# US 12,222,396 B2 · Battery internal temperature information processing method, computer device, and storage medium

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- Number: US 12,222,396 B2
- Document ID: US12222396B2
- Status: Granted
- Kind: B2
- Application number: 17/780,911
- Filed: 2020-10-23
- Published: 2023-01-26
- Granted: 2025-02-11
- Earliest claimed: 2019-11-28
- Assignees: BYD Company Limited
- Applicants: BYD Company Limited
- Inventors: Linwang Deng; Xiaoqian Li; Tianyu Feng; Sijia Liu
- Web page: https://patentgrep.com/patent/US12222396B2

## CPC

- G01R 31/374: with means for correcting the measurement for temperature or ageing
- H01M 10/625: Vehicles
- B60R 16/033: characterised by the use of electrical cells or batteries (for propulsion puposes B60K1/04; supplying batteries to, or removing batteries from, vehicles B60S5/06; testing of charge state G01R31/36)
- G01K 7/427: {Temperature calculation based on spatial modeling, e.g. spatial inter- or extrapolation}
- G01R 31/367: Software therefor, e.g. for battery testing using modelling or look-up tables
- G01R 31/3842: combining voltage and current measurements
- H01M 10/482: {for several batteries or cells simultaneously or sequentially}
- H01M 10/486: {for measuring temperature}
- H01M 10/613: Cooling or keeping cold
- H01M 10/6554: Rods or plates
- H01M 2220/20: Batteries in motive systems, e.g. vehicle, ship, plane
- Y02E 60/10: Energy storage using batteries
- G01R: MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES (indicating correct tuning of resonant circuits H03J3/12)
- H01M: PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- B60R: VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
- G01K: MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR (radiation pyrometry G01J5/00)
- Y02E: REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION

## Abstract

A battery internal temperature information processing method, a computer device, and a storage medium that first acquire off-line testing data for off-line testing a battery module and construct an equivalent thermal network model from the off-line testing data, determine optimal model parameters of the equivalent thermal network model based on a multi-objective function fitting method; thereafter, a first battery internal temperature estimate of the battery of the vehicle at a first moment in actual operation of the vehicle is determined, in turn, based on the acquired initial state vector values of the battery of the vehicle, first operational data at a first moment in actual operation of the vehicle, and an equivalent thermal network model including the optimal model parameters.

## Claims

### Claim 1

A method for processing battery internal temperature information, comprising:
obtaining off-line testing data of a battery module subject to an off-line test under different off-line operating conditions in an environment having a constant temperature, by:
obtaining off-line temperature data and equivalent circuit data of the battery module under the different off-line operating conditions for performing the off-line test, wherein the equivalent circuit data includes a battery terminal voltage value and a battery current value of the battery module;
wherein the obtaining off-line temperature data comprises:
disposing a first temperature sensing device inside a pole of a cell of the battery module and measuring an off-line internal temperature of the cell;
disposing second temperature sensing devices to surfaces of the cell and measuring off-line surface temperatures of the surfaces of the cell; and
disposing a third temperature sensing device to a cooling plate of a cooling system connected to the battery module and measuring an off-line temperature of the cooling plate, wherein the first temperature sensing device, the second temperature sensing devices, and the third temperature sensing device are of a same type of sensing devices;
obtaining multiple sets of initial parameters of an equivalent thermal network model according to the off-line testing data, and determining a set of optimal model parameters among the sets of initial parameters of the equivalent thermal network model based on a multi-objective function fitting method;
obtaining an initial state vector value of a battery comprising the battery module of a vehicle and first operating data of the battery of the vehicle at a first moment during an actual operation of the vehicle;
determining an estimate of an internal temperature of the battery at the first moment according to the initial state vector value, the first operating data, and the equivalent thermal network model including the set of optimal model parameters of the actual operation,
wherein after obtaining the off-line temperature data and the off-line testing data of the battery module, the method comprises:
obtaining a first open circuit voltage and a first temperature coefficient of the battery module from a database;
grouping and inputting the battery terminal voltage value, the battery current value, and the off-line internal temperature into a first heat production rate model, and obtaining a first battery heat production rate of the battery module output from the first heat production rate model under the different off-line operating conditions; wherein a set of a battery terminal voltage value, a battery current value, and an off-line internal temperature comprises the battery terminal voltage value, the battery current value, and the off-line internal temperature corresponding to a same moment under a same off-line operating condition.

### Claim 2 (depends on 1)

The method according to claim 1, wherein the first heat production rate model is based on a formular of
Q
heat
⁢
1
=
(
OC
⁢
V
(
SOC
1
)
-
U
1
)
×
I
2
⁢
1
+
I
1
⁢
T
1
⁢
dOC
⁢
V
d
⁢
T
⁢
(
S
⁢
O
⁢
C
1
)
;
the Qheat1 is the first battery heat production rate of the battery module under the different off-line operating conditions;
the OCV(SOC1) is the first open circuit voltage of the battery module;
the U1 is the battery terminal voltage value of the battery module;
the I1 is the battery current value of the battery module;
the T1 is the off-line internal temperature; and
the
dOC
⁢
V
d
⁢
T
⁢
(
S
⁢
O
⁢
C
1
)
is the first temperature coefficient of the battery module.

### Claim 3 (depends on 1)

The method according to claim 1, wherein
the obtaining the multiple sets of initial parameters of the equivalent thermal network model according to the off-line testing data, and determining the set of optimal model parameters among the sets of initial parameters of the equivalent thermal network model based on the multi-objective function fitting method comprises:
obtaining input data and output data of the equivalent thermal network model, and associating the input data with the output data corresponding to the same moment under the same off-line operating condition; wherein the input data includes the first battery heat production rate, the off-line cooling plate temperature, and an off-line ambient temperature, and the output data includes the off-line internal temperature and the off-line surface temperatures; and
inputting the input data into the equivalent thermal network model, and taking the output data associated with the input data as an output of the equivalent thermal network model to obtain a set of initial parameters of the equivalent thermal network model; wherein the set of the initial parameters includes a first equivalent thermal capacity inside the cell of the battery module in the equivalent thermal network model, and a first thermal resistance from an inside of the cell to a cell surface point corresponding to a heat transfer path, a second thermal resistance from the cell surface point corresponding to the heat transfer path to the environment, and a second equivalent thermal capacity of the cell surface point corresponding to the heat transfer path; and
the multi-objective function fitting method includes one or more of a least-squares algorithm, a genetic algorithm, or a particle swarm optimization algorithm.

### Claim 4 (depends on 1)

The method according to claim 1, wherein the obtaining the initial state vector value of the battery comprising the battery module of the vehicle and the first operating data of the battery of the vehicle at the first moment during the actual operation of the vehicle comprises:
obtaining the initial state vector value of the battery at an initial moment of the actual operation from a database; and
obtaining first operating data at a first moment of the actual operation, wherein the first operating data includes a first battery surface temperature, a first cooling plate temperature, a first ambient temperature, a first voltage value, and a first current value; the first battery surface temperature is measured by a fourth temperature sensing device provided on a battery surface of the vehicle, and the first cooling plate temperature is measured by a fifth temperature sensing device provided on a cooling plate of a cooling system connected to the battery of the vehicle; the first ambient temperature is an temperature of the environment at the first moment of the actual operation; the first voltage value is a voltage value of the battery of the vehicle at the first moment of the actual operation, and the first current value is a current value of the battery of the vehicle at the first moment of the actual operation.

### Claim 5 (depends on 4)

The method according to claim 4, wherein the determining the estimate of the internal temperature of the battery at the first moment according to the initial state vector value, the first operating data, and the equivalent thermal network model including the set of optimal model parameters of the actual operation comprises:
obtaining a second open circuit voltage and a second temperature coefficient of the battery of the vehicle from the database;
inputting the first voltage value, the first current value, and the first battery surface temperature into a second heat production rate model, and obtaining a second battery heat production rate outputted by the second heat production rate model at the first moment of the actual operation;
inputting the initial state vector value, the second battery heat production rate the first cooling plate temperature, and the first ambient temperature into the equivalent thermal network model containing the set of optimal model parameters, and obtaining a priori estimated value of a first state vector of the battery of the vehicle at the first moment of the actual operation output by the equivalent thermal network model;
determining an estimated value of a first battery surface point temperature according to the priori estimated value of the first state vector;
inputting the priori estimated value of the first state vector, the estimated value of the first battery surface point temperature, and the first battery surface temperature into a Luenberger observer including a first gain value and a second gain value, and obtaining a posteriori value of the first state vector after feedback correction of the Luenberger observer; and
determining a first estimated value of the internal temperature of the battery of the vehicle at the first moment of the actual operation according to the posteriori value of the first state vector.

### Claim 6 (depends on 5)

The method according to claim 5, wherein after the determining the estimate of the internal temperature of the battery at the first moment according to the initial state vector value, the first operating data, and the equivalent thermal network model including the set of optimal model parameters of the actual operation, further comprising:
obtaining second operating data of the battery of the vehicle at an Nth moment of the actual operation and a posteriori value of a second state vector at an (N−1)th moment; wherein N is a positive integer greater than or equal to 2; and
determining a second estimated value of the internal temperature of the battery at the Nth moment of the actual operation according to the posteriori value of the second state vector at the (N−1)th moment, the second operating data, and the equivalent thermal network model including the set of optimal model parameters at the Nth moment.

### Claim 7 (depends on 6)

The method according to claim 6, wherein the second operating data comprises a second battery surface temperature, a second cooling plate temperature, a second ambient temperature, a second voltage value, and a second current value; a second battery surface temperature is measured by the fourth temperature sensing device; the second cooling plate temperature is measured by the fifth temperature sensing device; the second ambient temperature is the temperature of the environment at the Nth moment of the actual operation; the second voltage value is the voltage value of the battery of the vehicle at the Nth moment of the actual operation; and the second current value is a current value of the battery of the vehicle at the Nth moment of the actual operation.

### Claim 8 (depends on 7)

The method according to claim 7, wherein the determining the second estimated value of the internal temperature of the battery at the Nth moment of the actual operation according to the posteriori value of the second state vector at the (N−1)th moment, the second operating data, and the equivalent thermal network model including the set of optimal model parameters at the Nth moment, comprises:
inputting the second voltage value, the second current value, and the second battery surface temperature into the second heat production rate model, and obtaining a third battery heat production rate of the battery of the vehicle output from the second heat production rate model at the Nth moment of the actual operation;
inputting the posteriori value of the second state vector at the (N−1)th moment, the third battery heat production rate, the second cooling plate temperature, and the second ambient temperature into the equivalent thermal network model including the set of optimal model parameters, and obtaining a priori estimated value of the second state vector of the battery of the vehicle at the Nth moment of actual operation output by the equivalent thermal network model;
determining a second estimated battery surface point temperature based on the priori estimated value of the second state vector;
inputting the priori estimated value of the second state vector, the estimated second battery surface point temperature, and the second battery surface temperature into the Luenberger observer, and obtaining a posteriori value of the second state vector after feedback correction of the Luenberger observer;
determining a second estimated value of the internal temperature of the battery at the Nth moment of the actual operation of the vehicle according to the posteriori value of the second state vector.

### Claim 9 (depends on 5)

The method according to claim 5, wherein the second heat production rate model is based on a formula of
Q
heat
⁢
2
⁢
k
=
(
OC
⁢
V
(
SOC
2
⁢
k
)
-
U
2
⁢
k
)
×
I
2
⁢
k
+
I
2
⁢
k
⁢
T
2
⁢
k
⁢
dOC
⁢
V
d
⁢
T
⁢
(
S
⁢
O
⁢
C
2
⁢
k
)
,
the k is a kth moment of the actual operation of the battery of the vehicle, 0≤k≤N and the k is an integer; at the initial moment of the actual operation of the vehicle, k=0; at the first moment of the actual operation of the vehicle, k=1; at an Nth moment of the actual operation of the vehicle, k=N;
the Qheat2k is the second battery heat production rate of the battery of the vehicle at kth moment;
the OCV(SOC2k) is the second open circuit voltage of the battery of the vehicle;
the U2k is a second voltage value of the battery of the vehicle at kth moment; and
the I2k is a second current value of the battery of the vehicle at the kth moment;
the T2k is a second battery surface temperature of the battery of the vehicle at kth moment; and
the
dOC
⁢
V
d
⁢
T
⁢
(
S
⁢
O
⁢
C
2
⁢
k
)
is the second temperature coefficient of the battery of the vehicle.

### Claim 10 (depends on 5)

The method according to claim 5, wherein the Luenberger observer is based on a formula of
X
state
(
k
)
=
X
state
time
(
k
)
+
K
p
·
(
T
s
measure
(
k
)
-
Y
mea
(
k
)
)
+
K
1
·
∑
i
=
1
k
(
T
s
measure
(
i
)
-
Y
mea
(
i
)
)
,
k is a kth moment of the actual operation of the battery of the vehicle, O≤k≤N, and the k is an integer; at the initial moment of the actual operation of the vehicle, k=0; at the first moment of the actual operation of the vehicle, k=1; at an Nth moment of the actual operation of the vehicle, k=N;
the Xstate(k) is a posteriori value of a state vector of the battery of the vehicle at the kth moment;
the Xstatetime(k) is a priori estimated value of the state vector of the battery of the vehicle at the kth moment;
the Kp is the first gain value;
the K1 is the second gain value;
the Tsmeasure(k) is a second battery surface temperature of the vehicle at the kth moment;
Ymea(k) is a second estimated battery surface point temperature of the vehicle at the kth the moment;
the Tsmeasure(i) is a battery surface temperature of the vehicle at an ith moment, the 1≤1≤k, and the i is an integer; and
the Ymea(i) is an estimated battery surface point temperature of the vehicle at the ith moment.

### Claim 11

A device for processing battery internal temperature information comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein the processor executes the computer-readable instructions to operations comprising:
obtaining off-line testing data of a battery module subject to an off-line test under different off-line operating conditions in an environment having a constant temperature, by:
obtaining off-line temperature data and equivalent circuit data of the battery module under the different off-line operating conditions for performing the off-line test, wherein the equivalent circuit data includes a battery terminal voltage value and a battery current value of the battery module;
wherein the obtaining off-line temperature data comprises:
disposing a first temperature sensing device inside a pole of a cell of the battery module and measuring an off-line internal temperature of the cell;
disposing second temperature sensing devices to surfaces of the cell and measuring off-line surface temperatures of the surfaces of the cell; and
disposing a third temperature sensing device to a cooling plate of a cooling system connected to the battery module and measuring an off-line temperature of the cooling plate, wherein the first temperature sensing device, the second temperature sensing devices, and the third temperature sensing device are of a same type of sensing devices;
obtaining multiple sets of initial parameters of an equivalent thermal network model according to the off-line testing data, and determining a set of optimal model parameters among the sets of initial parameters of the equivalent thermal network model based on a multi-objective function fitting method;
obtaining an initial state vector value of a battery comprising the battery module of a vehicle and first operating data of the battery of the vehicle at a first moment during an actual operation of the vehicle;
determining an estimate of an internal temperature of the battery at the first moment according to the initial state vector value, the first operating data, and the equivalent thermal network model including the set of optimal model parameters of the actual operation,
wherein after obtaining the off-line temperature data and the off-line testing data of the battery module, the method comprises:
obtaining a first open circuit voltage and a first temperature coefficient of the battery module from a database;
grouping and inputting the battery terminal voltage value, the battery current value, and the off-line internal temperature into a first heat production rate model, and obtaining a first battery heat production rate of the battery module output from the first heat production rate model under the different off-line operating conditions; wherein a set of a battery terminal voltage value, a battery current value, and an off-line internal temperature comprises the battery terminal voltage value, the battery current value, and the off-line internal temperature corresponding to a same moment under a same off-line operating condition.

### Claim 12

A non-transitory computer readable storage medium storing computer-readable instructions, wherein when the computer-readable instructions are executed by a processor, the processor is caused to perform operations comprising:
obtaining off-line testing data of a battery module subject to an off-line test under different off-line operating conditions in an environment having a constant temperature, by:
obtaining off-line temperature data and equivalent circuit data of the battery module under the different off-line operating conditions for performing the off-line test, wherein the equivalent circuit data includes a battery terminal voltage value and a battery current value of the battery module;
wherein the obtaining off-line temperature data is obtained by:
disposing a first temperature sensing device inside a pole of a cell of the battery module and measuring an off-line internal temperature of the cell;
disposing second temperature sensing devices to surfaces of the cell and measuring off-line surface temperatures of the surfaces of the cell; and
disposing a third temperature sensing device to a cooling plate of a cooling system connected to the battery module and measuring an off-line temperature of the cooling plate, wherein the first temperature sensing device, the second temperature sensing devices, and the third temperature sensing device are of a same type of sensing devices;
obtaining multiple sets of initial parameters of an equivalent thermal network model according to the off-line testing data, and determining a set of optimal model parameters among the sets of initial parameters of the equivalent thermal network model based on a multi-objective function fitting method;
obtaining an initial state vector value of a battery comprising the battery module of a vehicle and first operating data of the battery of the vehicle at a first moment during an actual operation of the vehicle;
determining an estimate of an internal temperature of the battery at the first moment according to the initial state vector value, the first operating data, and the equivalent thermal network model including the set of optimal model parameters of the actual operation,
wherein after obtaining the off-line temperature data and the off-line testing data of the battery module, the method comprises:
obtaining a first open circuit voltage and a first temperature coefficient of the battery module from a database;
grouping and inputting the battery terminal voltage value, the battery current value, and the off-line internal temperature into a first heat production rate model, and obtaining a first battery heat production rate of the battery module output from the first heat production rate model under the different off-line operating conditions; wherein a set of a battery terminal voltage value, a battery current value, and an off-line internal temperature comprises the battery terminal voltage value, the battery current value, and the off-line internal temperature corresponding to a same moment under a same off-line operating condition.

### Claim 13 (depends on 11)

The device according to claim 11, wherein the first heat production rate model is based on a formular of
Q
heat
⁢
1
=
(
OCV
⁡
(
SOC
1
)
-
U
1
)
×
I
1
+
I
1
⁢
T
1
⁢
dOCV
dT
⁢
(
SOC
1
)
;
the Qheat1 is the first battery heat production rate of the battery module under the different off-line operating conditions;
the OCV(SOC1) is the first open circuit voltage of the battery module;
the U1 is the battery terminal voltage value of the battery module;
the I1 is the battery current value of the battery module;
the T1 is the off-line internal temperature; and
the
dOCV
dT
⁢
(
SOC
1
)
is the first temperature coefficient of the battery module.

### Claim 14 (depends on 11)

The device according to claim 11, wherein
the obtaining the multiple sets of initial parameters of the equivalent thermal network model according to the off-line testing data, and determining the set of optimal model parameters among the sets of initial parameters of the equivalent thermal network model based on the multi-objective function fitting method comprises:
obtaining input data and output data of the equivalent thermal network model, and associating the input data with the output data corresponding to the same moment under the same off-line operating condition; wherein the input data includes the first battery heat production rate, the off-line cooling plate temperature, and an off-line ambient temperature, and the output data includes the off-line internal temperature and the off-line surface temperatures; and
inputting the input data into the equivalent thermal network model, and taking the output data associated with the input data as an output of the equivalent thermal network model to obtain a set of initial parameters of the equivalent thermal network model; wherein the set of the initial parameters includes a first equivalent thermal capacity inside the cell of the battery module in the equivalent thermal network model, and a first thermal resistance from an inside of the cell to a cell surface point corresponding to a heat transfer path, a second thermal resistance from the cell surface point corresponding to the heat transfer path to the environment, and a second equivalent thermal capacity of the cell surface point corresponding to the heat transfer path; and
the multi-objective function fitting method includes one or more of a least-squares algorithm, a genetic algorithm, or a particle swarm optimization algorithm.

### Claim 15 (depends on 11)

The device according to claim 11, wherein the obtaining the initial state vector value of the battery comprising the battery module of the vehicle and the first operating data of the battery of the vehicle at the first moment during the actual operation of the vehicle comprises:
obtaining the initial state vector value of the battery at an initial moment of the actual operation from a database; and
obtaining first operating data at a first moment of the actual operation, wherein the first operating data includes a first battery surface temperature, a first cooling plate temperature, a first ambient temperature, a first voltage value, and a first current value; the first battery surface temperature is measured by a fourth temperature sensing device provided on a battery surface of the vehicle, and the first cooling plate temperature is measured by a fifth temperature sensing device provided on a cooling plate of a cooling system connected to the battery of the vehicle; the first ambient temperature is an temperature of the environment at the first moment of the actual operation; the first voltage value is a voltage value of the battery of the vehicle at the first moment of the actual operation, and the first current value is a current value of the battery of the vehicle at the first moment of the actual operation.

### Claim 16 (depends on 15)

The device according to claim 15, wherein the determining the estimate of the internal temperature of the battery at the first moment according to the initial state vector value, the first operating data, and the equivalent thermal network model including the set of optimal model parameters of the actual operation comprises:
obtaining a second open circuit voltage and a second temperature coefficient of the battery of the vehicle from the database;
inputting the first voltage value, the first current value, and the first battery surface temperature into a second heat production rate model, and obtaining a second battery heat production rate outputted by the second heat production rate model at the first moment of the actual operation;
inputting the initial state vector value, the second battery heat production rate the first cooling plate temperature, and the first ambient temperature into the equivalent thermal network model containing the set of optimal model parameters, and obtaining a priori estimated value of a first state vector of the battery of the vehicle at the first moment of the actual operation output by the equivalent thermal network model;
determining an estimated value of a first battery surface point temperature according to the priori estimated value of the first state vector;
inputting the priori estimated value of the first state vector, the estimated value of the first battery surface point temperature, and the first battery surface temperature into a Luenberger observer including a first gain value and a second gain value, and obtaining a posteriori value of the first state vector after feedback correction of the Luenberger observer; and
determining a first estimated value of the internal temperature of the battery of the vehicle at the first moment of the actual operation according to the posteriori value of the first state vector.

### Claim 17 (depends on 16)

The device according to claim 16, wherein after the determining the estimate of the internal temperature of the battery at the first moment according to the initial state vector value, the first operating data, and the equivalent thermal network model including the set of optimal model parameters of the actual operation, the operations further comprise:
obtaining second operating data of the battery of the vehicle at an Nth moment of the actual operation and a posteriori value of a second state vector at an (N−1)th moment; wherein N is a positive integer greater than or equal to 2; and
determining a second estimated value of the internal temperature of the battery at the Nth moment of the actual operation according to the posteriori value of the second state vector at the (N−1)th moment, the second operating data, and the equivalent thermal network model including the set of optimal model parameters at the Nth moment.

### Claim 18 (depends on 17)

The device according to claim 17, wherein the second operating data comprises a second battery surface temperature, a second cooling plate temperature, a second ambient temperature, a second voltage value, and a second current value; a second battery surface temperature is measured by the fourth temperature sensing device; the second cooling plate temperature is measured by the fifth temperature sensing device; the second ambient temperature is the temperature of the environment at the Nth moment of the actual operation; the second voltage value is the voltage value of the battery of the vehicle at the Nth moment of the actual operation; and the second current value is a current value of the battery of the vehicle at the Nth moment of the actual operation.

### Claim 19 (depends on 18)

The device according to claim 18, wherein the determining the second estimated value of the internal temperature of the battery at the Nth moment of the actual operation according to the posteriori value of the second state vector at the (N−1)th moment, the second operating data, and the equivalent thermal network model including the set of optimal model parameters at the Nth moment, comprises:
inputting the second voltage value, the second current value, and the second battery surface temperature into the second heat production rate model, and obtaining a third battery heat production rate of the battery of the vehicle output from the second heat production rate model at the Nth moment of the actual operation;
inputting the posteriori value of the second state vector at the (N−1)th moment, the third battery heat production rate, the second cooling plate temperature, and the second ambient temperature into the equivalent thermal network model including the set of optimal model parameters, and obtaining a priori estimated value of the second state vector of the battery of the vehicle at the Nth moment of actual operation output by the equivalent thermal network model;
determining a second estimated battery surface point temperature based on the priori estimated value of the second state vector;
inputting the priori estimated value of the second state vector, the estimated second battery surface point temperature, and the second battery surface temperature into the Luenberger observer, and obtaining a posteriori value of the second state vector after feedback correction of the Luenberger observer;
determining a second estimated value of the internal temperature of the battery at the Nth moment of the actual operation of the vehicle according to the posteriori value of the second state vector.

### Claim 20 (depends on 16)

The device according to claim 16, wherein the second heat production rate model is based on a formula of
Q
heat
⁢
2
⁢
k
=
(
OCV
⁡
(
SOC
2
⁢
k
)
-
U
2
⁢
k
)
×
I
2
⁢
k
+
I
2
⁢
k
⁢
T
2
⁢
k
⁢
dOCV
dT
⁢
(
SOC
2
⁢
k
)
,
the k is a kth moment of the actual operation of the battery of the vehicle, 0≤k≤N, and the k is an integer; at the initial moment of the actual operation of the vehicle, k=0; at the first moment of the actual operation of the vehicle, k=1; at an Nth moment of the actual operation of the vehicle, k=N;
the Qheat2k is the second battery heat production rate of the battery of the vehicle at kth moment;
the OCV(SOC2k) is the second open circuit voltage of the battery of the vehicle;
the U2k is a second voltage value of the battery of the vehicle at kth moment; and
the I2k is a second current value of the battery of the vehicle at the kth moment;
the T2k is a second battery surface temperature of the battery of the vehicle at kth moment; and
the
dOCV
dT
⁢
(
SOC
2
⁢
k
)
the is the second temperature coefficient of the battery of the vehicle.