Device for selecting an operating strategy for a vehicle

Abstract

The invention relates to a device for selecting an operating strategy (BS) for a vehicle (F), comprising a backend (BE) which is implemented on a cloud (CL), wherein the backend (BE) comprises a programming interface (API) and is designed to calculate the operating strategy (BS) for an entire journey using a vehicle model and an optimisation method, wherein the device also comprises at least one frontend having an input window and access to the programming interface (API), wherein the input window is designed to enable a user (N) to enter and / or select and display influencing variables (EG) relating to the planned journey, wherein the backend (BE) comprises a navigation function for determining a route (FR) and is designed to determine and output, for the route (FR), a duration (T) and / or an arrival time (TD), an energy demand (E), and optionally costs (C) and / or an expected influence (ELT) on a component lifetime (LT) of at least one component of the vehicle (F).

Description

[0001] Daimler Truck AG Group

[0002] October 10, 2025

[0003] Device for selecting an operating strategy for a vehicle

[0004] The invention relates to a device for selecting an operating strategy for a vehicle according to claim 1.

[0005] Predictive cruise control systems are common in vehicles. These systems may be linked to a shift strategy and other vehicle functions, and are based on map forecasting. This function is implemented as a purely onboard feature in the vehicle. The user can adjust settings via the vehicle's menus, such as a set speed (standard cruise control operation), upper and lower speed hysteresis, the selection of a driving mode (e.g., Eco or Power), a dynamic factor for coasting into curves or roundabouts, etc.

[0006] EP 3647 087 B1 describes methods and systems for operating a vehicle's air conditioning system. The method includes sensing the state of charge of an energy storage device capable of powering the vehicle's air conditioning system; determining an energy level, including the state of charge; receiving a planned route for the vehicle; and receiving route status data associated with the planned route. The route status data includes traffic data, weather data, and / or geographical data that identify areas where the vehicle's air conditioning system is to be powered exclusively by the energy storage device.The procedure further includes determining whether the energy level is sufficient to complete the planned route for the vehicle, based on the planned route and route data, and if the energy level is insufficient to complete the planned route for the vehicle, providing a notification to a user via a display.

[0007] The invention is based on the objective of providing a novel device for selecting an operating strategy for a vehicle. This objective is achieved according to the invention by a device for selecting an operating strategy for a vehicle having the features of claim 1.

[0008] Advantageous embodiments of the invention are the subject of the dependent claims.

[0009] According to the invention, a device for selecting an operating strategy for a vehicle is proposed, wherein the device has a backend implemented in a cloud, the backend having a programming interface and being configured to calculate the operating strategy using a vehicle model and an optimization method for a complete trip, the device further comprising at least one frontend with an input window and access to the programming interface, the input window being configured for a user to input and / or select and display variables influencing the planned trip, the backend having a navigation function for determining a route and being configured to specify a duration and / or a time of arrival.to determine and output the energy demand and optionally costs and / or an expected impact on the lifetime of at least one component of the vehicle for the journey route, and to transmit the operating strategy or a parameter set representing it to a selected vehicle (for example, via wireless communication) when this operating strategy has been selected by pressing a specific button or using a specific control panel.

[0010] In one embodiment, several of the following influencing factors are provided:

[0011] - a velocity hysteresis,

[0012] - an average speed over an entire route, especially coupled with a desired arrival time at a point on the route or at the end of the route,

[0013] - a vehicle type and / or several specific vehicles for which data on technical equipment and / or energy consumption are stored,

[0014] - at least one route,

[0015] - a schedule, including a start time,

[0016] - Curve dynamics parameters and

[0017] - the weight of the vehicle.

[0018] In one embodiment, the backend is configured to calculate and display a recommendation for an operating strategy. The user can accept or reject this recommendation. In another embodiment, the input window displays an optimization triangle, an optimization polygon, or a multi-vertex optimization body, each associated with an optimization goal. The backend is configured to derive a weighting of the optimization goals based on the position of an input marker within the optimization triangle, optimization polygon, or optimization body, and to consider this weighting when determining the operating strategy.

[0019] In one embodiment, the optimization goals are driving performance, energy efficiency, and component lifespan and / or wear optimization. Alternatively, the optimization goals are energy efficiency, driving duration, fuel cell lifespan, and battery lifespan of a fuel cell vehicle.

[0020] In one embodiment, the backend is configured to take weather conditions and / or traffic conditions into account during optimization and / or scheduling.

[0021] In one embodiment, the input window is configured to allow the input of target values ​​at the end of the journey and / or at specific tour locations, with the backend being configured to implement these specifications in the strategy calculation and, if the target values ​​are not feasible, to indicate maximum possible values ​​that can be achieved with the inputs made.

[0022] In one embodiment, two or more of the following vehicle types are provided:

[0023] - battery-electric vehicle,

[0024] - Fuel cell vehicle,

[0025] - Vehicle with internal combustion engine for hydrogen and

[0026] - Vehicle with a diesel internal combustion engine.

[0027] In one embodiment, the data on the technical equipment and / or energy consumption of the vehicle type and / or specific vehicles include a battery size, an efficiency and maximum values ​​of a drive system, fuel cell data, in particular a maximum and a minimum power output and / or an efficiency curve and / or wear behavior models of vehicle components (in particular essential vehicle components) and / or data of an internal combustion engine, in particular an engine map and / or a consumption map.

[0028] In one embodiment, the time schedule includes data on at least one planned break and / or battery charging processes and / or refueling. The present invention proposes specifying a suitable and flexibly selectable operating strategy with feedback for the user, for example, a dispatcher or a driver.

[0029] The user receives feedback regarding the effect of the settings on fuel consumption, driving performance, and the impact on the wear and tear of key vehicle components. Furthermore, a fleet manager or dispatcher can directly influence these settings within the vehicle, making it possible to configure them to perfectly suit the transport order or the fleet operator's business model. Such optimization can lead to additional savings and improvements for a customer's fleet.

[0030] The solution according to the invention allows fleet management or dispatchers to directly specify or influence the parameter settings of the predictive driving strategy. For all involved (drivers, dispatchers, fleet managers), the setting and planning of transport orders (optimization goals, vehicle selection, route selection, scheduling, load (weight)) becomes significantly more transparent, as feedback on the most important data (time, consumption, wear, and costs) is displayed after optimization. Multiple optimization calculations at the extreme points of the optimization triangle can additionally demonstrate the entire range of possibilities and / or the arrival time and / or travel time in relation to consumption and / or wear. Based on this, a well-founded decision regarding the appropriate operating strategy is possible. The most suitable operating strategy can be selected for each trip.Changes can even be made during a project if the circumstances change. This leads to further, previously untapped cost savings.

[0031] Exemplary embodiments of the invention are explained in more detail below with reference to drawings.

[0032] This shows:

[0033] Fig. 1: a schematic view of a device in which an operating strategy for a

[0034] The vehicle is implemented in a backend and / or on a cloud and is calculated using a vehicle model.

[0035] Fig. 2: a schematic view of an exemplary display of an input window, Fig. 3: a schematic view of an optimization quadrilateral for a

[0036] fuel cell vehicle, and

[0037] Fig. 4: a schematic flowchart for operating the input window.

[0038] Corresponding parts are marked with the same reference symbols in all figures.

[0039] Figure 1 is a schematic view of a method for defining or optimizing an operating strategy BS in a vehicle F, in particular a commercial vehicle.

[0040] The invention relates to an optimized operating strategy BS for a vehicle F, which is implemented in a backend BE and / or in a cloud CL. An operating device is provided.

[0041] The operating strategy BS, for example, is calculated based on a vehicle model for a complete transport journey.

[0042] Relevant variables that influence the planned transport journey are evaluated for the BS operating strategy.

[0043] The relevant variables include, for example, a speed hysteresis setting (up and down), an average speed over the entire route R1, R2, a parameter specification in an optimization triangle, particularly with the goals of energy efficiency, component lifetime or wear optimization, as well as driving performance FL, a vehicle type FT (e.g., BEV, FCeV, ICE H2 or ICE Diesel), a route FR, a schedule, curve dynamics parameters KDP and / or a weight G of the vehicle F.

[0044] By varying the aforementioned parameters and recalculating NB (for example by pressing a button), an optimal setting for the respective transport order can be found or becomes more visible to a planner or driver.

[0045] After each calculation of a new data set, the results R are displayed, specifically: duration T or arrival time, average speed ATV, energy consumption and / or costs C. The user N can then decide whether to end the planning mode and can send the generated settings and optimization results to the selected vehicle F. Alternatively, they can continue searching for a better setting in another calculation run.

[0046] Figure 1 is a schematic view of a device in which an operating strategy BS for a vehicle F is implemented in a backend BE and / or on a cloud CL and calculated using a vehicle model, where complete trip planning FP of at least one day's duration is meaningful and / or possible. Relevant influencing factors EG on a planned transport trip can be interactively evaluated by a user N, for example, a logistics planner, who accesses the backend BE and / or the cloud CL via an API.

[0047] Figure 2 is a schematic view of an exemplary display of an input window on a touch-sensitive display unit, showing an optimization triangle OD.

[0048] Other influencing factors for the EC include, for example:

[0049] - a velocity hysteresis, that is, a lower velocity deviation LVB and / or an upper velocity deviation UVB,

[0050] - an average ATV speed over an entire route s, possibly coupled to a desired predetermined arrival time at a point on the route s or at the end of the route s,

[0051] - a parameter specification in the optimization triangle OD, as described for example in DE 10 2022 111 537 A1, whose corner areas are each assigned an optimization goal, namely driving performance FL, energy efficiency EE and component lifetime LT and / or wear optimization.

[0052] User N positions an input marker EM within the displayed optimization triangle OD. Based on the position of the input marker EM, a weighting of the optimization goals is derived, and this weighting of the optimization goals is taken into account when determining an operating strategy for the vehicle F.

[0053] DE 10 2022 111 537 A1 is hereby fully incorporated by reference into the present application.

[0054] Other possible exemplary influencing factors for the EC are:

[0055] - A vehicle type FT: for example, a battery electric vehicle (BEV), a fuel cell vehicle (FCeV), or a vehicle F with an internal combustion engine for hydrogen (H2-ICE) or diesel (Diesel-ICE). Alternatively, individual real vehicles can be selected and / or considered. Crucially, the technical specifications and / or energy consumption data of vehicle F must be available, such as battery size, efficiency and maximum values ​​of a drive system, fuel cell data (maximum and minimum power, efficiency curve), and data of an internal combustion engine (engine map, fuel consumption map).

[0056] - A route FR: To reach a desired destination, there may be several alternative routes R1, R2 that can be selected. These are determined and displayed by a navigation function and evaluated in an initial calculation with regard to travel time T, energy consumption E, and, if applicable, costs C.

[0057] - A schedule ZP: A start time TS and a planned break B can also modify other influencing factors EG, such as traffic V or weather influence WE, which can be provided to the backend BE and / or the cloud CL, for example, by third-party services TPS. Furthermore, the schedule ZP can also be optimized and combined with battery charging processes or refueling, including potential price fluctuations.

[0058] - Curve dynamics parameter KDP: For rural roads away from the motorway, this parameter can become relevant, as it influences the curve speed and the approach to obstacles such as roundabouts or intersections.

[0059] - Weight G: The weight G of the vehicle F significantly influences the energy consumption E, as well as the duration T and wear and tear during the transport operation and must be known as precisely as possible during the planning phase and subsequent implementation. It can also be varied for a selection of transport orders to assess its impact.

[0060] By varying the listed parameters or influencing factors EG and recalculating them NB (by pressing a button or using a control panel), an optimal setting for the corresponding transport order can be found, or the process can be made more transparent for the planner or driver. It is also possible to calculate and display a recommendation that the user N can follow, but is not obligated to do. Pressing another button or using a control panel transmits ST the operating strategy BS and / or a parameter set to the vehicle F.

[0061] Calculating the entire transport route in the backend BE has further advantages: Difficult weather or traffic conditions can be taken into account during optimization; for example, in areas with strong headwinds or difficult weather or road conditions, the speed can be reduced, and in areas with less wind or less difficult conditions, it can be increased to achieve the same average ATV speed.

[0062] A predicted traffic jam can be incorporated into the optimization. Taking into account the specifications of the optimization triangle OD, the impact on the driving trajectory and energy distribution of a fuel cell vehicle (FCeV) can be considered.

[0063] Target values ​​can be specified at the end of the tour or at specific tour locations (e.g., battery charge level (SOG), fuel levels, and / or time constraints). The optimization process then attempts to implement these specifications in the strategy calculation as effectively as possible. If the targets are not achievable, the maximum possible values ​​attainable with the current settings are displayed.

[0064] After each calculation of a new data set, the results R are displayed: duration T and / or time TD of arrival, average speed ATV, energy consumption E, cost C and / or expected influence ELT on component lifetime LT.

[0065] Alternatively, other options can be displayed in the optimization triangle OD and / or the optimization triangle OD can be extended to include further options, for example, an optimization quadrilateral or another optimization polygon or optimization body OK. Figure 3 is a schematic view of, for example, a pyramid-shaped optimization body OK for a fuel cell vehicle FCeV, where the optimization goals energy efficiency EE, driving time FD, fuel cell lifetime FC, and battery lifetime LTB are specified. For different positions of the input marker EM, exemplary values ​​for properties of the vehicle F or its components are given, which can facilitate the user N's ​​decision.

[0066] User N can then decide whether to complete the planning mode and send the generated settings and optimization results to the selected vehicle F, or whether to search for a better setting again in another calculation loop.

[0067] Figure 4 is a schematic flowchart for operating the display on the touch-sensitive display unit.

[0068] In step S1, the parameters are set in the input window. In step S2, the calculation is performed using the optimization method, and in step S3, a recommended parameter set is calculated. In step S4, the results R of the calculation are displayed. In step S5, the system either returns to step S1 if the recalculation button (NB) is pressed or the control panel is activated, or it proceeds to step S6 to transmit the operating strategy (BS) and / or the parameter set to the vehicle (F) if the transmission button (ST) is pressed or the control panel is activated.

[0069] Alternatively, input can be made using a pointing device, such as a mouse, and / or a keyboard.

[0070] Daimler Truck AG Vonend

[0071] October 10, 2025

[0072] Reference symbol list

[0073] API programming interface

[0074] ATV average speed

[0075] B Break

[0076] BE Backend

[0077] BEV (battery electric vehicle)

[0078] BS operating strategy

[0079] C Costs

[0080] CL Cloud

[0081] Diesel-ICE vehicle with internal combustion engine for diesel

[0082] Energy demand

[0083] EE Energy efficiency

[0084] EC influencing factor

[0085] ELT expected impact on component lifetime

[0086] EM entry mark

[0087] F vehicle

[0088] FC fuel cell

[0089] FCeV fuel cell vehicle

[0090] FL driving performance

[0091] FP journey planning

[0092] FR route

[0093] FT vehicle type

[0094] Weight

[0095] H2-ICE vehicle with combustion engine for hydrogen

[0096] KDP Curve Dynamics Parameters

[0097] LT component lifetime

[0098] LTB Battery Lifetime

[0099] LTFC Fuel Cell Lifetime

[0100] LVB lower speed deviation

[0101] N User NB Recalculation

[0102] OD optimization triangle

[0103] OK optimization body

[0104] R results

[0105] R1, R2 Routes

[0106] ST transmission

[0107] Step S1 to S6

[0108] T duration

[0109] TD Time of Arrival

[0110] TPS third-party service

[0111] TS start time

[0112] UVB upper velocity deviation

[0113] V Traffic

[0114] WE weather influence

[0115] ZP Timetable

Claims

Daimler Truck AG Group October 10, 2025 Patent claims 1. Device for selecting an operating strategy (OS) for a vehicle (F), wherein the device has a backend (BE) implemented on a cloud (CL), the backend (BE) having a programming interface (API) configured to calculate the operating strategy (OS) using a vehicle model and an optimization procedure for a complete trip, the device further comprising at least one frontend with an input window and access to the programming interface (API), the input window being configured for input and / or selection and display of influencing factors (EG) on the planned trip by a user (N), the backend (BE) having a navigation function for determining a route (FR) and being configured to determine a duration (T) and / or a time (TD) of arrival,to determine and output an energy requirement (E) and optionally costs (C) and / or an expected impact (ELT) on a component lifetime (LT) of at least one component of the vehicle (F) for the journey route (FR), and to transmit the operating strategy (BS) or a parameter set representing it to a selected vehicle (F) when it has been selected by pressing a specific button or operating a specific control panel.

2. Device according to claim 1, characterized in that several of the following influencing factors (EC) are provided: - a velocity hysteresis, - an average speed (ATV) over an entire route (s), especially coupled with a desired arrival time at a point on the route (s) or at the end of the route (s), - one vehicle type (FT) and / or several specific vehicles (F) for which data on technical equipment and / or energy consumption are stored, - at least one route (FR), - a schedule (ZP), including a start time (TS), - Curve Dynamic Parameters (CDP) and - a weight (G) of the vehicle (F).

3. Device according to claim 1 or 2, characterized in that the backend (BE) is configured to calculate and display a recommendation for an operating strategy (BS).

4. Device according to one of the preceding claims, characterized in that the input window displays an optimization triangle (OD) or an optimization polygon or an optimization body (OK) with multiple vertices, each of which is assigned an optimization goal, wherein the backend (BE) is configured to derive a weighting of the optimization goals based on the position of an input marker (EM) in the optimization triangle (OD), optimization polygon or optimization body (OK) and to take this into account when determining the operating strategy (BS).

5. Device according to claim 4, characterized in that the optimization goals driving performance (FL), energy efficiency (EE) and component lifetime (LT) and / or wear optimization are provided, or that the optimization goals energy efficiency (EE), driving time (FD), lifetime (LTFC) of a fuel cell (FC) and lifetime (LTB) of a battery of a fuel cell vehicle (FCeV) are provided.

6. Device according to one of the preceding claims, characterized in that the backend (BE) is configured to take weather conditions and / or traffic conditions into account during optimization and / or scheduling (ZP).

7. Device according to one of the preceding claims, characterized in that the input window is configured to allow the input of target values ​​at the end of the journey and / or at specific tour locations, wherein the backend (BE) is configured to implement these specifications in the strategy calculation and, if the target values ​​are not feasible, to indicate maximum possible values ​​that can be achieved with the inputs made.

8. Device according to one of the preceding claims, characterized in that the following vehicle types (FT) are provided: - battery electric vehicle (BEV), - Fuel cell vehicle (FCeV), - Vehicle (F) with internal combustion engine for hydrogen (H2-ICE) and - Vehicle (F) with internal combustion engine for diesel (Diesel-ICE).

9. Device according to one of the preceding claims, characterized in that the data on the technical equipment and / or energy consumption of the vehicle type (FT) and / or the specific vehicles (F) comprise a battery size, an efficiency and maximum values ​​of a drive, fuel cell data, in particular a maximum and a minimum power and / or an efficiency curve and / or wear behavior models of vehicle components and / or data of an internal combustion engine, in particular an engine map and / or a consumption map.

10. Device according to one of the preceding claims, characterized in that the time schedule (ZP) includes data on at least one planned break (B) and / or on battery charging processes and / or refueling.

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