DE102021201379A1 - Route planning method for an electric vehicle - Google Patents

Abstract

In order to provide 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, in a method (100) for route planning for an electric vehicle (200 ), 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), proposed that the data processing device (16) after a destination has been entered by a vehicle occupant in the navigation system (15) receives a large number of route suggestions (17a-17f) from the navigation system (15), that the data processing device (16) one La for each suggested route (17a-17f). Stprofil predicts that the data processing device (16) for each suggested route (17a-17f) based on the respective load profile determines a control strategy for the temperature management system (13), which optimizes a temperature curve of the battery (11) for the respective load profile, and that the data processing device (16) selects the suggested route (17a-17f) in which the period of time in which the battery (11) is operated in an optimum temperature range is at a maximum and/or at which a battery range is at a 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, the electric vehicle having 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 also relates to an electric vehicle, in particular a battery-electric vehicle or hybrid electric vehicle, comprising 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.

In the case of electric vehicles, particularly battery-powered vehicles, covering longer distances, above all in the case of distances that greatly exceed the daily driving distances, places high demands on the range of the battery of the electric vehicle. In order to meet the range requirements, it is known in the prior art to use a navigation system to display possible charging points when planning a route.

From the DE 10 2009 046 568 A1 a method for operating vehicles with an electric drive 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 store, with load profiles being determined based on the parameters of the route to be covered, which indicate which drive energy must be provided at what point in time. Temperature regulation of the energy store can be established on the basis of the load profiles.

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

The object of the present invention is to provide 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.

In order to achieve the object 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, the electric vehicle having 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 large number of route suggestions from the navigation system after a destination has been entered by a vehicle occupant in the navigation system, it being provided that the data processing device predicts a load profile for each route suggestion, that the data processing device for each route suggestion based on the respective load profile, a control strategy for the temperature management system is determined, which has a temporal temper optimized nature of the battery for the respective load profile, and that the data processing device selects the suggested route 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 in the navigation system. Each suggested route corresponds to a route on which the destination desired by the vehicle occupants can be reached. A load profile is predicted by the data processing device for each proposed route, and a control strategy for the temperature management is determined based on the load profile, which optimizes a temperature profile of the battery over time for the respective load profile. The period of time during which the battery can probably be operated in an optimal temperature range is then determined for each route proposal for the control strategy determined in each case. The suggested routes are then compared by the data processing device, taking into account the control strategy determined in each case, with regard to the maximum period 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 chen. The data processing device then selects the suggested route for which the period of time in which the battery can be operated in an optimum temperature range is at a maximum and/or at which the battery range is at a maximum. This suggested route is then preferably transmitted to the navigation system, which then guides the vehicle driver along this selected route.

What is essential to the present invention is that a control strategy is determined for each suggested route. The control strategies for different route suggestions can differ from one another. For example, a control strategy for a first suggested route may involve a high cooling rate or a high heating rate for the battery, while the control strategy for a second suggested route only provides low cooling or heating rates.

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

Provision is preferably made for the data processing device to additionally determine a control strategy for a drive management system of the electric vehicle for each suggested route based on the respective load profile, which strategy 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 route section prevents the battery from being excessively heated in the power-intensive route section and ensures that the additional heat development can be easily dissipated by the temperature management system.

It can therefore be provided with an even further advantage that the control strategy for the drive management system includes intelligent power release along the suggested route.

Provision can also be made for the load profile to be determined on the basis of route information, with the route information preferably including topographical information along the suggested route and/or a road profile and/or an expected volume of traffic along the suggested route.

The road profile can contain information about infrastructure devices, houses or a forest along the suggested route.

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

For example, with a cold battery starting temperature, a control strategy for the temperature management system and/or the powertrain management system for a route proposal that has a high proportion of stop-and-go or large differences in altitude can consist of allowing the battery to heat up itself before a maximum current release is issued, which could lead to irreversible damage to the battery when it is cold. In addition, the control strategy for a route suggestion that includes an uphill trip in the course of the route can include predictive cooling, i.e. the battery is actively cooled in the route section before the uphill section in order to keep the battery in an optimal temperature range during the ascent to keep.

The targeted approach or avoidance of larger differences in height enables more efficient battery cooling or battery heating.

Provision is preferably made for the load profile to be determined on the basis of a driving dynamics profile of a vehicle driver and/or a driving dynamics mode of the electric motor vehicle.

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

If the battery temperature is expected to be too low when a route is suggested along a route that is to be driven slowly and in the shade, for example through a wooded area, the control strategy can still do without actively heating up the battery if a sporty driving dynamics mode associated with higher power output is used is chosen.

Furthermore, it can be provided that the load profile is determined on the basis of environmental conditions, the environmental conditions including 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. In particular when the outside temperatures are low, the route suggestions must be optimized in such a way that an optimal operating temperature of the battery is reached as early as possible without the battery being damaged by the heating of the battery.

With a further advantage, it is provided that the control strategy for the temperature management system pre-cools and/or post-cools and/or pre-heats and/or post-heats the battery with regard to route sections of the route suggestions for which, according to the load profile, the battery is subject to high thermal loads or low thermal loads to the battery are expected includes.

This means that predictive cooling can be used to react in advance to severe thermal loads on the battery. In this way, the battery can be cooled to a greater extent on a section of the route before the start of a climb in order to be able to maintain the temperature range during the climb. It is thus possible to ensure optimal performance and service life of the battery by keeping the battery in an optimal thermal state at all times.

Provision is also preferably made for a control strategy to contain 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.

As a result, the battery can be operated in the optimum temperature range. Because the battery is operated in the most efficient temperature range, the service life can be increased. More efficient operation also reduces energy consumption. Both effects mean a more sustainable battery or a more sustainable electric vehicle, since overall fewer emissions are produced during battery production and when using the electric vehicle.

It is also advantageously 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 of the battery.

Provision can also be made for the load profile and/or the control strategy for the temperature management system and/or the control strategy for the drive management system to be determined using a machine learning method.

Since 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, before the method according to the invention is used, the machine learning method has already learned the optimal control strategies for different route profiles. This significantly minimizes the computing effort for determining 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 motor vehicle is being driven.

It is thus 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 the provision of an electric vehicle, in particular a battery-electric vehicle or hybrid electric vehicle, comprising 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 is designed to carry out a method as described above.

The invention is explained in more detail below with reference to the accompanying 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 suggested route,
• 4 a schematic representation of a suggested route with an ascent,
• 5 a road map with suggested routes while driving an electric vehicle to a destination, and
• 6 a block diagram of a method for route planning.

1 shows a schematic representation of an electric motor vehicle 200 designed to carry out a method 100 for route planning. Electric motor vehicle 200 is designed as a battery electric vehicle 10 and has a battery 11 for storing and delivering electrical energy for a drive 12 of 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 and 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 for a method 100 for route planning. After the driver has entered a destination on the navigation system 15 of the electric motor vehicle 200, the data processing device 16 receives a number of 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 on the suggested routes 17a, 17b, 17c, 17d and on the expected volume of traffic along the suggested routes 17a, 17b, 17c, 17d. In addition, the data processing device 16 can receive information on the road profiles, on the outside temperatures to be expected along the suggested routes 17a, 17b, 17c, 17d and on the current battery charge status and on the battery temperature. Using 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 and 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 suggested route 17d in which, based on the control strategies for the temperature management system 13 and for the drive management system 14, the time period in which the battery 11 is operated in the optimal temperature range is at a maximum, or at which a battery range is at a maximum. The selected recommended route 17d is returned to the navigation system 15, which then carries out the vehicle navigation.

In particular, the topography of the route along the respective suggested routes is taken into account for the control strategies determined in each case for the temperature management system 13 and for the drive management system 14 . So can, as in 4 shown, in the case of a suggested route 17e due to a mountain 18, a sharp increase 19 is to be expected. To overcome the difference in altitude, more battery power will have to be called up. The control strategy for the temperature management system 13 can therefore include predictive cooling of the battery 10 in a route section 20 lying before the expected increase 19 .

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

6 shows a block diagram of the procedure. The method 100 receives a planned travel destination 22 and information about the route suggestions 17a-17f as input. The information can be topographical information 23, environmental conditions 24 of the respective suggested routes 17a-17f, an expected volume of traffic 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 the thermal state 28 of the battery 11 as a further input parameter before departure and information 34 to the thermal management system 13. For each of the suggested routes 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 suggested routes 17a-17f with regard to the time period within which the battery 11 is likely to be in the optimum thermal range, or with regard to the maximum range. Finally, in a selection step 31, that suggested route is selected which either has a maximum period of time in which the temperature of the battery 11 is in the optimum temperature range or which enables a maximum range.

Reference List

100

Procedure for route planning

200

electric vehicle

10

battery electric vehicle

11

battery

12

drive

13

temperature management system

14

propulsion management system

15

navigation system

16

data processing device

17a-f

suggested route

18

Mountain

19

rise

20

track section

21

destination

22

holiday destination

23

topographical information

24

Environmental conditions

25

Expected traffic volume

26

Expected power release

27

driving dynamics mode

28

Battery thermal condition

29

processing step

30

valuation

31

selection step

32

roadside greening

33

Rain

34

information

QUOTES INCLUDED IN DESCRIPTION

This list of the documents cited 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.

Patent Literature Cited

• DE 102009046568 A1 [0003]
• DE 102017127029 A1 [0004]

Claims (10)

Method (100) for route planning for an electric vehicle (200), in particular for a battery-electric vehicle (10) or for a hybrid electric vehicle, the electric vehicle (200) having 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 input by a vehicle occupant into the navigation system (15) receives a large number of route suggestions (17a-17f) 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) for each route suggestion (17a-17f ) based on the respective load profile, a control strategy for the temperature management system (13 ) determines which optimizes a temporal temperature profile of the battery (11) for the respective load profile, and that the data processing device (16) selects the suggested route (17d) in which the period in which the battery (11) is operated in an optimal temperature range , is maximum, and/or at which a battery range is maximum. Method (100) according to claim 1 , characterized in that the data processing device (16) for each suggested route (17a-17f) based on the respective load profile also determines a control strategy for a drive management system (14) of the electric motor vehicle (200), which determines the temperature profile of the battery (11) over time for the respective Load profile optimized. Method (100) according to claim 2 , characterized in that the control strategy for the drive management system (14) includes intelligent power release along the suggested route (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 topographical information (23) along the suggested route (17a-17f) and/or a road profile and/or a expected volume of traffic (25) along the suggested route (17a-17f). Method (100) according to one of the preceding claims, characterized in that the load profile is determined on the basis of 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 determined on the basis of environmental conditions (24 ) is determined, the ambient conditions (24) including an outside temperature and/or weather influences, preferably along the suggested route (17a-17f). Method (100) according to one of the preceding claims, characterized in that the control strategy for the temperature management system (13) includes pre-cooling and/or post-cooling and/or pre-heating and/or post-heating of the battery (11) with regard to route sections of the suggested routes (17a-17f) , in which, according to the load profile, high thermal loads on the battery (11) or in which low thermal loads on the battery (11) are to be expected. 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 using 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 driving. Electric vehicle (200), in particular a battery-electric vehicle (10) or hybrid electric vehicle, comprising at least one battery (11) for storing and delivering electrical energy for a drive (12) of the electric 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.

Images