The rapid growth of transportation sector and related emissions are attracting the attention of policymakers to ensure environmental sustainability. Therefore, the deriving factors of transport emissions are extremely important to comprehend. The role of electric vehicles is imperative amid rising transport emissions. Electric vehicles pave the way towards a low-carbon economy and sustainable environment. Successful deployment of electric vehicles relies heavily on energy consumption models that can predict energy consumption efficiently and reliably. Improving electric vehicles’ energy consumption efficiency will significantly help to alleviate driver anxiety and provide an essential framework for operation, planning, and management of the charging infrastructure. To tackle the challenge of electric vehicles’ energy consumption prediction, this study aims to employ advanced machine learning models, extreme gradient boosting, and light gradient boosting machine to compare with traditional machine learning models, multiple linear regression, and artificial neural network. Electric vehicles energy consumption data in the analysis were collected in Aichi Prefecture, Japan. To evaluate the performance of the prediction models, three evaluation metrics were used; coefficient of determination ( R²), root mean square error, and mean absolute error. The prediction outcome exhibits that the extreme gradient boosting and light gradient boosting machine provided better and robust results compared to multiple linear regression and artificial neural network. The models based on extreme gradient boosting and light gradient boosting machine yielded higher values of R², lower mean absolute error, and root mean square error values have proven to be more accurate. However, the results demonstrated that the light gradient boosting machine is outperformed the extreme gradient boosting model. A detailed feature important analysis was carried out to demonstrate the impact and relative influence of different input variables on electric vehicles energy consumption prediction. The results imply that an advanced machine learning model can enhance the prediction performance of electric vehicles energy consumption.

In this paper, we present an ensemble stacked generalization (ESG) approach for better prediction of electric vehicles (EVs) energy consumption. ESG is a weighted combination of multiple base regression models to one meta-regressor, which enhances the model prediction and decreases the model variance over a single regressor model. For the current study, we develop ESG by combining three individual base machine learning algorithms, i.e., Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN), to predict the EVs’ energy consumption. Tackling the challenge of predicting EVs’ energy consumption, the data were collected from Aichi Prefecture, Japan, combining the digital elevation map with long-term GPS tracking data. EVs energy consumption in terms of energy efficiency (kWh/km) was estimated using several important variables such as average trip speed (km/h), trip distance, nighttime lighting, air conditioner (A/C), heater usage ratio, and road gradient. Several statistical evaluation metrics were used to evaluate the performance of the proposed methods. The prediction results show that ESG is more robust in predicting EVs’ energy consumption and outperformed other models by yielding more acceptable values for proposed evaluation metrics. The results further demonstrate that the accuracy of predictive models for EVs energy consumption can be reasonably accomplished by adopting stacking techniques. The finding of this study could provide essential guidance to decision-makers and practitioners for planned development and optimal placing of EV charging infrastructures in urban areas.

Precise charging time prediction can effectively mitigate the inconvenience to drivers induced by inevitable charging behavior throughout trips. Although the effectiveness of the machine learning (ML) algorithm in predicting future outcomes has been established in a variety of applications (transportation sector), the investigation into electric vehicle (EV) charging time prediction is almost new. This calls for the investigation of the ML algorithm to predict EV charging time. The study developed an EV charging time prediction model based on two years of charging event data collected from 500 EVs in Japan. To predict EV charging time, this paper employed three ML algorithms: extreme learning machine (ELM), feed-forward neural network (FFNN), and support vector regression (SVR). Furthermore, ML algorithms parameters are optimized by a metaheuristic techniques: the gray wolf optimizer (GWO), particle swarm optimizer (PSO), and genetic algorithm (GA) to achieve higher accuracy and robustness. The prediction results reveal that GWO-based ML models yielded better results compared to other models.

Traffic signal control is an integral component of an intelligent transportation system (ITS) that play a vital role in alleviating traffic congestion. Poor traffic management and inefficient operations at signalized intersections cause numerous problems as excessive vehicle delays, increased fuel consumption, and vehicular emissions. Operational performance at signalized intersections could be significantly enhanced by optimizing phasing and signal timing plans using intelligent traffic control methods. Previous studies in this regard have mostly focused on lane-based homogenous traffic conditions. However, traffic patterns are usually non-linear and highly stochastic, particularly during rush hours, which limits the adoption of such methods. Hence, this study aims to develop metaheuristic-based methods for intelligent traffic control at isolated signalized intersections, in the city of Dhahran, Saudi Arabia. Genetic algorithm (GA) and differential evolution (DE) were employed to enhance the intersection’s level of service (LOS) by optimizing the signal timings plan. Average vehicle delay through the intersection was selected as the primary performance index and algorithms objective function. The study results indicated that both GA and DE produced a systematic signal timings plan and significantly reduced travel time delay ranging from 15 to 35% compared to existing conditions. Although DE converged much faster to the objective function, GA outperforms DE in terms of solution quality i.e., minimum vehicle delay. To validate the performance of proposed methods, cycle length-delay curves from GA and DE were compared with optimization outputs from TRANSYT 7F, a state-of-the-art traffic signal simulation, and optimization tool. Validation results demonstrated the adequacy and robustness of proposed methods.

Electric vehicles (EVs) are rapidly becoming a sustainable and viable mode of future transportation due to their multitude of advantages, such as reduced CO₂ emissions, local air pollutants, and vehicular noise. This study aims to identify and analyze the scientific literature using bibliometric analysis to determine the main topics of authors, their sources, and the most-cited articles, countries, journals, and institutes in the literature on EVs. This bibliometric analysis included scientific work that was published from 2011 to 2022 to provide a thorough analysis of EVs, which will assist researchers and policymakers in understanding the most current global EV advancements. This analysis extracted all bibliometric information about EVs from the Scopus database, collecting 17,150 articles published between 2011 and 2022. The data were sorted for analysis by publication year, document type, author, institute, country, cited author, keyword, and keyword co-occurrence of the EVs. The VOSviewer software was employed to examine the sorted data due to its excellent analysis and visualization capabilities. We used VOSviewer to graphically represent the density, co-occurrence, trends, and linkage of the aforementioned data comprehensibly. The publishing patterns of EVs indicate that the research field is evolving, with a yearly increase in the number of publications. The analysis showed that China, the United States, and the United Kingdom are leading in EV research and large-scale applications. Furthermore, China is the leading country in terms of research institutions and authors involved in EVs. The journal Energies is the most prominent publication periodical. Keyword analysis revealed that during the past decade, EV research has concentrated on battery-management systems, energy storage, charging infrastructure, environmental concerns, etc. The bibliometric study offered pertinent details on the main themes explored concerning EVs and current technological developments.