WO2013060454A1

WO2013060454A1 - Method for controlling a hybrid drive having an internal combustion engine and an electric motor and an electric energy accumulator, in particular for a rail vehicle, a control device and hybrid drive

Info

Publication number: WO2013060454A1
Application number: PCT/EP2012/004451
Authority: WO
Inventors: Ingo Lehmann; Peter Riegger
Original assignee: Mtu Friedrichshafen Gmbh
Priority date: 2011-10-28
Filing date: 2012-10-25
Publication date: 2013-05-02
Also published as: DE102011085454A1

Abstract

The invention relates to a method for controlling a hybrid drive having an electrical energy accumulator, for a rail vehicle - in particular for controlling a hybrid drive with an internal combustion engine and an electric motor - in which an electronic route diagram (SPL) is predefined with respect to route sections by means of a train control apparatus, and a route-section-related driving mode (AA) of the rail vehicle is determined predictively by means of the electronic route diagram (SPL), and the operational control of the electrical energy accumulator is determined predictively using an energy accumulator model.

Description

Method for controlling a hybrid drive with an internal combustion engine and an electric motor and an electrical energy store, in particular for a rail vehicle, control device and hybrid drive

The invention relates to a method for controlling a hybrid drive with an internal combustion engine and an electric motor and an electrical energy storage, in particular for a rail vehicle, according to the preamble of claim 1, in which an electronic route timetable is given stretch-related via a train control unit, and a stretch-segment related drive type of the rail vehicle is determined predictively by means of the electronic route timetable.

In a method known from DE 102 26 143 B4 for controlling a hybrid drive, the drive type is proposed by a driving operating strategy. The proposed driving strategy is considered set if the driver does not intervene in the system. On the other hand, if the driver actuates the driving or the brake pedal at short notice, the proposed driving operating strategy is discarded and a new driving strategy is calculated. This takes place until a stationary state is reached, in which no further short-term changes occur more. The driving strategy is determined on the basis of an energy profile, for example with regard to consumption and the time required. The energy profile, in turn, is calculated from driver, vehicle, route and weather data as well as driver-specific data. The route data is taken from a digital road map with altitude information or from a GPS. sen. Using an RDS-T C system, the energy profile is to be adapted to expected changes in traffic route conditions or traffic conditions, such as congestion. No further comments are made on the implementation. The proposed method is geared to a motor vehicle and the final determination of the type of drive by the driver. This makes the fuel savings difficult to estimate.

DE 10 2008 038 753 A1 discloses a method mentioned at the outset, which makes it possible to predetermine the distance-segment-related drive types of the rail vehicle by means of the electronic route timetable before departure. When driving, a position deviation of the actual position can then be determined to a determined from the electronic distance timetable target position of the rail vehicle. In turn, a time reserve is calculated from the position deviation. Based on the time reserve then either the drive is maintained or changed. For example, in the case of a negative time reserve in the sense of a delay, a change is made to a drive with a higher power output. For safety reasons, before activation of the higher power output drive type, it is checked whether the expected speed deviates significantly from the speed defined by the electronic route timetable. In the event of a significant deviation, clearance must first be granted by the control center, for example by providing a modified electronic route timetable. The same consideration applies to the case that a drive of lower power output is to be initiated.

According to DE 10 2008 038 753 A1, nominal system variables of the hybrid drive, for example a desired engine power, can be determined via a predictive model. For example, actual system variables can be read during driving and a deviation of the actual system variables from the nominal system variables can be calculated. By means of a limit value analysis, it can then be decided whether there is no intervention in the system or whether the model is being trained by means of the actual system variables. As a safety measure it can be provided that the model is monitored by a safety management. This method is still in need of improvement. It is desirable to achieve an improved, in particular more comprehensive, control. At this point, the invention begins, whose object is to provide a method and a device which allows an improved, in particular more comprehensive, control of a hybrid drive. In particular, an improved method and a device for controlling a hybrid drive in a rail vehicle to be provided, which uses the conditions of rail transport and the framework of this transport system of rail transport is fair. In particular, a fuel saving in consideration of the energy storage is to be achieved.

This object is achieved by the invention with a method having the features of claim 1. Regarding the device, the invention also leads to a control control device for the predictive control of a hybrid drive for a rail vehicle according to claim 12. Regarding the device, the invention also leads to a hybrid drive for a rail vehicle, in particular a rail vehicle with a hybrid drive, according to claim 13.

The invention is based on the consideration that the framework conditions in the operation of a rail vehicle by the electronic route timetable are given, which, as known before driving z. B. is read via radio in the train control unit. In the electronic route timetable, the distance-segment-related speeds and thus also the time frame are stored between two waypoints. The method advantageously makes it possible, in particular before the start of the journey, to predetermine the route segment-related drive types of the rail vehicle by means of the electronic route timetable. In particular, in the context of a preferred development, it has proved to be advantageous, in principle, to start from a method as described in DE 10 2008 038 753 A1, the disclosure content of which is hereby fully incorporated by reference into this application. In the present case too, a corresponding model is used as the predictive observer for the predictive determination of the distance-segment-related drive types prior to driving.

The predictive control belongs to the class of model-based control methods and allows a prediction into the future, the so-called prediction horizon. Central idea is the use of the electronic route timetable, which, for. B. in conjunction with a height profile, as a forward-looking method advantageously utilizes the fuel-saving potential in full. This means that the energy used is minimized via the predictive control. For the operator decrease hence the operating costs. The model also offers the possibility of capturing and storing an individual behavior pattern of the rail vehicle driver. When re-driving through the same route with the same rail vehicle driver can then be accessed on the individual behavior patterns. The invention continues from the consideration that a sole specification of the route timetable z. B. can still be improved via a train control unit. In particular, a power control by means of a drive type and by system variables of a desired position and a target engine power with respect to a time reserve proves to be fundamentally advantageous; however, the approach underlying this predictive solution can still be supplemented by an advantageous control of the energy store; specifically a model-based predictive control of the energy storage. The invention has recognized that a hybrid drive must regularly resort to an energy storage regardless of a type of drive. Only the specification of actual values of the energy storage, z. B. part of a battery management, for a predictive control of a drive can be insufficient, z. B. when a battery is overcharged or completely emptied or otherwise operated beyond an advantageous load point.

The invention has recognized that the energy storage element as an element of the hybrid drive - over a limited battery and safety management also advantageous as possible to the drive should fit, but in principle, regardless of this, should be performed. The invention therefore provides that the operation management of the electrical energy storage is determined using a predictive energy storage model. The invention has recognized that the energy storage represents technically and in terms of cost an essential element of the hybrid drive. The beyond a normal control and regulation beyond predictive operation management of the electrical energy storage has the advantage that the energy storage, in particular the energy storage management, a reference point can be provided. Due to this, it is possible to perform the operation of the energy storage comparatively optimized. For example, the operation of the energy storage device-in the case of previously known framework conditions and on the basis of the predictively determined operation management-can be designed with regard to an imminent particularly high load on the energy storage device. In another example, the operational management may be in view of a particularly high impending charge of the Energy storage are designed. Such examples show that the prediction of certain operating management basically makes it possible to design the energy store with regard to a regeneration advantageous for its mode of operation; z. B. the highest possible amplitude of charge and discharge, in particular a complete charge and complete discharge can be sought. The concept of the invention provides the basis for guiding the energy store to an optimally optimized operating point; This has the advantage that excessive damage to the electrodes, in particular the cathodes, of the energy store is avoided.

Overall, the predictive certain operation of the electrical energy storage provides the basis for an advantageous extended life of the same. For a hybrid drive, the availability of an electrically wear-free brake is increased; namely due to the availability of the electrical energy storage with comparatively optimized charging potential; This is basically available due to the predictively determined management of the energy storage. The predictively determined management also increases the reliability of the electrical energy storage. Due to the predictively determined operation management in particular a compensation of latent charge in the electrical operating memory is possible.

Advantageous developments of the invention can be found in the dependent claims and specify in particular advantageous ways to realize the above-described concept within the scope of the task and with regard to further advantages.

Following the concept of the invention, the operational management of the electrical energy store can be determined predictively using the energy storage model, in particular using a prediction horizon tuned to the electrical energy store. Within the scope of a particularly preferred development, a prediction horizon is specified for the operation management of the electrical energy store. In particular, the prediction horizon can be matched to the operational management, in particular the operating mode, of the electrical energy store. The prediction horizon that is advantageously designed for the electrical energy store can be used to provide measures for extending the service life and availability of the storage capacity of the electrical energy store in an optimized manner. The electrical energy store is advantageously formed in the form of a battery. For rail vehicles, a battery is advantageous because it has a comparatively large storage capacity. Basically, the electrical energy storage can also be used as a supercapacitor. capacitor (super-cap) or the like electrical capacitor, which makes a faster availability of electrical energy possible than is possible with a battery.

In the context of a further particularly preferred development, the electronic route timetable is evaluated for the predictive operation of the electrical energy storage and converted into a driving profile of sections. The predictive determination of the operational management can thus be designed for a route section or the sequence of route sections.

Within the scope of a further particularly preferred development, provision is made for the predictive determination of the operational management of the electrical energy store to be based on an energy storage model, in particular a battery model. Under an energy storage model is basically a calculable physical model description of the energy storage to understand by means of the application related and in doubt as accurately as possible a controlled system over the energy storage can be reconstructed. In particular, such a procedure has proven itself in the predictive determination of the operation of the electrical energy storage; ie using an energy storage model, the energy storage can advantageously be predictively controlled. The energy storage model can largely replace a detailed measured value recording, since an initial state of the controlled system to be reconstructed on the energy storage, in particular battery, and also the development of the same - due to the route timetable and the stretch-section related Antriebsart- comparatively well known. Incident disturbances can be taken into account, for example, by coupling in an error variable. Thus, there is a good opportunity to estimate the operating state of the electrical energy storage predictive and high quality. In the context of an advantageous development, a number of different energy storage models can be made available and a variant of an energy storage model can be selected from the number of variants matched to a section of the driving profile. In other words, an energy storage model can be tuned to different energy storage modes and acute states as well as possibly the environment of the energy store, such as a rest state, a discharge state or a state of charge. In particular, it has proven to be advantageous for the drive type and / or the operational management of the electrical energy store to be determined predictively by means of an observer system. An observer system can have separate observers for the predictive determination of the drive type and for the predictive determination of the battery routing. The prediction for the type of drive can be made on the basis of a separate first prediction module and the prediction of the operational management of the electrical energy store on the basis of a separate second prediction module. The first and the second prediction module can be nested one behind the other or in one another or even calculate in parallel with one another. In particular, an observer system can be realized in the context of track management, in particular in the case of a railroad hybrid controller.

The prediction of the drive type and / or the operational management of the energy storage device can basically be carried out before the journey of the rail vehicle on the basis of the available electronic route timetable. However, due to the available time periods, it is also possible to carry out a renewed prediction for the type of drive and / or the operational management of the electrical energy store, even in the event of unexpected events occurring during the journey of the rail vehicle. Advantageously, a prediction model for the drive type and / or the operational management of the electrical energy store can be variably adapted. It has proved to be particularly advantageous that, depending on the driving profile, a driving mode is selected from the group consisting of: acceleration, driving, braking, standing or slow driving. These are the most essential driving modes that occur, e.g. B. in a drive profile, which is preferably selected from the group consisting of boosting, electrically driving, in particular steady driving, recuperation, in particular recuperation at the load point.

Preferably, an operating mode of the hybrid drive via shares of an internal combustion engine and an electric motor to a total power of the hybrid drive can be set. The determination can be fixed, for example, rigidly with regard to an operating mode. In the context of a particularly preferred development, the ratio of proportions of an internal combustion engine and an electric motor to a total power of the hybrid drive can also be variably adaptable, in particular predictively adaptable. In the context of a comparatively simple implementation of the concept of the invention, it has proved to be advantageous that the operation management of the electrical energy storage device is dependent on the stretch segment-related drive type of the rail vehicle or a drive profile of the hybrid drive. This has the advantage that a joint evaluation of the route timetable for the creation of driving profiles and drive profiles can be used both for the predictive determination of the operation of the electrical energy storage and the drive.

In the context of another development, the operation of the electrical energy storage can be independent of the stretch-segment-related drive; As a result, an independent predictive determination designed for the separate guidance of the energy store and drive type can be realized, ie. H. An operational management of the electrical energy storage device can be determined predictively independently of the route segment-related drive mode of the rail vehicle.

Preference is given to the possibility of optimizing battery parameters for operational management of the electrical energy store. In particular, this is possible in the case of a previously known prediction horizon based on an energy storage model, for which purpose the energy storage model can be designed, for example, for different states of a battery, in particular independently of a route section. On the basis of actual values of the battery management system, it is preferably possible to optimize parameters of the electrical energy store in an improved manner, in particular an SOC, SOH and / or RI parameter value.

Embodiments of the invention will now be described below with reference to the drawing in comparison with the prior art, which is also partly shown. This is not necessarily to scale the embodiments, but the drawing, where appropriate for explanation, executed in a schematized and / or slightly distorted form. With regard to additions to the teachings directly recognizable from the drawing reference is made to the relevant prior art. It should be noted that various modifications and changes may be made in the form and detail of an embodiment without departing from the general idea of the invention. The disclosed in the description, in the drawing and in the claims features of the invention may be essential both individually and in any combination for the development of the invention. In addition, all combinations of at least two fall within the scope of the invention the features disclosed in the description, the drawing and / or the claims. The general idea of the invention is not limited to the exact form or detail of the preferred embodiment shown and described below or limited to an article that would be limited in comparison with the subject matter claimed in the claims. For the given design ranges, values within the stated limits should also be disclosed as limit values and arbitrarily usable and claimable. Further advantages, features and details of the invention will become apparent from the following description of the preferred embodiments and from the drawing; this shows in: a particularly preferred embodiment of a rail vehicle with a control device comprising a rail hybrid controller with a prediction model for the equally predictive determination of a stretch section-related drive type AA as well as a predictive operation management of an electric battery storage; a schematic comparison of types of drive AA and operating modes of a conventional drive (A) and a hybrid drive (B) in terms of a driving profile with typical sections that are relevant, inter alia, in terms of a battery storage; a scheme for representing a varied prediction model as it can be used under restriction by means of the apparent in Fig. 2B Fahrprofile and tuned drive types AA and operating modes to different drive types in a model-based calculation in the context of a comparison with FIG. 1 varied prediction module 10A, 10B predictively determine and predetermine different battery modes designed for the modes of operation;

4 shows a schematic representation of a prediction of battery modes on the basis of a predefined operating management of a battery on the basis of corresponding battery models with regard to a route timetable with different route sections of a travel profile and a track section-related drive type of a rail vehicle; 5 is a control loop, for example, for implementation in the prediction module 10B of FIG. 4, which is advantageous on the basis of one or more battery models and which is suitable for specifying a prediction horizon that is modified with respect to a prediction horizon predetermined by the route timetable.

FIG. 1 shows the information structure of a hybrid drive 200 for a rail vehicle 1000 as a block diagram. Typically, a hybrid drive 200 includes an internal combustion engine 210, an electric motor 220, a transmission, electrical converters, and an electrical energy storage 230, such as a lithium-ion storage (super-caps). On a common electronic data bus 1, for example a CAN bus system or Ethernet, a train control unit 2 (ZSG), an engine control unit 3 (ECU), a transmission control unit 4 (GS), a battery management control unit 5 (BMS), an inverter Control unit 6 (VCU) and exemplarily a control unit 7 for determining the exhaust gas (SCR) connected. The controllers 2 to 7 connected to the data bus 1 are both receivers and transmitters.

Also connected to the data bus 1 is a unit 8 for receiving the Bahnrichtfunks and the data of the GPS. The electronic train timetable with stored electronic route map is set on the data bus via the railway direction radio. The data of the GPS includes the actual position and the current altitude. This information structure is supplemented by a prediction model 9 as a predictive observer and a safety management 15. The safety management 15 monitors the input variables, the output variable and the parameters of the prediction model 9. The input variables of the prediction model 9 are the electronic route timetable SPL, the actual system variables SG (IST ) and the current altitude Hh. The electronic route schedule SPL with stored electronic route map is provided by the train control unit 2. The electronic route timetable SPL contains the route-segment-related speeds, for example the permissible speed between a waypoint A and a waypoint B. Based on the link-segment-related speeds, the times between the waypoints are also defined. The current altitude Hh is provided by the unit 8 on the data bus 1. The actual system quantities SG (IST) are provided by the engine control unit 3 and generally correspond to the available individual data of all the control devices 2 to 7 connected to the data bus 1, including the unit 8. Below For example, the actual speed of the engine, the actual engine power, the actual fuel consumption, the status of the energy storage 230, the actual position of the rail vehicle and the oil, the energy storage, the cooling water and the inverter temperature to understand. The output variable of the prediction model 9 is the signal D1, which is fed to the engine control unit 3. Within the prediction model 9, a model-based calculation 10, a comparator 11, a data memory 12, an optimization 13 and a calculation algorithm 14 as a functional block are arranged as functional units. In the calculation 10, the controlled system is mapped mathematically. The input variables of the calculation 10 are the electronic route schedule SPL, the current altitude Hh and a signal S2. The model-based calculation 10 in a prediction module is used to calculate the setpoint system variables SG (SL), for example the setpoint position or the setpoint engine power. The desired system quantities SG (SL) are fed to the comparator 11 and stored in parallel in the data memory 12 in relation to the distance. The reference system variables SG (SL), the actual system variables SG (IST) and the data signal S1 stored in the data memory 12 are compared with one another via the comparator 11. The resulting deviation dS is firstly stored in the data memory 12 and, on the other hand, passed on to the optimization 13 as an input variable. By way of the optimization 13, the deviation dS is evaluated with regard to the costs as a function of a predefinable weighting factor k. If the deviation dS is below a predetermined limit value, the last value is retained, ie there is no further optimization. If the deviation dS is greater than the limit value, then the current value is set as the output variable, signal S1. The signal values S1 correspond to a cost function which is determined by means of the maximum principle according to Pontrjagin and by means of dynamic programming according to Bellman. In the subsequent calculation algorithm 14, the signal S1 is evaluated via a predetermined algorithm. Applicable known algorithms for this purpose are the Levenberg-Marquart method, the Newton method or the Nelder-Meat method. The output variable of the calculation algorithm 14 corresponds to a manipulated variable profile for the considered prediction horizon, which is fed back to the calculation 10 as signal S2. Via the feedback branch with optimization 13 and calculation algorithm 14, the model-based calculation 10 is trained until a minimum has been found. In the data memory 12, both the deviation dS and the desired system quantities SG (SL) are stored in relation to the distance.

Before the start of the journey, the route segment-related information is calculated via the prediction model 9 on the basis of the electronic route timetable SPL and the altitude profile of the route. NEN drive types calculated predictively. These are stored in the data memory 12 stretch-segment related. For example, a purely internal combustion engine type of drive in a section of track with slope or a drive type with recuperation (energy recovery) in a section of the road with a gradient. During the driving operation, the engine control unit 2 evaluates the current state of the overall system on the basis of the data D1 and optionally initiates a change of the drive type via the data bus 1. This is explained in more detail using the example of a position deviation.

The desired position is determined via the calculation 10 as one of the desired system variables SG (SL). The actual position is provided as one of the actual system variables SG (IST) from the engine control unit 3 to the prediction model 9 as an input variable. The comparator 11 calculates a deviation dS of the actual system quantities SG (IST) to the target system variables SG (SL). The deviation dS thus corresponds to a position deviation in this example. This is stored in the data memory 12 stretch-related. The data values D1 are cyclically read in by the engine control unit 2 and assessed. For this purpose, the engine control unit 2 calculates a time reserve based on the data value D1 which characterizes the position deviation. With a negative time reserve in the sense of a delay, a change is made to a drive with a higher power output. With a time reserve within a tolerance band, the drive mode is maintained and with a positive time reserve in the sense of a premature arrival is switched to a drive with the lowest energy consumption. If the rail vehicle 1000 reached the next waypoint, for example a train station (Bhf), too late, the engine control unit 3 initiates a change to the combined drive mode of internal combustion engine 210 and electric motor 220. The combined drive mode causes an increased expected speed. For safety reasons, before activation of the higher power output drive type, it is checked whether the expected speed differs significantly from the speed defined by the electronic section schedule SPL. If this is the case, clearance must first be granted by the control center, for example by providing a modified electronic route timetable.

In the context of the concept of the invention, it was recognized that it is fundamentally possible on the basis of this approach, which is known per se from DE 10 2008 038 753 A1, to also carry out the predictive determination of operational management of the electrical energy store 230. For this purpose, in the present case, a battery actual status is obtained by means of battery status actual values BS (IST) of the electrical energy store 230 of the model-based calculation 10 supplied. The actual status of the battery is provided to the model-based calculation 10 by the battery management controller 5; In principle, the battery actual status is also available to the engine control unit 3 as part of the actual system variables SG (IST) via the electronic data bus 1. In addition, current battery model data BM (IST) are also supplied to the model-based calculation 10, depending on the selected energy storage model BM--.

The current battery model data BM (IST) is provided to the model-based invoice 10 and the data bus. Thus, the model-based calculation 10 is not only able to provide a drive type AA for the target system sizes SG (SL), but also an operational management of the energy store 230 according to a predictive determination. The battery status setpoint values BS (SL) can also be passed on to the comparator 11 analogously to the aforementioned manner with the desired system variables SG (SL) or separately, where they are compared with the battery status actual values BS (IST) to become. In the context of the previously explained controlled system, these can be fed back into the model-based calculation 10 with the deviation dS or in an analogous manner in the further control loop as signal S1 and S2 or cyclically provided to the engine control unit 3 as data value D1.

In the following, with reference to a preferred embodiment, it will be described in detail how, based on the energy storage model BM 5 ', a predictive tuning of the operation of the electrical energy storage device 230 can take place.

FIG. 2 shows in views (A) and (B) the comparison of a flow of drive modes of a conventional diesel engine and an operation mode 500 of a hybrid drive 200 of a diesel engine and an electric motor; in both cases, taking into account a braking process with a brake. In both cases, the types of drive or operating modes are assigned to a driving profile, as specified for example by an electronic route timetable for a rail vehicle 1000. For example, it can be seen in detail from FIG. 2A that for a section that requires acceleration, a proportion of virtually 100% of the drive energy can be assigned to the diesel engine. For example, after reaching the maximum permissible vehicle speed or cruising speed, only that drive power is required which is necessary to maintain a steady-state condition. In the steady state, therefore, the operating points of an internal combustion engine 210, such as a diesel engine in the engine map, lie in an area with significantly lower driving power. energy but higher specific fuel consumption. Approximately 50% of the drive power of a diesel engine is appropriate for the "drive" section of the route, while the "braking" section usually requires full utilization of the brake, ie 100% braking energy. When driving slowly or standing, for example, in a station Bhf or the like, a drive energy of 10% of a diesel engine is usually to be set. For the same sequence of route sections 301, 302, 303, 304 of a driving profile 300 given by an electronic route timetable, a sequence of driving modes 400 I "acceleration", II "driving", III "braking", IV "standing" results in FIG. 2B. These driving modes 400 are assigned operating modes 500 of the hybrid drive 200 of engine 210 (diesel engine), electric motor 220 (motor / generator) and brake or generator as follows.

For the driving mode 400 I "accelerate" the hybrid drive 200 is currently operated in the drive profile APi "Boost." The proportion of an internal combustion engine 210 and an electric motor 220 in this operating mode 500 is 75% and 25% of the total power. In the present case, the operating mode 500 of the hybrid drive 200, referred to as driving or "hybrid / Eco", is implemented, in which case 50% of the total output from the internal combustion engine 210 (diesel engine) can be dispensed with 200 in drive profile APiii Recuperation, d. H. 80% of the braking power generated by braking can be supplied to the battery via the generator and 20% of the braking power can be supplied via the brake. For the driving mode 400 IV "standing or slow driving", the hybrid drive 200 can be operated in the drive profile APii "electric driving", for which approximately 10% of the energy potential of the electric motor must be applied.

As can be seen from the marking in FIG. 2B, for the driving mode 400 I of the hybrid drive 200 "accelerate" 25% of the electric motor (APi Boosten), in driving mode 400 III "braking" 80% of the generator (APiii "recuperation load point") and in driving mode 400 IV "standing or slow driving" (APii "electrically driving") 10% of the electric motor to provide power In these three cases, the electrical energy storage 230 as a battery or a supercapacitor significantly importance to cover or recording (recuperation) Thus, a battery mode 530 should be chosen advantageously, in the present case the predictive control thereof is described as an example. In this embodiment, the model-based calculation 10 is given a predictive model-based prediction module 10A, which alternatively takes into account the abovementioned operating modes for determining the desired system variables SG (SL). This is based on a subordinate prediction module 10B of FIG. 4, which predicts operating data for the optimized operation of the battery, taking account of aging effects, on the basis of the aforementioned energy storage model BM 5 '.

Referring first to FIG. 3, prediction module 10A is provided with data on actual altitude Hh of distance timetable SPL and actual system quantities SG (IST) and battery status actual values BS (IST) from battery management controller 5 via model-based calculation 10. The input variables are used to convert the electronic route schedule SPL into a driving profile 300 of route sections 301, 302, 303, 304, as is explained by way of example with reference to FIG. 2B. From this route analysis results in a restriction of the computing section 10A.1 concerning the prediction of operating modes 500 for the hybrid drive 200th This leads namely to the illustrated in the calculation section 10A.2 drive profiles APi "Boost", ii "electric driving" and iii "load point recuperation ". Based on this, a sequence of operating modes 500 may be created with the respective shares 520 of energy sales and power requirements, as shown in the upper part of FIG. 2B. This type of predictive determination of a route section-related drive type AA and operating modes 500 leads to a matrix of desired system variables SG (SL) which are available at the output of the model-based prediction module 10A or at the output of the model-based calculation 10. The matrix is also used for further analysis as input data for the predictive prediction module 10B illustrated in FIG. 4, based on the energy storage model BM.

The aforementioned battery status actual values BS (IST) of the battery management control unit 5 are also available to the predictive prediction module 10B, as well as the aforementioned current altitude Hh and the route timetable SPL. The restriction obtained from the route analysis enters the computing section 10B.1 and corresponds to that in the computing section 10A.1. Depending on this, ie evaluation of the electronic timetable SPL and on the basis of the restriction in the computing section 10B.1, a varied energy storage model BM is used for different operating modes 500 of the hybrid drive 200. In each case, slightly varied battery model Data BM (IST) are taken into account in the computing section 10B.2 on the basis of characteristic curves, which may also describe the aging effects of the battery. Shown is, for example, the varied energy storage mode 530 for the battery state "idle state" 531 assigned to an operating mode 500 Hybrid / Eco (driving mode 400 III "driving"). The energy storage model adapted for the battery state "Discharge" 532 is provided for the drive profile APi "Boost" or the drive profile APii "For the drive profile APIII" Recover load point "The energy storage model adapted for the battery state" Charge "533 is provided By calculating the battery status with regard to the route schedule SPL, a sequence of operating data can thus be specified as the battery status setpoint BS (SL) which is suitable for optimum operation of the battery, taking aging effects into consideration Likewise, the operating data for operating modes 500 of the hybrid drive 200 are available in the data memory 10A.3 at the output of the prediction module 10A.

The operating data of the battery may include, for example, charge times, discharge times or cycles thereof; these may also include load amplitudes or discharge amplitudes. The parameters mentioned can be designed with a view to optimizing the SOC, SOH or RI values of a battery. As a result, an optimized predictively determined operational management of the energy store 230 can be made available for the travel of a rail vehicle along the sequence of route sections 301, 302, 303, 304 of the travel profile 300 according to FIG. 2B. This can be done within the scope of the desired system variables SG (SL), which-as explained with reference to FIG. 1-are fed into the engine control unit 3 and are also optimized via signals S1, S2 and fed back into the model-based calculation 10 The result of the predictive control of the hybrid drive 200 of the present embodiment, not only an optimized stretch section related predictively determined drive type AA of the rail vehicle with fuel economy, but also a gentle and predictive optimized operation of the battery storage BS and energy storage 230th For example, the energy storage device 230 can be emptied as far as possible by means of the predictive operation of the energy storage device 230 against known and predictively usable braking processes. This has the consequence that, based on an operating mode for brakes shown in FIG. 2B, the generated braking energy (80% generator) can be completely fed into the energy store 230. For example, a measure in the context of predictive management may be to operate the electric motor via the battery in an operating mode 500 hybrid / eco "driving" beforehand, in order to empty the battery as much as possible a drive profile APi "boost" in advance, the electric energy storage 230 to be fully charged, this at least partially to cover the 25% electric drive energy of driving mode 400 I "accelerate" according to Fig. 2B from the battery storage. For example, the predictive operation management can also be used to perform the 10% power contribution of the electric motor via the battery storage in the usual driving mode - in particular in consideration of a steady ride or in the slow-travel- according to FIG 2B. but probably under the best possible tracking of the operating point.

It is also possible to store aging effects of the battery in the energy storage model BM 5 ', so that the entire predictive operation can take account of aging effects. Overall, the illustrated with the present embodiment, the concept of the invention means that the battery management control unit 5 BMS a reference point can be provided on the way the battery storage can be charged charged, discharged or operated.

Moreover, as explained in greater detail with reference to FIG. 5, a prediction horizon HOR (SPL) as initially predetermined or determined by a route timetable SPL (waypoints, stopping points, stations or other points for changing the driving profile) can be supplied to the energy storage model BM become. In other words, the predictive control loop 10C of FIG. 5 can first be supplied with the route timetable for determining a prediction horizon via the input calculation section 10C.1, and a modified assessment of the prediction horizon can be initiated using the energy storage model BM in the calculation section 10B.2. This can be achieved within the scope of calculation section 10C.3. The cause of a changed assessment of the prediction horizon HOR (SPL) can be, for example, that a drive profile APi "boosting" had to be terminated prematurely if insufficient energy of the battery storage is available. Also, a prediction horizon zont or a prediction length lower fail than a route schedule SPL basically allows; z. B. if, as part of a braking process, an energy transfer via the generator 220 is limited in the energy storage 230. Such and other processes may result in a modified prediction horizon modHOR (SPL), which is additionally or in modification of the prediction horizons HOR (SPL) specified by the route timetable SPL. The control loop 10C of FIG. 5 can be run through several times and thus lead to an optimized adaptation of the prediction horizons modHOR (SPL). The prediction horizons modHOR (SPL) can serve exclusively for the prediction of the operating data for optimal operation of the battery, as explained with reference to FIG. 4 in the prediction module 10B. However, the prediction horizons modHOR (SPL) can also be supplied to the prediction module 10A and lead to the adaptation of the driving modes AA or operating modes 500 of the hybrid drive 200.

reference numeral

1 data bus

2 train control unit (ZSG)

3 engine control unit (ECU)

4 gearbox control unit (GS)

5 Battery management control unit (BMS) 6 Inverter control unit (VCU)

7 Exhaust gas control unit (SCR) 8 Railway radio reception unit 9 Model

10 model-based calculation

10A, 10B prediction module

10A.1, 10A.2 calculation section

10A.3 data memory

10B.1. 10B.2 calculation section

10B.3 Data memory

10C control loop

10C.1 calculation section

11 comparators

12 data storage 13 optimization

14 calculation algorithm

15 security management

100 control device

110 train control

120 rail hybrid controller

200 hybrid drive

220 electric motor

230 energy storage

300 driving profile

301, 302, 303, 304 sections

400 driving mode

500 operating mode of hybrid drive

520 shares in a total power of a hybrid drive 530 energy storage mode

531 idle state

532 unloading

533 store

1000 rail vehicle

I, II, III, IV different driving profiles AA drive type

A, B waypoints

AP drive profile

APi boost

APii electric driving

APiii Recuperation at the load point

BM, 5 'energy storage model

BM (IST) battery model data

BS (SL) Battery status setpoints

BS (IST) Battery status actual values dS deviation

D1 data value

Hh height

k Weighting factor modHOR (SPL) Prediction horizon

S1, S2 signals

SG (IST) actual system sizes

SG (SL) Target system sizes

SPL electronic track schedule

Claims

claims

A method for controlling a hybrid drive (200) having an internal combustion engine (210) and an electric motor (220) and an electrical energy store (230), in particular for a rail vehicle, in which

- an electronic route timetable (SPL) is specified section-related via a train control unit ZSG, and

a track-segment-related drive type (AA) of the rail vehicle is determined predictively by means of the electronic course timetable (SPL), characterized in that

- The operation of the electrical energy storage device (230) using an energy storage model (B) is determined predictively.

2. The method according to claim 1, characterized in that the drive (AA) and / or the operation of the electrical energy storage device (230) before and / or during the journey of the rail vehicle are determined predictively.

3. The method of claim 1 or 2, characterized in that for the predictive operation of the electrical energy storage device (230) of the electronic route timetable (SPL) is evaluated and in a driving profile (300) of sections (301, 302, 303, 304) is implemented, in particular for a driving profile (300), a driving mode (400) is selected from the group consisting of: acceleration (I), driving (II), braking (III), standing or slow motion (IV).

4. The method according to claim 1 or 2, characterized in that the operation of the electric energy storage device depends on the stretch section related drive (AA) of the rail vehicle determined or determined independently of the stretch section related drive (AA) of the rail vehicle is predictive.

5. The method according to claim 1, wherein the drive profile is selected from the group comprising: boosting; electrically driving (ii), recuperation at the load point (iii), in particular an operating mode (500) of the hybrid drive (200) via shares (520) of an internal combustion engine (210) and an electric motor (220) is set to a total power of the hybrid drive (200) ,

6. The method according to any one of claims 1 to 5, characterized in that the predictive determination of the operation of the electrical energy storage device (230) based on an energy storage model (B) for a battery, a supercapacitor or the like energy storage (230).

7. Method according to one of claims 1 to 7, characterized in that a number of different variants of energy storage models (BM) are available and a variant of an energy storage model (BM) of the number of for a route section (301, 302, 303, 304) the driving profile (300) is selected variants, in particular depending on the drive (AA) of the rail vehicle and / or operating mode (500) of the hybrid drive.

8. The method according to any one of claims 1 to 7, characterized in that an energy storage model (BM) is tuned to a group of energy storage modes (530) consisting of: idle state (531), discharge (532), charging (533).

9. The method according to any one of claims 1 to 8, characterized in that for a mode of operation of the hybrid drive, the ratio of proportions of an internal combustion engine (210) and an electric motor (220) to a total power of

Hybrid drive (200) is variably adaptable, in particular predictively adaptable.

10. The method according to any one of claims 1 to 9, characterized in that on the basis of an energy storage model (BM) a prediction horizon is specified, in particular a prediction horizon (modHOR (SPL)) for energy storage management independently of a prediction horizon (modHOR (SPL)) given a drive becomes.

11. The method according to any one of claims 1 to 10, characterized in that an optimization of energy storage parameters for operation of the electrical energy storage device (230) and a prediction horizon (modHOR (SPL)) on Based on an energy storage model BM, in particular on the basis of battery status actual values (BS (IST)) of the battery management control unit (5, BMS), in particular a SOC, SOH and / or Rl value.

12. Control device (100) for the predictive control of a hybrid drive (200) for a rail vehicle (1000), in particular with a train control (110) with a predictive path hybrid controller (120), which is used to carry out a method according to one of claims 1 to 1 1 is formed.

13. hybrid drive (200) for a rail vehicle (1000), in particular rail vehicle with a hybrid drive, with a control device according to claim 12.