This article examines in depth the most recent thermal testing techniques for lithium-ion batteries (LIBs). Temperature estimation circuits can be divided into six divisions based on modeling and calculation methods, including electrochemical computational modeling, equivalent electric circuit modeling (EECM), machine learning (ML), digital analysis, direct impedance measurement and magnetic nanoparticles as a base. Complexity, accuracy and computational cost-based EECM circuits are feasible. The accuracy, usability and adaptability of diagrams produced using ML have the potential to be very high. However, none of them can anticipate the low-cost integrated BMS in real time due to their high computational costs. An appropriate solution might be a hybrid strategy that combines EECM and ML.

This study aims to evaluate the effectiveness of machine learning (ML) models in predicting concrete damage using electromechanical impedance (EMI) data. From numerous experimental evidence, the damaged mortar sample with surface-mounted piezoelectric (PZT) material connected to the EMI response was assessed. This work involved the different ML models to identify the accurate model for concrete damage detection using EMI data. Each model was evaluated with evaluation metrics with the prediction/true class and each class was classified into three levels for testing and trained data. Experimental findings indicate that as damage to the structure increases, the responsiveness of PZT decreases. Therefore, we examined the ability of ML models trained on existing experimental data to predict concrete damage using the EMI data. The current work successfully identified the approximately close ML models for predicting damage detection in mortar samples. The proposed ML models not only streamline the identification of key input parameters with models but also offer cost-saving benefits by reducing the need for multiple trials in experiments. Lastly, the results demonstrate the capability of the model to produce precise predictions.