Predictive energy ManageMent in virtual driving testsOften it is up to the test track to ultimately reveal whether the operating strategy of a new vehicle delivers the

targeted fuel consumption, CO2, and other emission results. To achieve a higher level of certainty earlier during

system development, IPG Automotive and AVL jointly use a new simulation method to evaluate networked

controller functions in a simulated complete vehicle.

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Energy Management

Link: https://www.springerprofessional.de/en/predictive-energy-management-in-virtual-driving-tests/6115156

Complex InterDepenDenCIes

With hybrid and electric vehicles in par-ticular, energy management greatly influ-ences fuel consumption, range, and/or emissions of the vehicle. So important it consequently is to the predict the opera-tion behaviour of the controllers involved, it is so difficult in practice. This is of the essence of the potentially very complex interdependencies of the individual con-trollers and systems, which are part of energy management.

Predictive functions, which use road characteristics ahead of the vehicle as an important element of energy manage-ment decision making, are of particular interest for simulation. The classical sin-gle domain approach, the use of follow-up signals and the use of open loop pro-file specifications which are widespread today [1] are approaching their limits in this context. It does not make the situa-tion any easier that the driver influences some systems of the controller network by his individual driving strategy. Advanced driver assistance systems (ADAS), which require a driver response, and which the driver himself influences, further add to the complexity.

Nevertheless, evaluating vehicle prop-erties at an early point of function devel-opment is essential to defining a success-ful system and function architecture [2]. To achieve this at an early point of the development process would require vehi-cle tests long before the first hardware is available. The only way to facilitate that is via simulation. To evaluate the control-

ler behaviour within simulation, the powertrain’s system and sub system architecture needs to be represented in sufficient complexity, 1, plus the driving maneuvers and test course must resemble real driving situations. This type of use cases can be reconstructed and driven with the fully networked simulation tools, portrayed here by IPG Auto motive in conjunction with AVL.

preDICtIve energy management

Monitoring the battery state is of special importance in a hybrid vehicle. So is controlling the internal combustion engine (ICE) and the electric motor (EM). To optimally control the state of charge (SoC), the electric motor’s operat-ing principle, the combustion engine’s load point, and the control of clutches, predictive strategies can be employed, 2. The road topography is an influential criterion when it comes to achieving the best possible efficiency in managing energy in the powertrain. One could imagine four cases: : When approaching an uphill stretch the

SoC could be increased to ensure that there is enough electric energy avail able for boosting the combustion engine.

: When approaching a downhill section the use of the electric motor as a gen-erator may not be necessary even if the SoC is low because the powertrain will be in overrun during the downhill travel, which will result in energy recu-peration that can be used to recharge the battery.

AuThOrs

DIpl.-Ing. BernharD sChICk is the Managing Director responsible for Innovation, Product Management

and Marketing at IPG Automotive Gmbh in Karlsruhe (Germany).

DIpl.-Ing. volker leonharDis Development Engineer for the

CarMaker simulation Environment at IPG Automotive Gmbh in Karlsruhe

(Germany).

DIpl.-Ing. steffen lange is Project Engineer at AVL

Deutschland Gmbh in haimhausen (Germany.)

1 System and subsystem interaction in a complex powertrain (presentation in AVL Cruise)

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: When approaching an urban area, the SoC could be increased in time to achieve the maximum purely ”elec-tric“ range. This can increase fuel effi-ciency, lower the noise and emissions levels and thus offers environmental benefits.

: Information about traffic signs can be used to enter speed limit zones or the entrance of a town in the sailing mode which increases fuel efficiency.

IntegratIon anD test platform

Integrating the powertrain, the predictive energy management, driver assistance, and navigation into a complete vehicle simulation is demonstrated within an example hybrid vehicle, 3. For that pur-pose a complex hybrid powertrain, based on the system simulation platform AVL Cruise [3], was integrated into the func-tional mock-up prototype of the CarMaker complete vehicle simulation environment by IPG.

In addition the complete vehicle was enhanced with the energy and driver assistance functions via Matlab/Simulink. To provide the predictive energy manage-ment functions with the information con-

tent [4] of digital maps and a predictive horizon (most probable path, MPP), a development platform for digital map functions, the tool ADAS RP from Navteq, was also connected [5].

sImulateD Complete vehICle

The level of detail of the complete vehicle model can be seen in the number of its components: It is a fully non-linear 3D driving dynamics model with body, axles, suspension, steering, engine mounting, powertrain, hydraulic brakes, tyres, and aerodynamics. In addition the vehicle is equipped with a freely scalable sensor kit for slip angle/side slip, IMUs (inertia meas-urement units), ADAS sensors (radar, lidar and ultrasonic), and a camera (Road Pre-view sensor).

The so-called ModelManager concept which allows an easy and efficient inte-gration of different domain models into the complete vehicle is of major impor-tance. The CarMaker simulation envi-ronment automatically registers these models within a cyclical check of the model library. This makes it possible to manage and organise many different models in parallel. The models can be

changed over with the user interface or on the fly from within the test automa-tion. The parameterisation, initialisation, the cyclical call and the concluding clean-up are performed automatically by the ModelManager. This routine and mechanism also facilitates an easy inte-gration of hardware components and systems into the functional mock-up prototype – not just the integration of models [1, 6].

powertraIn moDel

The complexity of modern powertrains is also reconstructed with an adequate level of detail in order to actively influ-ence the powertrain within the virtual driving test by the input of control and closed-loop control algorithms. Track characteristics which are anticipated can have a direct influence on the energy management and can significantly mod-ify the manoeuver.

As the evaluation based on standard-ised customer cycles will continue to play an important role for the concept analy-sis, basic design, component development and homologation of new vehicles in the era of 3D driving dynamics models, the

2 Analysis and evaluation of road-based virtual test drives (distance and speed) of a hybrid drive

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platform AVL Cruise was expanded so that it can be easily integrated into the CarMaker integration and test platform. Still, the previous scope of software func-tions can be fully used either “stand alone”, or can be integrated “at the click of the mouse” into a high performance driving dynamics model. This can be done in an office application and in real-time applications like CarMaker/HIL, AVL InMotion or on powertrain test benches by means of Puma Open.

Thus is becomes possible to consist-ently use complex powertrain models from the concept phase through the inte-gration and testing phases within the overall vehicle development process. Furthermore this fully integrated con-cept facilitates improvement iterations without the need to convert models, which brings about a considerable time and quality benefit. This actively sup-ports frontloading.

traCk anD envIronment CharaCterIstICs

The required track details are taken from digital maps, a navigation unit, and suit-able algorithms. From this information mix the most probable path, the MPP, is calculated and is provided to the control functions and electronic control units as a horizon. This is done via the connec-tion to ADAS RP, 4. During this process the map data on real road courses is imported into CarMaker. The track data also contains detailed information about road infrastructure (traffic signs, bend radii, etc.). The virtual driver follows the programmed route in a fully independ-ent way and obeys the most important traffic signs. Driving and trajectory strat-egy are easily parameterised for this purpose. To exchange the digital map and navigation information just like in a real car, an interface to the CAN based Adasis protocol [5] was integrated.

Realistic traffic situations can be simu-lated in CarMaker in real-time, as up to 1000 objects (at 1 ms cycle time) can be configured to build a traffic scenario. Fol-lowing a preceding car, approaching a stopped or slowing down vehicle, traffic jam scenarios, and turning-off or filtering-in scenarios among others are especially relevant for predictive energy manage-ment functions. Arbitrary manoeuver tasks (concerning longitudinal and lateral dynamics) can be allotted to every traffic

object. These tasks are either carried out in a sequential fashion (time/path) or trig-gered by events. The virtual traffic objects and road infrastructure are detected by a freely scalable sensor kit in the simulation environment: The virtual radar, lidar, and ultrasonic sensors deliver multi-object lists to the automatic speed control (ACC), while the Road Preview sensors cyclically scan the road section ahead of the vehicle.

vIrtual DrIver moDel

Real-world drivers use different driving strategies (such as defensive, economic, sporty, or aggressive) which influence vehicle behaviour and system behaviour. Within real driving manoeuvers the driv-

ing behaviour is also characterised by a driver’s different choice of trajectory, his/her way of holding course, steering, brak-ing and chosen acceleration strategy. To be able and to reliably calculate the fuel consumption and SoC values just as it is done in the mere simulation of longitudi-nal dynamics, the driver model IPGDriver was expanded to reconstruct this behav-iour in a way that is close to reality [7].

The new model for following a vehicle ahead plays a pivotal role in this: The virtual driver can “see” and is thus able to actively follow a preceding car. The time lapse and distance to the car ahead plus the strategy of following can be freely defined among other things. The driver can also follow recommendations

3 Virtual integration of hybrid powertrain, controller and navigation into a complete vehicle

4 Transfer of horizon, multi path (1D) and single path (1D) information from ADAS RP to the simulation environment CarMaker

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from driver assistance systems via an interface. An example is the timely rec-ommendation to either brake or step off the accelerator pedal respectively.

With these capabilities of the IPGDriver it is possible to systematically check and realistically evaluate integrated functions and to analyse predictive energy management strategies for vary-ing types of drivers, 5.

DrIver assIstanCe system In vIrtual DrIvIng test

In addition to knowing the track horizon from the navigation data, further informa-tion about the environment can be taken from driver assistance systems, such as the ACC. This data can be used to provide the driver with a recommendation of how to approach an object in the fuel efficient sailing mode. During this the vehicle approaches a traffic object (preceding vehicle), an exit, a village entrance, or speed limit zone without any drag torque.

To achieve the most energy efficient approach to the object, the controller cal-culates – depending on the boundary condition – the optimal moment of releasing the accelerator pedal and the level of recuperation in the powertrain. The virtual driver can operate the corre-sponding functions and follow their instructions. Thus the ACC function, for instance, can be checked against a real-istic variety of driver types.

summary anD outlook

The concept described by IPG Automo-tive and AVL jointly here, of virtual driv-

ing tests is based on the same principles that characterise real driving tests: The virtual driver performs the driving and test instructions in a realistic way in a simulated complete vehicle with a simu-lated powertrain and in a simulated 3D environment. The whole bandwidth of real driver types can be used for this with the driver being able to follow driv-ing recommendations coming from an assistance system. Thus it is possible to check within a virtual driving test at an early time whether the system design and operating strategy of a vehicle are robust.

Networked hybrid functions are tested in realistic driving scenarios to evaluate their proper operation. Among the insight gained is, for instance, the fre-quency of changing between electric and combustion engine driving mode and the subsequent battery cycles. In addi-tion fuel consumption, emission values, and vehicle range are determined.

referenCes[1] schyr, C.; schaden, T.; Jakubek, s.; schick, B.: new Frontloading Potentials Through Coupling of hIL simulation and Engine Test Bed. Lecture, Fisita World Congress, Munich, Germany, 2008[2] haraguchi, T.: Verbrauchsreduktion durch verbesserte Fahrzeugeffizienz. In: ATZ 113 (2011), no. 4, pp. 274 – 279[3] schick, B.; Leonhard, V.; Klein-ridder, B.: holistic Inspection of hybrid Powertrains and Chassis Control systems in a Continuous MIL/sIL/hIL Process. Lecture, AVEC, Kobe, Japan, 2008[4] Wilde, A.; schneider; J.; herzog, h.-G.: Fahr stil- und fahrsituationsabhängige Ladestrategie bei hybridfahrzeugen. In: ATZ 110 (2008), no. 5, pp. 412 – 421[5] ress, C.; Balzer, D.; Bracht, A.; Durekovic, s.; Löwenau, J.: Adasis Protocol for Advanced In- vehicle Applications. Lecture, ITs World Congress, new york, usA, november 2008

[6] Pfister, F.; schick, B.: The Future has a sensor. Location Awareness Meets Powertrain Controls. Vortrag, 4. symposium für Entwicklungsmethodik, Wiesbaden, 2011[7] Wurster, u.; schick, B.: substantial Progress of Virtual Driver skills in Interaction with Advanced Control systems to Meet the new Challenges of Vehicle Dynamics simulation. Lecture, AVEC, Loughborough, united Kingdom, 2010

5 Fuel efficiency impact of different styles of driving (follow-to-car)

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