Comparison of prediction performance of lithium titanate oxide battery discharge capacity with machine learning methods

Due to the non-linear characteristics of rechargeable batteries, many studies are carried out on battery life, state of charge and health status monitoring systems, and many models are developed using different methods. Within the scope of this study, lithium titanate oxide (LTO) battery was discharged at room temperature with different discharge currents. Through the experiments, the discharge capacity, current, voltage and temperature values of the LTO battery were recorded and the min-max scaling method was applied to the obtained discharge experiment data. 70% of the experimental data is reserved as training data and 30% as test data. Models have been developed to predict the discharge capacity of LTO batteries using machine learning algorithms. Random forest, K-nearest neighbor, decision tree and linear regression methods were used in the prediction models. By comparing the performance values obtained from the models used, the model that makes the best estimation of the solution of the problem has been determined. In the performance evaluations of machine learning methods explanatory coefficient (R2), mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE) values were used. The obtained research findings were compared with the findings of different studies conducted with similar methods. Research findings demonstrate that data-driven prediction methods can effectively predict the charge/discharge state of lithium-based batteries under various cycling conditions. As a result of the study, it was seen that the random forest model gave the most successful result in terms of success rates with a predictive value of % 99,8836.