In recent years, the Electric Vehicles (EVs) industry has experienced rapid growth, driven by advancements in battery technology, environmental awareness, and government incentives. However, traditional charging infrastructure’s limited availability and long charging times pose significant challenges, especially for long-distance travel and public service vehicles like taxis, buses, and law enforcement vehicles. This work explores the innovative concept of Battery Swap Stations (BSSs), an emerging technology poised to transform the EV charging landscape. It specifically focuses on electric taxis operating in Chicago’s urban environment, highlighting the substantial benefits this technology can offer. BSSs demonstrated to dramatically reduce charging times, improving taxi service efficiency and increasing revenue potential. Instead, conductive charging impacts the working time of taxis across all case studies (as observed in the Level 2 charger scenario) While BSS technology has its drawbacks, such as optimal location challenges and battery management complexities, it has the potential to significantly enhance service quality. Additionally, these stations hold the promise of not only increasing urban transportation system efficiency but also contributing to their sustainability.

The objective of this study is to provide a comprehensive analysis of the environmental impact of diesel and electric buses, with a focus on pollutant emissions along a mixed urban–rural route in small urban settings. Utilizing a detailed simulation model, this research compares emissions from a diesel bus and an electric bus on a specific route in a small town in central Italy. Key findings reveal that electric buses significantly reduce local exhaust emissions but are not entirely emission-free, considering the full life cycle, including electricity generation. The Well-to-Wheel analysis shows lower ${\mathrm{CO}}_2$ emissions for the electric bus compared with the diesel bus, with a substantial part of the emissions occurring at power generation facilities. Non-exhaust emissions, especially Total Suspended Particles, are similar for both bus types. This study highlights the advantages of adopting electric buses in urban areas to decrease local air pollution and greenhouse gas emissions. However, it also underscores the importance of cleaner electricity generation methods to fully leverage the environmental benefits of electric vehicles. The findings provide valuable insights for decision makers and urban planners in developing sustainable urban transportation systems.

In Italy, the availability of service areas (SAs) equipped with charging stations (CSs) for electric vehicles (EVs) on highways is limited in comparison to the total number of service areas. The scope of this work is to create a prototype and show a different approach to assessing the number of inlets required on highways. The proposed method estimates the energy requirements for the future electric fleet on highways. It is based on an energy conversion that starts with the fuel sold in the highway network and ends with the number of charging inlets. A proposed benchmark method estimates energy requirements for the electric fleet using consolidated values and statistics about refueling attitudes, with factors for range correction and winter conditions. The results depend on assumptions about future car distribution, with varying numbers of required inlets. The analysis revealed that vehicle traffic is a critical factor in determining the number of required charging inlets, with significant variance between different SAs. This study highlights the necessity of incorporating factors like weather, car charging power, and the future EV range into these estimations. The findings are useful for planning EV charging infrastructure, especially along major traffic routes and in urban areas with high-range vehicles relying on High-Power DC (HPDC) charging. The model’s applicability to urban scenarios can be improved by considering the proportion of energy recharged at the destination. A key limitation is the lack of detailed origin–destination (OD) highway data, leading to some uncertainty in the calculated range ratio coefficient and underscoring the need for future research to refine this model.

Accurately predicting the energy consumption of Battery Electric Vehicles (BEVs) is essential for addressing range anxiety, optimizing route planning, and governing infrastructure investments in a rapidly electrifying transportation sector. This paper presents a generalized, flexible, and probably the most comprehensive modeling framework designed to estimate BEV energy consumption under different driving conditions, vehicle configurations, and environmental influences. The model is structured in mechanical, electrical, and auxiliary sub-models. The model incorporates detailed input parameters, such as aerodynamic coefficients, transmission and motor characteristics, regenerative braking constraints, battery capacity, climate control demands, and ambient conditions. Validation results demonstrate a strong alignment between measured and predicted power profiles, with a high coefficient of determination, low RMSE, and low MAE confirming the model’s reliability and adaptability. The introduced framework can be extended to various BEV segments, driving cycles, and environmental conditions, providing valuable information for vehicle manufacturers, fleet managers, and policymakers aiming to improve EV performance, route efficiency, and charging infrastructure deployment.

This study presents a practical approach to quantify the economic benefits of dynamic price-based charging strategies for electric bus operators with overnight depot charging. The replication of the methodology allows for the precise estimation and charging schedule for the specific schedule. However, this work is also designed to target decision makers, allowing them to access the investment in this technology and its integration using the presented results. This paper defines a Mixed-Integer Linear Programming framework to optimize electric bus charging schedules using day-ahead electricity prices, incorporating real-world operational constraints and infrastructure limitations, and presents the potential savings achievable with the implementation of the proposed framework. The methodology includes a benchmark that schedules the charging as soon as possible, thereby replicating uncontrolled charging. Validated with actual operational data from a medium-sized Italian transit company, the model demonstrates substantial cost reductions by strategically shifting charging times to exploit lower-priced energy periods, especially aligning overnight charging with reduced electricity tariffs. The case study with real data from Italy shows a potential reduction of up to 1500 € (9% of the bill) for an 11-bus fleet. Scenario-based analyses highlight potential monthly savings with reductions in energy costs ranging between 7.8% and 15.1%, depending on operational contexts. Sensitivity analyses confirm that the operational variability further enhances cost-saving opportunities, while constrained grid power significantly limits these benefits, showing the impact for an adequately sized charging infrastructure. Also, a table is provided to easily extend the results to other European countries.

The scope of this paper is to assess the economic viability of bidirectional charging strategies for scheduled electric bus fleets, focusing on their potential to reduce operational costs under real-world market conditions. This paper presents a comprehensive and production-ready optimization framework for Vehicle-to-Grid integration, addressing day-ahead pricing, battery degradation, and grid constraints through a Mixed-Integer Linear Programming formulation. Unlike existing approaches that often rely on aggregated vehicle behavior, our framework preserves the full topology of the charging station, maintaining vehicle-level granularity. This enables precise evaluation of both marginal costs and savings, enhancing the model's practical relevance and deployment potential. Results from real-world fleet schedules and Italian market data show that both controlled charging and Vehicle-to-Grid achieve substantially lower costs than uncontrolled charging, with savings exceeding 35% in current scenarios. However, the cost gap between Vehicle-to-Grid and controlled charging remains narrow. These findings indicate that as battery pack prices continue to decline and energy markets evolve, Vehicle-to-Grid will become increasingly favorable for scheduled fleets.