Traction Energy Research Articles

Comparison of On-Road Highway Fuel Economy and All-Electric Range to Label Values: Are the Current Label Procedures Appropriate for Battery Electric Vehicles?

<div class="section abstract"><div class="htmlview paragraph">As consumers transition from internal combustion engine (ICE)-powered vehicles to battery electric vehicles (BEV), they will expect the same fuel economy label-to-on-road correlation. Current labeling procedures for BEVs allow a 0.7 or higher multiplier to be applied to the unadjusted fuel economy and range values. For ICE-powered vehicles, the adjustment factor decreases with increasing unadjusted fuel economy and can be lower than 0.7. To better inform consumers, starting in 2016, Car and Driver added an on-road highway fuel-economy test, conducted at 120 kph (75 mph), that augments the performance metrics that it's been measuring since the 1950s. For electric vehicles, testing includes an evaluation of the all-electric range.</div><div class="htmlview paragraph">The on-road test results were aligned with the certification information for each vehicle model including unadjusted and label fuel economy and range, road load force coefficients, and labeling options. Tractive energy and kinetic energy available for regenerative braking were computed from the certification information to evaluate the differences between the on-road testing and chassis-rolls testing conducted during the certification process.</div><div class="htmlview paragraph">Based on these results, the highway fuel economy label tends to be a very good predictor of the fuel economy observed during the on-road test at real highway speeds with vehicles powered by an ICE. However, most BEVs tested to date fall short of both their electric consumption and range label values. For BEVs, the difference between the label and on-road consumption and range is further exacerbated by other factors, such as extreme temperatures and suggestions by automakers to charge to less than 100 percent to extend battery life. Consequently, these results support the need to re-evaluate the labeling procedures for this emerging technology as it continues to become increasingly prominent in the marketplace.</div></div>

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.

Exploring hydrogen fuel cell and battery freight locomotive options using train dynamics simulation

As efforts are being made to quickly adopt zero carbon technologies and replace fossil fuel powered trains, the design requirements are explored using train dynamics simulations to establish energy needs. The test case was a freight train of 3030 gross tonnes consisting of two locomotives and thirty wagons operating on a relatively flat coastal rail route. Then, assuming rapid deployment of existing and near market technologies, speculative locomotive designs are proposed for both battery only and hydrogen-battery systems. Batteries were assumed as rack mounted systems and hydrogen assumed at 250 bar gaseous. The results showed that full recovery of dynamic braking required only a very small amount of storage to capture and allow re-use, requiring a battery of only 6% of the trip total of recovered energy. The haulage task was found to require traction energy of 10.7 MWh per locomotive but reduced to a net requirement of 7.6 MWh per locomotive with recovery of dynamic braking energy. The large mass of batteries needed for the operating trip, however, limits the practical operating range. The results showed that, although hydrogen storage and equipment occupy considerable space and the containment systems have large mass, the space and mass requirements for a hydrogen system in this case study could be as low as half that required for batteries, while higher pressure or cryogenic approaches to hydrogen storage will allow the scaling for longer range.

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.

Optimal intermittent electrification and its effect on battery sizing and energy saving using a high-fidelity train model

The fastest way to decarbonise traction energy of a railway network is full electrification of the network. However, full electrification can be costly and is often delayed due to complicated planning procedures. Often, it is not financially viable to electrify certain sections of the network due to geographical challenges such as hillsides and requirements to raise or lower the tracks. Intermittent electrification can provide a way to reduce emissions on a route without fully electrifying it. This paper presents an optimal intermittent electrification strategy and compares its effects on required battery size to a ‘common-sense’ approach. The “common-sense” approach is a heuristic electrification method where high-power sections of the route are electrified first. Energy savings are demonstrated using a validated high-fidelity bi-mode train model on the Newbury-Plymouth route of Great Western Railway in the UK. Unlike prior research, the optimal electrification strategy considers energy consumptions for both directions of a journey on the route and is therefore optimal regardless of travel direction. The 278-km Newbury-Plymouth route was divided into 50 equal discrete sections for the optimization process, which then identified sections that should be prioritised for electrification as they consume the maximum energy. A high charge/discharge lithium titanite-oxide battery was modelled and installed on the virtual vehicle to determine the required battery sizing for given optimal percentages of intermittent electrification. The battery sizing strategy includes battery wear effects from charge/discharge cycle life and shows the battery life to be 1.42 years if kept at 55°C and 6 years if kept at 25°C. At 36% electrification, using the ‘common-sense’ approach, the battery weighs 10,500 kg and the battery-OLE train reduces the CO2 emissions and energy usage on the route by 83% when compared to the diesel-only train.

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.

Collaborative Optimization Method for Multi-Train Energy-Saving Control with Urban Rail Transit Based on DRLDA Algorithm

With the traffic congestion problem deteriorating, people increasingly choose urban rail transit (URT) to travel. Although URT alleviates traffic congestion, the long-term operation of a large number of trains leads to huge energy consumption. In order to adapt the major social development concept of “Low carbon”, a multi-train energy-saving control collaborative optimization method is proposed in this paper. First, the composition of single train operating conditions is determined by the conversion of operating conditions between stations and the force changes under the premise of ensuring safe and on-time train operation. A single-train energy consumption calculation combinatorial optimization model with the dual control objectives of reducing passengers’ average waiting time as well as train traction energy consumption is established. The energy saving control strategy of a single train is investigated by ARMA-Radial Basis Function Neural Network (ARMA-RBFNN) and Genetic Algorithm (GA). Next, the queuing theory is introduced to analyze the variation in passenger waiting time for multiple trains at different arrival intervals. A Deep Reinforcement Learning (DRL) algorithm is designed to obtain the correlation among passenger waiting time, arrival interval and train stopping time. The optimization objective is to minimize the multi-train traction energy consumption and the average passenger waiting time while considering conditions such as train operating safety interval, speed limit, multiple operating state and single train energy-saving models, etc. Then, a multi-train cooperative energy-saving control model is proposed based on the Dragonfly Algorithm (DA). Finally, a case study of Beijing Metro Line 4 is conducted to illustrate the effectiveness of the proposed method. The results demonstrate that the total traction energy consumption and passenger waiting time are reduced by 3.1% and 5 s, respectively, compared with the method of independently optimizing the single-train control strategy. The findings can aid in the development of energy-saving strategies and also provide a basis for energy-saving operation control of multiple trains.

Energy Context: Analysis of Selected Studies and Future Research Developments

Energy context has been the subject of a great deal of research from different perspectives, including, but not limited to: energy transition, renewable energy, nuclear power, energy saving, energy use, energy level, energy trade, energy security, traction energy, energy communities, energy poverty, Energy Performance Contracting (EPC), climate change, climate policy, and climate sustainability [...]

Estimation of Traction Energy Consumption of Urban Service Buses in an Intermediate Andean City

The purpose of this work is to estimate the traction energy consumption of public transport buses in the urban sector of the city of Loja, Ecuador. Initially, with a data logger device, connected to the OBDII port, the speed and position variables of the transport units are acquired in real time, at a frequency of 1 Hz, during 25 round trips on 3 bus lines with the highest passenger flow; the effects of the slope profile are considered. To avoid information bias, 25 different HINO AK bus units, with different drivers, are monitored on full daily working days, where traffic is variable; different mass of the bus is defined in the three lines, based on historical data of the average number of passengers. Then, based on the fundamental theory of vehicle dynamics, the traction energy consumption of the buses is obtained in Matlab Simulink. For this purpose, a typical driving cycle (TDC) is de-fined, through a deterministic method, for both the outbound and the return trip of each line. The results highlight a higher traction energy consumption on line L8, with 50.36 kWh, where 52.43% is associated with slope resistance, 35.76% inertia resistance, 10.06% rolling resistance and 1.75% aerodynamic resistance. These findings represent a starting point for subsequent studies, in this Andean city, related to electromobility in mass transportation systems.

Reconciliation of Train-Run data and Train consumption data for a better traction energy consumption estimation, a new Tool

The present work addresses the estimation of the traction energy using train-run data. The proper quantification of electrical traction energy by Railway Undertaking is necessary for energy settlement and billing by the different energy stakeholders such as energy suppliers as well as the infrastructure managers. The system thus needs a fair estimation of the train consumption most notably in case of missing data. Train-run data are used to compute an estimation at the train level and then aggregated at the Railway Undertaking level. We used an aggregate tree-based model for the estimation models using a set of different physical variables extrapolated from a train dynamic physical model. In turn, it has imposed another tool for the reconciliation between train-run data and consumption data at the vehicle level because often train-run data and consumption data are siloed.This methodology has allowed the development of ORES, a new tool dedicated to the train-run data and consumption data reconciliation as well as train-run electrical traction energy estimation. Some additional approaches have also been tried using consumption at substations. The tools have been successfully used on various types of mission and rolling stocks and have enabled to better understand the limits of such systems.

Towards infrastructure energy labeling utilizing data from a connected fleet of electric vehicles

This work was motivated by mapping the energy consumption of road networks towards development of energy efficiency labeling – similar to what is done in consumer sectors. Specifically, the paper presents vehicle traction energy data from the LiRA project, focusing on the usability of it by looking at the effects of longitudinal slope, vehicle speed, and vehicle acceleration. Based on the results, it is shown that it is possible to extract traction energy from the LiRA database and also to normalize the measurements for slope and acceleration. The energy consumption remaining after normalization holds information associated with vehicle speed, wind conditions, vehicle-related inefficiencies, and lastly – the physical state/condition of the infrastructure. Isolation of the latter component will be the topic of further investigations aiming at developing a system for road energy labeling.

Economical Efficiency of Energy Storage for the Traction Energy System

Economical Efficiency of Energy Storage for the Traction Energy System

Analysis of the Abnormality of Traction Energy Consumption in Urban Rail Transit System

Traction energy consumption (TEC) is a critical part of the total energy consumption in urban rail transit (URT) systems. Energy consumption patterns and abnormal analysis of TEC guarantee the energy‐saving URT operation. With the rapid development of urbanization, the current energy consumption is becoming more and more prominent with some inherent drawbacks, such as complex original data, complicated statistical analysis, and abnormal energy consumption. This paper proposes a method for time accrual abnormal analysis of TEC. The system architecture of TEC typical values is presented, composed of three elements: research object, evaluation index, and time scale. The time series prediction algorithm calculates the typical values of the cumulative energy consumption index in each energy consumption mode. For the abnormality in TEC mode, the distance of the string vector is used as the similarity measure. Then, the similarity‐based anomaly analysis method is used to judge the pattern abnormality. By comparing the advantages and disadvantages of engineering practice and theoretical research methods, we analyze the applicability of traditional anomaly detection algorithms to perform anomaly analysis of TEC in URT systems. The adopted time accrual abnormal analysis achieves a high fault detection rate, outperforming other models.

Improving vehicle energy efficiency with braking energy recovery

In these days, improving energy efficiency of transport vehicles become one of the priorities. A vehicle must have enough traction power to overcome rolling, aerodynamic, acceleration and road grade resistances. Fortunately, acceleration and road grade resistances consume energy that is possible to recover at significant levels. At the present work, the operating characteristics affecting the braking energy of a 12 m long and 15 tons loaded weight city bus were analysed. The results indicated that Campus route has a cumulative braking energy of 26 kW-h. It is about 80% of the traction energy for the route, However, Karaman route has a cumulative braking energy of 38 kW-h. It is about 65% of the traction energy for the route. If 50% of the braking energies are recovered, percentages of the energy savings for the Campus and Karaman routes will be approximately 40% and 30%, respectively.

Comparison and Analysis of Prediction Models for Locomotive Traction Energy Consumption Based on the Machine Learning

Locomotive traction energy consumption is a multivariate coupled nonlinear system closely related to many factors such as locomotive properties, routing, line conditions, and operating methods. In order to accurately calculate and predict locomotive traction energy consumption, three prediction models are constructed based on a large number of measured operation data of the HXD1 locomotive. This research uses two neural network methods of backpropagation and radial basis functions, as well as support vector machines. Among them, the training group uses 600 data; the validation group uses 200 data. The above methods can compare and analyze prediction performance between different neural networks and machine learning algorithm models. The results show that the RBF and BP neural networks can effectively predict locomotive traction energy consumption among the different neural network models. The R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of the two neural network model test groups is 0.9926 and 0.9885, the MAPE is 2.91% and 7.28%, and the MSE is 0.02% and 0.08%, respectively. Moreover, we have avoided the influence of the randomness of the neural network algorithm through repeated running. It shows that RBF neural network is better than BP neural network in predicting locomotive traction energy consumption, with more powerful approximation performance and higher accuracy. Among the different machine learning algorithms, the R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of the SVM algorithm model test group is 0.9983, the MAPE is 2.01%, and the MSE is 0.02%, which shows the prediction accuracy and overall performance of the SVM algorithm model is better than the neural network model. Finally, we prove the broad generalization of the SVM algorithm through the application on other lines. The SVM algorithm model can be a powerful tool for calculating and predicting the traction energy consumption of HXD1 locomotives.

Safe Reinforcement Learning for Single Train Trajectory Optimization via Shield SARSA

The single train trajectory optimization, also known as speed profile optimization (SPO), is a traditional problem to minimize the traction energy consumption of trains. As a kind of optimal method, reinforcement learning (RL) has been used to solve the SPO problem. In the learning process of a common RL algorithm, a soft constraint (punishment) is always used to keep the agent away from unsafe states. However, a soft constraint can not guarantee and explain the safety of the result. For the SPO problem, it means that the optimized speed profile obtained by a simple RL may break the speed limit which is unacceptable in reality. This paper proposes a protection mechanism called Shield and constructs a Shield SARSA ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${S}$ </tex-math></inline-formula> -SARSA) algorithm to protect the learning process of the high-speed train. Four different reward functions are used to compare the protective efficacy between the proposed algorithm and the soft constraint. The numerical experiments based on the line data from Wuxi East to Suzhou North verify the protective efficacy and effectiveness.

Multiperiod metro timetable optimization based on the complex network and dynamic travel demand

Compared with the single metro timetable optimization aiming at minimizing passenger transfer waiting time, conflicts occur between the timetable optimization of different lines and directions when the metro forms the network. Previous studies often ignore these conflict issues and the uncertain dynamic passenger characteristic. What is more, few studies consider the consistency between the various periods with the same departure interval in consideration of simplifying the model formulation and solution algorithm. To handle the conflicts, this study establishes an importance model to rank the importance of different lines and directions at transfer stations using complex network theory for fully supporting schedules considering temporal and spatial heterogeneity of passenger demand. The concept of node importance is re-defined for better consistency with the metro schedule and passenger-oriented. Based on the importance model, we propose a multiperiod optimization model for solving the metro timetabling problem by considering dynamic passenger travel demand and the convergence between various intervals within a schedule-based metro network. The stable period optimization model generates timetables in a stable period with the same departure interval, which reduces dynamic passenger transfer waiting time and train traction energy. Given the time heterogeneity, we also gave a penalty factor to show the transfer failure impact of energy on passengers on the last train. A multi-objective optimization algorithm, NSGA-II, is adopted to obtain the Pareto Optimality in a stable period of metro timetable. Then, the transitional period optimization model selects timetabling in Pareto Optimality of disparate parts considering transfer connection coordination and operation stability to form the overall schedule timetabling using data envelopment analysis (DEA). Finally, the metro system in Suzhou City, China is chosen as a case study to illustrate the effectiveness and accuracy of the multiperiod metro timetable optimization method considering node and line importance. The method can theoretically and practically contribute to the metro department for scheduled operation.

Urban Rail Substation Parameter Optimization by Energy Audit and Modified Salp Swarm Algorithm

Auditing the energy consumption in urban rail is vital for energy consumption evaluation and system parameter design. There are multiple ways to audit energy consumption, but a universal and global approach is missing. The system-level traction energy consumption (STEC) is proposed. Compared to the main substation energy consumption (MSEC), STEC is more accurate by eliminating the influence of step-down loads based on field test studies. An optimization parameter designing model is built, which takes the system cost as the optimal object considering the life span of energy feedback system (EFS)s. The modified salp swarm algorithm (MSSA) is proposed as the optimization algorithm. The numerical tests show that MSSA has better converge performance than salp swarm algorithm (SSA) and particle swarm optimization (PSO). The impact factors of STEC are analyzed. Compared with SSA and PSO, the initial value of the MSSA is improved and it evolves faster. Compared with the case that does not take the no-load voltage of rectifiers and start voltage of EFSs as optimal parameters, the composite cost of the case that takes the abovementioned parameters as optimal parameters is 3.49% less. Compared to the system without EFSs, the optimal system with EFSs can save costs by 29.47%.

Rail train operation energy-saving optimization based on improved brute-force search

Rail train operation energy consumption mainly focuses on train traction energy consumption. Reducing train traction energy consumption in rail transit operation is significant to developing a green and low-carbon economy and reducing operation costs. The rail train operation energy-saving optimization framework is developed considering the utilization of regenerative braking energy. Firstly, three objectives of punctual arrival, fixed-point parking and minimum energy consumption are provided by train operation strategy analysis. Secondly, the improved brute-force search is developed to solve train operation energy-saving multi-objective problems. The running time, speed, distance, power, and energy consumption of operation intervals are calculated. Finally, Guangzhou Metro Line 7 is taken as an example to verify the effectiveness of the developed optimization model. The results show that the improved brute-force search method effectively finds a more energy-saving turning point under constant interval operation time and has a better energy-saving effect than two other heuristic algorithms.