IP Library › Granted Patent US 12,337,815
Granted Patent B2
US 12,337,815 · App. 17/291,852 · Granted Jun 24, 2025

Electrification control systems and methods for electric vehicles

Inventors: Martin T. Books (Columbus, IN); Jennifer K. Light-Holets (Greenwood, IN)
Assignee: Cummins Inc.
B60W10/26B60W10/08B60W20/12B60W20/13B60W10/06B60W2050/0028B60W2050/0088B60W2510/244B60W2510/248B60W2556/45B60W2710/086B60W2710/244
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Quick Facts
Patent No.
US 12,337,815
App. No.
17/291,852
Granted
Jun 24, 2025
Kind
B2
Abstract

A system is provided for performing an automated electrification operation for an electric vehicle ( 102 ) using a processor ( 122 ). An electrification controller ( 126 ) communicates with a model generation unit ( 128 ). The model generation unit ( 128 ) generates a model representative of a power consumption trend of the electric vehicle ( 102 ). The electrification controller ( 126 ) sets a target power margin for the electric vehicle ( 102 ) based on the model such that the target power margin is close to a minimum state-of-charge (SOC) threshold of an energy storage supply ( 124 ) of the electric vehicle ( 102 ). The target power margin represents a difference between the minimum SOC threshold and an ending power level of the energy storage supply ( 124 ) after completion of a mission associated with the electric vehicle ( 102 ). The processor ( 122 ) performs the automated electrification operation for the electric vehicle ( 102 ) based on the target power margin.

Claims (41)

1. A system for performing an automated electrification operation for an electric vehicle using a processor, comprising:

an electrification controller configured to communicate with a model generation unit;

the model generation unit configured to generate a model representative of a power consumption trend of the electric vehicle;

the electrification controller configured to set a target power margin for the electric vehicle based on the model such that the target power margin is close to a minimum state-of charge (SOC) threshold of an energy storage supply of the electric vehicle, the minimum SOC threshold representing a minimum power level of the energy storage supply to be maintained for normal operation of the electric vehicle, and the target power margin representing a difference between the minimum SOC threshold and an ending power level of the energy storage supply after completion of a mission associated with the electric vehicle, the mission including a route of the electric vehicle,

wherein the processor is configured to perform the automated electrification operation for the electric vehicle based on the target power margin,

wherein the model is generated based on data associated with one or more previous runs of the route of the electric vehicle,

wherein the model includes aging information of one or more components of the electric vehicle,

wherein the aging information comprises an age of a component of the one or more components of the electric vehicle, and

wherein the age is determined based on maintenance information of the component.

2. The system of claim 1 , wherein the model generation unit is disposed at least partially in a cloud server in communication with the electric vehicle via a network.

3. The system of claim 1 , wherein the processor is disposed in the electric vehicle.

4. The system of claim 1 , wherein the model is adjusted based on at least one filtering factor.

5. The system of claim 4 , wherein the at least one filtering factor is a time-based factor.

6. The system of claim 1 , wherein the aging information includes data related to a degree of deterioration of a respective component of the electric vehicle.

7. The system of claim 1 , wherein the aging information is used to detect a faulty component of the electric vehicle.

8. The system of claim 1 , wherein the aging information is used to modify the automated electrification operation of the electric vehicle.

9. The system of claim 1 , wherein:

the component comprises an electric motor, and

the maintenance information comprises a maintenance service on the electric motor or a replacement of the electric motor.

10. A method of performing an automated electrification operation for an electric vehicle using a processor, the method comprising:

generating a model representative of a power consumption trend of the electric vehicle, the power consumption trend based on aging information of one or more components of the electric vehicle;

setting a target power margin for the electric vehicle based on the model such that the target power margin is close to a minimum state-of-charge (SOC) threshold of an energy storage supply of the electric vehicle, the minimum SOC threshold representing a minimum power level of the energy storage supply to be maintained for normal operation of the electric vehicle, and the target power margin representing a difference between the minimum SOC threshold and an ending power level of the energy storage supply after completion of a mission associated with the electric vehicle the mission including a route of the electric vehicle,

wherein the model is generated based on data associated with one or more previous runs of the route of the electric vehicle;

performing the automated electrification operation for the electric vehicle based on the target power margin;

wherein the aging information comprises an age of a component of the one or more components of the electric vehicle, and

wherein the age is based on maintenance information of the component.

11. The method of claim 10 , wherein the model is a machine-learning model based on simulated data associated with the one or more previous runs of the route of the electric vehicle.

12. The method of claim 10 , further comprising adjusting the model based on at least one filtering factor or at least one auxiliary factor.

13. The method of claim 10 , further comprising comparing the target power margin to the minimum SOC threshold of the energy storage supply of the electric vehicle.

14. The method of claim 13 , further comprising modifying the automated electrification operation of the electric vehicle based on the comparing the target power margin to the minimum SOC threshold.

15. A method of performing an automated electrification operation for an electric vehicle using a processor, the method comprising:

generating a model having aging information of at least one component of the electric vehicle, the model being representative of a power consumption trend of the electric vehicle, the aging information including an age of a first component of the at least one component of the electric vehicle, wherein the age is based on maintenance information of the first component;

setting a target power margin for the electric vehicle based on the model such that the target power margin is close to a minimum state-of-charge (SOC) threshold of an energy storage supply of the electric vehicle, the minimum SOC threshold representing a minimum power level of the energy storage supply to be maintained for normal operation of the electric vehicle, and the target power margin representing a difference between the minimum SOC threshold and an ending power level of the energy storage supply after completion of a mission associated with the electric vehicle, the mission including a route of the electric vehicle,

wherein the model is generated based on data associated with one or more previous runs of the route of the electric vehicle; and

performing the automated electrification operation for the electric vehicle based on the target power margin.

16. The method of claim 15 , further comprising adjusting the model based on one or more vehicle characteristics associated with the electric vehicle.

17. The method of claim 15 , further comprising determining an aging rate of a second component of the at least one component of the electric vehicle.

18. The method of claim 17 , further comprising detecting a faulty component of the electric vehicle based on the aging rate.

19. The method of claim 15 , further comprising modifying the automated electrification operation of the electric vehicle based on the aging information.

20. The method of claim 15 , further comprising setting the target power margin for the electric vehicle within a range between a first percentage limit and a second percentage limit.

21. The method of claim 15 , further comprising setting the target power margin for the electric vehicle above the minimum SOC threshold by at least a predetermined amount.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 6, 2021
From: BOOKS, MARTIN T.; LIGHT-HOLETS, JENNIFER K.
To: CUMMINS INC.
Reel/Frame 056160/0102 →
Continuity (1)
Related Publication 20220009472A1 · Jan 13, 2022
References Cited (35)
US 4566634A · Wiegand · 1986 [cited by applicant]
US 5570841A · Pace et al. · 1996 [cited by applicant]
US 6285162B1 · Koo · 2001 [cited by applicant]
US 8332342B1 · Saha et al. · 2012 [cited by applicant]
US 9114727B2 · Choi et al. · 2015 [cited by applicant]
US 9499157B2 · Muller · 2016 [cited by examiner]
US 9539912B2 · Li · 2017 [cited by applicant]
US 11691518B2 · Holme · 2023 [cited by examiner]
US 20030132320A1 · Sebastian · 2003 [cited by applicant]
US 20030184307A1 · Kozlowski et al. · 2003 [cited by applicant]
US 20040011895A1 · Dantes et al. · 2004 [cited by applicant]
US 20110066308A1 · Yang et al. · 2011 [cited by applicant]
US 20130096858A1 · Amano et al. · 2013 [cited by applicant]
US 20130158755A1 · Tang et al. · 2013 [cited by applicant]
US 20130221741A1 · Stanek et al. · 2013 [cited by applicant]
US 20150291145A1 · Yu · 2015 [cited by applicant]
US 20160244044A1 · Miller et al. · 2016 [cited by applicant]
US 20170242080A1 · La Marca et al. · 2017 [cited by applicant]
US 20170261473A1 · Sung · 2017 [cited by examiner]
US 20180095141A1 · Wild et al. · 2018 [cited by applicant]
US 20180106868A1 · Sung · 2018 [cited by examiner]
US 20190202306A1 · Gurin · 2019 [cited by examiner]
US 20200198495A1 · Rizzoni · 2020 [cited by examiner]
CN 101519073A · 2009 [cited by examiner]
CN 102717797A · 2012 [cited by applicant]
CN 103287279A · 2013 [cited by applicant]
CN 103362712A · 2013 [cited by applicant]
CN 104973045A · 2015 [cited by applicant]
EP 0548748B1 · 1993 [cited by applicant]
JP 2008024124A · 2008 [cited by applicant]
JP 2011211869A · 2011 [cited by applicant]
WO 2012066242A1 · 2012 [cited by applicant]
Machine Translated CN101519073A (Year: 2009). [cited by examiner]
International Preliminary Report on Patentability received for PCT Patent Application No. PCT/US2018/060073, mailed on May 20, 2021, 9 pages. [cited by applicant]
International Search Report and Written Opinion issued by the ISA/US, Commissioner for Patents, dated Jan. 25, 2019, for International Application No. PCT/US2018/060073; 9 pages. [cited by applicant]