IP Library › Granted Patent US 12,509,096
Granted Patent B2
US 12,509,096 · App. 17/912,422 · Granted Dec 30, 2025

Method for operating a motor vehicle, and motor vehicle

Inventors: Esther Alberts (Munich, DE); Irmengard Ditzell (Freising, DE); Marcel Ewers (Dachau, DE); Simone Fuchs (Munich, DE); Harald Hofmeier (Eching, DE); Stefan Holder (Munich, DE); Tobias Straub (Munich, DE)
Assignee: Bayerische Motoren Werke Aktiengesellschaft
B60W50/0097G01C21/3815G01C21/3841G07C5/008B60W2510/244B60W2510/246B60W2520/10B60W2530/10B60W2540/043B60W2540/30B60W2556/10B60W2556/50
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Quick Facts
Patent No.
US 12,509,096
App. No.
17/912,422
Granted
Dec 30, 2025
Kind
B2
Abstract

A method for operating a motor vehicle. A map of corresponding event locations for one or more high load events is provided, wherein the one or more high load events have historically led to an above-average load on a vehicle component. A current position and/or route of the motor vehicle is determined. A high load event is identified from among the one or more high load events as likely to be relevant to the motor vehicle during the current operation. The identification of the high load event is based on the current position and/or route of the motor vehicle. The vehicle component is thermally preconditioned, via a corresponding automatic control of at least one device of the motor vehicle, before the motor vehicle has reached the corresponding event location of the identified high load event.

Claims (41)

1 . A method for operating a motor vehicle, the method comprising:

providing a map of corresponding event locations for one or more high load events, wherein the one or more high load events have historically led to an above-average load on a vehicle component;

during a current operation of the motor vehicle:

determining a current position and/or route of the motor vehicle;

identifying a high load event from among the one or more high load events as likely to be relevant to the motor vehicle during the current operation, wherein the identification of the high load event is based on the current position and/or route of the motor vehicle; and

thermally preconditioning the vehicle component for the identified high load event, via a corresponding automatic control of at least one device of the motor vehicle, before the motor vehicle has reached the corresponding event location of the identified high load event,

wherein the thermal preconditioning prevents the temperature of the vehicle component from reaching an operation threshold during the identified high load event,

wherein each high load event is associated with an occurrence probability, and

wherein the thermal preconditioning for the identified high load event increases with the occurrence probability of the identified high load event reaching progressively greater thresholds.

2 . The method of claim 1 , wherein the map is generated based on fleet data recording the one or more high load events as detected by one or more fleet vehicles during operation.

3 . The method of claim 2 ,

wherein the fleet data includes, for each high load event, the occurrence probability, and

wherein the occurrence probability: (a) reflects a proportion of the fleet vehicles having historically travelled through the corresponding event locations without occurrence of the high load event, and (b) is based on a recorded preceding driving history of the fleet vehicles travelling through the corresponding event locations.

4 . The method of claim 1 , further comprising:

generating the map, wherein generating the map includes, for each fleet vehicle:

detecting the high load event during the operation of the fleet vehicle,

recording a route segment travelled by the fleet vehicle prior to the occurrence of the high load event, and

assigning a distance-dependent classification number to the route segment in the map, the distance-dependent classification number characterizing the route segment as leading to the event location of the high load event;

wherein the identification of the high load event is based on the distance-dependent classification number.

5 . The method of claim 4 , wherein distinct distance-dependent classification numbers are assigned to the route segment for each corresponding high load event the route segment leads to.

6 . The method of claim 1 , further comprising:

ascertaining that an automatic navigation function is inactive during the operation of the motor vehicle; and

based on the ascertainment, identify a most-likely high load event from among a set of high load events whose corresponding event locations are within a given radius from the current position of the motor vehicle, wherein the most-likely high load event is the high load event that the motor vehicle is most likely to travel through,

wherein the thermal preconditioning of the vehicle component is based on the most-likely high load event.

7 . The method of claim 1 ,

wherein the map is managed by a central server device external to the motor vehicle, and

wherein the identification of the high load event is further based on vehicle-specific data of the motor vehicle, including: state-of-charge, operating mode, component temperature, and/or technical equipment, managed by the motor vehicle independent of the central server device.

8 . The method of claim 1 , wherein the identification of the high load event is further based on driver-specific characteristics of a driver of the motor vehicle, including: type of driver and/or automatically learned behavior of the driver.

9 . The method of claim 1 , further comprising:

assigning each of the high load events to one of a plurality of classes according to one or more operating conditions of the vehicle during or directly preceding the high load events, wherein the operating conditions include: speed and/or a load of the motor vehicle, wherein each class of the plurality of classes is associated with a control measure; and

during the thermal preconditioning, automatically executing the control measure associated with the identified high load event.

10 . A motor vehicle, comprising:

a navigation device configured to determine a current position and/or route of the motor vehicle;

a data interface configured to receive a map of corresponding event locations for one or more high load events, wherein the one or more high load events have historically led to an above-average load on a vehicle component;

a preconditioning device configured to precondition the vehicle component; and

a control device configured to:

identify a high load event from among the one or more high load events as likely to be relevant to the motor vehicle during the current operation, where the identification of the high load event is based on the map and at least one of the current position and/or route of the motor vehicle; and

thermally precondition the vehicle component for the identified high load event, via a corresponding automatic control of the preconditioning device, before the motor vehicle has reached the corresponding event location of the identified high load event,

wherein the thermal preconditioning prevents the temperature of the vehicle component from reaching an operation threshold during the identified high load event,

wherein each high load event is associated with an occurrence probability, and

wherein the thermal preconditioning for the identified high load event increases with the occurrence probability of the identified high load event reaching progressively greater thresholds.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 16, 2022
From: ALBERTS, ESTHER; DITZELL, IRMENGARD; EWERS, MARCEL; FUCHS, SIMONE; HOFMEIER, HARALD; HOLDER, STEFAN; STRAUB, TOBIAS
To: BAYERISCHE MOTOREN WERKE AKTIENGESELLSCHAFT
Reel/Frame 061125/0499 →
Priority Claims (1)
DE 10 2020 107 536.7 · Mar 19, 2020 · national
Continuity (1)
Related Publication 20230134925A1 · May 4, 2023
References Cited (67)
US 8620506B2 · Kummer · 2013 [cited by examiner]
US 9290108B2 · Payne · 2016 [cited by examiner]
US 9321441B1 · Shah · 2016 [cited by examiner]
US 9446682B2 · Gauthier · 2016 [cited by examiner]
US 9810543B2 · Hoch · 2017 [cited by examiner]
US 10343633B2 · Tseng · 2019 [cited by examiner]
US 10369899B2 · Hettrich · 2019 [cited by examiner]
US 10414240B2 · Eisele · 2019 [cited by examiner]
US 10780885B2 · Marcicki · 2020 [cited by examiner]
US 10800287B2 · Vallender · 2020 [cited by examiner]
US 10870368B2 · Ing · 2020 [cited by examiner]
US 10960785B2 · Villanueva · 2021 [cited by examiner]
US 11084398B2 · Marcicki · 2021 [cited by examiner]
US 11152653B2 · Carlson · 2021 [cited by examiner]
US 11577626B2 · Hettrich · 2023 [cited by examiner]
US 11654794B1 · Khattar · 2023 [cited by examiner]
US 11777153B2 · Hermann · 2023 [cited by examiner]
US 20070159757A1 · Moffatt · 2007 [cited by examiner]
US 20090293522A1 · Miyazaki · 2009 [cited by examiner]
US 20100235030A1 · Xue · 2010 [cited by examiner]
US 20140236466A1 · Doron · 2014 [cited by examiner]
US 20140277835A1 · Filev · 2014 [cited by examiner]
US 20150274030A1 · Payne · 2015 [cited by examiner]
US 20160059733A1 · Hettrich · 2016 [cited by examiner]
US 20160275730A1 · Bonhomme · 2016 [cited by examiner]
US 20170028978A1 · Dunlap · 2017 [cited by examiner]
US 20170080821A1 · Hughes · 2017 [cited by examiner]
US 20170101030A1 · Hughes · 2017 [cited by examiner]
US 20180072181A1 · Christen · 2018 [cited by examiner]
US 20180111486A1 · Kwon · 2018 [cited by examiner]
US 20180276485A1 · Heck · 2018 [cited by examiner]
US 20180334170A1 · Liu · 2018 [cited by examiner]
US 20180356835A1 · Gehring · 2018 [cited by examiner]
US 20190037545A1 · Jiao · 2019 [cited by examiner]
US 20190161076A1 · Plianos · 2019 [cited by examiner]
US 20190217721A1 · Marcicki · 2019 [cited by examiner]
US 20190286079A1 · Zang · 2019 [cited by examiner]
US 20190315232A1 · Ing · 2019 [cited by examiner]
US 20190339085A1 · Naef · 2019 [cited by examiner]
US 20200280842A1 · Liu · 2020 [cited by examiner]
US 20200317087A1 · Brinkmann · 2020 [cited by examiner]
US 20200376927A1 · Rajaie · 2020 [cited by examiner]
US 20220063445A1 · Lee · 2022 [cited by examiner]
US 20230099486A1 · Wiese · 2023 [cited by examiner]
US 20240247940A1 · Kotzor · 2024 [cited by examiner]
CN 108475465A · 2018 [cited by applicant]
CN 110418940A · 2019 [cited by applicant]
DE 102014008380A1 · 2014 [cited by applicant]
DE 102014004167A1 · 2015 [cited by examiner]
DE 102016102618A1 · 2016 [cited by examiner]
DE 102018111259A1 · 2018 [cited by applicant]
DE 102017006158A1 · 2019 [cited by examiner]
DE 102019101688A1 · 2019 [cited by examiner]
DE 102018211424A1 · 2020 [cited by applicant]
DE 102019114370A1 · 2020 [cited by applicant]
EP 2368751A2 · 2011 [cited by applicant]
EP 3254884A1 · 2017 [cited by examiner]
EP 3348964A1 · 2018 [cited by applicant]
KR 1020190042259A · 2019 [cited by applicant]
WO WO2016200762A1 · 2016 [cited by applicant]
DE-102014004167-A1 machine translation (Year: 2014). [cited by examiner]
DE-102016102618-A1 machine translation (Year: 2016). [cited by examiner]
DE-102017006158-A1 machine translation (Year: 2017). [cited by examiner]
DE-102019101688-A1 machine translation (Year: 2019). [cited by examiner]
PCT/EP2021/055110, International Search Report dated Jun. 1, 2021 (Two (2) pages). [cited by applicant]
German Search Report issued in German application No. 10 2020 107 536.7 dated Mar. 8, 2021, with Statement of Relevancy (Ten (10) pages). [cited by applicant]
Chinese-language Office Action issued in Chinese Application No. 202180008944.1 dated Apr. 30, 2025 with English translation (15 pages). [cited by applicant]