Digital twin (DT) is proposed as a solution to reduce financial and time losses for vehicle manufacturers by streamlining the expensive and time-consuming processes of designing and implementing electric vehicle types and road assessments. The use of digital twins to monitor, evaluate, and optimize vehicle performance based on real-time road data is increasingly crucial in the DT concept. In this study, the digital twin of the CERYAN brand vehicle model has been employed to compare the performance of different motor types (PMSM, PMSM Brushless, BLDC/PMSM Brushless, and BLDC) in terms of energy consumption and acceleration at various inclination angles, utilizing real-time road data. According to the World Motorcycle Test Cycle (WMTC) standards, the motor type with the best performance parameters was determined as a PMSM brushless motor (5 kW). The main superior aspects of the proposed motor type are to achieve a 25% higher range than the BLDC Motor, 30% better grade ascending capability than the PMSM Motor, and 26% lower energy consumption than the PMSM brushless motor (6 kW).

Parameter prediction for PV solar cells plays a crucial role in controlling and optimizing the performance of PV modules. In this study, the parameter prediction of a four‐diode PV model was carried out using the Improved Grey Wolf Optimization (IGWO) algorithm, which builds upon the Grey Wolf Optimization (GWO) algorithm. The parameters required for the four‐diode PV model were optimized based on a predefined objective function. Subsequently, the obtained data were compared with the data from RTCFrance Solar Cell to validate the accuracy and reliability of the optimization results. The evaluation of the optimization results revealed that the Sum Square Error (SSE) values for PSOGWO, AGWOCS, GWOCS, and GWO were 3.96E‐05, while the MSE value for IGWO was 3.6309E‐05. These findings clearly demonstrate that the proposed IGWO algorithm outperforms the other algorithms used in the study, based on the minimized SSE values. This study emphasizes the importance of parameter prediction in optimizing PV performance, and it contributes to thefield by introducing the novel IGWO algorithm for the four‐diode PV model. The algorithm's superior performance, as demonstrated through extensive testing and comparison with existing algorithms, validates its efficacy in accurately predicting the parameters for the PV solar cell model.

The transformation of conventional electrical networks into Smart Grids represents a significant advancement in power systems, driven by the integration of cutting-edge developments in artificial intelligence (AI), communication, and computational technologies. Unlike traditional grids, Smart Grids utilize vast volumes of data collected from smart meters, sensors, remote control units, and end-user devices. This data abundance enhances system monitoring, supports accurate demand forecasting, and enables intelligent decision-making to improve energy efficiency, reliability, and power quality. However, the complexity and scale of such datasets often exceed the capabilities of conventional computational methods, making AI-based approaches increasingly indispensable. Despite their advantages, AI models are often criticized for their lack of transparency and interpretability, which poses challenges in safety-critical and regulated environments such as power systems. To address these concerns, Explainable Artificial Intelligence (XAI) has emerged as a promising paradigm to make AI decisions more understandable and trustworthy. XAI techniques provide insights into model behavior, enabling greater user trust, regulatory compliance, and informed operational decisions. This paper highlights the essential role of XAI in the advancement of smart grid technologies and presents a concise review of current applications where XAI has been successfully integrated, including anomaly detection, load forecasting, energy management, and fault diagnosis. By bridging the gap between model performance and interpretability, XAI contributes significantly to the development of sustainable, secure, and transparent smart energy systems.

The increasing adoption of electric vehicles (EVs) has led to the widespread implementation of battery swapping stations. However, ensuring fairness in battery pricing remains a significant challenge since variations in battery health and performance among swapped batteries can result in user dissatisfaction and operational inefficiencies. This paper introduces a novel approach to enhance fairness in battery swapping by integrating a machine learning-based real-time prediction model with a pricing strategy based on remaining useful life (RUL) estimation to address this issue. The proposed solution comprises a real-time RUL estimation system and a dynamic pricing mechanism that ensures fair pricing based on battery health and performance. This integrated approach aims to improve user satisfaction and the operational efficiency of swapping stations. The paper evaluates various machine learning algorithms for real-time RUL estimation regarding accuracy, computation time, and memory usage. The results suggest that XGBoost provides the most suitable balance between accuracy and efficiency, making it an effective solution for real-world applications. Comparative analysis shows that the XGBoost model outperforms the second-best method (Random Forest) with a lower error (3.50 vs 3.79) while maintaining competitive computational efficiency (9.75 vs 8.52 seconds) and memory usage (2.12 vs 2.32 MB) when solving a typical numerical case study problem. The proposed approach has the potential to accelerate the adoption of electric vehicles and contribute to sustainability goals by promoting efficient battery utilization and fair pricing mechanisms.