Control method and control system for a hybrid powertrain

Abstract

The present invention relates to a control method and a control system (100) for a hybrid drive train (10) with a low-level control level for hardware-related control of an operating sequence and a high-level control level for optimization-related control of operating characteristics. Low-level control steps comprise parallel parameterized control (S21, S22, S23, S2N) of manipulated variables (u 1-N ) of components of the hybrid powertrain (10). High-level control steps include: determining (S43) optimized parameters (θ 1-N ) based on at least one stored parameter optimization model (M X,Y,Z ) and measured operating variables (y 1-N ), as well as updating (S44) the parameters used in the parameterized checking (S21, S22, S23, S2N) by the optimized parameters (θ 1-N ).

Description

[0001] The present invention relates to a control method and a control system for cooperatively controlling a hybrid drive train of a vehicle across control levels.

[0002] Control techniques for controlling, i.e., for controlling or regulating the joint operation of components of a hybrid powertrain—that is, drive sources such as an internal combustion engine, an electric motor, fuel metering, control of stator coils, or a transmission of drive forces such as a multi-step transmission, a continuous transmission, clutches, and the like—are known in the prior art. Two types of architectures for control and regulation technology are conventionally used: either decentralized control units, each of which specifically controls a subsystem, or a central control unit that controls an entire system, for example, by implementing numerous dedicated control subroutines for subsystems on a CPU.

[0003] The architectures mentioned above have the following structural disadvantages per se. The decentralized control units fundamentally do not operate optimally in the sense of the control-technically defined concept of optimal control, i.e. they do not operate at an operating point or optimal curve of such operating points of the overall system that is optimal with regard to a property. Such an optimum is not achieved precisely, for example, due to missing or inadequate coordination across parts of an operating range. Furthermore, a control system resulting from a plurality of decentralized control units can be unstable, i.e. it can cause oscillations in a reference variable or control or regulated variable, for example due to latencies in logical couplings between control elements of different decentralized control units.The fundamental disadvantages of a central control unit are that, depending on the complexity, numerical difficulties can arise, and that updates to optimizations or forecasts of changes cannot be implemented during operation, i.e., they cannot be implemented online without interrupting the entire control system for an update. In centralized systems, updates are therefore often only carried out with long delays. Furthermore, in such systems, only incomplete or inadequate updates are regularly carried out, meaning that the optimization problem is not solved correctly because the best possible update is not carried out. These approaches are not very efficient. If multiple subsystems have to be considered for an update, the problem is often far too complex to be carried out offline.It is an object of the invention to provide a control and regulation technology for controlling a hybrid powertrain that overcomes the aforementioned disadvantages of a prior art architecture of decentralized control units or a centralized control unit. It is an object of the invention to provide a technology that can use updated forecasts when controlling a hybrid powertrain and yet operates stably and approximately optimally within the meaning of the control-technology-defined concept of optimal control. A further object of the invention is to provide a cooperative control method that can be used for all decentralized control units.

[0004] The above objects are achieved by a control method having the control steps of claim 1 and a control system having the features of claim 7. Further features and details of the invention emerge from the subclaims, the description and the drawings.

[0005] Features and details that are described in connection with the device according to the invention naturally also apply in connection with the method according to the invention and vice versa, so that with regard to the disclosure of the individual aspects of the invention, reference is or can always be made to each other.

[0006] The control method according to the invention serves for the cooperative control of a hybrid powertrain of a vehicle across control levels. For this purpose, the control method comprises the following low-level control steps for hardware-related control of an operating sequence of the hybrid powertrain: - parallel parameterized control of control variables of components of the hybrid powertrain based on the respective stored parameters using several parameterized controllers; - Measuring operating variables during operation of the hybrid powertrain using several sensors; and - Feeding back the measured operating variables into the respective parallel parameterized control of the manipulated variables.

[0007] Essential to the invention, the control method also includes the following high-level control steps for the optimization-related control of operating characteristics of the hybrid powertrain: - Determining optimized parameters based on at least one stored parameter optimization model and the measured operating variables by means of a central unit; and - Updating the parameters used in the parallel parameterized control in the parameterized controllers in exchange for the optimized parameters determined by the central unit.

[0008] Likewise, the control system according to the invention serves for the cooperative control of a hybrid drive train of a vehicle across control levels. A low-level control level for hardware-related control of an operating sequence of the hybrid drive train comprises the following control means: a plurality of parameterized controllers for controlling manipulated variables of components of the hybrid drive train based on respectively stored parameters; and a measuring device for measuring operating variables during operation of the hybrid drive train with a plurality of sensors arranged to detect the respective operating variables on the vehicle; wherein the parameterized controllers are signal-connected to the measuring device for feedback of the measured operating variables into the respective control of the manipulated variables.

[0009] Essential to the invention, a high-level control system for optimization-related control of operating characteristics of the hybrid powertrain comprises the following control means: a central processing unit (CPU), which is signal-connected to the measuring device, for evaluating the measured operating variables, with: a parameter determination module for determining optimized parameters based on at least one stored parameter optimization model and the measured operating variables; and an update module for updating the stored parameters used by the parameterized controllers in exchange for the determined optimized parameters.

[0010] According to the present disclosure, the definition of a hybrid powertrain includes drives with at least two drive sources, which can be of different types or of the same type. Thus, the term "hybrid powertrain" within the meaning of the invention encompasses not only hybrid drives of HEV or P-HEV vehicles with an internal combustion engine and at least one electric motor, but also purely electrically powered BEV or FCEV vehicles whose drivetrain includes multiple electric motors as drive sources.

[0011] The invention thus provides, for the first time, a cooperative control and regulation technology that comprises an architecture with comprehensive cooperation between a high-level control plane and a low-level control plane. Furthermore, the control structure is adaptable to any hybrid powertrain control system with any powertrain components.

[0012] At the low-level control level, parameterized controllers located locally on individual components of the hybrid powertrain perform dedicated control or regulation, i.e., parameterized monitoring of the component's partial operation within the overall operation of the hybrid powertrain. The parameterized controllers receive feedback from sensors via a signal connection to sensors, providing actual values ​​of measured operating variables recorded by sensors during vehicle operation. This allows the local parameterized controllers of the components, or the parallel processes of parameterized control, to operate independently, hardware-specifically, with low complexity and stability, subject to the specification of stored low-level parameters such as target values, characteristic maps, or other parameter sets.

[0013] The high-level control level is implemented in a central unit that includes a CPU with data processing capacity, a data interface for receiving measurement data from the sensors and for outputting processed parameters via signal connections to the local parameterized controllers, as well as a memory containing software for processing the data and experimentally developed models for optimizing parameters based on measurement data from driving operation. The high-level control level performs operations that affect the interaction of the components of the hybrid powertrain or higher-level goals or properties resulting from the overall operation of the hybrid powertrain or the driving operation of the vehicle.On the one hand, the centralized perspective on actual and target values ​​in the hardware-specific, dedicated processes at the low-level control level and the centralized parameter output to these processes enable the consideration and coordination of the parallel processes of local, parameterized control. In other words, the parameters or parameter sets stored and used in the parameterized controllers are preferably subjected to global parameter optimization when necessary. This can be triggered by the detection of operating conditions that emerge from significant patterns in the measured data of driving operations and can be identified through real-time analysis.

[0014] Cooperative control between two control levels is an optimization-based control concept that has parameter optimization for different parameterized controllers as the central control task of the high-level control level. Various model-based parameter optimizations can be pursued for different optimization problems, i.e., desired properties for vehicle operation. Cooperative control thus encompasses cooperation between parameter optimization at the high-level control level and the storage of the newly optimized parameters at the low-level control level for exchanging the parameters used by the parameterized controllers.Cooperative control involves further cooperation between the measurement technology, which provides the respective recorded actual values ​​for the parameterized controllers at the low-level control level, and also provides the recorded actual values ​​at the high-level control level for evaluating and determining past, current, and upcoming operating states. The control method for cooperative, cross-control-level control of a hybrid powertrain also includes a corresponding control method for cooperative, cross-control-level control of a hybrid powertrain.

[0015] One advantage of the cooperative control system according to the invention is that it operates almost optimally in the sense of the control-technology-defined concept of optimal control, similar to a centralized control system. The parameters that are individually stored and used in the local controllers at the low-level control level can be previously adjusted in parallel to the parameterized controllers at the system-related high-level control level in the central unit, taking into account the combined measurement data. Furthermore, the parameters output to the low-level control level can always be model-based optimized with regard to the tracked properties of the overall system, which may also change depending on different operating states.

[0016] As a further advantage, the cooperative control according to the invention operates stably, comparable to a central control, since there is no coupling between controllers within a level, and thus no reaction phenomena such as overshoot or oversteering as well as latencies between control elements or other oscillations occur as in a decentralized control.

[0017] Further preferably, it can be provided that in the method only parameters of one controller are updated at any given time.

[0018] This avoidance of parallel updates significantly facilitates central optimization and allows the process to be carried out in real time.

[0019] Furthermore, a further advantage of the cooperative monitoring according to the invention is that the monitoring of hardware operation and the parameter optimization run separately from one another. This allows the monitoring of hardware operation to continue stably and without interruption throughout update processes for exchanging parameters. Thus, the monitoring of hardware operation is not dependent on fluctuations in the required utilization of a shared data processing resource for the parameter optimization models. Furthermore, there is no ultimate time window for the result of a run of a parameter optimization model, which may in particular comprise an iterative approach, which is predetermined, for example, by the timing of a subsequent process when monitoring hardware operation.

[0020] In a further advantage of the invention, the structure of the time-independent data processing between the control levels of the cooperative control according to the invention enables some of the control tasks of the high-level control level to be carried out or evaluated both online and, if necessary, offline in an external device, without impairing the operational operation of the control of the hardware.

[0021] The cooperative control system according to the invention is based on a simple hardware architecture, as readily available, dedicated parametric controllers are used for the components of the hybrid powertrain, and flexible calibration of the stored and used parameter sets is continuously provided. This results in the advantage that the parameterized controllers can be replaced, removed, or added to the control system with little effort and high compatibility.

[0022] Consequently, the advantage is that the cooperative control according to the invention offers a high degree of modularity, since the parameterized controllers dedicated to the individual components of the hybrid powertrain can be configured differently, e.g., in the event of modifications or improvements to the powertrain or similar reasons, or can be removed from the control system or newly added. Within the framework of this modularity, the stability of the remaining unchanged, dedicated parameterized controllers is maintained, and, in contrast to a central control system, no new global control law needs to be designed after a changed configuration. The invention can thus also be described as a modular control method for the cooperative control of a hybrid powertrain of a vehicle across control levels.

[0023] According to an advantageous aspect of the invention, the control method may comprise a high-level control step: - Determining a past, current or upcoming operating state based on the measured operating variables by means of the central unit.

[0024] Likewise, according to an advantageous aspect of the invention, the control system may comprise a state determination module for determining past, current and / or upcoming operating states based on the measured operating variables from the measuring device.

[0025] The recognition of typical, recurring operating states of the hybrid powertrain during driving enables a consistent or reasonable assignment of optimization criteria for the parameters.

[0026] According to an advantageous aspect of the invention, the determination of optimized parameters can be carried out depending on, in particular in response to, the determination of an operating state. Thus, a temporal and causal relationship is established between a change in the optimized parameters provided for the parameterized control and a driving dynamics situation of the vehicle.

[0027] According to an advantageous aspect of the invention, the control method may comprise several update routines, each with the following state-specific high-level control steps: - Selecting a parameter optimization model associated with the previously determined operating state by means of the central unit; - Determining at least one optimized parameter based on the selected parameter optimization model and the measured operating variables by means of the central unit; and - Updating at least one parameter used by at least one parameterized controller in at least one of the parameterized controllers in exchange for the at least one optimized parameter determined by the central unit.

[0028] Thus, a sequence of the steps mentioned, which is required for each continuous update of the parameterized control, is repeated for different individual model-based parameter optimizations independently of each other and in the same order.

[0029] In this context, according to an advantageous aspect of the invention, the control system can comprise a model selection module for selecting a parameter optimization model associated with the specific operating state, wherein a selection of different parameter optimization models for optimizing state-specific selected parameters for individual parameterized controllers in different operating states is stored in the central unit CPU.

[0030] According to an advantageous aspect of the invention, the update routines between the central unit and the parameterized controllers can be carried out asynchronously, in particular in response to the determination of an operating state, depending on state-specific selected parameters and / or depending on processing times of different, in particular iterative, parameter optimization models for the optimized parameters.

[0031] Thus, only those parameterized controllers whose components in the hybrid powertrain have a significant impact on the property being tracked in a specific operating state are specifically optimized. The update routines are asynchronous in that they are not clocked, but can be triggered by the initiation or termination of operating states. The update routines are asynchronous in that they are preferably independent of each other and can be executed simultaneously. The individual processing time for determining an optimized parameter depends on an associated and possibly iterative parameter optimization model and can vary accordingly.Because the update routines run asynchronously, it can be ensured that after driving conditions have been detected based on the measured operating variables, the state-specific update routines can be executed as quickly as possible for individual parameters. As a result, specific calibrations can be performed as quickly as possible using optimized parameters on parameterized controllers of manipulated variables on components of the hybrid powertrain with a significant impact on the tracked property. Consequently, the tracked property can be achieved for a specific operating state as quickly as possible after its occurrence.

[0032] According to an advantageous aspect of the invention, the low-level control plane of the control system may comprise at least one parameterized controller of a first drive source of the hybrid powertrain; a parameterized controller of a second drive source of the hybrid powertrain; and / or a parameterized controller of a transmission of the hybrid powertrain.

[0033] Thus, the technology according to the invention and its advantageous effects primarily focus on the drive sources and the power transmission, the interaction of which has the greatest impact on driving operation and the highest potential for optimization goals.

[0034] Further advantages, features, and details of the invention will become apparent from the following description, which describes an embodiment in detail with reference to the drawings. They show schematically: Fig. 1 is a block diagram of the control system controlling a hybrid powertrain according to an embodiment of the invention; and Fig. 2 is a block diagram of the control method that controls the hybrid powertrain according to an embodiment of the invention.

[0035] Fig. Figure 1 shows a system environment of the control system 100 for cooperatively controlling a hybrid drive train 10 (not shown in further detail) of a vehicle with drive sources in the form of an internal combustion engine and an electric motor, as well as a transmission and clutches. A measuring module 30 comprises sensors (not shown in further detail) that are attached to the hybrid drive train or to the vehicle to measure operating variables y1, y2, y3, y N, such as speed, various speeds of drive sources, torques or a drive load on elements of the power transmission, flow rates of air or fuel supply, an electrical power supply, a load requirement and the like (step S31 in Fig. 2).

[0036] The control system 100 comprises two cooperative control levels that communicate with each other via signal connections. A low-level control level comprises parameterized controllers 21, 22, 23, 2N, each of which carries out a parameterized control (steps S21, S22, S23, S2N in Fig. 2), ie in particular a dedicated control and / or regulation of a manipulated variable u1, u2, u3, u N , a hardware component of the hybrid drive based on a stored parameter or parameter set and a feedback (step S32 in Fig. 2) the operating variables y1, y2, y3, y recorded by the sensors NFor example, one of the parameterized controllers 21 controls a manipulated variable u1 of a throttle valve position of an air supply or an injection quantity metering unit of a fuel supply to an internal combustion engine, the parameterized controller 22 controls, for example, a manipulated variable u2 of an inverter for controlling stator coils of an electric motor with electrical power, the parameterized controller 23 controls, for example, a manipulated variable u3 of an actuator in a multi-step transmission for switching between gear ratios, the parameterized controller 2N controls, for example, a manipulated variable u N an actuator of a clutch between the transmission and the combustion engine or the electric motor to engage or release the clutch, etc. The parameterized control of the manipulated variables u1, u2, u3, u Ntakes place in parallel and independently of each other, for example by dedicated control elements such as a PID controller, which approximates an actual value to a target value, whereby a target value corresponds to the stored parameters or parameter set, and the actual value with the measured data from the sensors for the recorded operating variables y1, y2, y3, y N corresponds.

[0037] Further examples of components of the hybrid powertrain 10 or from its system environment and corresponding further control variables y N, which can be controlled by further parameterized controllers 2N of the low-level control level of the control system 100, relate to a torque distribution, possibly by a vectoring control specific for HEV, BEV, FCEV or P-HEV vehicles, different types of transmissions, components of an exhaust gas aftertreatment, such as a heating element, a device for dosing a urea additive, a regeneration of a particulate filter, a thermal conditioning of a traction battery, etc.

[0038] The high-level control level is formed by a central unit 40 in the form of a CPU. The CPU is signal-connected to the sensors via the preferably integrated measuring module. In addition, the central unit 40 is signal-connected to the low-level control level, i.e., all parameterized controllers 21, 22, 23, 2N, via a data bus. The central unit 40 comprises several functional modules 41, 42, 43, 44, which are implemented, for example, as software programs on the CPU and perform task-specific data processing or data transfer. In addition, the CPU of the central unit 40 has a memory in which a history of incoming measurement data from the feedback of the sensors (step S32 in Fig. 2) can be stored for predefined periods of time for the detection and analysis of past operating conditions. In addition, parameter optimization models M X,Y,Zwhich were previously created empirically, experimentally, or, if necessary, by simulations with respect to an optimization criterion. Using a parameter optimization model M X,Y,Z a parameter or parameter set of one or more of the parameterized controllers 21, 22, 23, 2N, whose component has an effect on a tracked operating property of the optimization criterion, can be modified.

[0039] The high-level control layer performs tasks that deal with the results and properties of the entire surrounding system, i.e., the operation of the hybrid drive train 10, or the driving state of the entire vehicle in interaction of the hybrid drive train 10 with other vehicle components. For example, if the property of lowest possible fuel consumption or low exhaust emissions is to be pursued as an optimization criterion, an increased drive load distribution to the electric motor may be expedient, for example, in partial load operation in a stop-and-go driving state of the vehicle or in operation with low drive load in a driving state at low speed. For a different optimization objective, such as high acceleration performance, a transmission stage with a lower gear ratio may be expedient.

[0040] The central unit 40 comprises a state determination module 41 which, based on the measurement data of the recorded operating variables y1, y2, y3, y N , ie, for example, by pattern recognition of patterns that emerge in the measured data curves, typified operating states are determined. The state determination module 41 can determine both past and current operating states (step S41 in Fig. 2), and also derived from these, predict future operating conditions, particularly those that are imminent, with a certain probability of occurrence. The forecast is based on stored characteristic maps from empirical studies of sequences of driving conditions.

[0041] As explained previously, the parameter optimization models M X,Y,Z, which are stored in the memory of the central unit 40, are assigned both to a specific optimization goal and a relevant selection of parameters or controlled components, as well as to specific operating states in which the optimization goal, i.e., the pursuit of an operating characteristic, is compatible with a requirement of the vehicle user, efficient, or sensible. Accordingly, the central unit 40 further comprises a model selection module 42, which generates a parameter optimization model M assigned to the current or predicted operating state. X,Y,Z selected (step S42 in Fig. 2).

[0042] A parameter determination module 43 of the central unit 40 inputs into a selected parameter optimization model M X,Y,Z required values ​​from the measured operating variables y1, y2, y3, y N and determines, for example in an iterative approach of the parameter optimization model M X,Y,Z, a parameter θ1, θ2, θ3, θ optimized according to the underlying optimization criterion N or parameter set (step S43 in Fig. 2). This process is initiated, for example, by the detection or initiation of a new, changed operating state of the hybrid powertrain 10. As soon as the optimized parameters θ1, θ2, θ3, θ N have been determined by the parameter determination module 43, these are provided to an update model 44.

[0043] The update module 44 of the central unit 40 carries out a data transfer by replacing the stored and used parameters or parameter sets in the relevant parameterized controllers 21, 22, 23, 2N, whose component has an effect on a tracked operating characteristic of the optimization criterion, with the optimized parameters θ1, θ2, θ3, θ Nor parameter sets. After the optimized parameters θ1, θ2, θ3, θ N are stored in the relevant parameterized controllers 21, 22, 23, 2N, these continue to operate with the changed parameter basis, whereby continuous operation is ensured in the transition between a previous and a subsequent parameter basis.

[0044] As in Fig. 2, the steps of the selection S42 of a parameter optimization model M X,Y,Z , the determination S43 of the optimized parameter and the updating S44 of the stored and used parameters in the respective parallel parameterized controls S21, S22, S23, S2N each one of several update routines R1, R2, R3, R N . These are calculated for individual selected parameterized controllers 21, 22, 23, 2N, which correspond to the optimized parameter θ1, θ2, θ3, θ N or parameter set from a parameter optimization model MX,Y,Z are performed separately. The update routines R1, R2, R3, R N are carried out continuously and at irregular intervals during the vehicle's journey, ie asynchronously to various parameterized controllers 21, 22, 23, 2N. In particular, the update routines R1, R2, R3, R N triggered by the state detection module 41 in the course of a change in a previously determined operating state.

[0045] In other words, the parameter optimization models M X,Y,Z within the asynchronous update routines R1, R2, R3, R N for specific parameters of individual parameterized controllers 21, 22, 23, 2N. The asynchronous update routines R1, R2, R3, R N, can be triggered, for example, by certain operating states of the hybrid drive train 10. At the high-level control level, individually specified properties for the operation of the vehicle are tracked in various operating states of the hybrid drive train 10. These tracked properties are achieved through individual optimization of parameters that are stored in the parameterized controllers at the low-level control level and used to control individual components of the hybrid drive train.

[0046] The individual optimization of individual parameters of certain parameterized controllers 21, 22, 23, 2N, ie a new calibration of the same by a set of optimized parameters θ1, θ2, θ3, θ N is carried out by the previously mentioned asynchronous update routines R1, R2, R3, R, which are preferably triggered by operating states. N . The update routines R1, R2, R3, R Nfor the pursued property in different operating states are thus a cooperation between the high-level control level and the low-level control level.

[0047] The above explanations of the embodiments describe the present invention exclusively by way of examples. Of course, individual features of the embodiments can be freely combined with one another, provided they are technically feasible, without departing from the scope of the present invention. List of reference symbols 10 Hybrid powertrain 21 parameterized controller (e.g. for a drive source) 22 parameterized controller (e.g. for another drive source) 23 parameterized controller (e.g. for a multi-step transmission) 2N parameterized controller (e.g. for a clutch) 30 measuring module with sensors 40 Central processing unit (CPU) 41 Condition determination module 42 Model selection module 43 Parameter determination module 44 Update module 100 control system u 1-N Control variables to be controlled y 1-N measured operating variables θ 1-N optimized parameters R 1-N Update routines

Claims

[1] Control method for cooperatively controlling a hybrid drive train (10) of a vehicle across control levels, comprising: Low-level control steps for hardware-related control of an operating sequence of the hybrid powertrain (10), comprising: - parallel parameterized control (S21, S22, S23, S2N) of manipulated variables (u 1-N ) of components of the hybrid drive train (10) based on respective stored parameters by means of several parameterized controllers (21, 22, 23, 2N); - Measuring (S31) operating variables (y 1-N ) during operation of the hybrid drive train (10) by means of several sensors; and - Feedback (S32) of the measured operating variables (y 1-N ) into the respective parallel parameterized control (S21, S22, S23, S2N) of the manipulated variables (u 1-N ); as well as High-level control steps for optimization-related control of operating characteristics of the hybrid powertrain (10), comprising: - Determination (S43) of optimized parameters (θ 1-N ) based on at least one stored parameter optimization model (M X,Y,Z ) and the measured operating variables (y 1-N ) by means of a central unit (40); and - updating (S44) the parameters used in the parallel parameterized control (S21, S22, S23, S2N) in the parameterized controllers (21, 22, 23, 2N) in exchange for the optimized parameters (θ 1-N ). [2] Control method for cooperatively controlling a hybrid powertrain (10) according to claim 1, comprising the high-level control step: - Determining (S41) a past, current or upcoming operating state based on the measured operating variables (y 1-N) by means of the central unit (40). [3] Control method for cooperatively controlling a hybrid powertrain (10) according to claim 2, wherein the determining (S43) of optimized parameters (θ 1-N ) is carried out as a function of, in particular in response to, the determination (S41) of an operating state. [4] Control method for cooperatively controlling a hybrid drive train (10) according to one of claims 2 or 3, comprising: several update routines (R 1-N ) each comprising the following condition-specific high-level control steps: - Selecting (S42) a parameter optimization model (M) assigned to the previously determined operating state X,Y,Z ) by means of the central unit (40); - Determining (S43) at least one optimized parameter (θ 1-N ) based on the selected parameter optimization model (M X,Y,Z ) and the measured operating variables (y1-N ) by means of the central unit (40); and - Updating (S44) at least one parameter used by at least one parameterized controller (S21, S22, S23, S2N) in at least one of the parameterized controllers (21, 22, 23, S2N) in exchange for the at least one optimized parameter determined by the central unit (40). [5] Control method for cooperatively controlling a hybrid drive train (10) according to claim 4, wherein the update routines (R 1-N ) between the central unit (40) and the parameterized controllers (21, 22, 23, 2N) are carried out asynchronously, in particular in response to the determination (S41) of an operating state, depending on state-specific selected parameters (θ 1-N ) and / or depending on the processing times of different, especially iterative, parameter optimization models (M X,Y,Z ) for the optimized parameters (θ1-N ). [6] Control system with control means for carrying out the low-level control steps and high-level control steps of the control method for cooperatively controlling a hybrid drive train (10) according to one of claims 1 to 5. [7] Control system (100) for cooperatively controlling a hybrid drive train (10) of a vehicle across control levels, comprising: a low-level control level with control means for hardware-related control of an operating sequence of the hybrid drive train (10), comprising: several parameterized controllers (21, 22, 23, 2N) for controlling manipulated variables (u 1-N ) of components of the hybrid drive train (10) based on stored parameters; and a measuring device (30) for measuring operating variables (y 1-N) during operation of the hybrid drive train (10) with a plurality of sensors arranged to record the respective operating variables (y 1-N ) on the vehicle; whereby the parameterized controllers (21, 22, 23, 2N) are signal-connected to the measuring module (30), for feedback of the measured operating variables (y 1-N ) in the respective control of the manipulated variables (u 1-N ); and a high-level control level with control means for optimizing the control of operating characteristics of the hybrid drive train (10), comprising: a central unit CPU (40), which is signal-connected to the measuring module (30), for evaluating the measured operating variables (y 1-N ), with: a parameter determination module (43) for determining optimized parameters (θ 1-N ) based on at least one stored parameter optimization model (M X,Y,Z ) and the measured operating variables (y 1-N ); and an update module (44) for updating the stored parameters used by the parameterized controllers (21, 22, 23, 2N) in exchange for the determined optimized parameters (θ 1-N ). [8] Control system (100) for cooperatively controlling a hybrid drive train (10) according to claim 7, comprising a state determination module (41) for determining past, current and / or upcoming operating states based on the measured operating variables (y 1-N ) from the measuring device (30). [9] Control system (100) for cooperatively controlling a hybrid drive train (10) according to claim 8, comprising a model selection module (42) for selecting a parameter optimization model (M X,Y,Z ), wherein in the central unit CPU (40) a selection of different parameter optimization models (M X,Y,Z) for the optimization of state-specific selected parameters (θ 1-N ) for individual parameterized controllers in different operating states. [10] Control system (100) for cooperatively controlling a hybrid powertrain (10) according to one of claims 7 to 9, wherein the low-level control level comprises at least: a parameterized controller (21) of a first drive source of the hybrid powertrain (10); a parameterized controller (22) of a second drive source of the hybrid drive train (10); and / or a parameterized controller (23) of a transmission of the hybrid powertrain (10).

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