# US 12,109,997 B2 · Path tracking control method and device for intelligent electric vehicles

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- Number: US 12,109,997 B2
- Document ID: US12109997B2
- Status: Granted
- Kind: B2
- Application number: 18/625,974
- Filed: 2024-04-03
- Published: 2024-08-01
- Granted: 2024-10-08
- Earliest claimed: 2022-11-29
- Assignees: Chang'an University
- Applicants: Chang'an University
- Inventors: Xuan Zhao; Shu Wang; Jian Ma; Rui Liu; Yilin He; Chenyu Zhou
- Web page: https://patentgrep.com/patent/US12109997B2

## CPC

- B60T 8/17551: {determining control parameters related to vehicle stability used in the regulation, e.g. by calculations involving measured or detected parameters}
- B60T 8/17552: {responsive to the tyre sideslip angle or the vehicle body slip angle}
- B60W 30/02: Control of vehicle driving stability
- B60W 30/045: Improving turning performance
- B60W 30/14: {Adaptive} cruise control
- B60W 40/10: related to vehicle motion
- 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}
- Y02T 10/72: Electric energy management in electromobility
- B60T: VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL (electrodynamic brake systems for vehicle, in general B60L; brakes per se, i.e. devices where braking effect occurs, including ultimate brake actuators, F16D); ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
- 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
- Y02T: CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION

## Abstract

A path tracking control method for an intelligent electric vehicle. The method includes the following steps. A lateral stability state of a vehicle is determined, where the lateral stability state includes a stable state, a critical-destabilized state, and a destabilized state. Path tracking control is performed on the vehicle according to the lateral stability state of the vehicle. This application also provides a path tracking control device for an intelligent electric vehicle, which includes a lateral stability state determination module and a path tracking control module.

## Claims

### Claim 1

A path tracking control method for an intelligent electric vehicle, comprising:
determining a lateral stability state of a vehicle, wherein the lateral stability state comprises a stable state, a critical-destabilized state, and a destabilized state;
performing path tracking control on the vehicle according to the lateral stability state of the vehicle;
determining, when the vehicle is in the stable state, a front wheel steering increment of the vehicle; and determining a front wheel steering of the vehicle based on the front wheel steering increment of the vehicle;
determining, when the vehicle is in the critical-destabilized state, a first yaw moment increment and an additional yaw moment weighting coefficient of the vehicle; determining a first additional yaw moment based on the first yaw moment increment and the additional yaw moment weighting coefficient; and determining a first longitudinal force on each wheel of the vehicle based on the first additional yaw moment; and
determining, when the vehicle is in the destabilized state, a second yaw moment increment of the vehicle; determining a second additional yaw moment based on the second yaw moment increment; and determining a second longitudinal force on each wheel based on the second additional yaw moment;
wherein the stable state is determined by
0
<
❘
"\[LeftBracketingBar]"
1
c
⁢
β
.
+
k
c
⁢
β
❘
"\[RightBracketingBar]"
≤
μ
;
the critical-destabilized state is determined by
μ
<
❘
"\[LeftBracketingBar]"
1
c
⁢
β
.
+
k
c
⁢
β
❘
"\[RightBracketingBar]"
≤
1
;
and
the destabilized state is determined by
❘
"\[LeftBracketingBar]"
1
c
⁢
β
.
+
k
c
⁢
β
❘
"\[RightBracketingBar]"
>
1
;
wherein β represents a center-of-mass sideslip angle of the vehicle; {dot over (β)} represents a center-of-mass sideslip angular velocity; k represents a slope; c is constant; and μ represents a road surface adhesion coefficient.

### Claim 2 (depends on 1)

The path tracking control method of claim 1, wherein when the vehicle is in the stable state, the front wheel steering increment of the vehicle is determined through steps of:
constructing an objective function J:
min
⁢
J
=
∑
j
=
i
+
1
N
p
⁢
η
⁡
(
t
+
j
|
t
)
-
η
ref
(
t
+
j
|
t
)
⁢
Q
2
+
∑
i
=
0
N
c
-
1
⁢
Δ
⁢
u
⁡
(
t
+
i
|
t
)
⁢
R
2
+
l
⁢
ε
2
;
wherein Np represents a prediction time domain; Nc represents a control time domain; t represents a current moment; η(t+j|t) represents a lateral position coordinate of the vehicle at a (t+j)th moment when the current moment is t; ηref(t+j|t) represents a reference value of the lateral position coordinate at the (t+j)th moment when the current moment is t; Q, R, and l represent weighting matrices of an output, a control increment, and a relaxation factor, respectively; E represents the relaxation factor; and Δu(t+i|t) represents a front wheel steering increment of the vehicle at a (t+i)th moment when the current moment is t;
solving the objective function J; and determining a front wheel steering increment Δu(t+i|t) of the vehicle at each moment within the control time domain when the objective function J reaches a minimum value; and
acquiring a front wheel steering u(t) of the vehicle at the current moment t based on a front wheel steering increment Δu(t+0|t) at a (t+0)th moment within the control time domain:
u(t)=u(t−1)+Δu(t+0|t).

### Claim 3 (depends on 1)

The path tracking control method of claim 1, wherein when the vehicle is in the critical-destabilized state, the first yaw moment increment is determined through steps of:
constructing an objective function J:
min
⁢
J
=

Y
⁡
(
k
)
-
Y
ref
(
k
)

Q
2
+

Δ
⁢
U
⁡
(
k
)

R
2
=
∑
j
=
i
+
1
N
p
⁢

Y
⁡
(
k
+
j
|
k
)
-
Y
ref
(
k
+
j
|
k
)

Q
2
+
∑
i
=
0
N
c
-
1
⁢

Δ
⁢
u
⁡
(
k
+
i
|
k
)

R
2
;
wherein Np represents a prediction time domain; Nc represents a control time domain; Y(k) represents a predicted value of an output of the vehicle in the prediction time domain Np; k represents a current moment; Yref( ) represents a reference value of the output; ΔU(k) represents a sequence of a yaw moment increment at a current moment k; Y(k+j|k) represents a predicted value of the output at a (k+j)th moment when the current moment is k; Yref(k+j|k) represents a reference value of the output at the (k+j)th moment when the current moment is k; Δu(k+i|k) represents a yaw moment increment at a (k+i)th moment when the current moment is k; and Q and R represent weight matrices of the output and a control increment, respectively;
solving the objective function J; and determining the yaw moment increment Δu(k+i|k) at each moment within the control time domain when the objective function J takes a minimum value.

### Claim 4 (depends on 1)

The path tracking control method of claim 1, wherein when the vehicle is in the critical-destabilized state, the additional yaw moment weighting coefficient is determined through steps of:
constructing a coordination controller based on a reinforcement learning deep deterministic policy gradient (DDPG) algorithm, wherein state inputs are yaw velocity wr, center-of-mass sideslip angle β, lateral displacement Y, heading angle φ, front wheel steering angle δf and lateral acceleration αy; and an action output is the additional yaw moment weighting coefficient α;
wherein a reward function of the coordination controller is as follows:
r
=
r
1
+
r
2
+
r
3
;
wherein
r
1
=
-
(
e
y
2
+
e
φ
2
)
+
C
1
;
r
2
=
-
❘
"\[LeftBracketingBar]"
1
c
⁢
β
.
+
k
c
⁢
β
❘
"\[RightBracketingBar]"
+
C
2
;
r
3
=
-
❘
"\[LeftBracketingBar]"
a
y
❘
"\[RightBracketingBar]"
+
C
3
;
ey represents a deviation between an actual lateral displacement of the vehicle and a reference path; eφ represents a deviation between the heading angle and a reference heading angle; αy represents a lateral acceleration of the vehicle; C1, C2 and C3 are constant; c represents a coefficient; k represents a slope; {dot over (β)} represents a center-of-mass sideslip angular velocity of the vehicle; and β represents a center-of-mass sideslip angle of the vehicle.

### Claim 5 (depends on 1)

The path tracking control method of claim 1, wherein the path tracking control is performed through steps of:
acquiring a yaw moment u(k) at a current moment k based on a yaw moment increment Δu(k+0|k) at a (k+0)th moment within a control time domain;
wherein u(k)=u(k−1)+Δu(k+0|k);
determining an additional yaw moment ΔM according to the yaw moment u(k) and the additional yaw moment weighting coefficient α;
wherein ΔM=αu(k); and
determining a longitudinal force on each wheel of the vehicle based on the additional yaw moment ΔM in a drive-brake-combined manner.

### Claim 6 (depends on 1)

The path tracking control method of claim 1, wherein when the vehicle is in the destabilized state, the second yaw moment increment of the vehicle is determined, and the path tracking control is performed based on the second yaw moment increment through steps of:
constructing an objective function J:
min
⁢
J
=

Y
⁡
(
k
)
-
Y
ref
(
k
)

Q
2
+

Δ
⁢
U
⁡
(
k
)

R
2
=
∑
j
=
i
+
1
N
p
⁢

Y
⁡
(
k
+
j
|
k
)
-
Y
ref
(
k
+
j
|
k
)

Q
2
+
∑
i
=
0
N
c
-
1
⁢

Δ
⁢
u
⁡
(
k
+
i
|
k
)

R
2
;
wherein Np represents a prediction time domain; Nc represents a control time domain; Y(k) represents a predicted value of an output of the vehicle in the prediction time domain Np; k represents a current moment; Yref( ) represents a reference value of the output; ΔU(k) represents a sequence of a yaw moment increment at a current moment k; Y(k+j|k) represents a predicted value of the output at a (k+j)th moment when the current moment is k; Yref(k+j|k) represents a reference value of the output at the (k+j)th moment when the current moment is k; Δu(k+i|k) represents a yaw moment increment at a (k+i)th moment when the current moment is k; and Q and R represent weight matrices of the output and a control increment, respectively;
solving the objective function J; and determining the yaw moment increment Δu(k+i|k) at each moment within the control time domain when the objective function J takes a minimum value;
acquiring a yaw moment u(k) at a current moment k based on a yaw moment increment Δu(k+0|k) at a (k+0)th moment within a control time domain;
wherein u(k)=u(k−1)+Δu(k+0|k);
determining an additional yaw moment ΔM according to the yaw moment u(k) and the additional yaw moment weighting coefficient α;
wherein ΔM=αu(k); and
determining a longitudinal force on each wheel of the vehicle based on the additional yaw moment ΔM in a drive-brake-combined manner.

### Claim 7

A path tracking control device for an intelligent electric vehicle, comprising:
a lateral stability state determination module; and
a path tracking control module;
wherein the lateral stability state determination module is configured to determine a lateral stability state of a vehicle, wherein the lateral stability state comprises a stable state, a critical-destabilized state, and a destabilized state; and
the path tracking control module is configured to perform path tracking control on the vehicle according to the lateral stability state of the vehicle, determine a front wheel steering increment of the vehicle when the vehicle is in the stable state, determine a front wheel steering of the vehicle based on the front wheel steering increment of the vehicle, determine a first yaw moment increment and an additional yaw moment weighting coefficient of the vehicle when the vehicle is in the critical-destabilized state, determine a first additional yaw moment based on the first yaw moment increment and the additional yaw moment weighting coefficient, determine a first longitudinal force on each wheel of the vehicle based on the first additional yaw moment, determine a second yaw moment increment of the vehicle when the vehicle is in the destabilized state, determine a second additional yaw moment based on the second yaw moment increment, and determine a second longitudinal force on each wheel based on the second additional yaw moment;
wherein the stable state is determined by
0
<
❘
"\[LeftBracketingBar]"
1
c
⁢
β
.
+
k
c
⁢
β
❘
"\[RightBracketingBar]"
≤
μ
;
the critical-destabilized state is determined by
μ
<
❘
"\[LeftBracketingBar]"
1
c
⁢
β
.
+
k
c
⁢
β
❘
"\[RightBracketingBar]"
≤
1
;
and
the destabilized state is determined by
❘
"\[LeftBracketingBar]"
1
c
⁢
β
.
+
k
c
⁢
β
❘
"\[RightBracketingBar]"
>
1
;
wherein β represents a center-of-mass sideslip angle of the vehicle; {dot over (β)} represents a center-of-mass sideslip angular velocity; k represents a slope; c is constant; and μ represents a road surface adhesion coefficient.

### Claim 8 (depends on 1)

A computer-readable storage medium, wherein the computer-readable storage medium is configured to store a computer program; and the computer program is configured to be executed by a processor to implement the path tracking control method of claim 1.

### Claim 9 (depends on 1)

A computer program product, comprising:
a computer program or a computer instruction;
wherein the computer program or the computer instruction is configured to be executed by a processor to implement the path tracking control method of claim 1.