However, an extensive analysis of the most relevant technologies that recover energy, the current topologies and configurations of such devices, and the state-of-the-art of control methods used to manage energy is necessary to identify the best solution. The analysis presented mainly focuses on finding maximum fuel economy, reducing emissions, minimizing the system's costs, and providing optimal driving performance. The evaluation of range extenders for electric vehicles aims to guide researchers and car manufactures to generate new topologies and configurations for EVs with optimized range, improved functionality, and low emissions. The problem of forecasting the electricity demand is not only linked to EVs. Today, buildings are still the main contributors of energy consumption within a smart city. In particular, the long-term electricity demand forecast is essential for the energy provider to analyze the future demand and for the accurate management of the demand response. Forecasting the consumer electricity demand with efficient and accurate strategies will help the energy provider to optimally plan generation points, such as solar and wind, and to produce energy accordingly to reduce the rate of depletion. An efficient and accurate forecasting model is provided by [5] to study the daily consumption of the consumers from their historical data and to forecast the necessary energy demand from the consumer's side. The proposed recurrent neural network gradient boosting regression tree (RNN-GBRT) forecasting technique allows the demand for electricity to be reduced by studying the daily usage patterns of consumers. The efficiency of the proposed forecasting model is compared with various conventional models. The prediction methods are also adopted in vehicle sharing systems. In this framework, the work in [6] deals with the long-term prediction of bike rental/drop-off demands at given bike station locations in the expansion areas. The real-world bike stations are mainly built-in batches for expansion areas. To address the problem, they propose LDA (Long-Term Demand Advisor), a framework that can be used to estimate the long-term characteristics of newly established stations. In LDA, several engineering strategies are proposed to extract discriminative and representative features for long-term demands. Moreover, for the original and newly established stations, several feature extraction methods and an algorithm are proposed to model the correlations between urban dynamics and long-term demands. Real-world data from New York City's bike-sharing system are evaluated to show that the LDA framework outperforms baseline approaches. Furthermore, ref. [7] proposes an analysis of European cycle logistics projects, studying how the corresponding supporting policies have an impact on their economic performance in terms of profit and profitability. First, they identify project success factors by geographic area and project-specific characteristics; then, they statistically test possible dependence relationships with supporting policies and economic results. Moreover, they provide a value-based identification of those characteristics and policies that more commonly lead to better economic results. The work can serve as a basis for the prioritization and contextualization of those project functionalities and public policies to be implemented in a European context. It was determined that cycle logistics projects in Europe achieve high profit and profitability levels and that the current policies are generally working well and the support of them as well as their profit and profitability vary across the bike model utilized: mixing cargo bikes and tricycles generates the highest profit and profitability, whilst a trailer-tricycle-cargo bike mix paves the way for high volumes and market shares. Some real-use smart city case studies are presented in [8; 9]. For instance, in [8], the Campus City project is presented.