DE102021203350A1 - Method for operating a hybrid vehicle with an electric drive and a combustion drive and associated device for operating a hybrid vehicle - Google Patents

Abstract

The present invention relates to a method (100) for operating a hybrid vehicle, in particular a plug-in hybrid, with an electric drive and a combustion drive, comprising determining (101) a route to be traveled by the vehicle, providing (102 ) a computational vehicle operating model, which is set up to simulate a journey of the vehicle along the determined route based on predefined restrictions and an operation of the vehicle defined by a state parameter and to determine a characteristic value, optimizing (103) the characteristic value according to a target specification, wherein such a value for the state parameter is determined by means of the mathematical vehicle operating model, which means that the characteristic value meets the target specification and providing (104) information that indicates how the vehicle is to be operated, so that it corresponds to the Opti the characteristic value determined value of the state parameter is operated.

Description

State of the art

The present invention relates to a method for operating a hybrid vehicle.

The number of registered vehicles with hybrid drive, in particular so-called plug-in hybrid vehicles, is increasing every year and will increase even faster in the future due to legal requirements, among other things. Half of the hybrid vehicles are currently used as company vehicles. However, the classic solution for tour and route planning does not take into account the special features of electromobility that result from the use of hybrid vehicles. For example, the need or the possibility of intermediate charging is not taken into account. In addition, emission-related driving bans or restrictions make tour and route planning more difficult for hybrid vehicles, especially when there is a need to move within inner-city zones.

Based on the latest studies, for example the study by the Fraunhofer Institute for Systems and Innovation Research entitled "Real world usage of Plug in hybrid electric vehicles", published in September 2020, the ecological effect of business use of hybrid vehicles is questioned. The reasons for this are that they are charged too seldom (on average only every other day) and as a result the electric drive is used too little (only for 18% of the total driving time). The resulting higher emissions figures are not in line with society's and government's expectations.

The lack of implemented charging processes is not due to the lack of charging stations. The charging infrastructure is evolving rapidly and there are more charging stations, more electricity providers and more charging tariffs, which makes the selection of an optimal charging option even more complex. There are also separate software solutions that allow a static search to be carried out in charging station databases, taking into account certain general conditions, such as plug type or charging capacity. However, these solutions are not integrated into tour and route planning routines. A search usually has to be carried out manually by a driver and is relatively complex. This is not enough to persuade a user to charge the vehicle more often and thus encourage the use of electric propulsion.

the CN108665342A discloses a method for searching for a charging station based on a vehicle type and the charging device required for this vehicle type. the CN110667428A discloses a method for recommending a charging station based on real-time position data.

Disclosure of Invention

The inventive method for operating a hybrid vehicle with an electric drive and a combustion engine includes determining a route to be traveled by the vehicle, providing a computational vehicle operating model, which is set up to drive the vehicle along the determined route based on predefined To simulate restrictions and an operation of the vehicle defined by a state parameter and to determine a characteristic value, the state parameter describing a charging process of the vehicle and/or a change in a drive mode of the vehicle, and the characteristic value describing a property of the trip that can be optimized, which an operation of the vehicle according to the state parameter results in an optimization of the characteristic value according to a target specification, such a value for the state parameter being determined by means of the computational vehicle operating model rd, which results in the characteristic value meeting the target specification, and providing information which indicates how the vehicle is to be operated in order for it to be operated in accordance with the value of the state parameter determined when optimizing the characteristic value.

The route is preferably determined by a conventional navigation system and when the route is determined, the route is provided for the method according to the invention. When determining the route, a destination is preferably entered by a driver and a route is calculated from a current location to the destination.

The computational vehicle operation model is a mathematical replica of a vehicle's behavior as it is moved along the route. Operation of the vehicle is thus simulated or modeled by computer. The vehicle is a hybrid vehicle, in particular a so-called plug-in hybrid. The computational vehicle operating model is preferably adapted to a specific vehicle, with the computational vehicle operating model basing its calculations on properties of the vehicle that were provided to the computational vehicle operating model. The calculations of the mathematical vehicle operating model are based, for example, on a battery capacity of the vehicle, a consumption of the vehicle, a weight of the vehicle, a performance of the vehicle and/or other properties and technical features of the vehicle total The computational vehicle operating model simulates driving the vehicle along the determined route based on predefined restrictions and vehicle operation defined by a status parameter, including any charging processes along the route.

In the calculations by the computational vehicle operating model, both a parameter describing the charging process of the vehicle and a parameter describing the change in the drive mode of the vehicle are used for the computational simulation of the operation of the hybrid vehicle. At least one of these parameters is used as a status parameter. The respective other of these parameters is preferably included in the calculation as a characteristic value or as a restriction.

In this case, the predefined restrictions are in particular those restrictions which a user provides to the computational vehicle operating model for a specific route and which the user wishes to meet when driving along the route. Furthermore, the restrictions are those restrictions that arise in the environment for the specific vehicle for which a journey is to be simulated. For example, the restrictions define when the electric drive must be used, for example if the vehicle is in an environmental zone.

The status parameter describes a charging process of the vehicle or a change in a drive mode of the vehicle. If a charging process is described by the status parameter, the status parameter preferably defines a time and/or a location for carrying out the charging process on the route, or a charging power during the charging process is defined. If the state parameter describes a change in a drive mode of the vehicle, the state parameter preferably describes a time and/or a place at which the drive mode of the vehicle changes from the electric drive to the combustion drive or from the combustion drive to the electric drive is switched.

The characteristic value describes a property of the journey that can be optimized, which results when the vehicle is operated according to the state parameter. The characteristic value does not necessarily describe a property of the vehicle per se, but preferably a property (mathematically: optimization criterion) that is relevant for the user when using the hybrid vehicle in order to use it optimally. Which property is described by the characteristic value is therefore dependent on the user and can either be selected by him or can be specified for the mathematical vehicle operating model. Exemplary properties that can be optimized are travel time, consumption, environmental pollution caused, battery life or similar parameters that result from the vehicle traveling along the route. The properties that can be optimized are mostly mutually contradictory or competing: i.e. if the state parameters are adjusted in such a way that one property improves, one of the remaining relevant properties will most likely deteriorate with the state parameter (concrete examples of this are given below). There is usually no set of "globally optimal" state parameters that would be equally optimal for all properties that can be optimized. Instead, one or more compromise solutions suitable for the specific situation must be sought. The solution according to the invention creates a basis for an analytical search for such compromise solutions with regard to the operation of PHEV vehicles.

The characteristic value, which can optionally also be described mathematically as a fitness function, is optimized according to a target specification. The target is typically dependent on the type of characteristic value. For example, the target can be maximizing or minimizing the characteristic value. Alternatively, a target window is defined by the target specification, in which the characteristic value should fall. For example, if the characteristic value is a travel time, this can either be maximized or minimized. Alternatively, the target specification can define that the travel time is such that the hybrid vehicle reaches the destination of the route at a specific point in time. An associated characteristic value is thus determined in each case for different possible state parameters, ie for a different behavior of the vehicle on the route, by means of the mathematical vehicle operating model. At the same time, the behavior of the vehicle is limited by the restrictions. It can thus be concluded which state parameters are to be applied to the vehicle in order to achieve an optimal characteristic value. Optionally, several values for different state parameters are determined by the computational vehicle operating model and these are selected in such a way that this leads to an optimized characteristic value.

In particular, the characteristic value itself consists of several aspects/optimization criteria that are taken into account at the same time during the optimization. The characteristic value does not necessarily reflect a single parameter, but can be formed from a combination of several parameters to be optimized.

Furthermore, information is provided which indicates how the vehicle is to be operated, so that it is operated in accordance with the value of the state parameter determined during the optimization of the characteristic value. This means that the result of the optimization of the characteristic value is provided, in particular the state parameters resulting therefrom are provided. This enables the vehicle to actually be operated such that its operation results in the optimized characteristic value. In this case, the information describes in particular how a charging process of the vehicle is to take place or when a change in a drive mode of the vehicle is to take place.

A method is thus created through which in particular a business use of hybrid vehicles, in particular plug-in hybrids, can be optimized. In this way, the use of the vehicle can be improved from several points of view, such as efficiency, battery life, environmental friendliness or sustainability, and the improvement can be quantitatively verified. For example, it can be avoided that the vehicles are charged too infrequently and that the electric drive is used too infrequently, which causes the vehicle to produce higher emissions figures than can be expected from a hybrid vehicle. The method preferably provides a driver of the vehicle with an instruction that tells him when the electric drive must be switched on or off and when, where and how the vehicle should be charged. This instruction is based on the optimized characteristic value. A large number of target values, parameters and framework conditions of the electromobility ecosystem are thus taken into account in a comprehensive mathematical model, i.e. the mathematical vehicle operating model, and the mathematical vehicle operating model is used to optimize the use of the hybrid vehicle in a multi-criteria manner.

Since operation of the vehicle on the route is simulated by the mathematical vehicle operating model, a large number of parameters and their influence on the behavior of the vehicle can be taken into account. How a vehicle is optimally operated depends on its function. A vehicle can thus be used optimally for its individual use by appropriate selection of the characteristic value.

The dependent claims show preferred developments of the invention.

The characteristic value is preferably formed from one or more of the following values: indicator for travel time along the route, indicator for costs for driving along the route, indicator for charging costs for charging a battery of the vehicle, indicator for environmental pollution caused when driving on the route, indicator for a service life of a battery of the vehicle, indicator for the effect of driving on a route (including operating mode and possible charging processes along the route) on a service life of a battery of the vehicle. The characteristic value therefore does not necessarily only describe a single property, but can also be formed from a combination of several properties. In this case, individual properties can optionally be weighted if they are combined in the characteristic value. For example, a characteristic value can be formed that reflects a travel time to be optimized along the route given a consumption to be optimized. Minimizing this characteristic value results in minimal travel time at minimal cost. If the travel time is more relevant to the user than the cost of traveling the route, the travel time is weighted accordingly higher. The characteristic value can preferably be selected manually by a user. Operation of the hybrid vehicle on the route can thus be adapted particularly efficiently to a user requirement.

More preferably, the status parameter describes a time and/or a place at which or at which an active drive of the vehicle is switched between the electric drive and the combustion drive. In this way, it can be determined when to switch between an electric drive and a combustion drive in order to travel the determined route with optimal use of the electric drive and the combustion drive. With regard to which property of the electric drive and the combustion drive is optimally used results from the properties that can be optimized, which the characteristic value describes.

The status parameter further preferably describes a time and/or a place at which a charging process of a battery of the vehicle takes place at a charging station on the route. It can thus be determined at which point or at what time the vehicle should be charged in order to travel the entire route with optimal use of the electric drive and the combustion drive.

More preferably, the status parameter describes a charging power with which a charging process of a battery of the vehicle takes place when it is being charged at a charging station on the route. At many charging stations, a possibility is provided to enable so-called fast charging (DC charging). However, this usually entails higher costs than regular charging (AC charging). Such rapid charging also often has a negative impact on the life of the battery. The method according to the invention thus makes it possible to select the charging mode that leads to the lowest costs, but in which the property that can be optimized according to the target specification is nevertheless met. For example, if the property that can be optimized is a travel time for traveling the route, the charging power can be selected such that the vehicle's battery is sufficiently charged but the destination of the route is still reached within the desired period of time.

More preferably, the status parameter describes a state of charge up to which the battery of the vehicle is charged when it is charged at a charging station on the route. For example, it can be determined that the battery should only be charged up to 70% so that the route can be traveled completely within a specified time according to the calculated vehicle operating model. This has the advantage that the battery is in an optimum state of charge in terms of the operating life of the battery.

It is also advantageous if the predefined restrictions include a maximum travel time along the route, a selection of permitted plugs at a charging station on the route, required stopovers on the route and/or a minimum state of charge of the vehicle at the destination of the route. In this case, the predefined restrictions can optionally be provided at least in part by a user. The predefined restrictions are fixed frameworks for the computational vehicle operating model, which means that the predefined restrictions must be complied with. The predefined restrictions differ from the characteristic value in that the predefined restrictions merely have to be complied with, but are not optimized. A travel time can be defined either as a predefined restriction or as a characteristic value. In the first case, the travel time is adhered to, in the second case, the travel time is optimized according to the target.

It is also advantageous if the information is provided at a point in time at which an operating parameter of the vehicle is to be changed, so that the vehicle is operated in accordance with the value of the state parameter which leads to the optimum characteristic value. Thus, a user, typically the driver of the vehicle, can react immediately to the information or a vehicle system can be adjusted correctly at the right point in time.

It is also advantageous if, when the information is provided, it is shown on a display in order to inform a user what action is to be taken on the vehicle so that it is operated according to the value of the state parameter which leads to the optimum characteristic value. For example, the driver of the vehicle is shown when to activate the electric drive and when to deactivate the electric drive. This is particularly advantageous since the method does not necessarily have to be carried out by a vehicle system, but can also be carried out on a smartphone, for example, and the corresponding information can be applied to the vehicle by the driver of the vehicle.

It is also advantageous if, when the information is provided, a signal is output by which an operating parameter of the vehicle is set on vehicle electronics such that the vehicle is operated according to the state parameter that leads to the optimal characteristic value. Thus, the adaptation of the vehicle parameters, ie the operating parameters of the vehicle, is carried out automatically and the user is relieved.

Furthermore, a device is advantageous which comprises a computing unit which is set up to carry out the method according to one of the preceding claims. Such a device has all the advantages of the method according to the invention.

character list

• 1 eine Darstellung eines Flussdiagrams des erfindungsgemäßen Verfahrens in einer ersten Ausführungsform,
• 2 eine schematische Darstellung eines Fahrzeuges, in dem eine erfindungsgemäße Vorrichtung gemäß einer Ausführungsform der Erfindung angeordnet ist,
• 3 eine schematische Darstellung eines rechnerischen FahrzeugBetriebsmodells gemäß einer Ausführungsform der Erfindung, und
• 4 eine beispielhafte Darstellung einer ermittelten Route, die von dem Fahrzeug befahren werden soll.

Exemplary embodiments of the invention are described in detail below with reference to the accompanying drawings. In the drawing is:

• 1 a representation of a flow chart of the method according to the invention in a first embodiment,
• 2 a schematic representation of a vehicle in which a device according to the invention is arranged according to an embodiment of the invention,
• 3 a schematic representation of a computational vehicle operating model according to an embodiment of the invention, and
• 4 an exemplary representation of a determined route that is to be traveled by the vehicle.

Embodiments of the invention

1 1 shows a flowchart of a method 100 for operating a hybrid vehicle 1 according to an exemplary embodiment of the invention. The hybrid vehicle is a vehicle with an electric drive 2 and a combustion drive 3, the vehicle 1 preferably being a so-called plug-in hybrid vehicle.

The method is preferably carried out by a computing unit 4 which is arranged in the vehicle 1 and which is, for example, the computing unit 4 of a navigation system. In such an embodiment, as also in 2 is shown, the computing unit 4 executing the method 100 is structurally arranged in the vehicle 1 . However, it is pointed out that the computing unit 4 that executes the method can alternatively also be arranged outside of the vehicle, in particular in a mobile terminal such as a smartphone.

If the method 100 is initiated, a first step 101 is carried out first. In the first step 101, a route 10 is determined, which is to be traveled by the vehicle 1. The route 10 is calculated by a navigation system, for example, and made available to the processing unit 4 for the method 100 . A user enters a destination 12 into a navigation system, for example, and the navigation system calculates the route 10 from a current location 11 to the destination 12.

After the first step 101 has been carried out, a second step 102 is carried out. In the second step 102, a computational vehicle operating model 20 is provided, which is set up to simulate a journey of the vehicle 1 along the determined route 10 based on predefined restrictions 21 and an operation of the vehicle 1 defined by a status parameter 22. A characteristic value 23 is determined as an initial value by the calculated vehicle operating model 20 .

A structure of the computational vehicle operating model 20 is shown schematically in FIG 3 shown. It is thus evident that the mathematical vehicle operating model 20 is provided with the predefined restrictions 21 and at least one state parameter 22 as input parameters. In addition, the route 10 is made available to the computational vehicle operating model 20 as a further input parameter 24 . A characteristic value 23 is determined by the calculated vehicle operating model 20 based on the information provided to the calculated vehicle operating model 20 . The characteristic value 23 describes a property of the journey that can be optimized, which results when the vehicle 1 is operated according to the state parameter 22 . State parameter 22 is either defined and can later be varied by vehicle operating model 20, or different values are specified for a specific state parameter 22.

State parameter 22 describes a charging process of vehicle 1 or a change in a drive mode of vehicle 1. In other words, this means that state parameter 22 describes an effect on hybrid vehicle 1. For example, the vehicle 1 can be influenced by a specific execution of a charging process of the vehicle 1 or the vehicle can be influenced by switching between the different drives, ie the electric drive 2 and the combustion drive 3 . Such a change is a change in the drive mode of the vehicle 1. It can be seen that the charging process can be carried out in different ways, for example at different locations, times, with different charging currents and/or with different charging durations, up to a respectively desired state of charge. Each of these properties can be defined as a state parameter 22 for the computational vehicle operating model 20 . In this case, different ways of carrying out the loading process can be described by different values of the status parameter 22 . A value of the status parameter 22 is not necessarily a positive integer number, but can have any format. For example, the status parameter describing the location can have a GPS position format in order to describe the location of the charging process. Correspondingly, the state parameter describing the time can have a time format, for example, in order to describe the point in time of the charging process. In a corresponding manner, the status parameters for the charging power and the charging duration also have a corresponding format.

The predefined restrictions 21 define restrictions that the computational vehicle operating model 20 must comply with. This includes in particular those restrictions that result from the actual environmental conditions and characteristics of the route. A predefined restriction is, for example, a definition of an area along the route 10 in which a specific drive mode, for example the electric drive 2, is to be used. However, the predefined restrictions 21 can also be user-defined be ned or be vehicle dependent. For example, the user can define a maximum travel time along route 10 if the user only has a certain amount of time to travel the route. A predefined restriction can also be a selection of allowed plugs at a charging station if only certain plugs are compatible for the vehicle 1 . Alternatively, certain intermediate stops on route 10 can be defined by the predefined restrictions, for example when certain intermediate destinations, for example for delivering packages, are to be approached on a trip along route 10 . A further advantageous restriction is a minimum state of charge of the vehicle 1 at the destination 12 of the route 10. The predefined restrictions 21 described above can be used in combination with one another or independently of one another and limit the arithmetical vehicle operating model 20 in such a way that only those characteristic values 23 are determined which result when the predefined restrictions 21 are complied with.

The characteristic value 23 describes a property of the trip along the route 10 that can be optimized. The characteristic value 23 is therefore not necessarily a property of the vehicle 1, but can define a property that is of interest to the user, for example to achieve time or cost optimization. For example, properties that are described by the characteristic value 23 are a travel time along the route 10, costs for driving along the route 10, environmental pollution caused when driving on the route 10 and/or a battery life of a battery of the vehicle 1. The characteristic value 23 is an indicator for these properties, whereby several of these properties can be combined by the characteristic value. For example, a characteristic value can be designed in such a way that it is an indicator for a journey time along route 10 and at the same time an indicator for the costs for driving along the route. In a simplified example, the characteristic value 23 could be formed such that the indicator for the travel time has a value from 1 to 10 and the indicator for the costs also has a value from 1 to 10. A new characteristic value 23 could be created by adding the two values. It is also possible to weight individual values. This formation of a characteristic value 23 is to be regarded as an example, but it shows that the characteristic value 23 does not necessarily represent only a single property of the journey. Alternatively, it is equally possible for the computational vehicle operating model to output a number of characteristic values that are optimized as a function of one another.

The vehicle operating model 20 is a mathematical model that simulates a journey of the vehicle 1 along the route 10 . A respectively associated characteristic value results for the different selection of the values for the state parameter 22 . It is assumed, for example, that state parameter 22 describes a time and/or a place at which or at which an active drive of the vehicle is switched between electric drive 2 and combustion drive 3 . The different possible values of the state parameter 22 thus define different points in time or locations at which there is a change from the electric drive 2 to the combustion drive 3 or at which there is a change back from the combustion drive 3 to the electric drive 2 . Since the route 10 was determined in advance, either a time or a location can be defined for the change, since a time is also associated with each location on the route. A journey along the route 10 is simulated by the computational vehicle operating model 20 , a specific value for the status parameter 22 being assumed for each simulation of the route 10 . A separate value for the characteristic value 23 results for each selected value of the state parameter 22. For example, the costs for driving along the route 10 can decrease the earlier a changeover from the combustion drive 3 to the electric drive 2 takes place. A third step 103 is carried out in order to now determine an optimal value for the state parameter 22, in the example described here an optimal point in time and/or place at which the active drive of the vehicle 1 should be changed.

In the third step 103, the characteristic value 23 is optimized according to a target specification, with the computational vehicle operating model 20 determining such a value for the state parameter 22 that the characteristic value 23 meets the target specification. The target can preferably be defined by a user. The characteristic value 23 is, for example, an indicator of the costs for driving along the route 10. The target specification by the user could therefore be that the costs for driving along the route should be minimized. The journey of vehicle 1 along route 10 is thus simulated multiple times by computational vehicle operating model 20 , each of the simulations being based on a value of status parameter 22 . In this way, the costs for traveling the route are simulated as an example, depending on the time and place at which a changeover between the active drive of the vehicle takes place. The simulation by the computational vehicle operating model 20, which leads to the lowest costs, is based on a specific value of the status parameter 22 and thus describes a specific one Time and/or a specific location at which the active drive of the vehicle should be changed. The characteristic value 23 was thus optimized by minimizing and an associated value for the state parameter 22 was determined. This determined value of the status parameter 22 thus also fulfills the target specification.

Furthermore, one or more different data sources 25 are preferably called up via one or more previously defined interfaces and taken into account in the calculation by the arithmetical vehicle operating model 20 . Such data sources 25 can be used to find an optimal operating solution or to define a permissible solution space. For example, an effect of the information provided by the data sources on individual solutions of the calculations of the computational vehicle operating model 20 and the associated characteristic value 243 can be evaluated. For example, data sources 25 provide information on charging infrastructure data (e.g. availability of individual charging stations, charging tariffs and charging prices, available charging plugs), environmental data (e.g. weather data, in particular on temperature, wind and/or precipitation, since these influence energy consumption along Route 10 have), traffic models (e.g. information on traffic jams, as this affects the travel time), map data (e.g. environmental zones).

After the third step 103, a fourth step 104 is carried out. In the fourth step 104 , information is provided which indicates how the vehicle 1 is to be operated, so that it is operated in accordance with the value of the state parameter 22 determined during the optimization of the characteristic value 23 . For example, corresponding information is displayed on a display 5 in the field of vision of the driver of vehicle 1. The information is displayed in such a way that it informs the driver what action is to be taken on vehicle 1, so that it can react according to the value of the State parameter 22 is operated, which leads to the optimal characteristic value 23. The value of the state parameter 22 previously selected as an example describes a time or a location for a change in the active drive of the vehicle 1. The display 5 thus shows, for example, that a drive mode of the vehicle should be changed at a specific location. In this case, the driver of the vehicle 1 can switch between the drive modes. This is particularly advantageous when there is no communication link between the processing unit 4 executing the method 100 and vehicle electronics, for example when the method 100 is executed on a smartphone.

It is particularly advantageous if the information is provided to the user, here the driver of the vehicle 1, at a point in time at which an operating parameter of the vehicle 1 is to be changed, so that the vehicle 1 is operated in accordance with the value of the status parameter 22, which leads to the optimal characteristic value 23. In other words, for the given example, this means that the driver of vehicle 1 is then informed that he should switch from electric drive 2 to combustion drive 3 or from combustion drive 3 to electric drive 2 when the time occurs or the Place is reached, which is described by the state parameter 22 and leads to the optimal characteristic value 23.

Alternatively or additionally, when the information is provided in the fourth step 104, a signal is output by which an operating parameter of the vehicle 1 is set on vehicle electronics such that the vehicle 1 is operated according to the state parameter 22, which leads to the optimal characteristic value 23 . For example, the computing unit 4 is connected to an engine controller of the vehicle 1 via a bus system of the vehicle 1 . If, in the example described above, the time or place has been reached at which the active drive of the vehicle 1 should be changed, the processing unit 4 sends a signal to the vehicle control system in order to automatically switch between the different drive modes of the vehicle 1 to perform.

In alternative embodiments, the status parameter 22 is a time and/or a place at which or at which a charging process of a battery of the vehicle 1 takes place at a charging station on the route 10 . In this way, for example, minimal costs are incurred for driving along route 10 if the vehicle 1 is charged at two specific charging stations 16, 17 along route 10. In particular, current loading prices are also taken into account. This could be particularly advantageous in particular for the costs of traveling along route 10, even if route 10 could in principle be traveled with only one charging process at one of the charging stations 16, 17.

Alternatively or additionally, the status parameter 22 describes a charging power with which a charging process of a battery of the vehicle 1 takes place when it is being charged at a charging station on the route 10 . For example, some charging stations support so-called fast charging operations, which, however, result in increased costs and a negative impact on battery life in the long term. The arithmetic vehicle operating model 20 and the associated optimization of the characteristic value can thus be used to determine, for example, whether a travel time defined as a predefined restriction must also be adhered to if no fast charging process is selected in order to minimize the costs for traveling the route.

Alternatively or additionally, the state parameter 22 describes a state of charge up to which a battery of the vehicle 1 is charged during a charging process when it is charged at a charging station on the route 10 . It is thus possible for a battery of the vehicle 1 not to be 100% charged, but for the battery to be only partially charged, for example. In this way, the time required to charge the battery of the vehicle can be reduced if the state of charge of the vehicle 1 achieved as a result is nevertheless sufficient to comply with the predefined restrictions 21 .

It is pointed out that the computational vehicle operating model 20 preferably works based on a plurality of state parameters 22 . Different combinations of values of the different status parameters are combined with one another and the journey is simulated according to the different combinations of values of the status parameters. A combination of state parameters 22 is thus determined, which leads to an optimization of the characteristic value 23 . This is explained in the following example, which is 4 is described.

4 shows a representation of an exemplary route 10 that is to be traveled by the vehicle 1 . For example, the destination 12 at which the user wants to attend an appointment was entered by a user who is at a starting point 11 of the route 10 . The route 10 is calculated and made available by the navigation system. This can be done, for example, using conventional route-finding algorithms. The user also defines as a predefined restriction 21 that the destination 12 can be reached in three hours at the latest. In addition, the user enters that the least possible environmental pollution should be caused when driving on Route 10.

The route is divided into a first section 13, a second section 14 and a third section 15. The first section 13 is a route section that leads through a suburban settlement. The second leg route 14 is a leg route passing through a rural area, and the third leg route 15 is a leg route passing through an urban area. Furthermore, a first charging station 16 and a second charging station 17 are arranged along the route 10 . Another restriction is that the second charging station 17 does not support fast charge mode. The computational vehicle operating model 20 optimizes a characteristic value 23, which indicates environmental pollution caused when driving on the route 10, the characteristic value 23 being minimized. The computational vehicle operating model 20 performs an optimization based on multiple state parameters 22 . A first state parameter describes a time and a place at which the alternative drive of the vehicle 1 changes between the electric drive 2 and the combustion drive 3, a second state parameter describes a time and/or a place at which or at which a charging process of the Battery of the vehicle takes place at one of the charging stations 16, 17 on the route and a third state parameter 22 describes a charging power with which the battery of the vehicle 1 is charged when it is charged at one of the charging stations 16, 17 on the route. Furthermore, a fourth state parameter describes a state of charge up to which the battery of the vehicle 1 is charged during a charging process when it is charged at one of the charging stations 16, 17 on the route 10. The computational vehicle operating model 20 simulates different applications of the state parameters on the vehicle 1 and calculates the environmental pollution that this leads to. However, this is done under the premise that a maximum travel time of three hours must be observed.

A scenario with associated values of the state parameters results, which leads to minimal environmental pollution. The information regarding these values of the state parameters is made available to the driver of the vehicle 1 via a display or via the display 4 . For example, the information is made available to the driver that he should switch from the combustion drive to the electric drive 2 after completing the first route section 13 . Furthermore, the user is informed that he should charge the vehicle 1 to 80% at the first charging station 16 and that the vehicle 1 should not be charged at the second charging station 17 . The driver is further informed that he should use a quick charge function so that the time frame of two hours, i.e. the predefined restriction, is adhered to. If these specifications are observed by the driver and applied correctly to the vehicle 1, this leads to minimal environmental pollution caused when driving on route 10.

It turns out, however, that the user is provided with other information if, for example, he has selected a different characteristic value for optimization. For example, the user could have selected that battery life of the vehicle 1 battery should be maximized and the user could have further waived the predefined constraint that a time frame of three hours for driving along the route 10 is to be observed. In this case, the optimization could come to the conclusion that the vehicle 1 should charge the battery of the vehicle to 100%, for example at the second charging station 17, without using a fast charging function that is not available at this charging station anyway. Although this could result in the journey taking more than three hours, it means that the vehicle's battery is charged in a particularly gentle manner, thereby increasing battery life.

With the computational vehicle operating model 20, the operation of the hybrid vehicle 1 is formulated as a problem of multi-criteria optimization. For this purpose, a mathematical model of the electromobility ecosystem will be built with a focus on PHEV operation problems.

The model consists of framework conditions/restrictions (order situation, current route, environmental zones, plug type, required state of charge at the destination, etc.) and target values for optimization (transport time, transport costs, environmental protection, battery life, etc.). To this end, several interfaces to individual parts of the electromobility ecosystem (charging infrastructure, vehicle data, etc.) are defined.

The optimal charging options and operating mode are carried out depending on current tour and route planning.

1. 1. Wo/wann/wie (Ladestrom/Ladeleistung etc.) muss geladen werden?
2. 2. In welchem Modus soll das Fahrzeug aktuell fahren?

Two questions are addressed by invention:

1. 1. Where/when/how (charging current/charging power etc.) does it have to be charged?
2. 2. In which mode should the vehicle currently drive?

At the same time, there is a multi-criteria optimization for several qualitative and quantitative target values related to official PHEV operation, an analytical calculation of the optimal PHEV operation and charging and operation planning parallel to tour and route planning.

The present invention opens up a new perspective on the operation of PHEVs through the approach of methods of multi-criteria optimization and formalization of the term "optimal PHEV operation" through the use of specific target values. Due to the approach of the invention, the electric drive is used more often in official PHEVs, which will reduce the number of emissions. In addition, the service life of the batteries is positively influenced because the aspect is taken into account as a target value in charging and operational planning.

The mathematical model for charging and operation planning described above serves as the basis for the use of meta-heuristic optimization algorithms (Kl-algorithms). The optimization algorithm runs in the backend and the driver receives the loading and operating instructions. The optimization loop is repeated every 30-40 seconds and the corresponding database (vehicle, environment, traffic, charging infrastructure data) is constantly updated so that, for example, the occupancy of charging stations (including booking perspective) and the traffic situation can be taken into account during optimization.

By using the invention, existing solutions for route and tour planning are improved with regard to PHEV fleets. This will result in the PHEVs being operated more effectively/environmentally friendly and the effects are also measurable.

In addition to the above disclosure, reference is made explicitly to the disclosure of 1 until 4 referred.

QUOTES INCLUDED IN DESCRIPTION

This list of documents cited by the applicant was generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Patent Literature Cited

• CN108665342A [0005]
• CN 110667428A [0005]

Claims (10)

Method (100) for operating a hybrid vehicle (1) with an electric drive (2) and a combustion drive (3), comprising: - Determining (101) a route (10) to be traveled by the vehicle (1); - Providing (102) a computational vehicle operating model (20), which is set up to drive the vehicle (1) along the determined route (10) based on predefined restrictions (21) and a status parameter (22) defined operation of the vehicle (1) to simulate and to determine a characteristic value (23), o wherein the status parameter (22) describes a charging process of the vehicle (1) and/or a change in a drive mode of the vehicle (1); and o wherein the characteristic value (23) describes a property of the trip that can be optimized, which results when the vehicle (1) is operated according to the state parameter (22); - Optimizing (103) the characteristic value (23) according to a target specification, with the computational vehicle operating model (20) being used to determine such a value for the state parameter (22) that the characteristic value (23) meets the target specification; and - Providing (104) information which indicates how the vehicle (1) is to be operated, so that it is operated in accordance with the value of the state parameter (22) determined during the optimization of the characteristic value (23). procedure according to claim 1 , wherein the characteristic value (23) is formed from one or more of the following values: indicator for a travel time along the route (10), indicator for costs for driving along the route (10), indicator for charging costs for charging a battery of the vehicle (1 ), indicator for environmental pollution caused when driving on the route (10), indicator for a battery life of a battery of the vehicle (1), indicator for the effect of driving on the route on an operating life of a battery of the vehicle. Method according to one of the preceding claims, wherein the state parameter (22) describes a time and/or a place at which or at which an active drive of the vehicle (1) is switched between the electric drive (2) and the combustion drive (3). . Method according to one of the preceding claims, wherein the status parameter (22) describes a time and/or a location at which or at which a charging process for a battery of the vehicle (1) at a charging station (16, 17) on the route (10) he follows. Method according to one of the preceding claims, • wherein the status parameter (22) describes a charging power with which a charging process of a battery of the vehicle (1) takes place when it is charged at a charging station (16, 17) on the route (10), and/or • wherein the state parameter (22) describes a state of charge up to which a battery of the vehicle is charged during a charging process when it is charged at a charging station (16, 17) on the route (10). Method according to one of the preceding claims, wherein the predefined restrictions (21) a maximum travel time along the route (10), a selection of permitted plugs at a charging station (16, 17) on the route (10), required intermediate stops on the route (10 ) and/or a minimum state of charge of the vehicle (1) at the destination of the route (10). Method according to one of the preceding claims, wherein the information is provided (104) at a point in time at which an operating parameter of the vehicle (1) is to be changed so that the vehicle (1) is operated in accordance with the value of the state parameter (22), which leads to the optimal characteristic value (23). Method according to one of the preceding claims, wherein when the information is provided (104), it is displayed on a display (5) in order to inform a user what action is to be taken on the vehicle (1) so that it can be adjusted according to the value of the State parameter (22) is operated, which leads to the optimal characteristic value (23). Method according to one of the preceding claims, wherein when the information is provided (104), a signal is output by which an operating parameter of the vehicle (1) is set on vehicle electronics such that the vehicle (1) is operated according to the status parameter (22). which leads to the optimal characteristic value (23). Device (20) comprising a computing unit (4) which is set up to carry out the method according to one of the preceding claims.

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