DE102023212729A1 - 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 - Google Patents

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 achieving a cost-optimal driving mode for a hybrid-electric vehicle. Furthermore, the invention relates to a control unit for regulating and/or controlling the driving mode of a hybrid-electric vehicle.

The following definitions, descriptions and statements retain their respective meaning for 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 encompasses three typical vehicle types: conventional vehicles, i.e., vehicles powered purely by an internal combustion engine; electric vehicles, for example, battery-electric vehicles; and hybrid vehicles, for example, hybrid-electric vehicles. A vehicle with an internal combustion engine 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 gasoline, diesel, LPG, or hydrogen from a fuel tank. The electric motor is supplied with electrical energy from a rechargeable energy storage device. The energy storage device is, for example, a solid-state or liquid battery, an accumulator, or a capacitor bank. Excess energy from the internal combustion engine is converted into electrical energy using a generator, which can be used to charge the energy storage device of the hybrid-electric vehicle. The hybrid electric vehicle can, for example, be a plug-in hybrid electric vehicle, also known as a plug-in hybrid electric vehicle (PHEV). The energy storage system of a PHEV can also be charged via a power grid, for example, via electric vehicle charging stations.

The DE 10 2020 200 826 A1 discloses a method for operating a plug-in hybrid vehicle, wherein the plug-in hybrid vehicle has an internal combustion engine, a fuel tank for providing fuel for the internal combustion engine, an electric machine and an electrical energy storage device for providing electrical energy for the electric machine, wherein for the operation of the plug-in hybrid vehicle on a route ahead of the plug-in hybrid vehicle, 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 the route ahead of the plug-in hybrid vehicle, wherein the route is divided into purely internal combustion engine route sections, purely electric route sections and hybrid route sections such that travel costs determined depending on the costs for the fuel, depending on the costs for the electrical energy and depending on the time costs are minimal.

The DE 10 2019 109 849 A1 discloses a driver assistance system for a vehicle for cost optimization of refilling processes of resources for the operation of the vehicle, configured to perform the following steps: determining a common optimal refilling strategy for at least a first and a second resource, taking into account a planned route of the vehicle; outputting the common optimal refilling strategy for the purpose of cost optimization of the refilling processes.

The object of the invention was to find out how a route, in particular a route with environmental zones, can be covered with a hybrid electric vehicle in a financially cost-optimized manner, while also being particularly energy-efficient and resource-saving.

The subject matter of the independent claim and the subordinate claims each solves this problem. Advantageous embodiments of the invention emerge from the definitions, the subclaims, the drawings, and the description of preferred embodiments.

According to one aspect, the invention provides a computer-implemented method for achieving a monetarily cost-optimized driving mode for a hybrid-electric vehicle. During driving mode, both the electric motor and the combustion engine can act directly on the drive train. This has the advantage that when high drive power is required, for example during acceleration or when driving uphill, the electric motor and the combustion engine drive the hybrid-electric vehicle together. During long-distance operation, for example when driving on the highway, the hybrid-electric vehicle can be driven solely by the combustion engine, with the electric drive motor being mechanically decoupled from the drive train. If the drive power requirement is sufficiently low, for example at low speeds, the electric motor functions as a generator driven by the drive train, converting kinetic energy into electrical energy, which is used to charge the energy storage device. This is also known as recuperation. When power requirements are particularly low, for example, when exiting a parking space, coasting, or in stop-and-go mode, the electric motor will drive the hybrid-electric vehicle alone, with the combustion engine shut down and, for example, mechanically decoupled from the drivetrain. In environmental zones, also known as low-emission zones, the most cost-effective operation involves the hybrid-electric vehicle being powered by the electric motor.

In one step of the method, a route to be traveled by the hybrid-electric vehicle, including speed specifications, is obtained via a first data interface. Furthermore, position information on nodes, where the nodes include 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 user interface. Via the human-machine user interface, a user enters, for example, the starting and destination locations and the number of intermediate stops. Furthermore, user preferences can be specified, such as rest periods or driving times, journey breaks, preferred operators and tariffs for charging points and filling stations, preferred payment methods, final SoC value, and final tank content.

For example, data from a navigation system can be received via the first data interface. The navigation system can exchange data with the aforementioned human-machine user interface, meaning that the navigation system, for example, constructs the route based on user input. The navigation system or the first data interface can exchange data with a traffic infrastructure. The route to be traveled is provided as an overall route via the first data interface, for example via the navigation system. For example, the route to be traveled can include six different real round trips between Aachen and Munich and between Aachen and Friedrichshafen. Case studies on the method disclosed here were conducted on these routes. The results of the case studies are disclosed below. The speed specifications can include speed limits and an intelligent speed adjustment 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 for the nodes, position information for route sections lying between the nodes is also received in the form of edges. This allows the steps of the procedure, particularly those related to minimization, to be solved using graph theory. Decision nodes are created for all stations along the route and linked to the individual nodes using station data such as position, type, and brand. The route can include sections in the form of environmental zones. These environmental zones are stored on a map, for example, and/or can be detected using geofencing. The procedure can determine purely electric operation of the hybrid-electric vehicle in environmental zones, thus contributing to a reduction in pollutant emissions.

In a further step of the process, the nodes' price structures are obtained. These price structures can include the price per liter, the price per kilowatt-hour, the price per minute, or the price per charging session. This price structure includes the prices for roaming (third-party services). The price structures can be obtained at the start of the trip for the entire route and updated throughout the trip, for example, from node to node.

1. a) Basierend auf den Geschwindigkeitsvorgaben wird ein Gesamtverbrauch an elektrischer Energie und/oder an Kraftstoff entlang der Strecke für mehrere Betriebsmodi des Hybrid-Elektro-Fahrzeugs bestimmt. Die erste Software- oder Hardwareeinheit liest also die Gesamtstrecke mit den Geschwindigkeitsvorgaben ein und bestimmt den Gesamtverbrauch in Abhängigkeit der Betriebsmodi. Dieser Schritt wird Vehicle Pre-Filter genannt. Beispielsweise umfasst das Hybrid-Elektro-Fahrzeug vier Betriebsmodi, nämlich einen konventionellen Fahrbetrieb, bei dem der Verbrennungsmotor alleine das erforderliche Antriebsmoment liefert, einen rein elektrischen Fahrbetrieb, bei dem der Elektromotor alleine das erforderliche Antriebsmoment liefert, einen Load Point Shift 1 Fahrbetrieb, in dem der Verbrennungsmotor zum Laden einer Batterie, beispielsweise einer Hochvolt-Batterie, für den Elektromotor betrieben wird bei normalen Fahren oder bei Stillstand, und einen Load Point Shift 2 Fahrbetrieb, bei dem der Verbrennungsmotor und der Elektromotor zusammen das erforderliche, aber auch das jeweils effizienteste Antriebsmoment liefern. Der Verbrennungsmotor kann beispielsweise eine Leistung von 110 Kilowatt haben. Der Elektromotor kann beispielsweise eine Leistung von 75 Kilowatt haben. Das Hybrid-Elektro-Fahrzeug kann beispielsweise eine Masse von 1796 kg haben.
2. b) Auf der Menge D aller Knoten i entlang der Strecke wird die Summe aus Tank- und/oder Aufladekosten basierend auf den Preisstrukturen minimiert. Ferner werden Tank- und/oder Aufladezeiten an den jeweiligen Knoten minimiert. Die Minimierungen erfolgen unter Berücksichtigung des Betriebsmodus in Umweltzonen. Ausgehend von einem initialen Kraftsoff- und/oder Ladezustand des Hybrid-Elektro-Fahrzeugs werden dabei die Kraftstoffzustände x_SoF und/oder Ladezustände x_SoC in den Knoten rekursiv bestimmt. Bei der Minimierung werden beispielsweise eine Menge von Zuständen, Kontrollvariablen und eine Anzeigefunktion betrachtet. Die Zustände umfassen Kraftstoffzustände, Ladezustände und Tank- und/oder Aufladezeiten, wobei alle Knoten entlang der Strecke betrachtet werden. Die Kontrollvariablen u_q geben die Entscheidungen für Auftank- und/oder Auflademengen an, wobei alle Knoten entlang der Strecke betrachtet werden. Die Kontrollvariablen u_m,i ∈ {1, 2, 3, 4} geben ferner den Betriebsmodus in dem jeweiligen Knoten i an. Die Menge der Kontrollvariablen ist U. Die Anzeigefunktion B_B;Drf;Drc gibt an, ob der jeweilige Knoten eine Kraftstoffstation oder eine Ladestation ist oder ob sich der jeweilige Knoten oder der Streckenabschnitt, beispielsweise die Kante zwischen zwei Knoten, innerhalb einer Umweltzone, auch low emission zone genannt, abgekürzt LEZ, befindet. Bei den Tankzeiten Δt_rf,i und/oder Aufladezeiten Δt_rc,i wird nach einem Aspekt die Zeit Δt_oh,i berücksichtigt, die für Brems- und Beschleunigungsvorgänge beim Tanken und/oder Laden benötigt wird. Ferner wird nach einem weiteren Aspekt bei den Tank- und/oder Aufladezeiten die Zeit Δt_dv,i berücksichtigt, die das Hybrid-Elektro-Fahrzeug benötigt, um von der Strecke den jeweiligen Knoten zu erreichen und von dem Knoten wieder zurück auf die Strecke zu kommen. Die entsprechenden Umfahrungsstrecken sind dabei in den Kosten enthalten. Wenn bei der Rekursion ein Knoten i eine Kraftstoffstation ist, wird der Kraftstoffzustand im Knoten (i+1) aus dem Kraftstoffzustand im Knoten i erhalten, dem eine Auftankmenge u_q,i an Kraftstoff, das heißt chemischer Energie, addiert und der Kraftstoffverbrauch Δq_fu,i(u_m,i) zwischen Knoten i und (i+1) subtrahiert wird. Wenn bei der Rekursion ein Knoten i eine Ladestation ist, wird der Ladezustand im Knoten (i+1) aus dem Ladezustand im Knoten i erhalten, dem eine Auflademenge u_q,i an elektrischer Energie addiert und der Verbrauch an elektrischer Energie Δq_el,i(u_m,i) zwischen Knoten i und (i+1) subtrahiert wird. ξ_tank ist die Tankkapazität des Hybrid-Elektro-Fahrzeugs. ξ_batt ist die Batteriekapazität des Hybrid-Elektro-Fahrzeugs. Entsprechend wird die Zeit im Knoten (i+1) aus der Zeit im Knoten i erhalten, der eine entsprechende Tank- und/oder Aufladezeit addiert wird.

The following further steps a) to d) of the method are carried out using a first software or hardware unit. The first software or hardware unit is called mission management. "Unit" means that the first software or hardware unit is an independent, self-contained part of an overall software or hardware unit, each of which may comprise multiple units, with the individual units exchanging data via software or hardware interfaces.

1. a) Based on the speed specifications, a total consumption of electrical energy and/or fuel along the route is determined for several operating modes of the hybrid-electric vehicle. The first software or hardware unit reads the total distance with the speed specifications and determines the total consumption depending on the operating modes. This step is called Vehicle Pre-Filter. For example, the hybrid-electric vehicle comprises four operating modes: conventional driving mode, in which the combustion engine alone provides the required drive torque, purely electric driving mode, in which the electric motor alone provides the required drive torque, and Load Point Shift 1 driving mode, in which the combustion engine for charging a battery, such as a high-voltage battery, for which the electric motor is operated during normal driving or when stationary, and a Load Point Shift 2 driving mode, in which the combustion engine and the electric motor together deliver the required, but also the most efficient, drive torque. The combustion engine, for example, can have an output of 110 kilowatts. The electric motor, for example, can have an output of 75 kilowatts. The hybrid-electric vehicle, for example, can have a mass of 1796 kg.
2. b) On the set D of all nodes i along the route, the sum of refueling and/or charging costs is minimized based on the price structures. Furthermore, refueling and/or charging times at the respective nodes are minimized. The minimizations are carried out taking into account the operating mode in environmental zones. Starting from an initial fuel 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. During the minimization, for example, a set of states, control variables, and an indicator function are considered. The states include fuel states, charge states, and refueling and/or charging times, with all nodes along the route being considered. The control variables u _q specify the decisions for refueling and/or charging quantities, with all nodes along the route being considered. The control variables u _m,i ∈ {1, 2, 3, 4} also specify the operating mode at the respective node i. The set of control variables is U. The indicator function B _{B;D rf ;Drc} indicates whether the respective node is a fuel station or a charging station or whether the respective node or route section, for example the edge between two nodes, is located within an environmental zone, also known as a low emission zone (LEZ). One aspect of the refueling times Δt _rf,i and/or charging times Δt _rc,i takes into account the time Δt _oh,i required for braking and acceleration during refueling and/or charging. Another aspect of the refueling and/or charging times also takes into account the time Δt _dv,i required by the hybrid electric vehicle to reach the respective node from the route and to return from the node back to the route. The corresponding detours are included in the costs. In the recursion, if a node i is a fuel station, the fuel state at node (i+1) is obtained from the fuel state at 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, if a node i is a charging station, the charge state at node (i+1) is obtained from the charge state at node i, to which a refueling amount u _q,i of electrical energy is added, and the electrical energy consumption Δq _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. Similarly, the time at node (i+1) is obtained from the time at node i, to which a corresponding refueling and/or charging time is added.

mit $$\mathrm{\Delta }{t}_i\left(u_i\right)=\mathrm{\Delta }{t}_{rf,i}\cdot B_{D_{rf}}+\mathrm{\Delta }{t}_{rc,i}\cdot \mathrm{\Delta }{t}_{oh,i}+\mathrm{\Delta }{t}_{dv,i}.$$

For example, the recursion rule is: $${\left[\begin{array}{c}{x}_{SoF}\\ x_{SoC}\\ x_t\end{array}\right]}_{i+1}={\left[\begin{array}{c}{x}_{SoF}\\ x_{SoC}\\ x_t\end{array}\right]}_i+\left[\begin{array}{c}{u}_q\cdot B_{D_{rf}}-\frac{\mathrm{\Delta }{q}_{fu}\left(u_m\right)}{{\xi }_{tank}}\\ u_q\cdot B_{D_{rc}}-\frac{\mathrm{\Delta }{q}_{el}\left(u_m\right)}{{\xi }_{batt}}\\ \mathrm{\Delta }t\left(u\right)\end{array}\right]$$

with $$\mathrm{\Delta }{t}_i\left(u_i\right)=\mathrm{\Delta }{t}_{rf,i}\cdot B_{D_{rf}}+\mathrm{\Delta }{t}_{rc,i}\cdot \mathrm{\Delta }{t}_{oh,i}+\mathrm{\Delta }{t}_{dv,i}.$$

mit Auftankkosten C_rf,i(u_i) = u_q,i · ξ_tank · p_fu,i, wobei p_fu,i Kraftstoffpreis pro Liter ist, Aufladekosten C_rc,i(u_i) und Kostenfaktor C_LEZ(u_i) · B_B für Umweltzonen.The minimization rule is, for example: $$\underset{u\in U}{\text{min}}{\displaystyle \sum _{i\in D}{C}_{rf,i}\left(u_i\right)}\cdot B_{D_{rf}}\left(i\right)+C_{rc,i}\left(u_i\right)\cdot B_{D_{rc}}\left(i\right)+C_{LEZ}\left(u_i\right)\cdot B_B\left(i\right)$$

with refueling costs C _rf,i (u _i ) = u _q,i · ξ _tank · p _fu,i , where p _fu,i is fuel price per liter, charging costs C _rc,i (u _i ) and cost factor C _LEZ (u _i ) · B _B for environmental zones.

In another aspect, constraints are imposed on the control variables to take into account 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 kilowatt hours. Furthermore, the condition can be set that the hybrid-electric vehicle always has sufficient fuel and/or electrical charge to reach the next node. This condition can also be specified directly by the driver as an input, for example, via the human-machine interface. Furthermore, the condition can be set that the battery is not overcharged during recuperation. Furthermore, the condition can be set that the accumulated refueling and/or charging times over the entire route are below a predefined threshold. For example, the threshold can be 60 minutes for a total distance of 1200 km. With this condition, the travel time, which can also be entered directly by the driver as an input, for example, via the human-machine interface, can be weighted in the cost function against the non-temporal conditions. At the nodes, the charging power can, for example, be between 50 and 350 kilowatts.

350 kilowatts corresponds to current high-power or fast charging. With such high charging power, charging takes 5 to 15 minutes. At the nodes, the fuel delivery rate can be, for example, 40 liters per minute. Furthermore, the preferences of a hybrid-electric vehicle user can be taken into account when minimizing the charging, for example, that the fuel tank and/or battery should each be charged to 50% at the start and/or end of the journey, the minimum charge should be 20%, and the minimum refueling should be 80%.

• c) Eine Referenztrajektorie entlang der Strecke wird erhalten, wobei die Referenztrajektorie die Knoten angibt, die die Summe aus Tank- und/oder Aufladekosten minimiert entsprechend den Tank- und/oder Aufladezeiten. Es kann eine sogenannte State of Charge und/oder State of Fuel Referenztrajektorie erhalten werden. Dieser Schritt kann durch die Wiederverwendung des Fahrzeugmodells aus dem Vehicle pre-filter berechnet werden und wird auch Vehicle Post-Filter genannt. Das Fahrzeugmodell hat die Auswahlmöglichkeit, bei jedem Berechnungsschritt aus den vier zuvor definierten Modi eine zu wählen.
• d) Der Schritt der Kostenminimierung wird periodisch oder bei Zielabweichungen wiederholt. Damit werden die Referenztrajektorien aktualisiert. Beispielsweise wird die erste Softwareeinheit mit einer Periode von 5 bis 15 Minuten neu aufgerufen und durchgeführt, und/oder nach einer Entfernungs-basierten Aktualisierungsrate, beispielsweise unter der Bedingung, dass das Fahrzeug mehr als 4 Kilometer gefahren worden ist.

All exemplary numerical values mentioned above and below are used as program parameters in the following case study.

• c) A reference trajectory along the route is obtained, where the reference trajectory specifies the nodes that minimize the sum of refueling and/or recharging costs corresponding to the refueling and/or recharging times. A so-called state of charge and/or state of fuel reference trajectory can be obtained. This step can be calculated by reusing the vehicle model from the vehicle pre-filter and is also called the vehicle post-filter. The vehicle model has the option of choosing one of the four previously defined modes at each calculation step.
• d) The cost minimization step is repeated periodically or when target deviations occur. This updates the reference trajectories. For example, the first software unit is re-invoked and executed every 5 to 15 minutes, and/or at a distance-based update rate, for example, if the vehicle has traveled more than 4 kilometers.

In another aspect, mission management determines an emission control process, determining the next or remaining cycle phases for emission control, for example, a specific vehicle mode over 15 to 45 minutes. Furthermore, the globally optimal phases can be determined based on time or distance.

The following further steps aa) to ff) of the method are carried out using a second software or hardware unit, wherein a vehicle model is implemented in the second software or hardware unit that determines a torque requirement at a wheel of the hybrid electric vehicle resulting from a driving resistance for a forecast horizon. The second software or hardware unit is called predictive energy management. The vehicle model can be, for example, a plug-in hybrid electric vehicle model.

• bb) Innerhalb des Vorausschauhorizonts entlang der Referenztrajektorie wird der jeweilige Drehmomentbedarf auf einen von einem Verbrennungsmotor des Hybrid-Elektro-Fahrzeugs erzeugten Drehmomentanteil und/oder einen von einem Elektromotor des Hybrid-Elektro-Fahrzeugs erzeugten Drehmomentanteil aufgeteilt basierend auf dem jeweiligen Betriebsmodus des Hybrid-Elektro-Fahrzeugs. Der Vorausschauhorizont wird auch Moving Horizon genannt. Aus der Aufteilung der Drehmomente kann die für das Aufbringen der jeweiligen Drehmomentanteile jeweils erforderliche elektrische Energie und die chemische Kraftstoffenergie berechnet werden, aus denen sich dann jeweils die Kraftstoff- und Ladezustände für den nächsten Vorausschauhorizont ergeben. Die jeweiligen Drehmomentanteile des Verbrennungsmotors und des Elektromotors bilden Kontrollvariablen für das Predictive Energy Management. Die Zustände, die im Predictive Energy Management betrachtet werden, umfassen einen Getriebegang, einen An- oder Aus-Zustand des Verbrennungsmotors und den Ladungszustand. Das Hybrid-Elektro-Fahrzeug umfasst beispielsweise sechs Getriebegänge.
• cc) Innerhalb des Vorausschauhorizonts wird ein Wechsel von Getriebegängen des Hybrid-Elektro-Fahrzeugs und/oder von An- und Aus-Zuständen des Verbrennungsmotors bestimmt. Bei jedem Wechsel eines Getriebeganges werden die jeweils resultierenden Tank- und/oder Aufladekosten über den Vorausschauhorizont bestimmt. Ein An-Zustand des Verbrennungsmotors wird beispielsweise mit 1, ein Aus-Zustand beispielsweise mit 0 gekennzeichnet.
• dd) Die zweite Software- oder Hardwareeinheit bestimmt die Aufteilung des jeweiligen Drehmomentbedarfs, den Wechsel der Getriebegänge und/oder der An- und Aus-Zustände des Verbrennungsmotors derart, dass in dem jeweiligen Vorausschauhorizont ein Kraftstoffverbrauch minimiert ist.
• ee) Die jeweilige Drehmomentaufteilung, der Wechsel der Getriebegänge und/oder der An- und Aus-Zustände des Verbrennungsmotors wird mittels eines Fahrzeug-Steuergeräts auf das Hybrid-Elektro-Fahrzeug angewendet. Das Fahrzeug-Steuergerät ist beispielsweise ein für eine Hybridregelung, auch Hybrid Power Coordination oder Hybrid Power Control genannt, spezialisiertes Fahrzeug-Steuergerät. Beispielsweise ist das Fahrzeug-Steuergerät ein Hybrid-Kontroll-Steuergerät, auch Hybrid Control Unit genannt. Dabei ist das Fahrzeug-Steuergerät, beispielsweise die Hybrid Control Unit, Hardware. Hybrid Power Coordination oder Hybrid Power Control ist im Wesentlichen Software.
• ff) Die vorangehenden Schritte werden in nächsten Vorausschauhorizonten entlang der Referenztrajektorie wiederholt bis zu einem Ende der Referenztrajektorie. Beispielsweise liegt eine Zeit basierte Aktualisierungsrate des Predictive Energy Mangements zwischen 1 und 10 Sekunden und eine Entfernungs-basierte Aktualisierungsrate unter 4 Kilometer. Die räumliche Auflösung der Vorausschauhorizonte beträgt beispielsweise 5 Meter. Damit wird der monetär kostenoptimale Fahrbetrieb für das Hybrid-Elektro-Fahrzeug erhalten.

aa) The 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 look-ahead horizon along the reference trajectory, the respective torque requirement is divided into a torque component generated by a combustion engine of the hybrid-electric vehicle and/or a torque component generated by an electric motor of the hybrid-electric vehicle based on the respective operating mode of the hybrid-electric vehicle. The look-ahead horizon is also called the moving horizon. From the distribution of the torques, the electrical energy and the chemical fuel energy required to apply the respective torque components can be calculated, from which the fuel and charge states for the next look-ahead horizon are then derived. The respective torque components of the The combustion engine and the electric motor constitute control variables for predictive energy management. The states considered in predictive energy management include a transmission gear, the on or off state of the combustion engine, and the charge state. For example, a hybrid-electric vehicle has six transmission gears.
• cc) Within the look-ahead horizon, a change in transmission gears of the hybrid electric vehicle and/or in the on and off states of the combustion engine is determined. For each gear change, the resulting refueling and/or charging costs are determined over the look-ahead horizon. For example, an on state of the combustion engine is designated with 1, and an off state with 0.
• dd) The second software or hardware unit determines the distribution of the respective torque requirement, the change of transmission gears and/or the on and off states of the combustion engine in such a way that fuel consumption is minimized in the respective forecast horizon.
• ee) The respective torque distribution, the change of transmission gears, and/or the on/off states of the combustion engine are applied to the hybrid-electric vehicle by means of a vehicle control unit. The vehicle control unit is, for example, a vehicle control unit specialized for hybrid control, also known as hybrid power coordination or hybrid power control. For example, the vehicle control unit is a hybrid control unit, also known as a hybrid control unit. The vehicle control unit, for example, the hybrid control unit, is hardware. Hybrid power coordination or hybrid power control is essentially software.
• ff) The preceding steps are repeated in subsequent look-ahead horizons along the reference trajectory until one end of the reference trajectory is reached. 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 less than 4 kilometers. The spatial resolution of the look-ahead horizons is, for example, 5 meters. This ensures the most cost-effective driving mode for the hybrid-electric vehicle.

The method provides a cost-driven reference trajectory using the Mission Management module and corresponding consumption-optimized operation of the hybrid-electric vehicle along the route, taking environmental zones into account, using the Predictive Energy Management module. 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 that efficiently optimizes fuel consumption and power distribution for a hybrid-electric vehicle.

According to a further aspect, the first software or hardware unit solves the minimization as an optimal control problem, comprising the shortest path routing problem, the traveling salesman problem, the station problem, the green vehicle routing problem, and the hybrid vehicle routing problem, wherein the fuel and/or charge states in the nodes are determined recursively using dynamic programming. The first software or hardware unit can execute the global optimization of the battery charging and refueling strategy for the entire route, taking into account the vehicle and route parameters as well as the driver preferences, in the vehicle on a vehicle control unit. This has the advantage that vehicle manufacturer-sensitive data remains on board. According to a further aspect, the global optimization of the battery charging and refueling strategy for the entire route, taking into account the vehicle and route parameters as well as the driver preferences, can be executed off board.

According to a further aspect, the second software or hardware unit determines the distribution of the respective torque requirements using Pontryagin's Maximum Principle and the change of transmission gears and/or the on/off states of the combustion engine using dynamic programming. The Hamilton function of the Pontryagin's Maximum Principle can include the fuel flow rate during refueling, a co-state, and the dynamic behavior of the battery charge states. The Hamilton function is an objective function in the discrete state space, the minimum of which is calculated. This allows fuel consumption to be minimized in the respective forecast horizons.

wobei x ein Zustand des Predictive Energy Managements, u eine Kontrollvariable des Predictive Energy Managements, λ ein Co-Zustand und SoC die Batteriedynamik parametrisiert. ṁ_f ist die Kraftstoffflussgeschwindigkeit. T_ICE ist der von dem Verbrennungsmotor bereitgestellte Drehmomentanteil. ω_dem ist die Rotationsgeschwindigkeit an einem Getriebeeingang des Hybrid-Elektro-Fahrzeugs. Die Hamilton-Funktion findet und minimiert den Ko-Zustand in den Vorausschauhorizonten nach einigen vorwärts iterativen Ausführungen.For example, the Hamilton function is: $$H\left(x,u,\lambda \right)={\dot{m}}_{f\left(T_{ICE},{\omega }_{dem}\right)}+\lambda \cdot SoC,$$

where x is a state of the predictive energy management system, u is a control variable of the predictive energy management system, λ is a co-state, and SoC parameterizes the battery dynamics. ṁ _f is the fuel flow rate. T _ICE is the torque fraction provided by the internal combustion 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 several forward iterative executions.

The combination of Pontryagin's Maximum Principle and dynamic programming has the advantage that predictive energy management can calculate efficient fuel consumption and power distribution within the look-ahead horizon without relying on convex quadratic programming, which is otherwise required for such optimization problems and requires highly specialized third-party software. The proposed approach, based on the combination of Pontryagin's 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 a hybrid electric vehicle.

In combination with dynamic programming in the first software or hardware unit, this results in an optimization procedure based on the sequential application of dynamic programming—Pontryagin's Maximum Principle. The results of the following case study show that this approach can achieve a 15.5% reduction in total monetary travel costs and a 7% reduction in fuel consumption.

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 digital map provider. The detailed route information 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 to exchange data via vehicle-to-infrastructure communication. The price structures of the nodes are transmitted to the hybrid-electric vehicle via vehicle-to-infrastructure communication, taking into account static day-based and/or dynamic second-based prices. This can improve monetary cost optimization. For charging stations, the price structure includes charging prices per kilowatt-hour, per minute, and/or per charging process, depending on a tariff. Using vehicle-to-infrastructure communication, the nodes resulting from the minimization are booked for the respective refueling and/or charging times. This ensures smooth travel along the route with nodes. In the case study, for example, the refueling prices fluctuate between €1.10 and €1.71 per liter. For the price per charging process pricing structure, the price fluctuates between €7.50 and €10 per charging process. For the price per time pricing structure, the price for a charging process is, for example, €0.50 per minute. For the price structure price per energy, for example, the price fluctuates between 0.33 and 0.79 euros per kilowatt hour.

According to another aspect, the first software or hardware unit is cloud-based. The second software or hardware unit reads the updated reference trajectories from the cloud. Storage space, computing power, and/or computer programs are provided as a service via the cloud. By outsourcing mission management to the cloud, predictive energy management on a vehicle control unit can perform more efficiently.

According to a further aspect, a user of the hybrid-electric vehicle is shown information about the route, including the nodes and price structures of the nodes, information about the reference trajectory, including the nodes obtained from the minimization along with their respective price structures, information about gear changes, and/or information about the on and off states of the combustion engine, via a human-machine user 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 user interface, and these selected nodes can be considered as the target deviations when updating the reference trajectory. This takes comfort functions and user preferences into account. According to a further aspect, the user can enter a change of operating mode via the human-machine user interface. The operating mode preferred by the user is then taken into account during optimization. According to a further aspect, the user can specify via the human-machine user interface that only certain operators of fuel and/or charging stations be taken into account or only those fuel and/or charging stations that allow a certain payment method.

For example, the globally optimal routes A, B, and C are visually presented to the user. Alternative routes with and without driver preferences can also be displayed. After a further aspect, the driver can select and confirm the routes.

Another aspect allows the driver to activate the Intelligent Speed Adaption function with the Adaptive Cruise Control (ACC) function. According to an EU regulation dated July 6, 2022, all motor vehicles must be equipped with a system that supports the driver in maintaining a speed appropriate for the road conditions. The driver can be alerted that the applicable speed limit is being exceeded via the acceleration control, for example, the accelerator pedal, or via targeted, appropriate, and effective feedback. The driver's ability to exceed the vehicle speed requested by the system must not be compromised. This system, called Intelligent Speed Adaption, uses GPS data and traffic sign recognition to determine the current speed limit and, if exceeded, warn the driver or automatically brake the vehicle by limiting engine power. However, the driver can override the system by pressing the accelerator pedal. When the Intelligent Speed Adaption system is activated, an energy-optimized torque distribution is determined based on globally optimal navigation.

According to a further aspect, driving operation for a battery-electric vehicle or an internal combustion engine vehicle is optimized in monetary terms. For example, the mission management module can set the control variable for fuel refill processes to zero, so that only electric operation of the hybrid-electric vehicle is considered, effectively resulting in a battery-electric vehicle. Analogously, an internal combustion engine vehicle can effectively result. A battery-electric vehicle uses only energy stored in rechargeable batteries and no other drive systems such as an internal combustion engine as its power source. Accordingly, an internal combustion engine vehicle uses only the internal combustion engine as its power source.

According to a further aspect, the invention provides a computer program for obtaining a monetarily 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 by or executed by the hardware component. The instructions of the computer program can comprise 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 that is provided, for example, via a data carrier or a data carrier signal using software over the air technology.

According to a further aspect, the method or the computer program is implemented in a driver assistance system. This, in particular, realizes a driver assistance system for a hybrid-electric vehicle for electric driving in environmental zones with monetarily cost-optimized driving operation through optimized resource replenishment. The driver assistance system determines an operating mode for driving through environmental zones through monetarily cost-optimized refilling processes, taking into account energy-optimized route planning of the vehicle. The driver assistance system outputs the route planning with drive energy costs to the driver via a human-machine user interface in order to decide whether the driver will "accept" the proposed operating mode change. With each operating mode change, the time required for the monetarily optimized refilling resources is taken into account. In environmental zones, an operating mode change of the plug-in hybrid vehicle takes place for the purpose of monetary cost optimization of the refilling 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 precisely taken into account via coordinates in the vehicle's navigation system and/or through geofencing. Furthermore, the driver assistance system can receive the static day-based and the dynamic second-based prices of the required energy resources from the coordinate-based filling stations and charging stations via an infrastructure-to-vehicle vehicle communication network for the optimization calculations. The calculation of a monetary cost-optimal charging and refueling strategy through discrete dynamic programming via nodes, i.e. the filling stations and charging stations, and edges, i.e., the routes between refueling stations and charging stations, can be performed in a hardware unit of the driver assistance system to obtain the global monetary optimality for changing the vehicle's operating state. According to another aspect, the driver assistance system outputs the route planning calculated by the optimization with monetary drive energy costs for the driver or for the decision of the control unit responsible for the operating mode change. According to another aspect, the driver assistance system is implemented in such a way that it reserves the preselected stopping positions at the stations for the predicted refueling resources via the vehicle communication network.

According to a further aspect, the invention provides a control unit for regulating and/or controlling the driving operation of a hybrid-electric vehicle. The control unit is configured to execute the steps of the method disclosed here or the computer program described above, wherein, in one application, the control unit regulates and/or controls a driving operation that is financially cost-optimal. The control unit 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.

According to one aspect, machine learning systems are used to learn a user's route history and, based on this, to predict the user's preferred routes.

According to a further aspect, a temperature of a high-voltage battery of the hybrid electric vehicle is another control variable limited by a predeterminable threshold value.

• 1 ein Ausführungsbeispiel eines Hybrid-Elektro-Fahrzeugs,
• 2 ein Ausführungsbeispiel von Komponenten des hier offenbarten Verfahrens,
• 3 ein Ausführungsbeispiel eines mittels des hier offenbarten Verfahrens erhaltenen monetär kostenoptimalen Fahrbetriebs für das Hybrid-Elektro-Fahrzeug am Beispiel einer hier offenbarten Fallstudie,
• 4 ein Ausführungsbeispiel einer Kraftstoff Ersparnis durch das hier offenbarte Verfahren,
• 5 ein Ausführungsbeispiel einer monetären Ersparnis durch das hier offenbarte Verfahren,
• 6 ein schematisches Ausführungsbeispiel eines hier offenbarten Verfahrens,
• 7 ein Ausführungsbeispiel eines hier offenbarten Steuergeräts und
• 8 ein Ausführungsbeispiel einer Mensch-Maschinen-Benutzungsschnittstelle.

The invention is illustrated in the following embodiments. They show:

• 1 an embodiment of a hybrid electric vehicle,
• 2 an embodiment of components of the method disclosed here,
• 3 an embodiment of a monetarily cost-optimal driving operation for the hybrid electric vehicle obtained by means of the method disclosed here, using the example of a case study disclosed here,
• 4 an embodiment of a fuel saving by the method disclosed here,
• 5 an embodiment of monetary savings through the method disclosed here,
• 6 a schematic embodiment of a method disclosed here,
• 7 an embodiment of a control device disclosed here and
• 8 an embodiment of a human-machine user interface.

In the figures, identical reference symbols denote identical or functionally similar reference parts. For clarity, only the relevant reference parts are highlighted in the individual figures.

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 gasoline engine. The hybrid electric vehicle 10 further comprises an electric motor 12. The electric motor 12 can be a basic electric motor with an output of 50 kW up to a 400 kW electric motor. The electric motor 12 can comprise 400 volt or 800 volt power electronics. Furthermore, the hybrid electric vehicle 10 comprises a transmission 13, for example an automatic transmission. According to one aspect, the transmission 13 is an 8-speed plug-in hybrid transmission. In addition, the hybrid electric vehicle 10 comprises a fuel tank 14 and a battery 15. For example, the fuel tank 14 and battery 15 are arranged in the region of a rear axle of the hybrid electric vehicle 10. The battery 15 can be a high-voltage battery. According to a further aspect, the hybrid electric vehicle 10 includes an additional battery, for example, a low-voltage battery. The hybrid electric vehicle 10 also includes a charging unit 16, via which the hybrid electric vehicle 10 can be charged as a plug-in hybrid electric vehicle using a power cable.

2 shows components of the method disclosed here. 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 refueling of the fuel tank 14 and a charging of the battery 15 over the entire route and obtains a reference trajectory for the state of charge (SoC) with environmental zones (LEZ).

For this purpose, a vehicle pre-filter 211 determines a total consumption of electrical energy and fuel along the route for several operating modes, for example Electric Vehicle EV mode, Conventional Vehicle CV mode, Load Point Shift LPS1 mode and Load Point Shift LPS 2 mode, of the hybrid electric vehicle 10. This corresponds to method step V3. A component 212 determines by means of dynamic programming DP in a step V4 on the set of all nodes along the route A, B, C the minimum of the sum of the refueling and/or charging costs C _rf,i (u _i ), C _rc,i (u _i ) based on the price structures and the minimum of refueling 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. Starting from an initial fuel 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 route A, B, C, wherein the reference trajectory specifies the nodes that minimize the sum of refueling and/or charging costs C _rf,i (u _i ), C _rc,i (u _i ) according to the refueling and/or charging times Δt _rf,i , Δt _rc,i . This corresponds to method step V5. The vehicle post-filter 213 thus calculates the fuel and electricity consumption along the globally optimized route based on the determined decision variables. Furthermore, the vehicle post-filter 213 predicts the tank fill level, predicts the battery charge level, and/or determines the speed profile, including stopping points, for the entire route.

The minimization is repeated periodically or in the event of target deviations, thus updating the reference trajectory. For example, the minimization is updated over a time horizon of 5–15 minutes. Vehicle pre-filter 211, dynamic programming minimization DP 212, and vehicle post-filter 213 together form the mission management module. The route information, including the total distance, the number of fuel stations 18 and charging stations 17, and the speed specifications, is received via a first data interface 20.

A second software or hardware unit 22 performs predictive energy management. The second software or hardware unit reads the respectively updated reference trajectory and, in doing so, regulates and/or controls the hybrid-electric vehicle 10 using a control unit HCU, for example, a hybrid control unit, such that the hybrid-electric vehicle follows the reference trajectory calculated by the mission management.

According to one aspect, the first and/or second software or hardware unit 21, 22 are implemented in the control unit HCU.

For this purpose, a vehicle model 221 determines a torque requirement at a wheel of the hybrid-electric vehicle 10 resulting from driving resistance for a look-ahead horizon. The look-ahead horizon is, for example, less than 4 kilometers. Within the look-ahead horizon along the reference trajectory, the module 222 uses Pontryagin's Maximum Principle PMP to divide the respective torque requirement between a torque component ICE generated by the combustion engine 11 and a torque component EM generated by the electric motor 12 based on the respective operating mode EV, CV, LPS1, LPS2 in such a way that fuel consumption is minimized within the respective look-ahead horizon. This corresponds to method step V8. Within the look-ahead horizon, a module 223 uses dynamic programming DP to determine a change of transmission gears of the hybrid-electric vehicle 10 and/or of on and off states of the combustion engine. This corresponds to method step V9. The torque distribution is repeated in calculation cycles of 1-10 seconds. Modules 221, 222, and 223 form the 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.

The outputs of the first and/or second software or hardware unit are visualized to a user of the hybrid-electric vehicle 10 via a human-machine user interface 23, allowing interaction. The human-machine user interface 23 can be a touchscreen of an infotainment system of the hybrid-electric vehicle 10.

3 summarizes the results of a case study. The case study considers a plug-in hybrid electric vehicle. Since charging is time-cost-based, the potential costs and fuel consumption depend on the infrastructure and driver preferences. In this case study, a maximum overhead time of up to 60 minutes was allowed for the 1200 km route.

• Aachen-München-Aachen AMA:

Route Entfernung [km] LEZ [km] TankStopps Lade-Stopps Tankzeit [min] Ladezeit [min] Verbrauch Tank [I] Kosten [EUR] A 1290 0 4 2 28,38 15,35 103,0 123,68 A 1290 106,7 4 4 28,38 26,83 101,9 126,56 B 1268 0 4 3 24,15 24,32 100,3 123,43 B 1268 90,8 4 3 27,94 23,32 100,4 124,31 C 1427 0 4 3 20,82 21,76 113,7 140,55 C 1427 80,4 4 5 19,74 31,12 113,6 143,37

• Aachen-Friedrichshafen-Aachen AFA:

Route Entfernung [km] LEZ [km] TankStopps Lade-Stopps Tankzeit [min] Ladezeit [min] Verbrauch Tank [I] Kosten [EUR] A 1207 0 4 0 19,23 0 99,5 108,67 A 1207 36,2 4 2 20,27 13,21 98,1 109,58 B 1203 0 4 3 18,71 18,52 91,9 105,07 B 1203 49 4 1 18,85 3,26 97,7 106,93 C 1205 0 3 4 10,42 35,72 84,7 112,30 C 1205 48,2 3 4 10,42 32,8 84,9 112,86 The method disclosed here was implemented on a hybrid control unit and simulated on six different round trips A, B, C between Aachen and Munich and between Aachen and Friedrichshafen:

• Aachen-Munich-Aachen AMA:

route Distance [km] LEZ [km] Fuel stops Charging stops Refueling time [min] Loading time [min] Tank consumption [I] Costs [EUR] A 1290 0 4 2 28.38 15.35 103.0 123.68 A 1290 106.7 4 4 28.38 26.83 101.9 126.56 B 1268 0 4 3 24.15 24.32 100.3 123.43 B 1268 90.8 4 3 27.94 23.32 100.4 124.31 C 1427 0 4 3 20.82 21.76 113.7 140.55 C 1427 80.4 4 5 19.74 31.12 113.6 143.37

• Aachen-Friedrichshafen-Aachen AFA:

route Distance [km] LEZ [km] Fuel stops Charging stops Refueling time [min] Loading time [min] Tank consumption [I] Costs [EUR] A 1207 0 4 0 19.23 0 99.5 108.67 A 1207 36.2 4 2 20.27 13.21 98.1 109.58 B 1203 0 4 3 18.71 18.52 91.9 105.07 B 1203 49 4 1 18.85 3.26 97.7 106.93 C 1205 0 3 4 10.42 35.72 84.7 112.30 C 1205 48.2 3 4 10.42 32.8 84.9 112.86

According to one aspect, the global optimization of route A is always calculated on board. The global optimization of alternative routes B and C can be performed in parallel on board, taking additional driver preferences into account, and/or in parallel off board using V2X communication in the cloud, with X2V communication receiving the optimization results.

A refueling process takes approximately 5 minutes at a fuel flow rate of 40 l/min. The charging stations provide power from 50 kW to 350 kW. Fuel and charging prices are as of July 10, 2020. All other program parameters are listed above in the description.

For the simulation, a vehicle model relevant to the vehicle manufacturer can be used to estimate the fuel and electricity consumption between the stations along the route, taking into account the topography of the route, for example using the Vehicle Pre-Filter.

• Aachen-München-Aachen AMA:

Route Entfernung [km] Verbrauch Tank [I] Kosten gesamt [EUR] A 1290 108,11 142,77 B 1268 106,24 141,88 C 1427 119,57 160,76

• Aachen-Friedrichshafen-Aachen AFA:

Route Entfernung [km] Verbrauch Tank [I] Kosten gesamt [EUR] A 1207 101,14 127,85 B 1203 100,82 128,80 C 1205 101,01 139,32 For comparison, the results for a benchmark PHEV are shown below:

• Aachen-Munich-Aachen AMA:

route Distance [km] Tank consumption [I] Total costs [EUR] A 1290 108.11 142.77 B 1268 106.24 141.88 C 1427 119.57 160.76

• Aachen-Friedrichshafen-Aachen AFA:

route Distance [km] Tank consumption [I] Total costs [EUR] A 1207 101.14 127.85 B 1203 100.82 128.80 C 1205 101.01 139.32

3 shows the cost-optimized driving operation of the hybrid electric vehicle 10 on the Aachen-Munich-Aachen route using the method disclosed here. EV operation was maintained within the existing LEZ environmental zones. A total of four refueling stops and four charging stops were made over a total distance of 106.7 km through LEZ environmental zones.

4 shows the fuel consumption savings achieved by the method disclosed here on the Aachen-Munich-Aachen AMA and Aachen-Friedrichshafen-Aachen AFA routes. The observed high fuel reduction on AFA C is explained by the relatively poor fuel station infrastructure on this route, which leads to extensive use of EV operation, thereby reducing the overall fuel consumption of the hybrid electric vehicle 10.

5 shows the total cost savings achieved on the routes Aachen-Munich-Aachen AMA and Aachen-Friedrichshafen-Aachen AFA using the method disclosed here.

6 shows the steps of the computer-implemented method for obtaining a monetary cost-optimal driving operation for a hybrid electric vehicle 10.

In a step V1, 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 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, in a step V3 a total consumption of electrical energy and/or fuel along the route A, B, C for several operating modes EV, CV, LPS1, LPS2 is determined. In a step V4, the sum of the refueling and/or charging costs C _rf,i (u _i ), C _rc,i (u _i ) is minimized for the set of all nodes along the route A, B, C based on the price structures and refueling 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. Starting from an initial fuel and/or charge state SoF, SoC, the fuel and/or charge states SoF, SoC in the nodes are determined recursively. In a step V5, a reference trajectory along the route A, B, C is obtained, wherein the reference trajectory indicates the nodes that minimize the sum of refueling and/or charging costs C _rf,i (u _i ), C _rc,i (u _i ) according to the refueling and/or charging times Δt _rf,i , Δt _rc,i . In a step V6, the minimization step V4 is repeated periodically or in the event of target deviations, thus updating the reference trajectory.

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 determines a torque requirement at a wheel of the hybrid-electric vehicle 10 resulting from a driving resistance for a look-ahead horizon: In a step V7, the respectively updated reference trajectory is read in. In a step V8, within the look-ahead horizon along the reference trajectory, the respective torque requirement is divided between a torque component ICE generated by an internal combustion engine 11 of the hybrid-electric vehicle 10 and/or a torque component EM generated by an electric motor 12 of the hybrid-electric vehicle 10 based on the respective operating mode EV, CV, LPS1, LPS2. In a step V9, a change of transmission gears and/or on and off states of the internal combustion engine 11 is determined within the look-ahead horizon. The second software or hardware unit 22 determines in a step V10 the distribution of the respective torque requirement, the change of the transmission gears and/or the on and off states of the combustion engine 11 such that fuel consumption is minimized in the respective forecast horizon. In a step V11 The respective torque distribution, the change of transmission gears, and/or the on/off states of the internal combustion engine 11 are distributed to the hybrid-electric vehicle (10) by means of a vehicle control unit. In a step V12, the immediately five preceding steps V7-V11 are repeated in the next look-ahead horizons along the reference trajectory up to the end of the reference trajectory. This results in the most cost-optimal driving operation for the hybrid-electric vehicle 10.

7 shows an embodiment of a control unit (HCU) for regulating and/or controlling the driving operation of a hybrid electric vehicle 10. The control unit (HCU) is configured to execute the steps of the method or the computer program disclosed herein, wherein, in one application, the control unit (HCU) regulates and/or controls a driving operation that is financially cost-optimal. The control unit (HCU) is embodied as a hybrid control unit.

According to one aspect, the first and/or second software or hardware unit 21, 22 are implemented in the control unit HCU.

8 shows a human-machine interface 23. The human-machine interface 23 can include a heads-up display. Route A is calculated in the vehicle. Routes B and C can be calculated in parallel in a cloud. A comparison of routes A, B, and C, whereby the comparison can be determined in the cloud, shows that route A is the globally optimal route. The heads-up display visually indicates to the driver that the vehicle must be charged at the next stop, with the nearest charging station being four kilometers away. Furthermore, the charging, charging time, and price are displayed. Furthermore, the bonus achieved on this route is displayed, for example, a rest stop with a 10 kWh profit.

Reference symbol

10

Hybrid electric vehicle

PHEV

Plug-in hybrid electric vehicle

11

combustion engine

12

electric motor

13

Gearbox

14

fuel tank

15

battery

16

loading 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

Pontryagin's Maximum Principle

223

dynamic programming

23

Human-machine user interface

A, B, C

Route

LEZ

environmental zone

EV

Operating mode

CV

Operating mode

LPS1

Operating mode

LPS2

Operating mode

SoF

Fuel condition

SoC

Charge level

Crf,i(ui)

Fuel costs

Crc,i(ui)

Top-up costs

Δtrf,i

refueling time

Δtcf,i

Charging time

ICE

Torque share of combustion engine

European Championship

Torque share of electric motor

V1-V12

Procedural steps

DP

dynamic programming

PMP

Pontryagin's Maximum Principle

HCU

control unit

QUOTES CONTAINED IN THE DESCRIPTION

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

Cited patent literature

• DE 10 2020 200 826 A1 [0004]
• DE 10 2019 109 849 A1 [0005]

Claims (10)

Computer-implemented method for obtaining monetary cost-optimal driving operation for a hybrid-electric vehicle (10), the method comprising the steps of: • obtaining a route (A, B, C) to be traveled 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) via a first data interface (20); • obtaining price structures of the nodes (V2); • carrying out the following steps (V3-V6) 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 fuel along the route (A, B, C) for a plurality of operating modes (EV, CV, LPS1, LPS2) of the hybrid-electric vehicle (10) (V3); ◯ on the set of all nodes along the route (A, B, C) minimizing the sum of refueling and/or charging costs (C _rf,i (u _i ), C _rc,i (u _i )) based on the price structures and minimizing refueling 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 the fuel and/or charging states (SoF, SoC) in the nodes are determined recursively (V4) starting from an initial fuel and/or charging state (SoF, SoC) of the hybrid electric vehicle (10); o Obtaining a reference trajectory along the route (A, B, C), wherein the reference trajectory indicates the nodes that minimize the sum of refueling and/or charging costs (C _rf,i (u _i ), C _rc,i (u _i )) according to the refueling and/or charging times (Δ _trf,i , Δt _rc,i ) (V5); ◯ wherein the minimization step (V4) is repeated periodically or in the event of target deviations and the reference trajectory is thus updated (V6); • Carrying out the following steps (V7-V12) 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 determines a torque requirement at a wheel of the hybrid electric vehicle (10) resulting from a driving resistance for a look-ahead horizon: ◯ Reading in the respectively updated reference trajectory (V7); ◯ within the look-ahead horizon along the reference trajectory, dividing the respective torque requirement between a torque component (ICE) generated by an internal combustion engine (11) of the hybrid-electric vehicle (10) and/or a torque component (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 look-ahead horizon, determining a change of transmission gears (13) of the hybrid-electric vehicle (10) and/or on and off states of the internal combustion engine (11) (V9); ◯ wherein the second software or hardware unit (22) determines the distribution of the respective torque requirement, the change of the transmission gears (13) and/or the on and off states of the internal combustion engine (11) such that fuel consumption is minimized in the respective look-ahead horizon (V10); ◯ applying the respective torque distribution, 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 (V7-V11) in the next look-ahead horizons along the reference trajectory up to an end of the reference trajectory and thus obtaining the monetarily cost-optimal driving operation for the hybrid-electric vehicle (10) (V12). Procedure 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 distribution of the respective torque requirement by means of Pontryagin's Maximum Principle (PMP) and the change of the transmission gears (13) and/or 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 to exchange data via 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 kilowatt hour, per minute and/or per charging process depending on a tariff, and/or wherein the nodes resulting from the minimization are booked for the respective refueling and/or charging times (Δt _rf,i , Δt _rc,i ) by means of the vehicle-to-infrastructure communication. 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 the respectively updated reference trajectories from the cloud. Method according to one of the preceding claims, wherein a user of the hybrid electric vehicle (10) is shown, by means of a human-machine user interface (23), information about the route (A, B, C), comprising the nodes and price structures of the nodes, information about the reference trajectory, comprising the nodes obtained from the minimization together with their respective price structures, information about changes in the transmission gears (13) and/or information about the on and off states of the internal combustion engine (11), the user can select nodes other than those obtained from the minimization via the human-machine user interface (23), and these selected nodes are taken into account as the target deviations when updating the reference trajectory. Method according to one of the preceding claims, wherein the driving operation for a battery-electric vehicle or an internal combustion engine vehicle is optimized in monetary terms. Computer program for obtaining a monetarily cost-optimal driving operation for a hybrid electric vehicle (10), the computer program comprising instructions which cause a hardware component of a computer to carry out the steps of the method according to one of the preceding claims when the computer program is loaded by the hardware component or executed by it. Control unit (HCU) for regulating and/or controlling a driving operation of a hybrid electric vehicle (10), wherein the control unit (HCU) is designed to carry out the steps of the method according to one of the Claims 1 until 8 or the computer program Claim 9 to be carried out, whereby in an application case the control unit (HCU) regulates and/or controls a driving operation that is financially cost-optimal.

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