Hybrid Train Research Articles

Design Interleaved Full Bridge Circuit DC-DC Converter With Fuel Cell Power Source On Hybrid Trains

Design Interleaved Full Bridge Circuit DC-DC Converter With Fuel Cell Power Source On Hybrid Trains

Air quality on UK diesel and hybrid trains

Concentrations of particulate matter (PM10, PM2.5), ultrafine (UFP), particle number (PNC), black carbon (BC), nitrogen dioxide (NO2) and nitrogen oxides (NOX) were measured in train carriages on diesel and bi-mode trains on inter-city and long-distance journeys in the United Kingdom (UK) using a high-quality mobile measurement system. Air quality on 15 different routes was measured using highly-time resolved data on a total of 119 journeys during three campaigns in winter 2020 and summer 2021; this included 13 different train classes. Each journey was sampled 4–10 times with approximatively 11,000 min of in-train concentrations in total. Mean-journey concentrations were 7.552 µg m−3 (PM10); 3.936 µg m−3 (PM2.5); 333–11,300 # cm−3 (PNC); 225–9,131 # cm−3 (UFP); 0.6–11 µg m−3 (BC); 28–201 µg m−3 (NO2); and 130–3,456 µg m−3 (NOX). The impact of different factors on in-train concentrations was evaluated. The presence of tunnels was the factor with the largest impact on the in-train particle concentrations with enhancements by a factor of 40 greater than baseline for BC, and a factor 6 to 7 for PM and PNC. The engine fuel mode was the factor with the largest impact on NO2 with enhancements of up to 14-times larger when the train run on diesel compared to the times running on electric on hybrid trains. Train classes with an age < 10 years observed the lowest in-train PM, BC and NOX concentrations reflecting improvements in aspects of rail technology in recent years. Air quality on UK diesel trains is higher than ambient concentrations but has lower PM2.5 and PNC than most other transport modes, including subway systems, diesel and petrol cars. This paper adds significantly to the evidence on exposure to poor air quality in transport micro-environments and provides the industry and regulatory bodies with reference-grade measurements on which to establish in-train air quality guidelines.

Energy Management Strategy Based on Reinforcement Learning and Frequency Decoupling for Fuel Cell Hybrid Powertrain

This study presents a Two-Layer Deep Deterministic Policy Gradient (TL-DDPG) energy management strategy for Hydrogen fuel cell hybrid train, that aims to solve the problem that traditional reinforcement learning strategies require high initial values and are difficult to optimize global variables. Augmenting the optimization capabilities of the inner layer, a frequency decoupling algorithm integrates into the outer layer, furnishing a fitting initial value for strategy optimization. This addition aims to bolster the stability of fuel cell output, thereby enhancing the overall efficiency of the hybrid power system. In comparison with the traditional reinforcement learning algorithm, the proposed approach demonstrates notable improvements: a reduction in hydrogen consumption per 100 km by 16.3 kg, a 9.7% increase in the output power stability of the fuel cell, and a 1.8% enhancement in its efficiency.

A preliminary design of a hybrid train’s on-board batteries for a 25 kV-50 Hz high speed railway line

The paper reports a technical-economic comparison for a Turkey high-speed railway line, between 25 kV AC electrification and the use of hybrid trains with on-board storage systems. On-board battery storage systems have been designed to operating in specific parts of the line. Different types of technologies have been taken into account, evaluating the use of high specific power/energy lithium-ion electrochemical cells. The models of the battery pack have been designed in the MATLAB/Simulink environment, in order to carry out the complete operation check, also evaluating the time and power required for recharging. An economic evaluation and comparison have been carried out in order to evaluate the convenience of using a hybrid train on specific part of a real line in order to not totally electrified the line.

Prediction of the optimal hybrid train trajectory by using artificial neural network models

AbstractThis paper presents the development and validation of two artificial neural networks (ANN) models, utilising time and power‐based architectures, to accurately predict key parameters of a hydrogen hybrid train profile and its optimal trajectory. The research employs a hybrid train simulator (HTS) to authenticate the ANN models, which were trained using simulated trajectories from five unique hybrid trains on a designated route. The models’ performance was evaluated by computing the mean square normalisation error and mean absolute performance error, while the output's reliability was confirmed through the HTS. The results indicate that both ANN models proficiently predict a hybrid train's critical parameters and trajectory, with mean errors ranging from 0.19% to 0.21%. However, the cascade‐forward neural network (CFNN) topology in the time‐based architecture surpasses the feed‐forward neural network (FFNN) topology concerning mean squared error (MSE) and maximum error in the power‐based architecture. Specifically, the CFNN topology within the time‐based structure exhibits a slightly lower MSE and maximum error than its power‐based counterpart. Additionally, the study reveals the average percentage difference between the benchmark and FFNN/CNFN trajectories, highlighting that the time‐based architecture exhibits lower differences (0.18% and 0.85%) compared to the power‐based architecture (0.46% and 0.92%).

3E analysis of a virtual hydrogen valley supported by railway-based H2 delivery for multi-transportation service

In Southern Italy, near the Mediterranean Sea, mobility services like cars, bicycles, scooters, and material-handling forklifts are frequently required in addition to multimodal local transportation services, such as trains, ferry boats, and airplanes. This research proposes an innovative concept of hydrogen valley, virtually simulated in Matlab/Simulink environment, located in Calabria. As a novelty, hydrogen is produced centrally and delivered via fuel cell hybrid trains to seven hydrogen refueling stations serving various mobility hubs.The centralized production facility operates with a nominal capacity of about 4 tons/day, producing hydrogen via PEM electrolysis, and storing hydrogen at 200 bar with a hydrogen compressor.As the size of vehicle fleets and the cost of acquiring renewable energy through power purchase agreements vary, the hydrogen valley is examined from both a technical and an economic perspective, analyzing: the values of the levelized cost of hydrogen, the energy consumption, and the energy efficiency of the energy systems. Specifically, the levelized cost of hydrogen reached competitive values, close to 5 €/kg of hydrogen, under the most optimistic scenarios, with fleet conversions of more than 60 % and a power purchase agreement price lower than 150 €/MWh.Then, the benefits of hydrogen rail transport in terms of emissions reduction and health from an economic standpoint are compared to conventional diesel trains and fully electric trains, saving respectively 3.2 ktons/year and 0.4 ktons/year of carbon dioxide equivalent emissions, and corresponding economic benefits of respectively 51 and 0.548 million euros.

Energy management strategy and simulation analysis of a hybrid train based on a comprehensive efficiency optimization

Since trains usually have long travel mileage requirements, designing an optimal energy management strategy has great potential for reducing energy consumption. First, a hybrid train power component model was developed, and the train operating efficiency, as well as a map of the optimal integrated efficiency of the entire vehicle operation, was derived for each mode from simulation calculations by analyzing the power flows in different operating modes. Next, the overall optimal efficiency of the entire vehicle was used to determine the mode division rules, and proportional integral control was used to ensure that the engine and motor operated at the target speed under the target torque. Finally, with the optimization objectives of reducing the fuel consumption of the entire vehicle and maintaining a balanced battery state of charge, a forward simulation model for the overall efficiency optimization of the entire vehicle was built in the MATLAB/Simulink software. Then, the trajectories of the engine and motor operating points, the fuel consumption, and the battery SOC were obtained for certain operating conditions. The effectiveness of the energy management strategy based on integrated efficiency optimization was verified by comparing the simulation results for the hybrid train with those of a conventional internal combustion engine train.

A Bi-Level Optimization of Speed and Energy Management for Diesel-Electric Hybrid Train

Diesel-electric hybrid trains (DEHT) equipped with battery systems can effectively reduce fuel consumption and greenhouse gas emissions. For chasing the best fuel consumption efficiency, the coupled optimization problem of driving strategy and energy management for DEHT is addressed in this paper. We propose a bi-level optimization method that jointly designs driving strategy and energy management for DEHT. The optimal driving strategy is obtained by modeling a particle swarm optimization problem in fuel consumption reduction at the top level. The power allocation for the diesel generator set and the battery is described as a convex optimization problem and solved at the bottom level. The proposed method can converge to an optimal value after cyclic iterations. In each iteration, the top-level outputs the current optimal driving strategy to the bottom problem. Based on this current optimal driving strategy, the bottom level calculates the current best fuel consumption and energy management, then feeds back to the top level to start a new iteration. To verify our introduced method, the proposed optimization is compared with the common problems, such as single energy management optimization, single-speed optimization, and sequence optimization. Results show that the maximum and minimum fuel consumption reduction by the introduced optimization method is 20.0% and 2.7%, respectively.

하이브리드 철도차량의 ECMS 기반 소비에너지 최적화 연구

In hybrid train it is essential for new technologies to improve energy efficiency such as power distribution between energy sources, SOC(State Of Charge) management, unlike general electric trains driven by single power through catenary lines. This paper suggests an energy management strategy for energy consumption optimization of train. A new energy optimization strategy for hydrogen electric train based on ECMS(Equivalent Consumption Management Strategy) is described using PMP(Pontryagin"s Minimum Principle). The EF(Equivalent Factor) that determines the power distribution between HFC(Hydrogen Fuel Cell) and battery system is changed according to the battery SOC through PI(Proportional-Integral) control. The proposed algorithm is implemented using Matlab/Simulink and verified through co-simulation with Amesim, which includes a vehicle model.

Power-Split Hybrid Train Configuration Design Based on a Single-Row Star Row

The fundamental component of a hybrid train system is the hybrid power-coupling mechanism. This paper proposes a single-row star-row power-split arrangement for the power-coupling method in a hybrid train. In order to create input and output power-split configuration schemes for a hybrid train power mechanism, a graphical–theoretical model and a relationship matrix were first established. An electromechanical path power proportion equation was then established to filter out two input power-split configurations. The working states of three developed hybrid power-coupling mechanism schemes were studied to find the scheme that fulfills hybrid train design criteria to meet the power, operating mode, and spatial structural arrangement requirements. Finally, by creating a simulation model for the entire vehicle, the dynamics, battery state of charge (SOC) value, and overall vehicle efficiency for the configuration were simulated and examined. The findings demonstrate that the single-row star-row power-split design technique presented here significantly enhances the train performance.

Optimal Hybridization with Minimum Fuel Consumption of the Hybrid Fuel Cell Train

This paper describes a numerical study of the optimal distribution of energy between fuel cells and auxiliary energy storages in the hybrid train. Internal combustion engines (ICEs) are currently under pressure from environmental agencies due to their harmful gas emissions, and pure battery vehicles have a short range; a hybrid train powered by fuel cells, batteries, and supercapacitors can provide a viable propulsion solution. In this study, special energy management on the mountain railway with optimal power distribution and minimum hydrogen consumption is proposed. Considering the characteristics of the mountain railway, the vehicle uses recuperation of regenerative braking energy and thus charges additional power devices, and hybridization optimization gives favorable power to each power source device with a minimum consumption of hydrogen in the fuel cell. In this study, a simulation model was created in a Matlab/Simulink environment for the optimization of hybridized power systems on trains, and it can be easily modified for the hybridization of any type of train. Optimization was performed by using Sequential quadratic programming (SQP). The results show that this hybrid train topology has the ability to recover battery and supercapacitor state of charge (SOC) while meeting vehicle speed and propulsion power requirements. The effect of battery and supercapacitor parameters on power distribution and fuel consumption was also simulated.

A Refinement-based Formal Development of Cyber-physical Railway Signalling Systems

For years, formal methods have been successfully applied in the railway domain to formally demonstrate safety of railway systems. Despite that, little has been done in the field of formal methods to address the cyber-physical nature of modern railway signalling systems. In this article, we present an approach for a formal development of cyber-physical railway signalling systems that is based on a refinement-based modelling and proof-based verification. Our approach utilises the Event-B formal specification language together with a hybrid system and communication modelling patterns to developing a generic hybrid railway signalling system model that can be further refined to capture a specific railway signalling system. The main technical contribution of this article is the refinement of the hybrid train Event-B model with other railway signalling sub-systems. The complete model of the cyber-physical railway signalling system was formally proved to ensure a safe rolling stock separation and prevent their derailment. Furthermore, the article demonstrates the advantage of the refinement-based development approach of cyber-physical systems, which enables a problem decomposition and in turn reduction in the verification and modelling effort.

Characterization of the French Railway Line Difficulties for Diesel and Hydrogen Hybrid Trains

Even if rail transport is the greenest mode of transport, 20% of rail operations are done by diesel traction in France. To reduce the greenhouse gas linked to these operations, SNCF, the French historical railway operator, is investing in the decarbonization of the fleet with the development of hybrid diesel and hybrid H2 trains, which present different performances than current diesel or electric rolling stocks. By developing and using a dedicated simulation tool called QUALESI, this paper shows the interest of coupling train energy modeling and Big Data for the qualification of the operation of these innovative trains on a whole territory, via a concrete example: the characterization of the suitability of diesel and hydrogen hybrid train operations for the whole French railway network.

Validation of a Lumped Parameter Model of the Battery Thermal Management System of a Hybrid Train by Means of Ultrasonic Clamp-On Flow Sensor Measurements and Hydronic Optimization

Electrification of the field of transport is one of the key elements needed to reach the targets of greenhouse gas emissions reduction and carbon neutrality planned by the European Green Deal. In the railway sector, the hybrid powertrain solution (diesel-electric) is emerging, especially for non-electrified lines. Electric components, especially battery power systems, need an efficient thermal management system that guarantees the batteries will work within specific temperature ranges and a thermal uniformity between the modules. Therefore, a hydronic balancing needs to be realized between the parallel branches that supply the battery modules, which is often realized by introducing pressure losses in the system. In this paper, a thermal management system for battery modules (BTMS) of a hybrid train has been studied experimentally, to analyze the flow rates in each branch and the pressure losses. Since many branches of this system are built inside the battery box of the hybrid train, flow rate measurements have been conducted by means of an ultrasonic clamp-on flow sensor because of its minimal invasiveness and its ability to be quickly installed without modifying the system layout. Experimental data of flow rate and pressure drop have then been used to validate a lumped parameter model of the system, realized in the Simcenter AMESim® environment. This tool has then been used to find the hydronic balancing condition among all the battery modules; two solutions have been proposed, and a comparison in terms of overall power saved due to the reduction in pressure losses has been performed.

Techno-Economic Analysis of Hydrogen Storage Technologies for Railway Engineering: A Review

According to the specific requirements of railway engineering, a techno-economic comparison for onboard hydrogen storage technologies is conducted to discuss their feasibility and potentials for hydrogen-powered hybrid trains. Physical storage methods, including compressed hydrogen (CH2), liquid hydrogen (LH2), and cryo-compressed hydrogen (CcH2), and material-based (chemical) storage methods, such as ammonia, liquid organic hydrogen carriages (LOHCs), and metal hydrides, are carefully discussed in terms of their operational conditions, energy capacity, and economic costs. CH2 technology is the most mature now but its storage density cannot reach the final target, which is the same problem for intermetallic compounds. In contrast, LH2, CcH2, and complex hydrides are attractive for their high storage density. Nevertheless, the harsh working conditions of complex hydrides hinder their vehicular application. Ammonia has advantages in energy capacity, utilisation efficiency and cost, especially being directly utilised by fuel cells. LOHCs are now considered as a potential candidate for hydrogen transport. Simplifying the dehydrogenation process is the important prerequisite for its vehicular employment. Recently, increasing novel hydrogen-powered trains based on different hydrogen storage routes are being tested and optimised across the world. It can be forecasted that hydrogen energy will be a significant booster to railway decarbonisation.

Hierarchical eco-driving and energy management control for hydrogen powered hybrid trains

Climate change and emissions reductions are important issues on the rail industry’s agenda. The use of hydrogen as an alternative fuel for rail brings many potential benefits, most notably that it is a clean energy source and supports zero carbon strategies. Compared to fossil fuels, hydrogen fuel cell technology can also provide a more powerful and efficient energy output. In this work, we explore the possibility of reducing the overall operating cost of hydrogen and battery-powered trains, from train eco-driving control to energy distribution along the power chain, while satisfying safety, punctuality, and energy efficiency of travel. To address this multi-objective problem, we split it into a long-horizon model predictive controller (MPC) to optimize the driving profile online, a short-horizon MPC to ensure on-time train operation, and an energy management MPC to distribute fuel cell and battery power to achieve energy savings and battery charge sustaining. Finally, simulations are performed with realistic railway routes to verify fuel economy, charge sustaining, location accuracy and robustness to temporary speed restrictions (TSR). The results show the superiority of the proposed hierarchical control.

Net Hydrogen Consumption Minimization of Fuel Cell Hybrid Trains Using a Time-Based Co-Optimization Model

With increasing concerns on transportation decarbonization, fuel cell hybrid trains (FCHTs) attract many attentions due to their zero carbon emissions during operation. Since fuel cells alone cannot recover the regenerative braking energy (RBE), energy storage devices (ESDs) are commonly deployed for the recovery of RBE and provide extra traction power to improve the energy efficiency. This paper aims to minimize the net hydrogen consumption (NHC) by co-optimizing both train speed trajectory and onboard energy management using a time-based mixed integer linear programming (MILP) model. In the case with the constraints of speed limits and gradients, the NHC of co-optimization reduces by 6.4% compared to the result obtained by the sequential optimization, which optimizes train control strategies first and then the energy management. Additionally, the relationship between NHC and employed ESD capacity is studied and it is found that with the increase of ESD capacity, the NHC can be reduced by up to 30% in a typical route in urban railway transit. The study shows that ESDs play an important role for FCHTs in reducing NHC, and the proposed time-based co-optimization model can maximize the energy-saving benefits for such emerging traction systems with hybrid energy sources, including both fuel cells and ESD.

Cost optimization of multi-mode train conversion for discontinuously electrified routes

Rail decarbonization is a popular topic in the transportation industry. The transition from diesel-fed trains on unelectrified routes to hybrid trains or infrastructure electrification has been widely studied. However, previous studies rarely considered the installation of pantographs, fuel cells, and battery packs on multi-mode trains. This paper proposes an approach to evaluate and optimize the performance of multi-mode trains. It aims to achieve the minimum total cost, including multi-mode train conversion and route rebuilding costs, energy costs, and other maintenance costs. A simulator was developed to compute energy consumption, journey time, and onboard device replacement for multi-mode trains on discontinuously electrified routes. An evaluation has been made using the fitness function for the total cost and journey time to rank the new optimal method. A particle swarm optimization (PSO) algorithm was used to find the optimal solution. For the case study of the rebuilding plan from London St. Pancras to Leicester, before the energy price change, the optimal train costs are 34.9% less than the benchmark diesel train. After considering the future increase in energy prices, the benefits of the optimal train with low energy consumption become more significant to 40.4%.

Experimental study of the explosive welding process applicability for train weight decreasing

For the weight decreasing of the trains, it needs to use a new construction conception. For the weight decreasing of the trains, it needs to use a new construction conception, which means a hybrid material construction. The new conception (steel and aluminium for the vehicle construction) requires a suitable welding process for joining the steel with aluminium. Explosive welding is a suitable process to realize this idea. The authors attempt to examine this possibility by experimental methods and suggest a suitable solution for the reduction of the train’s weight. The authors made explosive welding experimental tests to join aluminium (EN-AW-6082) and low carbon steel (EN 10025 S235J2). The cladding of the test samples was made by an explosive welding process. The welded joint suitability was tested by destructive and non-destructive tests. It was tested the joint cross-section microstructure by microscopy. Based on the results it can declare that explosive welding is a suitable process to realize hybrid train construction.

Operation and energy evaluation of diesel and hybrid trains with smart switching controls

Energy and environmental sustainability in urban rail transport have attracted substantial attention in the last decade. In the UK, about 29% of the current train fleets are run solely on diesel fuel, and the UK Government has pledged to remove all diesel-only trains by 2040. One option to achieve this ambitious goal is replacing diesel trains with hybrid trains. However, little has been done to evaluate the operational performance of replacing diesel trains with hybrid trains on the same track. This paper develops a novel Hybrid Train Simulator which can analyse the driving performance and energy flow among multiple energy sources (diesel, hydrogen and battery). Several practical case studies are presented based on typical mainline railways in the UK. Operation performance using diesel trains and hybrid trains is compared. This paper also proposes automatic smart switching control between multiple power sources according to the altitude of the route for hybrid trains. The case studies indicate that hybrid trains increase the journey time due to their low power capacity but improve energy efficiency significantly. Besides, implementing auto-switching on hybrid trains can reduce energy consumption by 6% compared with benchmark hybrid trains.