Masafumi Miyatake

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.

Recently, Energy Storage Devices (ESDs) are introduced to railway vehicles in order to operate even in an emergency case such as power outage. However, no simultaneous design methods of power capacity and energy capacity of onboard ESD for emergency operation have been proposed. In this paper, a model for the calculation of power and energy capacity of onboard ESD which are utilized in an emergency case is proposed. Furthermore, we proposed a method to design power and energy capacity of onboard ESD considering required power for traction and auxiliary power supply system.

In dense traffic railway networks, trains may often slow down or stop between stations owing to previous train delays. If preceding train trajectory can be predicted, energy-efficient driving can be achieved by suppressing unnecessary speed changes. In this paper, we propose an algorithm to find energy-efficient driving considering fixed-block signaling (FBS) system by using dynamic programming (DP). DP is suitable for use because it can optimize the control inputs with discrete and state constraints. In this paper, we discuss energy-efficient driving by considering a FBS system using some case studies of simulation. In the simulation, we examine a technique to drive an express train in an energy-efficient way when the preceding local train is running toward the station with passing loops. The results show that the proposed method can derive complex speed profiles for energy-efficient driving and the train can be operated with a maximum reduced energy consumption of 8.3%.

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 most striking characteristic of linear-motor railway system is that the structure consists of a primary and secondary linear induction motor (LIM). The primary side of the LIM is installed in a rolling stock, and the secondary side of LIM is installed on the track. The magnetic attractive vertical force produced by the LIM increases the running resistance since this force is in the same direction as the gravitational force due to which rolling stocks gain weight virtually from the track side of view. In addition, the efficiency of the LIM has different characteristics of efficiency compared with the rotary motor. Previous studies have focused on the design method of LIM to improve motor efficiency and decrease energy consumption. However, it is a well-known fact that this kind of approach requires hardware renewal which requests large amount of investments. The purpose of this study is to analyze these characteristics of LIM effects and design the optimal speed profile to minimize the energy consumption as a linear-motor railway system. This smart and economic energy-saving approach is based on the optimization of speed profiles of the linear-motor railway system using dynamic programming.

For large photovoltaic power generation plants, number of panels are interconnected in series and parallel to form a photovoltaic (PV) array. In this configuration, partial shade will result in decrease in power output and introduce multiple peaks in the P-V curve. As a consequence, the modules in the array will deliver different row currents. Therefore, to maximize the power extraction from PV array, the panels need to be reconfigured for row current difference minimization. Row current minimization via Su Do Ku game theory do physical relocation of panels may cause laborious work and lengthy interconnecting ties. Hence, in this paper, an alternative to physical relocation based on particle swarm optimization (PSO) connected modules is proposed. In this method, the physical location of the modules remains unchanged, while its electrical connections are altered. Extensive simulations with different shade patterns are carried out and thorough analysis with the help of I-V, P-V curves is carried out to support the usefulness of the proposed method. The effectiveness of proposed PSO technique is evaluated via performance analysis based on energy saving and income generation. Further, a comprehensive comparison of various electrical array reconfiguration based is performed at the last to examine the suitability of proposed array reconfiguration method.

In recent years, continuous depletion of conventional energy resources and the fluctuating fuel cost have motivated research on alternative source for power generation. To effectively harvest power from the green energy resources, extensive research have been carried out in literature. Solar energy being the foremost among all other renewable energy resources has evolved as a reliable substitute for the conventional power generation. Unlike other sources, real time research on Photovoltaic (PV) Systems is a difficult assignment as it necessitates an accurate PV emulator that can precisely replicate the nonlinear characteristics of a PV cell. PV Researchers have developed emulators of different type utilizing various configurations. This paper attempts to present a comprehensive review on different PV emulator topologies so as to gather all the research activities that are scattered in this arena under a common umbrella. Further this paper provides a detailed analysis of each technique emphasizing on its (i) Realization cost (ii) Accuracy (iii) Level of complexity (iv) Sensitiveness to varying environmental conditions (v) Hardware implementation and (vi) Efficiency.

In dense traffic railway networks, trains may often slow down or stop between stations owing to previous train delays. If preceding train trajectory can be predicted, energy-efficient driving can be achieved by suppressing unnecessary speed changes. In this paper, we propose an algorithm to find energy-efficient driving considering fixed-block signalling (FBS) system by using dynamic programming (DP). DP is suitable for use because it can optimize the control inputs with discrete and state constraints. In this paper, we discuss energy-efficient driving by considering a FBS system using some case studies of simulation. In the simulation, we examine a technique to drive an express train in an energy-efficient way when the preceding local train is running toward the station with passing loops. The results show that the proposed method can derive complex speed profiles for energy-efficient driving and the train can be operated with a maximum reduced energy consumption of 8.3%.

In recent years, most of the loads in a typical home operate at DC voltage. Some of the loads to mention are LED lights, laptops, LED T. Vs, washing machines, and computers. The use of DC voltage at load minimizes the power losses that happen with centralized power generation. Moreover, tendency of using DC voltage provoke the path for smart micro-girds that form local distributed system and supply the loads locally. Further it encourages use of renewable energy resources as well. However, power conversion units such as DC-DC converter for obtaining desired voltage introduces non-linearity. Hence controllers/compensators are designed to maintain the system stabile at all operating conditions. In this work, a Home DC microgrid system fed from solar Photovoltaic array and battery is built and its performance is studied. A robust Energy Management System is designed to regulate the system voltage levels. Three different converters such as Boost, Bidirectional and Buck converters are used to serve the purpose. Various simulation studies are performed in MATLAB/SIMULINK to understand the system stability.

Recent times have witnessed a significant positive trend in adoption of renewable energy sources and DC-based loads, which questions the efficiency and reliability of existing structure of electrical power system. DC microgrids are identified as potential solutions for addressing India's and other developing nations' rural electrification. This paper presents an energy management technique for isolated DC microgrids for academic readers, as well as provides an introduction to not-familiarized readers for understanding the common design challenges for implementing off-grid power systems. The principle of the proposed technique is based on law of energy conservation defined by the energy balance equation and is implemented by synthesizing the instantaneous current reference and hysteresis control with source priority, which was validated by rigors simulation studies in Matlab/Simulink. Furthermore, in order to demonstrate the modularity and scalability of the algorithm, it was tested on a prototype hardware test setup at the Solar Energy Research Centre, VIT University.

Electric model based in the duality between the electric and magnetic circuit taking into account the misalignment characteristic of transformer of a Wireless Power Transfer, WPT, is proposed. Being a model based in the constructive characteristic of the transformer the model, it represents better the misalignment effect and the simulation values are more accurate in comparison with the T model that is widely used.

Increased penetration of wind and solar PV system in Distributed Generation (DG) and isolated micro grid environment necessitates the use of maximum power point tracking method for wind and solar PV resources. Considering the change in environmental conditions and non-linearity, a variety of publications reporting various MPPT algorithms for solar and wind energy systems are put forward in recent times. But the review reports on common MPPT techniques used for solar and wind applications for hybrid power generation have not yet been reported. Hence, in this paper, conventional techniques and artificial intelligent techniques found extensively used in the power generation platform are peerly reviewed and compared. Historical MPPT methods like Perturb & Observe (P & O) / Hill Climbing, Incremental Conductance (INC), Fuzzy and Neural Network methods benchmarked in MPPT province are comprehensively compared in a common platform. In addition to the common existing techniques, recent swarm intelligence and bio-inspired techniques in solar PV and sensor less adaptive techniques in wind MPPT are also been reviewed provided for quality assessment. Finally an economic analysis is arrived for MPPT methods based on (i) Capacity utilisation factor (ii) Cost (iii) Energy Savings (iv) payback period (v) Income generated and (vi) stability.

The most striking characteristics of linear-motor railway system is that the structure consists of a primary and secondary linear induction motor (LIM). The primary side of the LIM is installed in a rolling stock, and the secondary side of LIM is installed on the track. The magnetic attractive vertical force produced by the LIM increases the running resistance since this force is in the same direction as the gravitational force due to which rolling stocks gain weight virtually from the track side of view. In addition, the efficiency of the LIM has a different characteristics of efficiency compared with the rotary motor. Previous studies have focused on the design method of LIM to improve motor efficiency and decrease energy consumption. However, it is a well-known fact that this kind of approach requires hardware renewal which requests large amount of investments. The purpose of this study is to analyze these characteristics of LIM effects and design the optimal speed profile to minimize the energy consumption as a linear-motor railway system. This smart and economic energy-saving approach is based on the optimization of speed profiles of the linear-motor railway system using dynamic programming.

This paper describes a concept based on energy-efficient train operation technology. It is known that the energy-saving train driving operation for a train takes the form of an operation of power-coast-brake. However, drivers cannot operate in such strategy when the previous train delays. Some drivers experientially drive trains without stopping between stations. But it is very difficult for less-experiential drivers to drive without stopping stations. We have developed prototype hardware and software for helping driver's operation and evaluated energy consumption and running time by numerical simulation.

Recently, the railway sector is required to improve energy efficiency. Since no additional hardware investment is required, the authors are focused on the optimisation of the train timetable for less energy consumption. Train timetable contains the slack time for each interstation for robustness against delay. Although energy consumption can be reduced by the slack time, the slack time distribution was not always optimal in the viewpoint of energy saving. The authors already proposed to optimise the slack time distribution based on the "Law of Identical Incremental Energy Consumption", however, it could be applied to the line only with local trains. The authors propose the extended train timetable optimisation technique considering minimum train intervals between local and express trains. In the formulation, the relation between the energy consumption and the running time for each interstation was investigated by means of a train simulator. The condition of the minimum interval was added to the model as a non-linear inequality constraint under the fixed total time between the origin and destination of each train. The proposed method was demonstrated by a case study of a typical Japanese commuter line. It was found that several percent of energy was reduced by the proposed method.

This paper focuses on the methodology to apply the Dynamic Programming (DP) to the optimal operation control of trains with onboard Lithium-ion batteries in order to accumulate knowledge of energy-efficient driving technique. In this methodology, the energy-efficient driving control is formulated as an optimal control problem with two-point boundary conditions. We propose the model to consider the influence of the voltage variation of the batteries which depends on their State Of Charge (SOC) on the tractive effort of the train. The simulation results show that this methodology can minimize the energy consumption by deriving the optimal train speed profile taking the track condition such as speed limitations as well as the influence of the battery SOC. It is also found that the DP-based optimization could search against the error of the SOC.

Reduction of energy consumption is one of the recent key topics in railway sector. There are many investigations such as SiC devices, permanent magnet motors, onboard and stationary energy storage and reversible substations to absorb regenerative braking, energy efficient driving and traffic management. Multi-train simulators play an important role in evaluating the effect of these countermeasures. In the simulator, power feeding circuit must be modeled to calculate the energy consumption with high accuracy. There are some literatures of modeling and solving feeding circuits, however most of them linearized some nonlinear but important characteristics of trains and substations. We have also proposed a methodology of precise modeling and solving power feeding circuits of railway systems implemented in the multi-train simulator. The methodology is flexible and characterized by the special node numbering and introduction of 'tie nodes' which is solvable by multipurpose solvers. In this paper, we analyze the solvability of the methodology and introduce the virtual 'tie nodes' to decrease errors of circuit equation if the distance of two nodes is very near. The criterion whether the virtual tie node should be used or not is finally analyzed.

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.

The optimal operation of catenary free Light Rail Transit (LRT) with onboard energy storage device minimizing energy consumption is discussed in this paper. The Electric Double Layer Capacitor (EDLC) is assumed as an energy storage device because of its high power density, long lifetime and quick charge/discharge. The proposed method can determine the optimal capacitance of the EDLC and placement of charging facility at different cases for the purpose of saving the energy consumption. The trend of the optimal design of the EDLC sizing and the charging facility placement is finally discussed qualitatively.

The crescent interest in greener transportation systems increases the search for coherent, intelligent, efficient and more cost effective solutions. In this scenarios Light Rail Transits are becoming more and more used. In the direction or more flexible and safer system, contactless power systems are being used in LRT. This paper present an Improved core shape for the contactless transformer used in a model LRT system. The electrical and the magnetic analysis of the transformer are performed and compared to a Planar core transformer. The Improved core allows to suppress the utilization of core in the secondary reducing the weight in the train car with keeping the required transmitting power and input voltage.