WO2012119197A1 - Amélioration de la ponctualité et de l'efficacité énergétique de trains - Google Patents

Amélioration de la ponctualité et de l'efficacité énergétique de trains Download PDF

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Publication number
WO2012119197A1
WO2012119197A1 PCT/AU2012/000232 AU2012000232W WO2012119197A1 WO 2012119197 A1 WO2012119197 A1 WO 2012119197A1 AU 2012000232 W AU2012000232 W AU 2012000232W WO 2012119197 A1 WO2012119197 A1 WO 2012119197A1
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WIPO (PCT)
Prior art keywords
train
progress
speed
rail network
profile
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PCT/AU2012/000232
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English (en)
Inventor
Peter John Pudney
Philip George Howlett
Amie Renee ALBRECHT
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Ausrail Technologies Pty Limited
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Publication date
Priority claimed from AU2011900824A external-priority patent/AU2011900824A0/en
Application filed by Ausrail Technologies Pty Limited filed Critical Ausrail Technologies Pty Limited
Publication of WO2012119197A1 publication Critical patent/WO2012119197A1/fr

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L15/00—Indicators provided on the vehicle or train for signalling purposes
    • B61L15/0058—On-board optimisation of vehicle or vehicle train operation
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
    • B61L27/10—Operations, e.g. scheduling or time tables
    • B61L27/16—Trackside optimisation of vehicle or train operation

Definitions

  • This invention relates to a method and system for the operation of trains on a rail network.
  • Fuel or energy savings of 10% or greater can be achieved by providing train drivers with information on energy-efficient driving strategies.
  • PCT/AU03/00604 provides an in-cab system that displays driving advice to help train drivers improve timekeeping and reduce energy consumption. It uses optimal control theory to calculate a control strategy which minimizes the mechanical work done by the traction system to move a train from its current location and speed to the next scheduled stop. The calculation of the optimal control strategy takes into account the required arrival time at the stop as well as train performance parameters and track gradients, curves and speed limits. [0007] However, the method does not calculate optimal driving strategies for situations where the train must pass an intermediate timing location, without stopping, at a specified time.
  • a method of monitoring the progress of a train on a rail network and providing driving advice in real time to an operator of said train comprising:
  • a method of monitoring the progress of a train on a rail network and providing information on the progress of the train in real time to an operator of said train comprising:
  • said adjoint variable evolves according to a differential equation along with the position and speed of the train.
  • the value of the adjoint variable is calculated directly from the speed of the train.
  • a numerical method is used to solve a system of differential equations for said speed profile of the train and for the value of said adjoint variable.
  • steps (i) to (iv) are performed as required so that said driving advice automatically adjusts to compensate for any operational disturbances encountered by the train.
  • said discrete control modes for said train include drive, hold, coast and brake modes.
  • said parameters include train mass and mass distribution.
  • said parameters further include maximum tractive effort and maximum braking effort as functions of speed.
  • said parameters further include coefficient(s) of rolling resistance.
  • step (iii) involves processing data from a GPS unit and train controls to determine the location and speed of the train.
  • said optimal journey profile is precomputed.
  • a plurality optimal journey profiles corresponding to different journey times are calculated and the profile that has an arrival time at the target location closest to the desired arrival time is selected.
  • the optimal journey profile comprises driving in a hold mode (i.e. at constant speed), calculated by the Pontryagin Principle of optimal control, wherever possible and where it is not possible changing as quickly as is safely possible at exactly the right location, calculated by the Pontryagin Principle of optimal control, to drive (i.e. full power), hold, coast or brake modes as necessary.
  • the present invention provides an optimal driving strategy in the case where a train must pass an intermediate timing location at a specified time.
  • the method of the present invention can also be used on problems with more than one intermediate timing point.
  • Figure 1 shows a block diagram of the system according to a preferred embodiment of the present invention, illustrating the main data flows between various elements of the system;
  • Figure 2 illustrates an optimal speed profile for a train over a fictitious section of track
  • Figure 3 illustrates an optimal speed profile for a train over another fictitious section of track
  • Figure 4 illustrates an optimal journey for a coal train
  • Figure 5 shows the processing of precomputed speed profiles
  • Figure 6 illustrates a preferred embodiment of the system display which provides the train operator with driving advice in real time.
  • Figure 7 illustrates speed graphs for two optimal journeys
  • Figure 8 illustrates speed graphs l/, and l/ 2 ;
  • Figure 9 illustrates journey energy as a function of hold speed change location.
  • the present invention in one preferred form, provides a fully automatic system that monitors the progress of a train on a long-haul network, calculates efficient control profiles for the train, and displays driving advice to the train crew.
  • the system works in conjunction with a dynamic rescheduling tool that coordinates interactions between various trains operating on the network.
  • the system assists the crew of a long-haul train by calculating and providing driving advice that assists to keep the train on time and reduce the energy used by the train.
  • the system performs four main tasks:
  • journey optimisation calculates or selects an energy-efficient driving strategy that will get the train to the next key location as close as possible to the desired time
  • advice generation generates and provides driving advice for the driver. [0039] These tasks are performed continually so that the driving advice automatically adjusts to compensate for any operational disturbances encountered by the train.
  • the system includes:
  • the station estimation task processes observations from a GPS unit and the train controls to determine the location and speed of the train and the current control setting.
  • Location is the position of the train on a given route, and is used to look up track gradient, curvature and speed limits.
  • the state estimation task uses absolute and relative position data to determine the location of the train.
  • Control setting is required for train parameter estimation, and for estimating the energy use of the train if direct measurement of energy use is not available.
  • the train parameter estimation task estimates parameters of a train performance model from the sequence of observed journey states.
  • the train model used by the in-cab system has the following train parameters:
  • any of these parameters that are not known with sufficient accuracy before the journey commences must be estimated during the journey.
  • the unknown parameters can be estimated using a Kalman filter.
  • mass is to be estimated, the mass distribution is assumed to be uniform. If tractive effort is to be estimated it is assumed to take the form where P is the maximum power of the train and v 0 is the speed below which maximum tractive effort is assumed to be constant.
  • the optimal journey profile between a given journey state and a target journey state is found by solving a set of differential equations for the motion of the train and an additional differential equation that determines the optimal control.
  • the optimal journey profile specifies the time, speed and control at each location of the track between the current train location and the next target.
  • journey profiles can be precomputed or else calculated during the journey. If precomputed, several different journeys corresponding to different journey times are used on the train and the journey optimisation task then simply selects the precomputed profile that has the arrival time at the target closest to the desired arrival time.
  • This model is based on simple physics. It does not model the complexities of traction motors, braking systems, in-train forces or wheel-rail interations. Nor does it need to; in practice, the driving advice derived from this simple model is both realistic and effective.
  • the force u is controlled by the driver, and satisfies the constraints F B (v) ⁇ u ⁇ F D (v) where F D (v) > 0 is the maximum drive force that can be achieved at speed i and F B (v) > 0 is the maximum braking force that can be achieved at speed v.
  • the optimal control is founded by forming the Hamiltonian function 1 u - R(v) + G(x)
  • the optimal control maximises the Hamiltonian, and so the optimal control depends on the value of the adjoint variable ⁇ .
  • Track intervals can be divided into four speed-dependent classes:
  • the optimal strategy anticipates steep gradients by speeding up before a steep incline and slowing down before a steep decline.
  • the optimal journey trajectory can be constructed in this way as a sequence of trajectory segments between speed-holding phases, where speed holding can occur at the hold speed l/or at a speed limit.
  • trajectory segment will have start type 1 .
  • the optimal journey profile comprises driving in a hold mode (i.e. at constant speed), calculated by the Pontryagin Principle of optimal control, wherever possible and where it is not possible changing as quickly as is safely possible at exactly the right location, again calculated by the Pontryagin Principle of optimal control, to drive (i.e. full power), coast or brake modes as necessary.
  • the advice generation task compares the current state of the train to the corresponding state on the optimal journey profile and then generates and displays advice for the train operator that will keep the train close to the optimal profile.
  • Brake advice is given if braking is required to avoid exceeding a speed limit or a speed on the journey profile that has braking as the optimal control.
  • Coast advice is given if:
  • Hold advice is given if the speed of the train is near or above a holding speed indicated by the optimal journey profile.
  • the speed to be held will be either a speed limit or the journey holding speed.
  • the optimisation software is used to calculate optimal speed profiles for six difference total journey times. Each profile is designed to minimise fuel consumption for the given journey time. As the time allowed for the journey decreases the minimum possible fuel consumption increases.
  • the system uses a GPS unit to determine the position of the train. Given the speed and position of the train and the time remaining until the train is due at the next key location, the system selects the most appropriate of the pre- computed profiles. Advice is generated to keep the train as close as possible to the selected profile. The crew will enter necessary information such as the arrival time at the next key location.
  • the advice given to the driver will be one of:
  • brake advice is not displayed and the driver is solely responsible for deciding when to brake.
  • the system is able to work with pre-computed profiles because, in practice, if the control is changed too early or too late, switching between the different pre- computed profiles will automatically adjust future control changes to compensate. Alternatively, the calculations are generally fast enough that new profiles can be computed in real time.
  • Energy savings can be achieved simply by demonstrating efficient control techniques to the train operator. Effective techniques can either be demonstrated onboard or by using simulations. However, because of the relationship between fuel consumption and journey time some form of on-board advice system is required to achieve the best possible fuel consumption, and is the reason why coasting boards by the side of the track do not work.
  • the system of the present invention achieves significant fuel savings without increasing running times because the system is an adaptive system based on optimal control theory.
  • the system can adjust the driving strategy using the actual observed train performance. All systems that rely on pre-computed profiles must take into account the current state of the train with regard to location, time and speed. Any system of non- adaptive control will give unreliable advice when the train is not in the right place at the right time doing the right speed. Whilst non-adaptive systems could possibly be used on Metropolitan railways with fixed timetables and identical trains or on tightly controlled networks with unit trains carrying consistent loads using dedicated track, they are not suitable on networks where the trains are subjected to unpredictable delays.
  • the length and mass distribution of a train can be used with a simple averaging procedure to transform the track gradients and speed limits so that the motion of a point mass train on the transformed track corresponds to the motion of the real train on the real track.
  • the optimal journey has the train coasting 2km before the start of the decline, and driving 500m before the start of the incline.
  • Figure 4 shows an optimal journey for a coal train.
  • the hold speed is 70km/h.
  • the elevation profile has been smoothed to compensate for the length and mass distribution of the train.
  • the speed-holding strategy for long-haul trains is different to the drive-coast-brake strategy for suburban trains, but this is not so.
  • the hold speed required to achieve the timetable on short journey sections is usually greater than the maximum speed that can be achieved before coasting and braking are required.
  • the suburban drive-coast-brake strategy is simply a subset of the speed holding strategy used on longer journeys.
  • the invention is designed to work on a train with optimisation working as a background task continually updating the optimal speed prof ile from the current state of the journey to the next target.
  • Advice is provided from the result of comparing the current state to the optimal journey and generating appropriate control advice.
  • Figure 5 shows the processing of precomputed speed profiles
  • Figure 6 shows a typical advice task provided to a driver.
  • Figure 6 shows a preferred embodiment of the driver display 10 for providing real time driving advice to the driver of the train.
  • the target location 12 (in this example, "Crystal Brook") is selected by the driver and represents the next destination the train must reach by a certain time.
  • the estimated time of arrival (ETA) 14 is calculated by the system and represents the predicted time the train will reach the target location 12 based on the current location of the train, the distance to the target location, and the selected journey profile (i.e. driving strategy). If the calculated ETA does not satisfy the driver's requirements (i.e. by being too early or too late) the driver can select a "faster” or "slower” journey profile from a series of journey profiles. These profiles may be selected by the driver from a graduated scale 16. In the preferred embodiment depicted the driver has a choice of seven (7) different journey profiles. As may be appreciated, the slower the journey profile the less fuel is used, whilst the faster the journey profile the less fuel-efficient the journey.
  • Line 18 on the display illustrates the vertical profile of the section of track on the display, whilst line 20 depicts the track curvature, or horizontal profile, of the section of track on display.
  • the line 22 represents the train, with the vertical line 24 denoting the location of the front of the train and vertical line 26 denoting the location of the rear of the train.
  • the train 22 progresses from left to right on the display.
  • Line 28 and associated numbers 30 indicate the speed limits (in km/h) in various zones of the section of track on display.
  • the speed limit over the first zone is 75km/h, then the speed limit reduces to 50km/h, then increases to 60km/h, then reduces to 50km/h, and finally increases to 55km/h.
  • the coloured line 32 indicates the recommended driving profile for the train over the various zones of track.
  • the colour of line 32 at any point denotes the control mode the driver is required to use at that point on the track (i.e. brake, coast, or power).
  • red represents "brake” mode
  • white represents “coast” mode
  • green represents "power” mode.
  • the shade of the colour varies to indicate the degree of braking or power required. The darker the shade of colour, the greater the degree of braking or power required at the particular point on the track. This is particularly useful when the control mode is 'hold' which is, by nature, somewhere between full power and coast modes.
  • Indicator 34 provides a visual indication to the driver as to how the train is progressing against the recommended speed profile.
  • the indicator comprises a pair of spaced apart arrows which move horizontally across the display as the train progresses and vertically to indicate how the train is progressing against the recommended speed profile.
  • the pair of arrows will span the recommended speed profile. If the train is travelling too slowly the arrows will fall below the line 32, whilst if the train is travelling too quickly the arrows will lie above the line 32. In the example shown, the arrows lie slightly below the line, indicating that the train is travelling slightly slower then recommended.
  • Figure 7 shows speed graphs for two optimal journeys on the range x
  • the shaded area at the bottom of the graph indicates the track altitude.
  • the upper orange curve is the track speed limit.
  • the colours on the two speed profiles indicate control— green is power, grey is coast, and red is brake.
  • Ji( i) has hold speed V 2 and finishes at (X, ⁇ f).
  • Figure 8 shows these two journey parts.
  • the upper speed profile is V which passes through the desired timing point.
  • the lower speed profile is V 2 , which finishes at the correct time.
  • the composite journey, l/, on [X 0 ,X ] and V 2 on [ ⁇ , ⁇ ], arrives at both the timing location and at the end of the journey at the correct times, and both parts of the journey are journeys of optimal type.
  • This journey changes hold speed at location X, .
  • a procedure for constructing a journey where the hold speed changes at a given location a ⁇ ⁇ is:
  • the parameter a can then be varied to find the composite journey with the minimum cost.
  • Figure 9 shows how cost J varies with a for our example problem.
  • the data points do not lie exactly on a smooth curve because of inaccuracies in the numerical procedures used to calculate the optimal trajectories.
  • changing hold speed at the timing location result in energy use that is close to the minimum.
  • changing hold speed at the timing location is likely to be good enough.
  • the speed profile ⁇ does not need to be calculated all the way to the end of the journey; it only needs to be calculated far enough beyond ⁇ to ensure that speed limits beyond ⁇ will not be exceeded.
  • the method can be extended to handle multiple timing points before the next stop.
  • the ideal hold speed from any location and speed taking into account future timing locations where the earliest desired arrival time and the latest desired arrival times are specified, can be found using a numerical search procedure, such as a binary search:
  • V L : V
  • V H : V
  • the method of the present invention is typically embodied in software.
  • the invention provides an automated system that monitors the progress of a train on a long-haul network, calculates efficient control profiles for the train, and displays driving advice to the train crew.
  • the system works in conjunction with a dynamic rescheduling tool that coordinates interactions between various trains operating on the network.
  • the invention is designed to work on a train with optimisation working as a background task continually updating the optimal speed profile from the current state of the journey to the next target.
  • Advice is provided to the driver from the result of comparing the current state to the optimal journey and generating appropriate control advice.
  • the present invention at least in the preferred form provides one or more of the following benefits:

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  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Train Traffic Observation, Control, And Security (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)

Abstract

La présente invention concerne un procédé et un système pour le fonctionnement de train sur un réseau ferroviaire. L'invention concerne un procédé et un système qui surveille la progression d'un train sur un réseau, calcule des profils de commande efficaces pour le train et affiche des conseils de conduite pour l'équipe de train. Le système calcule et donne des conseils de conduite qui permettent au train d'être ponctuel et réduisent l'énergie utilisée par le train (i) en surveillant la progression d'un trajet afin de déterminer l'emplacement actuel et la vitesse du train ; (ii) en estimant certains paramètres d'un modèle de performances du train ; (iii) en calculant ou en sélectionnant une stratégie de conduite économe en énergie qui amènera le train à l'emplacement clé suivant à une heure aussi proche que possible de l'heure souhaitée ; et (iv) en générant et fournissant des conseils de conduite au conducteur.
PCT/AU2012/000232 2011-03-08 2012-03-08 Amélioration de la ponctualité et de l'efficacité énergétique de trains WO2012119197A1 (fr)

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AU2011900824 2011-03-08
AU2011900824A AU2011900824A0 (en) 2011-03-08 Improving timekeeping and energy efficiency for trains

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CN106585669A (zh) * 2016-11-29 2017-04-26 中国铁路总公司 机车辅助操控系统
CN106671995A (zh) * 2016-12-12 2017-05-17 交控科技股份有限公司 一种重载列车驾驶曲线建立方法及装置
WO2017184308A1 (fr) * 2016-04-22 2017-10-26 Electro-Motive Diesel, Inc. Système de cadencement de train basé sur l'état de locomotive
CN110877616A (zh) * 2018-09-06 2020-03-13 阿尔斯通运输科技公司 电能消耗优化方法、存储介质以及自动驾驶和监控系统
DE102019203919A1 (de) * 2019-03-22 2020-09-24 Siemens Mobility GmbH Verfahren zum Betrieb eines Fahrerassistenzsystems für ein spurgebundenes Fahrzeug
CN112885113A (zh) * 2019-11-29 2021-06-01 阿尔斯通运输科技公司 用于公交车辆的驾驶辅助方法
CN113361150A (zh) * 2021-08-11 2021-09-07 华东交通大学 一种城市列车运行多目标优化方法及系统
CN113715877A (zh) * 2021-09-16 2021-11-30 交控科技股份有限公司 列车控制方法、系统、计算机设备和存储介质
CN113753009A (zh) * 2021-10-09 2021-12-07 株洲中车时代电气股份有限公司 列车长大下坡控制方法、装置及电子设备
CN114162189A (zh) * 2021-11-05 2022-03-11 中国铁路兰州局集团有限公司 铁路客整所一体化作业进度监控系统

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US7822491B2 (en) * 2002-05-20 2010-10-26 Ausrail Technologies Pty Limited System for improving timekeeping and saving energy on long-haul trains
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Cited By (18)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2017184308A1 (fr) * 2016-04-22 2017-10-26 Electro-Motive Diesel, Inc. Système de cadencement de train basé sur l'état de locomotive
US10029714B2 (en) 2016-04-22 2018-07-24 Progress Rail Locomotive Inc. Locomotive health-based train pacing system
CN106585669A (zh) * 2016-11-29 2017-04-26 中国铁路总公司 机车辅助操控系统
CN106671995A (zh) * 2016-12-12 2017-05-17 交控科技股份有限公司 一种重载列车驾驶曲线建立方法及装置
CN106671995B (zh) * 2016-12-12 2018-09-04 交控科技股份有限公司 一种重载列车驾驶曲线建立方法及装置
CN110877616A (zh) * 2018-09-06 2020-03-13 阿尔斯通运输科技公司 电能消耗优化方法、存储介质以及自动驾驶和监控系统
DE102019203919A1 (de) * 2019-03-22 2020-09-24 Siemens Mobility GmbH Verfahren zum Betrieb eines Fahrerassistenzsystems für ein spurgebundenes Fahrzeug
EP3828660A1 (fr) * 2019-11-29 2021-06-02 ALSTOM Transport Technologies Procédé d'aide à la conduite d'un véhicule de transport public
CN112885113A (zh) * 2019-11-29 2021-06-01 阿尔斯通运输科技公司 用于公交车辆的驾驶辅助方法
FR3103915A1 (fr) * 2019-11-29 2021-06-04 Alstom Transport Technologies Procédé d’aide à la conduite d’un véhicule de transport public
US11644321B2 (en) 2019-11-29 2023-05-09 Alstom Transport Technologies Driving assistance method for a public transport vehicle
CN113361150A (zh) * 2021-08-11 2021-09-07 华东交通大学 一种城市列车运行多目标优化方法及系统
CN113715877A (zh) * 2021-09-16 2021-11-30 交控科技股份有限公司 列车控制方法、系统、计算机设备和存储介质
CN113715877B (zh) * 2021-09-16 2022-09-02 交控科技股份有限公司 列车控制方法、系统、计算机设备和存储介质
US11912323B2 (en) 2021-09-16 2024-02-27 Traffic Control Technology Co., Ltd Train control method, system, computer device and storage medium
CN113753009A (zh) * 2021-10-09 2021-12-07 株洲中车时代电气股份有限公司 列车长大下坡控制方法、装置及电子设备
CN113753009B (zh) * 2021-10-09 2022-07-19 株洲中车时代电气股份有限公司 列车长大下坡控制方法、装置及电子设备
CN114162189A (zh) * 2021-11-05 2022-03-11 中国铁路兰州局集团有限公司 铁路客整所一体化作业进度监控系统

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