a 3d simulationmodel of train dynamics for testing odometry algorithms · 2012-06-26 · challenge...

Challenge H: For an even safer and more secure railway

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A 3D SimulationModel of Train Dynamics for Testing OdometryAlgorithms

Luca Pugi1,2, Alessandro Ridolfi1,2, Benedetto Allotta1,2, Monica Malvezzi3,Gregorio Vettori1,2, Fabrizio Cuppini4, Filippo Salotti4

1 Mechatronics and Dynamic Modelling Lab (MDM Lab), University of Florence @Pistoia, Italy

2 Dept. of Energy Engineering, University of Florence, Italy

3 Dept. of Information Engineering, University of Siena, Italy

4 ECM S.p.a., Pistoia, Italy

1INTRODUCTION

Automatic Train Protection systems (ATP) are planned to increase the railway running security asthey are able to react to theengine driver’s conduct mistakes, which might causeaccidents and theycan stop automatically the train in these situations. The European Rail Traffic Management System(ERTMS)[1][2][3][4]represents an advanced ATP,which are provided three different levels for: ERTMSof Level 2 and 3setting aside the existence of a traditional type of a railway signalling system andERTMS of Level 1requiring, on the contrary, a traditional system of semaphore signalling.Odometry[5]is basically important for the rail running security as it has the aim to estimate theinstantaneous speed of the train and the distance the train covers (train position);all these data arefundamental for the correctbehaviourof the protection functions of the ATP system (and consequentlyfor the safety and efficiency of the automatic train protection and control system). Within theestimation of the complete motion of a vehicle the term odometry refers to the use of data fusiontechniques of measures thatgenerally are provided by a set of different types of sensors.For instance,the odometric algorithm inside SCMT (Italian acronym for “Sistema Controllo Marcia Treno”) uses twotachometers which are put on two independent axles[5]; one if its developments integrates theinformation of the two GITs (Italian acronym for “Generatore di Impulsi Tachimetrici”: TachometricImpulse Generator), with the acceleration measurement given by a longitudinal accelerometer[6].Other chances are the use of a Doppler Radar sensor combined with the two GITs [7]or, consideringpresent and future developments too, the use of Inertial Navigation Systems (INS) based on inertialsensors (MEMS accelerometers and gyroscopes)which can be integrated with magnetometers and/orGPS, GNSS-GALILEO or like location systems[8].The common aim of the different systems is to get amore and more precise and reliable odometric estimate; currently the research of more accuratesolution is leading to the introduction of different types of sensors and in particular the integration ofINS (Inertial Navigation System) devices in the traditional odometric system seems to be a promisingsolution. In particular INS may be used to improve speed estimations of the algorithms, in particularworking conditions in which the information of some sensors are not available or the correlationbetween the sensor measurements and the vehicle longitudinal motion is in some way “weak”. Forexample, in case of traction or braking manoeuvres with degraded adhesion conditions themeasurements of the axle speed are deeply influenced by the behaviour of WSP (Wheel SlideProtection) and Anti-Skid systems. Anyway as a general rule, to exceed the restrictions of any singlesensor typology, sensor fusion techniques of redundant measures thatare got from the train sensorset can be used.

The development and prototyping of odometry algorithms involve the simulation of realisticenvironmental conditions which are usually produced using recorded experimental data and syntheticresults from simulation models [9] which may be also used for the development of HIL testing devicessuch as the Trenitalia MI-6 Test rig of Firenze Romito [10]. Even when experimental data areavailable from previous activities, the use of synthetic inputs from dynamic simulation models isrecommendable in order to simulate the wide variety of different working conditions that have to beverified to assure the respect of performance, safety and reliability specifications of the testedalgorithms. Furthermore such types of tests are completely reproducible and the operative conditionsare fully controllable, even critical conditions can be safely be performed (for example, extremelydegraded wheel/rail adhesion conditions).To test and calibrate correctly the odometryalgorithms it istherefore necessary to reproduce in a simulation environment the train sensor outputs, that is 3D railkinematics measurements, which have to be consistent the one with the other; simulated signals vary


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1INTRODUCTION




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mailto:@Pistoia

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according to the kind of measurements needed as inputs by the tested algorithms: typically redundantmeasurements of two axle tachometers (GITs), but also the estimation of vehicle longitudinal speedthrough Doppler Radar sensors or the acceleration measures.There are some troubles both usingexperimental data, in the event they are available (as often they donot fill the whole sensor set whichis necessary for these applications), and to yield results with two-dimensional simulation models (thedevelopment of inertial navigation systems involves the use of accelerometer and gyroscopic sensorswhose response is sensitive to the three-dimensional behaviour of the vehicle).As a consequence,simplified planar models of railway vehicles that have been widely used for example for Hardware inthe Loop testing of SCMT odometry algorithms [9]are not suitableto develop this innovative solutions.In addition to 3D nature of kinematics outputs to produce it also needs to simulate complexinteractions between the mechatronics on board rail subsystems (for instance anti-skid and WSP) andthe dynamics of rail vehicle itself; in order to correctly test the odometric algorithm, extremely differentrailway tracks and running conditions have also to be enquired. Then the authors chose,rather usingtraditional multibody modelling programs, preferred to carry out a series of “ad hoc” simulations with arailway vehiclemultibody model, developed using Matlab-SimulinkTM(tool SimMechanicsTM).Anaccurate simulationof degraded adhesion conditions according to applicable norms [11] withcorresponding interactions with on board subsystems is also quite difficult to be performed usingconventional or commercial multibody codes (degraded adhesion conditions are critical in the locationalgorithm testing). In order to avoid these limitations a complete three-dimensional multibody model ofa railway vehicle has been developed using Matlab-Simulink™ including an efficient contact model[12] which has been further modified in order to simulate degraded adhesion conditions: a widevariety of on board subsystems(for example WSP, anti-slip devices) is available and, as aconsequence, the model can simulate various running conditions, with arbitrary tracks, includingconditions that may stress the sensor behaviour, like i.e. low adhesion between the wheels and therails, track irregularities, curves, line gradient, etc..The use of Matlab-SimulinkTM allows testing easilythe on board components with real-time implementation; for instance, considering the WSPsubsystem as a safety relevant component for railway running and a device affecting directly therotational dynamics of axles (and consequently GIT measurements), you can carry outHIL tests(Hardware in the loop) on suitable test rigs[10], using Matlab-SimulinkTMas a real-time simulationsoftware. On this point you can find current regulations[13][11]including all the requirements torespect in the simulation environmentin order to make valid HIL tests,in a partial or full substitution ofthe corresponding trials on the real railway line.In addition to WSP systems, you can also testATP/ATC systems, odometry boards, anti-skid systems, etc..

2RAILWAY VEHICLE MULTIBODY MODEL

The correct evaluation of the performances of an odometric algorithm requires the simulation ofextremely different railway tracks and running conditions. In order to make a series of significantsimulations for the location algorithm test, the authors have developed a complete 3D multibodymodel of a high speed train using SimMechanicsTM tool inside Matlab-SimulinkTM. Forcommercial/industrial policy reasons authors have not used data of a known high speed train;however data used are quite realistic and obtained introducing light modification on values taken fromsome well-known existing high speed applications.The aim of the 3D mechanical model used for thesimulations is to reproduce kinematics outputs which are necessary for the algorithm test(outputscorresponding to on board sensor set measurements): coach accelerations and angular velocities (3Dinertial sensor outputs), axle angular velocities (GITs), coach longitudinal speed (radar-Doppler), etc..

The rail vehicle multibody model has to be supported by a well-run 3D contact model, reproducing thereal forces exchanged between wheel and rail; the wheel-rail contact force hasbeen calculatedthrough an algorithm developed during otherresearch activities [12], which has been implementedwith Matlab-SimulinkTM too. This model, based on the Hertzian contact theory and on the Kalker non-linear one (with saturation), can calculate multiple points of contact (up-to 4 points for every singlewheel).Inside this model an accurate law of degraded adhesion, allowing the reproduction of a seriesof running conditions which are critical for the working of the different algorithms, has beenimplemented;in degraded adhesion conditions on board mechatronics devices are activated (i.e.WSP, traction control), affecting the dynamic behaviour of the whole system and consequently thelocation algorithm estimation too. Besides the authors used a track model allowing a high flexibility inthe creation of the railway line with the possibility of planning, in a parametric way and as a function ofthe covered space, straights/curves, slopes, cant angles, connection tracks, etc.. This way you can“stress” the different sensors and enquire efficiency of location algorithms as a function of the differentparameters of interest.


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2.1 RAIL TRACK MODEL

The parametric track model allows getting the required geometries of the rail lines:particularly straightstretches, curves (with the corresponding connection tracks and cant angles), rail line slopes(altimetry with the corresponding connection tracks),introducing a series of actions (of inertial typetoo) which may affect the measurements of the on board sensor set (i.e. inertial sensors such asaccelerometers and gyroscopes). Some patterns of irregularitiesof the rail line (rail gaugeirregularities, cant, etc.) have even been planned; the irregularities in the track(put inside at the wheel-rail contact model)are useful for the analysis of the sensor response if disturbances occur: i.e.you canexcite accelerometers and gyroscopes to estimate their sensitivity (analysis of the influence ofpossible irregularities, which may be present on the rail line, on the inertial sensors) or to imposesome secondary motions, caused by irregularities, for the analysis of the Doppler-Radar response(the accuracy of its measure also depends on the assembly tolerance of the Doppler-Radar sensor onthe rail vehicle). Depending on the covered space you can also set the desired adhesion pattern andother significant features, such as, for instance the availability or unavailability of the signal of theGPS system (for examplewhile crossing a tunnel) and the state of the reflective surface (scattering):this last parameter is basically important for the correct behaviour of the Doppler-Radartype speedsensor, whose efficiency significantlydepends onthe state of the surface it directs towards (withreference to studies about this subject[14][15]).

2.2 MECHANICAL MODEL OF THE RAIL VEHICLE

The coach motion of the first rail vehicle (with its own 3D motion dynamics),where the sensors of thelocation system are set up, was simulated and estimated.The authors chose to model a single unitcomposed of a coach attached to two bogies (Figure 1). This rail vehicle has been modelled with amultibody approach; the system was divided in the followingrigid bodies: one coach, two bogieframes, eight axle boxes (four for eachbogie), and fourwheelsets (two for eachbogie).Eachcomponent was modelled with geometrical and inertial properties whose values are very close tothe real typical values of the actual high-speedtrains. The rail vehicle characteristics can be easilychanged in a parametric way (customizable and flexible model). The model used in Matlab-SimulinkTM

is a rail vehicle with wheel-and-axle set Bo-Bo (2 engine axles for every bogie, with independenttraction control).

Figure 1 – Rail vehicle bogie model

The main featuresof the rail vehicle thathas been employed for simulations are the following (Table 1):

Total Mass About 56000 kgCoach Mass About 42000 kg


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Wheel-and-axle set Bo-BoBogie wheelbase 2.42 mBogie distance 16.9 mWheel diameter 0.92 mPrimary suspension own frequency About 4.5 HzSecondary suspension own frequency About 0.8 HzWheel-rail profiles ISO-ORE 1002

Rail UIC60 with 1/20 poseTable 1 – Characteristics of the rail vehicle

The coach is held by a rear and front bogie with a two-stage suspension system. Particularly the railvehicle is provided with a double suspension stage (vertical and lateral) between the coach, thebogies and the axles, damping devices (vertical, lateral, anti-yaw dampers) with non-linearcharacteristics, anti-roll bar and bump-stop plugs to reduce souplesse and other coach motions (inorder to respect in every condition the rules concerning the loading gauge of the rail vehicle). Thebodies are supposed to be rigid and the degrees of freedom of each body is defined by jointsconnecting each component to the other, reproducing the available movement of the real parts of thetrain. The primary suspension system, comparatively hard, links the axles to the bogie frame, whilethe secondary one, typically softer for comfort reasons, is interposed between bogie and coach andsupports the weight of the latter. The transmission of the longitudinal efforts between coach andbogies takes place through elastic elements simulating the effect of the main solutions thatare utilizedfor traction vehicles and carriages (for instance push-pull bar, Watt’s quadrilateral, etc.).The forceelements (i.e. the twosuspension stages and the bump-stop) have been modelled bymeans of forceelements such as springs and dampers, with opportunely defined non-linear characteristics (due tothis the default Simscape elements have been modified introducing lookup tables with real componentbehaviours).The primary suspension stage is constituted by an axle box with one of its oscillatingarms attached to the bogie frame by means of a rubber element (sutuco) while the other arm is linkedto the frame with a damper oriented vertically. The upper part of the axlebox is the base for a shearspring, which represents the elastic element of the primary suspension. The secondary suspensionstage consists of a pair of air spring: usage of air spring permits better performances in terms ofcomfort and the possibility to control spring characteristics by means of the pneumatic system actingon it.Concerningmotion resistance,the authors considered the contribution of the rolling resistance (astrongly limited value) through the contact model thathas been employed and the resistantcontribution due to the track slope directly from the combination of the Multibody model and trackgeneration.Estimating some further resistant contributions such as the cushion friction and theaerodynamic resistance, a longitudinal force has been applied at the centre of mass of the coach. Theoverall resistance is modelled according to a second order polynomial function of the longitudinalspeed (the resistant forces have a quadratic trend as to the rail vehicle velocity): the coefficients ofthis polynomial are estimated under the dataavailable in literature [15][17].In order to carry out somestraight stretch tests, the motion resistances corresponding to the presence of towed vehicles can bemodelled in a completely similar way, so you can just spare the Multibody modelling of the wholetrain. The relative resistant concentrated force is applied just next to the driving hook which is locatedin the rear of the front rail vehicle: this way the longitudinal dynamics of a whole train is rebuilt, withthe consequent locomotive pitch phenomena (coach load transfer and bogie load transfer).Briefly thefront rail vehicle is modelled as a Multibody vehicle, while the remaining part of the train and themotion resistances of the front rail vehicle are modelled with a concentrated longitudinal force(lumped system).

2.3 BRAKING SYSTEM AND WSP

Sincethe necessary sensor set for the rail vehicle location implementations is typically located in thefront vehicle cab (rail vehicle modelled in Multibody environment), during a braking manoeuversimulation the typical braking delays which are connected to the train length can be neglected (thetrain vehicles get the starting braking signal with a delay which is nearly proportional to the distancefrom the front locomotive).This remark stands both in consideration of an electro-pneumatic brakingplant [18] and in case of a pneumatic plant [19]. The braking plant modelled in Matlab-Simulink is justof pneumatic type.Moreover the authors have modelled a WSP system intervening if the axle slipsincrease too much; the locomotive is usually in front of the train and consequently its wheels find a railwhich isstatistically dirtier than the one which is found by the towed rail vehicles and therefore theavailable adhesion is lower.


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In the pneumatic plant (you can see its elementary layout in Figure 2) there are two electromagneticvalves directly modulating the pressure in the brake cylinder: the electromagnetic bleed valve EVScauses a pressure decrease in the brake cylinder, connecting it to the atmosphere through acalibrated orifice, the electromagnetic back pressure valve EVR causesthe increase of the brakingpressure connecting the brake cylinder to the distributor through a calibrated orifice.

Figure 2 – Simplified scheme of the pneumatic braking plant

WSP system dynamically modulates the braking torque thatis applied on each axle with the aim ofavoiding the axle skid and using the available adhesion as much as possible. In order to estimate theadhesion loss state during the braking manoeuver the vehicle longitudinal velocity, called referencevelocity is needed; the logic of the anti-skid system used offers the chance to plan a periodic brakerelease of one of the four axles to guarantee that at least one of the four linear velocities of the axlesis nearly close on the effective longitudinal speed of the vehicle. Usually adhesion losses areestimated by comparing recorded axle speeds and accelerations with the corresponding referencevalues; the system tries to keep the slip of each axle in an optimized interval thatis variable accordingto the speed value.In the simulation environment four braking axles and a pair of EVR/EVS valves forevery axle were modelled. The working logic of the implemented WSP systemcauses the activation ofthe two electromagnetic valves EVR and EVS according to three possible stages: the pressureincrease in the brake cylinder (braking), the pressure preservation (tube plug) or the pressuredecrease (loosing pressure).Two different time constants have been planned for the description of thedynamics response of EVR and EVS electromagnetic valves. Besides the distributor has a differenttiming response according to its filling or emptying.Both the distributor and the EVR and EVS valvesare modelled as first order systems, characterized by two different time constants (which fix the typicaldynamic response of a pneumatic system): one at the pressure increase stage and the other one atthe pressure decrease stage. The series of these dynamics causes the law of the brake cylinderfilling/emptying and consequently the effective braking (as shown in the simplified diagram in Figure3).

Figure 3 – Working logic of the pneumatic plant and of the implemented WSP system

In the scheme in figure 3, is the control pressure at the bottom of the pneumatic distributor, andare the time constants of the distributor,respectively in course of pressure increase(loading) and

decrease (unloading) stage, is the distributor output pressure, the logic state 1 represents the


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activation of the electropneumatic valve, is the reference pressure, and are the timeconstants of the electromagnetic back pressure valve EVR and of the bleed valve EVS and atlast is the effective pressure of the braking cylinder which sets out the mechanical braking action.For further explanations you can also see[20].Moreovera variable clamping effort as a function of therail vehicle velocity can be set out, an effort depending on the typology of the considered brakinggaskets (characterized by the trend friction factor-velocity) [21].

2.4 TRACTION AND ANTI-SKID SYSTEM

Concerning the traction plant modelling, the following simplifying hypotheses are considered: thetorsional dynamics of the transmission system is omitted, the electrical time constants of the systemare considered much smaller compared to the mechanical ones, the rotational inertia of the engine isreduced to the axle. Simulations are carried out considering that the vehicle axles (with wheel-and-axle set Bo-Bo) are independent both from the mechanical point of view and the electrical one;practically you consider four power independent groups formed by electrical driving gear-mechanicaltransmission-axle (previousresearches were carried out in order to study the effects of the mutualinteraction of parallel connected induction motors[22]). The torque is therefore directly applied to theaxles: the inertia of the single axle is properly increased to consider the reduced inertia value of thetransmission system (engine, reduction gear, etc.).The group engine-reduction gear-axle can bestudied referring to the only axle velocity, introducing the inertia equivalent value (reduced inertia); forinstance to calculate the engine reduced inertia related to the axle rotation axis, the following kineticenergy equivalence relation is used: = ⇒ = (1)

whereJis the moment of inertia of the engine, represents the reduction ratio, that is the ratiobetweenthe engine angular velocity and the axle one . The torque value applied to every singleaxle is properly modulated by the on board anti-skid subsystem, limiting the axle creepages under lowadhesion conditions. Obviously the wheel-rail contact is characterized by rather low adhesioncoefficients. Moreover as the electric locomotive is the first carriage (at the head of the train), thelocomotive wheels find a rail which is statistically dirtier than the one which is found by the drawncarriage, and in case of dirty contact surfaces the available adhesion is obviously lower. The anti-skidaim is to regulate in real time the braking torque in order to keep the slips within tolerable limits,avoiding high axle slips and consequently wear and overheating of the rolling surfaces. The anti-skidsystem calculates the rail vehicle longitudinal velocity (reference speed) and on the basis of this one itmodulates the engine torque, whenever the axle creepage is identified: the evaluation of the slipstage occurs according to an accelerometric principle and a tachometric one (in a few words,comparing the reference values with the one the sensors placed on the axles have measured). Thetraction torque is taken away, in case of slip, and afterwards given back, when the slip stops,according to suitable laws.

2.5 ABOUT ADHESION

Testing and calibrating location algorithmsinvolve the simulation of degraded adhesion conditions.Thecontact forces werecalculated using a wheel-rail contact model developed during previous researches[12]. In this model, forces are calculated according to Kalker non-linear theory: the creep forces weresaturated using the modified Johnson-Vermeulen formulation suggested by [23].As regards testsmade in degraded adhesion conditions the authors implemented, inside the contact model, anadhesion law with the addition of an hysteretic cycle to the classical friction law of Coulomb’s model(with a transition from the static adhesion coefficient to the kinematics one). More specifically, whenlow adhesion conditions occur, longitudinal and lateral contact forces coming from Kalker’s non-linear theory are corrected (according to (2)) with a multiplicative coefficient ( )thatis a function ofthe relative creepage . = ( )= ( ) ⇒ = ( ) + (2)

where is the total creep force. The creep relative slip (3) is defined as:


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= ∗∗= ∗∗ ⟹ = ( ) + (3)

where and ∗ are respectively actual and pure rolling forward velocities, and and ∗are actualand pure rolling lateral velocities.The correction factor ( ), which realizes the transition betweenstatic and kinematical friction factor, is defined according to (4):

≥ 0 ⟹ ≤ ⟹ = 1> ⟹ = + (1 − ) ( ) < 0 ⇒ ( ) = min , ( − ) (4)

Where is the value of corresponding to the maximum adhesion value, is the kinematicalfriction reduction factor, is an exponential slope factor, isthe force saturation coefficient usedinside Kalker non-linear theory(according to [23]) and dt is the integration interval. In order toreproduce a hysteretic behaviour of the adhesion,the dependence from creep time derivative hasbeen added. Figure 4 shows the corresponding behaviour of the contact force , during a typicalcycle due to a loss and subsequent recover of adhesion.

Figure 4–Contact force behaviour during a loss and subsequent recover of adhesion

The exact estimationof the effective state of the adhesion coefficient is quite difficult, because of itsdependence on several parameters; particularly the interaction of the two contact rolling surfaces isinfluenced by the presence and the nature of the contamination between them: it is just thecontamination which causes conditions of low adhesion. Substantially there is a lack of a stabletheoretical background for high degraded adhesion conditions; that’s why the authors have followedheuristic models based on experimental results and common sense engineeringconsiderations[10][24][25][26][27][28][29]. These studiesshow how the available adhesion during aloss phaseisdifferent from the subsequent recover phase.In the original contact model[12]thesaturationproposed by [23]is applied only to creep forces, as spin torques are considered nearto negligible terms; in case of heavily degraded adhesion conditions an unsaturated calculation of thespin torques will lead to results that are quite unrealistic since computed spin torques become equalor higher than creep forces which are limited by the saturation coefficient. In real conditions whenmacro-slip occurs, the spin torque is expected to be null or negligible. Since the proposed model wasdevoted to the simulation of much degraded adhesion conditions the spin torques have beenneglected.

3ACHIEVED RESULTS

As regards the generation of synthetic signals, a family of relatively short simulations has beendetected in order to make computation on more workstations and processors faster and easier. Theintention is to carry out tests about relatively short tracks, but they have to be individually significant(in order to reproduce typical runningsituations), to build some “modules” which can be settled the onewith the other (assisted if necessary by an appropriate interpolation and filtering) to form the desiredcourse. Some arbitrarily elaborate tracks (as regards length, characteristics of the track drawing,adhesion and running conditions) can consequently be produced by combining the resultscorresponding to the “elementary” track simulations; this operation can be directly made in a post-


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processing stage.This way time consumption is reduced since the computational effort is distributedon a limited set of shorter simulations; the combination of the different elementary simulations enablesanyway to get the desired rail track geometry and the desired real conditions.

Figure 5shows the results of a braking simulation in degraded adhesion conditions on a straight andlevel rail track, without any irregularity; the vehicle has initially a 200 /ℎ longitudinal speed andafter a brief stretch of coasting it’s braked up to a complete stop. Because of the conditions ofdegraded adhesion the profiles of the peripheral speed of the axles don’t match the speed profile ofthe rail vehicle (the axles creep). The WSP system sets a limit for the axle slips, avoiding theirlocking(down to low speeds); the relative slips are kept around 25 − 30% values.

Figure 5–Longitudinal speed profiles of a braking simulation in degraded adhesion conditions

Hereunder figure 6shows the corresponding odometric results got through SCMT algorithm (that isusing only 2 GITs as sensors)[5]. In figure 6 the velocity error is shown, which is defined according to(5): = ∗ − (5)where ∗is the estimated speed of the train and is the real speed (measured on the coach).

Figure 6 – Vehicle speed, GITs measurements and corresponding SCMT speed estimation. Velocity error calculatedbySCMT odometry algorithm compared with corresponding ERTMS specifications

The reconstruction of the speed profile, and consequently the evaluation of the train position, suffersfrom errors thatare rather high: these errors are caused by the low adhesion conditions (criticalconditions for GITs).You can get definitely better results with location inertial-type algorithms using a3D sensor set (accelerometers and gyroscopes). So the modelling of the only longitudinal dynamicsof the rail vehicle (two-dimensional dynamic models), traditionally employed for a first setting-up ofsome odometric systems [6], is not enough anymore. On the contrary, for testing correctly theseadvanced algorithms, modelling in a complete way the 3D dynamics of the rail vehicle is important, inorder to reproduce in a simulation the typical outputs of the 3D sensor set which has been employed.For instance, running a curve, the lateral accelerometer will measure the so-called non-compensatedacceleration (given by the difference between the centrifugal effect and the gravitational one linkedtothe superelevation of the rail line[30]). The planned curve has, at a steady state, a 1800 curve

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radius and a superelevation ℎ = 150 ; in the connection tracks of the curve input-output thesevalues change linearly (clothoid line). The rail vehicle runs the curve with a 200 /ℎlongitudinalspeed. Figure 7 shows in red the lateral non-compensated acceleration, whichis the lateralaccelerometer output; this result is obtained in a simulation test thanks to the 3D multibody model ofthe rail vehicle (described on paragraph 2.2). The track is superelevated so that, in the plane of thetrack, the component of gravity (generated by the rail cant) will provide some fraction of the so-called“centrifugal lateralacceleration”; the resultant non-compensated lateral acceleration on the vehicle isgiven by (6): = − (6)

Where is the running speed [ / ], is the curve radius [ ], ℎ is thesuperelevation [ ] and s isthe track gauge [ ≅ 1500 ].

Figure 7 – Lateral acceleration of a curve simulation with / longitudinal speed

The theoretical value of the “centrifugal lateral acceleration” is about 1.7 / (this value representsthe non-compensated lateral acceleration for the curve without cant). The theoretical value of theresultant non-compensated lateral acceleration is about 0.74 / . The difference between thistheoretical value and the result of the simulation (that’s about 0.9 / ) has to be given to the carbodyroll towards the outside of the curve (souplesse), tending to reduce the gravitational effect ofcompensation. As it’s shown in Figure 7, the lateral “non-compensated” acceleration value is positiveand physically that means the passenger feels to be pushed towards the outside of the curve.

As further example of the 3D simulation model,Figures 8-10 show the results obtained starting fromthe previous braking trial and applying some irregularity patterns to the track. Irregularities are appliedas imposed displacements on the track.The authors considered the following types of irregularities:

lateral irregularities: both rails have a lateral displacement perpendicularly to the originalrailway track;

vertical irregularities: both rails of the track have vertical displacement; gauge irregularities: one rail is moved perpendicularly to the original railway track; cant irregularities: a superelevation is set rotating the track plane respect to an axis oriented

along the track.

In Figure 8you can see the reference path of lateral irregularitywhich has been employed:among theplanned irregularities, the lateral ones have the highest sizeand lead to high values of imposedacceleration measured on the coach.


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Figure 8 –Lateral irregularity applied on the railway track

In simulationsthe referring irregularity patterns(700 long) have been carried out in a periodic way forthe whole length of the simulation. Figures 9 and 10 show the trends of the longitudinal and lateralacceleration in presence of irregularities.

Figure 9 - Longitudinal acceleration of a braking simulationin degraded adhesion conditions (with and without

irregularities)

Figure 10 - Lateral acceleration of a braking simulation indegraded adhesion conditions (with and without

irregularities)

The longitudinal acceleration profile is affected by “noise” in the rail stretches thatare interested toirregularities. The lateral acceleration trend is the one which more differs from the trend of the trial inabsence of irregularities, coherently with the imposed irregularity patterns: the lateral accelerometerwill be subject to “disturbances” just nextto the irregularity stretches. The 3D rail vehicle modellingallowed testing the INS-type location algorithm [31]by getting results which are definitely bettercompared to SCMT. In figure 11 the results of the braking test (previously analyzed with SCMTalgorithm) are shown: it should be noted that the speed estimated error is decidedly lower and thisbrings advantages as regards the train localization and consequently for the railway safety.

Figure 11 - Vehicle speed, GIT measurement and corresponding INS localization algorithm speed estimation. Velocityerror calculatedby INS localization algorithm compared with corresponding ERTMS specifications

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LastlyFigure 12 shows the results obtained analyzing with SCMT and INS location algorithm a morecomplex rail track: a straight railway which is about 20 long, with slope and descent stretches witha ±30/1000 gradient, characterized by mixed adhesion conditions(good adhesion stretches alternatedto degraded adhesion ones).

Figure 12 -Vehicle speed, GIT measurement and corresponding SCMT and INS algorithm speed estimation. Velocity errorcalculatedby SCMT and INS algorithm compared with corresponding ERTMS specifications

The results obtained with INS localization algorithm respect, more than satisfactorily, ERTMSrequirements [1][2][3][4].

CONCLUSIONS AND FUTURE DEVELOPMENTS

The availability of a complete three-dimensional model able to reproduce complex interactions amongon board safety relevant subsystems is an important instrument for the preliminary design andcalibration of innovative odometry and localization algorithms. The application of this kind of modelscan be extended to HIL testing of safety relevant on board subsystems: reliable offline testing of trainposition and speed estimation methods by means of 3D real-time train dynamic simulation maysignificantly reduce time and cost of the development and tuning of new onboard systems.

Further activities will be carried out in order to improve the following aspects:

Real-time implementation on a multiprocessor system for the development of an HIL testingplatform;

Further improvement and validation of wheel-rail adhesion models.

REFERENCES

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[2] EEIG ERTMS Users Group, “Performance Requirements for STMs”, Reference EEIG Subset-059, Issue 0.0.6, 28/03/00.

[3] EEIG ERTMS Users Group, “Performance Requirements for STMs”, Reference EEIG Subset-041, Issue 2.0.0, 30/03/00.

[4] EEIG ERTMS Users Group, ”Odometer FFFIS”, Reference EEIG 97E2675B, DocumentVersion 5-, 31/07/98.

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[9] P.TONI, M.MALVEZZI, L.PUGI, M.RINCHI, P.PRESCIANI:Sviluppo e validazione di algoritmi diodometria per sistemi di controllo e monitoraggio ferroviari, (in Italian) Ingegneria Ferroviariamaggio 2003 pag. 433/457.

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[13] UIC 541-05: “Brakes - Specifications for the construction of various brake parts - Wheel SlideProtection device (WSP)”, November 2005.

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override (EBO)”, May 2006.[19] UIC 540: “Brakes - Air Brakes for freight trains and passenger trains”, November 2006.[20] L.PUGI, M.MALVEZZI, B.ALLOTTA, L.BANCHI, P.PRESCIANI: A parametric library for the

simulation of a Union Internationale des Chemins de Fer (UIC) pneumatic braking system,Proc. Instn Mech. Engrs Vol. 218 Part F: J. Rail and Rapid Transit F01403 # IMechE 2004pages 117-132.

[21] UIC 541-3: “Brakes - Disc brakes and their application - General conditions for the approval ofbrake pads”, October 2010.

[22] B.ALLOTTA, L.PUGI, F.BARTOLINI: Mutual interaction of parallel connected induction motorson degraded adhesion conditions The 1st Joint International Conference on Multibody SystemDynamics May 25-27, 2010, Lappeenranta, Finland.

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[25] M.BOITEUX: Le probleme de l’adherence en freinage, Revue generale des chemins de fer, pp.59-72, February 1986.

[26] O.POLACH: Creep forces in simulations of traction vehicles running on adhesion limit, ElsevierB.V., 2004.

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[28] M.MALVEZZI, B.ALLOTTA, L.PUGI:Feasibility of Degraded AdhesionTests in a LocomotiveRoller Rig, Proc. of the ImechE, Part F, Journal of Rail and Rapid Transit, Volume 222 Number1/2008.

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