Engine or battery powered two-wheeled vehicles are an essential part of a transport system. Its users are however more vulnerable in comparison with personal vehicles. In general, compared to four-wheel vehicles even a small percentage of accidents that include motorcycles causes a significant rate of all fatalities in the Czech Republic. Two-wheelers nowadays face the challenge of electric propulsion and wider user adoption. This fact emphasizes the need to assess rider´s behaviour. One way of examination of rider´s behaviour in safe and scientific environments is an interactive vehicle simulator. In order to develop a complex semi-active motorcycle simulator, a virtual model in Unity platform was created. The main purpose of this paper is to verify the longitudinal dynamics of the physical model for emerging interactive simulator. To achieve this goal, crucial parameters of the model were set. For validation of the model we defined several riding scenarios. Based on the data measured during a track test experiment in a real environment, model parameters further needed were indirectly defined. The results show that the output of the longitudinal dynamics model closely correlates with the real data gathered on a track

Orlický, Adam

Svoboda, Josef

Toman, Přemysl

CTU Open Journal Systems (Czech Technical University, Prague / České vysoké učení technické v Praze)

https://doi.org/10.14311/APP.2022.39.0055Acta Polytechnica CTU Proceedings 39:55–59, 2022 © 2022 The Author(s). Licensed under a CC-BY 4.0 licencePublished by the Czech Technical University in PragueVALIDATION OF A LONGITUDINAL MOTORCYCLE RIDINGDYNAMIC MODEL FOR A POWERED TWO-WHEELERINTERACTIVE SIMULATORJosef Svoboda∗, Přemysl Toman, Adam OrlickýCzech Technical University in Prague, Faculty of Transportation Sciences, Department of Vehicle Technology,Horská 3, 128 03 Prague 2, Czech Republic∗ corresponding author: svoboj88@fd.cvut.czAbstract. Engine or battery powered two-wheeled vehicles are an essential part of a transport system.Its users are however more vulnerable in comparison with personal vehicles. In general, compared tofour-wheel vehicles even a small percentage of accidents that include motorcycles causes a significantrate of all fatalities in the Czech Republic. Two-wheelers nowadays face the challenge of electricpropulsion and wider user adoption. This fact emphasizes the need to assess rider’s behaviour. Oneway of examination of rider’s behaviour in safe and scientific environments is an interactive vehiclesimulator. In order to develop a complex semi-active motorcycle simulator, a virtual model in Unityplatform was created. The main purpose of this paper is to verify the longitudinal dynamics of thephysical model for emerging interactive simulator. To achieve this goal, crucial parameters of the modelwere set. For validation of the model we defined several riding scenarios. Based on the data measuredduring a track test experiment in a real environment, model parameters further needed were indirectlydefined. The results show that the output of the longitudinal dynamics model closely correlates withthe real data gathered on a track.Keywords: Riding dynamics, powered two wheelers, vehicle simulator, longitudinal dynamics.1. IntroductionIn the Czech Republic (based on Yearbooks of RoadAccidents in the Czech Republic and Traffic 2017-2020,published by the Police of the Czech Republic) weobserve a steady ratio of number of road accidentscaused by motorcycle riders and total number of peo-ple killed in road accidents. It means that 2 % ofaccidents caused by motorcycle riders stand behind10 % of people killed. In comparison with cars thereis the ratio circa 40-50 % caused road accidents to60-70 % people killed. This makes us see more than 3times higher seriousness of accidents caused by motor-cycle riders. Compared with the situation in the EU(European Union) the average fatalities percentageis 15 % of total fatalities from 2010 to 2018 basedon European Commission (2020) Facts and FiguresMotorcyclists and Moped Riders.Powered Two Wheelers (PTW) are the way to in-crease mobility and accessibility for citizens. Thisstatement presents the ACEM (the European Associ-ation of Motorcycle Manufacturers) in the documentDecarbonisation of Transport: Powered Two-Wheelers(PTWs) on the road to 2050. Towards this approachthe question of decarbonization and implementationof electric PTW is also mentioned. The similar fore-cast is also reported in paper [1]. As for the transitionto electric PTW we also have to be aware of differentrisks coming from riders facing electric propulsion ofPTW [2]. The rider style can differ significantly. Asan extreme example of potential risk of easily availableelectric micro-mobility vehicles nowadays we presente-scooters. As reported in [3] the injury rate per mil-lion miles travelled was 180 times higher than theoverall group of motor vehicles.To assess rider style adaptation to electric propul-sion, when using several research methods, it is prefer-able to perform experiments in driving, respectivelyriding simulators. As the riding itself could be dan-gerous and the consequences of small error could leadto several injuries, the simulators are considered asatisfactory way. Similar effects arise when assess-ing potentially dangerous activities of drivers suchas fatigue or aggressive driving styles as documentedin [4] and [5]. Concerning motorcycle simulators, theapproach turned out to be a successful tool to assessriders’ behaviour as presented in [6].The development of a motorcycle simulator is spe-cific for several reasons. If we compare it with con-ventional four-wheel simulators, we can determinedifferences. If we focus on motorcycle riding dynamicsfrom the physical point of view, we can observe severalspecific effects which we cannot neglect.A motorcycle is a spontaneously unstable vehicle.If we consider the weight ratio between the rider andthe bike, it is clear that the rider’s movement af-fects the COG (Centre of Gravity) of the whole rider-motorcycle system. That is why the manoeuvrability(especially leaning) of powered two wheelers is stronglyaffected by the rider’s changing body position. All ofthese conditions have to be taken into account whendeveloping a virtual physical model as well as control55J. Svoboda, P. Toman, A. Orlický Acta Polytechnica CTU Proceedingshardware [7].In addition to physics, there are other technicalchallenges such as control and command of the wholemotorcycle and its HMI (Human-Machine Interface)system. Another technical specific relate to the visu-alization of virtual scenery projected to a rider. Therider compared to a driver has a potentially widerrange of view including the view near the rider’s feet,open rear view or the sky view while looking up. In or-der to improve the feeling of accurate speed adoptionand virtual environment perception the visualizationis a significant element of the whole simulation andits devices should be designed precisely [8].The motorcycle riding simulator reveals challengestypical of PTW such as e.g. roll motion height andvehicle motion controlled by a rider [8]. When wedesign the simulator the motorcycle physical model isan essential part of the whole structure. This phys-ical model can be divided into two main systems ofthe straight movement and the turning movementsystem [7].Straight forward movement or the so-called longitu-dinal riding dynamics includes in general accelerationand braking in the motorcycle riding field even shift-ing gears, recuperation braking and deceleration asa typical feature of each engine or motor type andsettings.Based on findings above we see that PTW causeshigh percentage of fatalities and that they are anintegral part of future mobility planning moreover en-larged by the group of electric PTW. It is important tobe able to examine accurately rider’s behaviour withinthe context of electric PTW mobility. It means boththe risks and benefits. The safety of testing riders,repeatability and reproducibility is very important.Therefore, we decided to develop a motorcycle simula-tor aiming at problematic issues of electric propulsionof PTW. One of the main sub-systems of such a simu-lator is the longitudinal dynamics model. The primarymotivation for creation of an accurate longitudinalmodel is the possibility of future accurate assessmentof riders’ behaviour in safe environment. One of thekey factors of the simulator of electric motorcycle isthe authenticity of specific riding propulsion character-istics of longitudinal movement associated with sucha motorcycle.2. MethodOur approach towards accurate longitudinal motor-cycle dynamic models combine real test track andvirtual computational development in the softwareUnity. The basis for the following work is the archi-tecture (see Figure 1) of the whole simulator systemwhich is divided into two parts – “Set-up geometry”module, where the basic geometry and mass charac-teristic of the bike is defined. The second module (seeFigure 2) provides the communication between theindividual blocks of the physical model as shown inFigure 1. Module 1 – Motorcycle input parameters.Figure 2. Module 2 – Motorcycle simulator architec-ture.the diagram. This step provides the possibility of acomplex view on the whole development and testing.As the whole system is developed according to thepresented architecture, we created the first draft ver-sion of the longitudinal dynamic model able to operatewithin the system. The following step is to validatethe output of the simulations according to the realtrack test data.For the testing and as the first steps in the devel-opment of the simulator we also consider the futurepossibility of implementation of control hardware andthus we created a simplified handlebar controller (seeFigure 3) able to communicate with the model.For the validation we defined three motorcycle-ridersystem movement scenarios. Acceleration from zeroup to 80 km·h−1, emergency braking from 80 km·h−1.The third scenario describes the straight free move-ment from the speed of 80 km·h−1 to a complete halt.In this scenario the motorcycle with a rider stopsonly due to riding resistances of the whole systemmotorcycle-rider. Test scenarios are defined ideallywith 0 % slope and wind speed 0 km·h−1.In the first longitudinal model parameters set-upwe focus on the road motorcycle similar to the cate-gory MOTO3 of the world road racing championship.56vol. 39/2022 Validation of a longitudinal motorcycle riding dynamic modelFigure 3. Diagram of the simplified motorcycle han-dlebar for simulator.Figure 4. Visualization from the development Unityplatform.Virtual development of environment and visualisationof the bike presents Figure 4. The motorcycle is pro-duced by the student team CTU Lions of the Facultyof Transportation Sciences, Czech Technical Univer-sity in Prague. Basic parameters are weight of 165 kg,electric motor with peak power up to 42 kW, torqueup to 95 Nm. The rider has testing weight of 75 kg.This particular motorcycle (see Figure 5) was usedfor the measurement for track tests according to thepresented scenarios.For data acquisition we used our self-developedDAQ (Data Acquisition System) verified in a seriesof previous track tests as presented in [2, 9]. For thisapplication the main parameters were the motorcyclevelocity and throttle openings. Data was collectedwithin closed area.The basis for the validation is the learning algo-rithm for the exact bike from which the measureddata comes from. In the first step we use the sce-nario of free movement to adjust parameters of ridingresistances. Then we use acceleration scenario data.Throttle openings and velocity curves are input to thesimulation model. The model afterwards computesthe optimal engine power curve to correlate with theinput speed curve. Then we tune the parameters ofbraking to correlate the most with the speed curve inbraking scenario.When the learning phase ends we perform the ex-act same testing scenario in the unity through thesimplified handlebar controller. The data from the vir-tual testing are stored as the simulation output dataand then compared with the input velocity curves.For the evaluation of the accuracy of the longitudi-nal dynamics model we define a final speed differenceerror.Figure 5. Motorcycle used for track test and DAQ.Figure 6. Free movement scenario speed profilescomparison.3. ResultsThe results chapter is divided into two parts in compli-ance with the presented method and testing scenariosequence. In each section the results of the final set-upof the longitudinal dynamics model compared to thedata from the track test are presented. The first onefocuses on the final speed profiles and the second onelooks up closely to the absolute speed differences.3.1. Speed profilesFollowing Figures 6, 7 and 8 present the speed pro-file within time in the acceleration, braking and freemovement testing scenario. We observe very close cor-relation of the track test and simulation output data.The correlation coefficient in the all three scenarios isalmost equal to 1. The track test data are representedby blue line and the simulation output data by blackline.3.2. Speed profiles differencesWhen we look closely (see Figure 9, 10 and 11) at thespeed profiles or to its differences, we see maximalabsolute values 2.8 km·h−1. In all cases the speeddifference is calculated as test track data minus simu-lation output data. The total average speed differencevalues in order of testing scenarios are 0.78 km·h−1,0.77 km·h−1 and 0.24 km·h−1. When the speed simu-lation output values are higher than test track valuesthe maximal differences are 1.61 km·h−1, 0.57 km·h−1and 2.80 km·h−1. In the opposite situation the max-57J. Svoboda, P. Toman, A. Orlický Acta Polytechnica CTU ProceedingsFigure 7. Acceleration scenario speed profiles com-parison.Figure 8. Braking scenario speed profiles comparison.imal differences are 2.63 km·h−1, 2.30 km·h−1 and0.76 km·h−1.4. DiscussionWe performed validation of our motorcycle longitu-dinal dynamic model. As the result of validation,we see very closely correlating speed profiles of tracktest and simulation output data. Focusing on theresults in the form of speed differences we observerelatively small average values lower than 1 km·h−1.The Maximum speed difference across all scenarios is2.8 km·h−1 after our validation process. This result wefind accurate and in proper dimensions based on otherstudies e.g. [10] where the maximum speed differencein comparable speed level reaches 7.46 km·h−1.From this point we achieved a very realistic rep-resentation of the longitudinal dynamics motorcyclebehaviour. The main limitation is the influence of therider’s weight and the rider’s aerodynamic positionwhich can cause inaccuracies especially in aerody-namic drag. Both will be subject of further analysisand development. The second main limitation is thefact that this process is oriented on a specific mo-torcycle. On the other hand, we have very precisesimulation outputs. From the simulation point of viewthe validation process is easily repeatable with newdata from track tests.Figure 9. Free movement scenario speed profilesdeviation.Figure 10. Acceleration scenario speed profiles devi-ation.Figure 11. Braking scenario speed profiles deviation.5. ConclusionMotorcyclists are relatively vulnerable road users. Ac-cording to the current trends of electromobility, ridersare very often facing challenges in form of new propul-sion systems of PTW: On the other hand, these newprimarily electric PTW are facing unexperienced users.An important fact is that electric propulsion systemhas many different aspects compared to petrol ones.Thus, riders may behave in a different way. Rider’sbehaviour assessment in scope of new types of PTWis a path towards safe ride. From this point of view aninteractive vehicle simulator serves as a tool for safetesting under laboratory conditions. Focusing moreon motorcycle and PTW in general, they have severalspecifics such as e.g. spontaneous instability.In this article we validated the longitudinal dynamicmodel of the interactive motorcycle simulator. Our val-idation process is designed to reproduce precisely thebehaviour of a real motorcycle based on data collectedduring track tests. We created 3 testing scenarios onwhich the validation was done. The whole process is58vol. 39/2022 Validation of a longitudinal motorcycle riding dynamic modelapplicable to wide palette of motorcycles so that anaccurate database of virtual vehicles could be created.We assessed speed profiles of a track test and of anoutput of simulation. Afterwards we calculated speedprofiles deviations. As the results present, the finalvirtual model precisely reproduces the longitudinaldynamic behaviour within the simulation with maxi-mal speed deviation of 2.8 km·h−1. The limitation ofthe study is primarily influenced by rider’s variablepositions that will become the subject of our futureresearch. Our next steps will also aim to validationof other parts of our physical motorcycle simulatormodel.References[1] N. Haworth. Powered two wheelers in a changingworld – Challenges and opportunities. AccidentAnalysis & Prevention 44(1):12–18, 2012. Safety andMobility of Vulnerable Road Usears: Pedestrians,Bicyclists, and Motorcyclists.https://doi.org/10.1016/j.aap.2010.10.031[2] P. Toman, J. Svoboda, J. Pinkas. Study of ridingspecifics of the electric and conventional poweredtwo-wheeler users. In 2021 Smart City SymposiumPrague (SCSP), pp. 1–6. 2021.https://doi.org/10.1109/SCSP52043.2021.9447401[3] K. Rix, N. J. Demchur, D. F. Zane, L. H. Brown.Injury rates per mile of travel for electric scootersversus motor vehicles. The American Journal ofEmergency Medicine 40:166–168, 2021.https://doi.org/10.1016/j.ajem.2020.10.048[4] P. Bouchner, R. Piekník, S. Novotny, et al. Fatigue ofcar drivers-detection and classification based on theexperiments on car simulators. WSEAS Transactionson Systems 6(12):2789–2794, 2006.[5] P. Bouchner, S. Novotny, D. Rozhdestvenskiy, et al.Drivers aggressive behavior experiments on the drivingsimulator. In Proceedings of the 8th InternationalConference on Energy and Development, Environmentand Biomedicine, pp. 148–153. 2014.[6] D. Crundall, A. Stedmon, E. Crundall, R. Saikayasit.The role of experience and advanced training onperformance in a motorcycle simulator. AccidentAnalysis & Prevention 73:81–90, 2014.https://doi.org/10.1016/j.aap.2014.08.009[7] Y. Miyamaru, G. Yamasaki, K. Aoki. Development ofa motorcycle riding simulator. JSAE Review23(1):121–126, 2002.https://doi.org/10.1016/S0389-4304(01)00147-3[8] K. Nagaseak, A. Yamasaki, H. Ishii. Development of ariding simulator for motorcycles. SAE Technical Paper2018-32-0031:7, 2018. ISSN 0148-7191, e-ISSN2688-3627. https://doi.org/10.4271/2018-32-0031[9] P. Toman, J. Svoboda, A. Orlický. Assessment of themotorcycle rider behaviour – Experimental study on therace track. In 2020 Smart City Symposium Prague(SCSP), pp. 1–6. 2020.https://doi.org/10.1109/SCSP49987.2020.9133760[10] L.-T. Hieu, N. X. Khoa, O. Lim. An investigation onthe effects of input parameters on the dynamic andelectric consumption of electric motorcycles.Sustainability 13(13):7285, 2021.https://doi.org/10.3390/su1313728559

Validation of a longitudinal motorcycle riding dynamic model for a powered two-wheeler interactive simulator

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