Masafumi Miyatake

Global warming has become an increasingly important issue, prompting the need for more sustainable solutions in energy-intensive sectors such as railways. Utilizing renewable energy and regenerative braking energy (RBE) in stations could be an effective approach. However, depending on the varying renewable energy potentials across regions, different optimized combinations of renewable energy capacities will be required. Therefore, appropriate capacity determination and efficient energy management for such multiple-power-supplied railway stations are crucial. This paper formulates a mixed-integer linear programming (MILP) model considering both short-term energy flow and long-term planning, aiming to optimize the energy management plan while determining the optimal capacity configuration. Furthermore, the cost-reduction and energy-saving effects are evaluated through case studies. The findings indicate that this approach can significantly reduce costs and save energy, offering insights for developing efficient and sustainable energy solutions in future smart grid and railway infrastructure.

In DC electric railway systems, energy-saving can be expected by using regenerative brake. However, since the regenerative brake force decreases at high-speed range, the use of a mechanical brake to obtain a constant brake force results in losses and reduces the energy-saving effect. The “regenerative brake notch”, brake method proposed in a previous study, suggests a way of informing the brake start point for stopping only with the regenerative brake force. However, when this method is applied to an actual train running, it is not always possible to stop at the stopping position due to disturbances such as the running resistance and the gradient of railways. In this study, we proposed a driver advisory system and a control method to solve this problem. We verified the responsivity of this control method in actual motors drive system and the energy-saving effects on applying the proposed method by using a numerical simulation assuming of an actual railway running.

The transition towards environmentally friendly transportation solutions has prompted a focused exploration of energy-saving technologies within railway transit systems. Energy Storage Systems (ESS) in railway transit for Regenerative Braking Energy (RBE) recovery has gained prominence in pursuing sustainable transportation solutions. To achieve the dual-objective optimization of energy saving and investment, this paper proposes the collaborative operation of Onboard Energy-Storage Systems (OESS) and Stationary Energy-Storage Systems (SESS). In the meantime, Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is applied to optimize the ESS capacity and reduce its redundancy. The simulation is programmed in MATLAB. The results show that the corporation of OESS and SESS offers superior benefits (70 kWh energy saving within 30 min operation) compared to using SESS alone. Moreover, the OESS plays a significant role, emphasizing its significance in saving energy and investment, therefore presenting a win–win scenario. It is recommended that the capacity of OESS be designed to be two to three times that of SESS. The findings contribute to the ongoing efforts in developing more sustainable and energy-efficient transportation solutions, with implications for the railway industry’s investment and broader initiatives in energy saving for sustainable urban mobility.

This paper is focussed to solve the optimal train control problem using Pseudospectral method and application of Dynamic Programming. The primary objective in Energy-Efficient Train Control is to generate optimum speed profile under the given speed and gradient constraints so that the train reaches the destination within the allotted time. In this proposed methodology, the state and control variables are approximated using a global polynomial and collocation is performed at Legendre-Gauss-Lobatto points. Further, the transformed problem is solved using Dynamic Programming. The advantage of obtaining global optimum with Dynamic Programming is combined with possibility of reducing the calculation time by employing the Pseudospectral method is investigated. The choice of number of collocation points and its effect on the quality of optimum speed trajectory and computation times are provided in the simulation results. It is observed that the proposed method converges faster when compared with compared to Dynamic Programming performed in time domain.

Abstract The energy consumption of a battery‐powered train in an interstation depends on the running time and state of energy (SOE) at departure. In this paper, we develop an optimization method of train timetables to minimize energy consumption in line with several stations. The variables in this proposed optimization model are running, dwell, and charging times as real numbers and places of charging facilities as binaries. Additionally, we conducted a case study using the real‐world light rail transit (LRT) route, vehicle, and onboard battery model to confirm the effectiveness. In the case study, the proposed method can optimize the timetable and placement of charging facilities by considering the track gradient and battery SOE.

The energy consumption of a battery-powered train in an interstation depends on the running time and state of energy (SOE) at departure. In this paper, we develop an optimization method of train timetables to minimize energy consumption in line with several stations. The variables in this proposed optimization model are running, dwell, and charging times as real numbers and places of charging facilities as binaries. Additionally, we conduct a case study using the real-world light rail transit (LRT) route, vehicle, and onboard battery model to confirm the effectiveness. In the case study, the proposed method can optimize the timetable and placement of charging facilities by considering the track gradient and battery SOE.

The timetable of urban rail greatly affects its daily energy consumption. To improve the utilization of renewable energy between trains using timetabling has become an effective way to reduce energy consumption. Previous studies ignore or simplify the modelling of traction power supply network, which failed to accurately describe the flow of energy between trains through the power network. This paper proposed an optimisation method of energy efficiency timetabling considering the power flow of traction power supply network. First, an urban rail transit DC traction network model is established, and the current-vector iterative method is used to characterize the energy consumption. Then, a train timetable optimisation model is proposed to minimize the total energy consumption of the traction network system by adjusting the dwell time and section running time. The genetic algorithm is used to solve the optimisation problem. Finally, simulation result shows that the proposed method can accurately characterize the energy flow and effectively reduce the total energy consumption of the urban rail transit.

This paper presents automatic software for E-liked shaped contactless inductive power transfer (CIPT) device study and design that provides attended-free, multiple-case auto-generating and auto-deploying analysis in one go. It provides visualized and listed results in a design space or for optimizing solutions. To satisfy the demand for static and dynamic charging devices, the software provides specific cores, such as EE-, EI-, IE-, and II-shaped, with or without legs as optional core structures. The software contains three main parts: a user-friendly interface, analytic approaches providing grid analysis that represent the general performance in a designated parameter range, and optimal analysis for multi-objective optimization using a genetic algorithm (GA). The post-analysis processor converts the analysis results to easy-to-read outputs. Users can customize various parameters, such as core type, structural size, circuit configuration, materials, and analysis setting. Automatic functions, such as resistance and compensation calculation, are available for the convenience of the user. By applying one approach, or by combining them in a specific order, the software achieves designs that satisfy the user’s demands within the user-provided range. The software is built in Python and collaborates with a finite element method (FEM) solver, which is JMAG in this paper. Some examples are given to demonstrate the performance of the software.

Utilization of wireless power transfer in light rail transits is seen as one solution for electrification of lines. The main advantage of this supply system is the reduction of installation; moreover, the alignment between the transmitter coil in the track and the receiver coil in the train should be perfect in order not to affect the power transfer. To reduce the effects of misalignment on the input and output electric parameters of the system, a new planar core and coil design, called hybrid intercore coil, is proposed. The proposed design uses a magnetic material layer between the windings in the inner half of the coil to create a non-uniform magnetic field distribution, which makes the system more robust against the effects of coil misalignment on the system current and voltage. Simulations with finite element method software were conducted to compare designs. The results show that the proposed design is less susceptible to the effects of misalignment and is more efficient. Prototype cores were constructed to verify the simulation results. Measurements show a smaller input overcurrent and output overvoltage when operating in resonance mode. The proposed design reduced the effects of coil misalignment on electrical parameters.

The train on energy storage device called on-board rail vehicle is able to store regenerative energy on storage at breaking and run under catenary-free condition, but we must think how to charge to energy storage device. If it charged at station during stopping, it has one of the methods that include the charge from catenary. Then, if run the on-board rail vehicles for long railroad, the major problem is where to place the quick charging points. We propose to be focused on energy consumption by appropriate placing of charging stations because energy storage device dropped the open circuit voltage as SOC (state of charge) decrease and became low tractive force of motors. As a result, the low SOC caused more energy consumption for running trains. In this paper, we create a mathematical model of onboard energy storage train with an onboard high-power rechargeable lithium-ion battery under catenary-free conditions and placed of charging station and simulated to know relationship between electric energy and running under different SOC. Furthermore, we make the line model of 90[km] in total length and simulated the train electric energy consumptions the change of the electric energy consumptions by changing station to put on 2 patterns. We consider the change of the electric energy consumption by the difference of the charging station and suggest the systems of the onboard energy storage train, which can run on catenary-free condition.

In order to develop the train operation control algorithm for a group of trains, it is necessary to evaluate the energy efficiency and the operational robustness by simulations. Especially, energy efficient operation of a group of trains is so attracted as well as of each train. In this paper, a flow of the train traffic simulator and delay occurrence for operational robustness are stated briefly, and methods of modeling and calculation of a feeding circuit are stated in details, which are important technical elements to evaluate the energy efficiency in the train traffic simulation. The feature of our feeding circuit model is introduction of the "tie nodes" and the "tie matrix", which show that some nodes are in the same voltage. Introduction of them improves the pliability of the modeling and calculation according to change of feeding network composition. Applying the proposed model and calculation method in C++, the converged results were acquired in three test cases using the general-purpose calculation solver. The computation time and the calculation error are sufficiently less, even compared to the referential results by MATLAB. We are to verify accuracy of our model using field data and use it for development of train operation control algorithms.