DE102021201379B4 - Route planning method for an electric vehicle - Google Patents

Abstract

Method (100) for route planning for an electric motor vehicle (200), in particular for a battery-electric vehicle (10) or for a hybrid electric motor vehicle, wherein the electric motor vehicle (200) has at least one battery (11) for storing and delivering electrical energy for a drive (12) of the electric motor vehicle (200), a temperature management system (13) for the battery (11), a navigation system (15) and a data processing device (16), wherein the data processing device (16) after a destination has been entered into the navigation system (15) by a vehicle occupant A plurality of route suggestions (17a-17f) are received from the navigation system (15), characterized in that the data processing device (16) predicts a load profile for each route suggestion (17a-17f), that the data processing device (16) predicts for each route suggestion (17a-17f ) uses the respective load profile to determine a control strategy for the temperature management system (13), which optimizes a temporal temperature profile of the battery (11) for the respective load profile, and that the data processing device (16) selects the route suggestion (17d) in which the period of time in which the battery (11) is operated in an optimal temperature range is maximum, and/or at which a battery range is maximum.

Description

The present invention relates to a method for route planning for an electric vehicle, in particular for a battery-electric vehicle or a hybrid electric vehicle, wherein the electric vehicle has at least one battery for storing and delivering electrical energy for driving the electric vehicle, a temperature management system for the battery, a navigation system and a data processing device includes. The present invention further relates to an electric motor vehicle, in particular a battery-electric vehicle or hybrid electric motor vehicle, comprising at least one battery for storing and delivering electrical energy for driving the electric motor vehicle, a temperature management system for the battery, a navigation system and a data processing device.

Covering longer distances places high demands on the range of the battery of the electric vehicle in electric vehicles, especially in battery-electric vehicles, especially for distances that greatly exceed the daily distances traveled. In order to meet the range requirements, it is known in the prior art to display possible charging points when planning routes using a navigation system.

The DE 10 2013 016 569 A1 discloses an operating method for a hybrid drive of a motor vehicle that can be operated with multiple operating modes. The operating method includes the steps of dividing a route into a sequence of route sections; determining at least one predicted target variable for each of the operating modes of the hybrid drive on each section along the route; Determining an optimal path for the travel route, wherein a path is a sequence of operating modes along the travel route, such that each route section is assigned an operating mode and the optimal path is the one that has an optimal value for the at least one target variable.

The DE 10 2012 208 613 A1 discloses a method for thermally conditioning a rechargeable energy storage system (RESS) for a vehicle. A thermal conditioning system is configured to apply a plurality of thermal conditioning actions to the RESS. A control system is configured to determine a travel route for the vehicle for a driving cycle of the vehicle, select one of the plurality of thermal conditioning measures based at least in part on the travel route, and provide instructions to the thermal conditioning system to implement the selected one to apply one of the plurality of thermal conditioning measures to the RESS.

From the DE 10 2009 046 568 A1 a method for operating vehicles with electric drives is known, which can be used to provide intelligent control strategies for the electric drive and intelligent temperature control of traction batteries in motor vehicles. The method includes temperature management for the energy storage, whereby load profiles are determined based on the parameters of the distance to be covered, which indicate which drive energy must be provided at which point in time. Temperature control of the energy storage can be determined based on the load profiles.

From the DE 10 2017 127 029 A1 is known a method for controlling an electrified vehicle based on a route selected for desired thermal management of a battery. A control system analyzes the current battery thermal state in relation to possible driving routes and selects a route in which a minimum heating or cooling rate can be achieved.

The present invention is based on the object of providing a method for route planning for an electric vehicle, in particular for a battery-electric vehicle or for a hybrid electric vehicle, in which route information is included to increase the battery range and/or the battery life.

To solve the problem on which the invention is based, a method for route planning for an electric vehicle, in particular for a battery-electric vehicle or for a hybrid electric vehicle, is proposed, wherein the electric vehicle has at least one battery for storing and delivering electrical energy for driving the electric vehicle, a temperature management system for the Battery, a navigation system and a data processing device, wherein the data processing device receives a plurality of route suggestions from the navigation system after a destination has been entered into the navigation system by a vehicle occupant, it being provided that the data processing device predicts a load profile for each route suggestion that the data processing device predicts for each route suggestion Based on the respective load profile, a control strategy for the temperature management system is determined, which shows a temperature curve of the battery over time for the respective load profile optimized, and that the data processing device selects the route suggestion in which the period in which the battery is operated in an optimal temperature range is maximum, and / or in which a battery range is maximum.

According to the method, the data processing device, for example a computer or a control unit of the electric motor vehicle, receives a large number of route suggestions from the navigation system after a vehicle occupant has entered a destination on the navigation system. Each route suggestion corresponds to a route on which the destination desired by the vehicle occupant can be reached. For each route suggestion, a load profile is predicted by the data processing device, and based on the load profile, a control strategy for temperature management is determined, which optimizes a temperature profile of the battery over time for the respective load profile. For the control strategy determined in each case, the period of time in which the battery can probably be operated in an optimal temperature range is then determined for each route suggestion. The route suggestions are then compared by the data processing device, taking into account the respective control strategy determined, with regard to the maximum period of time in which the battery can probably be operated in an optimal temperature range, or with regard to the maximum range that can be achieved with the control strategy. The data processing device then selects the route suggestion in which the period of time in which the battery can be operated in an optimal temperature range is maximum and/or in which the battery range is maximum. This route suggestion is then preferably transmitted to the navigation system, which then guides the vehicle driver along this selected route.

What is essential about the present invention is that a control strategy is determined for each route suggestion. The control strategies for different route suggestions can be different from one another. For example, a control strategy for a first route suggestion may include a strong cooling rate or a strong heating rate for the battery, while the control strategy for a second route suggestion only provides for low cooling or heating rates.

The data processing device can also be integrated into the navigation system or be part of the navigation system. It is also possible for the vehicle occupant to suspend the implementation of the method in order to carry out a navigation method known from the prior art.

It is preferably provided that the data processing device additionally determines a control strategy for a drive management system of the electric vehicle for each route suggestion based on the respective load profile, which optimizes the temperature profile of the battery over time for the respective load profile.

For example, the power release can be optimized for each of the route suggestions. The released current can be reduced in certain sections of the route if a constantly high current is required or more effective in a subsequent section of the route. The reduced power release before the power-intensive subsequent section of the route prevents the battery from being excessively heated in the power-intensive section of the route and ensures that the additional heat development can be easily dissipated by the temperature management system.

With even further advantage, it can therefore be provided that the control strategy for the drive management system includes intelligent power release along the suggested route.

Furthermore, it can be provided that the load profile is determined on the basis of route information, the route information preferably comprising topographical information along the route suggestion and/or a road profile and/or an expected traffic volume along the route suggestion.

The road profile can contain information about infrastructure facilities, houses or forest cover along the proposed route.

The route information required to determine the load profiles can be obtained, for example, from OpenStreetMap infrastructure data.

For example, in the case of a cold battery starting temperature, a control strategy for the temperature management system and/or the drive management system for a route suggestion that has a high stop-and-go component or large altitude differences may consist of allowing the battery to heat itself up before a maximum current release which could lead to irreversible damage when the battery is cold. In addition, for a route suggestion in which a mountain journey is planned during the route, the control strategy can include predictive cooling, that is to say that the battery is in the route section before the mountain section is actively cooled to keep the battery in an optimal temperature range during the rise.

Targeted approaching or avoiding larger height differences enables more efficient battery cooling or heating.

It is preferably provided that the load profile is determined based on a driving dynamics profile of a vehicle driver and/or a driving dynamics mode of the electric motor vehicle.

The vehicle driver's driving dynamics profile can be determined, for example, using machine learning methods. If a specific driver usually only requires low power from the motor vehicle on motorway sections, a control strategy for a route suggestion that includes a motorway section can include preheating the battery in a section before the motorway section.

If the temperature of the battery is expected to be too low when a route is suggested along a route that is slow to drive and is shaded, for example through a forest area, the control strategy can still forego active heating of the battery if a sporty driving dynamics mode with a higher power output is used is selected.

Furthermore, it can be provided that the load profile is determined on the basis of environmental conditions, wherein the environmental conditions include an outside temperature and/or weather influences, preferably along the suggested route.

The respective control strategy can therefore also take cooling influences such as rainfall into account. Particularly when outside temperatures are low, the route suggestions must be optimized so that the optimal operating temperature of the battery is reached as early as possible, but without the battery being damaged by heating.

Another advantage is that the control strategy for the temperature management system involves pre-cooling and/or post-cooling and/or pre-heating and/or post-heating of the battery with respect to sections of the route suggestions in which, depending on the load profile, there are strong thermal loads on the battery or low thermal loads on the battery are expected.

This means that predictive cooling can be used to react to strong thermal loads on the battery in advance. In this way, the battery can be cooled more strongly in a previous section of the route before the start of a mountain journey in order to be able to maintain the temperature range during the climb. This makes it possible to ensure optimal performance and service life of the battery by keeping the battery in an optimal thermal state at all times.

It is further preferably provided that a control strategy includes a minimization of the maximum temperature and/or a minimization of the temperature difference in the battery, in particular in the battery cells or between the battery cells.

This means that the battery can be operated in the optimal temperature range. By operating the battery in the most efficient temperature range, its service life can be increased. A more efficient operation also reduces energy consumption. Both effects mean a more sustainable battery or a more sustainable electric vehicle, since overall there are fewer emissions during battery production and when using the electric vehicle.

With further advantage it is provided that the control strategy for the temperature management system and/or the control strategy for the drive management system and/or the load profile is determined on the basis of a battery condition, the battery condition preferably being a temperature, and/or a state of charge, and/or an age the battery includes.

With further advantage it can be provided that the load profile and/or the control strategy for the temperature management system and/or the control strategy for the drive management system are determined using a machine learning method.

Because the possible control strategies for the drive management system and/or for the temperature management are determined using a machine learning method, it is not necessary to calculate every possible control strategy for each route proposal in a time-resolved manner. Rather, the machine learning method has already learned the optimal control strategies for different route profiles before using the method according to the invention. This significantly minimizes the computational effort required to determine the possible control strategies.

Furthermore, it can be provided that the data processing device continuously or periodically checks the route suggestions and/or new possible route suggestions while the electric vehicle is driving.

This makes it possible to react to changing road conditions or changing traffic volumes or changing weather conditions while driving, and an optimal route can be suggested.

A further solution to the problem on which the invention is based is to provide an electric vehicle, in particular a battery-electric vehicle or hybrid electric vehicle, comprising at least one battery for storing and dispensing electrical energy for driving the electric vehicle, a temperature management system for the battery, a navigation system and a data processing device, wherein the data processing device is designed to carry out a method described above.

The invention is explained in more detail below with reference to the attached figures.

• 1 ein Elektrokraftfahrzeug,
• 2 eine Straßenkarte mit Routenvorschlägen von einem Navigationssystem,
• 3 eine Straßenkarte mit einem ausgewählten Routenvorschlag,
• 4 eine schematische Darstellung eines Routenvorschlags mit einem Anstieg,
• 5 eine Straßenkarte mit Routenvorschlägen während der Fahrt eines Elektrokraftfahrzeugs zu einem Zielort, und
• 6 ein Blockschaltbild eines Verfahrens zur Routenplanung.

Show it:

• 1 an electric vehicle,
• 2 a road map with route suggestions from a navigation system,
• 3 a road map with a selected route suggestion,
• 4 a schematic representation of a suggested route with a climb,
• 5 a road map with route suggestions while driving an electric vehicle to a destination, and
• 6 a block diagram of a route planning method.

1 shows a schematic representation of an electric motor vehicle 200 designed to carry out a method 100 for route planning. The electric motor vehicle 200 is designed as a battery-electric vehicle 10 and has a battery 11 for storing and releasing electrical energy for a drive 12 of the electric motor vehicle 200. Furthermore, the electric motor vehicle 200 includes a temperature management system 13 for the battery 11, a drive management system 14 as well as a navigation system 15 and a data processing device 16. The navigation system 15 and the data processing device 16 are designed to carry out a method 100 explained below.

2 shows an application case for a method 100 for route planning. After the vehicle driver has entered a destination on the navigation system 15 of the electric motor vehicle 200, the data processing device 16 receives several route suggestions 17a, 17b, 17c and 17d from the navigation system 15. The data processing device 16 also receives, preferably also from the navigation system 15 and/or from a traffic information system, topographical information 23 about the route suggestions 17a, 17b, 17c, 17d and the expected traffic volume along the route suggestions 17a, 17b, 17c, 17d. In addition, the data processing device 16 can receive information about the road profiles, the expected outside temperatures along the route suggestions 17a, 17b, 17c, 17d and the current battery charge status as well as the battery temperature. Based on this information, the data processing device 16 determines a load profile for each route suggestion 17a, 17b, 17c, 17d, as well as control strategies for the temperature management system 13 and for the drive management system 14 that are adapted to the respective load profile for each route suggestion, which determine the temperature profile of the battery over time for the respective route suggestion Optimize 17a, 17b, 17c, 17d.

The load profile, the control strategy for the temperature management system 13 and the control strategy for the drive management system 14 can be determined, for example, using a machine learning method, in particular using a trained neural network.

The data processing device 16 then selects, as in 3 shown, that route suggestion 17d in which, based on the control strategies for the temperature management system 13 and for the drive management system 14, the period in which the battery 11 is operated in the optimal temperature range is maximum, or in which a battery range is maximum. The selected route suggestion 17d is returned to the navigation system 15, which then carries out the vehicle navigation.

For the respectively determined control strategies for the temperature management system 13 and for the drive management system 14, the topography of the route along the respective route suggestions is taken into account in particular. So can, as in 4 shown, a strong climb 19 can be expected for a route suggestion 17e due to a mountain 18. In order to overcome the height difference, a higher battery power will be required. The control strategy for the temperature management system 13 can therefore be a pre dictive cooling of the battery 10 in a route section 20 lying before the expected climb 19.

Another application of the method 100 for route planning is in 5 shown. During the journey to the destination 21, the weather conditions along a previously selected route suggestion 17f have improved and increased outside temperatures can therefore be expected. The data processing device 16 periodically determines new possible route suggestions while driving. At the one in the 5 In the new route proposal 17g shown, due to the roadside greenery 32 and expected rain 33. With suitable control strategies, the period of time in which the battery 11 can be operated in the optimal temperature range is longer than in the original route proposal 17e. Since the cooling along the new route suggestion 17f is more efficient, the new route suggestion 17f is selected and used for navigation.

6 shows a block diagram of the process flow. The method 100 receives as input a planned travel destination 22 as well as information about the route suggestions 17a-17f. The information can be topographical information 23, environmental conditions 24 of the respective route suggestions 17a-17f, an expected traffic volume 25 and the expected power release 26 along the route. In addition, the method 100 can include information about a driving dynamics mode 27 of the electric vehicle 200. The method 100 also receives as further input parameters the thermal state 28 of the battery 11 before the start of the journey as well as information 34 about the thermal management system 13. For each of the route suggestions 17a-17f, a battery-optimized control strategy is determined in a processing step 29 using a machine learning method. This is followed by an evaluation 30 of the route suggestions 17a-17f with regard to the period within which the battery 11 is expected to be in the optimal thermal range, or with regard to the maximum range. Finally, in a selection step 31, that route suggestion is selected which either has a maximum period in which the temperature of the battery 11 is in the optimal temperature range or which enables a maximum range.

Reference symbol list

100

Route planning procedure

200

electric vehicle

10

Battery electric vehicle

11

battery

12

drive

13

Temperature management system

14

Drive management system

15

navigation system

16

Data processing device

17a-f

Route suggestion

18

Mountain

19

rise

20

Section of the route

21

Destination

22

holiday destination

23

Topographic information

24

Environmental conditions

25

Expected traffic volume

26

Expected power release

27

Driving dynamics mode

28

Thermal condition of the battery

29

Processing step

30

Evaluation

31

Selection step

32

Roadside greening

33

Rain

34

information

Claims (10)

Method (100) for route planning for an electric motor vehicle (200), in particular for a battery-electric vehicle (10) or for a hybrid electric motor vehicle, wherein the electric motor vehicle (200) has at least one battery (11) for storing and delivering electrical energy for a drive (12) of the electric motor vehicle (200), a temperature management system (13) for the battery (11), a navigation system (15) and a data processing device (16), wherein the data processing device (16) after a destination has been entered into the navigation system (15) by a vehicle occupant A plurality of route suggestions (17a-17f) receives from the navigation system (15), characterized in that the data processing device (16) predicts a load profile for each route suggestion (17a-17f), that the data processing device (16) predicts for each route suggestion (17a-17f ) based on the respective load profile, a control strategy for the temperature management system (13) is determined, which optimizes a temporal temperature profile of the battery (11) for the respective load profile, and that Data processing device (16) selects the route suggestion (17d) in which the period in which the battery (11) is operated in an optimal temperature range is maximum, and / or in which a battery range is maximum. Procedure (100) according to Claim 1 , characterized in that the data processing device (16) additionally determines a control strategy for a drive management system (14) of the electric motor vehicle (200) for each route suggestion (17a-17f) based on the respective load profile, which determines the temperature profile of the battery (11) over time for the respective one Load profile optimized. Procedure (100) according to Claim 2 , characterized in that the control strategy for the drive management system (14) includes intelligent power release along the route suggestion (17a-17f). Method (100) according to one of the preceding claims, characterized in that the load profile is determined on the basis of route information, the route information preferably being topographical information (23) along the route suggestion (17a-17f) and/or a road profile and/or a to expected traffic volume (25) along the proposed route (17a-17f). Method (100) according to one of the preceding claims, characterized in that the load profile is determined based on a driving dynamics profile of a vehicle driver and/or a driving dynamics mode (27) of the electric motor vehicle (200), and/or that the load profile is based on environmental conditions (24 ) is determined, wherein the environmental conditions (24) include an outside temperature and/or weather influences, preferably along the route suggestion (17a-17f). Method (100) according to one of the preceding claims, characterized in that the control strategy for the temperature management system (13) involves pre-cooling and/or post-cooling and/or pre-heating and/or post-heating of the battery (11) with respect to sections of the route suggestions (17a-17f) , in which, according to the load profile, strong thermal loads on the battery (11) or in which low thermal loads on the battery (11) are to be expected, includes. Method (100) according to one of the preceding claims, characterized in that the control strategy for the temperature management system (13) and/or the control strategy for the drive management system (14) and/or the load profile is determined on the basis of a battery condition, the battery condition preferably being one Temperature, and / or a state of charge, and / or an age of the battery (11). Method (100) according to one of the preceding claims, characterized in that the load profile and/or the control strategy for the temperature management system (13) and/or the control strategy for the drive management system (14) are determined by means of a machine learning method. Method (100) according to one of the preceding claims, characterized in that the data processing device (16) continuously or periodically checks the route suggestions (17a-17f) and/or new possible route suggestions (17a-17f) while the electric motor vehicle (200) is traveling. Electric motor vehicle (200), in particular battery-electric vehicle (10) or hybrid electric motor vehicle, comprising at least one battery (11) for storing and delivering electrical energy for a drive (12) of the electric motor vehicle (200), a temperature management system (13) for the battery (11) , a navigation system (15) and a data processing device (16), characterized in that the data processing device (16) is designed to carry out a method (100) according to one of the preceding claims.

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