Traction Energy Consumption Research Articles

Battery-Assisted Trolleybuses: Effect of Battery Energy Utilization Ratio on Overall Traction Energy Consumption

Urban public transport companies worldwide are introducing environmentally friendly bus solutions, and in cities with an existing trolleybus network, battery-assisted trolleybuses are an efficient alternative. Equipped with a traction battery, these vehicles allow a short range outside of the trolley lines and on-board recuperation, and they combine the advantages of electric buses and trolleybuses without the need for costly infrastructure expansions. Two similar battery-assisted trolleybuses are in operation in Žilina, where the unitary traction energy consumption has been observed to decrease as a function of the battery-powered and on-trolley-line vehicle run ratio. This theory was confirmed by statistical regression analysis of real operational data for one year of operation in different situations. This research also includes an analysis of battery-assisted trolleybuses’ operation on conventional trolleybus lines, which brought surprising findings, as well as an analysis of other selected indicators that can be dependent on the battery run ratio. These findings can contribute to reducing traction energy consumption by simply modifying the operating technologies without the need for extensive investment in infrastructure or vehicle equipment. This research is pilot research ready for in-depth research.

Integrating Energy-Efficient Train Control in railway Vertical Alignment Optimization: A novel Mixed-Integer Linear Programming approach

Incorporating train control into the railway design process enables a practical and comprehensive evaluation of the lifecycle utility of a track profile. This paper proposes a novel integrated approach, termed EETC-VAO, which combines railway track Vertical Alignment Optimization (VAO) and Energy-Efficient Train Control (EETC). Initially formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem, EETC-VAO aims to meet various geometric constraints and simultaneously minimize construction costs, traction energy consumption, and section running times in both directions. The model is subsequently reformulated into an equivalent Mixed-Integer Linear Programming (MILP) model using linearization methods and is further enhanced with valid inequalities, logic cuts, and a warm start algorithm with random velocity generation. The model has been extensively tested across a variety of case studies and train types, from synthetic small-scale scenarios to challenging real-world cases spanning from 3 to 71.2 km. Our findings demonstrate that operational costs can be significantly reduced with only marginal increases in construction costs. The integrated approach achieves reductions in total lifecycle costs of up to 40%, revealing a critical trade-off between construction and operational expenses. Notably, our results also indicate that lower construction costs do not inherently conflict with reduced operational costs, emphasizing the critical importance of integrating the train control scheme into the VAO problem.

Energy-saving design of underground metro vertical alignment with deep reinforcement learning

The design of metro vertical alignment greatly affects train traction energy consumption. This study develops a deep reinforcement learning (DRL) algorithm for an energy-saving design of underground metro vertical alignment and analyzes the influence of train traction energy consumption calculation factors (CFs) on energy-saving design. First, a metro train operation simulation model is adopted using the approximate integral method, and the value range of CFs as input parameters is determined. Second, the 'agent', 'state', 'action', and 'reward' of the metro vertical alignment environment and the corresponding alignment constraints are determined. Third, a Dueling Double Deep Q Network (D3QN) is constructed to design the energy-saving metro vertical alignment. The proposed model is applied to a real-world metro case study, and the influence of CFs (design speed, limiting gradient in reverse slopes, and passenger capacity) on energy-saving design is analyzed. Finally, compared to manual design, the D3QN-based design outperforms GA-based design and DQN-based design, and demonstrates a remarkable energy savings of 6.17%.

Friction Characteristics and Lubrication Properties of Spherical Hinge Structure of Swivel Bridge

A spherical hinge structure is a key swivel bridge element that must be considered when evaluating friction characteristics and lubrication properties to meet the rotation requirement. Polytetrafluoroethylene (PTFE)-based spherical hinge sliders and lubrication coating have been employed for over 20 years, but with the growing tonnage of swivel bridge construction, their capacity to accommodate the required lubrication properties can be exceeded. In this manuscript, the optimal friction coefficient of the spherical hinge is obtained through the finite element analysis method. Four lubrication coatings and four spherical hinge sliders are prepared and tested through a self-developed rotation friction coefficient test, four-ball machine test, dynamic shear rheological test, and compression and shear performance test to evaluate the lubrication and friction properties of the spherical hinge structure. The results of the finite element analysis show that the optimum rotation friction coefficient of the spherical hinge structure is 0.031–1.131. The test results illustrate that the friction coefficient, wear scar diameter, maximum non-seize load, phase transition point, and thixotropic ring area of graphene lubrication coating are 0.065, 0.79 mm, 426 N, 14.6%, and 64,878 Pa/s. The graphene lubrication coating has different degrees of improvement compared with conventional polytetrafluoroethylene lubrication coating, showing more excellent lubrication properties, bearing capacity, thixotropy, and structural strength. Compressive and shear tests demonstrate that polyether ether ketone (PEEK) has good compressive and shear mechanical properties. The maximum compressive stress of PEEK is 87.7% higher than conventional PTFE, and the shear strength of PEEK is 6.07 times higher than that of PTFE. The research results can provide significantly greater wear resistance and a lower friction coefficient of the spherical hinge structure, leading to lower traction energy consumption and ensuring smooth and precise bridge rotation.

Energy-saving operation in urban rail transit: A deep reinforcement learning approach with speed optimization

The energy consumption of urban rail transit plays a significant role in the operating costs of trains. It is particularly crucial to decrease the energy consumption of the traction power supply in subway systems, as it accounts for approximately half of the total energy consumption of the subway operating organization. To overcome the limitations of traditional real-time speed profile generation methods and the limited exploration capabilities of popular reinforcement learning algorithms in the speed domain, this paper presents the Energy-Saving Maximum Entropy Deep Reinforcement Learning (ES-MEDRL) algorithm. The ES-MEDRL algorithm incorporates Lagrange multipliers and maximum policy entropy as penalties to formulate a novel objective function. This function aims to intensify exploration in the speed domain, minimize train traction energy consumption, and ensure a balance between ride comfort, punctuality, and safety within the subway system. This leads to the optimization of speed profile strategies. To further reduce energy consumption, this paper proposes a secondary optimization strategy for the energy-saving speed profile. This approach involves trading acceptable travel time for improved energy efficiency. To validate the performance of the proposed model and algorithm, numerical experiments are conducted using the Yizhuang Line of the Beijing Metro. The findings demonstrate a minimum 20 % increase in energy efficiency with the ES-MEDRL algorithm compared to manual driving. This algorithm can guide energy-efficient train operations at the planning level.

The Impact of Priority in Coordinated Traffic Lights on Tram Energy Consumption

Traffic signal priority issues have been a research subject for several decades in Poland and worldwide. Traffic control algorithms have evolved considerably during this period and have become increasingly advanced. Most of them operate within coordinated street sequences, which adds to their complexity. Tramway priority affects traffic conditions for other road users, so many aspects must be taken into account when choosing a priority solution. Typically, one of the main criteria for evaluating the effectiveness of priority is reducing travel time for the priority vehicle while ensuring that the travel times of other traffic participants through the intersection are maintained or slightly deteriorated. However, the energy aspects are often overlooked. This publication aims to investigate how local priority for tramways in traffic signals of coordinated streets affects energy consumption for tramway traction needs. The study was conducted using a microscopic modeling method with PTV Vissim software (ver. 2021). The models were built for coordinated sequences with different levels of priority. Real traffic control algorithms with priority were implemented into the model on the sequence of Marymoncka Street and Grochowska Street in Warsaw. Then, by introducing changes to the parameters of the algorithms, their effect on traffic characteristics, including estimated power consumption, was studied. The results obtained from the computer simulation were statistically processed using R software (ver. 4.3.2). The analysis results prove the effectiveness of tramway priority operation, show its impact on electricity consumption, and allow us to determine the limits of its effective application. Thus, they complement the knowledge of the impact of tramway priority on traffic. The research results also have practical value, as they help us to make rational decisions in the process of designing traffic control algorithms at intersections with a multi-criteria approach.

The Modelling of Traction Energy Consumption of a Container Train

The authors focus in the paper on the procedure of calculating payments for traction electricity in the conditions of Správa železnic, s.o. (Railway Administration, the national railway infrastructure manager in the Czech Republic). Specific measures to technological factors that cause higher train power consumption are discussed. In the next section, the numerical solution of the train motion and the design of a simulation model for the possibility of computational assessment of the traction energy consumption of the train are described. A simulation model is proposed to numerically solve the train equation of motion. For the purpose of this paper, a specific track section and a specific train are selected. The results are compared with real recorded data obtained from the operation.

Definition of a Typical Driving Cycle of an electric taxi in an Andean city

This research aims to define a Typical Driving Cycle (TDC) of an electric taxi in Loja, Ecuador, an intermediate Andean city, considering the unit in service. In the first instance, the speed, taxi position, current, and battery voltage are acquired in real time through the OBDII port of the KIA SOUL EV, using a data logger device at a sampling rate of 1 Hz. The variables are read and stored using a program code developed in Labview. In addition, the start and end of rides are recorded. The taxi is monitored for a month, and the variable mass of the unit in service is recorded; road gradient effects are considered. Then, based on the fundamental theory of vehicle dynamics, the traction energy consumption of the taxi is obtained using Matlab Simulink; the TDC is defined by applying the Minimum Weighted Differences, whose characteristic parameters are the energies of the different forces opposing the vehicle motion. The vehicle completed 660 rides in the whole month, which is equivalent to an average of 54% of the traction energy; the rest of the energy, the taxi circulated without users. The TDC corresponds to ride 5, on day 11, with a traction energy consumption of 0.57 kWh, being 49.48% associated with inertia resistance.

A bi-objective optimization model of metro trains considering energy conservation and passenger waiting time

Continuous growth of energy consumption for metro systems has raised the public's concern. Meanwhile, both operators and passengers pursue for efficient metro services, especially during peak hours. This paper aims at designing a bi-objective optimization model and a new solution approach so that the energy conservation of metro trains and less passenger waiting time can be achieved. Firstly, considering complex routes, a multi-particle train operation model with the objectives of punctuality, parking accuracy, energy efficiency and safety provided is established for the minimization of traction energy consumption through investigating the optimal force coefficients and coast positions, while suitable dwell times and headway times of metro trains are found trough building a timetable model, which balances the regenerative energy, transfer and non-transfer passenger waiting time. Secondly, a deep reinforcement learning (DRL) and non-dominated sorting genetic algorithm II (NSGA-II) based two-layer solution approach is introduced for model calculation. Finally, comparison shows that the proposed model can achieve the energy conservation of metro trains by 9.16% with passenger waiting time decreased by 15.88%. More experiments further demonstrate the superiority of the developed model and solution approach.

Determination of Parameters for Limiting Longitudinal Acceleration to Optimize Energy Consumption for Train Traction

Purpose. The main purpose of the article is to improve traction calculations using mathematical modeling when solving the equation of train motion. The train model is adapted to optimize the parameters of train movement during speed gain with the determination of functional relationships between the power of the locomotive power plant and the profile of the track section with the consumption of energy resources by the locomotive. Methodology. Modern methods of mathematical modeling were used to solve the tasks. The modeling of the power plant operation was performed using interpolation and approximation methods. The simulation modeling method was used to create a model of the electrical part of the diesel locomotive transmission. The method of integrating the differential equation of motion was used to calculate the parameters and nature of the train's movement and the problem was solved, which consists in finding a solution to this equation over the length of the traction section, taking into account the variable values of the locomotive power and the slopes of the track profile. Findings. To analyze the results of traction calculations, we obtained such indicators as technical speed and diesel fuel consumption, and compared these indicators. The results show that in the case of acceleration limitation, there is a decrease in diesel fuel consumption at almost unchanged technical speed. Originality. The improvement of energy efficiency of train operation has been further developed, namely, the functional dependence of the relative power of the locomotive power plant, which takes into account the actual profile of the site, has been obtained, which makes it possible to determine energy-saving modes of locomotive control and reduce energy consumption, load on the diesel engine and elements of the diesel locomotive traction transmission during transient modes, which, in turn, in addition to saving diesel fuel, will affect the reliability of the power plant elements. The practical value of the study is to save diesel fuel in the modes of movement that provide speed gain and increase the reliability of the power plant due to reduced load.

Cooperative optimal train operation algorithm for utilizing regenerative braking energy

The efficient use of regenerative braking energy between trains can significantly reduce the net traction energy consumption in urban rail transit systems, and has attracted considerable attention in recent years. An effective way of using regenerative braking energy is to adjust the train's coasting or cruising phase to an acceleration phase to match the braking phase of a nearby train within the same substation. This paper formulates the train driving phase adjustment problem as a cooperative optimal control problem, where a novel energy constraint is introduced to ensure efficient use of regenerative braking energy during the adjusted phase with optimal traction effort. By applying the control parameterisation method and elaborate handling of the energy constraint and the non-smooth control bound constraint, we transform the formulated problem into a nonlinear programming problem. We then use the sequential quadratic programming algorithm to solve the resulting problem, with the gradients of the cost and constraint functions computed using the sensitivity method. Numerical examples based on real-world data for a subway line show the effectiveness of the proposed method and its advantages over a genetic algorithm in terms of energy savings and computational efficiency.

Reducing Traction Energy Consumption with a Decrease in the Weight of an All-Metal Gondola Car

The paper presented studies on reducing traction energy consumption with a decrease in the weight of an all-metal gondola car. Based on the proposed mathematical criterion, a new form of a blind floor was obtained, which makes it possible to reduce the weight of an all-metal gondola car. The aim of the paper was to reduce traction energy consumption with a decrease in the weight of an all-metal gondola car. For an all-metal gondola car with a modified form of a blind floor, strength studies were performed based on the finite element method. The equivalent stresses of the blind floor of an all-metal gondola car were 140.6 MPa, and the equivalent strains were 7.08 × 10−4. The margin of safety of the blind floor of an all-metal gondola car was 1.57. The weight of an all-metal gondola car with a modified form of a blind floor was reduced by 5.1% compared to a typical all-metal gondola car. For an all-metal gondola car with a modified form of a blind floor, a comparison was made of the traction energy consumption with typical all-metal gondola cars. Traction energy consumption with empty all-metal gondola cars were reduced by 2.5–3.1%; with loaded all-metal gondola cars by 2.4–7.3%, depending on the travel time interval.

On-time and energy-saving train operation strategy based on improved AGA multi-objective optimization

On-time and energy-saving train operation is important for the sustainable development of rail transit. As for the problems of traction energy consumption and on-time arrival at stations faced by trains in rail transit, an optimization strategy of energy-saving speed curves of trains based on an improved adaptive genetic algorithm (AGA) was proposed in this paper. First, weight coefficients of operation time and energy consumption were designed through an analytic hierarchy process, and an optimization model that targets train operation time and energy consumption was established according to a basic train operation model with constraints such as speed limits and precise train stopping. Then, on-time and energy-saving speed curves of trains were generated based on the improved AGA. Finally, a simulation was carried out with actual rail transit lines. The results show that the proposed method has strong efficiency for energy conservation and better optimization performance than the simple genetic algorithm in solving train trajectory optimization problem.

Energy Saving Optimal Operation Strategy of Freight Trains under Complex Scenarios

Background: For the optimization of energy-saving driving of freight trains in complex operating environments, the use of reasonable train maneuvering methods can largely reduce the energy consumption of train traction. Recent patents on energy-efficient maneuvering strategies for complex scenarios of freight trains have been researched. Objective: Using the receding horizon algorithm and the improved NSGA-II algorithm to solve the target speed curve of freight trains to cope with the complex and changing operating environment, and to explore the recent patents of energy-saving maneuvering strategies for freight trains and methods. Methods: The recent patents of energy-efficient maneuvering strategies for freight trains in complex scenarios are investigated in this research. A multi-objective optimization model for freight train maneuvering with electrical phasing was developed with the objectives of reducing the traction energy consumption and running time of the train. A method for determining the optimal operating conditions of freight trains under complex line conditions is proposed. The offline optimization of the target speed curve under the electrical phasing constraints of freight trains and the online adjustment under the temporary speed restriction (TSR) are achieved by using the RH-INSGA-II (receding horizon-improved NSGA- II) algorithm. Results: Combined with an actual freight railroad line data as an example, simulation experiments were conducted and verified with HXD1 electric locomotive hauling 50 C80 wagons. Conclusion: The speed curve considering the split-phase constraint can effectively reduce the traction energy consumption. The electrical split-phase constraint affects the whole speed optimization process, not only the speed curve at the split-phase zone. Although the traction energy consumption is increased with the addition of the TSR on the line, the RH-INSGA-II algorithm dynamically changes the sequence of optimal train maneuvering conditions according to the planned train running time in order to avoid further amplification of the late train time.

The cost differential for an optimal train journey on level track

The classic optimal train control problem is to drive a train on a track with known gradient over a fixed distance and within a specified time in such a way as to minimize tractive energy consumption. On level track the optimal strategies take two basic forms—a truncated strategy of optimal type with phases of maximum acceleration, coast and maximum brake which is typical of shorter metropolitan journeys, and an extended strategy of optimal type with phases of maximum acceleration, speedhold at the optimal driving speed, coast to the optimal braking speed, and maximum brake which is typical of longer journeys by freight trains and intercity passenger trains. The cost of these optimal strategies is uniquely determined by the journey distance and journey time. In this paper we extend a previously known formula for the partial rate of change of cost with respect to journey time to a formula for the full rate of change of cost that also incorporates the partial rate of change of cost with respect to journey distance.

Integrated energy‐efficient optimization for urban rail transit timetable

AbstractThe energy‐efficient optimization of the train timetable should be carried out on the basis of meeting passenger demand and determining the train operation plan. This paper proposes an integrated energy‐efficient optimization framework for the train timetable. Firstly, two energy‐efficient optimization strategies for this problem are proposed: inter‐station running time allocation and regenerative braking energy utilization. The first strategy aims to reduce the traction energy consumption, realized by selecting the profile based on a given set of speed profiles for each inter‐station sector when ensuring a stable travel time of train routing. The second strategy is intended to fully utilize the regenerative energy feedback from braking trains, realized by setting the departure time of trains based on a small‐range headway adjustment. Then, an integrated energy‐efficient optimization model of the timetable is developed and solved using the genetic algorithm. Finally, a case study of the Guangzhou Metro Line 8 is presented to verify the effectiveness of the method. The results show that the utilization rate of regenerative braking energy in the optimized timetable increases by 8.60%, and the total energy consumption decreases by 9.52%. Regenerative energy utilized roughly doubles during peak hours and increases more than twice as much during off‐peak hours.

A Learning Based Intelligent Train Regulation Method With Dynamic Prediction for the Metro Passenger Flow

With the acceleration of urbanization, the dynamic passenger flow has an ever-growing impact on the actual train operation. In this paper, we propose a learning based intelligent train regulation method with dynamic passenger flow prediction. To capture the characteristics of the dynamic metro passenger flow, a convolutional neural network is established to predict the real-time passenger flow from two dimensions including space and time. As the prediction accuracy is restricted by the insufficiency of the practical passenger flow data, a deep convolutional generative adversarial network is constructed to generate data that have the same distribution as the original passenger flow dataset. Then, by considering the effects of the dynamic passenger flow on the train operation and the train capacity constraints, the dynamic train regulation is formulated as a multi-stage optimal control problem with the objective function of minimizing the train traction energy consumption and the total traveling time of passengers. To efficiently obtain the optimal regulation strategy at each decision step, a deep Q-network algorithm is proposed to solve the formulated problem such the dimensionality curse caused by the excessive state space is avoided. The numerical experiments demonstrate the high efficiency and effectiveness of our proposed algorithm and model.

A Two-Stage Optimization Algorithm of the Train Traction Energy Consumption in Urban Rail Transit

The optimization of the train traction energy consumption in urban rail transit is a nonlinear optimization problem, and its solution is a very difficult task. This article focuses on the optimization of train traction energy consumption on the line with multiple stations. First, a single-objective nonlinear energy-saving optimization model is established for a given line and total travel time. Second, a two-stage optimization algorithm is proposed to solve this model. In the first stage, for a given interval and travel time, an optimization model of the traction energy consumption is established by using the distance discretization method. A dichotomous iterative algorithm based on energy consumption is proposed to search the optimal traction energy consumption. By calling this algorithm repeatedly, the energy–time curve of each interval can be obtained. In the second stage, according to these energy–time curves, the total travel time of a train on the line is allocated to each interval one by one in the form of time slices. And finally the optimal travel time and optimal traction energy consumption of the train on each interval are obtained. This method does not need to set the train operation mode sequence in advance but adaptively selects the energy-saving operation mode according to the line constraints and the train parameters. The up-direction from Yizhuang to Tongjinan of the Beijing Metro Yizhuang line in China is selected as the test section. The experimental results demonstrate the effectiveness and computational efficiency of our proposed methods and algorithms. Moreover, the dichotomous iterative algorithm runs so fast that it can be used in real-time driver advisory systems or automatic train operation systems.

Efficient Deployment of Dual Locomotives in Regional Freight Rail Transport

The present article focuses on the efficient deployment of dual locomotives in regional rail freight transport considering the quantification of traction energy and energy savings. In the first part of the article, a categorization of dual locomotives, according to their power output in electric and alternative traction (and ratio of both power outputs) is proposed. The potential of deployment of chosen dual locomotives in Central European conditions (a sub-network of Czech railway network around mainlines electrified with AC) is verified by calculation of traction energy consumption of the model train (with two examples of dual locomotives). In addition to non-stop running through the entire line, traction energy consumption of stop (and following acceleration) in each intermediate station is calculated, for a particular direction. Then, appropriate freight train paths for passing passenger trains and saving of traction energy are proposed. The results are supplemented by sensitivity analysis in the form of calculation of traction energy consumption with variable numbers of loaded wagons, with the help of iPLAN/FBS timetabling software. The limitations are the maximum length or gross mass of the train. Finally, the conclusions obtained from the computational examples are evaluated and recommendations for appropriate deployment of dual locomotives and planning of targeted improvements of infrastructure are formulated.

Energy-Efficient Optimization Method of Urban Rail Train Based on Following Consistency

Because of the short distance between stations in urban rail transit, frequent braking of urban rail trains during operation will generate a large amount of regenerative braking energy. Urban rail trains can reduce their actual traction energy consumption using regenerative braking energy. Therefore, an energy-efficient optimization method for urban rail trains is proposed. By taking the punctuality of trains as the premise, the weighted acceleration of trains is taken as the synergetic variable, the synergetic coefficient is introduced to construct the following consistency model, and its convergence is proved. By analyzing the influencing factors of the following consistency coordination time, an adaptive parameter adjustment strategy is designed to solve the latest secondary traction time and the corresponding maximum speed of the primary traction. In order to save communication resources, the event trigger function is used to construct trigger conditions, and the consistency algorithm is used to update the cooperative controller. The simulation results show that the weighted acceleration of the follower train achieves the following consistency on the premise of ensuring punctuality, and the actual traction energy consumption of the follower train is reduced by 5.73%. The proposed method provides a new strategy for the energy-efficient operation of urban rail trains.