Li-ion battery capacity prediction using improved temporal fusion transformer model

Abstract

Lithium-ion (Li-ion) batteries have near-zero energy emissions and provide power to various devices, such as automobiles and portable equipment. The strategy predicts the capacity of Li-ion in advance and can also help arrange maintenance tasks. To improve state of health (SOH) and remaining useful life (RUL) prediction accuracy, we propose an improved temporal fusion transformer (ITFT) method based on bidirectional long short-term memory (Bi-LSTM) encoder-decoder layer replacing the long short-term memory for probabilistic online RUL prediction. A novel interpretable hyperparameter tuning method called Bayesian optimization based on tree-structure Parzen estimator (TPE) is coupled with a unique ITFT model to improve RUL prediction accuracy. Furthermore, we consider the effects of keen-onset to establish the starting point of our training. The root mean square error for four batteries using the proposed model for the test data are 0.0018, 0.0019, 0.0013, and 0.0025, respectively, which outperforms the other models, with an improvement accuracy rate above 25%. The proposed model SOH results indicate that our proposed approach outperforms some previously published methods. Our online RUL prediction demonstrates relative errors of 1.18%, 1.54%, 1.06%, 2.70%, 0.67%, 2%, 3.90%, 0%, and 3.08% for nine batteries, respectively. These results for SOH and RUL predictions emphasize the excellent performance of our proposed method.