• Energy Research
• 2021-2025close
• HK close
• Transport Research close

In this paper, the allocation of charging station (CS) is optimized to alleviate the “range anxiety” of electric vehicle (EV) drivers by reducing the time of medium-to-long distance travel, which is raised due to the potential en-route charging. The problem is defined to explicitly consider the spatial differences in urban land price. Although many works take spatial land price into consideration, few of them notice what the gap of spatial land price bring to the charging system. Our objective function is the expected traveling time under an optimized distribution of urban EV flows, and models of spatial network and CSs allocation are then established. Based on Tabu Search algorithm (TSA), a fixed budget charging resources planning algorithm (FBCRPA) is proposed. The proposed method is compared with methods based on betweeness centrality, and results show that our method can find more effective allocation strategy. It is found that users’ traveling time would decrease with increase in difference in land price. Meanwhile, budget would transfer from central region to other regions and carrying capacity of charging system would improve in the above situation. This paper also finds that increase in budget is beneficial to a reduction in drivers’ time, but the improvement is limited.

An efficient pack-level battery thermal management system is essential to ensure the safe driving experience of electric vehicles. In this work, we perform three-dimensional modeling of a liquid thermal management system for a real-world battery pack powering electrical vehicles. The effects of system structures, coolant flow direction layout, coolant flow rates, and inlet temperatures on the thermal performance are investigated. Under 2.0 C discharge, the system structure with interspersed cooling plates reduces the maximum temperature of the battery pack to 36.3 °C, which is 15% lower than that of the conventional design with bottom-mounted cooling plates. With the present system structure, around 62.2% of the heat produced by the batteries is dissipated. Moreover, the temperature difference is decreased by 10.5% with appropriately arranging coolant flow directions in different liquid cooling plates. In addition, increasing the coolant flow rate and lowering the inlet coolant temperature can greatly reduce the maximum battery temperature, while the latter parameter shows little influence on the pack temperature difference, which only changes 0.3 °C when the inlet temperature is decreased by 6 °C. This work provides valuable guidance for the development of pack-level liquid battery thermal management systems in electric vehicles.

Efficient thermal management is crucial for ensuring the safety and performance of lithium-ion batteries powering electric vehicles. Here, we develop a passive battery thermal management system with thermally enhanced water adsorbents by evenly loading MIL-101(Cr) particles onto a copper foam. MIL-101(Cr) particles can absorb water from the ambient and release water at an elevated temperature to dissipate heat, while the copper foam acts as a thermal conductive network to transfer the heat among the particles. The cooling performance of the system is tested under various battery working conditions. Results show that the thermal conductivity of the composite water adsorbent is increased to 1.9 W m−1 K−1, nearly 10 times of the pure MIL-101(Cr). Compared with natural cooling, air cooling and solid–liquid phase change material cooling, the proposed passive cooling method reduces the battery temperature by 7.5, 2.6, and 2.1 °C, respectively, at 3 C discharge. Additionally, the battery temperature and the temperature difference are confined below 37.6 and 1.5 °C in the dynamic discharge–charge cycling test. All these encouraging results indicate that the developed passive thermal management system achieves high cooling capability, good temperature uniformity, and zero energy consumption, which possesses a broad application prospect in electric vehicles. © 2022 Elsevier Ltd

With the dynamic development of renewable energies, energy storage devices, and electric vehicles, microgrids have been playing an increasingly vital role in smart power grids. Under the recent development of carbon neutralisation, microgrid systems containing multiple clean energy sources have become significant modules for energy conservation and emission reduction. Considering technological and environmental elements, we investigated the economic operation of microgrids with the integration of electric vehicles. In this paper, carbon trading mechanisms and operation scheduling strategies are analysed in the simulation models. Then, transaction costs and power balance are discussed. Industrial applications and policy implications are also presented.

With the growing dominance of inverter-based resources (IBRs), the synchronous inertia of the interconnected power systems (IPSs) is affected. The increase in IBRs results in a decrease in the system inertia. The decrease in inertia impacts the initial rate of decline of the frequency. Thus, there is a need for faster frequency response requirements to enhance the dynamic performance of the IPS. With the massive penetration of the plug-in hybrid electric vehicles (PHEVs) into expanding smart cities, PHEVs can act as controllable loads which support the inertial response of the system in a rapid manner. This gives a scope to monitor a large amount of EV operational data to ensure reliable operation considering extensive penetration of EVs. This study proposes a stochastic and iterative based optimization for a two-area interconnected power system (IPS) coupled with a hybrid energy storage system (HESS). The HESS uses 10,000 plug-in hybrid electric vehicles (PHEVs) in each area and a superconducting magnetic energy storage (SMES) device to aid load frequency control (LFC). The 10,000 PHEVs would contribute to massive operational data, which needs to be considered while studying the IPS dynamic performance. Here, we investigated two discrete tie lines: HVDC links parallel to the alternating current (AC) tie line and a virtual synchronous power-based (VSP)-HVDC link parallel to the AC tie line. The controller’s optimal parameters are recorded using two meta-heuristic algorithms, that is, particle swarm optimization (PSO) and biogeography-based optimization (BBO) along with simultaneous coordinated tuning of secondary controller and storage units. Results are taken both in the presence and absence of a HESS with two types of tie links. The analysis is performed with typical load changes and sensitivity analysis scenarios for an accurate record of variations in the outcomes. Thus, the proposed BBO-based LFC tracks the supply and demand variations, ensuring precision and accuracy, indicating improved IPS dynamic performance in smart cities.

Private sector investment, the mainstream financing method for procuring public road transport development projects, has encountered several profound difficulties and risks during execution, particularly in developing countries. However, there needs to be more extensive investigations on the major barriers facing road transport infrastructure projects in these countries. In this vein, the present study aims to identify and assess the perceived barriers inhibiting private sector investment in delivering public road transport infrastructure projects in the developing country of Iran. The research method adopted is based on a descriptive survey with a three-round Delphi technique with 35 experts from both the private and public sector in Iran. According to the research study results, four main groups of legal and organizational, political, economic, and operational barriers have been found to significantly impact the attraction of private sector investment in such projects. The three most significant obstacles for public road transport infrastructure projects in developing countries include: (i) a lack of financial and investment safety; (ii) a lack of proficient managers and policies of public organizations in order to facilitate the process of privatization; and (iii) corruption in the privatization process. The survey findings can help the government and policymakers to eliminate or alleviate the potential barriers towards private sector participation in future public road infrastructure projects, particularly in those developing countries such as Iran.

Electric vehicles (EVs) possess untapped potential as mobile power banks for actively delivering electricity between different energy communities, known as Community-to-Vehicle-to-Community (C2V2C) service. While C2V2C represents an effective means of inter-community electricity sharing, limited research explores EVs' role in electricity delivery between locations. Suitable control approaches of EV charging for the C2V2C service are lacking, and it is unclear how robust the C2V2C service is and how its performance is affected by different factors. This paper aims to bridge these research gaps by developing an advanced control of EV smart charging/discharging to facilitate the C2V2C service. By comparing the power balance in the EVs' current-connecting and next-destination communities, the advanced control derives a target state-of-charge for the EVs in the current-connecting community, which can optimize the electricity delivery between the two communities. Then, the robustness of the C2V2C service is analyzed by evaluating its performances under different scenarios. Major factors like community combinations, renewable energy system (RES) configurations, EV battery capacity and numbers are examined for their impacts on C2V2C performance. The findings demonstrate that the C2V2C service significantly enhances energy balance across diverse community combinations, particularly in workplaces with substantial RES capacity. A large EV battery capacity is beneficial for performance improvements, but the impact diminishes at higher values due to limited surplus renewables availability. The increasing EV number enhances both electricity delivery capability and utilization of self-produced renewables. This study validated the effectiveness of the C2V2C service and provides valuable insights into optimizing its application across different scenarios. © 2024 The Author(s)

Abstract This paper reviews existing literature on the CO2 emissions implications of high-speed rail (HSR) projects due to its interactions with air, road, and ordinary-speed rail. We conceptually classify the studies into three levels, with the first focusing on the emission comparison between HSR and the other modes, the second on the emission implication due to HSR-induced traffic reallocation, and the third on the life cycle assessment (LCA). We find that the literature is still in a relatively premature state. We also identify future research opportunities, including: i) to carry out a more detailed and comprehensive analysis regarding how HSR affects cargo operations of other transport modes and the corresponding CO 2 emission implications, ii) to further develop the game-theoretical models to be applied to estimate a fuller picture of modal splits and the corresponding CO2 emission impacts, and iii) to apply LCA by considering the avoided life-cycle emissions associated with infrastructure and vehicles due to traffic diversion and the intricacies of modal interactions.

This paper proposes a coordinated charging scheduling approach for battery electric buses (BEBs) in a hybrid charging scheme, i.e., both plug-in fast charging and battery-swapping charging modes are incorporated in a single charging station. To accommodate the uncertain battery energy consumption during bus operation, a two-stage stochastic program is formulated, where the first stage decision determines the battery inventory level of each station and the second stage determines the charging mode and designs when, where, and how long each bus should be charged. Future uncertainties associated with energy consumption are captured by a set of possible discrete scenarios from historical data. A progressive hedging algorithm is developed to decompose the two-stage stochastic program into sub-problems. A case study is conducted to verify the proposed models and solution algorithms.