by utilizing the NASA battery datasets. In addition, systematic sampling was observed to extract the data from the parameters of charging profile of the battery. Four batteries namely B0005, B0006, B0007 and B0018 are utilized and experiment was performed while training the model with 70:30 ratios. The prediction accuracy of the model in case of B0006 and B0018 was lower as compared with B0005 and B0007 due to the effect of capacity regeneration phenomena. The proposed methodology of CFNN based MBIP is validated with Single-Battery Input Profile (SBIP). Several performance metrics such as Root Mean Square Error (RMSE), Mean Squared Error (MSE) and Mean Absolute Error (MAE) are observed.

Automated summary

The proposed model in this paper utilizes a Multi-Battery Input Profile (MBIP) based Cascade Forward Neural Network (CFNN) to predict the Remaining Useful Life (RUL) of Lithium-ion batteries. The model was trained using NASA battery datasets and showed varying prediction accuracy for different batteries due to capacity regeneration phenomena. Performance metrics such as RMSE, MSE, and MAE were observed to evaluate the model's performance.