Estimation of state of charge of a battery assembly
A system for estimating a parameter of a battery assembly includes a sensing device connected to a cell of the battery assembly, the battery assembly having a first battery chemistry, the sensing device including a measurement cell having a second battery chemistry that is different than the first battery chemistry. The system includes an estimator configured to acquire a set of first estimates of a first state of charge (SOC) of the battery assembly, acquire a set of second estimates of a second SOC of the sensing device, and blend the sets of estimates to generate a blended SOC relation describing the second SOC of the sensing device as a function of the first SOC of the battery assembly. The estimator is also configured to filter the set of first estimates using the blended SOC relation to generate a final estimate of the SOC of the battery assembly.
1 . A system for estimating a state of charge (SOC) of a battery assembly, comprising:
a sensing device connected in series to a battery cell of the battery assembly, the battery assembly having a first battery chemistry, the sensing device including a measurement cell having a second battery chemistry that is different than the first battery chemistry;
an estimator configured to perform:
acquiring a set of first estimates of a first state of charge (SOC) of the battery assembly, the set of first estimates based on a measurement of a parameter of the battery assembly performed at a set of sample times;
acquiring a set of second estimates of a second SOC of the sensing device, the set of second estimates based on a measurement of the parameter of the sensing device at the set of sample times;
blending the set of first estimates and the set of second estimates to generate a blended SOC relation, the blended SOC relation describing the second SOC of the sensing device as a function of the first SOC of the battery assembly; and
filtering the set of first estimates using the blended SOC relation to generate a final estimate of the SOC of the battery assembly, the final estimate having an accuracy that is greater than an accuracy of the set of first estimates; and
a charger module configured to control a charging operation based on the final estimate of the SOC of the battery assembly.
2 . The system of claim 1 , wherein the filtering is performed using a Kalman filter algorithm.
3 . The system of claim 1 , wherein the blending is based on a ratio of a capacity of the battery assembly to a capacity of the sensing device.
4 . The system of claim 3 , wherein the blended SOC relation is represented by:
SOC
SD
(
k
)
=
(
CAP
BP
/
CAP
SD
)
*
SOC
BP
(
k
)
+
d
%
+
v
(
i
,
T
)
,
wherein SOC SD (k) is a state of charge of the sensing device for a plurality of measurement times k, CAP BP is a capacity of the battery assembly, CAP SD is a capacity of the sensing device, d % is a minimum charge offset, SOC BP (k) is a state of the charge of the battery assembly, and v(i,T) is a noise model of the sensing device based on a current i and a temperature T.
5 . The system of claim 1 , wherein the set of first estimates is acquired based on a Coulomb counting process.
6 . The system of claim 5 , wherein the set of second estimates is acquired based on a Coulomb counting process in combination with a battery state estimation process.
7 . The system of claim 1 , wherein the estimator is further configured to determine a state of health of the battery assembly based on a capacity of the sensing device, a first offset value and a second offset value, the first offset value based on a difference between a SOC of the sensing device in a rest condition and a SOC of the battery cell in the rest condition, the second offset value based on a difference between a maximum SOC of the sensing device and a measured SOC of the sensing device.
8 . The system of claim 1 , wherein the battery cell is part of a battery pack of a vehicle.
9 . A method of estimating a state of charge (SOC) of a battery assembly, comprising:
acquiring a set of first estimates of a first state of charge (SOC) of the battery assembly, the battery assembly having a first battery chemistry, the set of first estimates based on a measurement of a parameter of the battery assembly performed at a set of sample times;
acquiring a set of second estimates of a second SOC of a sensing device connected in series to a battery cell of the battery assembly, the sensing device including a measurement cell having a second battery chemistry that is different than the first battery chemistry, the set of second estimates based on a measurement of the parameter of the sensing device at the set of sample times;
blending the set of first estimates and the set of second estimates to generate a blended SOC relation, the blended SOC relation describing the second SOC of the sensing device as a function of the first SOC of the battery assembly;
filtering the set of first estimates using the blended SOC relation to generate a final estimate of the SOC of the battery assembly, the final estimate having an accuracy that is greater than an accuracy of the set of first estimates; and
controlling a charging operation based on the final estimate of the SOC of the battery assembly.
10 . The method of claim 9 , wherein the filtering is performed using a Kalman filter algorithm.
11 . The method of claim 9 , wherein the blending is based on a ratio of a capacity of the battery assembly to a capacity of the sensing device.
12 . The method of claim 11 , wherein the blended SOC relation is represented by:
SOC
SD
(
k
)
=
(
CAP
BP
/
CAP
SD
)
*
SOC
BP
(
k
)
+
d
%
+
v
(
i
,
T
)
,
wherein SOC SD (k) is a state of charge of the sensing device for a plurality of measurement times k, CAP BP is a capacity of the battery assembly, CAP SD is a capacity of the sensing device, d % is a minimum charge offset, SOC BP (k) is a state of the charge of the battery assembly, and v(i,T) is a noise model of the sensing device based on a current i and a temperature T.
13 . The method of claim 9 , wherein the set of first estimates is acquired based on a Coulomb counting process.
14 . The method of claim 13 , wherein the set of second estimates is acquired based on a Coulomb counting process in combination with a battery state estimation process.
15 . The method of claim 9 , further comprising determining a state of health of the battery assembly based on a capacity of the sensing device, a first offset value and a second offset value, the first offset value based on a difference between a SOC of the sensing device in a rest condition and a SOC of the battery cell in the rest condition, the second offset value based on a difference between a maximum SOC of the sensing device and a measured SOC of the sensing device.
16 . The method of claim 9 , wherein the battery cell is part of a battery pack of a vehicle.
17 . A vehicle system comprising:
a memory having computer readable instructions; and
a processing device for executing the computer readable instructions, the computer readable instructions controlling the processing device to perform a method including:
acquiring a set of first estimates of a first state of charge (SOC) of a battery assembly, the battery assembly having a first battery chemistry, the set of first estimates based on a measurement of a parameter of the battery assembly performed at a set of sample times;
acquiring a set of second estimates of a second SOC of a sensing device connected in series to a battery cell of the battery assembly, the sensing device including a measurement cell having a second battery chemistry that is different than the first battery chemistry, the set of second estimates based on a measurement of the parameter of the sensing device at the set of sample times;
blending the set of first estimates and the set of second estimates to generate a blended SOC relation, the blended SOC relation describing the second SOC of the sensing device as a function of the first SOC of the battery assembly;
filtering the set of first estimates using the blended SOC relation to generate a final estimate of the SOC of the battery assembly, the final estimate having an accuracy that is greater than an accuracy of the set of first estimates; and
controlling a charging operation based on the final estimate of the SOC of the battery assembly.
18 . The vehicle system of claim 17 , wherein the filtering is performed using a Kalman filter algorithm.
19 . The vehicle system of claim 17 , wherein the blending is based on a ratio of a capacity of the battery assembly to a capacity of the sensing device, and the blended SOC relation is represented by:
SOC
SD
(
k
)
=
(
CAP
BP
/
CAP
SD
)
*
SOC
BP
(
k
)
+
d
%
+
v
(
i
,
T
)
,
wherein SOC SD (k) is a state of charge of the sensing device for a plurality of measurement times k, CAP BP is a capacity of the battery assembly, CAP SD is a capacity of the sensing device, d % is a minimum charge offset, SOC BP (k) is a state of the charge of the battery assembly, and v(i,T) is a noise model of the sensing device based on a current i and a temperature T.
20 . The vehicle system of claim 17 , wherein the set of first estimates is acquired based on a Coulomb counting process, and the set of second estimates is acquired based on a Coulomb counting process in combination with a battery state estimation process.