METHOD FOR OPERATING A VEHICLE WITH A HYBRID DRIVETRAIN SYSTEM

Description

[0001] The invention relates to a method for operating a vehicle with a hybrid powertrain system, wherein the hybrid powertrain system comprises a non-electrically driven drive motor and an electrically driven torque machine which is connected to an energy storage device for energy transfer, and wherein the drive motor and the torque machine are controlled by a control unit and connected to an output element via a hybrid transmission, wherein the method comprises determining a desired state-of-charge trajectory for a time-dependent state-of-charge profile of the energy storage device, and optimizing and controlling the operation of the hybrid powertrain system with regard to the desired state-of-charge trajectory by means of an optimization method, taking into account an estimated expected vehicle drive power.

[0002] The powertrain of a vehicle can include all components that generate power for propulsion within the vehicle and transfer it to the vehicle's roadway.

[0003] A drivetrain can, for example, consist of a rigid drive shaft or flexible drive belts to transmit the torque that propels the vehicle. Vehicles can be powered by various torque-generating devices that convert energy stored in the vehicle into torque to provide propulsion. Efforts are regularly made to operate the torque-generating device as efficiently as possible in order to propel the vehicle for as long or as far as possible, based on the energy stored in the vehicle.

[0004] In vehicles with a hybrid powertrain system, the vehicle has two different torque-generating devices that can be operated with two different forms of energy. During operation, a suitably configured control system can select the most advantageous energy form or torque-generating device for the current driving situation or with regard to a predefined target criterion. In many different operating situations, it is advantageous if the respective contributions of the two torque-generating devices are continuously adjusted to the specific driving situation, individual driving characteristics, or other target criteria.In this process, one of the two torque-generating devices can be temporarily deactivated, and the drive power required for the desired driving situation can be generated and provided exclusively by the non-deactivated torque-generating device. Even with hybrid powertrain systems, the most efficient possible use of the total available energy is regularly a primary focus and an important target parameter for controlling optimized operation of the hybrid powertrain system. Furthermore, attempts are often made to optimize other parameters, such as reducing unwanted pollutant emissions, increasing the all-electric range, or improving or maintaining the health of the energy storage device for as long as possible.

[0005] In many vehicles with a hybrid powertrain system, the system includes an internal combustion engine as the non-electrically driven propulsion motor and at least one electrically driven torque motor. The internal combustion engine can typically run on a liquid fuel such as gasoline. The electrically driven torque motor can be, for example, a DC motor, which can also operate as a generator. The torque motor is connected to an electrical energy storage device, usually an electric battery system or a fuel cell, for energy transfer.In a hybrid drive system, the drive power can be generated either by the drive motor, the torque generator, or simultaneously by both. When the drive motor and torque generator are used simultaneously, the respective share of the total drive power transmitted to a driven element can be varied and adjusted to a predefined target value. Furthermore, the torque generator can also be operated temporarily as a generator, for example, to convert a portion of the vehicle's kinetic energy into electrical energy during braking and feed it into a suitable energy storage device.If the hybrid powertrain system has several electrically operated torque machines, the multiple torque machines can often be controlled and operated independently of each other, whereby individual or all torque machines can also be connected to the energy storage device in an energy-transferring manner in order to convert kinetic energy into electrical energy and store it.

[0006] The operation of the hybrid powertrain system is controlled by the control unit. This typically involves a continuously performed optimization process to optimize operation with regard to one or more target variables, such as energy consumption or the utilization of the energy forms currently stored in the vehicle. In many cases, the state of charge is a beneficial target variable for optimization. By specifying a target state-of-charge trajectory for the state of charge of the energy storage device over time, the contribution of the electrically driven torque motor, and consequently the contribution of the combustion engine, to the total required drive power can be specified and controlled at any given point during operation.

[0007] The specification of a desired state-of-charge trajectory can be improved with regard to the respective target parameter by estimating the expected road load within a forecast period during the operation of the hybrid powertrain system and, based on this, estimating the expected vehicle drive power that will likely be required during the forecast period to move the vehicle according to the driver's instructions. For example, the current vehicle location and a likely selected route can be determined using global position sensors (GPS) and digital mapping systems to estimate the expected road load within the upcoming forecast period.Then, for example, the expected road load can be estimated as one of the key parameters for the vehicle drive power expected within the forecast period, based on the route and speed profiles determined for the likely selected route, particularly the elevation and speed profiles. Starting from the estimated vehicle drive power that the hybrid powertrain system is expected to deliver within the forecast period, suitable control strategies and power management schemes can then be used to specify and control the respective use of the electric torque motor and the combustion engine in such a way that the one or more target parameters of the optimization procedure used are achieved in the best possible way, whereby the aim is usually to achieve the lowest possible energy consumption for the drive power transmitted to the output element.

[0008] The optimization methods frequently used in practice require a model of the hybrid powertrain system's operation during typical driving situations that is as realistic as possible. In many cases, multi-criteria optimization is performed. Scalarization allows for optimization using either linear or non-linear methods. However, achieving the most efficient operation of the hybrid powertrain system requires considerable effort for both modeling and optimization. Under favorable conditions, for example, the energy required to propel the vehicle along a predicted route can be optimized or minimized if energy consumption is defined as a target variable for the optimization. Additional target variables can also be defined and optimized.

[0009] It is also known, and the subject of intensive research, to consider other optimization targets alongside minimizing energy consumption, such as minimizing pollutant emissions during operation. The respective pollutant emissions can be taken into account in both the modeling and optimization processes, which significantly increases the overall optimization effort. Other targets might include temperature management for the hybrid powertrain system or state optimization of the energy storage system.

[0010] During operation of the hybrid powertrain system, energy consumption can often be minimized and the system's operation optimized when the vehicle travels as expected along a predetermined or accurately estimated route. Depending on the chosen optimization method, pollutant emissions can also be minimized, and other target parameters for optimized hybrid powertrain operation can be considered.

[0011] It has been shown that longer forecast periods often lead to better optimization results. However, longer forecast periods require larger amounts of data and result in increasingly higher computational costs. With the currently available electronic data processing capabilities in a vehicle, a forecast period of a few minutes is achievable. However, as soon as the forecast period is extended and, for example, exceeds 10 minutes, the effort required for data acquisition and processing increases disproportionately.

[0012] It is known from practical experience that the duration of the forecast period can be adjusted depending on the expected route and other boundary conditions. Such an adjustment allows the operating mode of the hybrid powertrain system to be dynamically adapted to the current conditions. However, such adjustments often only achieve minor improvements. A comparable method is described, for example, in German patent application DE 10 2020 104693 A1 and the subsequently published patent application WC 2021 / 239402 A1.

[0013] It is also known to use control units with self-learning capabilities. These units can store the respective vehicle characteristics for previously traveled routes. A new, expected route can then be compared with previously stored routes to improve the optimization and control of the hybrid powertrain system based on the stored vehicle characteristics. However, the effort required for this is comparatively high.

[0014] It is therefore considered an object of the present invention to further develop a method mentioned at the outset in such a way that the most advantageous operation of the hybrid powertrain system can be achieved in the vehicle with the least possible effort.

[0015] According to the invention, this problem is solved by a method for operating a vehicle with a hybrid powertrain system, wherein the hybrid powertrain system comprises an internal combustion engine and an electrically operated torque machine which is connected to an energy storage device for energy transfer, and wherein the internal combustion engine and the torque machine are controlled by a control device and connected to an output element via a hybrid transmission, wherein the method comprises determining, for a prediction period starting from an expected driving route, a desired state-of-charge trajectory for a time course of a state of charge of the energy storage device,and that the operation of the hybrid powertrain system is optimized and controlled by the control unit using an optimization procedure with regard to the desired state-of-charge trajectory, taking into account the estimated expected vehicle propulsion power, wherein the procedure further comprises retrieving an experience-based state-of-charge trajectory from an external database for the expected driving route before the start of the prediction period, which covers at least the prediction period, estimating an expected vehicle propulsion power taking into account an expected driving load, specifying at least one optimization constraint for each of the predetermined adverse operating events, specifying an associated event response time for each optimization constraint, and estimating, starting from the estimated expected vehicle propulsion power,whether an adverse operating event will occur within an event prediction period, that in the event of an adverse operating event being expected to occur, at least one optimization constraint associated with this adverse operating event is specified for controlling the operation of the hybrid powertrain system over the associated time-limited event response period, and that the desired state-of-charge trajectory is determined from the experience-based state-of-charge trajectory by modification with the at least one specified optimization constraint.

[0016] Adverse operating events can include, for example, gear changes of the hybrid transmission while the combustion engine is running, or the imminent need to engage the combustion engine after a prolonged period of drive power generated exclusively by the torque motor. By means of a suitably defined optimization constraint, the adverse operating situation can either be avoided for each adverse operating event or transformed into a less adverse operating situation.

[0017] For example, undesirably high pollutant emissions from the combustion engine during gear changes can be significantly reduced by introducing a temporary change in a target variable as an optimization constraint. To temporarily increase the proportion of drive power generated by the torque motor, the state of charge of the target energy storage device can be significantly reduced for a limited period as an optimization constraint. Within the optimization process, the proportion of electrical drive power is then increased because electrical energy can be drawn from the energy storage device due to the optimization constraint. Simultaneously, the proportion of drive power generated by the combustion engine is correspondingly reduced, thereby lowering, for example, the combustion engine's pollutant emissions.

[0018] Each optimization constraint has a defined event response time. This response time specifies the period during which the associated constraint is applied during the optimization process after an impending adverse operational event is detected. The event response time can be a single, identical response time for all optimization constraints. Alternatively, each adverse operational event, and potentially each individual optimization constraint, may have its own response time during which the constraint must be applied and considered during the optimization process.

[0019] Since the time-limited event response time can be significantly shorter than the total journey duration and, for example, less than a minute or only a few seconds, the desired state-of-charge trajectory can easily be adjusted over the journey duration so that deviations from the desired state-of-charge trajectory caused by unfavorable operating events are very small and, in particular, the desired state of charge is reached by the end of the journey. At the same time, the adverse effects of unfavorable operating events can be mitigated by detecting them early, without having to fully model these adverse effects and permanently consider them as additional parameters or target variables in the optimization process.

[0020] By accessing an external database and using the state-of-charge trajectories stored there, an experience-based state-of-charge trajectory can be retrieved in the vehicle for a long prediction period with comparatively little data processing effort and made available for an optimization procedure.

[0021] The experience-based state-of-charge trajectory may be determined through prior modeling and optimization procedures and may have already been modified taking into account further known information. These modeling and optimization procedures were performed outside the vehicle and possibly long before the experience-based state-of-charge trajectory was retrieved from the external database, for example, in central data processing facilities that are connected to the external database. Since the necessary calculations are performed outside the vehicle, the effort required within the vehicle can be kept very low.

[0022] Based on the experience-based state-of-charge trajectory determined outside the vehicle, this trajectory is then modified within the vehicle by at least one optimization constraint to ensure the best possible adaptation of the target state-of-charge trajectory to the current driving situation. Unusual traffic situations, such as traffic jams or roadworks, can be taken into account. It is also possible to consider current vehicle characteristics, environmental conditions, or driver input. The resulting target state-of-charge trajectory can then be used, via standard optimization methods and the control unit, to operate the hybrid powertrain system in such a way that the target state-of-charge trajectory is achieved.

[0023] The experience-based state-of-charge trajectory retrieved from the external database can cover any length of time. For example, after entering the desired destination for the upcoming vehicle trip, the experience-based state-of-charge trajectory can encompass the entire route. Provided the driving behavior during the journey to the desired destination does not deviate from this route, a single retrieval of the relevant experience-based state-of-charge trajectory can provide the essential basis for subsequently determining the desired state-of-charge trajectory for that trip.The experience-based state-of-charge trajectory is expediently longer than a prediction period that is specified during the operation of the hybrid powertrain system for the optimization of the operation of the hybrid powertrain system, so that, starting from the experience-based state-of-charge trajectory, a target state-of-charge trajectory that fully covers the prediction period can be determined, which forms the basis for the optimized operation of the hybrid powertrain system.

[0024] The effort required in the vehicle for acquiring the experience-based state-of-charge trajectory and for determining or adapting the desired state-of-charge trajectory can thus be kept very low, even though a very large amount of information is taken into account and a very large modeling and optimization effort can be carried out in advance when determining the experience-based state-of-charge trajectory.

[0025] It is also possible to specify a large number of individual information data points per unit of time for the experience-based state-of-charge trajectory, since the experience-based state-of-charge trajectory can be determined in advance using external data processing devices, and only the amount of data required for the experience-based state-of-charge trajectory needs to be transmitted to the vehicle. When determining the target state-of-charge trajectory, fewer information data points per unit of time, or support points, can then be used for the subsequent optimization procedures. This further reduces the effort required in the vehicle for operating the hybrid powertrain system without adversely affecting the quality of operation.

[0026] Optionally, the experience-based state-of-charge trajectory is determined using operating data from the hybrid powertrain systems of multiple vehicles. This means the experience-based state-of-charge trajectory incorporates information from several vehicles. The experience-based state-of-charge trajectory can represent an averaged state-of-charge trajectory, in which the state-of-charge trajectories of several vehicles comparable with respect to a specific vehicle characteristic are averaged or considered using a suitable method. It is also possible to give greater weight to, or preferentially select, those stored state-of-charge trajectories that show a higher degree of agreement with other characteristics, such as individual driving behavior or environmental conditions during the respective journey.

[0027] According to an advantageous embodiment of the invention, the experience-based state-of-charge trajectory retrieved from the database is selected from a number of state-of-charge trajectories stored in the database, with at least one vehicle characteristic of the hybrid powertrain system being used as a selection criterion. Using this vehicle characteristic, for example, a state-of-charge trajectory that is optimally suited to the vehicle type in question can be selected. If several state-of-charge trajectories are available for the vehicle type in question, further vehicle characteristics, such as the distances already traveled or the age of the hybrid powertrain system, can also be taken into account.

[0028] Alternatively, or in addition to considering vehicle parameters, it can optionally be provided that the experience-based state-of-charge trajectory retrieved from the database is selected from a number of state-of-charge trajectories stored in the database. For this selection, at least one trip parameter is used as a selection criterion, which is determined based on at least one operating parameter of the vehicle and recorded during at least one previous trip with the vehicle. If necessary, a number of identical or similar trips completed in the past with the same vehicle and, if applicable, the same driver can be considered for a preliminary determination of the experience-based state-of-charge trajectory.This is possible and advantageous, for example, for commuters or professional drivers who repeatedly travel the same route, as experience gained from previous journeys with the same vehicle and, if applicable, the same driver, can be taken into account and used to optimize the control of the hybrid powertrain system. In this way, the results of past journeys with the vehicle in question can be considered when selecting the experience-based state-of-charge trajectory, allowing for a beneficial adaptation of the experience-based state-of-charge trajectory to the current conditions of the vehicle.

[0029] According to a further advantageous embodiment of the invention, it can also be provided that at least one optimization constraint is specified by a driver before the start of the prediction period. For example, the driver can specify the fastest possible arrival time or shortest possible journey duration, or the most energy-efficient driving style, in order to influence the control of the hybrid powertrain system during the journey.

[0030] In principle, a suitable optimization constraint can also be used to cause a short-term deviation of the desired state-of-charge trajectory from the experience-based state-of-charge trajectory in order to take into account short-term events or suddenly occurring situations that cannot be captured in the experience-based state-of-charge trajectory.

[0031] Advantageously, the system provides that at least one predefined adverse operating event would result in increased pollutant emissions, and that the associated optimization constraint results in a corresponding reduction in pollutant emissions. A complete recording and modeling of pollutant emissions during the operation of the hybrid powertrain system, as well as a complete and continuous consideration of pollutant emissions through additional parameters or target variables during optimization, would involve a significantly greater effort compared to currently used optimization methods. However, the operating events that are particularly adverse for pollutant emissions are often only of relatively short duration and can be relatively well recorded and characterized in advance by measurements on a hybrid powertrain system in a vehicle test bench.If necessary, the required measurements can also be carried out on an engine test bench or powertrain test bench, further reducing the effort required. For a number of adverse operating events, one or even several optimization constraints can be determined and specified. These constraints influence the optimization process in such a way that pollutant emissions can be significantly reduced without noticeably increasing energy consumption or adversely affecting the state of charge of the energy storage system over a significant period.

[0032] According to one embodiment of the invention, it is provided that, based on a predefined prioritization, one of the optimization constraints associated with these unfavorable operating events is selected and specified for controlling the operation of the hybrid powertrain system if more than one optimization constraint is assigned to the identified unfavorable operating event. By means of a suitable prioritization, which can be developed and specified in advance, for example, based on measurements on a hybrid powertrain system in a vehicle test bench, the optimization constraint that best reduces or avoids the adverse effects of the unfavorable operating event can be selected simply and without significant effort.

[0033] Similarly, according to a further embodiment of the invention, it is provided that, based on a predetermined prioritization, an associated optimization constraint is selected and specified for controlling the operation of the hybrid powertrain system if more than one adverse operating event is detected within the event prediction period. Particularly in an urban environment, unforeseen events such as suddenly changing traffic lights, other road users, or rapidly changing routes can lead to two or more adverse operating events being predicted or triggered within the event prediction period or even within the event response time.It is expected that different or even contradictory optimization constraints would be provided for different adverse operating events, and that with a suitable, usually probability-based, prioritization, the optimization constraint that can best reduce the adverse effects of the two or more adverse operating events can be identified and selected.

[0034] Prioritization can be defined, for example, by a one-dimensional weighting of the various optimization constraints. It is also possible to determine and predefine a multi-dimensional prioritization, allowing the prioritization to be adapted and specified for different target variables. Furthermore, it is conceivable that at least one characteristic describing the driving situation is recorded during a driving scenario, and that a prioritization adapted to or specifically designed for this characteristic is used to select the optimization constraint.

[0035] With a view to subsequent adaptation and improvement of the method according to the invention, it is optionally provided that operating parameters are recorded during the operation of the hybrid powertrain system, and that, based on the recorded operating parameters, the prioritization of the associated optimization constraints is reviewed and, if necessary, changed. The operating parameters, such as speeds, accelerations, or preferred driving path decisions, make it possible to recognize an individual driving style and, depending on the individual unfavorable operating events, to change and adjust the prioritization of the several possible optimization constraints. For example, a fuel-efficient driving style or a sporty driving style of a driver can be identified based on the operating parameters recorded during a vehicle movement along a driving path.Differentiated parameters can be used to subsequently adjust the prioritization of the multiple optimization constraints and adapt them to the individual driving style. The operating parameters can be recorded using suitable sensors or estimated or determined based on other parameters. It is also possible for a driver to specify a desired driving style via an on-board communication system before or during a journey, and the prioritization can be adjusted accordingly.

[0036] A recorded operating parameter can also be a target parameter such as the actual energy consumption caused by the operation of the hybrid powertrain system or a measurable pollutant emission, so that the effects of the method according to the invention can be recorded during operation and, if necessary, taken into account for a controlled intervention in the optimization of the operation.

[0037] It is also conceivable that one or more operating parameters are recorded and used for periodic or continuous adjustment of the optimization method. In this way, the actual effects of the optimization method's influence on the operation of the hybrid powertrain system over the limited event response time can be recorded or determined based on the operating parameters. By comparing the actual reduction in the adverse effects of unfavorable operating events during a driving situation with previously recorded adverse effects that would occur without the application of the method according to the invention, the efficiency or even the appropriateness of individual optimization constraints can be verified and, if necessary, adjusted by changing their prioritization.

[0038] It is also conceivable that, in the event of a foreseeable adverse operational event, optimization parameters are recorded during a predefined maximum response time, and that the event response time is terminated as soon as the recorded optimization parameters meet a predefined event response termination criterion. For example, by recording and evaluating operational parameters, it can be estimated whether the desired effect on the operation of the hybrid powertrain system, as intended by the optimization constraint, has already been achieved or sufficiently initiated, such that it is no longer necessary to further influence the optimization process by specifying an optimization constraint. It is also possible, if necessary, to extend the event response time in order to bring about or, if applicable, amplify the effect desired by the optimization constraint.

[0039] In practice, particulate emissions are a particularly significant component of pollutant emissions from combustion engines. The application of the method according to the invention makes it possible to reduce particulate emissions without having to change parameters of the driving route itself, in particular speed and / or acceleration, and without requiring extensive calculations or optimizations.

[0040] Driving situations in which the method according to the invention is particularly advantageous are the so-called high-speed sections, such as those encountered on highways. In optimization methods known from the prior art, the combustion engine of the hybrid drive system is switched off due to the relatively low torque required to maintain the driving speed. This allows the combustion engine to cool down. Experience shows that overtaking maneuvers occur more frequently in high-speed sections, driven by the driver's subjective perception of the surroundings or by information about the road ahead, such as junctions. Since more torque is required during such overtaking maneuvers than the engine can provide, the combustion engine must be engaged.

[0041] In previously known methods, such overtaking maneuvers take place with a cooled combustion engine. However, such a cooled combustion engine emits an excessive number of particles because neither the combustion engine itself nor any exhaust gas purification system connected to it has reached its respective operating temperature.

[0042] In contrast, the method according to the invention advantageously adapts the experience-based state-of-charge trajectory to the state-of-charge trajectory targeted for the prediction period. During the high-speed phase described above, the application of the method according to the invention leads to an increase in the targeted state-of-charge trajectory, thus achieving extended operation of the combustion engine. This reduces the proportion of the combustion engine's cooling phase, thereby also reducing particulate emissions.

[0043] Another relevant operating parameter of hybrid powertrain systems is the state of the energy storage device associated with the hybrid powertrain. A significant proportion of known energy storage devices are damaged by deep discharge. Deep discharge, as defined in the invention, refers to the drop in the state of charge of the energy storage device below a device-specific value, the so-called discharge cut-off voltage. The deep discharge state is characterized by the fact that the energy storage device is discharged to such an extent that the electrical voltage provided by the energy storage device falls below this discharge cut-off voltage. The discharge cut-off voltage is a parameter of the energy storage device that depends in particular on the type and design of the energy storage device.

[0044] To prevent deep discharge of the energy storage device, the method according to the invention provides that one of the optimization constraints, based on which the desired state-of-charge trajectory is determined from the experience-based state-of-charge trajectory, is the state of charge that must never be undercut. The invention provides that the desired state-of-charge trajectory can be adapted to a target state of charge, wherein the target state of charge is selected such that deep discharge of the energy storage device is prevented during the entire journey.

[0045] It is particularly advantageous that the target state of charge can also be changed along the route, so that changing environmental influences can be taken into account. For example, changing outside temperatures, but also changes in the operating temperature of the energy storage device, can lead to a state of deep discharge being reached with varying actual states of charge.

[0046] Another optimization criterion for hybrid powertrain systems, which is particularly relevant in practice, is the so-called drivability of a vehicle equipped with a hybrid powertrain system. In this context, drivability refers to a subjective characteristic of a vehicle, which, however, can be measured using physical parameters and formulated on the basis of mathematical forms.

[0047] In an advantageous implementation of the inventive concept, it is therefore provided that the drivability of the motor vehicle equipped with the hybrid powertrain can be improved by means of the method according to the invention, by reducing the number of starts of the combustion engine required along the route and / or reducing the operating time of the combustion engine at low speeds by adjusting at least one optimization constraint. Low speed within the meaning of the inventive concept means speeds of less than 60 km / h, preferably less than 50 km / h.

[0048] It is advantageously provided that the optimization constraint to be adjusted is the target state of charge of the energy storage device. Lowering the target state of charge is particularly preferred if further engine starts are to be prevented. A decision as to whether engine starts should be prevented can, for example, be based on an evaluation factor.

[0049] It is therefore advantageously provided according to the invention that the method also includes an evaluation step in which an evaluation factor is first determined, and then it is determined whether further engine starts should be carried out based on the determined evaluation factor. An advantageously provided method for determining the evaluation factor involves determining a time Tnorm, which is the average time between two engine starts of a known hybrid powertrain system. This time Tnorm is continuously multiplied along the route by the number of engine starts that have already occurred and then divided by the time that has elapsed since the start of the journey. If the resulting evaluation factor is greater than 1, then an above-average number of engine starts have already taken place and further engine starts should be prevented.It is advantageously provided that the actual state of charge of the energy storage device is also taken into account, so that deep discharge of the energy storage device can be prevented.

[0050] Furthermore, it is also provided that the method according to the invention can be adapted such that the optimization criterion consists of properties of a position of the vehicle equipped with the hybrid powertrain system along the driving path. For example, it is possible to prevent engine starts as much as possible in urban areas, in order to reduce pollution, particularly there.The invention also relates to a hybrid powertrain system for a vehicle, wherein the hybrid powertrain system comprises a control device, a non-electrically driven drive motor and an electrically driven torque machine which is connected to an energy storage device for energy transfer, and wherein the drive motor and the torque machine are controlled by the control device and connected to an output element via a hybrid transmission, characterized in that the control device is configured such that a previously described method is carried out during operation of the hybrid powertrain system.

[0051] The following section explains various embodiments of the invention in more detail, which are illustrated by way of example in the drawing. It shows: Figure 1a schematic representation of a hybrid powertrain system with an internal combustion engine, an electric torque machine, an energy storage device, a control device and a data transmission device for retrieving information on an experience-based state-of-charge trajectory from an external database, Figure 2 a schematic representation of a process flow according to the invention for operating the in Figure 1 depicted hybrid powertrain system, Figure 3 a schematic representation of a process sequence according to the invention during the execution of a determination and adjustment of the desired state-of-charge trajectory starting from the experience-based state-of-charge trajectory, Figures 4 to 6b schematic representations of the state of charge along a route, Figure 7a a progression of the rating factor along a route, Figure 7bdesired state-of-charge trajectories along the route Figure 7a , Figure 8a a speed profile along a route, Figure 8b Trends in the cumulative number of engine starts along the route Figure 8a and Figure 8c Charging state profiles along the route from the Figures 8a and 8b .

[0052] In Figure 1An exemplary embodiment of a hybrid drivetrain system 1 according to the invention is shown. The hybrid drivetrain system 1 comprises an internal combustion engine 2 and an electrically driven torque machine 3, which are connected via a common hybrid transmission 4 to an output element 5, through which torque generated by the hybrid drivetrain system 1 can be transmitted to two drive wheels 6 of a vehicle (not shown in detail). The electric torque machine 3 is connected to an electrical energy storage device 7 for energy transfer.The electric torque machine 3 can be used either to drive the drive wheels 6 and convert energy from the energy storage device 7 into kinetic energy of the vehicle, or it can be used as a generator and convert kinetic energy of the vehicle or kinetic energy generated by the internal combustion engine 2 into electrical energy and supply it to the energy storage device 7. The torque machine 3 can, for example, be a DC motor that can also be operated as a generator. It is also possible to integrate several electric torque machines 3 into the hybrid powertrain system 1. A different, non-electrically driven drive motor can also be used instead of the internal combustion engine 2.

[0053] The hybrid powertrain system 1 includes a control unit 8. The control unit 8 is connected to the internal combustion engine 2, the torque machine 3, and the hybrid transmission 4 via signal transmission, and the respective operation of the internal combustion engine 2, the torque machine 3, and the hybrid transmission 4 can be controlled by the control unit 8. The control unit 8 can also be connected to at least one sensor 9, which can detect operating parameters such as the current vehicle speed, etc., and transmit them to the control unit 8.

[0054] The control unit 8 is further connected to a data transmission unit 10 and a data processing unit 11 via signal transmission. Using the data transmission unit 10, information about an experience-based state-of-charge trajectory can be retrieved from an external database 12 and stored in an internal database 13 in the vehicle. Based on the experience-based state-of-charge trajectory thus provided, the data processing unit 11 can, for example, determine a desired state-of-charge trajectory, taking into account current environmental conditions or driver preferences. This desired state-of-charge trajectory is then used by the control unit 8 to control and operate the hybrid powertrain system 1. The control unit 8 is configured to carry out the inventive method for operating the hybrid powertrain system 1 described below.

[0055] In Figure 2The figure schematically illustrates the sequence of the method according to the invention, summarizing the essential process steps for obtaining the experience-based state-of-charge trajectory and for determining the desired state-of-charge trajectory. In a route determination step 14, an expected route is determined. Information can be obtained either directly from an activated navigation device 15 or from a combination 16 of global position sensors (GPS) and digital mapping systems, or from the current vehicle location and a presumably selected route.

[0056] For the expected driving route, in a data acquisition step 17, an experience-based state-of-charge trajectory, or comprehensive information on the experience-based state-of-charge trajectory, is retrieved from the external database 12. The experience-based state-of-charge trajectory is selected from a number of experience-based state-of-charge trajectories stored in the external database 12 based on predefined selection criteria. The external database 12 can, for example, be provided by the vehicle manufacturer. The experience-based state-of-charge trajectory could already be used as a basis for operating the hybrid powertrain system. However, it does not yet include any adjustments to current requirements, such as the current traffic situation or individual driver settings.If the expected route was determined by the driver's input into a navigation device 15 and the driver follows the suggestions of the navigation device 15, i.e., follows the suggested and thus expected route, the experience-based state-of-charge trajectory retrieved from the external database 12 can cover the entire route. In this case, an update of the experience-based state-of-charge trajectory is no longer necessary during the journey. If the driver deviates from the expected route, a new expected route can be determined and a new experience-based state-of-charge trajectory retrieved from the external database 12.

[0057] In adaptation step 18, the experience-based state-of-charge trajectory is adjusted to reflect current conditions. This process can take into account all available information about the current traffic situation along the expected route, such as increased traffic volume, roadworks, mandatory detours, or current weather conditions. This information can also be obtained through communication between the vehicle and other vehicles on the expected route or through communication between the vehicle and fixed communication devices within the traffic infrastructure. Furthermore, current driver preferences can be considered, such as a preferred driving style (e.g., as fast or energy-efficient as possible) or a preferred optimization criterion (e.g., driving in a way that conserves battery power or minimizes emissions).Starting with the experience-based state-of-charge trajectory, at least one optimization constraint is specified in the adaptation step. This constraint modifies the experience-based state-of-charge trajectory and determines a target state-of-charge trajectory. This target state-of-charge trajectory can then be used for controlling and operating the hybrid powertrain system 1.

[0058] The effort required to determine the adapted state-of-charge trajectory is comparatively low, as the experience-based state-of-charge trajectory was retrieved from the external database 12 and is available, if necessary, along with further information, without requiring extensive calculations or optimizations. The experience-based state-of-charge trajectory can cover a relatively long period, from several minutes up to the entire journey time. Only a comparatively small amount of computational effort is required in the vehicle to adapt it to current conditions, or to determine the desired state-of-charge trajectory, which is used for operating the hybrid powertrain system, as required in adaptation step 18.The modified target state-of-charge trajectory, based on the retrieved experience-based state-of-charge trajectory, is then used in implementation step 19 to control and monitor the operation of the hybrid powertrain system 1 via the control unit 8. The adaptation step 18 can be repeated continuously, and the target state-of-charge trajectory can be updated. If a deviation of the actual driving path from the expected driving path is detected, a new experience-based state-of-charge trajectory can and should be retrieved from the external database 12 in a repeated data acquisition step 17 and subsequently modified in an adaptation step 18 and converted into a new target state-of-charge trajectory.

[0059] In Figure 3The figure schematically illustrates the sequence of the method according to the invention, wherein the desired state-of-charge trajectory for a presumed adverse operating event is adapted for a predetermined event duration by a further optimization constraint. As described above, in the route determination step 14, an expected route profile is determined for the duration of a prediction period. Information can be obtained either directly from the activated navigation device 15 or from a combination 16 of global position sensors (GPS) and digital map systems, or from the current vehicle location and a presumably selected route.With the help of a further module 20, the prediction period Δt is specified, which in simple variants corresponds to a fixed predetermined time period, but in more complex variants of the inventive method can be determined and specified on the basis of the determined route information and further parameters describing the driving situation, such as the speed of the vehicle.

[0060] The expected route is described in the Figure 3 In the data acquisition and adaptation steps 17, 18, which are not shown separately, an experience-based state-of-charge trajectory is obtained and a desired state-of-charge trajectory is determined on the basis of which.

[0061] Based on the desired state-of-charge trajectory and the vehicle drive power expected for the further course of the journey, the control variables required for the control of the hybrid powertrain system 1 are determined using a model 21 based on current operating parameters such as the current speed and the expected road load.

[0062] A driver can make interventions at any time, for example, to increase or decrease the vehicle's speed.

[0063] A verification module 23, based on the expected road load or the expected vehicle drive power, checks whether and with what probability an unfavorable operating event, such as a gear change or an increase in speed after a longer journey without the combustion engine 2 engaged, will occur within the forecast period. If an unfavorable operating event is identified with a sufficiently high probability using the verification module 23, an optimization constraint is generated and forwarded, together with the control variables determined from the model 21, to an optimization module 24.

[0064] In optimization module 24, a suitable optimization method is used to determine a desired state-of-charge trajectory for the time-dependent state-of-charge of the energy storage device. The optimization method can be a multi-criteria scalar optimization or another optimization method suitable for controlling the operation of a hybrid powertrain system. The optimization constraints, which may have been generated by verification module 23, must be taken into account.

[0065] The control variables determined or changed using the optimization procedure are transferred to a control module 25, which converts the control variables into control commands with which the operation of the hybrid powertrain system 1 is controlled.

[0066] In Figure 4Figure 26 shows a schematic representation of the course of the state of charge, the experience-based state of charge trajectory, and the desired state of charge trajectory during an example journey along a route. The y-axis of the diagram is shown in Figure 26. Figure 4 The charge level diagram 29 shows the charge of the energy storage device as a percentage. The x-axis of the charge level diagram 29 represents the elapsed time in seconds since the start of a journey along a route. The time of the start of the journey, which lies at the origin of the charge level diagram 29, is subsequently denoted by T.

[0067] The state of charge 26 corresponds to the actual charge of the energy storage device in percent and is represented by a dash-dot line. The experience-based charge trajectory 27 was determined before the start of the journey based on operating data from hybrid powertrain systems of several vehicles and retrieved from an external database for the expected route. The experience-based charge trajectory 27 is represented by a solid line. The prediction period Δt of the experience-based charge trajectory 27 comprises, in the case of the Figure 4 The charge level diagram shown shows 29,700 seconds.

[0068] In the example shown, the control unit is capable of making predictions with a forecast horizon of 200 seconds and influencing the desired state-of-charge trajectory 28. The desired state-of-charge trajectory 28 is represented by a dashed line.

[0069] Up to T + 50 seconds, the state of charge 26, the experience-based state of charge trajectory 27, and the target state of charge trajectory 28 are identical. At T + 50 seconds, the control unit determines that a reduction in the state of charge 26 compared to the experience-based state of charge trajectory 27 is necessary and defines a target state of charge trajectory 28 by which the determined, necessary deviation can be achieved. In the example shown, the target state of charge trajectory 28 runs at 20% from T + 50 seconds to T + 100 seconds, thus diverging the state of charge 26 from the experience-based state of charge trajectory 27. From T + 100 seconds onward, the experience-based state of charge trajectory 27 and the target state of charge trajectory 28 are again identical.

[0070] At T + 200 seconds, the control unit determines that a further correction of the state of charge 26 is required compared to the experience-based state of charge trajectory 27. For this purpose, the target state of charge trajectory 28 is again reduced to 20% from T + 200 seconds to T + 250 seconds. The state of charge 26 of the energy storage device follows this adjustment, and the difference between the state of charge 26 and the experience-based state of charge trajectory 27 is increased between T + 200 seconds and T + 250 seconds.

[0071] At T + 350 seconds, the control unit determines that the difference between state of charge 26 and the experience-based state of charge trajectory 27 should be reduced. To achieve this, the target state of charge trajectory 28 is increased to 80% from T + 350 seconds to T + 450 seconds. State of charge 26 follows this adjustment, and the difference between state of charge 26 and the experience-based state of charge trajectory 27 is reduced until state of charge 26 again follows the experience-based state of charge trajectory 27.

[0072] In the Figures 5a and 5b The graph shows the charging status (26) over a 1200-second driving period. Figure 5b shows a partial enlargement of the Figure 5ain the range of T + 400 seconds. The experience-based state-of-charge trajectory 27 remains constant at a value of 22% in the example shown, meaning that the hybrid powertrain system is supplied with electrical energy from the energy storage system as long as the state of charge 26 does not fall below this value. As soon as the state of charge 26 falls below 22%, the operating times of the combustion engine are increased to raise the state of charge 26.

[0073] In the Figures 5a and 5b Furthermore, a deep discharge line 30 is shown. This line runs at a value of 19.9%. As soon as the state of charge 26 of the energy storage device falls below this value, it is in a state of deep discharge and is at risk of being damaged. As shown in Figure 5bAs can be clearly seen, the state of charge 26 falls below the depth charge line 30 in the range of T + 400 seconds when a constant value of 22% is specified for the experience-based state of charge trajectory 27.

[0074] In the Figures 6a and 6b is an alternative course of the charge state 26 along the same route as in the Figures 5a and 5b The control unit is programmed to prevent the charge level from falling below the deep discharge line 30 under all circumstances. Accordingly, the target charge level trajectory 28 is raised by the control unit to a charge level of 28% in the range where the deep discharge line 30 is expected to fall below when using the experience-based charge level trajectory 27. This increase begins earlier than T + 400 seconds to ensure that the charge level does not fall below the deep discharge line 30 under any circumstances.

[0075] In Figure 7aFigure 1 is a schematic representation of the progression of an evaluation factor 31 provided according to the invention, along an alternative driving route lasting 1200 seconds. The evaluation factor 31 increases sharply when the internal combustion engine is started and decreases over time. A value of 1 for the evaluation factor 31 means that the number of engine starts performed so far is average. If the evaluation factor 31 is greater than 1, then a disproportionately high number of engine starts have occurred. The in Figure 7a The depicted course of the evaluation factor 31 arises when, in the method according to the invention, the evaluation factor 31 is taken into account in such a way that further engine starts of the internal combustion engine are prevented as long as the evaluation factor 31 is greater than 1.

[0076] In Figure 7bThe curves of a first state of charge 26' and a second state of charge 26'' are shown. The curve of the first state of charge 26' occurs when the hybrid powertrain system is operated without taking into account the weighting factor 31. The curve of the second state of charge 26'' occurs when the weighting factor 31 is taken into account.

[0077] In the Figures 8a to 8c It is shown how taking a driving speed 32 into account within the framework of the method according to the invention influences a number of engine starts 33 and the state of charge 26. Figure 8aThe driving speed 32 is shown along another alternative route, 1200 seconds long. Until approximately time T + 400 seconds, the driving speed 32 is below 50 km / h. The control unit is programmed such that, at a driving speed 32 of less than 50 km / h, operation of the combustion engine should preferably be avoided, since such a speed profile suggests urban driving, in which local emissions should be prevented as far as possible.

[0078] In Figure 8bA first curve of the cumulative number of engine starts 33' and a second curve of the cumulative number of engine starts 33'' are shown. The first curve of the cumulative number of engine starts 33' results when the previously described optimization criterion, namely that the combustion engine should not be operated if possible at a driving speed 32 of less than 50 km / h, is not taken into account. This results in 4 engine starts 33' in the range of the assumed city driving, i.e., up to time T + approximately 400 seconds.

[0079] In contrast, the second curve for the cumulative number of engine starts 33'' arises when the previously described optimization criterion is taken into account within the framework of the inventive method. As a result, no engine starts 33'' occur in the area of ​​the presumed urban driving when the inventive method is applied. Figure 8cIt has been shown that the optimization constraint, which is adjusted to take the optimization criterion into account, is the desired state-of-charge trajectory 28. The figure further shows two state-of-charge trajectories, namely a third trajectory of the state-of-charge 26‴ and a fourth trajectory of the state-of-charge 26‴′.

[0080] In the example shown, the experience-based state-of-charge trajectory 27 remains constant at 50%, meaning that the hybrid powertrain system is supplied with electrical energy from the energy storage system as long as the state of charge 26 does not fall below this value. To prevent engine starts 33 of the combustion engine, the control unit reduces the target state-of-charge trajectory 28 to 45% during the anticipated urban driving period. This results in the fourth state-of-charge profile 26''. After the anticipated urban driving period ends, i.e., after approximately 400 seconds (T + 400), the control unit increases the target state-of-charge trajectory 25 back to 50%, ensuring that the energy storage system is charged to a state of charge of 50% by the end of the driving route. REFERENCE MARK LIST

[0081] 1. Hybrid powertrain system 2. Internal combustion engine 3. Torque machine 4. Hybrid transmission 5. Output element 6. Drive wheels 7. Energy storage device 8. Control device 9. Sensor 10. Data transmission device 11. Data processing device 12. External database 13. Internal database 14. Route determination step 15. Navigation device 16. Combination 17. Data acquisition step 18. Adaptation step 19. Implementation step 20. Module 21. Model 22. Intervention 23. Verification module 24. Optimization module 25. Control module 26. State of charge 27. Experience-based state of charge trajectory 28. Target state of charge trajectory 29. State of charge diagram 30. Deep discharge line 31. Evaluation factor 32. Driving speed 33. Engine start

Claims

1. Method for operating a vehicle having a hybrid powertrain system (1), wherein the hybrid powertrain system (1) comprises an internal combustion engine (2) and an electrically operated torque machine (3) that is connected, in an energy-transferring manner, to an energy storage device (7), and wherein the internal combustion engine (2) and the torque machine (3) are controlled by a control unit (8) and are connected via a hybrid transmission (4) to an output element (5), wherein the method comprises determining, for a prediction period Δt, starting from an expected route profile, a target state-of-charge trajectory (28) for a temporal progression of a state of charge (26) of the energy storage device (7), and optimizing and controlling, by the control unit (8), the operation of the hybrid powertrain system (1), while taking into account the estimated expected vehicle drive power, by using an optimization method with respect to the target state-of-charge trajectory (28), wherein the method further comprises retrieving, before the start of the prediction period Δt, from an external database (12), for the expected route profile, an experience-based state-of-charge trajectory (27) that covers at least the prediction period Δt, estimating an expected vehicle drive power while taking into account an expected route load, specifying, for each of predefined adverse operating events, at least one optimization constraint, specifying, for each optimization constraint, an associated event reaction duration tER, estimating, starting from the estimated expected vehicle drive power, whether an adverse operating event will occur within an event prediction period, specifying, in the case of an adverse operating event that is expected to occur, for the associated time-limited event reaction duration tER, at least one optimization constraint associated with this adverse operating event for controlling the operation of the hybrid powertrain system (1), and determining the target state-of-charge trajectory (28), starting from the experience-based state-of-charge trajectory (27), by modification with the at least one specified optimization constraint.

2. Method according to claim 1, characterized in that the experience-based state-of-charge trajectory (27) was determined on the basis of operating data of hybrid powertrain systems of a plurality of vehicles.

3. Method according to claim 1 or 2, characterized in that the experience-based state-of-charge trajectory (27) retrieved from the external database (12) is selected from a number of state-of-charge trajectories stored in the external database (12), wherein, for the selection, at least one vehicle characteristic variable of the hybrid powertrain system (1) is used as a selection criterion.

4. Method according to one of the preceding claims, characterized in that the experience-based state-of-charge trajectory (27) retrieved from the external database (12) is selected from a number of state-of-charge trajectories stored in the external database (12), wherein, for the selection, at least one trip characteristic variable is used as a selection criterion, which was determined starting from at least one operating characteristic variable of the vehicle that was determined during at least one previous trip with the vehicle.

5. Method according to one of the preceding claims, characterized in that at least one optimization constraint is specified by a driver before the start of the prediction period Δt.

6. Method according to one of the preceding claims, characterized in that at least one predefined adverse operating event would cause increased pollutant emissions, and that, with an optimization constraint assigned to the adverse operating event, pollutant emissions reduced relative thereto are brought about.

7. Method according to one of the preceding claims, characterized in that at least one predefined adverse operating event would cause an adverse temperature development within the hybrid powertrain system (1) or an adverse condition of the energy storage device (7), and that, with an optimization constraint assigned to the adverse operating event, a development of the temperature or of the condition of the energy storage device (7) that is more favorable relative thereto is brought about.

8. Method according to one of the preceding claims, characterized in that, on the basis of a specified prioritization, one of the optimization constraints assigned to these adverse operating events is selected and specified for controlling the operation of the hybrid powertrain system (1) if more than one optimization constraint is assigned to the determined adverse operating event.

9. Method according to one of the preceding claims, characterized in that, on the basis of a specified prioritization, an associated optimization constraint is selected and specified for controlling the operation of the hybrid powertrain system (1) if more than one adverse operating event is determined within the event prediction period.

10. Method according to one of the preceding claims, characterized in that, during operation of the hybrid powertrain system (1), operating characteristic variables are recorded, and that, starting from the recorded operating characteristic variables, a prioritization of the associated optimization constraints is checked and, if applicable, changed.

11. Method according to one of the preceding claims, characterized in that, in the case of an adverse operating event that is expected to occur, optimization characteristic variables are recorded during a specified maximum reaction duration, and that the event reaction duration tER is ended as soon as the recorded optimization characteristic variables meet a specified event reaction termination criterion.

12. Hybrid powertrain system (1) for a vehicle, wherein the hybrid powertrain system (1) comprises a control unit (8), a non-electrically operated drive engine (2), and an electrically operated torque machine (3) that is connected, in an energy-transferring manner, to an energy storage device (7), and wherein the drive engine (2) and the torque machine (3) are controlled by the control unit (8) and are connected via a hybrid transmission (4) to an output element (5), characterized in that the control unit (8) is configured in such a way that, during operation of the hybrid powertrain system (1), a method as defined in claims 1 to 11 is carried out.