# US 12,214,772 B2 · Model predictive control of a motor vehicle

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 12,214,772 B2
- Document ID: US12214772B2
- Status: Granted
- Kind: B2
- Application number: 17/777,120
- Filed: 2019-11-14
- Published: 2023-02-02
- Granted: 2025-02-04
- Earliest claimed: 2019-11-14
- Assignees: ZF Friedrichshafen AG
- Applicants: ZF Friedrichshafen AG
- Inventors: Timon Busse; Timo Wehlen
- Web page: https://patentgrep.com/patent/US12214772B2

## CPC

- B60W 10/30: including control of auxiliary equipment, e.g. air-conditioning compressors or oil pumps
- B60W 20/13: in order to stay within battery power input or output limits; in order to prevent overcharging or battery depletion
- B60L 1/003: {to auxiliary motors, e.g. for pumps, compressors}
- B60L 1/02: to electric heating circuits
- B60L 15/2045: {for optimising the use of energy}
- B60L 50/60: using power supplied by batteries (in combination with fuel cells B60L50/75)
- B60L 58/26: by cooling
- B60W 10/184: with wheel brakes
- B60W 10/22: including control of suspension systems
- B60W 10/26: for electrical energy, e.g. batteries or capacitors
- B60W 20/11: using model predictive control [MPC] strategies, i.e. control methods based on models predicting performance {(utilising navigation and traffic information in the control strategy B60W20/12)}
- B60W 20/12: using control strategies taking into account route information {(estimation or calculation of non-directly measurable driving parameters B60W40/00)}
- B60W 30/1843: {Overheating of driveline components (B60W30/186 takes precedence)}
- B60W 50/00: Details of control systems for road vehicle drive control not related to the control of a particular sub-unit {, e.g. process diagnostic or vehicle driver interfaces}
- B60W 50/0097: {Predicting future conditions}
- B60L 2240/12: Speed
- B60L 2240/36: Temperature of vehicle components or parts
- B60L 2240/425: Temperature
- B60L 2240/485: Temperature
- B60L 2240/545: Temperature
- B60L 2240/62: Vehicle position
- B60L 2240/68: Traffic data
- B60L 2260/46: by self learning
- B60L 2260/56: Temperature prediction, e.g. for pre-cooling
- B60W 2050/0013: {Optimal controllers}
- B60W 2050/0025: {Transfer function weighting factor}
- B60W 2050/0031: {Mathematical model of the vehicle}
- B60W 2050/0039: {of the propulsion unit}
- B60W 2510/081: Speed
- B60W 2510/085: Power
- B60W 2510/087: Temperature
- B60W 2556/50: of positioning data, e.g. GPS [Global Positioning System] data
- B60W 2710/244: Charge state
- B60W 2710/305: target power to auxiliaries
- B60W 2720/103: Speed profile
- Y02T 10/64: Electric machine technologies in electromobility
- Y02T 10/72: Electric energy management in electromobility
- Y02T 90/16: Information or communication technologies improving the operation of electric vehicles
- B60W: CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60L: PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES (arrangements or mounting of electrical propulsion units or of plural diverse prime-movers for mutual or common propulsion in vehicles B60K1/00, B60K6/20; arrangements or mounting of electrical gearing in vehicles B60K17/12, B60K17/14; preventing wheel slip by reducing power in rail vehicles B61C15/08; dynamo-electric machines H02K; control or regulation of electric motors H02P); SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES (electric coupling devices combined with mechanical couplings of vehicles B60D1/64; electric heating for vehicles B60H1/00); ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL (control or regulation of electric motors H02P); MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- Y02T: CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION

## Abstract

A processor unit (3) is configured for executing an MPC algorithm (13) for model predictive control of a motor vehicle (1). The MPC algorithm (13) includes a longitudinal dynamic model (14) of the motor vehicle (1) and a cost function (15) to be minimized. The cost function (15) includes multiple terms, a first term of which represents an output of the cooling pump (28). In addition, the processor unit (3) is configured for, by executing the MPC algorithm (13) as a function of the longitudinal dynamic model (14), ascertaining a speed trajectory of the motor vehicle (1) situated within a prediction horizon and simultaneously ascertaining a pump operating value trajectory situated within the prediction horizon such that the first term of the cost function (15) is minimized.

## Claims

### Claim 1

A processor unit (3) for model predictive control, MPC, of a motor vehicle (1) that includes a cooling pump (28), operable with different pump operating values (Q1, Q2, Q3) of at least one operating parameter (30), wherein:
the processor unit (3) configured for executing an MPC algorithm (13) for model predictive control of the motor vehicle (1), the MPC algorithm (13) comprising a longitudinal dynamic model (14) of the motor vehicle (1) and a cost function (15) to be minimized, the cost function (15) comprising a plurality of terms, including a first term representing an output of the cooling pump (28); and
the processor unit (3) is configured for, by executing the MPC algorithm (13), ascertaining a speed trajectory of the motor vehicle (1) situated within a prediction horizon as a function of the longitudinal dynamic model (14), and
simultaneously ascertaining a pump operating value trajectory situated within the prediction horizon such that the first term of the cost function (15) is minimized,
controlling a drive train of the motor vehicle (1) based on the speed trajectory of the motor vehicle (1), and
controlling a pump of the motor vehicle (1) based on the pump operating value trajectory.

### Claim 2 (depends on 1)

The processor unit (3) of claim 1, wherein the cooling pump (28) is arranged in a drive train (7) of the motor vehicle (1) and is configured for delivering a coolant through a cooling circuit (29), wherein the cooling circuit (29) cools one or more of the following components of the drive train (7):
an electric machine (8);
a transmission (10);
a power electronics unit (34); and
a battery (9).

### Claim 3 (depends on 1)

The processor unit (3) of claim 1, wherein the cooling pump is configured for delivering a coolant through a cooling circuit, and the cooling circuit is configured to cool an interior space of the motor vehicle (1).

### Claim 4 (depends on 1)

The processor unit (3) of claim 1, wherein the cooling pump is configured for delivering a coolant through a cooling circuit, and the cooling circuit is configured to cool an on-board charger of the motor vehicle (1).

### Claim 5 (depends on 1)

The processor unit (3) of claim 1, wherein the cooling pump is a vane pump in a transmission of the motor vehicle (1) and is configured for delivering a coolant through a cooling circuit, and the cooling circuit is configured to cool components of the transmission.

### Claim 6 (depends on 1)

The processor unit (3) of claim 1, wherein:
the motor vehicle (1) further comprises a first component (18) operable with different values (h1, h2, h3) of a first operating parameter (20);
the motor vehicle (1) comprises a second component (19) operable with different values (y1, y2, y3) of a second operating parameter (24);
the longitudinal dynamic model (14) comprises a loss model (27) of the motor vehicle (1);
the loss model (27) describes an overall loss of the motor vehicle (1);
the cost function (15) comprises a second term representing the overall loss of the motor vehicle (1);
the overall loss depends on a combination of operating values, including a first value (h1; h2; h3) of the first operating parameter (20) and a second value (y1; y2; y3) of the second operating parameter (24); and
the processor unit (3) is configured for ascertaining, by executing the MPC algorithm (13) as a function of the loss model (14), a combination of operating values by which the second term of the cost function (15) is minimized.

### Claim 7 (depends on 6)

The processor unit (3) of claim 6, wherein:
the second term comprises an electrical energy weighted with a first weighting factor and predicted according to the longitudinal dynamic model (14), which is provided within a prediction horizon by a battery (9) of the drive train (7) for driving the electric machine (8);
the cost function (15) comprises, as a third term, a driving time weighted with a second weighting factor and predicted according to the longitudinal dynamic model (14), which the motor vehicle (1) requires in order to cover the entire distance predicted within the prediction horizon; and
the processor unit (3) is configured for ascertaining an input variable for the electric machine (8) by executing the MPC algorithm (13) as a function of the second term and as a function of the third term such that the cost function (15) is minimized.

### Claim 8 (depends on 1)

A motor vehicle (3) comprising:
the processor unit (3) of claim 1;
a driver assistance system (16);
a drive train (7); and
a cooling pump (18) operable with different pump operating values (Q1, Q2, Q3) of at least one operating parameter (30),
wherein the driver assistance system (16) is configured for:
accessing a speed trajectory of the motor vehicle (1) situated within a prediction horizon and ascertained by the processor unit (3);
accessing a pump operating value trajectory situated within the prediction horizon and simultaneously ascertained by the processor unit (3);
controlling the drive train (7) of the motor vehicle (1) based on the speed trajectory of the motor vehicle (1); and
controlling the cooling pump (28) based on the pump operating value trajectory.

### Claim 9

A method for model predictive control, MPC, of a motor vehicle (1) that includes a cooling pump (28) operable with different pump operating values (Q1, Q2, Q3) of at least one operating parameter (30), the method comprising:
executing an MPC algorithm (13) for model predictive control of the motor vehicle (1), the MPC algorithm (13) comprising a longitudinal dynamic model (14) of the motor vehicle (1) and a cost function (15) to be minimized, the cost function (15) comprising a plurality of terms with a first term that represents an output of the cooling pump (28);
ascertaining a speed trajectory of the motor vehicle (1) situated within a prediction horizon by executing the MPC algorithm (13) as a function of the longitudinal dynamic model (14); and
ascertaining a pump operating value trajectory situated within the prediction horizon by executing the MPC algorithm (13) such that the first term of the cost function (15) is minimized;
controlling a drive train of the motor vehicle (1) based on the speed trajectory of the motor vehicle (1); and
controlling a pump of the motor vehicle (1) based on the pump operating value trajectory,
wherein the ascertainment of the speed trajectory and of the pump operating value trajectory take place simultaneously.

### Claim 10

A computer program product (11) for model predictive control, MPC, of a motor vehicle (1) that includes a cooling pump (28) operable with different pump operating values (Q1, Q2, Q3) of at least one operating parameter (30), wherein the computer program product (11), when run on a processor unit (2) instructs the processor unit (3) to:
execute an MPC algorithm (13) for model predictive control of the motor vehicle (1), the MPC algorithm (13) comprising a longitudinal dynamic model (14) of the motor vehicle (1) and a cost function (15) to be minimized, the cost function (15) comprising a plurality of terms with a first term that represents an output of the cooling pump (28), and
by executing the MPC algorithm (13),
compute a speed trajectory of the motor vehicle (1) situated within a prediction horizon as a function of the longitudinal dynamic model (14), and
simultaneously compute a pump operating value trajectory situated within the prediction horizon such that the first term of the cost function (15) is minimized,
controlling a drive train of the motor vehicle (1) based on the speed trajectory of the motor vehicle (1), and
controlling a pump of the motor vehicle (1) based on the pump operating value trajectory.

### Claim 11 (depends on 1)

The processor unit (3) of claim 1, wherein:
ascertaining the speed trajectory of the motor vehicle (1) as the function of the longitudinal dynamic model (14) comprises computing the speed trajectory of the motor vehicle (1) as the function of the longitudinal dynamic model (14); and
simultaneously ascertaining the pump operating value trajectory such that the first term of the cost function (15) is minimized comprises simultaneously computing the pump operating value trajectory such that the first term of the cost function (15) is minimized.