The car-sharing system based on booming autonomous connected electric vehicles (ACEV-based car-sharing system) has a large potential to significantly improve mobility, safety, and environmental ...friendliness of current transportation systems. However, the ACEV-based car-sharing system lacks comprehensive investigations which considering new emerging features. Also, it is constrained by inherent shortages of traditional car-sharing policies and various involved electric vehicle technologies, e.g., high-cost vehicle relocation, limited vehicle range and long recharge time. To address these problems, a general two-stage multi-objective optimization model is proposed, where it comprehensively formulates the emerging features and mitigates the inherent shortages. Two major goals are achieved, where the appropriated geographical service area is optimized in the first stage and the charging infrastructure allocation is efficiently processed in the second stage. In each stage, the multi-objective optimization model would simultaneously benefit both users and service providers. Also, a novel hybrid parking mechanism is proposed to compromise user flexibility and system management efficiency. The results demonstrate the effectiveness of the proposed method from multiple perspectives. (1) With appropriate estimations of a geographical service area and a reasonable ratio of vehicles to charging infrastructures, the proposed system can offer users efficient service qualities while maintaining a high vehicle usage frequency simultaneously. (2) Further, we find that the electric vehicle (EV) range, the charging speed, and the vehicle supply have a significant impact on the system. Especially, it noted that a fast charging speed gains a vital operation efficiency improvement. (3) In addition, more benefits are achieved when AV and CV technologies are adopted in the system. (4) Also, the adoption of ACEVs can almost reduce the overall CO2 emissions by 42.03% and energy consumption by 31.34% than internal combustion vehicles.
•An electric, autonomous, connected vehicle sharing system is investigated.•Optimal service area and charging infrastructure allocation can be obtained.•Both the benefits of users and service providers are considered.•A hybrid parking mechanism for the electric vehicle sharing system is proposed.•Affecting factors of the system are identified and investigated in detail.
The traditional full-scan method is commonly used for identifying critical links in road networks. This method simulates each link to be closed iteratively and measures its impact on the efficiency ...of the whole network. It can accurately identify critical links. However, in this method, traffic assignments are conducted under all scenarios of link disruption, making this process prohibitively time-consuming for large-scale road networks. This paper proposes an approach considering the traffic flow betweenness index (TFBI) to identify critical links, which can significantly reduce the computational burden compared with the traditional full-scan method. The TFBI consists of two parts: traffic flow betweenness and endpoint origin-destination (OD) demand (rerouted travel demand). There is a weight coefficient between these two parts. Traffic flow betweenness is established by considering the shortest travel-time path betweenness, link traffic flow and total OD demand. The proposed approach consists of the following main steps. First, a sample road network is selected to calibrate the weight coefficient between traffic flow betweenness and endpoint OD demand in the TFBI using the network robustness index. This index calculates changes in the whole-system travel time due to each link's closure under the traditional full-scan method. Then, candidate critical links are pre-selected according to the TFBI value of each link. Finally, a given number of real critical links are identified from the candidate critical links using the traditional full-scan method. The applicability and computational efficiency of the TFBI-based approach are demonstrated for the road network in Changchun, China.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Enzyme-based biofuel cells are attracting attention rapidly partially due to the promising advances reported recently. However, there are issues to be addressed before biofuel cells become ...competitive in practical applications. Two critical issues are short lifetime and poor power density, both of which are related to enzyme stability, electron transfer rate, and enzyme loading. Recent progress in nanobiocatalysis opens the possibility to improve in these aspects. Many nano-structured materials, such as mesoporous media, nanoparticles, nanofibers, and nanotubes, have been demonstrated as efficient hosts of enzyme immobilization. It is evident that, when nanostructure of conductive materials are used, the large surface area of these nanomaterials can increase the enzyme loading and facilitate reaction kinetics, and thus improving the power density of biofuel cells. In addition, research efforts have also been made to improve the activity and stability of immobilized enzymes by using nanostructures. It appears to be reasonable to us to expect that progress in nanostuctured biocatalysts will play a critical role in overcoming the major obstacles in the development of powerful biofuel cells.
Currently, signal control mode is the main control method of urban road intersections. Given that the traffic efficiency of road intersections is mainly affected by signal timing schemes, it is ...important to optimize signal timing at road intersections. Therefore, signal timing optimization methods of urban road intersections are explored in this work. When optimizing the timing of the signal at the intersections, the selection of optimization targets play an important role. At present, there are multiple objectives considered while designing signal timing scheme, including capacity, delays, and automobile exhaust. However, from the perspective of the traveler, they are more concerned about their own delay while passing intersections. In this work, we propose a novel multi-objective signal timing optimization model with goals of per capita delay, vehicle emissions, and intersection capacity. Considering the problem characteristics of the target problem, a meta-heuristic algorithm combining difference operator, which is based on Particle Swarm Optimization Algorithm, is developed. To test the validity of proposed approach, we applied it to real-world intersection signal timing problems in China. The results show that the optimized signal timing scheme obtained by the proposed algorithm is better than the realistic one. Also, the effectiveness of the developed algorithm is demonstrated by comparing it with other efficient algorithms.
Perovskite oxides (ABO3) have been studied extensively to promote the kinetics of the oxygen evolution reaction (OER) in alkaline electrolytes. However, developing highly active catalysts for OER at ...near-neutral pH is desirable for many photoelectrochemical/electrochemical devices. In this paper, we systematically studied the activity and stability of well-known perovskite oxides for OER at pH 7. Previous activity descriptors established for perovskite oxides at pH 13, such as having an eg occupancy close to unity or having an O p-band center close to Fermi level, were shown to scale with OER activity at pH 7. Stability was a greater challenge at pH 7 than at pH 13, where two different modes of instability were identified from combined transmission electron microscopy and density functional theory analyses. Perovskites with O p-band close to Fermi level showed leaching of A-site atoms and surface amorphization under all overpotentials examined at pH 7, while those with O p-band far from Fermi level were stable under low OER current/potential but became unstable at high current/potential accompanied by leaching of B-site atoms. Therefore, efforts are needed to enhance the activity and stability of perovskites against A-site or B-site loss if used at neutral pH.
The catalyst intrinsic area-specific activity for the oxygen reduction reaction (ORR) was constrained by the scaling relations governing the adsorption of reaction intermediates. In this study, we ...strategically modified the electronic band structures of Pt(CuNi)x alloy nanoparticles by varying their composition, resulting in a specific activity trend resembling a volcano shape. The introduction of MoOy shattered the existing scaling relations, leading to a significant enhancement in ORR activity of Pt alloys, surpassing the activity of Pt(CuNi)x catalysts. These findings proved the effectiveness of MoOy deposition on Pt(CuNi)x in disrupting the scaling relations, ultimately improving ORR activity.
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•MoOy-Pt(CuNi)x heterojunction structured nanoparticles were synthesized.•All samples showed significant improvement in oxygen reduction reaction activity.•MoOy-Pt(CuNi)0.66 showed exceptional 5.0 mA/cm−2Pt ORR activity at 0.9 V vs. RHE.•The creation of dual active sites significantly promoted ORR kinetics.
Fuel cells utilize the chemical energy of liquid or gaseous fuels to generate electricity. As fuel cells extend their territory to include heavy-duty vehicles, new demands for proton conductors, a ...critical component of fuel cells, have emerged. A near-term need is ensuring the chemical and mechanical stability of proton exchange membranes to enable long lifetime vehicles. In the mid-term, achieving stable conductivity of proton conductors under hot (>100°C) and dynamic fuel cell operating conditions is desirable. In the long term, targeting high thermal stability and tolerance to water enables the utilization of high energy density liquid fuels that will increase pay-load space for heavy-duty vehicles. This article presents our perspective on these near-, mid-, and long-term targets for proton conductors of heavy-duty fuel cells.
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Fuel cells are an attractive technology to power zero-emission vehicles. Compared with battery-powered vehicles, fuel cells offer fast fueling and adequate fuel storage for long-range applications. Heavy-duty fuel cell vehicles have strenuous requirements with the most challenging target being the development of fuel cells with the durability to return capital investment over a longer lifetime. Fuel cell operation under hot and dry conditions enables simpler, low-cost fuel cell systems through better heat and water management. Utilizing high energy density liquid fuels can also increase pay-load space and eliminate the need for an expensive hydrogen infrastructure. Advanced proton conductors that can resolve these issues associated with heavy-duty fuel cell applications are needed. Here, we present the progress and promising options in meeting near-, mid-, and long-term targets with respect to performance, durability, and technical readiness to stimulate research on proton conductors for heavy-duty fuel cell vehicles.
Fuel cell technology is an attractive electrification platform for heavy-duty vehicles. As fuel cells expand their territory to include heavy-duty vehicles, new demands for proton conductors—a critical component of fuel cells—have emerged. This article summarizes the perspective of original equipment manufacturers on the research needs for heavy-duty fuel cell vehicle proton conductors in the near, mid, and long terms.
With the advent of the data-driven era, deep learning approaches have been gradually introduced to short-term traffic flow prediction, which plays a vital role in the Intelligent Transportation ...System (ITS). A hybrid predicting model based on deep learning is proposed in this paper, including three steps. Firstly, an improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method is applied to decompose the nonlinear time series of highway traffic flow to obtain the intrinsic mode function (IMF). The fuzzy entropy (FE) is then calculated to recombine subsequences, highlighting traffic flow dynamics in different frequencies and improving prediction efficiency. Finally, the Temporal Convolutional Network (TCN) is adopted to predict the recombined subsequences, and the final prediction result is reconstructed. Two sensors of US101-S on the main road and on-ramp were selected to measure the prediction effect. The results show that the prediction error of the proposed model on two sensors is notably decreased on single-step and multistep prediction, compared with the original TCN model. Furthermore, the proposed improved CEEMDAN-FE-X framework can be combined with prevailing prediction methods to increase the prediction accuracy, among which the improved CEEMDAN-FE-TCN model has the best performance and strong robustness.
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