Computer-implemented method and computer program for obtaining a monetarily cost-optimal driving operation for a hybrid electric vehicle and control unit for regulating and / or controlling a driving operation of a hybrid electric vehicle

Abstract

A computer-implemented method for obtaining a monetarily cost-optimal driving mode for a hybrid-electric vehicle (10), the method comprising the steps of: obtaining, via a first data interface (20), a route (A, B, C) to be traveled for the hybrid-electric vehicle (10), comprising speed specifications, position information on nodes, wherein the nodes include fuel and charging stations (17, 18), and information on environmental zones (LEZ) along the route (A, B, C) (V1); obtaining price structures of the nodes (V2); performing the following steps (V3-V6) by means of a first software or hardware unit (21): based on the speed specifications, determining a total consumption of electrical energy and / or fuel along the route (A, B, C) for several operating modes (EV, CV, LPS1, LPS2) of the hybrid-electric vehicle (10) (V3); (...)

Description

The invention relates to a computer-implemented method and computer program for obtaining a monetary, cost-optimal driving mode for a hybrid electric vehicle. The invention further relates to a control device for regulating and / or controlling a driving mode of a hybrid electric vehicle.The following definitions, descriptions, and statements retain their respective meaning and apply to the entire disclosed subject matter.A hybrid electric vehicle comprises an internal combustion engine and / or an electric motor as drive units. In this disclosure, the term hybrid electric vehicle includes three typical types of vehicles, namely conventional, i.e., purely internal combustion engine-driven vehicles, electric, for example battery electric vehicles, and hybrid vehicles, for example hybrid electric vehicles. In this case, an internal combustion engine vehicle has only one operating mode CV and an electric vehicle has only one operating mode EV. The internal combustion engine is supplied with chemically liquid energy in the form of petrol, diesel, LPG or hydrogen from a fuel tank. The electric motor is supplied with electrical energy from a rechargeable energy store. The energy store is, for example, a solid-state or liquid battery, an accumulator or a capacitor block. Excess energy of the internal combustion engine is converted into electrical energy using a generator, with which the energy store of the hybrid electric vehicle can be charged. The hybrid electric vehicle can be, for example, a plug-in hybrid electric vehicle, also called a plug-in hybrid electric vehicle, abbreviated as PHEV. The energy store of a PHEV can additionally be charged via a power grid, for example charging stations for electric vehicles.DE 10 2020 200 826 A1 discloses methods for operating a plug-in hybrid vehicle, wherein the plug-in hybrid vehicle has an internal combustion engine, a fuel tank for supplying fuel to the internal combustion engine, an electric machine and an electrical energy store for supplying electrical energy to the electric machine, wherein, for the operation of the plug-in hybrid vehicle on a route situated ahead of the same, a consumption of fuel by the internal combustion engine, a consumption of electrical energy by the electric machine and a required travel time are determined as a function of the route situated ahead of the plug-in hybrid vehicle, wherein the route is divided into purely internal combustion engine route sections, purely electrical route sections and hybrid route sections in such a way that, as a function of the cost of the fuel, a consumption of fuel by the internal combustion engine, a consumption of electrical energy by the electric machine and a required travel time are determined, depending on costs for the electrical energy and depending on time costs, ascertained travel costs are minimal.DE 10 2019 109 849 A1 discloses a driver assistance system for a vehicle for cost optimizing replenishment processes of resources for operating the vehicle, configured to carry out the following steps: ascertaining a common optimum replenishment strategy for at least a first and a second resource while taking into account a planned route of the vehicle; outputting the common optimum replenishment strategy for the purpose of cost optimizing the replenishment processes.It was an object of the invention to allow a route, in particular a route with environmental zones, to be passed through with a hybrid electric vehicle in a monetary cost-optimum manner, and in this case also in particular in an energy-efficient and resource-saving manner.The subject matters of the independent claim and the subordinate claims each solve this task. Advantageous embodiments of the invention are evident from the definitions, the subclaims, the drawings and the description of preferred exemplary embodiments.In one aspect, the invention provides a computer-implemented method for obtaining a monetary cost-optimal driving mode for a hybrid electric vehicle. During the driving operation, both the electric motor and the internal combustion engine can act directly on the drive train. This has the advantage that, in the event of a high drive power requirement, for example during acceleration or during hill climbing, the electric motor and the internal combustion engine drive the hybrid electric vehicle jointly. During long-distance operation, for example during highway driving, the hybrid electric vehicle can be driven by the internal combustion engine alone, wherein, for example, the electric drive motor is mechanically decoupled from the drive train. If there is a sufficiently low drive power requirement, for example at low speed, the electric motor functions as a generator driven by the drive train, which converts kinetic energy into electrical energy with which the energy store is charged, which is also referred to as recuperation. If the drive power requirement is particularly low, for example when parking is removed or in coasting mode or in stop-and-go mode, the electric motor will drive the hybrid electric vehicle alone, the internal combustion engine being switched off and, for example, being mechanically decoupled from the drive train. In environmental zones, also called low emission zones, the monetary cost-optimal operation provides that the hybrid electric vehicle is driven by the electric motor.In one step of the method, a route to be driven for the hybrid electric vehicle comprising speed specifications is obtained via a first data interface. Furthermore, position information on nodes, wherein the nodes comprise fuel and charging stations, and information on environmental zones along the route are obtained. The first data interface can be an interface to a driver assistance system or to an autonomous driving system of the hybrid electric vehicle.According to a further aspect, the first data interface is an interface to a human-machine use interface. Via the human-machine use interface, a user inputs, for example, starting and destination locations and number of intermediate stops. Furthermore, user preferences can be established therewith, for example idle times or steering times, travel interruptions, preferred operators and tariffs of charging points and gas stations, preferred payment methods, end SoC value, end gas content.By way of example, data of a navigation system can be obtained via the first data interface. The navigation system may be in communication with the aforementioned human-machine use interface, i.e., the navigation system constructs the route based on the user inputs, for example. The navigation system or the first data interface can be in data exchange with a traffic infrastructure. The route to be driven is provided as an overall route via the first data interface, for example via the navigation system. For example, the route to be driven may comprise six different real round rails between aachen and munich and between aachen and friedrichshafen. On these lines, case studies were conducted on the method disclosed herein. The results of the case studies are disclosed below. The speed specifications can comprise speed limits and an intelligent speed adaptation adapted thereto, for example obtained from a digital map. For example, a maximum speed of 120 km / h can be imposed for environmental zones. According to one aspect, in addition to the position information relating to the nodes, position information relating to route sections lying between the nodes is also obtained in the form of edges. The steps of the method, in particular relating to minimization, can thus be solved by means of graph theory. In this case, decision nodes are created for all stations along the route and are linked to the individual nodes with station data such as position, type and mark. The route can comprise route sections in the form of environmental zones. The environmental zones are stored in a map, for example, and / or can be detected by geofencing. The method can determine purely electric driving operation of the hybrid electric vehicle in environmental zones and thus contribute to a reduction in pollutant emissions.In a further step of the method, price structures of the nodes are obtained. The price structures may include price per liter, price per kilowatts hour, price per minute, or price per charging operation. This price structure includes prices for roaming (third party services). The price structures can be obtained at the start of a drive over the entire route and updated in the course of the drive, for example from node to node.The following further steps a) to d) of the method are carried out by means of a first software or hardware unit. The first software or hardware unit is called mission management. Unit means that the first software or the first hardware can be a separate, closed part of total software or total hardware, which can each comprise a plurality of units, wherein the individual units are in data exchange via software or hardware interfaces. a) Based on the speed specifications, a total consumption of electrical energy and / or of fuel along the route is determined for a plurality of operating modes of the hybrid electric vehicle. The first software or hardware unit thus reads in the total route with the speed specifications and determines the total consumption as a function of the operating modes. This step is called a vehicle pre-filter. For example, the hybrid electric vehicle comprises four operating modes, namely a conventional driving mode, in which the internal combustion engine alone supplies the required drive torque, a purely electric driving mode, in which the electric motor alone supplies the required drive torque, a load point shift 1 driving mode, in which the internal combustion engine is operated for charging a battery, for example a high-voltage battery, for the electric motor during normal driving or during standstill, and a load point shift 2 driving mode, in which the internal combustion engine and the electric motor together supply the required but also the respectively most efficient drive torque. The internal combustion engine may have a power of 110 kilowatts, for example. The electric motor can have a power of 75 kilowatts, for example. The hybrid electric vehicle can have a mass of 1796 kg, for example. b) On the set D of all nodes i along the route, the sum of tank and / or charging costs based on the price structures is minimized. Further, refueling and / or charging times at the respective nodes are minimized. The minimizations are performed taking into account the operating mode in environmental zones. Based on an initial force-off state and / or charge state of the hybrid electric vehicle, the fuel states x SoF and / or charge states x SoC in the nodes are determined recursively. In the minimization, for example, a set of states, control variables, and a display function are considered. The states include fuel states, state of charge, and tank and / or charge times, with all nodes viewed along the route. The control variables u q indicate the decisions for refueling and / or charging amounts, considering all nodes along the route. The control variables u m,i ∈{1, 2, 3, 4} furthermore indicate the operating mode in the respective node i. The set of control variables is U. The display function B B;Drf;Drc indicates whether the respective node is a fuel station or a charging station or whether the respective node or the route section, for example the edge between two nodes, is located within an environmental zone, also called a low emission zone, abbreviated LEZ. In one aspect, the tank times Δt rf,i and / or charging times Δt rc,i take into account the time Δt oh,i required for braking and accelerating processes during tanking and / or charging. Furthermore, according to a further aspect, the tank and / or charging times take into account the time Δt dv,i which the hybrid electric vehicle requires in order to reach the respective node from the route and to come back from the node to the route. The corresponding bypass routes are included in the costs. In the recursion, when a node i is a fuel station, the fuel state in node (i+1) is obtained from the fuel state in node i, to which a refueling amount u q,i of fuel, i.e. chemical energy, is added and the fuel consumption Δq fu,i( u m,i) between nodes i and (i+1) is subtracted. In the recursion, when a node i is a charging station, the state of charge in the node (i+1) is obtained from the state of charge in the node i to which a charging amount u q,i of electric energy is added and the consumption of electric energy Aq el,i( u m,i) between nodes i and (i+1) is subtracted. ξ tank is the tank capacity of the hybrid electric vehicle. ξ batt is the battery capacity of the hybrid electric vehicle. Accordingly, the time in node (i+1) is obtained from the time in node i, which is added a corresponding refueling and / or charging time.The recursion rule is, for example:The minimization rule is, for example: with refueling costs C rf,i( u i) = u q,i · ξ tank · p fu,i, wherein p fu,i is a fuel price per liter, charging costs C rc,i( u i) and cost factor C LEZ( u i) · BBfor environmental zones.In another aspect, constraints are imposed on the control variables to account for the physical limitations of the fuel tank and the battery of the hybrid electric vehicle. For example, the hybrid electric vehicle has a tank volume of 40 liters and a battery capacity of 8.7 kilowatts hours. Furthermore, the condition can be set that the hybrid electric vehicle always has sufficient fuel and / or electrical charge to reach the respective next node. This condition can also be defined directly by the driver as input, for example via the human-machine user interface. Furthermore, the condition can be set that the battery is not overcharged upon recuperation. In addition, the condition can be set that the accumulated tank and / or charging times are below a predefinable threshold value over the entire route. For example, the threshold value may be 60 minutes at a total distance of 1200 km. With this condition, the travel time, which can also be input directly by the driver as input, for example via the human-machine use interface, can be weighted in the cost function with respect to the non-temporal conditions. At the node, the charging power may be between 50 to 350 kilowatts, for example.350 Kilowatts corresponds to current high power charging or fast charging. At such a high charging performance, the time for charging is 5 to 15 minutes. At the node, the rate of delivery of fuel may be, for example, 40 liters per minute. Furthermore, preferences of a user of the hybrid electric vehicle can be taken into account in the minimization, for example that at the start of travel and / or at the end of travel the fuel tank and / or the battery should be charged, for example, in each case to 50%, the minimum charge should be, for example, 20% and the minimum refueling should be, for example, 80%.All of the above and below example numerical values are used as program parameters in the following case study.c) A reference trajectory along the route is obtained, the reference trajectory indicating the nodes that minimize the sum of refueling and / or charging costs corresponding to refueling and / or charging times. A so-called state of charge and / or state of fuel reference trajectory can be obtained. This step can be calculated from the vehicle prefilter by re-using the vehicle model and is also called a vehicle post filter. The vehicle model has the option of selecting one from the four previously defined modes at each calculation step.d) The step of minimizing costs is repeated periodically or at target deviations. The reference trajectories are thus updated. For example, the first software unit is called up anew and executed with a period of 5 to 15 minutes, and / or after a distance-based update rate, for example under the condition that the vehicle has been driven for more than 4 kilometers.In another aspect, mission management determines exhaust cleaning wherein the next or pending cycle phase is determined for exhaust cleaning, for example, determined vehicle mode over 15 to 45 minutes. Further, the globally optimal phases may be determined based on time or distance.The following further steps aa) to ff) of the method are carried out by means of a second software or hardware unit, wherein a vehicle model is implemented in the second software or hardware unit, which vehicle model determines a torque requirement at a wheel of the hybrid electric vehicle for a look-ahead horizon, which torque requirement results from a driving resistance. The second software or hardware unit is called predictive energy management. The vehicle model may be, for example, a plug-in hybrid electric vehicle model.aa) The respectively updated reference trajectory is read in. For example, the vehicle post filter forwards its results to the vehicle model. For example, the vehicle post filter forwards the states and control variables resulting from mission management to the predictive energy management unit.bb) Within the preview horizon along the reference trajectory, the respective torque demand is divided into a torque portion generated by an internal combustion engine of the hybrid electric vehicle and / or a torque portion generated by an electric motor of the hybrid electric vehicle based on the respective operating mode of the hybrid electric vehicle. The preview horizon is also called moving horizon. From the division of the torques, the electrical energy required for applying the respective torque components and the chemical fuel energy can be calculated, from which the fuel and charge states for the next look-ahead horizon then result. The respective torque components of the internal combustion engine and of the electric motor form control variables for predictive energy management. The conditions considered in predictive energy management include a transmission gear, an on or off state of the engine, and the state of charge. The hybrid electric vehicle comprises, for example, six transmission gears.cc) Within the preview horizon, a change of transmission gears of the hybrid electric vehicle and / or of on and off states of the internal combustion engine is determined. Upon each change of a transmission gear, the respectively resulting refueling and / or charging costs are determined via the look-ahead horizon. An on state of the internal combustion engine is identified by 1, for example, and an off state is identified by 0, for example.dd) The second software or hardware unit determines the division of the respective torque demand, the change of the transmission gears and / or the on and off states of the internal combustion engine in such a way that fuel consumption is minimized in the respective look-ahead horizon.The respective torque distribution, the changes of the transmission gears and / or the on and off states of the internal combustion engine is applied to the hybrid electric vehicle by means of a vehicle control unit. The vehicle control device is, for example, a vehicle control device specialized for hybrid control, also called hybrid power coordination or hybrid power control. For example, the vehicle control device is a hybrid control device, also called a hybrid control unit. The vehicle control device, for example the hybrid control unit, is hardware. Hybrid power coordination or hybrid power control is essentially software.The preceding steps are repeated in next preview horizons along the reference trajectory until an end of the reference trajectory. For example, a time-based update rate of the predictive energy management is between 1 and 10 seconds and a distance-based update rate is below 4 kilometers. The spatial resolution of the preview horizons is, for example, 5 meters. Thus, the monetary cost-optimal driving operation for the hybrid electric vehicle is obtained.The method provides a cost-driven reference trajectory by means of the mission management module and a corresponding consumption-optimized operation of the hybrid electric vehicle along the route taking into account environmental zones by means of the predictive energy management module, wherein mission management and predictive energy management exchange data. Mission management and predictive energy management thus represent modular optimization units and together implement a two level model predictive control algorithm which efficiently optimizes fuel consumption and power split for a hybrid electric vehicle.According to a further aspect, the first software or hardware unit solves the minimization as a problem of optimum control, comprising shortest path routing problem, traveling salesman problem, station problem, green vehicle routing problem and hybrid vehicle routing problem, wherein the fuel and / or charge states in the nodes are determined recursively by means of dynamic programming. The first software or hardware unit can execute the global optimization of the battery charging and refueling strategy for the overall route on a vehicle control device, taking into account the vehicle and route parameters and the driver preferences in the vehicle. This has the advantage that vehicle manufacturer-sensitive data remain on board. In another aspect, the global optimization of the battery charging and refueling strategy for the overall route may be performed off-board in consideration of the vehicle and route parameters as well as driver preferences.According to a further aspect, the second software or hardware unit determines the division of the respective torque demand by means of Pontryagenin Maximum Principle and determines the change of the transmission gears and / or of the on and off states of the internal combustion engine by means of dynamic programming. The Hamilton's function of the Pontryagenin Maximum principle may include the flow rate of fuel during refueling operations, a co-state, and the dynamic behavior of the charge states of the battery. The Hamiltonian function is an objective function in discrete state space, the minimum of which is calculated. Fuel consumption can thus be minimized in the respective look-ahead horizons.The Hamiltonian function is, for example: where x is a predictive energy management state, u is a predictive energy management control variable, λ is a co-state, and SoC parametries battery dynamics. f is fuel flow rate. T ICE is the torque portion provided by the engine. ω dem is the rotational speed at a transmission input of the hybrid electric vehicle. The Hamiltonian finds and minimizes the co-state in the look-ahead horizons after some forward iterative executions.The combination of Pontryagenin Maximum Principle and Dynamic Programming has the advantage that predictive energy management can calculate efficient fuel consumption and power sharing within the look-ahead horizon without relying on convex quadratic programming otherwise required in such optimization problems, highly specialized third party providers requiring software to execute. The proposed approach based on the combination of pontryagenin maximum principle and dynamic programming can solve a so-called two-point boundary value problem for an initial and a final state of charge of a high-voltage battery of the hybrid electric vehicle.In combination with the dynamic programming in the first software or hardware unit, an optimization method is thus achieved based on the sequential application of dynamic programming-Pontryagenin Maximum Principle-dynamic programming. The results of the following case study show that this approach can achieve a total monetary travel cost reduction of 15.5% and a fuel consumption reduction of 7%.According to a further aspect, the first data interface is an interface to a navigation system of the hybrid electric vehicle, to sensors of the hybrid electric vehicle and / or to a provider of a digital map. The detailed information on the route thus obtained is read in by the mission management module and processed in the optimization task.According to a further aspect, the hybrid electric vehicle and the nodes are each designed for data exchange via a vehicle-to-infrastructure communication. The price structures of the nodes are communicated to the hybrid electric vehicle via the vehicle-to-infrastructure communication taking into account static day-based and / or dynamic second-based prices. Thus, the monetary cost optimization can be improved. For charging stations, the price structure comprises charging prices per kilowatts hour, per minute and / or per charging process as a function of a rate. By means of the vehicle-to-infrastructure communication, the nodes resulting from the minimization are booked for the respective tank times and / or charging times. This achieves a smooth passage of the route with nodes. In the case study, the tank prices vary, for example, between 1.10 and 1.71 euros per liter. For the price structure price per charging operation, the price varies, for example, between 7.50 and 10 euros per charging operation. For the price structure price per time, the price for a charging process is, for example, 0.5 euros per minute. For the price structure price per energy, the price varies, for example, between 0.33 and 0.79 euros per kilowatts hour.According to a further aspect, the first software or hardware unit is cloud-based. The second software or hardware unit reads in the respectively updated reference trajectories from the cloud. Storage space, computing power and / or computer programs are provided as a service via the cloud. By offloading mission management to the cloud, predictive energy management may more efficiently perform on a vehicle controller.According to a further aspect, information on the route, comprising the nodes and price structures of the nodes, information on the reference trajectory, comprising the nodes obtained from the minimization together with their respective price structures, information on changes of the transmission gears and / or information on the on and off states of the internal combustion engine, is displayed to a user of the hybrid electric vehicle by means of a human-machine use interface. The user, for example a driver of the hybrid electric vehicle, can select nodes other than those obtained from the minimization via the human-machine interface, also called human-machine interface, and these selected nodes can be taken into account as the target deviations during updates of the reference trajectory. Thus, convenience functions and preferences of the user are taken into account. According to a further aspect, the user can input a change of operating mode via the human-machine user interface. The operating mode thus preferred by the user is then taken into account in the optimization. According to a further aspect, the user can input via the human-machine use interface that only specific operators of fuel and / or charging stations are taken into account or only those fuel and / or charging stations which allow a specific payment method.For example, the globally optimal routes A, B and C are visually displayed to the user. Alternative routes without and with driver preferences can also be displayed. In another aspect, the driver may select and confirm the routes.According to a further aspect, the driver can activate the intelligent speed adaptation function with the adaptive cruise control ACC function. According to an EU Regulation of July 6, 2022, all motor vehicles will have to be equipped with a system in the future which assists the vehicle driver in maintaining the speed appropriate for the road conditions. The driver can be alerted via the acceleration controller, for example the accelerator pedal, or via targeted, appropriate and effective feedback that the applicable speed restriction is exceeded. The ability of the driver to exceed the system demanded vehicle speed must not be compromised. This system is called intelligent speed adaptation and is intended to determine the currently applicable maximum speed with the aid of GPS data and traffic sign recognition and to warn it if it is exceeded or to brake the vehicle automatically by limiting the engine power. However, the driver may override the system by stepping on the accelerator pedal. When the intelligent speed adaptation system is activated, an energy-optimized torque distribution is determined on the basis of the globally-optimized navigation.According to a further aspect, a driving mode for a battery electric vehicle or an internal combustion engine vehicle is optimized with a high cost. For example, the mission management module may set the control variable for fuel replenishment operations to zero, such that only an electric operation of the hybrid electric vehicle is considered, i.e., a battery electric vehicle is effectively resulting. Similarly, an engine vehicle may effectively result. A battery electric vehicle uses only energy stored in rechargeable batteries and does not use any other drives such as an internal combustion engine as a drive source. Accordingly, an engine vehicle exclusively uses the engine as a drive source.According to a further aspect, the invention provides a computer program for obtaining a monetary cost-optimal driving mode for a hybrid electric vehicle. The computer program comprises instructions that cause a hardware component of a computer to execute the steps of the method disclosed above when the computer program is loaded from or executed by the hardware component. The computer program instructions may include machine instructions, source text or object code written in assembly language, an object oriented programming language, for example C++, or in a procedural programming language, for example C. According to one aspect of the invention, the computer program is a hardware-independent application program which is provided, for example, via a data carrier or a data carrier signal by means of software over the air technology.According to a further aspect, the method or the computer program is implemented in a driver assistance system. In this way, in particular a driver assistance system for a hybrid electric vehicle for electric driving in environmental zones with a monetary cost-optimal driving operation is realized by optimized resource filling. The driver assistance system determines an operating driving mode for driving through environmental zones by means of monetary-cost-optimal refilling operations taking into account energy-consumption-optimal route planning of the vehicle. The driver assistance system outputs the route planning with drive energy costs for the driver via a human-machine user interface in order to decide whether the driver "accepts" the suggested operating mode change. Each mode change takes into account the time required for the monetaryly optimized refill resources. In environmental zones, a change of operating mode of the plug-in hybrid vehicle takes place for the purpose of the monetary cost optimization of the replenishment resources. According to a further aspect, the driver assistance system is implemented in the vehicle in such a way that it takes into account an operating mode of the vehicle and / or an input of an operating mode change by the driver in the vehicle. The environmental zones can be taken into account exactly via coordinates in the navigation system of the vehicle and / or by geofencing. Further, the driver assistance system may obtain the static day-based and dynamic second-based prices of the required energy resources from the coordinate-based gas stations and charging stations via an infrastructure-to-vehicle communication network for the optimization calculations. The calculation of a monetary cost-optimal charging and refueling strategy by discrete dynamic programming via nodes, i.e., the gas stations and charging stations, and edges, i.e., the distances between gas stations and charging stations, may be performed in a hardware unit of the driver assistance system to obtain the global monetary optimality for the change of the vehicle operating state. According to a further aspect, the driver assistance system outputs the route planning calculated by the optimization with a monetary drive energy cost for the driver or for the decision of the control unit that is responsible for the mode change. According to a further aspect, the driver assistance system is implemented in such a way that it reserves the preselected stop positions at the stations for the predicted refill resources via the vehicle communication network.According to a further aspect, the invention provides a control device for regulating and / or controlling a driving mode of a hybrid electric vehicle. The control device is configured to execute the steps of the method disclosed here or the computer program described above, wherein, in an application, the control device regulates and / or controls a monetary cost-optimal driving mode. The control device is, for example, an engine control unit or a hybrid control unit.According to a further aspect, the invention provides a vehicle, for example a hybrid electric vehicle, comprising a driver assistance system as described above.In one aspect, route histories of a user are learned by machine learning systems, and routes preferred by the user are predetermined based thereon.According to a further aspect, a temperature of a high-voltage battery of the hybrid electric vehicle is a further control variable which is limited by a predefinable threshold value.The invention is illustrated in the following working examples. The following are shown: FIG. 1 shows an exemplary embodiment of a hybrid electric vehicle, FIG. 2 shows an exemplary embodiment of components of the method disclosed here, FIG. 3 shows an exemplary embodiment of a monetary, cost-optimal driving mode for the hybrid electric vehicle obtained by means of the method disclosed herein, using the example of a case study disclosed herein, FIG. 4 shows an embodiment of a fuel economy by the method disclosed herein, FIG. 5 shows an embodiment of a monetary saving by the method disclosed here, FIG. 6 shows a schematic exemplary embodiment of a method disclosed here, FIG. 7 shows an exemplary embodiment of a control unit disclosed here, and FIG. 8 shows an embodiment of a human-machine use interface.In the figures, like reference numerals designate like or functionally like reference parts. For clarity, only the relevant reference parts are highlighted in the individual figures.FIG. 1 shows the topology of a hybrid electric vehicle 10, for example a plug-in hybrid electric vehicle PHEV. The hybrid electric vehicle 10 comprises an internal combustion engine 11, for example a four-cylinder spark ignition engine. The hybrid electric vehicle 10 further comprises an electric motor 12. the electric motor 12 can be a basic current generator at 50 kW up to a 400 kW electric machine. The electric motor 12 may include 400 volt or 800 volt power electronics. Furthermore, the hybrid electric vehicle 10 comprises a transmission 13, for example an automatic transmission. In one aspect, the transmission 13 is an 8-speed plug-in hybrid transmission. The hybrid electric vehicle 10 also comprises a fuel tank 14 and a battery 15. The battery 15 may be a high-voltage battery. According to a further aspect, the hybrid electric vehicle 10 comprises a further battery, for example a low-voltage battery. The hybrid electric vehicle 10 also comprises a charging unit 16, via which the hybrid electric vehicle 10 can be charged as a plug-in hybrid electric vehicle by means of a power cable.FIG. 2 illustrates components of the method disclosed herein. The components shown are, for example, components of a control structure based on a parallel P2 PHEV reverse vehicle model.A first software or hardware unit 21 performs mission management. Mission management calculates a monetary global cost-optimal fueling of the fuel tank 14 and charging of the battery 15 over the entire route and receives a reference trajectory for the state of charge SoC with environmental zones LEZ.For this purpose, a vehicle prefilter 211 determines a total consumption of electrical energy and of fuel along the route for a plurality of operating modes, for example electric vehicle EV mode, conventional vehicle CV mode, load point shift LPS 1 mode and load point shift LPS 2 mode, of the hybrid electric vehicle 10. A component 212 determines, by means of dynamic programming DP, in a step V 4, on the set of all nodes along the route A, B. C, the minimum of the sum of the tank and / or charging costs C rf,i( u i), C rc,i( u i) based on the price structures and the minimum of tank and / or charging times Δt rf,i, Δt rc,i at the respective nodes taking into account the operating mode EV, CV, LPS 1, LPS 2 in environmental zones LEZ. In this case, starting from an initial force-off state and / or charge state SoF, SoC of the hybrid electric vehicle 10, the fuel and / or charge states SoF, SoC in the nodes are determined recursively.A vehicle post filter 213 receives the reference trajectory along the route A, B, C, wherein the reference trajectory indicates the nodes that minimize the sum of the tank and / or charging costs C rf,i( u i), C rc,i( u i) corresponding to the tank and / or charging times Δt rf,i, Δt rc,i. This corresponds to method step V 5. Thus, the vehicle post filter 213 calculates the fuel and power consumption along the globally optimized route based on the determined decision variables. Furthermore, the vehicle post filter 213 carries out a prediction of the tank fill quantity, a prediction of the battery state of charge and / or a determination of the speed profile including stop points for the overall route.The minimization is repeated periodically or in the case of target deviations and the reference trajectory is thus updated. For example, the minimization is updated on a time horizon of 5-15 minutes. Vehicle pre-filter 211, minimization by means of dynamic programming DP 212 and vehicle post-filter 213 together form the mission management module. The route information, comprising total route, number of fuel stations 18 and charging stations 17, speed specifications, is obtained via a first data interface 20.A second software or hardware unit 22 performs predictive energy management. The second software or hardware unit reads in the respectively updated reference trajectory and regulates and / or controls the hybrid electric vehicle 10 by means of a control unit HCU, for example a hybrid control unit, in such a way that the hybrid electric vehicle follows the reference trajectory calculated by the mission management.The first and / or second software or hardware unit 21, 22 are implemented in the control device HCU according to one aspect.For this purpose, a vehicle model 221 determines a torque requirement at a wheel of the hybrid electric vehicle 10 for a preview horizon, said torque requirement resulting from a driving resistance. The preview horizon is, for example, less than 4 kilometers. Within the preview horizon along the reference trajectory, the module 222 divides the respective torque demand by means of Pontryagins Maximum Principle PMP to a torque portion ICE generated by the internal combustion engine 11 and a torque portion EM generated by the electric motor 12 based on the respective operating mode EV, CV, LPS 1, LPS 2 in such a way that the fuel consumption is minimized in the respective preview horizon. This corresponds to method step V 8. Within the look-ahead horizon, a module 223 determines a change of transmission gears of the hybrid electric vehicle 10 and / or of on and off states of the internal combustion engine by means of dynamic programming DP. This corresponds to method step V 9. The torque split is repeated in computation cycles of 1-10 seconds. The modules 221, 222 and 223 form predictive energy management.The structure of the first and second software or hardware units 21, 22 represents the so-called DP-PMP-DP multi-objective optimization.Via a human-machine use interface 23, the outputs of the first and / or second software or hardware unit are visualized to a user of the hybrid electric vehicle 10 with the possibility of interaction. The human-machine use interface 23 may be a touch screen of an infotainment system of the hybrid electric vehicle 10.FIG. 3 summarizes the result of a case study. In the case study, a plug-in hybrid electric vehicle is considered. Since charging is time cost based, the potential costs and fuel consumptions are dependent on the infrastructure and the preferences of the driver. In this case study, a maximum overhead time of up to 60 minutes was allowed for the 1200 km distance.The method disclosed here was implemented on a hybrid control controller and simulated on six different round bars A, B, C between Aachen and Munich and between Aachen and Friedrichshafen:Aachen-Munich-Aachen AMA:A. A129004228,3815,35103,0123,68A. A1290106,74428,3826,83101,9126,56B. B126804324,1524,32100,3123,43B. B126890,84327,9423,32100,4124,31C. C142704320,8221,76113,7140,55C. C142780,44519,7431,12113,6143,37Aachen-Friedrichshafen-Aachen AFA:A. A120704019,23099,5108,67A. A120736,24220,2713,2198,1109,58B. B120304318,7118,5291,9105,07B. B1203494118,853,2697,7106,93C. C120503410,4235,7284,7112,30C. C120548,23410,4232,884,9112,86According to one aspect, the global optimization of route A is always calculated on board. The global optimization of the alternative routes B and C can take place in parallel on board taking into account further driver preferences and / or in parallel off board by means of V2X communication in the cloud, wherein X2V communication receives the optimization results.A refueling operation takes about 5 minutes at a fuel flow rate of 40 l / min. The charging stations supply power of 50 kW to 350 kW. Fuel and charge prices originate from 10.07.2020. All further program parameters were mentioned above in the description.For the simulation, a vehicle manufacturer can use a relevant vehicle model for estimating the fuel and power consumption between the stations along the route, taking into account the topography of the route, for example by means of the vehicle prefilter.For comparison, the results for a benchmark PHEV are as follows:Aachen-Munich-Aachen AMA:A. A1290108,11142,77B. B1268106,24141,88C. C1427119,57160,76Aachen-Friedrichshafen-Aachen AFA:A. A1207101,14127,85B. B1203100,82128,80C. C1205101,01139,32FIG. 3 shows the driving mode, which is optimized in terms of costs, in terms of the monetary terms by means of the method disclosed here, for the hybrid electric vehicle 10 on the route Aachen-Munich-Aachen. Operation EV was maintained in the existing environmental zones LEZ. In this case, a total of four tank stops and four charge stops were carried out at a total distance of 106.7 km through environmental zones LEZ.FIG. 4 shows the fuel consumption saving achieved by the method disclosed herein on the routes Aachen-Munich-Aachen AMA and Aachen-Friedrichshafen-Aachen AFA. The observed high fuel reduction to AFA C is explained by the relatively poor fuel station infrastructure on this route, which results in extensive use of EV operation, thereby reducing the overall fuel consumption of the hybrid electric vehicle 10.FIG. 5 shows the overall cost savings achieved on the routes Aachen-Munich-Aachen AMA and Aachen-Friedrichshafen-Aachen AFA achieved using the method disclosed herein.FIG. 6 shows the steps of the computer-implemented method for obtaining a monetary cost-optimal driving mode for a hybrid electric vehicle 10.In a step V 1, a route A, B, C to be driven for the hybrid electric vehicle 10 comprising speed specifications, position specifications for nodes, wherein the nodes comprise fuel and charging stations 17, 18, and information about environmental zones LEZ along the route A, B, C are obtained via a first data interface 20.In a step V2, price structures of the nodes are obtained.The following steps are carried out by means of a first software or hardware unit 21: Based on the speed specifications, a total consumption of electrical energy and / or of fuel along the route A, B, C for a plurality of operating modes EV, CV, LPS 1, LPS 2 is determined in a step V 3. In a step V4, on the basis of the set of all nodes along the route A, B, C, the sum of the tank and / or charging costs C rf,i( u i), C rc,i( u i) is minimized on the basis of the price structures and of tank and / or charging times Δt rf,i, Δt rc,i at the respective nodes taking into account the operating mode EV, CV, LPS1, LPS2in environmental zones LEZ. Starting from an initial force-off and / or charge state SoF, SoC, the fuel and / or charge states SoF, SoC in the nodes will be determined recursively. In a step V 5, a reference trajectory along the route A, B, C is obtained, wherein the reference trajectory indicates the nodes that minimize the sum of the tank and / or charging costs C rf,i( u i), C rc,i( u i) corresponding to the tank and / or charging times Δt rf,i, Δt rc,i. In a step V 6, the step V 4 of minimizing is repeated periodically or in the case of target deviations and the reference trajectory is thus updated.The following steps are carried out by means of a second software or hardware unit 22, wherein a vehicle model 221 is implemented in the second software or hardware unit 22, which model determines a torque requirement at a wheel of the hybrid electric vehicle 10 for a preview horizon, which torque requirement results from a driving resistance: in a step V 7, the respectively updated reference trajectory is read in. In a step V 8, within the preview horizon along the reference trajectory, the respective torque demand is divided into a torque portion ICE generated by an internal combustion engine 11 of the hybrid electric vehicle 10 and / or a torque portion EM generated by an electric motor 12 of the hybrid electric vehicle 10 based on the respective operating mode EV, CV, LPS 1, LPS 2. In a step V 9, a change of transmission gears and / or of on and off states of the internal combustion engine 11 is determined within the preview horizon. In a step V 10, the second software or hardware unit 22 determines the division of the respective torque demand, the change of the transmission gears and / or the on and off states of the internal combustion engine 11 in such a way that fuel consumption is minimized in the respective look-ahead horizon. In a step V 11, the respective torque distribution, the changes of the transmission gears and / or the on and off states of the internal combustion engine 11 are divided into the hybrid electric vehicle ( 10) by means of a vehicle control unit. In a step V 12, the immediately five preceding steps V 7-V 11 are repeated in next look-ahead horizons along the reference trajectory up to one end of the reference trajectory. Thus, the monetary cost-optimal driving operation for the hybrid electric vehicle 10 is obtained.FIG. 7 shows an exemplary embodiment of a control unit HCU for regulating and / or controlling a driving mode of a hybrid electric vehicle 10. The control device HCU is designed as a hybrid control unit.The first and / or second software or hardware unit 21, 22 are implemented in the control device HCU according to one aspect.FIG. 8 shows a human machine use site 23. Route A is calculated in the vehicle. The routes B and C may be calculated in parallel in a cloud. A comparison of routes A, B and C, which comparison can be determined in the cloud, reveals that route A is the global optimal route. The head-up display visually indicates to the driver that the vehicle needs to be charged at the next stop, with the next charging station being four kilometers away. Charging, charging duration and price are also indicated. Furthermore, the bonus obtained on this route is displayed, for example, stop station with a 10-kWh gain.Reference numerals denote reference numerals10 PHEV plug-in hybrid electro vehicle 11 internal combustion engine 12 electric motor 13 transmission 14 fuel tank 15 battery 16 charging unit 17 charging station 18 fuel station 20 first data interface 21 first software or hardware unit 211 vehicle pre-filter 212 dynamic programming 213 vehicle post-filter 22 second software or hardware unit 221 vehicle model 222 pontryagins maximum principle 223 dynamic programming 23 human-machine use interface A, B, c route LEZ environmental zone EV operating mode CV operating mode LPS 1 operating mode LPS 2 operating mode SoF fuel state SoC state of charge C rf,i( u i) tank costs C rc,i( u i) charging costs Δt rf,i tank time Δt cf,i charging time ICE torque component of internal combustion engine EM torque component of electric motor V 1-V 12 method steps DP dynamic programming PMP pontryagenins maximum principle HCU control deviceReferences included in the specificationThis list of documents cited by the applicant has been produced in an automated manner and is only included for the better information of the reader. The list is not part of the German patent application or utility model application. The DPMA does not take any adhesion for any faults or omissions.Patent Literature citedDE 10 2020 200 826 A1

[0004] DE 10 2019 849 A1

[0005]

Claims

Computer-implemented method for obtaining a monetary cost-optimal driving mode for a hybrid electric vehicle (10), the method comprising the steps of: • via a first data interface (20), obtaining a route (A, B, C) to be driven for the hybrid electric vehicle (10) comprising speed specifications, position information on nodes, wherein the nodes comprise fuel and charging stations (17, 18), and information on environmental zones (LEZ) along the route (A, B, C) (V1); • obtaining price structures of the nodes (V2); • Carrying out the following steps (V 3-V 6) by means of a first software or hardware unit (21): o, based on the speed specifications, determining a total consumption of electrical energy and / or of fuel along the route (A, B, C) for a plurality of operating modes (EV, CV, LPS 1, LPS 2) of the hybrid electric vehicle (10) (V 3); ◯ on the set of all nodes along the route (A, B, C) minimizing the sum of tank and / or charging costs (C rf,i( u i), C rc,i( u i)) based on the price structures and minimization of tank and / or charging times (Δt rf,i, Δt rc,i) at the respective nodes taking into account the operating mode (EV, CV, LPS1, LPS2) in environmental zones (LEZ), wherein, starting from an initial force-release and / or charge state (SoF, SoC) of the hybrid electric vehicle (10), the fuel and / or charge states (SoF, SoC) in the nodes are recursively determined (V4); obtaining a reference trajectory along the route (A, B, C), the reference trajectory indicating the nodes that minimize the sum of the tank and / or charging costs (C rf,i( u i), C rc,i( u i)) according to the tank and / or charging times (Δ trf,i, Δt rc,i) ( V5); ◯ wherein the step (V4) of minimizing is repeated periodically or in the case of target deviations and thus the reference trajectory is updated (V6); • Carrying out the following steps (V 7-V 12) by means of a second software or hardware unit (22), wherein a vehicle model (221) is implemented in the second software or hardware unit (22), said vehicle model determining a torque requirement at a wheel of the hybrid electric vehicle (10), said torque requirement resulting from a driving resistance, for a preview horizon: ◯ Reading in the respectively updated reference trajectory (V 7); ◯, within the preview horizon along the reference trajectory, dividing the respective torque demand into a torque portion (ICE) generated by an internal combustion engine (11) of the hybrid electric vehicle (10) and / or a torque portion (EM) generated by an electric motor (12) of the hybrid electric vehicle (10) based on the respective operating mode (EV, CV, LPS1, LPS2) of the hybrid electric vehicle (10) (V8); ◯, within the preview horizon, determining a change of transmission gears (13) of the hybrid electric vehicle (10) and / or of on and off states of the internal combustion engine (11) (V9); ◯ wherein the second software or hardware unit (22) determines the division of the respective torque demand, the change of the transmission gears (13) and / or the on and off states of the internal combustion engine (11) in such a way that a fuel consumption is minimized (V10) in the respective preview horizon; ◯ applying the respective torque division, the change of the transmission gears (13) and / or the on and off states of the internal combustion engine (11) to the hybrid electric vehicle (10) by means of a vehicle control unit (HCU) (V11); ◯ Repeating the immediately five preceding steps (V 7-V 11) in next forward-view horizons along the reference trajectory up to one end of the reference trajectory and thus obtaining the monetary-cost-optimal driving mode for the hybrid electric vehicle ( 10) (V 12).Method according to claim 1, wherein the first software or hardware unit (21) solves the minimization as an optimal control problem, comprising shortest path routing problem, traveling salesman problem, station problem, green vehicle routing problem and hybrid vehicle routing problem, wherein the fuel and / or charge states (SoF, SoC) in the nodes are determined recursively by means of dynamic programming (DP).Method according to one of the preceding claims, wherein the second software or hardware unit (22) determines the division of the respective torque demand by means of Pontryagenin Maximum Principle (PMP) and the change of the transmission gears (13) and / or of the on and off states of the internal combustion engine (11) by means of dynamic programming (DP).Method according to one of the preceding claims, wherein the first data interface (20) is an interface to a navigation system of the hybrid electric vehicle (10), to sensors of the hybrid electric vehicle (10) and / or to a provider of a digital map.Method according to one of the preceding claims, wherein the hybrid electric vehicle (10) and the nodes are each designed for data exchange via a vehicle-to-infrastructure communication and the price structures of the nodes are transmitted to the hybrid electric vehicle (10) via the vehicle-to-infrastructure communication taking into account static day-based and / or dynamic second-based prices, wherein for charging stations (17) the price structure comprises charging prices per kilowatts hour, per minute and / or per charging process as a function of a rate, and / or wherein by means of the vehicle-to-infrastructure communication the nodes resulting from the minimization are transmitted for the respective tank and / or charging times (Δt rf,i, Δt rc,i).Method according to one of the preceding claims, wherein the first software or hardware unit (21) is cloud-based and the second software or hardware unit (22) reads in the respectively updated reference trajectories from the cloud.Method according to one of the preceding claims, wherein information on the route (A, B, C) comprising the nodes and price structures of the nodes, information on the reference trajectory comprising the nodes obtained from the minimization together with their respective price structures, information on changes of the transmission gears (13) and / or information on the on and off states of the internal combustion engine (11) are displayed to a user of the hybrid electric vehicle (10) by means of a human-machine use interface (23), the user can select nodes other than the nodes obtained from the minimization via the human-machine use interface (23) and these selected nodes are taken into account as the target deviations during updates of the reference trajectory.Method according to one of the preceding claims, wherein the driving mode is optimized for a battery electric vehicle or an internal combustion engine vehicle at a monetary cost.A computer program for obtaining a monetary cost-optimal driving mode for a hybrid electric vehicle (10), the computer program comprising instructions that cause a hardware component of a computer to execute the steps of the method according to any one of the preceding claims when the computer program is loaded from or executed by the hardware component.Control device (HCU) for regulating and / or controlling a driving mode of a hybrid electric vehicle (10), wherein the control device (HCU) is configured to execute the steps of the method according to one of Claims 1 to 8 or the computer program according to Claim 9, wherein, in an application, the control device (HCU) regulates and / or controls a monetaryly cost-optimal driving mode.

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