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For high-speed rail with high energy consumption, the recovery and utilization of regenerative braking energy is essential to improve the energy consumption of high-speed rail. As a technical link, the energy bidirectional feed inductively coupled power transfer (ICPT) system can realize the regenerative braking energy recovery of the contactless traction power supply system. Furthermore, considering that the braking energy of the high-speed rail is the largest when entering the station during the whole line operation, the braking section of the station is mainly considered. This paper proposes a preset control method for segmented power supply of the energy bidirectional feed ICPT system considering regenerative braking energy recovery. By establishing the steady-state mathematical model of the bidirectional ICPT system, the influence of the internal phase-shift angles φ1 and φ2 and the external phase-shift angle γ on the operating state of the system is analyzed. To realize system synchronization under the operation of EMUs, a train braking model is established through force analysis, and a power preset controller is designed to realize the synchronous control of the power flow of the bilateral system. According to the braking process of the train entering the station, the switching control method of the segment coil under the different conditions of the single train entering the station and the multitrain entering the station is proposed to ensure the reliability and flexibility of the train power supply. The simulation results of the 350 kW ICPT system simulation model show that the system can operate stably when the power transmission simulation is switched, and the transmission efficiency can reach 89%, which proves the feasibility of the control method. Energy-saving estimates show that a single train can recover about 200–300 kWh of electric energy during single braking. The comparison with the measured data verifies the accuracy of the modeling in this paper. [ABSTRACT FROM AUTHOR]

In recent decades, two different approaches to mine ventilation control have been developed: ventilation on demand (VOD) and automatic ventilation control (AVC) systems. The latter was primarily developed in Russia and the CIS countries. This paper presents a comparative analysis of these two approaches; it was concluded that the approaches have much in common. The only significant difference between them is the optimal control algorithm used in automatic ventilation control systems. The paper describes in greater detail the algorithm for optimal control of ventilation devices that was developed at the scientific school of the Perm Mining Institute with the direct participation of the authors. One feature of the algorithm is that the search for optimal airflow distribution in the mine is performed by the system in a fully automated mode. The algorithm does not require information about the actual topology of the mine and target airflows for the fans. It can be easily programmed into microcontrollers of main fans and ventilation doors. Based on this algorithm, an automated ventilation control system was developed, which minimizes energy consumption through three strategies: automated search for optimal air distribution, dynamic air distribution control depending on the type of shift, and controlled air recirculation systems. Two examples of the implementation of an automated ventilation control system in potash mines in Belarus are presented. A significant reduction in the energy consumption for main fans' operation obtained for both potash mines. [ABSTRACT FROM AUTHOR]

With the increasing awareness of carbon neutrality, the application of energy-efficient train control (EETC) to rail transportation systems continues to attract attention from industry and academia. In many classic EETC studies, train models are commonly simplified with pure regenerative braking and constant power characteristics during high speeds, to simplify the complexity of the model. In this paper, a realistic model incorporating hybrid braking characteristics combining regenerative and mechanical braking, and reduced-power characteristics at high speeds into the EETC problem is proposed to improve control precision of the train and the modeling precision of the energy consumption and time. This study addresses the minimum-time train control (MTTC) problem and EETC problem considering nonlinear traction characteristics based on the mixed-integer linear programming (MILP) method, and nonlinear traction and braking characteristics are approximated via a piecewise linear (PWL) modeling technique. Results indicate that the proposed alternative models achieves some deviations from the realistic model in terms of time, energy consumption, and control strategies. The deviations between models in energy consumption and time accumulate as the number of operating stations increases. Therefore, the choice of an appropriate model depends on the precision requirements of various scenarios. In scenarios demanding higher precision, selecting the proposed realistic model is crucial for more accurate computation of energy consumption and time and for obtaining more precise control strategies. • A novel MILP model addresses EETC and MTTC problems with varying nonlinear traction system characteristics, using PWL modeling for approximation. • The impact of realistic nonlinear traction system characteristics model on the EETC problem is considered, and the optimal train control strategy is derived. • Comprehensive case studies on urban rail transit and high-speed railways demonstrate the impact of realistic traction/braking forces on eco-driving strategies. • Sensitivity analysis of critical speed assesses energy consumption variations. [ABSTRACT FROM AUTHOR]

The train generates regenerative braking energy (RBE) during braking. When the RBE generated by the braking train is too large to be absorbed and utilized, the OCS voltage will rise rapidly. It may even exceed the rated voltage threshold, resulting in hidden dangers for driving safety. How to use RBE efficiently and reasonably has become a hot issue in recent years. This paper presents a metro timetable optimization method for multi-train operation to improve the RBE utilization efficiency of trains. Firstly, an energy-saving multi-objective model for multi-train operation is established, which takes the total energy consumption and travel time as the objective functions and the dwell time and departure interval as the constraints. Then, the genetic algorithm (GA) is used to generate the composition sequence of dwell time and departure interval, and the multi-node nonlinear model is used to calculate the total energy consumption of the metro line. Finally, Guangzhou metro line 7 is selected as an example to illustrate the effectiveness of the developed optimization model. The results show that the energy consumption of the whole line can be effectively reduced by adjusting the dwell time and departure interval. Meanwhile, the timetable optimization model with GA has better energy-saving effects by comparative analysis. • A metro timetable optimization method based on improved genetic algorithm is developed. • The multi-node nonlinear model is developed to calculate the total energy consumption. • The developed optimization model has better energy-saving effects than previous methods. [ABSTRACT FROM AUTHOR]

This paper investigates the nonlinear model predictive control to automatic train regulation in metro systems under dynamically disturbed environments. A coupled state-space model for the evolution of train traffic and passenger load dynamics is explicitly developed. With the safety constraints, a mixed-integer nonlinear optimal control model is formulated to generate train regulation strategies. An efficient solution algorithm, based on spatial branch and bound combined with generalized Benders decomposition, is particularly designed to obtain exact solutions quickly for the embedded applications. The original complex problem can be converted into several smaller convex quadratic subproblems with reduced domains to be solved quickly. The effectiveness of our train control model and approaches are verified in numerical simulations. Computational results illustrate that the proposed control method effectively improves the operational efficiency and meanwhile reduces energy consumption during operations. Besides, the efficient embedded solution algorithm performs satisfactory computational efficiency, facilitating a real-time implementation. [ABSTRACT FROM AUTHOR]

This paper studies the problem of automatic train operation (ATO) robust nonlinear model predictive control under considering multiple objectives and constraints. After establishing a nonlinear multipoint model with uncertain bounded disturbance, a robust nonlinear model predictive control algorithm to meet the punctuality of train operation and energy consumption for ATO is proposed based on constraint tightening strategy. Moreover, theoretical analysis of the feasibility and stability for the speed loop system are presented. Then, with the objective of reference electromagnetic torque tracking and low switching frequency, a model predictive direct torque control algorithm with one‐step delay compensation is proposed. Feasibility of the proposed algorithm is ensured by using deadlock prediction method, and convergence analysis of the torque loop is given simultaneously. Lastly, the effectiveness of these two algorithms are verified by numerical simulation. [ABSTRACT FROM AUTHOR]

SUMMARY Recently, interest in energy savings in railway systems has been increasing because of its environment-friendly aspects, for example, CO2 emissions. In this paper, the authors propose a scheduling and control system for automatic train operation (ATO) that saves energy. This research in ATO concentrates on the optimization of speed profiles to save energy. The differences in this system from previous work are substantiative experiments on the track and a design that explicitly considers the following energy-saving operations. First, coasting is installed in speed profiles and maximum speed is decreased by jerk regulation. Second, power-limiting braking is used in the braking section and regenerative energy is increased. To achieve this braking efficiency, notch operations are updated. Finally, second-order scheduling is achieved by high speed control using ATO. For the experiments on the track, the efficiency of a linear-motor train was measured in a pre-experiment and used to perform accurate numerical calculations. In conclusion, the numerical study shows an energy efficiency increase by 7.3% and the plan for further experiments is determined. [ABSTRACT FROM AUTHOR]

In high-density metro lines, frequent disturbances could lead to a domino effect of train delays if no adjustment strategy is imposed timely. In this paper, to enhance train timetable adherence and reduce energy consumption, we provide a hierarchical optimal control framework for the automatic train regulation problem with energy-efficient speed trajectory based on the model predictive control method. Specifically, by considering the dynamic passenger flows, the non-linear train regulation model is proposed at the higher level to enhance timetable adherence, while at the lower level the energy-efficient speed trajectories of the trains are generated on-line in a distributed manner. The interaction of real-time information exists between the higher level and the lower level, i.e., the upper model provides the train running time and the numbers of onboard passengers to the lower model, while the lower model feedbacks the updated train information (e.g., the actual arrival time) to the upper model, which provides the potential for the proposed framework to respond to disturbances at the operational stage. Moreover, a customized model predictive control method combined with Radau pseudospectral method (RPM) is designed to generate the energy-efficient train speed trajectory at the lower level in response to uncertain operational conditions. Numerical cases based on the Beijing Metro Yizhuang line demonstrate the effectiveness and robustness of our proposed hierarchical optimal control framework to deal with uncertain disturbances. • A hierarchical optimal control framework is designed to realize complexity reduction. • A non-linear model is built for the train regulation problem at the higher level. • The energy-efficient speed trajectories are generated on-line at the lower level. • The hierarchical framework reduces both timetable deviation and energy consumption. [ABSTRACT FROM AUTHOR]

Most existing automatic train operation (ATO) models are based on different train control algorithms and aim to closely track the target velocity curve optimized offline. This kind of model easily leads to some problems, such as frequent changes of the control outputs, inflexibility of real-time adjustments, reduced riding comfort and increased energy consumption. A new data-driven train operation (DTO) model is proposed in this paper to conduct the train control by employing expert knowledge learned from experienced drivers, online optimization approach based on gradient descent, and a heuristic parking method. Rather than directly to model the target velocity curve, the DTO model alternatively uses the online and offline operation data to infer the basic control output according to the domain expert knowledge. The online adjustment is performed over the basic output to achieve stability. The proposed train operation model is evaluated in a simulation platform using the field data collected in YiZhuang Line of Beijing Subway. Compared with the curve tracking approaches, the proposed DTO model achieves significant improvements in energy consumption and riding comfort. Furthermore, the DTO model has more advantages including the flexibility of the timetable adjustments and the less operation mode conversions, that are beneficial to the service life of train operation systems. The DTO model also shows velocity trajectories and operation mode conversions similar to the one of experienced drivers, while achieving less energy consumption and smaller parking error. The robustness of the proposed algorithm is verified through numerical simulations with different system parameters, complicated velocity restrictions, diverse running times and steep gradients. [ABSTRACT FROM AUTHOR]

A power-split-type transmission with independently controllable output speed (ICTs) was recently proposed. It is capable of converting a variable or constant input shaft speed into a controllable output shaft speed independent of the input speed — without ordinary speed control system. However, this transmission has no practical significance, because of the huge control motor power and uncontrollable speed of one output shaft. The novel type of the hybrid ICT is proposed in this paper that consists of three differential gear trains and two ordinary gear drives, with input shaft of arbitrary speed and three more external shafts with mutually controllable speeds. The kinematic synthesis of the series of the proposed type of ICTs is carried out. The numerical example, assigned to a vehicle, is demonstrated where a continuous variation of required vehicle speeds at arbitrary riding conditions is achieved without using a speed control system. For the optimal speed of driving engine, i.e. for minimum fuel consumption at any vehicle speed, the engine speed control system is provided, with control motor that runs at speed forcing engine to run at optimal speed. [ABSTRACT FROM AUTHOR]

This paper presents a design methodology for the development of self-powered active lateral secondary suspensions for rail vehicles. It first investigates the energy flow in the active lateral secondary suspensions and analyzes the conditions for self-powered control in detail. The impact of the controller design on both ride quality and energy consumption is then used to guide the design/specification of actuators and to define key actuator parameters to achieve both expected performance improvement and zero-energy consumption for the actuators. Furthermore, a control strategy for dealing with larger-than-expected energy consumption by the active suspensions is proposed not only to eliminate excessive power requirements but to ensure ride quality improvement, in comparison to that by passive suspensions, as well. Computer simulations are used to validate the control strategy for the self-powered active suspension. [ABSTRACT FROM AUTHOR]

One of the main priorities for metro line operators is the reduction of energy consumption, due to the environmental impact and economic cost. The new moving block signalling system CBTC (Communication Based Train Control) is being installed in order to increase the transport capacity of new metro lines, and to upgrade lines equipped with the former fixed block signalling systems. In addition, this new technology could be used to improve energy efficiency in the traffic operation, due to its capability to update the ATO (Automatic Train Operation) driving commands not only at stations but also along the journey. In this paper a new optimization algorithm is proposed to design ATO CBTC speed profiles to minimize energy consumption, generating the Pareto optimal curve. The algorithm is a multi-objective NSGA-II-F based on simulation of the train motion under uncertainty. The main source of uncertainty in the consumption calculation is the mass of the train, which is dependent on the passenger load, and it is modeled as a fuzzy number. In addition, a new method is proposed to fill running time gaps with dominated points, providing a pseudo-Pareto curve. The NSGA-II-F algorithm has been applied to design ATO CBTC optimal speed profiles in a case study of Metro de Madrid, providing additional energy savings up to 7.3% compared to former fixed block signalling systems and a well time-distributed pseudo-Pareto curve for the running times required by the traffic regulation system. [ABSTRACT FROM AUTHOR]

Issues regarding environment sustainability and energy saving have been receiving much attention in the worldwide railway society. Saving energy via train regulation is cost effective and has become an issue to be considered in railway operations. Automatic train regulation (ATR) plays an important role in maintaining metro service quality; however, designing ATR is an optimal control problem with large scale, high nonlinearity, heavy constraints, and stochastic characteristics. Considering issues regarding energy saving in the ATR design would further complicate the optimization problem. In this paper, traffic environment associated with energy consumption resulting from traffic regulation is investigated, and a model for describing the environment is developed; thereby, a dual heuristic programming (DHP) method for designing ATR with energy saving via coasting and station dwell control is proposed. The evaluation of the ATR design is conducted with field data. The evaluation shows that better traffic regulation with higher energy efficiency is attainable with the ATR design. [ABSTRACT FROM PUBLISHER]

In this paper, we present an automatic train control method adaptable to disturbed train traffic conditions. The proposed method presumes transmission of detected time of a home track clearance to trains approaching the station by employing equipment of Digital ATC (Automatic Train Control). Using the information, each train controls its acceleration by a method that consists of two approaches. First, by setting a designated restricted speed, the train controls its running time to arrive at the next station in accordance with predicted delay. Second, the train predicts the time at which it will reach the current braking profile generated by Digital ATC, along with the time when the braking profile transits ahead. By comparing them, the train correctly chooses the coasting drive mode in advance to avoid deceleration due to the current braking profile. We evaluated the effectiveness of the proposed method regarding driving conditions, energy consumption, and reduction of delays by simulation. © 2011 Wiley Periodicals, Inc. Electr Eng Jpn, 175(3): 65-73, 2011; Published online in Wiley Online Library (). DOI 10.10020/eej.21080 [ABSTRACT FROM AUTHOR]