DE102022118589A1 - METHODS AND SYSTEMS FOR PREDICTING THE ENERGY CONSUMPTION OF A VEHICLE FOR ITS TRAVEL ALONG A DEFINED ROUTE AND FOR ROUTE PLANNING - Google Patents

Abstract

The invention relates to a method for predicting the energy consumption of a vehicle, in particular a battery electric vehicle (BEV) or a hybrid electric vehicle (HEV), for its journey along a defined route between a specific starting point and a specific destination. The method includes obtaining, in particular calculating based on input data or receiving, corresponding values for a set of one or more energy consumption impact parameters of an energy consumption model for the vehicle. The set of energy consumption influence parameters represents in the energy consumption model one or more of the following influence factors on the energy consumption of the vehicle along the route: a surface influence factor (21) which defines a road surface dependent influence on the energy consumption of the vehicle; a cornering influence factor (23) defining a road curvature dependent influence on the energy consumption of the vehicle; a wind impact factor (25), e.g., drag factor, defining a wind-dependent impact on the vehicle's energy consumption; a driving style influencing factor (34) which defines a driving style-dependent influence on the energy consumption of the vehicle; a tire pressure influencing factor (31) which defines a tire pressure-dependent influence for a selected driver on the energy consumption of the vehicle; a temperature-related battery consumption influencing factor (32), which has an influence, dependent on the ambient temperature, on the energy supply capability of a drive battery of the vehicle and/or on the energy consumption, in particular for heating or cooling the battery, of an active cooling and/or heating system of the drive battery of the vehicle Are defined. The method further includes determining a vehicle energy consumption prediction for the route based on the route, particularly its length, the energy consumption model, and the obtained one or more values of the set of energy consumption impact parameters.

Description

The present invention relates to the field of vehicle technology, in particular the field of battery electric vehicles (BEV) or hybrid electric vehicles (HEV), i.e. vehicles whose drive train is based at least in part on electrical energy provided by a battery. The invention relates in particular to a method and a system for predicting an energy consumption of a vehicle for its journey along a defined route and for determining an optimal route between a starting point and a destination point for a vehicle.

Since the maximum amount of energy that can be made available by the battery and therefore the range of the vehicle is limited per se, it is important for the driver to know the energy consumption of the vehicle and especially in the context of navigating a specific route to know whether the current battery charge would be sufficient for the entire route to the destination or not. Fear of range is a crucial issue for many customers when it comes to electric vehicles. One reason for this so-called range anxiety is the driver's uncertainty about the remaining range that the vehicle can drive with a certain battery capacity. This is not only because the range of electric vehicles depends on a larger number of factors compared to conventional vehicles with internal combustion engines, but also because the network of charging stations is not (yet) as dense as that of gas stations and most of them Charging stations are located in urban areas or along highways. Apart from that, the range of an electric vehicle is difficult to predict, especially in rural areas, since e.g. B. highly dependent on road surface and conditions, elevation profiles and weather conditions.

Therefore, it is in the driver's best interest to better understand if a given route can be driven without draining the battery anywhere along the route without being able to recharge it. Therefore, predicting the vehicle's energy consumption for driving along a given route can be an important aspect of route planning.

Current methods for predicting the energy consumption of a vehicle, in particular a BEV or HEV, are largely based on determining the distances of route segments and the average energy consumption rate of the vehicle at a maximum permissible speed per segment.

The aim of the present invention is to provide a method and a system for improving the prediction of the energy consumption of a vehicle for its journey along a specific route. In particular, it is desirable to improve one or more of the efficiency, effectiveness, and accuracy of predicting a vehicle's energy consumption for its travel along a defined route.

A solution to this problem is provided by the teaching of the independent claims. Various preferred embodiments of the present invention are provided by the teachings of the dependent claims.

• - einen Oberflächen-Einflussfaktor, der einen von der Straßenoberfläche abhängigen Einfluss auf den Energieverbrauch des Fahrzeugs definiert;
• - einen Kurven-Einflussfaktor, der einen von Straßenkurven abhängigen Einfluss auf den Energieverbrauch des Fahrzeugs definiert;
• - einen Wind-Einflussfaktor, z. B. einen Luftwiderstandsfaktor, der einen windabhängigen Einfluss auf den Energieverbrauch des Fahrzeugs definiert;
• - einen Fahrstil-Einflussfaktor, der einen vom Fahrstil abhängigen Einfluss auf den Energieverbrauch des Fahrzeugs definiert;
• - einen Reifendruck-Einflussfaktor, der einen reifendruckabhängige Einfluss für einen ausgewählten Fahrer auf den Energieverbrauch des Fahrzeugs definiert;
• - einen temperaturbezogenen Batterieverbrauchs-Einflussfaktor, der einen von der Umgebungstemperatur abhängigen Einfluss auf die Energieversorgungsfähigkeit einer Antriebsbatterie des Fahrzeugs und/oder auf den Energieverbrauch, insbesondere zum Heizen oder Kühlen der Batterie, eines aktiven Kühl- und/oder Heizsystems der Antriebsbatterie des Fahrzeugs definiert; und

(ii) Bestimmen einer Vorhersage für den Energieverbrauch des Fahrzeugs für die Route auf der Grundlage der Route, insbesondere ihrer Länge, des Energieverbrauchsmodells und des erhaltenen einen oder mehrerer Werte des Satzes von Energieverbrauchseinflussparametern.A first aspect of the invention relates to a method, in particular a computer-implemented method, for predicting the energy consumption of a vehicle, in particular a battery electric vehicle (BEV) or a hybrid electric vehicle (HEV), for its journey along a defined route between a specific starting point and a specific destination . The method includes: (i) Obtaining, in particular calculating on the basis of input data or receiving, corresponding values for a set of one or more energy consumption influencing parameters of an energy consumption model for the vehicle, the set of energy consumption influencing parameters in the energy consumption model having one or more of the following influencing factors on the energy consumption of the vehicle along the route:

• - a surface influence factor, which defines a road surface dependent influence on the energy consumption of the vehicle;
• - a curve influence factor that defines a road curve-dependent influence on the energy consumption of the vehicle;
• - a wind influencing factor, e.g. B. an aerodynamic drag factor, which defines a wind-dependent influence on the energy consumption of the vehicle;
• - a driving style influencing factor that defines a driving style-dependent influence on the energy consumption of the vehicle;
• - a tire pressure influencing factor that defines a tire pressure-dependent influence for a selected driver on the energy consumption of the vehicle;
• - A temperature-related battery consumption influencing factor that depends on the ambient temperature influence on the energy supply capability of a drive battery of the vehicle and / or defines the energy consumption, in particular for heating or cooling the battery, of an active cooling and/or heating system for the drive battery of the vehicle; and

(ii) determining a vehicle energy consumption prediction for the route based on the route, in particular its length, the energy consumption model and the obtained one or more values of the set of energy consumption impact parameters.

Accordingly, the determination of the energy consumption of the vehicle is based on an energy consumption model that is based on a set of one or more other influencing factors that have not previously been used for, or are based on, a prediction of the energy consumption. Since the vehicle's energy consumption is related to the range of the vehicle, i. H. with the route that the vehicle can travel with the current state of charge of the battery, the energy consumption model can be regarded as a model for predicting the range. The method according to the first aspect of the invention thus offers an improved possibility of using data such as live vehicle data, weather forecasts or information about road quality to predict the energy consumption and thus the range of the vehicle, which can enable a customer to choose a suitable route select to arrive at a desired destination.

As used herein, the term “vehicle” specifically refers to an electric vehicle, including a battery electric vehicle (BEV) or a hybrid electric vehicle (HEV). This can include any type of vehicle such as: As cars, trucks, vans, buses, tractors or agricultural machinery and e-bikes, e-scooters or the like. An electric vehicle includes in particular an electric motor that drives the vehicle. The electric motor is powered by a battery, typically a rechargeable battery, which may be referred to as an "electric vehicle battery" ("EVB") or "traction battery". The traction battery can be charged from an outlet or other power source, e.g. B. with hydrogen via a fuel cell.

The term "route" as used herein refers in particular to a connection between a starting point and a destination that can be traversed by the vehicle. The route can contain paved roads (from small roads to highways), but also unpaved paths, such as dirt roads or similar, or even no defined paths, e.g. fields, meadows, deserts, etc., as long as such rough terrain is compatible with the vehicle can be driven on. The route can contain intermediate points, in particular manually selected intermediate points between the starting point and the destination. It goes without saying that any smallest route segment between two points (starting point, ending point and intermediate point(s)) can be considered as a "route" between a starting point and an ending point with no (hand-selected) intermediate points.

As used herein, the term “energy consumption model” specifically refers to a conceptual model of a vehicle's energy consumption, particularly a computer-implemented model that represents various factors that may have an impact on the vehicle's energy consumption. Specifically, as used herein, energy consumption refers to the energy consumption of an electric vehicle, e.g. B. a battery electric vehicle (BEV) or a hybrid electric vehicle (HEV). As explained in more detail below, the energy consumption model is based on one or more energy consumption parameters that represent certain influencing factors for the energy consumption of the vehicle. By executing the energy consumption model, energy consumption of the vehicle can be predicted, in particular for a defined route between a starting point and a destination.

The term "energy consumption impact parameter" as used herein specifically refers to a parameter of the energy consumption model that represents a particular "impact factor" (see below). In particular, specific values that are suitable for being calculated in the energy consumption model can be assigned to the “energy consumption influencing parameters”.

The term “influencing factor” as used herein specifically refers to a factor or aspect affecting the vehicle's energy consumption. More precisely, a factor that in some way has an impact on energy consumption. The effect or influence can be either a reduction or an increase in energy consumption. Although the term "influencing factor" generally means that the factor in question can have an impact on energy consumption, there may be situations where a particular factor does not change energy consumption, e.g. B. when there is no wind.

The term "receiving" data, e.g. B. of values for a set of one or more energy consumption impact parameters of an energy consumption model for the vehicle, relates in particular to (i) the calculation of these Data based on input data, e.g. B. be derived with the help of one or more sensors, or (ii) the receipt of this data from an external data source.

The terms "first," "second," "third," and the like in the specification and claims are used to distinguish between like elements and not necessarily to describe a sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances, and that the embodiments of the invention described herein may operate in a different order than that described or illustrated herein.

As used in this specification and claims, the term "comprising" or "including" does not exclude other elements or steps. If an indefinite or definite article is used when referring to a singular noun, e.g. B. "a" or "an", "the", this includes a plural of that noun, unless expressly stated otherwise.

Unless expressly stated otherwise, "or" refers to an inclusive or and not to an exclusive or. For example, a condition A or B is met if any of the following conditions are true: A is true (or present) and B is false (or absent), A is false (or absent) and B is true (or present) , and both A and B are true (or present).

Preferred embodiments of the method are described below, which can be combined with one another or with other aspects of the present invention as desired, provided that such a combination is not expressly excluded or is not technically impossible.

• - einen Massen-Einflussfaktor, der einen von der Gesamtmasse des Fahrzeugs abhängigen Einfluss auf den Energieverbrauch des Fahrzeugs definiert;
• - einen Temperatur-Einflussfaktor, der einen von der Umgebungstemperatur abhängigen Einfluss auf den Energieverbrauch des Fahrzeugs definiert, insbesondere beim autonomen oder hochautomatisierten Fahren;
• - einen Routentopologie-Einflussfaktor, der einen vom Höhenprofil abhängigen Einfluss auf den Energieverbrauch des Fahrzeugs definiert, insbesondere beim autonomen oder hochautomatisierten Fahren.

In some embodiments, the set of energy consumption impact parameters of the energy consumption model also includes one or more of the following additional energy consumption impact parameters:

• - a mass influencing factor which defines an influence on the energy consumption of the vehicle which is dependent on the total mass of the vehicle;
• - a temperature influencing factor, which defines an influence on the energy consumption of the vehicle that is dependent on the ambient temperature, in particular in the case of autonomous or highly automated driving;
• - a route topology influencing factor that defines an influence on the energy consumption of the vehicle that is dependent on the height profile, in particular in autonomous or highly automated driving.

In such embodiments, obtaining, in particular calculating based on input data or receiving, the respective values for the set of energy consumption impact parameters comprises obtaining respective values for these one or more additional energy consumption influence parameters, and determining a prediction for the energy consumption of the vehicle for the route is further based on the obtained one or more values of the additional energy consumption impact parameters.

The use of such additional energy consumption parameters can further improve the prediction of the vehicle's energy consumption and thus the remaining range of the vehicle, in particular with regard to the accuracy of the prediction. The above data, including data on road conditions, weather and elevation profile, as well as data relating to one or more of the other energy consumption parameters, can be used in the model, in particular through the use of static formulas or characteristics of real vehicles .

In some embodiments, one or more energy consumption impact parameters are each defined as a numerical parameter that is related as a factor to a corresponding predefined reference parameter. Using numeric parameters can make it easier to calculate the energy consumption model. The use of reference parameters can further simplify the method. For example, a reference surface impact factor may refer to a factor of 1 for a reference or standard road. Road surfaces that increase energy consumption can then be assigned a factor greater than 1. Other roads that contribute to energy saving can be given a factor of <1. It goes without saying that other scales of factors and any suitable reference parameter can also be used.

In some embodiments, the route is divided into a set of route segments. Determining, in particular calculating on the basis of input data or receiving respective values for a set of one or more energy consumption influencing parameters can then determine respective segment-specific values for at least one of the energy include consumption impact parameters on a per route segment basis. Furthermore, determining a prediction for the energy consumption of the vehicle for the route can include calculating a respective segment-specific energy consumption of the vehicle for each of the route segments on the basis of the obtained one or more values of the energy consumption influencing parameters including the segment-specific values and integrating the calculated segment-specific energy consumption, to get the energy consumption of the vehicle for the entire route.

Dividing the route into a number of route segments can further improve the prediction of vehicle energy consumption. The various energy consumption parameters, which represent one or more of the following influencing factors on the energy consumption of the vehicle along the route in the energy consumption model, can vary along the route. It can therefore be advantageous, in particular for the accuracy of the prediction, to subdivide the route into route sections. More precisely, a prediction for the energy consumption can be made for each of the route segments, which can be more accurate than e.g. B. Using average values for the energy consumption parameters along the entire route.

In some embodiments, the method further comprises updating, in particular replacing or refining, one or more of the obtained values of the set of energy consumption influence parameters depending on the consumption data obtained, which have a measured actual energy consumption and/or measured actual respective values for one or more of the energy consumption influence parameters Set of energy consumption influencing parameters for a journey of one or more reference vehicles, in particular the vehicle itself or an identical or comparable vehicle or a fleet of several identical or comparable vehicles along the entire route or at least one road section thereof. Taking into account actually measured values (for the energy consumption itself or for one or more energy consumption parameters) can further improve the accuracy of the energy consumption prediction. In particular, the energy consumption prediction may be updated as the vehicle travels along the route, making it more accurate as the vehicle travels along the route.

In some related embodiments, the determination of the vehicle energy consumption prediction for the entire route or at least a road segment thereof is further based on the application of a machine learning-based classifier to the consumption data and/or the updated one or more values of the set of energy consumption impact parameters. The classification of the consumption data can be carried out on the basis of a consumption database. In this way, the prediction method can be further improved to produce more accurate results by training a classifier with machine learning. Earlier consumption forecasts can be used to improve the energy consumption forecast for future journeys along a route.

In some of these embodiments, the method further comprises determining whether, and if so to what extent, the consumption data has been affected by traffic; and selectively using only such consumption data or components thereof as a basis for updating one or more of the values of the set of energy consumption influencing parameters and/or as a basis for determining the vehicle energy consumption prediction for the route for which consumption data or Components of which no such impairment was found. In addition to the influencing factors mentioned above, traffic can also play a role in predicting the energy consumption of a vehicle. For example, the energy consumption of a vehicle can depend on how often the vehicle has to brake, stop, accelerate, etc. It can be advantageous to eliminate these traffic-related effects when updating the values. In other words, only those consumption data that are free from traffic influences, i.e. that represent the energy consumption of a vehicle that can drive without regard to other road users, can be used to update the values. This is particularly because traffic is an unpredictable, or at least hardly predictable, parameter. A machine learning classifier could be misled by traffic-impaired data.

• - Vorberechnen und/oder Speichern entsprechender Werte für einen oder mehrere der Faktoren Oberflächen-Einflussfaktor, Kurven-Einflussfaktor und Routentopologiefaktor entweder für die Route als Ganzes oder, wenn die Route in einen Satz von Routensegmenten unterteilt ist, für jedes der einzelnen Routensegmente;
• - Bestimmen eines jeweiligen Wertes für den Wind-Einflussfaktor auf der Grundlage einer jeweiligen linear angenäherten Windrichtung, die für die Route als Ganzes oder für jedes der Routensegmente einzeln bestimmt wird;
• - Bestimmen eines jeweiligen Wertes für den Wind-Einflussfaktor auf der Grundlage des Klassifizierens der Windrichtungen gemäß einem diskreten Satz von Klassen verschiedener Windrichtungen und der Fahrzeugfahrtrichtungen gemäß einem diskreten Satz von Klassen verschiedener Fahrzeugfahrtrichtungen, und durch Erhalten des Wertes für den Wind-Einflussfaktor durch Lesen aus einer vordefinierten Datenstruktur, die für jede Kombination einer Klasse von Windrichtungen und einer Klasse von Fahrzeugfahrtrichtungen einen jeweiligen voreingestellten Wert für den Wind-Einflussfaktor speichert;
• - Vorhersagen, basierend auf Wetterdaten, die sich auf eine geographische Region beziehen, durch die die Route führt, ob irgendwelche signifikanten wind- oder temperaturbedingten Einflüsse auf den Energieverbrauch des Fahrzeugs während seiner Fahrt entlang der Route zu erwarten sind, und Verwenden oder Ignorieren eines oder mehrerer des Wind-Einflussfaktors, des temperaturbezogenen Batterieverbrauchsfaktors und des Temperatur-Einflussfaktors als eine Funktion des Ergebnisses der Vorhersage;
• - Darstellen des jeweiligen erhaltenen Wertes von mindestens zwei der Einflussfaktoren, die zum Bestimmen der Vorhersage für den Energieverbrauch des Fahrzeugs für die Route verwendet werden, durch einen diskreten Wert aus einem jeweiligen diskreten Satz zulässiger Werte, Codieren der diskreten Werte der mindestens zwei Einflussfaktoren, um einen Code zu erhalten, der alle diskreten Werte darstellt, und Erhalten eines Wertes des kombinierten Einflusses der mindestens zwei Einflussfaktoren auf den Energieverbrauch des Fahrzeugs durch Lesen aus einer vordefinierten Datenstruktur, die für jede mögliche Variation des Codes einen jeweiligen voreingestellten Wert speichert, der einen kombinierten Einfluss der mindestens zwei Einflussfaktoren auf den Energieverbrauch des Fahrzeugs darstellt.

In some embodiments, determining the respective values for a set of one or more energy consumption influencing parameters of the energy consumption model for the vehicle includes one or more of the following measures:

• - pre-calculating and/or storing corresponding values for one or more of the factors surface influence factor, curve influence factor and route topology factor either for the route as a whole or, if the route is divided into a set of route segments, for each of the individual route segments;
• - determining a respective value for the wind influence factor based on a respective linearly approximated wind direction determined for the route as a whole or for each of the route segments individually;
• - determining a respective value for the wind influence factor on the basis of classifying the wind directions according to a discrete set of classes of different wind directions and the vehicle travel directions according to a discrete set of classes of different vehicle travel directions, and by obtaining the value for the wind influence factor by reading out a predefined data structure storing, for each combination of a class of wind directions and a class of vehicle travel directions, a respective default value for the wind influence factor;
• - Predicting, based on weather data relating to a geographical region through which the route passes, whether any significant wind or temperature-related influences on the energy consumption of the vehicle during its travel along the route are to be expected, and using or ignoring one or a plurality of the wind factor, the temperature-related battery consumption factor, and the temperature factor as a function of the result of the prediction;
• - representing the respective obtained value of at least two of the influencers used to determine the vehicle energy consumption prediction for the route by a discrete value from a respective discrete set of allowed values, encoding the discrete values of the at least two influencers in order to to obtain a code that represents all discrete values, and obtaining a value of the combined influence of the at least two influencing factors on the energy consumption of the vehicle by reading from a predefined data structure that stores for each possible variation of the code a respective preset value that has a combined Represents influence of at least two influencing factors on the energy consumption of the vehicle.

Such embodiments can provide an effective way to reduce the required computational resources and improve the efficiency of the method. Instead of using exact values, one or more of the influencing factors can be discretized and combined in a fingerprint/bitfield-like structure. In an exemplary embodiment, e.g. B. 18 bits encode the current consumption situation and the dynamic impact could be retrieved from a lookup table with only 2^18=262144 values. Such a look-up table can be calculated in advance and the entire impact calculation would only require a single memory access. This could also be implemented in hardware to further reduce runtime. Discretization could also reduce the amount of data to be transmitted to a mobile device, i.e. not the raw weather data could be transmitted, only the reduced set (e.g. 3 bits for temperature and 3 bits for wind direction).

In some embodiments, the method further comprises updating the energy consumption model by adjusting it based on one or more of the following elements: a comparison of the energy consumption predicted for the route using the energy consumption model with a corresponding actually measured energy consumption that is calculated for the same vehicle or a or several comparable other vehicles along the route has been detected, and training data relating to previously undetected geographic regions or unknown route conditions. Comparing the predicted energy consumption with the corresponding energy consumption can improve the prediction. For example, data for at least one comparable region, a comparable vehicle or a comparable weather situation can be taken into account.

A second aspect of the present invention is directed to a route planning method for determining an optimal route between a starting point and a destination point for a vehicle, the route planning method comprising: (i) predicting a respective energy consumption of the vehicle, in particular a battery electric vehicle (BEV) or a hybrid electric vehicle (HEV), for each of a set of different possible routes between the starting point and the destination point according to the method of any one of the preceding claims; and (ii) selecting or suggesting, in particular using a network-based route planning algorithm, an optimal route from the set of routes according to a defined optimization criterion depending on the predicted respective energy consumption of the vehicle for the various routes in the set of routes.

According to the route planning method of the second aspect of the present invention, there is provided a method for determining a route between a starting point and a destination using the aforementioned prediction of energy consumption. Especially for electric driving vehicles (BEV or HEV), the energy consumption may be of interest for the selection or suggestion of an optimal route, while the energy consumption in route planning for traditional internal combustion engine vehicles, which typically have a longer range or can at least operate in a well-equipped filling station network, is not of particular importance.

In particular, the method of the second aspect can be carried out according to one or more of the following embodiments.

In some embodiments of the route planning method of the second aspect, the optimization criterion is defined such that the route that is selected as the optimal route from the set of routes is optimal in that it has the lowest predicted energy consumption. Selecting the route with the lowest predicted energy consumption can ensure that the route can be traveled safely without draining the battery. Apart from that, the route with the lowest energy consumption is a sensible choice to optimize the travel costs and is also advantageous from an environmental point of view. In some embodiments, the route planning method further comprises: predicting a respective travel time between the starting point and the destination point along the respective route for the vehicle and each of the routes in the group of routes; wherein the optimization criterion is defined such that the route selected or suggested as the optimal route from the group of routes is optimal in that it has the lowest predicted travel time weighted by a factor reflecting the predicted energy consumption of the same route. Instead of the route with the lowest energy consumption, other users could prefer a route with the lowest estimated travel time in order to reach their destination sooner. Because the vehicle's energy consumption and travel time can be related, it is advantageous to weight the travel time with a factor that reflects the expected energy consumption. For example, the energy consumption can be higher at a higher driving speed and, conversely, lower at a lower driving speed.

In some embodiments of the route planning method of the second aspect, the prediction of a respective energy consumption of the vehicle for each of a set of different possible routes between the starting point and the destination comprises the prediction of a respective energy consumption of the vehicle for each of the possible routes depending on different energy consumption-related settings of the vehicle; and the selection or suggestion of an optimal route from the set of routes according to a defined optimization criterion, the selection or suggestion of the optimal route as a function of both the predicted respective energy consumption of the vehicle for the various routes in the set of routes and the various settings of the vehicle includes. The energy consumption of a vehicle can also depend on various settings of the vehicle, e.g. B. settings that control a driving mode, or settings for passenger comfort. Therefore, it can be advantageous to provide an optimization criterion that not only takes into account the predicted energy consumption, but also the different settings of the vehicle.

• (i) Ermitteln eines Referenzwerts für den Energieverbrauch eines Fahrzeugs, insbesondere eines batteriebetriebenen Elektrofahrzeugs (BEV) oder eines Hybridelektrofahrzeugs (HEV), für seine Fahrt entlang einer bestimmten Route zwischen einem bestimmten Startpunkt und einem bestimmten Zielpunkt;
• (ii) Erhalten, insbesondere Berechnen auf der Grundlage von Eingabedaten oder Empfangen, von entsprechenden Werten für einen Satz von einem oder mehreren Energieverbrauchseinflussparametern eines Energieverbrauchsmodells für das Fahrzeug für seine Fahrt entlang der Route, wobei der Satz von Energieverbrauchseinflussparametern in dem Energieverbrauchsmodell einen oder mehrere der folgenden Einflussfaktoren auf den Energieverbrauch des Fahrzeugs entlang der Route darstellt:
  • - einen Kurven-Einflussfaktor, der einen von Straßenkurven abhängigen Einfluss auf den Energieverbrauch des Fahrzeugs definiert;
  • - einen Wind-Einflussfaktor, der den windabhängigen Einfluss auf den Energieverbrauch des Fahrzeugs definiert;
  • - einen Fahrstil-Einflussfaktor, der einen vom Fahrstil abhängigen Einfluss auf den Energieverbrauch des Fahrzeugs definiert;
  • - einen Reifendruck-Einflussfaktor, der einen reifendruckabhängige Einfluss für einen ausgewählten Fahrer auf den Energieverbrauch des Fahrzeugs definiert;
  • - einen temperaturbedingten Batterieverbrauchs-Einflussfaktor, der einen von der Umgebungstemperatur abhängigen Einfluss auf die Energieversorgungsfähigkeit einer Antriebsbatterie des Fahrzeugs und/oder auf den Energieverbrauch eines aktiven Kühl- und/oder Heizsystems der Antriebsbatterie des Fahrzeugs definiert;
  • - einen Massen-Einflussfaktor, der einen von der Gesamtmasse des Fahrzeugs abhängigen Einfluss auf den Energieverbrauch des Fahrzeugs definiert;
  • - einen Temperatur-Einflussfaktor, der einen von der Umgebungstemperatur abhängigen Einfluss auf den Energieverbrauch des Fahrzeugs definiert;
  • - einen Routentopologie-Einflussfaktor, der einen vom Höhenprofil abhängigen Einfluss auf den Energieverbrauch des Fahrzeugs definiert;
• (iii) Schätzen eines Wertes eines Oberflächen-Einflussfaktors, der in dem Energieverbrauchsmodell einen straßenoberflächenabhängigen Einfluss auf den Energieverbrauch des Fahrzeugs definiert; und
• (iv) Bestimmen eines Oberflächenzustands einer Straße auf der Grundlage der Route, des Energieverbrauchsmodells und des erhaltenen einen oder mehrerer Werte des Satzes von Energieverbrauchseinflussparametern.

A third aspect of the present invention relates to a method for determining the surface condition of a road, the method comprising:

• (i) determining a reference value for the energy consumption of a vehicle, in particular a battery-powered electric vehicle (BEV) or a hybrid electric vehicle (HEV), for its journey along a specific route between a specific starting point and a specific destination;
• (ii) obtaining, in particular calculating on the basis of input data or receiving, corresponding values for a set of one or more energy consumption influencing parameters of an energy consumption model for the vehicle for its journey along the route, wherein the set of energy consumption influencing parameters in the energy consumption model contains one or more of following influencing factors on the energy consumption of the vehicle along the route:
  • - a curve influence factor that defines a road curve-dependent influence on the energy consumption of the vehicle;
  • - a wind influence factor, which defines the wind-dependent influence on the energy consumption of the vehicle;
  • - a driving style influencing factor that defines a driving style-dependent influence on the energy consumption of the vehicle;
  • - a tire pressure influencing factor that defines a tire pressure-dependent influence for a selected driver on the energy consumption of the vehicle;
  • - a temperature-related battery consumption influencing factor, which has an influence on the energy supply capability of a battery which is dependent on the ambient temperature Drive battery of the vehicle and / or defined on the energy consumption of an active cooling and / or heating system of the drive battery of the vehicle;
  • - a mass influencing factor which defines an influence on the energy consumption of the vehicle which is dependent on the total mass of the vehicle;
  • - a temperature influencing factor which defines an influence on the energy consumption of the vehicle which is dependent on the ambient temperature;
  • - a route topology influencing factor which defines an influence on the energy consumption of the vehicle which is dependent on the height profile;
• (iii) estimating a value of a surface impact factor defining a road surface dependent impact on the energy consumption of the vehicle in the energy consumption model; and
• (iv) determining a surface condition of a road based on the route, the energy consumption model and the obtained one or more values of the set of energy consumption impact parameters.

According to the third aspect of the invention, a method can be provided which does not use the surface condition as a surface factor for predicting the energy consumption, but conversely determines the surface condition of a road with the energy consumption model where the surface condition is not known. This determined parameter can then be used, for example, for other users and/or future energy consumption forecasts.

A fourth aspect of the present invention is directed to a data processing system comprising means for performing the method of the first aspect and/or the method of the second aspect and/or the third aspect, in particular according to one of the embodiments described herein.

A fifth aspect of the present invention relates to a computer program or a computer program product containing instructions which, when the program is executed by a computer or a distributed computing system, cause the computer or the distributed computing system to perform the method of the first aspect and/or the carry out the method of the second aspect and/or the third aspect, in particular according to one of the embodiments described herein.

The computer program (product) can in particular be implemented in the form of a data carrier on which one or more programs for carrying out the method are stored. This is preferably a data carrier, such as a CD, DVD or other optical medium, or a flash memory module. This can be advantageous if the computer program product is to be traded as a single product independent of the processor platform on which the one or more programs are to be executed. In another embodiment, the computer program product is provided as a file on a data processing unit, in particular on a server, and can be accessed via a data connection, e.g. B. the Internet or a dedicated data connection, such as a company or local area network, can be downloaded.

The system of the second aspect can accordingly have a program memory in which the computer program is stored. Alternatively, the system can also be set up to access a computer program that is available externally, for example on one or more servers or other data processing units, via a communication connection, in particular to exchange data with it that are used in the course of executing the computer program or outputs of the computer program represent.

The explanations, embodiments and advantages described above in connection with the method of the first aspect also apply in a similar manner to the other aspects of the invention.

character list

• 1 zeigt schematisch eine beispielhafte Ausführungsform eines Energieverbrauchsmodells;
• 2 zeigt schematisch eine beispielhafte Ausführungsform eines Verfahrens zur Vorhersage des Energieverbrauchs;
• 3 zeigt schematisch eine beispielhafte Ausführungsform eines Routenplanungsverfahrens; und
• 4 zeigt schematisch eine beispielhafte Ausführungsform eines Verfahrens gemäß der vorliegenden Erfindung.

Further advantages, features and applications of the present invention are illustrated in the following detailed description and the accompanying drawings, in which:

• 1 schematically shows an exemplary embodiment of an energy consumption model;
• 2 12 schematically shows an exemplary embodiment of a method for predicting energy consumption;
• 3 schematically shows an exemplary embodiment of a route planning method; and
• 4 Figure 12 shows schematically an exemplary embodiment of a method according to the present invention.

1 shows an exemplary embodiment of an energy consumption model 1, the how can be created as described below. The model 1 can then be used to predict a vehicle's range and determine the most efficient route between a starting point and a destination using data such as live vehicle data, weather forecasts and road quality information. An overview of such an overall process is given below, while exemplary embodiments of methods according to the present invention are described with reference to FIG 2 until 4 to be discribed. It goes without saying that the energy consumption model 1 described can be used wherever it is suitable. The same applies to the overview given of phases 1 to 4 of an example process, which also refers to each of the in connection with the 2 until 4 described exemplary methods can be used wherever appropriate.

The energy consumption model 1 can include various energy consumption influencing parameters that represent corresponding influencing factors. The model takes into account several types of influencing factors, which can be referred to as static vehicle specifications 10 , local weather and road conditions 20 , and dynamic usage parameters 30 . All these different parameters influence in some way the energy consumption of a vehicle, especially an electric vehicle, e.g. B. a battery electric vehicle (BEV) or a hybrid electric vehicle (HEV). The values for the respective energy consumption parameters 10, 20, 30 can be determined as described below. The parameters may be determined in any suitable manner, including reading sensor data from the vehicle, receiving data from an external data source, or entering it manually.

A battery capacity 11 can be taken from the technical data of the vehicle. The average consumption 12 can be obtained from a data set present in the vehicle, e.g. B. from sensor data. The current state of charge 13 can also be taken from such a data set. In addition, a charge strategy 14 may be considered, which may relate to user-defined levels to which the battery should be charged or discharged. A route planning method can search for nearby charging stations to take into account the charging strategy, e.g. B. to avoid the battery falling below a critical value. A charging plug type 15 can also be taken into account in order to only find suitable charging stations. Such data can e.g. B. can be obtained from the OpenChargeMap. The charging strategy can also contain other restrictions, such as e.g. B. personal preferences of a user, e.g. B. the preference for a charging station at home, maximum prices to be taken into account or the preference for fast charging stations. For example, charging at home or at subsidized charging stations can be significantly cheaper than at other locations. Manual selection of such route options is almost impossible. A charging strategy can also be important because the network of charging stations is not (yet) as dense as that of petrol stations and most charging stations are located in urban areas or along motorways. And even if there is a dense network of charging stations, the driver may not want to wait half an hour to continue driving.

Road conditions can have a major impact on vehicle energy consumption. The road condition 21 can e.g. B. can be taken from the street classification and the street surface tag of OpenStreetMap. The topology of the route or the height profile 22 can be taken from a digital model, e.g. B. the SRTM Digital Elevation Model. The energy consumption increases on road sections that lead uphill, whereas a positive effect on consumption can be achieved when driving downhill through recuperation. The road curvature 23 (road curves) can be calculated for each road segment or obtained from map data or vehicle sensors. With respect to weather conditions, the outside temperature 24 may be determined from weather data or vehicle sensors. Likewise, wind data 25 can be obtained from weather data. Depending on the strength and direction of the wind, energy consumption can increase or decrease.

The vehicle's energy consumption can also depend on dynamic usage parameters 30, ie parameters that can change from trip to trip or even during a trip. The tire pressure 31 can be determined by corresponding vehicle sensors. For example, driving with tire pressure that is too low can increase energy consumption. Heating, ventilation and air conditioning (HVAC) 32 can have a major impact on energy consumption. In other words, the vehicle's energy consumption is greatly affected when a user turns the heating or air conditioning on or off. The mass 33 of the vehicle may depend on the number of passengers, e.g. B. can be determined by the respective seat sensors. Other aspects affecting the mass of the vehicle, e.g. B. additional cargo or luggage, can be entered manually or derived from sensor data. Driving style 34 also has one Impact on energy consumption. It can be determined from current vehicle data.

With the energy consumption model 1 off 1 an example of how model 1 can be set up and used will now be described. It becomes clear that the energy consumption model 1 can be a model for predicting the range, since the range of the vehicle depends on the energy consumption. The process described below is divided into four phases for better understanding. It may be advantageous to determine some of the parameters for the entire trip while other parameters are determined for each road segment. In phase 1, the energy consumption model is created. In particular, the values for the energy consumption parameters are determined, as also briefly described above and explained in more detail below.

In the example, an average vehicle consumption 12, a mass factor 33, a driving style factor 34 and a tire pressure factor 31 are determined for the entire journey. Average vehicle mileage 12 may be determined from vehicle specification, historical average mileage, or average mileage of comparable vehicles in a comparable geographic region. The mass factor 33 may specify how much heavier the vehicle is than the standard vehicle in the vehicle specification, taking into account the number of passengers and additional cargo or luggage. This factor can be obtained from manual inputs, average passenger weight (e.g. 75 kg) or from vehicle sensors, e.g. B. weight sensors or door opening and closing processes are determined. The driving style factor 34 can be determined from manual inputs or from measurements, e.g. B. "How often has the driver exceeded the speed limit in the past", "How often has the calculated power consumption been exceeded", "How often has recuperation taken place", "How often has emergency braking been carried out", "How often have systems such as ESP, ABS triggered" or something like that. For example, the tire pressure factor 34 may represent how much lower the tire pressure is than the optimal value. This impact can be determined based on physical assumptions (e.g. 5% per bar below the optimal pressure) or measurements from the vehicle sensors.

With the help of the influencing factors mentioned above, a basic consumption can be calculated, e.g. B. by multiplying the average vehicle consumption 12, the mass factor 33, the driving style factor 34 and the tire pressure factor 31.

In the further course of determining values for the energy consumption influencing parameters, certain influencing factors can be determined for each road section, such as e.g. a surface influence factor 21, a curve influence factor 23, a wind influence factor 25, a temperature-related energy consumption influence factor 24, an influence factor for weather-related consumption profiles of heating, ventilation and air conditioning systems 32 and an altitude influence factor 22.

The surface influence factor 21 can be determined depending on the road surface. For example, it can be determined how much more consumption can be expected than on a slippery road (which can be assigned a value of 1.0). The surface type can be derived from OpenStreetMap data, satellite imagery, or vehicle sensor recordings from vehicles traveling on the same road or similar roads in the same region. Typical values might be: paved, smooth roads = 0.0; cobbled, rough roads = 0.02; compacted roads = 0.1; dirt roads = 0.3; gravel roads = 1.0; almost impassable roads = 10.0.

In particular, the curve influence factor 23 can express that the energy consumption for a curved road is higher than for a straight road. The influence can be determined by deriving the road curvature from the road geometries on a map.

The wind influence factor 25 indicates the influence of the wind on the energy consumption. Headwinds increase energy requirements, while tailwinds reduce energy consumption. A relative wind influence can be determined from the vehicle orientation, the wind direction and the wind force. This can be weighted with a factor that is determined by manual input or by a vehicle-specific drag coefficient that is defined by the shape of the vehicle and z. B. comes from CAD data or the vehicle specifications. This can be important as wind influence can depend on the shape of the vehicle. The aerodynamics of the vehicle have a major impact on drag. This value may not even be constant for a vehicle, e.g. For example, the bed of a typical US pickup truck may be open or closed, or contain structures that increase the drag coefficient.

The temperature-related battery consumption influencing factor 24 can be determined using static formulas or characteristic curves. The ambient temperature influences the consumption in the form of active battery heating or cooling as well as (chemical) energy losses when it is below or above the sweet spot of around 23°C. At temperatures above 23 °C, increased consumption can be assumed in a range up to 35 °C, and even higher consumption at temperatures below 23 °C in a range down to -7 °C. For example, the temperature impact factor can be set to 5% for high temperatures and 10% for low temperatures. The properties of the vehicle or battery properties specific to the region or climate zone can be taken into account.

The effects of weather-related heating, ventilation, and air conditioning (HVAC) 32 consumption profiles can be determined by obtaining weather data from a global weather forecast model. If the HVAC is active, a significant temperature-dependent increase in consumption can be assumed. Again, lower temperatures lead to higher consumption as it is more expensive to heat cold air in winter. Here the HVAC influencing factor e.g. For example, set to 20% for high temperatures and 70% for low temperatures.

The elevation impact factor 22 may be determined based on the maximum uphill and downhill gradients for each road segment. This data can be taken from a digital elevation model (e.g. SRTM with a grid resolution of 30 m, which is assigned to the road segment). Based on this information, it can be assumed that there is increased consumption going uphill and energy gain going downhill. Since there are other consumers in the vehicle besides the engine that have to be supplied with energy, the advantage of a negative gradient can be provided with a threshold value that defines when the recuperated energy becomes effective.

A total consumption within a road section can, for. B. can be determined by multiplying the length of the road section with the basic consumption and all section-related influencing factors. In this way, a prediction can be made for the energy consumption of a vehicle for its journey along the stated route.

In phase 2, the energy prediction can be used to find the most efficient route between the start point and the destination point. If all or some of the above parameters are known, the total energy consumption can be integrated as a cost factor into a route planning algorithm, so that a route can be selected that is the most efficient or a combination of fastest and most efficient route (weighed by an efficiency factor) . The route planning algorithm can also provide suggestions for saving energy (e.g. through recuperation, showing the effects of air conditioning or personal driving style on consumption). Technically, this can be done by calculating the optimal route for a number of different settings (ensemble) and visualizing the consumption differences in relation to the current settings.

In this scenario, a challenge can be how to make the dynamic data available to the route planning algorithm. Typically, a routing algorithm (e.g., bidirectional A*) must visit and cost many edges of the routing graph, where an edge of the routing graph may represent a road segment or a portion of a road segment. Therefore, a complex calculation of many influencing factors would be difficult or not possible at all. As explained below, some simplification steps can be performed in phase 2 in order to approximate the road-specific influencing factors without calculating the exact value as in phase 1.

The (almost) static influencing factors (surface, curves and heights) can be calculated and stored in advance for each road section (this step only needs to be performed when the surface information is updated, but generally remains constant). Wind direction can only be calculated for the general direction of the street segment (the vector from the start point to the destination point) and not for each edge within a street segment. This significantly reduces the number of wind direction queries.

In a pre-processing step, it can first be determined whether significant weather influences are to be expected in the region through which the route is to lead. First, an area of interest can be defined, e.g. B. Regions where the route is most likely to pass. This can e.g. B. be a rectangle, a circle or an ellipse, which is greater than the distance between the starting point and the destination point by a certain amount, with the midpoint between the starting point and the destination point. Then it can be checked whether there are relevant temperatures (more than x degrees above/below the temperature sweet spot) in this region. It can also be checked whether the wind strength in this region is above a certain threshold. If neither the temperature nor the wind force are above the threshold values, the dynamic influence factors for this route planning request can be set to a fixed value of 1. To this way, no weather queries would be carried out and the query would be significantly faster.

Determining wind direction usually requires evaluating trigonometric functions, which is usually slow (slower than simple math). Therefore, the wind direction and vehicle direction can be partitioned into a discrete set of directions (e.g. 8 or 16 discrete directions; e.g. North = 0, North-East = 1, etc.) and the trigonometric functions replaced by a matrix search (e.g. vehicle direction North = 0 and wind direction South = 4 would result in a value of 1 indicating the maximum influence).

All of these steps can of course also be performed in phase 1, but this is particularly critical when the cost function of the route planning algorithm has to be evaluated thousands and millions of times.

In a further optimization step, all influencing factors can be discretized and combined in a fingerprint/bitfield-like structure, e.g. B. 3 bits for 8 different road surface influence classes, 3 bits for 8 vehicle directions, 3 bits for 8 wind directions, 2 bits for 4 wind speeds, 3 bits for 8 temperatures, 4 bits for 8 slope values (e.g. one bit for up/ down and 3 bits for the amount of incline). Thus 18 bits would encode the current consumption situation and the dynamic effects could be retrieved from a lookup table with only 2^18=262144 values. This look-up table can be precomputed and the entire calculation of the effects would require only a single memory access.

The optimization steps mentioned above could also be implemented in hardware to further reduce runtime. Discretization could also reduce the amount of data to be transmitted to a mobile device, i.e. not the raw weather data is transmitted, only the reduced set (e.g. 3 bits for temperature and 3 bits for wind direction).

In a phase 3, live vehicle data can be used to obtain more accurate energy consumption estimates (consumption profiles) for each road segment. For this purpose, a live consumption recording can be carried out in the electric vehicle or in a vehicle fleet. The consumption values and the associated dynamic data (elevation, weather, driving style, tire pressure, vehicle configuration) can be stored in a database for this location, this data and this time.

A machine learning classifier can then be trained to predict road segment specific consumption based on the consumption database (the simplification steps above can also be applied). The classifier can be evaluated for each route planning query, range forecast or consumption forecast in order to obtain a consumption estimate from comparable regions, comparable vehicles or comparable weather situations.

It may be preferable to only record data when no traffic impact is known (as this is an unpredictable parameter and can mislead the classifier). Other filter methods can be used alternatively or additionally in order to avoid unrealistic consumption forecasts. Based on the machine learning-based prediction, the static and dynamic formulas from phase 1 can then be adjusted.

In phase 4, the current consumption values can be compared with the consumption forecasts in order to (i) continuously update the forecast model or (ii) derive estimates for the impacts on the road surface. The energy consumption forecast model can be continuously updated, e.g. B. if the consumption forecast is too far off. The model can also be retrained with data from previously undiscovered regions or unknown conditions. The comparison can also be used to derive estimates for the impacts on the road surface. If all other values are available but no information about the road surface (this is the most difficult value to obtain as it can change over time and manual estimation is prone to error), the influence of the road surface can be inferred from the consumption data . Strictly speaking, it is not the type of covering that can be derived, but its influence on energy consumption. In general, this could be used to fill in gaps in the basemap for road segments where no road surface or road condition data is available by default.

If all the values are available, provision can also be made for deriving deterioration of the roads, in particular of the road surface, when consumption increases continuously over time. To do this, the consumption trend for each road section must be continuously monitored, i.e. a deviation from the forecast consumption can be saved for a passage of a road section and trends can be analyzed.

In 2 an exemplary embodiment of a method according to the first aspect of the invention is described. More precisely shows 2 an example method 100 for predicting energy consumption of a vehicle. The method 100 may essentially include the steps discussed above with respect to phase 1.

In a first step S11, the values for the energy consumption parameters are determined. These parameters can include one or more or all of the in 1 reflect the influencing parameters shown. They can be determined as described above in relation to phase 1 of the overall process. The values obtained for the energy consumption parameters are the input data for the energy consumption model 1 as described above. In a further step S12, a prediction for the energy consumption of the vehicle can then be made on the basis of a route, the energy consumption model 1 and the values obtained from the set of energy consumption parameters.

A further step S13 for updating the values of the energy consumption parameters can be carried out. The updated values can then be used to evaluate the energy consumption model 1. The update can be done using live vehicle data as described above in relation to phase 3 of the overall process. In particular, at least one of the measured actual energy consumptions and the measured actual values for the respective energy consumption influencing parameters can replace the previous values or at least refine them. In this way, more accurate consumption estimates (forecasts) can be created.

A step S14 for updating the energy consumption model 1 can also be performed as described above in relation to phase 4(i). This can be done by comparing the predicted energy consumption with an actually measured energy consumption measured for the same vehicle on this route or for one or more comparable vehicles on this route. Updating the energy consumption model 1 can also include training the energy consumption model with data from geographical regions or route conditions not previously recorded.

With reference to 3 an exemplary embodiment of a method according to the second aspect of the invention will now be described. More precisely shows 3 an exemplary route planning method 200 for determining an optimal route between a starting point and a destination point for a vehicle. The method 200 may essentially include the steps discussed above with respect to phase 2.

In a first step S21, the energy consumption of the vehicle can be predicted according to the method 100 described above. In particular, the energy consumption can be predicted for each of the different possible routes between the starting point and the destination point. In a step 23, an optimal route can be selected or suggested from the set of routes. This can be done using a defined optimization criterion, e.g. the route with the lowest predicted energy consumption. The method 200 can include a further step S22 for predicting a travel time for each route from the set of routes. The travel time can then also be used as an optimization criterion, e.g. for selecting or suggesting the route with the lowest predicted travel time.

In 4 an exemplary embodiment of a method according to the third aspect of the invention will now be described. More precisely shows 4 an example method 300 for determining a surface condition of a road. The method 300 may essentially include the steps discussed above in relation to phase 4(ii).

As described above in phase 4(ii) of the overall process, it is conceivable to obtain information about the road surface when no value for the surface influence parameter 21 is available. In a first step S31, a reference value for the energy consumption of a vehicle for its journey along a defined route can be determined. In a step S32, the values for the aforementioned energy consumption parameters, with the exception of the surface influencing factors, can be determined as described above in relation to phase 1. A value for the surface influence parameter 21 can then be estimated in a step S33. A surface condition of the road can then be determined in a step S34 on the basis of this estimated surface influencing parameter, the energy consumption model 1 and the values obtained for the other energy consumption influencing parameters.

While some exemplary embodiments of the present invention have been described above, it should be appreciated that there are a large number of variations thereto. Furthermore, it is noted that the exemplary embodiments described represent only non-limiting examples of how the present invention may be implemented and is not intended to limit the scope of application or to limit the configuration of the devices and methods described herein. Rather, the foregoing description is intended to provide those skilled in the art with constructions for implementing at least one exemplary embodiment of the invention, and it is understood that various changes in functionality and arrangement of elements of the exemplary embodiment may be made without departing from the scope of the appended claims and their statutory instruments Correspondence defined object to deviate.

Reference List

1

energy consumption model

10

static vehicle specifications

11

battery capacity

12

average consumption

13

current load

14

loading strategy

15

charging plug type

20

local weather and road conditions

21

Surface Impact Factor

22

Route Topology Impactor

23

curve influencer

24

temperature influence factor

25

wind factor

30

dynamic usage parameters

31

tire pressure influencing factor

32

Ventilation and Air Conditioning Influencing Factor

33

Mass Influencer

34

driving style influencer

100

Exemplary embodiment of a method for predicting the energy consumption of a vehicle

200

Exemplary method for determining an optimal route between a starting point and a destination point for a vehicle

300

Example of a method for determining the condition of a road surface

Claims (16)

A method (100) for predicting an energy consumption of a vehicle for its journey along a defined route between a given starting point and a given destination, the method comprising: Obtaining (S11) corresponding values for a set of one or more energy consumption influencing parameters of an energy consumption model (1) for the vehicle, wherein the set of energy consumption influencing parameters in the energy consumption model represents one or more of the following influencing factors on the energy consumption of the vehicle along the route: - a surface influence factor (21) which defines a road surface-dependent influence on the energy consumption of the vehicle; - a curve influence factor (23) which defines a curve-dependent influence on the energy consumption of the vehicle; - a wind influence factor (25) which defines a wind-dependent influence on the energy consumption of the vehicle; - A driving style influencing factor (34) which defines a driving style-dependent influence on the energy consumption of the vehicle; - a tire pressure influencing factor (31) which defines a tire pressure-dependent influence for a selected driver on the energy consumption of the vehicle; - a temperature-related battery consumption influencing factor (32) which defines an influence on the energy supply capability of a drive battery of the vehicle and/or on the energy consumption of an active cooling and/or heating system of the drive battery of the vehicle which is dependent on the ambient temperature; determining (S12) a prediction for the energy consumption of the vehicle for the route based on the route, the energy consumption model and the obtained one or more values of the set of energy consumption impact parameters. procedure after claim 1 , wherein: the set of energy consumption influence parameters of the energy consumption model further comprises one or more of the following additional energy consumption influence parameters: - a mass influence factor (33) defining an influence on the energy consumption of the vehicle dependent on the total mass of the vehicle; - a temperature influencing factor (24) which defines an influence on the energy consumption of the vehicle which is dependent on the ambient temperature; - a route topology influencing factor (22) which defines an influence on the energy consumption of the vehicle which is dependent on the elevation profile; obtaining (S11) the respective values for the set of energy consumption impact parameters; obtaining the respective values for said one or more additional energy consumptions its flow parameters includes; and determining (S12) a vehicle energy consumption prediction for the route further based on the obtained one or more values of the additional energy consumption impact parameters. A method according to any one of the preceding claims, wherein one or more of the energy consumption impact parameters are each defined as a numerical parameter related as a factor to a corresponding predefined reference parameter. A method according to any one of the preceding claims, wherein: the route is divided into a number of route segments; obtaining (S11) corresponding values for a set of one or more energy consumption impact parameters comprises obtaining for at least one of the energy consumption impact parameters corresponding segment-specific values on a per route segment basis; and determining (S12) a forecast for the energy consumption of the vehicle for the route, calculating a corresponding segment-specific energy consumption of the vehicle for each of the route segments on the basis of the obtained one or more values of the energy consumption influencing parameters including the segment-specific values and integrating the calculated segment-specific energy consumption in order to to obtain the energy consumption of the vehicle for the entire route. The method according to any one of the preceding claims, further comprising updating (S13) one or more of the received values of the set of energy consumption impact parameters as a function of received consumption data that includes a measured actual energy consumption and/or measured actual respective values for one or more of the Represent energy consumption influencing parameters of the set of energy consumption influencing parameters for a journey of one or more reference vehicles along the entire route or the at least one road section thereof. procedure after claim 5 , wherein determining (S12) the prediction for the vehicle's energy consumption for the entire route or the at least one road segment thereof is further based on the application of a machine learning-based classifier to the consumption data and/or the updated one or more values of the set of Energy consumption influencing parameters is based. procedure after claim 5 or 6 , further comprising: determining whether and, if so, to what extent the consumption data has been affected by traffic; and selectively use only such consumption data or components thereof as a basis for updating one or more of the values of the set of energy consumption influencing parameters and/or as a basis for determining the prediction for the vehicle's energy consumption for the route, none for the consumption data or components thereof such impairment has been determined. Method according to one of the preceding claims, wherein obtaining (S11) the respective values for a set of one or more energy consumption influencing parameters of the energy consumption model for the vehicle comprises one or more of the following measures: - pre-calculating and/or storing corresponding values for one or more of the surface influence factor, curve influence factor and route topology factor either for the route as a whole or, if the route is divided into a set of route segments, for each of the individual route segments; - determining a respective value for the wind influence factor based on a respective linearly approximated wind direction determined for the route as a whole or for each of the route segments individually; - determining a respective value for the wind influence factor on the basis of classifying the wind directions according to a discrete set of classes of different wind directions and the vehicle travel directions according to a discrete set of classes of different vehicle travel directions, and by obtaining the value for the wind influence factor by reading out a predefined data structure storing, for each combination of a class of wind directions and a class of vehicle travel directions, a respective default value for the wind influence factor; - Predicting, based on weather data relating to a geographical region through which the route passes, whether any significant wind or temperature-related influences on the energy consumption of the vehicle during its travel along the route are to be expected, and using or ignoring one or a plurality of the wind factor, the temperature related battery consumption factor, and the temperature factor as a function of the outcome of the prediction; - presenting the respective obtained values of at least two of the influencing factors used to determine the prediction for the vehicle's energy consumption for the route den, by a discrete value from a respective discrete set of allowed values, encoding the discrete values of the at least two influencers to obtain a code representing all discrete values, and obtaining a value of the combined influence of the at least two influencers on the energy consumption of the Vehicle by reading from a predefined data structure that stores a respective preset value for each possible variation of the code, which represents a combined influence of the at least two influencing factors on the energy consumption of the vehicle. Method according to any one of the preceding claims, further comprising updating (S14) the energy consumption model by adjusting it based on one or more of the following: - a comparison of the energy consumption predicted for the route using the energy consumption model with a corresponding energy consumption actually measured, which was recorded for the same vehicle or one or more comparable other vehicles along the route; and - Training data related to previously undiscovered geographical regions or unknown route conditions. A route planning method (200) for determining an optimal route between a starting point and a destination point for a vehicle, the route planning method comprising: Predicting (S21) a respective energy consumption of the vehicle for each of a set of different possible routes between the starting point and the destination according to the method (100) according to any one of the preceding claims; and Selecting or suggesting (S23) an optimal route from the set of routes according to a defined optimization criterion as a function of the predicted respective energy consumption of the vehicle for the different routes in the set of routes. route planning procedure claim 10 , wherein the optimization criterion is defined such that the route that is selected as the optimal route from the set of routes is optimal in that it has the lowest predicted energy consumption. route planning procedure claim 10 or 11 , further comprising: predicting (S22) a respective travel time between the starting point and the destination point along the respective route for the vehicle and each of the routes in the set of routes; wherein the optimization criterion is defined such that the route selected or proposed as the optimal route from the set of routes is optimal in that it has the lowest predicted travel time weighted by a factor reflecting the predicted energy consumption of the same route. Route planning method according to one of Claims 10 until 12 wherein predicting a respective vehicle energy consumption for each of a set of different possible routes between the starting point and the destination comprises predicting a respective vehicle energy consumption for each of the possible routes as a function of different vehicle energy consumption-related settings; and the selection or suggestion of an optimal route from the set of routes according to a defined optimization criterion, the selection or suggestion of the optimal route as a function of both the predicted respective energy consumption of the vehicle for the various routes in the set of routes and the various settings of the vehicle includes. A method (300) for determining a surface condition of a road, the method comprising: determining a reference value for the energy consumption of a vehicle for its journey along a specified route between a specified starting point and a specified destination; Obtaining (S31) corresponding values for a set of one or more energy consumption influencing parameters of an energy consumption model (1) for the vehicle for its journey along the route, the set of energy consumption influencing parameters in the energy consumption model having one or more of the following influencing factors on the energy consumption of the vehicle along of the route represents: - a curve influence factor (23) which defines a road curve-dependent influence on the energy consumption of the vehicle; - a wind influence factor (25) which defines a wind-dependent influence on the energy consumption of the vehicle; - A driving style influencing factor (34) which defines a driving style-dependent influence on the energy consumption of the vehicle; - a tire pressure influencing factor (31) which defines a tire pressure-dependent influence for a selected driver on the energy consumption of the vehicle; - A temperature-related battery consumption influencing factor (32), the one of the environment temperature-dependent influence on the energy supply capability of a drive battery of the vehicle and/or on the energy consumption of an active cooling and/or heating system of the drive battery of the vehicle; - a mass influencing factor (33) which defines an influence on the energy consumption of the vehicle which is dependent on the total mass of the vehicle; - a temperature influencing factor (24) which defines an influence on the energy consumption of the vehicle which is dependent on the ambient temperature; - a route topology influencing factor (22) which defines an influence on the energy consumption of the vehicle which is dependent on the elevation profile; Estimating (S33) a value of a surface influence factor (21) which defines a road surface-dependent influence on the energy consumption of the vehicle in the energy consumption model (1); and determining (S34) a surface condition of a road based on the route, the energy consumption model and the obtained one or more values of the set of energy consumption impact parameters. Data processing system with means for carrying out the method according to one of Claims 1 until 14 . Computer program with instructions which, when the program is executed by a computer or a distributed computing system, cause the computer or the distributed computing system to carry out the method according to one of Claims 1 until 14 to execute.

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