US 2026/0274271 A1 · METHOD TO DETERMINE VEHICLE MASS AND LONGITUDINAL ACCELERATION MEASUREMENT ERROR

Abstract

A method for estimating both vehicle mass and longitudinal acceleration in a vehicle based on a measured longitudinal acceleration from a sensor of the vehicle, includes obtaining a measured longitudinal acceleration from the sensor and a predetermined vehicle mass, providing a regression formula to determine a total force as a function of vehicle mass and vehicle acceleration, where the vehicle acceleration is the measured longitudinal acceleration modified by a sensor error, using a Kalman filter to solve the regression formula where vehicle mass is a first of two states and the sensor error is a second of the two states, and determining both the sensor error and the vehicle mass as a function of a Kalman filter gain and the total force. From this method both a real-time or instantaneous vehicle mass and an acceleration sensor bias or error can be determined.

Claims19

Dependent claims

A method for estimating both vehicle mass and longitudinal acceleration in a vehicle based on a measured longitudinal acceleration from a sensor of the vehicle, comprising:

obtaining a measured longitudinal acceleration from the sensor and a predetermined vehicle mass;
providing a regression formula to determine a total force as a function of vehicle mass and vehicle acceleration, where the vehicle acceleration is the measured longitudinal acceleration modified by a sensor error;
using a Kalman filter to solve the regression formula where vehicle mass is a first of two states and the sensor error is a second of the two states;
and
determining both the sensor error and the vehicle mass as a function of a Kalman filter gain and the total force.

2 more elements

A method of estimating vehicle mass and an error in a measured longitudinal acceleration from an accelerometer of a vehicle, comprising:

setting an initial state vector as
X
^
(
0
)
=
[
x
^
1
(
0
)
x
^
2
(
0
)
]
=
[
m
^
(
0
)
m
^
⁢
a
^
bias
(
0
)
]
;
where an initial value a first state {circumflex over (m)}(k) is a predetermined vehicle mass and an initial value of a second state {circumflex over (m)}âbias(k) is zero;
initializing an error covariance matrix P as
P
⁡
(
0
)
=
[
1
0
0
1
]
;
predicting a next state vector as:
X
^
(
k
)
=
[
x
^
1
(
k
)
x
^
2
(
k
)
]
=
[
m
^
(
k
)
m
^
⁢
a
^
bias
(
k
)
]
=
[
1
0
0
1
]
︸
A
[
m
^
(
k
-
1
)
m
^
⁢
a
^
bias
(
k
-
1
)
]
;
predicting a next error covariance as Pk=APk−1AT+Q;
where
Q
=
[
q
11
0
0
q
22
]
,
q11 represents the variance of the first state {circumflex over (m)}(k), and q22 represents the variance of the second state {circumflex over (m)}âbias(k);
obtaining a measured longitudinal acceleration from the accelerometer;
determining C=[ameas(k)−1], and determining a residual covariance as:
S=CP k−1CT+R;
where R is a design matrix that represents a measurement variance;
determining a Kalman gain as:
K=Pk−1CTS−1;
determine a total force Ftotal as a function of a powertrain torque;
determining a residual error as:
ϵ
=
y
-
C
⁢
X
^
(
k
-
1
)
=
F
total
(
k
)
-
C
⁢
X
^
(
k
-
1
)
,
where
⁢
y
=
F
Total
(
k
)
⁢
and
⁢
X
^
(
k
-
1
)
=
[
m
^
(
k
-
1
)
m
^
⁢
a
^
bias
(
k
-
1
)
]
is the estimated state vector;
determining an updated error covariance Pk and an updated state vector X(k) as:
Pk=(I−KC)Pk−1;
where I is an identity 2×2 matrix, and X(k)= (k−1)+Ke;
and
determining a corrected vehicle acceleration aactual by modifying the measured acceleration by the determined sensor error as:
a
actual
-
a
meas
-
m
^
⁢
a
^
bias
(
k
)
m
^
(
k
)
.

238 more elements

Dependencies are inferred from the claim text.

Method to determine vehicle mass and longitudinal acceleration measurement error

Life of this patent

Method to determine vehicle mass and longitudinal acceleration measurement error

Life of this patent

Abstract

Claims19