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).

In crowded city centers, drivers looking for available parking space generate extra traffic and in addition, the resulting excessive exhaust gases cause air pollution. Therefore, directing the drivers to a parking spot in an intelligent way is an important task for smart city applications. This task requires the prediction of occupancy states of parking lots which involves appropriate processing of the historical parking data. In this work, Long-Short Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) methods were applied to parking data collected from curbside parking spots of Adana, Turkey for predicting the parking lot occupancy rates of future values. The experiments were performed for making predictions with different prediction horizons that are 1 minute, 5 minutes, and 15 minutes. The performances of the methods were compared by calculating root mean squared error (RMSE) and mean absolute error (MAE) values. The experiments were performed on data from five different days. According to the results, when the prediction horizon is set to 1 minute, LSTM achieved RMSE and MAE values of 0.98 and 0.72, respectively. For the same prediction horizon, ARIMA achieved RMSE and MAE values of 0.62 and 0.35, respectively. On the other hand, LSTM achieved smaller error values for larger prediction horizons. In conclusion, it was shown that LSTM is more suitable for larger prediction horizons, however, ARIMA is better at predicting near-future values.

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.

Cigarette smoke significantly impacts teeth and dental composites, making smoking simulators vital for studying discoloration, staining, surface roughness, and topography. Conventional simulators face challenges such as incomplete process simulation, inadequate smoke exposure unit design, and improper specimen positioning. This study presents a novel, accurate, and automatic tobacco smoking simulator addressing these issues. The simulator includes a life-sized artificial mouth unit, 3D-printed jaw models for natural dental sample positioning, a vacuum system to simulate positive/negative pressure, and a reprogrammable microcontroller for customizable smoking profiles. Tested with 140 cigarettes over 14 days, the system effectively demonstrated color changes in dental materials. Adjustable parameters such as puff volume, inhalation/exhalation time, smoke density, and smoking speed allow replication of low, moderate, and high-intensity smoking profiles. Portable, low-cost, and versatile, the simulator provides a robust solution for research and education, enabling investigations into the effects of smoke on teeth and dental materials under realistic conditions.