Search Results (138)

Boda boda motorbike taxis are a primary mode of transport in Nairobi, Kenya, and a major source of urban air pollution. This study investigates the environmental and electrical grid impacts of electrifying Nairobi’s boda boda fleet. Using real-world tracking data from 118 motorbikes, we simulated the effects of a full-scale transition from internal combustion engine (ICE) vehicles to electric motorbikes. We analysed various scenarios, including different battery charging strategies (swapping and home charging), motor efficiencies, battery capacities, charging rates, and the potential for solar power offsetting. The results indicate that electrification could reduce daily ${\mathrm{CO}}_2$ emissions by approximately 85% and eliminate tailpipe particulate matter emissions. However, transitioning the entire country’s fleet would increase the national daily energy demand by up to 6.85 GWh and could introduce peak grid loads as high as 2.40 GW, depending on the charging approach and vehicle efficiency. Battery swapping was found to distribute the grid load more evenly and better complement solar power integration compared to home charging, which concentrates demand in the evening. This research provides a scalable, data-driven framework for policymakers to assess the impacts of transport electrification in similar urban contexts, highlighting the critical trade-offs between environmental benefits and grid infrastructure requirements. Full article

Battery-swapping stations (BSSs) are pivotal for supplying energy to electric heavy-duty trucks. However, their operations face challenges in accurate demand forecasting for battery-swapping and fair revenue allocation. This study proposes an optimization strategy for the economic operation of BSSs that optimizes revenue allocation and load balancing to enhance financial viability and grid stability. First, factors including geographical environment, traffic conditions, and truck characteristics are incorporated to simulate swapping behaviors, supporting the construction of an accurate demand-forecasting model. Second, an optimization problem is formulated to maximize the weighted difference between BSS revenue and squared load deviations. An economic operations strategy is proposed based on an adaptive Shapley value. It enables precise evaluation of differentiated member contributions through dynamic adjustment of bias weights in revenue allocation for a strategy that aligns with the interests of multiple stakeholders and market dynamics. Simulation results validate the superior performance of the proposed algorithm in revenue maximization, peak shaving, and valley filling. Full article

This paper analyzes the control strategy for urban battery-swapping stations by optimizing the charging policy based on real-time battery demand and the time required for a full charge. The energy stored in available batteries serves as an electricity buffer, allowing energy to be drawn from the grid when costs or equivalent ${\mathrm{CO}}_2$ emissions are low. An optimized charging policy is derived using dynamic programming (DP), assuming average battery demand and accounting for both the costs and emissions associated with electricity consumption. The proposed algorithm uses a prediction of the expected traffic in the area as well as the expected cost of electricity on the net. Battery tests were conducted to assess charging time variability, and traffic density measurements were collected in the city of Valencia across multiple days to provide a realistic scenario, while real-time data of the electricity cost is integrated into the control proposal. The results show that incorporating traffic and electricity price forecasts into the control algorithm can reduce electricity costs by up to 11% and decrease associated ${\mathrm{CO}}_2$ emissions by more than 26%. Full article

This study shows a comprehensive simulation to assess and enhance the throughput capacity of unmanned air system vertiports, one of the most essential elements of urban air mobility ecosystems. The framework integrates dynamic grid-based spatial management, probabilistic mission duration algorithms, and EASA-compliant operational protocols to address the infrastructural and logistical demands of high-density UAS operations. It was focused on two use cases—high-frequency food delivery utilizing small UASs and extended-range package logistics with larger UASs—and the model incorporates adaptive vertiport zoning strategies, segregating operations into dedicated sectors for battery charging, swapping, and cargo handling to enable parallel processing and mitigate congestion. The simulation evaluates critical variables such as vertiport dimensions, UAS fleet composition, and mission duration ranges while emphasizing scalability, safety, and compliance with evolving regulatory standards. By examining the interplay between infrastructure design, operational workflows, and resource allocation, the research provides a versatile tool for urban planners and policymakers to optimize vertiport layouts and traffic management protocols. Its modular architecture supports future extensions. This work underscores the necessity of adaptive, data-driven planning to harmonize vertiport functionality with the dynamic demands of urban air mobility, ensuring interoperability, safety, and long-term scalability. Full article

Recently, the global landscape of public transportation has witnessed a transformative shift towards sustainable and efficient modes of mobility, with particular emphasis on electric vehicles (EVs) and their integration into industrial applications. The mining industry, including the underground mining of the mineral resources sector is following this trend. However, underground mines are critical environments and the adoption of EVs needs to be carefully analysed. This study investigates the associated hazards and risks of adopting EVs (such as dumpers and loaders) focusing on the swapping battery operations. First, current hazards related to battery swapping are identified—21 in total, occurring in 25 instances. After, risks are assessed and associated with specific hazards. Finally, possible measures and solutions for reducing the impacts of these risks on the performance of the EVs are offered. Full article

Currently, the layout planning of power exchange facilities in urban areas is not perfect, which cannot effectively meet the power exchange demand of urban operating vehicles and restricts the operation of urban operating vehicles. The article proposes a vehicle power exchange demand-oriented power exchange station siting planning scheme to meet the rapid replenishment demand of operating vehicles in urban areas. The spatial and temporal distribution of power exchange demand is predicted by considering the operation law, driving law, and charging decision of drivers; the candidate sites of power exchange stations are determined based on the data of power exchange demand; the optimization model of the site selection of power exchange stations with the lowest loss time of vehicle power exchange and the lowest cost of the planning and construction of power exchange stations is established and solved by using the joint algorithm of MLP-NSGA-II; and the optimization model is compared with the traditional genetic algorithm (GA) and the Density Peak. The results show that the MLP-NSGA-II joint algorithm has the lowest cost of optimizing the location of switching stations. The results show that the MLP-NSGA-II algorithm improves the convergence efficiency by about 30.23%, and the service coverage of the optimal solution reaches 94.30%; the service utilization rate is 85.35%, which is 6.25% and 19.69% higher than that of the GA and DPC, respectively. The research content of the article can provide a design basis for the future configuration of the number and location of power exchange stations in urban areas. Full article

This work analyzes the electric vehicle (EV) sales trends of plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs) and trends in the growth of Alternating Current (AC) and Direct Current (DC) charging infrastructure station scenarios in Europe and Italy. It offers a comprehensive view of market trends, technical developments, infrastructure development, and worldwide standardization initiatives for policymakers, researchers, and industry. A detailed classification of the charging technologies of EVs, i.e., conductive, wireless power transfer (WPT), battery swapping (BS), and different EV types, is presented. Finally, this work provides a comparative overview of charging standards and protocols, including the ones established by the Society of Automotive Engineers (SAE), International Electrotechnical Commission (IEC), and Standardization Administration of China (SAC), emphasizing interoperability and cross-border integration to accelerate the transition to clean transportation. Full article

With the proliferation of electric bicycle battery swapping models, spatial supply demand imbalances of battery resources across swapping stations have become increasingly prominent. Existing studies predominantly focus on location optimization but struggle to address dynamic operational challenges in battery allocation efficiency. This paper proposes an integer programming (IP)-based dynamic scheduling optimization method for shared batteries, aiming to minimize transportation costs and balance battery distribution under multi-constraint conditions. A resource allocation model is constructed and solved via an interior-point method (IPM) combined with a branch-and-bound (B&B) strategy, optimizing the dispatch paths and quantities of fully charged batteries among stations. This study contributes to urban sustainability by enhancing resource utilization efficiency, reducing redundant production, and supporting low-carbon mobility infrastructure. Using the operational data from 729 battery swapping stations in Shanghai, the spatiotemporal heterogeneity of rider demand is analyzed to validate the model’s effectiveness. Results reveal that daily swapping demand in core commercial areas is 3–10 times higher than in peripheral regions. The optimal scheduling network exhibits a ‘centralized radial’ structure, with nearly 50% of batteries dispatched from low-demand peripheral stations to high-demand central zones, significantly reducing transportation costs and resource redundancy. This study shows that the proposed model effectively mitigates battery supply demand mismatches and enhances scheduling efficiency. Future research may incorporate real-time traffic data to refine cost functions and introduce temporal factors to improve model adaptability. Full article

High battery swapping demand from electric taxis and drivers’ subjective station selection often leads to congestion and the uneven utilization of battery swapping stations (BSSs). Efficient vehicle guidance is essential for improving the operational performance of electric taxis. In this study, we have developed a vehicle-to-station guidance model that considers dynamic demand and diverse driver response-time preferences. We have proposed two decision-making strategies for BSS recommendations. The first is a real-time optimization method that uses a greedy algorithm to provide immediate guidance. The second is a delayed optimization framework that performs batch scheduling under high demand. It integrates a genetic algorithm with KD-tree search to handle dynamic demand insertion. A case study based on Beijing’s Fourth Ring Road network was conducted to evaluate the strategies under four driver preference scenarios. The results show clear differences in vehicle waiting times. A balanced consideration of travel distance, waiting time, and cost can effectively reduce delays for drivers and improve station utilization. This research provides a practical optimization approach for real-time vehicle guidance in battery swapping systems. Full article

The development of vehicle-to-grid (V2G) technique and the growth of battery swapping stations are expected to enhance the resilience of power networks. However, V2G battery swapping stations exhibit inconsistencies among internal battery packs, where the power capacity is significantly affected by the battery swapping behavior of electric vehicle (EV) users. To address this issue, this paper proposes a secondary frequency control strategy for V2G battery swapping stations that accounts for battery pack heterogeneity. First, a user behavioral model is developed through quantitative analysis of key factors such as economic incentives, time costs, and battery degradation, which is then used to optimize the operation of V2G battery swapping stations. Moreover, active balancing of EV battery energy levels is achieved by incorporating penalty terms into the objective function. Finally, a distributed secondary frequency control strategy based on the consensus algorithm is established to minimize total frequency control loss. Simulation results demonstrate that the proposed strategy effectively meets the secondary frequency control requirements of the power grid. Full article

To effectively address the challenges of imbalanced equipment utilization, frequent congestion, and poor economic benefits faced by charging and swapping stations (ICSSs), this paper innovatively proposes a comprehensive scheduling strategy that combines user behavior regulation and battery management. In terms of user regulation, an intention-reshaping model for changing user cognition is proposed to equalize the use of charging and swapping (CAS) equipment, easing ICSS congestion. Moreover, an off-station scheduling model for electric vehicles (EVs) is developed to enhance overall ICSS revenue. Within the battery management terms, the suggested inventory battery threshold adjustment method and charging strategy by charging time segmentation are employed to ensure consistent inventory battery supply and cost-effective battery charging. Finally, a two-stage scheduling strategy of in-station and off-station scheduling is suggested for the ICSS, and an improved northern goshawk optimization algorithm (INGO) is used to solve it. The results showed that this strategy reduced the overall congestion of ICSSs by 34% and increased the average annual net revenue by 64%. The goal of alleviating congestion and improving the economic efficiency of ICSSs has been achieved. Full article

To enhance the logistics scheduling efficiency of automated guided vehicles (AGVs) in automated ports and achieve the orderly charging and battery swapping of AGVs as well as self-sufficient clean energy, this paper proposes an integrated optimization method. The method first utilizes graph theory to construct a theoretical model that includes AGVs, the port road network, and charging and battery-swapping stations, in order to analyze the optimal logistics scheduling and charging and swapping strategies. Subsequently, for the multi-objective optimization problems in AGV logistics scheduling and charging and swapping, a fast solution method based on the immune optimization algorithm is proposed, with scheduling time and the self-sufficiency rate of clean energy for port AGVs as the constraint conditions. Finally, the effectiveness of the proposed model and algorithm is verified through a simulation scenario. The results show that in the simulated port logistics scenario, after optimization, the total operation time of AGVs is significantly reduced. Compared with the cases that only consider scheduling time, the charging strategy, or wind and solar output, the average clean energy self-sufficiency rate under the proposed strategy increased by 82.7%, 27.5%, and 53.9%, respectively. In addition, as the weight of the self-sufficiency rate increases, both the total driving time and the total clean energy self-sufficiency rate of AGVs show an upward trend and are approximately linearly related. Within the specified maximum scheduling time, the actual scheduling time and self-sufficiency rate can be flexibly coordinated, with significant carbon reduction benefits. Full article

With the escalating environmental crisis, electric vehicles have emerged as a key solution for emission reductions in logistics due to their low-carbon attributes, prompting significant attention and extensive research on the electric vehicle routing problem (EVRP). However, existing studies often overlook charging infrastructure (CI) capacity constraints and fail to fully exploit the synergistic potential of heterogeneous energy replenishment infrastructures (HERIs). This paper addresses the EVRP with HERIs under various capacity constraints (EVRP-HERI-CC), proposing a mixed-integer programming (MIP) model and a hybrid ant colony optimization (HACO) algorithm integrated with a variable neighborhood search (VNS) mechanism. Extensive numerical experiments demonstrate HACO’s effective integration of problem-specific characteristics. The algorithm resolves charging conflicts via dynamic rescheduling while optimizing charging-battery swapping decisions under an on-demand energy replenishment strategy, achieving global cost minimization. Through small-scale instance experiments, we have verified the computational complexity of the problem and demonstrated HACO’s superior performance compared to the Gurobi solver. Furthermore, comparative studies with other advanced heuristic algorithms confirm HACO’s effectiveness in solving the EVRP-HERI-CC. Sensitivity analysis reveals that appropriate CI capacity configurations achieve economic efficiency while maximizing resource utilization, further validating the engineering value of HERI networks. Full article

To explore the operation strategy of battery leasing and battery swapping services (the two services), this paper constructs a battery swapping supply chain consisting of the battery manufacturer and the vehicle company. Taking the battery manufacturer as a core enterprise, this paper examines four service operation strategies: two self-operated services, self-operated battery swapping services, self-operated battery leasing services and two outsourcing services. Through comparative analysis, the findings indicate that the optimal strategy for the battery manufacturers depends on the vehicle body price. Specifically, when the vehicle body price is low, self-operating both services maximizes profitability and effectively stimulates demand for battery-swapping vehicles. Conversely, when the price is high, a complete outsourcing strategy is preferable, as it is the most effective way to stimulate battery-swapping vehicle demand. Similarly, the optimal strategy for the vehicle company is influenced by the vehicle body price. The vehicle company should provide the two services only when the vehicle body price is low; otherwise, they should focus on producing battery-swapping vehicles. Moreover, to stimulate demand for battery-swapping services, the determination of the optimal strategy is contingent upon several key variables, including the vehicle body price, the battery-swapping service price sensitivity, and the battery-swapping operating cost-sharing ratio. Full article

Recently, the rapid increase in the adoption of electric vehicles (EVs) has been driven by considerable technological advancements and a growing focus on environmental sustainability. As consumers and governments increasingly recognize EVs as a viable alternative to traditional internal combustion engine vehicles, the demand for a reliable and accessible charging infrastructure has surged. However, establishing a robust network of charging stations is no longer crucial only to fulfill the demands of EV proprietors but also to relieve range anxiety and improve user convenience, thereby facilitating wider EV adoption. This paper provides a comprehensive global analysis of charging station infrastructure, exploring international standards and regulations, various charging modes, the key parameters of leading electric vehicles, and the importance of RE deployment and ES solutions. Full article