Data-driven modelling techniques have recently been used in the development of engine design and control systems for defining the engine in-cylinder complex combustion process. The aim of this investigation is to improve exhaust emissions and fuel consumption by designing an electromechanical exhaust gas recirculation (EGR) cooling system consisting of an electric water pump and fan unlike conventional systems. To determine the effects of the EGR ratio and the temperature of the exhaust gas entering the intake manifold on the diesel engine parameters of nitrogen oxides (NOx) emission and brake specific fuel consumption (BSFC), four learning (ML) algorithms were adapted according to statistical evaluation criteria such as root mean squared error (RMSE), coefficient of determination (R²), mean squared error (MSE) and mean absolute error (MAE). The hyper parameters of the selected best model among four learning algorithms were determined by using grid search method. The results showed that the Gaussian process regression model (GPR) outperformed other ML models according to success error prediction of NOx and BSFC. Then, performance of the designed electromechanical EGR cooling system was analyzed under global driving conditions, the New European driving cycle (NEDC), and the world-wide harmonized light duty test procedure (WLTP). In these test cycles, global optimization was utilized with the GPR model as the objective function based on minimizing NOx and BSFC. Consequently, this study demonstrates the potential of the proposed system based on ML-GA to reduce NOx and BSFC by achieving reductions of 13.7%(NEDC)–9.98%(WLTP) and 2.61%(NEDC)–2.07%(WLTP) in NEDC and WLTP conditions, respectively compared to the conventional EGR cooling approach.

Robots are used as machine tools via some advanced properties such as versatility, adaptation and mobility with other control systems. These advantages can be the solution to overcome the encountered problems in electrochemical machining (ECM). In this manner, the usability of a new machining method that is Robotic electrochemical machining (RECM) in freeform surface machining is investigated. Firstly, ECM parameters and their effects on the material removal rate (MRR) is discussed and compared with the literature. Experimental results showed that MRR variation for RECM acts similar characteristics with conventional ECM processes. The increase in voltage, electrolyte conductivity and feed rate increase MRR. The highest effect on MRR for both materials is voltage, electrolyte conductivity, and feed rate, respectively. Secondly, three different machine learning models are designed as Gaussian Process Regression (GPR), regression tree and deep neural network (DNN) models and the relationships between their actual and predicted values are discussed. The performance of the DNN model is better than other models with a correlation of determination values equal for Inconel 718 and AISI 1040. Furthermore, the DNN model with four input parameters outperforms developed models such as GPR and DTR with optimized hyperparameters regarding the R², MSE, RMSE, and MAE. The change in height in the Z-axis and the surface roughness of the machined surfaces are also measured. The results show that the decrease in the Z axis is homogeneous, that is, uniform machining occurs and also the surface roughness has improved. Therefore, RECM can be a solution to machine free-form surfaces for different materials with high predictability.

This study proposes a comprehensive methodology for accurate State of Charge (SOC) estimation in lithium-ion batteries by optimizing equivalent circuit model (ECM) parameters under varying temperature conditions using the Taguchi method. Analysis of Variance (ANOVA) was employed to evaluate the influence of these parameters on ECM accuracy. Experiments were conducted at −10 °C, 25 °C, and 40 °C to evaluate the effects of pulse time gap, discharge pulse time, and C-rate on SOC estimation accuracy. A genetic algorithm-based multi-objective optimization technique was employed to minimize RMSE in the extended Kalman filter (EKF) SOC estimation process. The results showed that temperature significantly impacts SOC prediction, with deviations most pronounced at low (−10 °C) and high (40 °C) temperatures. When assessments are conducted for different SOC levels (SOC90, SOC50, SOC30), the key results highlight the substantial influence of pulse time gap and discharge pulse time on model accuracy. Also, it was observed that there is a significant reduction in RMSE, indicating improved performance under optimized conditions. The findings are particularly relevant for real-time applications, such as electric vehicles, where accurate SOC estimation is crucial for battery management.