Green Energy and Intelligent Transportation最新文献

This paper evaluates rail transit within the context of the transit policies implemented in Lima, Peru. First it reviews the implementation of rapid transit, and bus reform. Secondly, it evaluates the outcomes of such policies by using Total Factor Productivity for policy effectiveness, Data Envelopment Analysis for rapid transit performance, and Generalized Cost of Travel for improvements. This paper finds that implementation failed in enforcing key requirements for rail transit regarding penetration of CBD and short transfers to bus transit; and that the basic assumptions of bus reform did not hold regarding bus oversupply, bus congestion or bus pollution. This paper also finds that outcomes of policies failed dramatically in achieving the planning goals; however, rail transit (Metro) shows high level of resilience in serving large ridership at high speed. On the other hand, bus reform was associated with a disproportionate increase of motorization, well over the effect of income growth or car attractiveness, and more related to the excessive reduction of bus transit capacity ill-advised from unproved bus reform assumptions. This paper recommends expanding rail rapid transit due to its intensive use of green renewable energy and its potential of demand growth if combined with modern Intelligent Transportation services, but this opportunity can be wasted without the proposed policy constraint to achieve lower Generalized Cost of Travel at any governmental intervention for bus reform, instead of just reducing bus transit capacity as implemented. Finally, this paper recommends government to government contracts to build rail transit and to enforce proper planning.

Lithium-ion batteries have been rapidly developed as clean energy sources in many industrial fields, such as new energy vehicles and energy storage. The core issues hindering their further promotion and application are reliability and safety. A digital twin model that maps onto the physical entity of the battery with high simulation accuracy helps to monitor internal states and improve battery safety. This work focuses on developing a digital twin model via a mechanism-data-driven parameter updating algorithm to increase the simulation accuracy of the internal and external characteristics of the full-time domain battery under complex working conditions. An electrochemical model is first developed with the consideration of how electrode particle size impacts battery characteristics. By adding the descriptions of temperature distribution and particle-level stress, a multi-particle size electrochemical-thermal-mechanical coupling model is established. Then, considering the different electrical and thermal effect among individual cells, a model for the battery pack is constructed. A digital twin model construction method is finally developed and verified with battery operating data.

The successful application of new technologies such as remotely piloted aircraft systems, distributed electric propulsion systems, and automatic control systems on electric vertical take-off and landing(eVTOL) aircraft has prompted Urban Air Mobility (UAM) to be mentioned frequently. UAM is a newly raised transport mode of using eVTOL aircraft to transport people and cargo in urban areas, which is thought to share some of the traffic on the ground. One of the prerequisites for UAM to operate on a regular basis is that its demand can support the operating costs, so forecasting UAM demand is necessary. We conduct UAM demand forecasting based on the four-step method, focusing on improving the third-step modal split, and propose a demand forecasting model based on the logit model. The model combines a nested logit (NL) model with a multinomial logit (MNL) model to solve the problem of non-existent UAM sharing rates. We use Chengdu, China as an example, and focus on forecasting the UAM traffic demand in 2030 with the help of the four-step method. The results show that UAM is suitable for shared operation during the early stages. With a fully shared operation, the UAM share rate increases by 0.73% for every kilometer increase in distance. Moreover, UAM is more competitive than other modes for delivery distances exceeding 15 ​km. Finally, using the distributions of the share rate and traffic flow pattern from the simulation, we propose the routes that can be prioritized for UAM operations in Chengdu.

The estimation of State of Health (SOH) for battery packs used in Electric Vehicles (EVs) is a complex task with significant importance, accompanied by several challenges. This study introduces a data-fusion model approach to estimate the SOH of battery packs. The approach utilizes dual Gaussian Process Regressions (GPRs) to construct a data-driven and non-parametric aging model based on charging-based Aging Features (AFs). To enhance the accuracy of the aging model, a noise model is established to replace the random noise. Subsequently, the state-space representation of the aging model is incorporated. Additionally, the Particle Filter (PF) is introduced to track the unknown state in the aging model, thereby developing the data-fusion-model for SOH estimation. The performance of the proposed method is validated through aging experiments conducted on battery packs. The simulation results demonstrate that the data-fusion model approach achieves accurate SOH estimation, with maximum errors less than 1.5%. Compared to conventional techniques such as GPR and Support Vector Regression (SVR), the proposed method exhibits higher estimation accuracy and robustness.

Iron-chromium redox flow battery (ICRFB) is an electrochemical energy storage technology that plays a vital role in dealing with the problems of discontinuity and instability of massive new energy generation and improving the acceptance capacity of the power grid. Carbon cloth electrode (CC) is the main site where the electrochemical reaction occurs, which always suffers from the disadvantages of poor electrochemical reactivity. A new N-B co-doped co-regulation Ti composite CC electrode (T-B-CC) is firstly generated and applied to ICRFB, where the REDOX reaction can be promoted significantly owing to the plentiful active sites generated on the modified electrode. As contrasted with ICRFB with normal CC electrode, after 50 battery charge/discharge cycles, the discharge capacity (1,990.3 ​mAh vs 1,155.8 ​mAh) and electrolyte utilization (61.88% vs 35.94%) of ICRFB with CC electrode (T-B-CC) are significantly improved. Furthermore, the energy efficiency (EE) is maintained at about 82.7% under 50 cycles, which is 9.3% higher than that of the pristine electrically assembled cells. The co-modulation of heteroatom doping and the introduction of Ti catalysts is a simple and easy method to improve the dynamics of the Cr³⁺/Cr²⁺ and Fe³⁺/Fe²⁺ reactions, enhancing the performance of ICRFBs.

Analyzing capacity degradation characteristics and accurately predicting the knee point of capacity are crucial for the safety management of lithium-ion batteries (LIBs). However, the degradation mechanism of LIBs is complex. A key but challenging problem is how to clarify the degradation mechanism and predict the knee point. According to the external characteristics such as capacity decline gradievnt and the peak value of increment capacity curve (IC curve), the capacity degradation can be divided into four stages, including initial decline stage, slow decline stage, transition stage and high-speed decline stage. The degradation mechanism of LIBs is compared from the longitudinal and horizontal aspects, respectively. Among them, the battery usage from the initial stage to the end of life (EOL) is longitudinal analysis. The battery under different conditions, such as charging and discharging, different discharge rate, different cathode material degradation mechanism is horizontal analysis. Moreover, a method based on neural network is proposed to predict the knee point. Two features are used to predict the capacity and cycle of the knee point, which are the gradient of the capacity degradation curve and the difference of the IC curve with the maximum correlation. The experimental results show that a two-dimensional surface can be obtained using only the first 100 cycles, which can provide a reference for the position of the knee point accurately prediction.

Battery life prediction is of great significance to the safe operation, and reduces the maintenance costs. This paper proposes a hybrid framework considering feature extraction to achieve more accurate and stable life prediction performance of the battery. By feature extraction, eight features are obtained to fed into the life prediction model. The hybrid framework combines variational mode decomposition, the multi-kernel support vector regression model and the improved sparrow search algorithm to solve the problem of data backward, uneven distribution of high-dimensional feature space and the local escape ability, respectively. Better parameters of the estimation model are obtained by introducing the elite chaotic opposition-learning strategy and adaptive weights to optimize the sparrow search algorithm. The algorithm can improve the local escape ability and convergence performance and find the global optimum. The comparison is conducted by dataset from National Aeronautics and Space Administration which shows that the proposed framework has a more accurate and stable prediction performance. Compared with other algorithms, the SOH estimation accuracy of the proposed algorithm is improved by 0.16%–1.67%. With the advance of the start point, the RUL prediction accuracy of the proposed algorithm does not change much.

The super volume changes and severe mechanical degradation have been a hindrance in the wide application of silicon based composite electrodes in commercial lithium-ion batteries (LIBs). Calendering, one procedure in producing LIBs' electrodes, is indispensable to ensure low porosity and energy density. However, the repercussions of the calendering process on the physical characteristics related to the behavior of silicon (Si) based electrodes during the electrochemical reaction have not been well understood. Thus, on account of the deformation characteristic of cantilever electrodes, an in-situ technique is employed to analyze the repercussions of calendering status on the coupled electro-chemo-mechanical performances. During the electrochemical cycling, Young's modulus and diffusion-induced stress in composite electrodes are quantified. The results show that the swelling strain, the stress and the modulus of the Si-based electrode and the calendering degree are positively correlated. Meanwhile, the stress induced by diffusion in the active layer tends to increase in the stage of lithiation and reverses during the delithiation process. Accompany with the SEM analysis, we conclude that the calendering process can induce larger stress, driving the formation of cracks in electrodes. These findings can help understand how the calendering process could affect the capacity dissipating and lifetime of Si based electrodes.

Electric vehicles (EVs) are going to overrule the transportation sector due to their pollution-free technology and low running costs. However, charging the EVs causes significant power demand and stress on the power delivery network. The challenge can be tackled well when charging and discharging scheduling are coordinated with intelligent EV routing. In this work, two-stage charging and discharging scheduling are proposed. In the first stage, a time scheduling algorithm is structured to identify EV charging/discharging slots at different hours, and at a later stage, the slots are optimally distributed among different charging stations. Routing of the EVs towards the EVCSs has been designed to enhance the useful participation of the EVs in the charging and discharging program. In this regard, a possible number of EVs in the test region has been forecasted with a regression model. The adequacy of the combined charging-discharging and location scheduling model is tested on a typical PV-enhanced 28-bus Indian distribution network. Three case studies containing three sub-cases in each have been performed incorporating the choice of the EV owners towards charging and discharging in different time slots in a day. The case studies have resulted in a peak-to-average ratio (PAR) of 1.151,0, 1.165,0, 1.196,8, 1.165,0, 1.180,9, 1.196,8, 1.196,8, 1.196,8 and 1.196,8 for the 24-h demand pattern in Case-1a, Case-1b, Case-1c, Case-2a, Case-2b, Case-2c, Case-3a, Case-3b and Case-1c respectively in comparison to a PAR of 1.2 for the 24-h demand in base case.

Due to the inherent nature of being highly digitalized, networked and intelligent, Unmanned Aerial System (UAS) operations pose a huge challenge to traditional aviation regulation and technical systems. How to keep safe, efficient and integrated operation for different Airspace users has become a pressing issue faced by civil aviation around the world. This paper focuses on the main operational scenarios and characteristics of unmanned aviation development in China. New operational characteristics and associated challenges due to diverse low-altitude users are analyzed, including operation concepts, UAS traffic management, technological test and verification, and standards. Drawing light on the practices in Europe and the United States, this paper summarizes China's practices and progress in low-altitude operations management, and analyzes future technological development needs and trends, as well as feasible implementation pathways and measures based on actual needs.