The continuous technical improvements involving electric motors, battery packs, and general powertrain equipment make it strictly necessary to predict or evaluate the energy consumption of electric vehicles (EVs) with reasonable accuracy. The significant improvements in computing power in the last decades have allowed the implementation of various simulation scenarios and the development of strategies for vehicle modelling, thus estimating energy consumption with higher accuracy. This paper gives a general overview of the strategies adopted to model EVs for evaluating or predicting energy consumption. The need to develop such solutions is due to the basis of each analysis, as well as the type of results that must be produced and delivered. This last point strongly influences the whole set-up process of the analysis, from the available and collected dataset to the choice of the algorithm itself.

The integration of Artificial Intelligence (AI) in Energy Storage Systems (ESS) for Electric Vehicles (EVs) has emerged as a pivotal solution to address the challenges of energy efficiency, battery degradation, and optimal power management. The capability of such systems to differ from theoretical modeling enhances their applicability across various domains. The vast amount of data available today has enabled AI to be trained and to predict the behavior of complex systems with a high degree of accuracy. As we move towards a more sustainable future, the electrification of vehicles and integrating electric systems for energy storage are becoming increasingly important and need to be addressed. The synergy of AI and ESS enhances the overall efficiency of electric vehicles and plays a crucial role in shaping a sustainable and intelligent energy ecosystem. To the best of the authors’ knowledge, AI applications in energy storage systems for the integration of electric vehicles have not been explicitly reviewed. The research investigates the importance of AI advancements in energy storage systems for electric vehicles, specifically focusing on Battery Management Systems (BMS), Power Quality (PQ) issues, predicting battery State-of-Charge (SOC) and State-of-Health (SOH), and exploring the potential for integrating Renewable Energy Sources with EV charging needs and optimizing charging cycles. This study examined all topics to identify the most commonly used methods, which were analyzed based on their characteristics and potential. Future trends were identified by exploring emerging techniques introduced in recent literature contributions published since 2017.

Advancements in deep learning enable sophisticated algorithms to learn from vast data sets, driving innovation and efficiency in power systems. The global push for electrification has increased focus on deep learning, leading to numerous recent publications in this field. This paper presents a bibliometric analysis and future trends of deep learning in power systems, aiming to identify its fundamental characteristics and summarize the research hot topics and future trends. This study used the Scopus database with the keywords "deep learning" and "power systems," yielding 20,202 publications from 1970 to 2023. Over 37,600 authors contributed, averaging 12 citations per paper. Keyword trends show traditional deep learning techniques like LSTMs and CNNs are widely used in power systems, while newer methods like advanced reinforcement learning, graph neural networks, and physics-informed neural networks are emerging, promising advancements in optimal power flow, V2G integrations, and grid resilience. Further analysis highlights ubiquitous deep learning applications like load forecasting and power quality analysis in power systems. Emerging topics include microgrid optimization and electric vehicle charging demand prediction, with growing interest in IoT management, digital twins, and cybersecurity. Future research will focus on self-healing grids, optimal power flow, and energy trading for improved reliability and security.

The location of Electric Vehicle Charging Stations (EVCSs) is gaining significant importance as part of the conversion to a full-electric vehicle fleet. Positive or negative impacts can be generated mainly based on the quality of service offered to customers and operational efficiency, also potentially involving the electrical grid to which the EVCSs are connected. The EVCS location problem requires an in-depth and comprehensive analysis of geographical, market, urban planning, and operational aspects that can lead to several potential alternatives to be evaluated with respect to a defined number of features. This paper discusses the possible use of a multi-criteria decision-making approach, considering the differences between multi-objective decision making (MODM) and multi-attribute decision-making (MADM), to address the EVCS location problem. The conceptual evaluation leads to the conclusion that the MADM approach is more suitable than MODM for the specific problem. The identification of suitable attributes and related features is then carried out based on a systematic literature review. For each attribute, the relative importance of the features is obtained by considering the occurrence and the dedicated weights. The results provide the identification of the most used attributes and the categorization of the selected features to shape the proposed MADM framework for the location of the electric vehicle charging infrastructure.

Environmental policies and commitment is forcing a strong push towards electrification of human activities. Efforts are oriented to progressively dismiss old and outdated Internal Combustion Engines, replacing oil energy sources with electricity vectors. An important switch is involving transportation sector, responsible for 16% of global emissions produced, where the electrification process is converting the private mobility to the use of more sustainable electric vehicles. The drawbacks related to the battery capacity and range is limiting the diffusion among the whole territory, especially where a lack of charging infrastructure is persistent. Therefore, a focus on the adopted driving behaviour must be set to carry benefits in energy saving and potentially extending range. The present paper provides an analysis on different driving behaviour based on real-world data. An experimental campaign is described to acquire different driving styles during test-runs on a real route. An electric vehicle model is also developed in parallel and validated on the real dataset. A test route is identified in the southern region of Puglia in Italy, centered around the city of Lecce, and altitude and speed profiles are acquired and processed into the model. Test-runs were first clustered based on the driver type, then according to the level of auxiliary power consumption. Then, with the help of EV model, a more detailed analysis could be set to retrieve meaningful motivations behind the trends gathered between the two driving styles, especially focusing on the type of road. Results allowed to extrapolate trends between energy consumption and driving styles, showing a considerable impact on the battery usage. A +5% SoC is estimated if an aggressive driving style is adopted, while the impact of auxiliary power is predominant with an eco-driving style, taking into account uneven testing conditions.