This paper describes the characteristics of worldwide scientific contributions to the field of electric vehicles (EVs) from 1955 to 2021. For this purpose, a search within the Scopus database was conducted using “Electric Vehicle” as the keyword. As a result, 50,195 documents were obtained through analytical and bibliometric techniques and classified into six communities according to the subject studied and the collaborative relationships between the authors. The most relevant publications within each group, i.e., those related to the most publications, were analyzed. The result shows 104,344 authors researching on EVs in 149 different countries with 225,445 relations among them. Furthermore, the most frequent language in which these publications were written as well as the h-index values of their authors were analyzed. This paper also highlights the wide variety of areas involved in EV development. Finally, the paper raises numerous issues to consider in order to broaden knowledge about EVs, their efficiency, and their applications in the near future for the development of sustainable cities and societies.

Non-intrusive load monitoring (NILM) enables the disaggregation of appliance-level energy consumption from aggregate electrical signals, offering a scalable solution for improving efficiency. This study compared the performance of traditional NILM algorithms (Mean, CO, Hart85, FHMM) and deep neural network-based approaches (DAE, RNN, Seq2Point, Seq2Seq, WindowGRU) under various experimental conditions. Factors such as sampling rate, harmonic content, and the application of power filters were analyzed. A key aspect of the evaluation was the difference in testing conditions: while traditional algorithms were evaluated under multiple experimental configurations, deep learning models, due to their extremely high computational cost, were analyzed exclusively under a specific configuration consisting of a 1-s sampling rate, with harmonic content present and without applying power filters. The results confirm that no universally superior algorithm exists, and performance varies depending on the type of appliance and signal conditions. Traditional algorithms are faster and more computationally efficient, making them more suitable for scenarios with limited resources or rapid response requirements. However, significantly more computationally expensive deep learning models showed higher average accuracy (MAE, RMSE, NDE) and event detection capability (F1-SCORE) in the specific configuration in which they were evaluated. These models excel in detailed signal reconstruction and handling harmonics without requiring filtering in this configuration. The selection of the optimal NILM algorithm for real-world applications must consider a balance between desired accuracy, load types, electrical signal characteristics, and crucially, the limitations of available computational resources.