• Energy Research
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This paper proposes Python models for a photovoltaic-based charging station for electric vehicles considering technical, economic, and environmental aspects. The proposed models consider two main cases of photovoltaic-based charging systems, which are photovoltaic/grid-charging systems and photovoltaic/grid/battery-charging systems. Moreover, additional operational options, such as exporting energy to the grid and zero-export, are added to the proposed models. The proposed techno-economic models can be used to evaluate the location of the electric vehicle charging station and the financial and environmental benefits of the electric vehicle charging station that is installed in a residential, commercial, or industrial context. The models are tested by proposing a simulation based on load demand, and then different cases, including the actual size case and additional trading cases, are investigated.

Purpose.The railways of Ukraine have been operated the locomotives, which are both morally and physically obsolete. Therefore, to ensure the competitiveness of rail transport it is necessary to update the locomotive fleet, and first of all the fleet of electric locomotives, because electrified railways provide the greater part of passenger and freight traffic. In this connection it is of special importance to determine the optimum parameters of the nominal mode of electric rolling stock. The purpose of the work is to examine the features of solution of these problems with respect to electric locomotives. Methodology. Assuming that the limit values of traction force are determined by the conditions of wheel-rail grip, then the power of the nominal mode can be represented as the product of rated speed, estimated friction coefficient, train weight and the coefficients that represent the ratio of the estimated (starting) value of traction force to value of traction force the nominal mode and the ratio of the mass of the locomotive to the train weight. Since the mass of the train is not a constant value, there is always a surplus power of the locomotive fleet required for the mastering of a predetermined volume of transportations. Reduced overcapacity of the locomotive fleet can be achieved by introduction of the locomotives of different power, designed for driving trains of different weight that will result in increased completeness of the power use but also in difficulty in selecting of locomotives for trains in operation. The paper shows the method of calculating the optimum values of power, speed and traction force of the nominal mode. It presents the mathematical model of the relationship of traction rate, excessive capacity and power of the traction unit. Findings.It is proved that the power of the traction unit, the total fleet power requirement and the excess of power in absolute units are proportional to the speed of the nominal mode. To reduce the total power of the fleet when selecting the optimum power of the traction unit it is necessary to take into consideration the speed of the nominal mode, defined by the condition of minimization of power consumption for traction, i.e. the smallest value that enables the implementation of the given running speed and the powerredundancy level required for operation. Originality. It consists in the development of a unified algorithm for determining the optimal parameter values of the nominal mode of passenger, freight and freight-passenger electric locomotives. Practical value. The authors determined the minimization costs during production, acquisition and maintenance of electric locomotives, whose nominal mode parameters are designed according to the above procedure.

AbstractIn order to realise sustainable urban transport, it is necessary to combine different kinds of decision-making, including vision-led, plan-led and consensus-led approaches. In this paper, a cross-assessment model that supports both vision-led and consensus-led approaches is proposed as an analytical tool for developing sustainable urban transport and land use strategies for a low-carbon society. It is applied to an impact analysis of public transport and land use strategies in 2030 for all of Japan's 269 urban areas, with outcomes – including the financial balance of public transport operation, user benefits, and CO2 emissions reduction – compared by strategy and urban area.The analytical results show that three value factors related to efficiency, equity and the environment do not necessarily conflict with each other. In particular, it is clarified that CO2 emissions reduction targets can contribute to the improvement of financial balance and user benefits at the national level. In addition, the results of comparative analysis among the LUTI (land use and transport integration) scenarios demonstrate that a combination of urban transport strategies with land use control in the form of ‘corridors and multi-centres’ provides greater emissions reduction and increased user benefits.

This article presents the value of further increase of the energy efficiency in railway transport in combination with transition to the non-standard sources of energy. This is done within the paradigm of sustainable development and decarbonization in the global economy and by also taking into account the objectives stated in the document “Global perspective of railway transport development until 2050.” Hydrogen fuel cells, which are being studied by the Technical and Scientific Council of the JSC “RZD”, have demonstrated to be the most beneficial. The global trends of commercial use of fuel cells for transport are based on the unique properties of hydrogen as a fuel: high energy efficiency, ecological safety, and capability to be used without electric grids. Taking into account technological and ecological value, the transition from diesel traction to hydrogen fuel cell traction is justified and can be evaluated as a step of evolution that is as valuable as the transition from steam traction to diesel traction. Empirical proof is available that hydrogen is not hazardous to handle with properly designed equipment. The potential of the Russian science allows fulfilling appropriate research and development. We have taken into account that the required scientific and technological competences are available in different scientific centers and their effective integration is required. The conclusion states that the required conditions exist for introduction of the hydrogen fuel cells to the rolling stock in Russia. Although manufacturing of the critical fuel cells is not currently available in the Russian Federation, it can be arranged if demand is sufficient. This will require the collaboration of customers and scientific investigators in the project development of electric rolling stock in which hydrogen fuel cells can be used.

Abstract Vehicular velocity prediction is of great significance to intelligent transportation system, as it provides a possible future velocity sequence for vehicle's decision‐making system. A velocity prediction method via meta learning is proposed, which provides an adaptive and generative framework for multiple‐driving cycles. The prediction model is devised using a deep neural network structure. The model's training is performed by the recently proposed meta‐supervised learning, which ensures that one trained model could meet the adaptability to multiple driving cycles. The complete framework consists of three parts: Pre‐training, fine‐tune‐training and real‐time prediction, which is tested to predict the hybrid electric city buses’ future velocity in a variable traffic scenario. The average prediction accuracy of 3, 5 and 10 s horizons is 0.51, 0.63 and 0.88 m s−1, which is 25.9%, 16.78% and 7.47% higher than that trained by the conventional supervised learning method. As suggested, the proposed prediction method is effective and could meet the requirement of energy‐saving control for hybrid electric city buses. With further study, potential application of this method may also exist in the field of driving behaviour prediction and transportation mode recognition.

The private motor vehicles are significantly important means of transportation in modern lifestyle, however, these also contribute to a large proportion of the total air pollution and primary energy consumption. In order to develop green transportation system, it becomes imperative to use integrated technologies to achieve reduced emissions and utilize renewable energy. Electric vehicles (EVs) have been considered as one of these technologies to transform the traditional vehicle mix. However, the uptake of EV has been debated on factors like cost, performance (autonomous mileage), charging point infrastructure construction, energy saving, policy and end users’ adaptation. Present study investigates the technology feasibility (which usually refer to EVs’ cost, EV charging, supplier’s customer services quality, EV travel performance) and users’ adaptation of EV in Beijing, which is a key driver for the EV uptake into the Beijing transportation system. The relevant data have been collected and analyzed in the form of questionnaire survey around all of these factors. While considering the user perception and satisfaction, safety of charging and energy bills have also been investigated. According to the data analysis, it has been found the policy of ‘No traffic restrictions for EVs’ (the traffic restrictions means for certain date, from Monday to Friday the motor vehicles with the last register number of 1 and 6, 2 and 7, 3 and 8, 4 and 9, 5 and 0, are restricted to travel, respectively), the availability of the charging infrastructure and technical support are the most significant factors affecting the users’ opinions on using EVs.

Abstract Reducing energy consumption, regardless of fuel or electricity, of ground vehicles is a paramount pursuit in academia and industry. Current research concentrates exclusively on the improvement of the vehicle, but accepts the road conditions as external influences. The innovation of this article is to consider the road conditions, particularly the grade angle, as design variables. It is assumed that the stochastic speed trajectories of all vehicles on the road can be modelled by a Markov chain. The expected value of the average energy consumptions of all vehicles running on the road is defined as the objective function. The optimisation problem is solved by dynamic programming with Markov model. Evaluations have been made on both simulated and measured speed trajectories. For the simulated speed trajectories, the optimal road grade profile designed by the method saves up to 22% energy compared with a flat road. For the measured speed trajectories, the optimal road grade profile saves up to 2.7% transportation energy compared with the actual road profile. Application of this method on building road could lead to considerable energy saving.

Electric vehicles (EVs) parking lots are representing significant charging loads for relatively a long period of time. Therefore, the aggregated charging load of EVs may coincide with the peak demand of the distribution power system and can greatly stress the power grid. The stress on the power grid can be characterized by the additional electricity demand and the introduction of a new peak load that may overwhelm both the substations and transmission systems. In order to avoid the stress on the power grid, the parking lot operators are required to limit the penetration level of EVs and optimally distribute the available power among them. This affects the EV owner’s quality of experience (QoE) and thereby reducing the quality of performance (QoP) for the parking lot operators. The QoE is represents the satisfaction level of EV owners; whereas, the QoP is a measurement representing the ratio of EVs with QoE to the total number of EVs. This study proposes a fuzzy logic weight-based charging scheme (FLWCS) to optimally distribute the charging power among the most appropriate EVs in such a way that maximizes the QoP for the parking lot operators under the operational constraints of the power grid. The developed fuzzy inference mechanism resolves the uncertainties and correlates the independent inputs such as state-of-charge, the remaining parking duration and the available power into weighted values for the EVs in each time slot. Once the weight values for all EVs are known, their charging operations are controlled such that the operational constraints of the power grid are respected in each time slot. The proposed FLWCS is applied to a parking lot with different capacities. The simulation results reveal an improved QoP comparing to the conventional first-come-first-served (FCFS) based scheme.