DE102023122897A1 - Method for predicting the power requirements of auxiliary units of a vehicle - Google Patents

Abstract

The invention relates to a method for predicting a power requirement (PN,1) of auxiliary units of a vehicle, a control device set up to carry out the method and a computer program product. The method includes the steps: - Acquiring vehicle operating data (B) and environmental data (U); - Providing a plurality of prediction models for the power requirement (PN,1) of the auxiliary units, the prediction models being classified via value ranges (KM,1) of a first classification parameter are, wherein a current value (KA,1) of the first classification parameter is part of the recorded vehicle operating data (B) or environmental data (U) and wherein the vehicle operating data (B), the environmental data (U) and a predicted speed (v̂) as input data of the Prediction models serve; - Selecting the prediction model whose value range (KM,1) of the first classification parameter includes the current value (KA,1) of the first classification parameter; - Predicting the power requirement (PN,1) of the auxiliary units for a previous route or for a future one Period using the selected forecast model.

Description

The invention relates to a method for predicting a power requirement of auxiliary units of a vehicle, a control device set up to carry out the method and a computer program product.

From the EP2600106A1 a device for calculating the power requirement of a vehicle is known. In the device, predicted weather data and predicted settings of an air conditioning system are taken into account and the resulting electricity consumption is calculated.

• - Erfassen von Fahrzeugbetriebsdaten (B) und Umgebungsdaten (U);
• - Bereitstellen einer Mehrzahl von Vorhersagemodellen für den Leistungsbedarf (P_N,1) der Nebenaggregate, wobei die Vorhersagemodelle über Wertebereiche (K_M,1) eines ersten Klassifizierungsparameters klassifiziert sind, wobei ein aktueller Wert (K_A,1) des ersten Klassifizierungsparameters Bestandteil der erfassten Fahrzeugbetriebsdaten (B) oder Umgebungsdaten (U) ist und wobei die Fahrzeugbetriebsdaten (B), die Umgebungsdaten (U) sowie eine vorhergesagte Geschwindigkeit (v̂) als Eingangsdaten der Vorhersagemodelle dienen;
• - Auswählen des Vorhersagemodells, dessen Wertebereich (K_M,1) des ersten Klassifizierungsparameters den aktuellen Wert (K_A,1) des ersten Klassifizierungsparameters beinhaltet;
• - Vorhersagen des Leistungsbedarfs (P_N,1) der Nebenaggregate für eine vorausliegende Fahrstrecke oder für einen zukünftigen Zeitraum mittels des ausgewählten Vorhersagemodells.

The method according to the invention for predicting a power requirement (P _N,1 ) of one or more auxiliary units of a vehicle comprises the steps:

• - Acquiring vehicle operating data (B) and environmental data (U);
• - Providing a plurality of prediction models for the power requirement (P _N,1 ) of the auxiliary units, the prediction models being classified over value ranges (K _M,1 ) of a first classification parameter, a current value (K _A,1 ) of the first classification parameter being part of the captured vehicle operating data (B) or environmental data (U) and wherein the vehicle operating data (B), the environmental data (U) and a predicted speed (v̂) serve as input data of the prediction models;
• - Selecting the prediction model whose value range (K _M,1 ) of the first classification parameter includes the current value (K _A,1 ) of the first classification parameter;
• - Predictions of the power requirement (P _N,1 ) of the auxiliary units for a route ahead or for a future period using the selected prediction model.

By predicting the power requirement of the vehicle's auxiliary units, the method according to the invention enables a more precise prediction of the vehicle's total power requirement, an improved range determination and more precise results for other long-term prediction functions, such as the planning of thermal management measures.

Using multiple forecast models allows for better consideration of seasonal effects. Since the power requirement is predicted using different models depending on the boundary conditions, overfitting of the models is advantageously prevented. Such overfitting could occur in particular if a single prediction model is trained over a longer period of time with similar boundary conditions. If changed boundary conditions eventually occur, an overfitted prediction model is unable to provide a sufficiently accurate prediction.

• - Bestimmen des tatsächlichen Leistungsbedarfs (P_N,0) von Nebenaggregaten des Fahrzeugs;
• - Erfassen von Fahrzeugbetriebsdaten (B) und Umgebungsdaten (U), umfassend einen tatsächlichen Wert (K_A,2) eines zweiten Klassifizierungsparameters;
• - Auswählen des Vorhersagemodells, dessen Wertebereich (K_M,2) des zweiten Klassifizierungsparameters mit dem tatsächlichen Wert (K_A,2) des zweiten Klassifizierungsparameters übereinstimmt, als zu trainierendes Vorhersagemodell;
• - Trainieren des ausgewählten Vorhersagemodells mittels des tatsächlichen Leistungsbedarfs (P_N,0), der Fahrzeugbetriebsdaten (B), der Umgebungsdaten (U) und der tatsächlichen Fahrzeuggeschwindigkeit (v).

Preferably, the prediction models are regression models for which the following steps are carried out in a training mode:

• - Determining the actual power requirement (P _N,0 ) of the vehicle’s accessories;
• - collecting vehicle operating data (B) and environmental data (U), comprising an actual value (K _A,2 ) of a second classification parameter;
• - selecting the prediction model whose value range (K _M,2 ) of the second classification parameter corresponds to the actual value (K _A,2 ) of the second classification parameter as the prediction model to be trained;
• - Training the selected prediction model using the actual power demand (P _N,0 ), the vehicle operating data (B), the environmental data (U) and the actual vehicle speed (v).

The steps of selecting the prediction model to be trained via the second classification parameter and training the selected prediction model using the variables mentioned (actual power requirement, vehicle operating data, environmental data and current vehicle speed) enable only the prediction model intended for the respective boundary conditions to be trained and the prediction model based on real data.

The training mode can be used, for example, at the end of each trip, at fixed intervals, after covering a certain distance or depending on other parameters. Frequent and regular training generally increases the accuracy of the prediction models.

Preferably, the time profiles of the actual power requirement (P _N,0 ), the recorded vehicle operating data (B), the environmental data (U) and the actual vehicle speed (v) are determined and temporarily stored during operation of the vehicle and for training when the training mode is used the prediction models used. Accordingly, a large amount of data is available for training the models and, if conditions change, several prediction models can be trained during a single use of the training mode.

In principle, any mathematical or statistical model that can be used to draw conclusions from input variables (here: vehicle operating data and environmental data) to target variables (here: power requirements of auxiliary units) can be used as a regression model. These include, for example, decision tree models, Gaussian process regression models, artificial neural networks and multivariate polynomial regression models.

In a preferred embodiment of the method, as part of training the prediction models, the actual power requirement (P _N,0 ) of the auxiliary units is determined from the difference between a power (P _B ) of a battery of the vehicle and a power requirement (P _D ) of a drive train of the vehicle. This enables a simple but reliable calculation of the actual power requirements of the auxiliary units. In alternative versions, the individual auxiliary units are set up to determine their actual power requirements and transmit them directly to a computing unit, for example via a CAN system.

The auxiliary units preferably include one or more of the following systems: a heating, ventilation and air conditioning system (HVAC system), vehicle lighting, a power steering device, a vacuum pump, a windshield wiper motor, a sensor system, an on-board computer, a multimedia system.

The vehicle operating data (B) preferably includes one or more of the following parameters: elapsed time since the start of the current journey, vehicle speed, driver settings of the auxiliary units, number of occupants, interior temperature.

The driver settings of the auxiliary units can include, for example, a target interior temperature of the HVAC system, settings for seat heating or seat cooling, settings for the operating mode of the HVAC system or settings for a window heater.

The settings for the operating mode of the HVAC system include, for example, the choice of recirculation mode or outside air mode, as well as the status (switched on or switched off, intensity level) of individual fans.

The environmental data (U) preferably includes one or more of the following parameters: ambient temperature, air humidity, irradiance, visibility, precipitation intensity.

In preferred embodiments of the method, the first classification parameter is equal to the second classification parameter; in particularly preferred embodiments, the first and second classification parameters are the ambient temperature T _Env . The ambient temperature T _Env is one of the largest influencing factors on the power requirement of the auxiliary units and is therefore particularly suitable as a classification parameter.

Alternatively, different sizes are used for the first and second classification parameters. In general, a large number of parameters of the environmental data or vehicle operating data come into question for the first and second classification parameters. Furthermore, it is possible for the first or second classification parameters to include several physical variables, for example the ambient temperature T _Env and the relative air humidity φ. It is also possible for the first and second classification parameters to result from a combination of several physical quantities, for example from the product of the ambient temperature T _Env and another quantity of the ambient data.

A control device according to the invention is set up to carry out the method according to the invention.

A computer program product according to the invention comprises commands which, when the program is executed by a control device, cause it to carry out the method according to the invention.

• 1 schematisch ein Ausführungsbeispiel des erfindungsgemäßen Verfahrens und
• 2 schematisch das Prinzip des Trainingsmodus.

Preferred exemplary embodiments are explained in more detail using the following figures. This shows

• 1 schematically an exemplary embodiment of the method according to the invention and
• 2 schematically the principle of the training mode.

1 shows schematically an exemplary embodiment of the method according to the invention for predicting a power requirement (P _N,1 ) of one or more auxiliary units of a vehicle. In the exemplary embodiment, the auxiliary units include the components of a heating, ventilation and air conditioning system (HVAC system).

The method includes the step of acquiring vehicle operating data (B) 20 and environmental data (U) 10. In this embodiment, the environmental data (U) 10 includes, for example, an ambient temperature T _Env , a solar irradiance q _Sun and a relative air humidity φ _rel . The vehicle operating data (B) 20 includes, for example, a target interior temperature T _Tgt and a parameter b _SH for describing the setting of a seat heating.

A plurality of prediction models 50 for the power requirements (P _N,1 ) of the auxiliary units are provided. In the exemplary embodiment, these are four prediction models 50 (M _a , M _b , M _c , M _d ). The prediction models 50 are classified via value ranges (K _M,1 ) of a first classification parameter, whereby a current value (K _A,1 ) of the first classification parameter is part of the recorded vehicle operating data (B) 20 or environmental data (U) 10 and where the vehicle operating data ( B) 20, the environmental data (U) 10 and a predicted speed (v̂) serve as input data for the prediction models 50. In the exemplary embodiment, the first classification parameter is the ambient temperature T _Env . The value ranges (K _M,1 ) of the first classification parameter are: $${\text{K}}_{\text{M},\text{1}}\left({\text{M}}_{\text{a}}\right)=<0°\text{C};{\text{K}}_{\text{M},\text{1}}\left({\text{M}}_{\text{b}}\right)=0\dots 10°\text{C};{\text{K}}_{\text{M},\text{1}}\left({\text{M}}_{\text{c}}\right)=10\dots 25°\text{C};{\text{K}}_{\text{M},\text{1}}\left({\text{M}}_{\text{d}}\right)=>25°\text{C}$$

The prediction of the vehicle speed v̂ in step 30 is carried out, for example, on the basis of a route set in a navigation system of the vehicle as well as on the basis of information about speed limits, traffic signs, traffic lights, traffic volume, environmental conditions, speed of other vehicles, route topology and driving behavior of the vehicle driver.

In a step 40, the prediction model 51 is selected, the value range (K _M,1 ) of the first classification parameter containing the current value (K _A,1 ) of the first classification parameter. Based on the exemplary embodiment, the current ambient temperature T _Env , which is 17° C. for example, is compared with the value ranges of the four prediction models 50 and the appropriate model 51 is selected, in this case model M _c .

As a further step in the method, the power requirement (P _N,1 ) of the auxiliary units is predicted for a route ahead or for a future period using the selected prediction model 51.

The process can be carried out continuously or at discrete time intervals in order to enable accurate prediction of the power requirements (P _N,1 ) of the auxiliary units.

2 shows schematically the principle of the training mode of the method according to the invention.

In the exemplary embodiment, the training mode is used at the end of each trip or once per hour, depending on which condition occurs first.

In training mode, the actual power requirement (P _N,0 ) of the vehicle's auxiliary units is determined in one step. In the exemplary embodiment, the actual power requirement (P _N,0 ) of the auxiliary units is determined from the difference between a power (P _B ) 80 of a battery of the vehicle and a power requirement (P _D ) 90 of a drive train of the vehicle.

The vehicle operating data (B) 20 and the environmental data (U) 10 are also recorded in training mode. These include an actual value (K _A,2 ) of a second classification parameter. In the exemplary embodiment, the second classification parameter is the ambient temperature T _Env . Accordingly, the second classification parameter in the exemplary embodiment is equal to the first classification parameter. The value ranges (K _M,2 ) of the second classification parameter are, analogous to the first classification parameter: $${\text{K}}_{\text{M}\mathrm{,2}}\left({\text{M}}_{\text{a}}\right)=<0°\text{C};{\text{K}}_{\text{M}\mathrm{,2}}\left({\text{M}}_{\text{b}}\right)=0\dots 10°\text{C};{\text{K}}_{\text{M}\mathrm{,2}}\left({\text{M}}_{\text{c}}\right)=10\dots 25°\text{C};{\text{K}}_{\text{M}\mathrm{,2}}\left({\text{M}}_{\text{d}}\right)=>25°\text{C}$$

In alternative embodiments, the value ranges (K _M,2 ) of the second classification parameter can differ from the value ranges (K _M,1 ) of the first classification parameter, even if the same physical quantity is used for the first and second classification parameters.

In further alternative embodiments of the method, different variables are used for the first and second classification parameters, for example the ambient temperature T _Env as the first classification parameter and the relative air humidity φ _rel as the second classification parameter.

In a further step of the training mode, the prediction model 51 is selected from the plurality of prediction models 50, whose value range (K _M,2 ) of the second classification parameter matches the actual value (K _A,2 ) of the second classification parameter, as the prediction model 51 to be trained .

The selected prediction model 51 is trained using the actual power requirement (P _N,0 ), the vehicle operating data (B) 20, the environmental data (U) 10 and the actual vehicle speed (v) 60.

For this purpose, in the exemplary embodiment, the time profiles of the actual power requirement (P _N,0 ), the vehicle operating data (B) 20, the environmental data (U) 10 and the actual vehicle speed (v) 60 are determined and temporarily stored during operation of the vehicle. The training is carried out, for example, by converting the time progressions into discrete points in time and feeding them as input data to the prediction model - suitable for the respective point in time. Based on this, the prediction model, in which the power requirement represents the dependent variable and the other variables represent the independent variables, is created (when the prediction models are first trained) or optimized.

QUOTES INCLUDED IN THE DESCRIPTION

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

Cited patent literature

• EP 2600106 A1 [0002]

Claims (10)

Method for predicting a power requirement (P _N,1 ) of one or more auxiliary units of a vehicle, comprising the steps: - Acquiring vehicle operating data (B) and environmental data (U); - Providing a plurality of prediction models for the power requirement (P _N,1 ) of the auxiliary units, the prediction models being classified over value ranges (K _M,1 ) of a first classification parameter, a current value (K _A,1 ) of the first classification parameter being part of the captured vehicle operating data (B) or environmental data (U) and wherein the vehicle operating data (B), the environmental data (U) and a predicted speed (v̂) serve as input data of the prediction models; - Selecting the prediction model whose value range (K _M,1 ) of the first classification parameter includes the current value (K _A,1 ) of the first classification parameter; - Predictions of the power requirement (P _N,1 ) of the auxiliary units for a route ahead or for a future period using the selected prediction model. Procedure according to Claim 1 , whereby the prediction models are regression models for which the following steps are carried out in a training mode: - determining the actual power requirement (P _N,0 ) of the vehicle's auxiliary units; - Acquiring vehicle operating data (B) and environmental data (U), comprising an actual value (K _A,2 ) of a second classification parameter; - selecting the prediction model whose value range (K _M,2 ) of the second classification parameter matches the actual value (K _A,2 ) of the second classification parameter as the prediction model to be trained; - Training the selected prediction model using the actual power requirement (P _N,0 ), the vehicle operating data (B), the environmental data (U) and the actual vehicle speed (v). Procedure according to Claim 2 , whereby the actual power requirement (P _N,0 ) of the auxiliary units is determined from the difference between a power (P _B ) of a battery of the vehicle and a power requirement (P _D ) of a drive train of the vehicle. Method according to one of the preceding claims, wherein the auxiliary units include one or more of the following systems: a heating, ventilation and air conditioning system (HVAC system), vehicle lighting, a device for steering power assistance, a vacuum pump, a windshield wiper motor, a sensor system, a On-board computer, a multimedia system. Method according to one of the preceding claims, wherein the vehicle operating data (B) comprises one or more of the following parameters: elapsed time since the start of the current journey, vehicle speed, driver settings of the auxiliary units, number of occupants, interior temperature. Method according to one of the preceding claims, wherein the environmental data (U) comprises one or more of the following parameters: ambient temperature, air humidity, irradiance, visibility, precipitation intensity. Method according to one of the preceding claims, wherein the first classification parameter is equal to the second classification parameter. Procedure according to Claim 7 , where the first and second classification parameters are the ambient temperature T _Env . Control device set up to carry out the method according to one of the preceding claims. Computer program product comprising commands which, when the program is executed by a control device, cause it to carry out the method according to one of the Claims 1 until 8th to carry out.

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