CN112964992A - Method, device and medium for processing temperature information in battery based on AUKF - Google Patents

Method, device and medium for processing temperature information in battery based on AUKF Download PDF

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CN112964992A
CN112964992A CN201911190827.2A CN201911190827A CN112964992A CN 112964992 A CN112964992 A CN 112964992A CN 201911190827 A CN201911190827 A CN 201911190827A CN 112964992 A CN112964992 A CN 112964992A
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battery
value
vehicle
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temperature
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CN112964992B (en
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李晓倩
冯天宇
邓林旺
刘思佳
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BYD Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
    • G01K13/00—Thermometers specially adapted for specific purposes
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

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Abstract

The invention discloses a battery internal temperature information processing method, equipment and medium based on AUKF. The method comprises the following steps: acquiring initial parameters of an equivalent thermal network model according to offline test data of offline test of the battery module, and determining an initial value of an optimal model parameter in the initial parameters based on a multi-objective function fitting method; a first estimated battery internal temperature value and a first estimated model parameter value of the battery of the vehicle at a first moment of actual operation are determined according to the initial AUKF combined vector value of the battery of the vehicle, the first operation data and an equivalent thermal network model containing the initial values of the optimal model parameters. The method and the device can accurately estimate the internal temperature of the battery in real time and optimize the model parameters of the equivalent heat network model at the same time, so that the internal temperature of the battery can be estimated more accurately according to the optimized model parameters, the working condition of the battery is optimized according to the accurately estimated internal temperature of the battery in real time, and the safety of the battery is improved.

Description

Method, device and medium for processing temperature information in battery based on AUKF
Technical Field
The invention relates to the technical field of battery temperature, in particular to a battery internal temperature information processing method, equipment and medium based on an AUKF (adaptive unscented Kalman filter).
Background
The battery serving as the power of the new energy automobile generally has a temperature rise phenomenon in the charging and discharging processes, and heat generation and heat dissipation in the battery are uneven, so that temperature field distribution exists in the battery, a large temperature difference exists between the inside and the outside of the battery, and the battery is particularly obvious in high-power demand application; however, in the actual battery thermal management, the battery surface temperature can only be measured on the external surface of the battery in real time, and cannot be measured on the internal temperature of the battery in real time, and the internal temperature of the battery has a significant influence on the performance of the battery and is directly related to the safety performance of the battery, so that the thermal management of the battery, especially the estimation of the internal temperature of the battery, has become one of the most challenging parts of the battery management system.
In the prior art, in the estimation method of the internal temperature of the battery, the method for estimating the internal temperature of the battery based on the electrochemical impedance spectrum test has higher requirements on a test system, so that an actual vehicle cannot meet the test conditions; the method for estimating the internal temperature of the battery based on the functional relationship between the internal temperature and the surface temperature has no practical physical significance, and is difficult to adapt to the accurate calculation of the internal temperature of the battery under various complex working conditions; the method for enabling the battery surface to be equivalent to one temperature point by using the battery heat transfer model in the prior art considers that the temperatures of all points of the battery surface are the same. In addition, in part of the prior art, an offline identification algorithm or an online parameter identification method based on an estimator is adopted to obtain model parameters, and the scheme cannot eliminate the influence of the estimation error of the internal temperature of the battery on the parameter identification result, so that the model parameters are not matched, and a larger estimation error of the internal temperature is further caused.
Disclosure of Invention
The embodiment of the invention provides a battery internal temperature information processing method, equipment and medium based on AUKF, which can be used for optimizing model parameters of an equivalent heat network model while accurately estimating the internal temperature of a battery in real time, so that the internal temperature of the battery can be estimated more accurately according to the optimized model parameters, and further, the working condition of the battery can be optimized according to the accurately estimated internal temperature of the battery in real time, and the safety of the battery is improved.
In order to achieve the above object, the present invention provides a battery internal temperature information processing method based on an AUKF, including:
acquiring offline test data of the battery module for offline testing under different offline working conditions of a constant temperature environment;
acquiring initial parameters of an equivalent thermal network model according to the off-line test data, and determining an optimal model parameter initial value in each initial parameter of the equivalent thermal network model based on a multi-objective function fitting method;
acquiring an initial AUKF combined vector value of a battery of the vehicle;
acquiring first operation data of a battery of the vehicle at a first moment when the vehicle actually operates;
and determining a first internal battery temperature estimated value and a first model parameter estimated value of the battery of the vehicle at a first moment when the vehicle actually operates according to the initial AUKF combined vector value, the first operation data and the equivalent thermal network model containing the initial value of the optimal model parameter.
The invention also provides computer equipment which comprises a memory, a processor and computer readable instructions stored in the memory and capable of running on the processor, wherein the processor executes the computer readable instructions to realize the AUKF-based battery internal temperature information processing method.
The invention also provides a computer readable storage medium, which stores computer readable instructions, and the computer readable instructions are executed by a processor to realize the battery internal temperature information processing method based on AUKF.
According to the method, the device and the medium for processing the temperature information in the battery based on the AUKF, provided by the invention, the initial parameters of an equivalent thermal network model are obtained according to offline test data of a battery module for offline test under different offline working conditions of a constant temperature environment, and the initial values of the optimal model parameters in the initial parameters of the equivalent thermal network model are determined based on a multi-objective function fitting method; acquiring an initial AUKF combined vector value of a battery of the vehicle; acquiring first operation data of a battery of the vehicle at a first moment when the vehicle actually operates; and determining a first internal battery temperature estimated value and a first model parameter estimated value of the battery of the vehicle at a first moment when the vehicle actually operates according to the initial AUKF combined vector value, the first operation data and the equivalent thermal network model containing the initial value of the optimal model parameter. The method and the device can accurately estimate the internal temperature of the battery in real time and optimize the model parameters of the equivalent heat network model at the same time, so that the internal temperature of the battery can be estimated more accurately according to the optimized model parameters (thus, the influence of estimation errors of the internal temperature of the battery on parameter identification results can be eliminated, the model parameters are more matched, the estimation errors of the internal temperature of the battery are reduced), and further, the working conditions of the battery can be optimized according to the accurately estimated internal temperature of the battery in real time, and the safety of the battery is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without inventive labor.
FIG. 1 is a flow chart of a method for processing battery internal temperature information based on AUKF in one embodiment of the invention;
fig. 2 is a schematic structural diagram of a cell internal temperature calculation model in an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a cell equivalent thermal network model according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a heat transfer path according to an embodiment of the present invention;
FIG. 5 is a schematic structural diagram of an equivalent circuit model according to an embodiment of the present invention;
fig. 6 is a flowchart of step S50 of the method for processing the internal temperature information of the battery based on the AUKF according to the embodiment of the present invention;
FIG. 7 is a flow chart of a method for processing battery internal temperature information based on AUKF in another embodiment of the present invention;
fig. 8 is a flowchart of step S60 of the method for processing the battery internal temperature information based on the AUKF according to the embodiment of the present invention;
FIG. 9 is a schematic diagram of experimental verification results in accordance with an embodiment of the present invention;
FIG. 10 is a block diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, there is provided a battery internal temperature information processing method based on AUKF, including the following steps S10-S40:
s10, acquiring offline test data of the battery module for offline test under different offline working conditions in a constant temperature environment; the off-line working conditions comprise constant current charging and discharging with different multiplying factors (wherein the different multiplying factors are preferably 0.1C-3C), NEDC (New European Driving Cycle, New European Cycle Test), WLTC (World Light Vehicle Test Procedure) and other dynamic working conditions. In the invention, firstly, a battery module for off-line testing needs to be prepared, wherein the battery module comprises a plurality of battery cells connected in series and parallel and a connecting piece for connecting the battery cells with an external component; the battery module is characterized in that a first temperature sensing device for detecting the internal temperature of the battery core is arranged inside the battery core of the battery module (the first temperature sensing device is preferably arranged inside a pole core of the battery core to ensure that the highest temperature inside the whole battery core can be measured), a second temperature sensing device for detecting the surface temperature of the battery core is arranged on the surface of the battery core, and a third temperature sensing device for detecting the temperature of an offline cooling plate is arranged on a cooling plate of a cooling system connected with the battery module. Understandably, the first temperature sensing device, the second temperature sensing device and the third temperature sensing device are preferably the same type of sensing device, so that errors between detected temperature data are smaller. When performing an offline test, the battery module needs to be placed in a constant temperature environment (for example, in a thermostat, the thermostat can provide a constant temperature environment with a certain temperature, and the temperature of the constant temperature environment is adjustable) to perform the offline test under different offline working conditions, so as to obtain offline test data for performing the offline test under different offline working conditions, and understandably, the offline test data includes equivalent circuit data of a battery cell of the battery module, for example, a battery terminal voltage value and a battery current value of the battery cell of an equivalent circuit model of the battery module, and the like; the offline test data further includes offline temperature data of the battery core of the battery module, for example, the offline test internal temperature of the battery core of the battery module, which is measured by the first temperature sensing device, the offline test surface temperature of the battery core surface of the battery module, which is measured by the second temperature sensing device (if the battery core is square, the battery core includes 6 surfaces, the second sensing device can be set on 6 surfaces of the battery core at the time, and a set of offline test surface temperatures acquired at the time includes 6 offline test surface temperatures respectively measured on 6 surfaces of the battery core), the offline cooling plate temperature of the cooling plate measured by the third temperature sensing device, the offline environment temperature of the constant temperature environment, and the like. Understandably, after the offline test data is obtained, a first battery heat generation rate calculated in real time in the charging and discharging processes of the battery core of the battery module under different offline working conditions (understandably, the charging and discharging processes can be a complete offline working condition or one section of the whole offline working condition) can be determined according to the battery end voltage value, the battery current value and the offline test internal temperature in the offline test data, that is, each moment in the charging and discharging processes under the offline working conditions corresponds to one first battery heat generation rate.
Understandably, the heat transfer process from the inside of the battery cell to the outside of the battery module can be equivalent to the heat transfer process from the center to the surface inside the battery cell and the heat convection process between the surface of the battery cell and the external environment, and according to the thermal characteristics, the process can be described by the characteristics of an electrical network, namely, the thermal resistance can be equivalent to the resistance, the thermal capacity can be equivalent to the capacitance, the temperature can be equivalent to the voltage, and the heat production rate can be equivalent to the current source; based on the equivalent correspondence, a cell internal temperature calculation model shown in fig. 2, as shown in fig. 2, T, may be establishedinCell internal temperature, T, of the battery modulesCell surface temperature, T, of the battery moduleambOff-line ambient temperature, T, representing a constant temperature environmentcoolIndicating the offline cold plate temperature (T in the case of a cooling system where the cold plates are not cooledcool＝Tamb)，QheatRepresents the heat generation rate inside the battery cell (i.e., the first battery heat generation rate), CinIt should be noted that the resistance 1 in fig. 2 represents the thermal resistance between two temperature nodes (e.g., between two temperature nodes corresponding to the heat transfer paths from the inside of the battery cell and the surface points of the battery cell, or between two temperature nodes corresponding to the surface points of the battery cell and the external environment), the capacitance 2 represents the equivalent heat capacity of the corresponding node, and it is understood that, in fig. 2, only the heat transfer in directions perpendicular to three mutually perpendicular planes of the square cell, respectively, is indicated (in fig. 2, indicated as positive directions in the three x, y, z directions), in practice, as is preferred, for a square cell, heat transfer in three other directions (negative directions of three directions x, y, and z in fig. 2) symmetrical to those in fig. 2 should be considered at the same time, and heat transfer in each direction can be regarded as one heat transfer path, that is, for a square cell, heat transfer paths in six directions need to be considered. To further illustrate the heat transfer path for the above heat transfer, FIGS. 3 and 4 are used to further illustrateTo illustrate, the schematic structure of the heat transfer paths shown in fig. 4 corresponds to one of the heat transfer paths 3 in the cell equivalent thermal network model shown in fig. 3 (i.e. corresponds to the heat transfer path k in fig. 3, where k is 1, 2, …, n), that is, each of the heat transfer paths 3 in fig. 3 is equivalent to a "T" type network of R-C-R shown in fig. 4, where R is shown in fig. 4inkRepresenting the thermal resistance from the inside of the battery cell to the surface point of the battery cell corresponding to one heat transfer path; routkRepresenting the thermal resistance between the surface point of the battery cell corresponding to one heat transfer path and the external environment; cskRepresents the equivalent heat capacity, T, of the corresponding cell surface point of one heat transfer pathskThe cell surface temperature of a cell surface point corresponding to one heat transfer path is indicated. Understandably, the offline temperature data of the battery cell of the battery module in the offline test data of the present invention is a parameter corresponding to the battery cell equivalent thermal network model shown in fig. 3 and 4. In the invention, the equivalent of the battery cell equivalent thermal network model is considered as the circuit model, and the calculation can be carried out by combining with the circuit simulation software, so that the parameters in the battery cell equivalent thermal network model are conveniently optimized, the battery cell equivalent thermal network model can be expressed without establishing a complex system function relationship, and the subsequent calculation process is simplified.
S20, obtaining initial parameters of the equivalent thermal network model according to the off-line test data, and determining the initial value of the optimal model parameter in each initial parameter of the equivalent thermal network model based on a multi-objective function fitting method.
That is, based on the offline test data determined in the above step S10, initial parameters of an equivalent thermal network model (the equivalent thermal network model corresponds to the above cell equivalent thermal network model in fig. 3) may be determined; specifically, each set of input parameters and output parameters of the equivalent heat network model are determined according to the offline test data (one set of input parameters corresponds to one set of output parameters, and the corresponding input parameters and output parameters are determined according to the offline test data corresponding to the same moment in the charging and discharging process under the offline working condition), and then the determined input parameters are input into the equivalent heat network model, and the output parameters corresponding to the input parameters and output by the equivalent heat network model are obtained; determining multiple groups of initial parameters of the equivalent heat network model through the process; preferably, the set of input parameters may include the first battery heat generation rate, the offline cooling plate temperature, and the offline ambient temperature determined in step S10 above; a set of said output parameters comprising an off-line test internal temperature and said off-line test surface temperature; the set of initial parameters includes equivalent heat capacity inside the battery cell in the equivalent network model of the battery module, heat resistance from the inside of the battery cell to the surface points of the battery cell corresponding to each heat transfer path, and heat resistance from the surface points of the battery cell corresponding to each heat transfer path to an external environment (the external environment here refers to an environment where the battery cell or the battery is located, for example, the battery cell of the battery module is placed in a constant temperature environment, at this time, the external environment is the constant temperature environment, and if the equivalent network model is applied to the battery of a vehicle which actually runs, at this time, the external environment is an environment where the battery of the vehicle is actually located), and equivalent heat capacity of the surface points of the battery cell corresponding to each heat transfer.
Understandably, after determining multiple sets of initial parameters of the equivalent thermal network model, a multi-objective function fitting method (including but not limited to a least square method, a genetic algorithm, a particle swarm optimization algorithm, etc.) may be used to determine optimal model parameter initial values in the multiple sets of initial parameters, that is, parameter optimization may be performed by the multi-objective function fitting method (the method for performing parameter optimization by using the multi-objective function fitting method is known and is not described herein again), and finally a set of optimal model parameter initial values is obtained by searching, so that after inputting input parameters into the equivalent thermal network model including the optimal model parameter initial values, actual output parameters output by the equivalent thermal network model are most consistent with experimental test results.
And S30, acquiring an initial AUKF joint vector value of the battery of the vehicle. Understandably, the initial AUKF combined vector value can be determined according to the initial value of the battery state and the initial value of the optimal model parameter known in the step S20; in the present invention, the initial state vector value of the battery may have a certain error, and the present invention may perform a step-by-step feedback correction on the initial state vector value of the battery having the error in a subsequent step (for example, a first combined vector posterior value after the feedback correction is obtained after performing a first correction at a first time, and then may determine a first internal temperature estimation value of the battery of the vehicle after the feedback correction at the first time when the vehicle actually operates according to the first combined vector posterior value and a first combined vector covariance posterior value), so that the accuracy of the battery initial state vector values (e.g., the first combination vector posterior value, the third combination vector posterior value, etc., mentioned later) after correction according to the feedback becomes higher and higher, and further, the accuracy of the real-time internal temperature of the battery of the vehicle (e.g., the first battery internal temperature estimated value, the second battery internal temperature estimated value, etc., mentioned later) becomes higher and higher.
Preferably, the step S30 is specifically:
determining an initial value of parameter covariance according to the initial value of the optimal model parameter; that is, the initial value of the parameter covariance may be determined from the initial value of the optimal model parameter known in the above step S20.
Acquiring a battery state initial value of a battery of the vehicle at an initial time of actual operation from a database, and determining a battery state covariance initial value according to the battery state initial value; that is, the battery state covariance initial value may be determined according to the battery state initial value.
Determining an initial AUKF combined vector value according to the initial value of the optimal model parameter and the initial value of the battery state; preferably, AUKF combined vector value X corresponds to equivalent thermal network model cell shown in FIG. 3jointCan be expressed as:
Xjoint＝[Pparameter T,Xstate T]T
among them, the battery for a vehicle includes n (in a rectangular battery, n is 6) surface nodes, Pparameter＝[Cin,Rin1～Rinn,Rout1～Routn,Cs1～Csn]TUnderstandably, PparameterIs a column vector composed of model parameters of the battery (such as the initial values of the optimal model parameters, including the equivalent heat capacity in the cell, the heat resistance from the cell to the cell surface points corresponding to the heat transfer paths, the heat resistance from the cell surface points corresponding to the heat transfer paths to the external environment, and the equivalent heat capacity of the cell surface points corresponding to the heat transfer paths), XstateA column vector, X, of battery states (e.g. initial values of battery states at initial moments of actual operation of the battery)state＝[Tin,Ts1～Tsn]T. Therefore, the initial AUKF combined vector value can be determined according to the initial value of the optimal model parameter and the initial value of the battery state.
And determining an initial value of the covariance of the joint vector according to the initial value of the covariance of the parameters and the initial value of the covariance of the battery state. That is, after both the above-described parameter covariance initial value and the battery state covariance are determined, the joint vector covariance initial value may be determined from both.
S40, acquiring first operation data of a battery of the vehicle at a first moment when the vehicle actually operates; that is, after the equivalent thermal network model including the initial values of the optimal model parameters is determined, the equivalent thermal network model may be applied to a battery of a vehicle (the battery of the vehicle refers to a battery cell of the vehicle which does not include a temperature sensing device, and therefore, an internal temperature of the battery of the vehicle cannot be measured by the temperature sensing device), and further, a real-time internal temperature of the battery of the vehicle when the vehicle is actually operated is determined based on first operation data of the actual operation of the battery of the vehicle (for example, a first internal temperature estimation value of the battery of the vehicle at a first time of the actual operation, a second internal temperature estimation value of the battery of the vehicle at an nth time of the actual operation, and the like, which are mentioned later).
S50, determining a first battery internal temperature estimated value and a first model parameter estimated value of the battery of the vehicle at a first moment when the vehicle actually operates according to the initial AUKF combined vector value, the first operation data and the equivalent heat network model containing the initial value of the optimal model parameter.
That is, the initial AUKF joint vector value having the error may be subjected to the stepwise feedback correction in this step, so that the accuracy of the initial AUKF joint vector value after the feedback correction becomes higher and higher, and since the AUKF joint vector value is formed by combining the model parameter and the battery state, on the one hand, the accuracy of the real-time battery internal temperature of the battery of the vehicle may become higher and higher by performing the feedback correction on the AUKF joint vector value, that is, the more accurate battery internal temperature of the vehicle may be obtained by the present invention; on the other hand, the model parameters of the equivalent heat network model can be optimized, so that the internal temperature of the BATTERY can be estimated more accurately according to the optimized model parameters (so, the influence of estimation errors of the internal temperature of the BATTERY on parameter identification results can be eliminated, the model parameters are more matched, and the estimation errors of the internal temperature of the BATTERY are reduced), further, the acquired accurate internal temperature of the BATTERY can be output to a BATTERY management system (BATTERY MANAGEMENT SYSTEM), the BMS can optimize the working state of the BATTERY according to the received internal temperature of the BATTERY (for example, the BATTERY is subjected to high-temperature pre-tightening, the working current is selected according to the internal temperature of the BATTERY at low temperature, and the like), the BATTERY of the vehicle is ensured to work in a safe temperature range, the flammable and explosive hidden danger is solved, and the safety and the reliability of. According to the embodiment of the invention, the real-time internal temperature of the battery of the vehicle can be determined according to the running data (first running data) of the actual running of the vehicle and the equivalent heat network model containing the initial value of the optimal model parameter under the condition that the temperature sensing equipment for collecting the temperature data of the vehicle has a fault, so that the dependence on the effectiveness of the temperature sensing equipment for collecting the temperature data of the vehicle is reduced. Meanwhile, the method can simultaneously carry out feedback correction on the internal temperature of the battery of the vehicle and the model parameters of the equivalent heat network model, and can effectively eliminate the error of the equivalent heat network model and the error influence of the error of the initial AUKF combined vector value on the real-time internal temperature of the battery.
The invention sets up a plurality of heat transfer paths by starting from an actual physical model (a circuit model equivalent to a battery cell equivalent heat network model) and considering the uneven surface temperature caused by uneven heat generation and heat dissipation of a battery; meanwhile, the heat exchange between the battery of the vehicle in actual operation and the cooling system under the high-temperature condition is considered, and the heat transfer path between the battery of the vehicle and the cooling plate is increased in the equivalent heat network model (the temperature of the cooling plate of the cooling system is considered), so that various factors of actual heating of the battery in actual operation are comprehensively considered, the error between the finally estimated internal temperature of the battery and the actual temperature of the battery is smaller, and the internal temperature of the battery and the surface temperature of the battery can be more accurately calculated.
In an embodiment, in step S10, the obtaining of the offline test data of the battery module performing the offline test under different offline conditions of the constant temperature environment includes:
acquiring offline temperature data and equivalent circuit data of the battery module when offline testing is performed under different offline working conditions; wherein the offline temperature data includes an offline test internal temperature (corresponding to T shown in FIGS. 2, 3, and 4)in) Off-line test surface temperature (corresponding to T shown in fig. 2)sx、TsyOr TszT shown in FIG. 4sk) Off-line cooling plate temperature (corresponding to T shown in fig. 2, 3 and 4)cool) And an off-line ambient temperature (corresponding to T shown in fig. 2, 3, and 4) of a constant temperature environment in which the battery module is under an off-line testamb) (ii) a The off-line test internal temperature is measured by first temperature sensing equipment arranged inside a battery core of the battery module, the off-line test surface temperature is measured by second temperature sensing equipment arranged on the surface of the battery core of the battery module, and the off-line cooling plate temperature is measured by third temperature sensing equipment arranged on a cooling plate of a cooling system connected with the battery moduleMeasuring; the equivalent circuit data includes a battery terminal voltage value and a battery current value of the battery module.
That is, in this embodiment, the offline operating conditions include constant current charging and discharging at different multiplying powers (where the different multiplying powers are preferably 0.1C to 3C), and dynamic operating conditions such as NEDC and WLTC. After the offline test data is obtained, determining a first battery heat generation rate of a battery cell of a battery module in real time in the charging and discharging processes under different offline working conditions according to a battery end voltage value, a battery current value and the offline test internal temperature in the offline test data; determining an initial parameter of the equivalent heat network model according to the first battery heat production rate and the offline test data; after determining multiple groups of initial parameters of the equivalent thermal network model, determining an optimal model parameter initial value in the multiple groups of initial parameters by adopting a multi-objective function fitting method (the multi-objective function fitting method comprises but is not limited to a least square method, a genetic algorithm, a particle swarm optimization algorithm and the like); to finally determine a first estimate of the internal temperature of the battery of the vehicle at a first instant of time when the vehicle is actually operating, by means of the equivalent thermal network model containing initial values of the optimal model parameters.
In an embodiment, after the step S10 of acquiring the offline temperature data and the offline test data of the battery module, the method includes:
acquiring a first open-circuit voltage and a first temperature coefficient of the battery module from a database; understandably, the first open-circuit voltage and the first temperature coefficient are both related to a State of Charge (SOC) value of the battery, so that as long as the SOC value of the battery module is determined, the first open-circuit voltage and the first temperature coefficient can both be determined accordingly, and the first open-circuit voltage and the first temperature coefficient of the battery module are both stored in the database in association with the SOC value of the battery cell of the battery module.
Grouping and inputting the battery terminal voltage value, the battery current value and the offline test internal temperature into a preset first heat generation rate model, and acquiring first battery heat generation rates of the battery module output by the first heat generation rate model under different offline working conditions; the battery terminal voltage value, the battery current value and the offline test internal temperature are set to be corresponding to the battery terminal voltage value, the battery current value and the offline test internal temperature at the same moment under the same offline working condition.
That is, after the offline test data is obtained, the first battery heat generation rate of the battery cell of the battery module in the charging and discharging process under different offline working conditions may be determined in real time according to the battery terminal voltage value, the battery current value and the offline test internal temperature in the offline test data. Understandably, each moment in the charging and discharging process of the off-line working condition corresponds to a first battery heat generation rate.
Further, the electrical characteristics of the cells of the battery module may be described by an equivalent circuit model shown in fig. 5, as shown in fig. 5 (R shown in fig. 5)0And RplThermal resistance, C, of a cell that can be equated to a battery moduleplWhich may be equivalent to the heat capacity of the cells of the battery module), in one embodiment, the first heat generation rate model is:
Figure BDA0002293520840000131
wherein:
Qheat1generating heat rate for a first battery of the battery module under an off-line working condition; that is, the internal heat generation rate of the battery cell of the battery module. (OCV (SOC)1)-U1)×I1Showing a polarized heat portion which is the sum of ohmic polarization and concentration polarization;
Figure BDA0002293520840000132
representing the heat of reversible reaction, entropy heating.
OCV(SOC1) A first open circuit voltage of the battery module; OCV (SOC)1) The SOC value of the battery cell of the battery module is stored in an OCV-SOC table of a BMS or other databases in a correlated manner, and the battery module can be obtained by inquiring the SOC value corresponding to the battery cell of the battery module from the OCV-SOC tableOCV (SOC)1)。
U1Is the battery terminal voltage value, U, of the battery module1The voltage of the battery cell of the battery module in the charging and discharging process can be actually measured through voltage measuring equipment in the offline testing process.
I1The battery current value of the battery module is obtained; i is1The current of the battery cell representing the battery module in the charging and discharging process can be actually measured by current measuring equipment in the offline test process, and I1Negative during charging and positive during discharging.
T1For the off-line measurement of the internal temperature, T1May be measured by the first temperature sensing device.
Figure BDA0002293520840000141
The first temperature coefficient is the first temperature coefficient of the battery module, the first temperature coefficient and the SOC value of the battery core of the battery module are stored in a BMS or other databases in an associated mode, and the SOC value corresponding to the battery core of the battery module can be inquired from the BMS or other databases to obtain the SOC value of the battery module
Figure BDA0002293520840000142
In an embodiment, the step S20, that is, obtaining initial parameters of an equivalent thermal network model according to the offline test data, and determining an initial value of an optimal model parameter in each of the initial parameters of the equivalent thermal network model based on a multi-objective function fitting method includes:
acquiring input parameters and output parameters of an equivalent heat network model, and associating the input parameters and the output parameters corresponding to the same moment (the same moment can be a preset moment in an offline test process) under the same offline working condition; the input parameters include the first battery heat production rate (corresponding to Q in the first heat production rate model)heat1) The offline cooling plate temperature (corresponding to T shown in fig. 2, 3, and 4)cool) And the off-line ambient temperature (corresponding toT shown in FIGS. 2, 3 and 4amb) (ii) a The output parameters include the offline test internal temperature (corresponding to T shown in FIGS. 2, 3, and 4)in) And the off-line test surface temperature (corresponding to T shown in FIG. 2)sx、TsyOr TszT shown in FIG. 4sk)。
Inputting the input parameters into an equivalent network model, and taking the output parameters associated with the input parameters input into the equivalent network model as the output of the equivalent network model to obtain initial parameters of the equivalent network model; one set of the initial parameters includes the equivalent heat capacity inside the battery cell in the equivalent network model of the battery module (i.e. C shown in fig. 2)in) And thermal resistance from the inside of the cell to the cell surface points corresponding to the respective heat transfer paths (i.e., R shown in fig. 4)ink) And the thermal resistance from the cell surface point corresponding to each heat transfer path to the external environment (i.e., R shown in fig. 4)outk) And equivalent heat capacity of the cell surface points corresponding to each heat transfer path (i.e., C shown in fig. 4)sk)。
And determining a group of optimal model parameter initial values in each group of initial parameters based on a multi-objective function fitting method, wherein the multi-objective function fitting method comprises one or more of a least square method, a genetic algorithm and a particle swarm optimization algorithm. And performing parameter optimization through a multi-objective function fitting method, and finally searching to obtain a group of optimal model parameter initial values, so that after input parameters are input into the equivalent thermal network model containing the optimal model parameter initial values, actual output parameters output by the equivalent thermal network model are most consistent with an experimental test result. Understandably, in the invention, in the subsequent calculation process, the initial value of the optimal model parameter of the equivalent heat network model is iteratively updated, so that the accuracy is higher, and the internal temperature of the battery can be estimated more accurately according to the optimized model parameter; therefore, the influence of the estimation error of the internal temperature of the battery on the parameter identification result can be eliminated, the model parameters are more matched, the estimation error of the internal temperature of the battery is reduced, and the actual output parameters output by the equivalent heat network model are more consistent with the experimental test result.
In one embodiment, the step S40, the acquiring first operation data of the battery of the vehicle at a first time when the vehicle is actually operated includes:
acquiring first operation data of a battery of the vehicle at a first moment when the vehicle actually operates, wherein the first operation data comprises a first battery surface temperature, a first cooling plate temperature, a first environment 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 vehicle cooling system connected to a battery of the vehicle; understandably, the fourth temperature sensing device and the fifth temperature sensing device are set to the same type of sensing device (preferably, the same as the first temperature sensing device) so that an error between detected temperature data is smaller. The first environment temperature is the temperature of the environment where the vehicle actually operates at the first moment, the first voltage value is the voltage value of the battery of the vehicle at the first moment when the vehicle actually operates, and the first current value is the current value of the battery of the vehicle at the first moment when the vehicle actually operates. Understandably, the first voltage value and the first current value may be measured in real time at every moment during the actual operation of the vehicle. In this embodiment, the cell internal temperature calculation model shown in fig. 2 may also be equivalently applied to the heat transfer process of the battery of the vehicle, and in the actual operation process of the battery of the vehicle, as shown in fig. 2, TinIndicating the internal temperature, T, of the battery of the vehiclesRepresents the first battery surface temperature, TambA first ambient temperature, T, representative of the environment in which the vehicle is actually operating at said first moment in timecoolDenotes the first cooling plate temperature (T in the case of a cooling system in which the cooling plates are not cooledcool＝Tamb)，QheatIndicating the internal battery heat generation rate of the vehicle.
In one embodiment, as shown in fig. 6, the step S50, namely, the determining a first estimated battery internal temperature and a first estimated model parameter of the battery of the vehicle at a first time when the vehicle actually operates according to the initial AUKF combined vector value, the first operation data and the equivalent heat network model including the initial value of the optimal model parameter, includes:
s501, inputting the initial AUKF combined vector value and the combined vector covariance initial value into a preset symmetrical sampling model to generate an initial AUKF combined vector feature point set.
In this embodiment, the battery state space equation may be first established based on the equivalent thermal network model shown in fig. 3, as follows:
Xjoint(k)＝f(Xjoint(k-1))，Qheat(k)，Tamb(k)，Tcool(k))+ω(k)
Y(k)＝g(Xjoint(k))+γ(k)
wherein: xjoint(k) An AUKF joint vector value representing a kth moment of actual operation of a battery of the vehicle, k being an integer; at a first moment when the vehicle is actually running, k is 1; at the Nth moment when the vehicle actually runs, k is N; and Xjoint(0) I.e. the initial AUKF joint vector value. f (-) corresponds to the equivalent thermal network model shown in FIG. 3, i.e., f (-) can be described by the equivalent thermal network model shown in FIG. 3; y (k) is an estimate of the surface point temperature of the battery of the vehicle at the kth instant of actual operation; can be described as the combination of the above AUKF with vector value Xjoint(k) G (·) function of (c); qheat(k) A heat generation rate for a battery of the vehicle (such as the above-described second battery heat generation rate or a third battery heat generation rate mentioned later); t isamb(k) A temperature of an environment in which the vehicle is actually running (such as the above-described first ambient temperature or a second ambient temperature mentioned later); t iscool(k) Is the cooling temperature of the cooling plate of the cooling system of the vehicle (such as the above-mentioned first cooling plate temperature or the later-mentioned second cooling plate temperature). ω (k) represents the process noise at the kth instant of actual operation of the vehicle, from which process noise variance q (k) can understandably be determined; gamma (k) represents the second of the actual operation of the vehicleMeasurement noise at time k; and the measurement noise variance r (k) may be determined from the measurement noise. Understandably, in an initial state where the vehicle is not actually running, the initial process noise ω (0) and the initial measurement noise ω (0) are both preset known values, and the initial process noise variance and the initial measurement noise variance may be determined based on the initial process noise ω (0) and the initial measurement noise ω (0).
Then, determining an initial joint vector feature point set by adopting a symmetric sampling model
Figure BDA0002293520840000171
Preferably, the symmetric sampling model is:
Figure BDA0002293520840000172
Figure BDA0002293520840000173
Figure BDA0002293520840000174
wherein the content of the first and second substances,
k is the kth moment of actual operation of the battery of the vehicle, k is more than or equal to 0 and less than or equal to N, and k is an integer; at a first moment when the vehicle is actually running, k is 1; at the Nth moment when the vehicle actually runs, k is N;
Figure BDA0002293520840000175
the value is a combined vector posterior value of the ith characteristic point at the k-1 moment;
m is a combined vector posterior value XjointLength of (d);
mu is a preset scaling coefficient;
Pjoint(k-1) is a combined vector covariance posterior value at the k-1 time;
Figure BDA0002293520840000176
and the AUKF joint vector feature point set at the k-1 moment.
Understandably, at a first moment when the vehicle is actually running, k is 1; at this time, the symmetric sampling model is:
Figure BDA0002293520840000181
Figure BDA0002293520840000182
Figure BDA0002293520840000183
in step S30, the initial AUKF combined vector value X is obtainedjoint(0) Initial value of covariance of joint vector Pjoint(0) All known, initial AUKF combined vector value Xjoint(0) Is also known, and therefore the scaling factor mu, is also known
Figure BDA0002293520840000184
And
Figure BDA0002293520840000185
can be determined according to the parameters; further, an initial set of joint vector feature points
Figure BDA0002293520840000186
May be determined.
S502, acquiring a second open-circuit voltage and a second temperature coefficient of a battery of the vehicle from a database; understandably, the second open-circuit voltage and the second temperature coefficient are both related to the SOC value of the battery of the vehicle, so that the second open-circuit voltage and the second temperature coefficient can be determined therewith as long as the SOC value of the battery of the vehicle is determined, and the second open-circuit voltage and the second temperature coefficient of the battery of the vehicle are both stored in the database in association with the SOC value of the battery, so that the second open-circuit voltage and the second temperature coefficient thereof at the current time can be determined according to the real-time SOC value of the battery of the vehicle (during actual operation, the real-time SOC value of the battery of the vehicle can be measured).
And S503, inputting the first voltage value, the first current value and the first battery surface temperature into a preset second heat generation rate model, and acquiring a second battery heat generation rate of the vehicle battery at a first time of actual operation, which is output by the second heat generation rate model. Understandably, the first moment in the charging and discharging process of the actual operation of the vehicle corresponds to a second battery heat generation rate in real time.
Preferably, the second heat generation rate model is:
Figure BDA0002293520840000187
wherein:
k is the kth moment of actual operation of the battery of the vehicle, k is more than or equal to 0 and less than or equal to N (N is the Nth moment of actual operation of the vehicle), and k is an integer; at the initial moment when the vehicle actually runs, k is 0; at a first moment when the vehicle is actually running, k is 1; at the nth time when the vehicle is actually running, k is equal to N.
Qheat2kA battery heat generation rate at a kth time for a battery of the vehicle; i.e., the internal heat generation rate of the battery of the vehicle. (OCV (SOC)2k)-U2k)×I2kShowing a polarized heat portion which is the sum of ohmic polarization and concentration polarization;
Figure BDA0002293520840000191
representing the heat of reversible reaction, entropy heating.
OCV(SOC2k) A second open circuit voltage of a battery of the vehicle; OCV (SOC)2k) Storing the SOC value of the battery of the vehicle in an OCV-SOC table of a preset storage region of the vehicle in association with the SOC value, and acquiring the battery model by inquiring the SOC value corresponding to the battery of the vehicle from the OCV-SOC tableOCV (SOC) of the group2k)。
U2kThe voltage value of a battery of the vehicle at the k moment; u shape2kThe voltage representing the voltage of the battery of the vehicle during charge and discharge in actual operation may be actually measured by the voltage measuring device during actual operation.
I2kA current value of a battery of the vehicle at a k-th moment; i is2kRepresenting the current of the battery of the vehicle during charging and discharging in actual operation, which can be actually measured by a current measuring device during actual operation, and I2kNegative during charging and positive during discharging.
T2kIs a battery surface temperature of a battery of the vehicle at a kth time; t is2kMay be measured by a fourth temperature sensing device disposed on a surface of a battery of the vehicle. Understandably, in the second heat generation rate model, relative to (OCV (SOC))2k)-U2k)×I2kIn the case of a composite material, for example,
Figure BDA0002293520840000192
this heat transfer has little influence on the second battery heat generation rate, and therefore, in this embodiment, in the process of actually calculating the second battery heat generation rate, the battery surface temperature on the battery surface of the vehicle may be equivalently replaced by the battery internal temperature (since the battery internal temperature cannot be measured because the temperature sensing device is not provided inside the battery in the vehicle), and then the second battery heat generation rate is calculated.
Figure BDA0002293520840000193
Is a second temperature coefficient of a battery of the vehicle. The second temperature coefficient is stored in a BMS or other database in association with the SOC value of the battery of the vehicle, and the SOC value of the battery can be acquired by inquiring the SOC value corresponding to the battery of the vehicle from the BMS or other database
Figure BDA0002293520840000194
According to the above, at the first time of the actual operation of the vehicle, k is 1, and at this time, the second heat generation rate model is:
Figure BDA0002293520840000201
wherein the first voltage value U21The first current value I21The first battery surface temperature T21Second temperature coefficient
Figure BDA0002293520840000202
And a second open-circuit voltage OCV (SOC)21) It is known that the second heat generation rate Q corresponding to the first moment can be obtained according to the second heat generation rate modelheat21。
S504, inputting the initial AUKF joint vector characteristic point set, the second battery heat generation rate, the first cooling plate temperature and the first environment temperature into the equivalent heat network model containing the optimal model parameters, and acquiring a first characteristic point state value of the battery of the vehicle at a first actual running moment output by the equivalent heat network model.
Understandably, at a first moment when the vehicle is actually running, k is 1; at this time, it can be known from the above battery state space equation:
Figure BDA0002293520840000203
wherein the second battery heat generation rate Qheat(1) (at the first moment of actual operation of the vehicle, Qheat(1)＝Qheat21) The first cooling plate temperature Tcool(1) And the first ambient temperature Tamb(1) Are all known, and the initial AUKF joint vector feature point set
Figure BDA0002293520840000204
A symmetric sampling model has been used for the determination. Furthermore, a first characteristic point state value of the battery of the vehicle at a first time of actual operation can be obtained from the above known parameters
Figure BDA0002293520840000205
S505, inputting the state value of the first characteristic point and a preset initial value of the noise variance into a preset state space model, and determining a first joint vector prior value, a first joint vector covariance prior value and a first measurement correction matrix of a battery of the vehicle at a first moment of actual operation of the vehicle; understandably, ω (k) represents the process noise at the k-th instant of actual operation of the vehicle, from which process noise variance q (k) can be determined; γ (k) represents the measurement noise at the k-th instant of actual operation of the vehicle; and the measurement noise variance r (k) may be determined from the measurement noise. In the present embodiment, the initial value of the noise variance at the first time includes an initial process noise variance Q (1) and an initial measurement noise variance R (1), and the initial process noise variance Q (1) and the initial measurement noise variance R (1) may be determined according to a preset initial process noise ω (0) and an initial measurement noise ω (0), that is, the initial value of the noise variance is a known preset value. At this time, the noise variance initial value and the first characteristic point state value obtained by the calculation are used as the basis
Figure BDA0002293520840000211
A first joint vector prior value of a battery of the vehicle at a first instant of actual operation of the vehicle may be determined
Figure BDA0002293520840000212
First joint vector covariance prior value
Figure BDA0002293520840000213
And a first measurement correction matrix Kjoint(1)。
Preferably, the state space model includes:
Figure BDA0002293520840000214
wherein:
k is the kth moment of actual operation of the battery of the vehicle, k is more than or equal to 0 and less than or equal to N, and k is an integer; at a first moment when the vehicle is actually running, k is 1; at the Nth moment when the vehicle actually runs, k is N;
alpha is a positive constant, and alpha is less than or equal to 1;
β is a positive constant, β ═ 2;
m is a combined vector posterior value XjointLength of (d);
mu is a preset scaling coefficient;
Figure BDA0002293520840000215
the joint vector prior value at the k moment is taken as a joint vector prior value;
Figure BDA0002293520840000216
the state value of the characteristic point of the jth characteristic point of the AUKF joint vector characteristic point set at the kth moment is obtained;
Figure BDA0002293520840000221
a weight coefficient of the j-th feature point in calculating an expected value of the prior value of the joint vector at the k-th moment is calculated;
Figure BDA0002293520840000222
and calculating the weight coefficient of the prior value of the covariance of the joint vector at the kth moment for the jth characteristic point.
At a first moment when the vehicle is actually running, k is 1; at this time, the first joint vector prior value at the first time
Figure BDA0002293520840000223
Can be calculated according to the formula.
Figure BDA0002293520840000224
Wherein:
Figure BDA0002293520840000225
is composed of
Figure BDA0002293520840000226
And
Figure BDA0002293520840000227
the difference between the two;
Figure BDA0002293520840000228
the prior value of the covariance of the joint vector at the kth moment;
q (k) is the process noise variance of the equivalent thermal network model at the k time of actual operation of the vehicle, and in some embodiments, the process noise variance of the equivalent thermal network model at the k time is the theoretical process noise variance in the corrected theoretical noise variances at the k-1 time. Understandably, the initial process noise variance Q (1) at the first time is a preset value.
At a first moment when the vehicle is actually running, k is 1; at this time, the first joint vector covariance prior value at the first time
Figure BDA0002293520840000229
Can be calculated according to the formula.
Figure BDA00022935208400002210
Wherein:
Figure BDA00022935208400002211
updating an output value for the measurement of the characteristic point state value of the jth characteristic point of the AUKF joint vector characteristic point set at the kth moment;
Figure BDA0002293520840000231
is at k timeMeasuring and updating an expected value of an output value of the carved AUKF joint vector feature point set;
Figure BDA0002293520840000232
is composed of
Figure BDA0002293520840000233
And
Figure BDA0002293520840000234
the difference between them.
Figure BDA0002293520840000235
Wherein:
Figure BDA0002293520840000236
updating the covariance between the characteristic point state value of the AUKF joint vector characteristic point set at the Kth moment and the measurement update output value of the characteristic point state value;
Figure BDA0002293520840000237
updating the variance between the output values for the measurement of the characteristic point state value and the characteristic point state value of the AUKF joint vector characteristic point set at the Kth moment;
and R (k) is the measured noise variance of the equivalent thermal network model at the k time when the vehicle actually runs, and in some embodiments, the measured noise variance of the equivalent thermal network model at the k time is the theoretical measured noise variance in the corrected theoretical noise variances at the k-1 time. Understandably, the initial measurement noise variance R (1) at the first time is a preset value. Kjoint(k) And correcting the matrix for the measurement of the k moment when the vehicle actually runs.
At a first moment when the vehicle is actually running, k is 1; at this time, the first measurement correction matrix K at the first timejoint(1) Can be calculated according to the formula.
S506, inputting the first battery surface temperature, the first joint vector prior value, the first joint vector covariance prior value and the first measurement correction matrix into a preset estimation model, and determining a first joint vector posterior value and a first joint vector covariance posterior value of the battery of the vehicle at a first moment when the vehicle actually runs; preferably, the estimation model is:
Figure BDA0002293520840000238
wherein the content of the first and second substances,
Xjoint(k) the posterior value of the combined vector at the k moment of the actual running of the vehicle is obtained;
Pjoint(k) the combined vector covariance posterior value at the k moment of actual running of the vehicle is obtained;
Figure BDA0002293520840000241
the joint vector prior value at the k moment is taken as a joint vector prior value;
Kjoint(k) correcting a matrix for the measurement at the kth moment when the vehicle actually runs;
Ts measure(k) is the battery surface temperature of the vehicle at time k;
Figure BDA0002293520840000242
updating the expected value of the output value for the measurement of the AUKF joint vector feature point set at the kth moment;
Figure BDA0002293520840000243
the prior value of the covariance of the joint vector at the kth moment;
Figure BDA0002293520840000244
the state value and the characteristic point of the AUKF joint vector characteristic point set at the Kth momentThe measurement of the state values updates the variance between the output values.
At a first moment when the vehicle is actually running, k is 1; at this time, the first battery surface temperature T is generated due to the first times measure(1) The temperature of the vehicle can be measured in real time at a first moment by a fourth temperature sensing device arranged on the surface of a battery of the vehicle; the first joint vector prior value
Figure BDA0002293520840000245
First joint vector covariance prior value
Figure BDA0002293520840000246
And a first measurement correction matrix Kjoint(1) It is known that other parameters in the estimation model are also obtained in the above step S505 or other steps, so that the first combined vector posterior value X of the battery of the vehicle at the first moment of actual operation of the vehicle can be determined according to the above estimation modeljoint(1) And a first joint vector covariance posterior value Pjoint(1)。
S507, determining a first battery internal temperature estimated value and a first model parameter estimated value of the battery of the vehicle at a first moment when the vehicle actually runs according to the first joint vector posterior value and the first joint vector covariance posterior value. That is, in the present embodiment, vector value X is united due to AUKFjointCan be expressed as:
Xjoint＝[Pparameter T,Xstate T]T
and P isparameter＝[Cin,Rin1～Rinn,Rout1～Routn,Cs1～Csn]T；Xstate＝[Tin,Ts1～Tsn]T(ii) a Thus, the posterior value X of the first joint vector at the first time instant can be determinedjoint(1) And a first joint vector covariance posterior value Pjoint(1) A first battery internal temperature estimate and a first model parameter estimate are determined. I.e. the first combined vector posterior value, first combinationThe resultant vector covariance posterior value, the first battery internal temperature estimated value, the first model parameter estimated value and the like are all results of first iteration updating, and the parameters after the iteration updating can be continuously iterated in the subsequent process, so that the estimation of the battery internal temperature is more and more accurate.
In an embodiment, as shown in fig. 7, after the step S50, that is, after determining the first estimated battery internal temperature and the first estimated model parameter of the battery of the vehicle at the first time when the vehicle actually operates according to the initial AUKF combined vector value, the first operation data and the equivalent heat network model including the initial value of the optimal model parameter, the method further includes:
s60, obtaining a first theoretical noise variance of the equivalent thermal network model at a first time when the vehicle actually runs according to the first battery internal temperature estimated value and the first model parameter estimated value.
In one embodiment, as shown in fig. 8, the step S60 includes:
s601, acquiring a preset time sequence length and a first battery surface temperature of a battery of the vehicle at a first time of actual operation; is provided with LAUKFIs a preset time sequence length; l isAUKFK is not more than k; that is to say LAUKFThe specific value of (a) can be set according to the requirements of users. The first cell surface temperature of the cell at the first moment of actual operation can be measured directly.
S602, determining a first model output residual error of the equivalent heat network model at a first moment of actual operation of the vehicle according to the first battery internal temperature estimated value, the first model parameter estimated value and the first battery surface temperature; in this step, a first model output residual of the equivalent thermal network model at the first time may be calculated according to a preset residual model. Preferably, the residual model is:
Figure BDA0002293520840000251
wherein:
e (k) is a model output residual error of the equivalent thermal network model at the kth moment;
Ts measure(k) is the battery surface temperature of the vehicle at time k;
Figure BDA0002293520840000252
and updating the expected value of the output value for the measurement of the AUKF joint vector characteristic point set at the kth moment.
At a first moment when the vehicle is actually running, k is 1; at this time, Ts measure(1) And
Figure BDA0002293520840000261
have been calculated in step S505 above, the first model output residual E (1) can be calculated.
S603, determining a first theoretical noise variance of the equivalent thermal network model at the first time when the vehicle actually runs according to the historical model output residual within the time sequence length before the first time and the first model output residual. That is, the output residual array at the corresponding time may be obtained according to the following residual array model:
Figure BDA0002293520840000262
wherein:
LAUKFis a preset time sequence length; l isAUKFK is not more than k; that is to say LAUKFThe specific value of (a) can be set according to the requirements of users.
L is the first time of the historical operation of the vehicle, k-LAUKFL is more than or equal to 1 and less than or equal to k, and k is an integer; at a first time of the historical operation of the vehicle, l is 1; at the kth moment of the historical operation of the vehicle, k;
h (k) is an output residual error array of the equivalent heat network model at the k moment when the vehicle actually runs;
and E (l) outputting residual errors for the historical model of the equivalent heat network model at the l moment of the historical operation of the vehicle. Understandably, when l is k, the historical model output residual is the model output residual of the equivalent thermal network model at the k-th time.
At a first moment when the vehicle is actually running, k is 1; at this time, the first output residual matrix H (1) of the equivalent thermal network model at the first time may be determined according to the residual matrix model. Furthermore, according to the first output residual array, a first theoretical noise variance of the equivalent thermal network model at a first time when the vehicle actually operates can be obtained, specifically, the theoretical noise variance is obtained according to the following formula:
Figure BDA0002293520840000263
wherein:
Qid(k) the noise variance of the theoretical process in the theoretical noise variance of the equivalent heat network model at the kth moment of actual operation of the vehicle;
Rid(k) theoretically measuring the noise variance in the theoretical noise variances of the equivalent heat network model at the kth moment of actual operation of the vehicle;
Kjoint(k) correcting a matrix for the measurement at the kth moment when the vehicle actually runs;
h (k) is an output residual error array of the equivalent heat network model at the k moment when the vehicle actually runs;
Figure BDA0002293520840000271
updating the variance between the output values for the measurement of the characteristic point state value and the characteristic point state value of the AUKF joint vector characteristic point set at the Kth moment;
r (k) is the measured noise variance of the equivalent thermal network model at the k-th moment when the vehicle actually runs. In some embodiments, the measured noise variance of the equivalent thermal network model at the k-th time is a theoretical measured noise variance among the corrected theoretical noise variances at the k-1 th time. Understandably, the initial measurement noise variance R (1) at the first time is a preset value.
At a first moment when the vehicle is actually running, k is 1; at this time, the initial measurement noise variance R (1) is known, Kjoint(1) H (1) and
Figure BDA0002293520840000272
are known, and therefore, the first theoretical process noise variance Q can be directly obtainedid(1) And a first theoretical measurement noise variance Rid(1) That is, a first theoretical noise variance of the equivalent thermal network model at a first instant in time when the vehicle is actually operating is determined.
And S70, according to a preset noise updating rule, correcting a first theoretical noise variance of the equivalent heat network model at a first moment when the vehicle actually runs through an AUKF filter. That is, in the present invention, the AUKF filter may implement the parameter P of the battery state when the condition of the present embodiment is satisfiedparameterBattery state XstateAnd a function of updating the first theoretical noise variance.
In an embodiment, the first theoretical noise variance comprises a first theoretical measurement noise variance and a first theoretical process noise variance; at this time, the step S70 includes:
when the first theoretical measurement noise variance is smaller than or equal to a preset noise boundary value, keeping the first theoretical measurement noise variance and the first theoretical process noise variance unchanged;
when the first theoretical measurement noise variance is larger than a preset noise boundary value, updating the first theoretical measurement noise variance to the larger value of the measurement noise initial value in the first theoretical measurement noise variance and the preset noise variance initial value through the AUKF filter, and simultaneously updating the first theoretical process noise variance to the larger value of the matrix trace of the process noise initial value in the first theoretical process noise variance and the preset noise variance initial value through the AUKF filter. In this embodiment, the AUKF filter may perform the action of updating the first theoretical measurement noise variance and the first theoretical process noise variance.
The noise update rule in this embodiment may be specifically explained by the following formula:
Figure BDA0002293520840000281
Figure BDA0002293520840000282
wherein:
q (K +1) is a theoretical process noise variance in theoretical noise variances of the equivalent heat network model at the K-th time when the vehicle actually runs;
δ is a preset noise boundary value, which can be set according to requirements.
Q (k) is the process noise variance of the equivalent thermal network model at the k time of actual operation of the vehicle, and in some embodiments, the process noise variance of the equivalent thermal network model at the k time is the theoretical process noise variance in the corrected theoretical noise variances at the k-1 time. Understandably, the initial process noise variance Q (1) at the first time is a preset value. trace (Q (k)) is the trace of matrix Q (k);
Qid(k) the noise variance of the theoretical process in the theoretical noise variance of the equivalent heat network model at the kth moment of actual operation of the vehicle; trace (Q)id(k) Is a matrix Qid(k) The trace of (2);
Rid(k) the measured noise variance in the theoretical noise variance of the equivalent heat network model at the kth moment of actual operation of the vehicle;
r (K +1) is a theoretical measurement noise variance in theoretical noise variances of the equivalent thermal network model at the K-th time when the vehicle actually runs;
and R (k) is the measured noise variance of the equivalent thermal network model at the k time when the vehicle actually runs, and in some embodiments, the measured noise variance of the equivalent thermal network model at the k time is the theoretical measured noise variance in the corrected theoretical noise variances at the k-1 time. Understandably, the initial measurement noise variance R (1) at the first time is a preset value.
In an embodiment, the step S70, namely, the modifying the first theoretical noise variance of the equivalent thermal network model at the first time when the vehicle actually operates according to the preset noise update rule, includes:
keeping the first theoretical measurement noise variance and the first theoretical process noise variance unchanged when the theoretical measurement noise variance at all moments of actual operation of the vehicle is less than or equal to the noise boundary value, and degrading the AUKF filter (adaptive unscented Kalman filter) into a UKF filter (unscented Kalman filter); the theoretical measured noise variance includes a first theoretical measured noise variance at a first time when the vehicle is actually operating. That is, in the present invention, when the theoretical measurement noise variance at all times of the actual operation of the vehicle is less than or equal to the noise boundary value, it is indicated that the theoretical measurement noise variance does not satisfy the noise update rule, and at this time, the theoretical measurement noise variance and the theoretical process noise variance in the theoretical measurement noise variance do not need to be changed, and at this time, since the AUKF filter does not need to update the first theoretical measurement noise variance and the first theoretical process noise variance, it is only necessary to implement the update of the battery state parameter PparameterAnd battery state XstateThe update function is performed, so in this embodiment, the auck filter is degraded to be used by the UKF filter, and at this time, used in the subsequent process; that is, in the subsequent step, if the theoretically measured noise variance at all times of the actual operation of the vehicle is always less than or equal to the noise boundary value, the AUKF filter always performs only the operation of the battery state parameter P as the UKF filterparameterAnd battery state XstateThe update operation may be performed.
In an embodiment, after the step S70, that is, after the modifying the first theoretical noise variance of the equivalent thermal network model at the first time when the vehicle actually operates according to the preset noise update rule, the method further includes:
acquiring second operation data of a battery of the vehicle at the Nth moment of actual operation of the vehicle, a second joint vector posterior value at the Nth-1 moment and a second joint vector covariance posterior value at the Nth-1 moment; n is a positive integer greater than or equal to 2;
and determining a second battery internal temperature estimated value and a second model parameter estimated value of the battery of the vehicle at the Nth moment when the vehicle actually operates according to the second combination vector posterior value, the second combination vector covariance posterior value, the second operation data and the equivalent thermal network model containing the second model parameter estimated value at the Nth moment when the vehicle actually operates.
Understandably, in the present invention, the feedback correction of the internal temperature of the battery of the vehicle can be performed by combining the vector posteriori values, and thereafter, further correcting the battery internal temperature after the feedback correction in the actual operation (for example, at the nth time, performing feedback correction on the second coupling vector posterior value at the nth-1 time to obtain a third coupling vector posterior value after the feedback correction, and the like, and determining a second battery internal temperature estimated value of the battery of the vehicle at the nth time of the actual operation of the vehicle according to the third coupling vector posterior value), that is, the battery internal temperature is always in an iterative correction process in the actual operation process of the vehicle, as this iterative process progresses, the battery internal temperature estimate will become more accurate and, as such, the error influence on the real-time internal temperature of the battery caused by the error of the equivalent heat network model and the error of the initial AUKF combined vector value can be effectively eliminated. On the other hand, the invention can also optimize the model parameters of the equivalent thermal network model (for example, at the nth time, the third combination vector posterior value of the battery of the vehicle at the nth time when the vehicle actually operates can be determined according to the second combination vector posterior value and the equivalent thermal network model containing the second model parameter estimated value at the nth-1 time, and the like, so that the second model parameter estimated value of the battery of the vehicle at the nth time when the vehicle actually operates can be determined according to the third combination vector posterior value), therefore, the internal temperature of the battery can be estimated more accurately according to the optimized model parameters, so that the influence of the estimation error of the internal temperature of the battery on the parameter identification result can be further eliminated, the model parameters are more matched, and the estimation error of the internal temperature of the battery is reduced.
In an embodiment, the second operational data includes a second battery surface temperature, a second cooling plate temperature, a second ambient temperature, a second voltage value, and a second current value; the second battery surface temperature is measured by a fourth temperature sensing device provided on a battery surface of the vehicle, and the second cooling plate temperature is measured by a fifth temperature sensing device provided on a cooling plate of a vehicle cooling system connected to a battery of the vehicle; the second environment temperature is the temperature of the environment of the actual operation of the vehicle at the Nth moment, 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 the current value of the battery of the vehicle at the Nth moment of the actual operation. Understandably, the second voltage value and the second current value may be measured in real time at the nth time during the actual operation of the vehicle.
In one embodiment, said determining a second battery internal temperature estimate and a second model parameter estimate of the battery of the vehicle at a time N when the vehicle is actually operating based on the second combined vector a posteriori value, the second combined vector covariance a posteriori value, the second operating data, and the equivalent thermal network model including the second model parameter estimate at the time N-1 comprises:
and inputting the second joint vector posterior value and the second joint vector covariance posterior value into the symmetric sampling model to generate a first AUKF joint vector feature point set.
Understandably, at the nth time when the vehicle is actually running, k is N; at this time, according to the symmetric sampling model:
Figure BDA0002293520840000311
Figure BDA0002293520840000312
Figure BDA0002293520840000321
in step S30, the second combined vector posterior value X is obtainedjoint(N-1) and the second bigement vector covariance Pjoint(N-1) are all known, and the posterior value X of the second combined vector isjointThe length M of (N-1) and the scaling factor μ are also known, and therefore
Figure BDA0002293520840000322
And
Figure BDA0002293520840000323
can be determined according to the parameters; further, a first set of AUKF joint vector feature points
Figure BDA0002293520840000324
May be determined.
Inputting the second voltage value, the second current value and the second battery surface temperature into the second heat generation rate model, and acquiring a third battery heat generation rate of the battery of the vehicle at an N-th time of actual operation, which is output by the second heat generation rate model; understandably, the nth moment in the charge and discharge process of the actual operation of the vehicle corresponds to a third battery heat generation rate in real time. According to the second heat generation rate model described above, at the nth time of actual operation of the vehicle, k is equal to N, and at this time, the second heat generation rate model is:
Figure BDA0002293520840000325
wherein the second voltage value U2NThe second current value I2NThe surface temperature T of the second battery2NSecond temperature coefficient
Figure BDA0002293520840000326
And a second open circuitVoltage OCV (SOC)2N) It is known that the third battery heat generation rate Q corresponding to the nth time can be obtained according to the second heat generation rate modelheat2N。
Inputting the first AUKF joint vector feature point set, the third battery heat generation rate, the second cooling plate temperature and the second ambient temperature into the equivalent heat network model containing a second model parameter estimation value at the N-1 th moment, and acquiring a second feature point state value of the battery of the vehicle at the N th moment of actual operation, which is output by the equivalent heat network model; at the nth time of actual operation of the vehicle, k is equal to N, and at this time, as can be seen from the above battery state space equation:
Figure BDA0002293520840000327
wherein the third battery heat generation rate Qheat(N) (at the Nth moment when the vehicle is actually running, Qheat(N)＝Qheat2N) The second cooling plate temperature Tcool(N) and the second ambient temperature Tamb(N) are all known, and the first AUKF joint vector feature point set
Figure BDA0002293520840000331
A symmetric sampling model has been adopted for determination. Further, the second characteristic point state value of the battery of the vehicle at the nth time of actual operation may be derived from the above known parameters
Figure BDA0002293520840000332
Acquiring a corrected second theoretical noise variance at the N-1 th moment, inputting the state value of the second characteristic point and the second theoretical noise variance into the state space model, and determining a second joint vector prior value, a second joint vector covariance prior value and a second measurement correction matrix of a battery of the vehicle at the N th moment when the vehicle actually runs; at the nth time of the actual operation of the vehicle, k is equal to N, in which case the second connection may be first determined according to the state space model described in step S505Sum vector prior value
Figure BDA0002293520840000333
And further determining a second joint vector covariance prior value
Figure BDA0002293520840000334
Finally determining a second measurement correction matrix Kjoint(N); the specific calculation process is described in step S505, and is not described herein again.
Inputting the second battery surface temperature, the second combination vector prior value, the second combination vector covariance prior value and the second measurement correction matrix into the estimation model, and determining a third combination vector posterior value and a third combination vector covariance posterior value of the battery of the vehicle at the Nth moment of actual operation of the vehicle; at an nth time instant of actual operation of the vehicle, k being equal to N, at which time the second battery surface temperature due to the nth time instant is measured by a fourth temperature sensing device provided at a battery surface of the vehicle; the first joint vector prior value
Figure BDA0002293520840000335
Second combined vector prior value
Figure BDA0002293520840000336
And a second joint vector covariance prior value
Figure BDA0002293520840000337
Finally determining a second measurement correction matrix Kjoint(N) all are known, and the other parameters in the estimation model mentioned in step S506 can also be obtained by referring to step S505 or other steps, so that the posterior value X of the third combination vector of the battery of the vehicle at the nth time when the vehicle actually runs can be determined according to the estimation modeljoint(N) and a third combined vector covariance posterior value Pjoint(N)。
Determining the third running state of the battery of the vehicle in the actual vehicle according to the posterior value of the third combination vector and the posterior value of the covariance of the third combination vectorA second battery internal temperature estimate at time N and a third model parameter estimate. That is, in the present embodiment, vector value X is united due to AUKFjointCan be expressed as:
Xjoint＝[Pparameter T,Xstate T]T
and P isparameter＝[Cin,Rin1～Rinn,Rout1～Routn,Cs1～Csn]T；Xstate＝[Tin,Ts1～Tsn]T(ii) a Thus, the posterior value X can be determined from the third combination vectorjoint(N) and a third combined vector covariance posterior value Pjoint(N) determining a second battery internal temperature estimate and a third model parameter estimate. That is, the third combination vector posterior value, the third combination vector covariance posterior value, the second battery internal temperature estimated value, the third model parameter estimated value and the like are all the results of the nth iteration update, and the above parameters after the iteration update can be continuously iterated in the subsequent process, so that the estimation of the battery internal temperature is more and more accurate. Understandably, N is a positive integer greater than or equal to 2, when N is 2, the nth-1 time is the first time, and at this time, the second joint vector posterior value at the nth-1 time in the above embodiment is the first joint vector posterior value corresponding to the first time; if N is greater than 2, iteration may be performed according to the above embodiment.
As shown in fig. 9, fig. 9 is a verification result of experimental verification performed by the method for processing temperature information in a battery based on the auckf of the present invention, where a verification object of the experimental verification is a battery cell of a battery module in which a first temperature sensing device is disposed, and fig. 9 shows a temperature T in the battery cell during charging and discharging processes with different multiplying powers and during a standing processinexpCell surface temperature TsexpMeasured value and estimated value T of internal temperature of battery corresponding to battery core of battery moduleinestThe experimental result shows that the error between the estimated value and the measured value of the internal temperature of the battery is very small, and the accurate estimation of the temperature of each node can be realized.
Further, a computer device is provided, the computer device may be a server, and the internal structure thereof may be as shown in fig. 10. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer readable instructions, and a database. The internal memory provides an environment for the operating system and execution of computer-readable instructions in the non-volatile storage medium. The computer readable instructions are executed by a processor to realize a battery internal temperature information processing method based on AUKF.
Further, a computer device is provided, which comprises 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 realize the above-mentioned AUKF-based battery internal temperature information processing method.
The invention also provides a computer readable storage medium, which stores computer readable instructions, and the computer readable instructions, when executed by a processor, implement the method for processing the temperature information in the battery based on the AUKF.
The invention also provides a vehicle which comprises a battery and a control module in communication connection with the battery, wherein the control module is used for executing the AUKF-based battery internal temperature information processing method.
For specific limitations of the control module, reference may be made to the above limitations of the method for processing the battery internal temperature information based on the AUKF, and details are not repeated here. Each of the above control modules may be implemented in whole or in part by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
It will be understood by those of ordinary skill in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware associated with computer readable instructions, which can be stored in a non-volatile computer readable storage medium, and when executed, can include processes of the embodiments of the methods described above. Any reference to memory, storage, databases, or other media used in embodiments provided herein may include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), Direct Rambus Dynamic RAM (DRDRAM), and Rambus Dynamic RAM (RDRAM).
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of each functional unit or module is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units or modules according to requirements, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.

Claims (20)

1. A battery internal temperature information processing method based on AUKF is characterized by comprising the following steps:
acquiring offline test data of the battery module for offline testing under different offline working conditions of a constant temperature environment;
acquiring initial parameters of an equivalent thermal network model according to the off-line test data, and determining an optimal model parameter initial value in each initial parameter of the equivalent thermal network model based on a multi-objective function fitting method;
acquiring an initial AUKF combined vector value of a battery of the vehicle;
acquiring first operation data of a battery of the vehicle at a first moment when the vehicle actually operates;
and determining a first internal battery temperature estimated value and a first model parameter estimated value of the battery of the vehicle at a first moment when the vehicle actually operates according to the initial AUKF combined vector value, the first operation data and the equivalent thermal network model containing the initial value of the optimal model parameter.
2. The method for processing the temperature information in the battery based on the AUKF of claim 1, wherein the obtaining of the offline test data of the battery module for offline test under different offline conditions in the constant temperature environment comprises:
acquiring offline temperature data and equivalent circuit data of the battery module when offline testing is performed under different offline working conditions; the off-line temperature data comprises an off-line test internal temperature, an off-line test surface temperature, an off-line cooling plate temperature and an off-line environment temperature of a constant temperature environment in which the battery module is in an off-line test; the offline testing internal temperature is measured by first temperature sensing equipment arranged inside a battery core of the battery module, the offline testing surface temperature is measured by second temperature sensing equipment arranged on the surface of the battery core of the battery module, and the offline cooling plate temperature is measured by third temperature sensing equipment arranged on a cooling plate of a cooling system connected with the battery module; the equivalent circuit data includes a battery terminal voltage value and a battery current value of the battery module.
3. The method for processing the temperature information in the battery based on the AUKF of claim 2, wherein after acquiring the offline temperature data and the offline test data of the battery module, the method comprises:
acquiring 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 offline test internal temperature into a preset first heat generation rate model, and acquiring first battery heat generation rates of the battery module output by the first heat generation rate model under different offline working conditions; the battery terminal voltage value, the battery current value and the offline test internal temperature are set to be corresponding to the battery terminal voltage value, the battery current value and the offline test internal temperature at the same moment under the same offline working condition.
4. The AUKF-based battery internal temperature information processing method of claim 3, wherein said first heat generation rate model is:
Figure FDA0002293520830000021
wherein:
Qheat1generating heat rate for a first battery of the battery module under an off-line working condition;
OCV(SOC1) A first open circuit voltage of the battery module;
U1the terminal voltage value of the battery module is obtained;
I1the battery current value of the battery module is obtained;
T1testing the internal temperature for said off-line;
Figure FDA0002293520830000022
is the first temperature coefficient of the battery module.
5. The method for processing the temperature information in the battery based on the AUKF of claim 3, wherein the obtaining initial parameters of the equivalent thermal network model according to the off-line test data and determining the initial value of the optimal model parameter in each initial parameter of the equivalent thermal network model based on the multi-objective function fitting method comprises:
acquiring input parameters and output parameters of an equivalent heat network model, and associating the input parameters and the output parameters corresponding to the same moment under the same off-line working condition; the input parameters include the first battery heat generation rate, the offline cooling plate temperature, and the offline ambient temperature; the output parameters include the offline test internal temperature and the offline test surface temperature;
inputting the input parameters into an equivalent network model, and taking the output parameters associated with the input parameters input into the equivalent network model as the output of the equivalent network model to obtain initial parameters of the equivalent network model; the group of initial parameters comprise the equivalent heat capacity inside the battery cell in the equivalent network model of the battery module, the heat resistance from the inside of the battery cell to the surface points of the battery cell corresponding to each heat transfer path, the heat resistance from the surface points of the battery cell corresponding to each heat transfer path to the external environment and the equivalent heat capacity of the surface points of the battery cell corresponding to each heat transfer path;
and determining a group of optimal model parameter initial values in each group of initial parameters based on a multi-objective function fitting method, wherein the multi-objective function fitting method comprises one or more of a least square method, a genetic algorithm and a particle swarm optimization algorithm.
6. The method for processing the AUKF-based battery internal temperature information of claim 1, wherein said obtaining an initial AUKF combined vector value of the battery of the vehicle comprises:
determining an initial value of parameter covariance according to the initial value of the optimal model parameter;
acquiring a battery state initial value of a battery of the vehicle at an initial time of actual operation from a database, and determining a battery state covariance initial value according to the battery state initial value;
determining an initial AUKF combined vector value according to the initial value of the optimal model parameter and the initial value of the battery state;
and determining an initial value of the covariance of the joint vector according to the initial value of the covariance of the parameters and the initial value of the covariance of the battery state.
7. The method for processing battery internal temperature information based on the AUKF of claim 6, wherein the acquiring first operation data of the battery of the vehicle at a first timing when the vehicle is actually operated, comprises:
acquiring first operation data of a battery of the vehicle at a first moment when the vehicle actually operates, wherein the first operation data comprises a first battery surface temperature, a first cooling plate temperature, a first environment 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 vehicle cooling system connected to a battery of the vehicle; the first environment temperature is the temperature of the environment where the vehicle actually operates at the first moment, the first voltage value is the voltage value of the battery of the vehicle at the first moment when the vehicle actually operates, and the first current value is the current value of the battery of the vehicle at the first moment when the vehicle actually operates.
8. The method for processing the AUKF-based battery internal temperature information of claim 7, wherein said determining a first battery internal temperature estimate and a first model parameter estimate of the vehicle's battery at a first time when the vehicle is actually operating based on the initial AUKF combined vector value, the first operation data, and the equivalent thermal network model including the initial values of the optimal model parameters, comprises:
inputting the initial AUKF joint vector value and the initial value of the covariance of the joint vector into a preset symmetrical sampling model to generate an initial AUKF joint vector feature point set;
obtaining a second open-circuit voltage and a second temperature coefficient of a battery of the vehicle from a database;
inputting the first voltage value, the first current value and the first battery surface temperature into a preset second heat generation rate model, and acquiring a second battery heat generation rate of the battery of the vehicle at a first moment of actual operation, which is output by the second heat generation rate model;
inputting the initial AUKF joint vector feature point set, the second battery heat generation rate, the first cooling plate temperature and the first environment temperature into the equivalent heat network model containing the optimal model parameters, and acquiring a first feature point state value of the vehicle battery at a first actual running moment, which is output by the equivalent heat network model;
inputting the state value of the first characteristic point and a preset initial value of the noise variance into a preset state space model, and determining a first joint vector prior value, a first joint vector covariance prior value and a first measurement correction matrix of a battery of the vehicle at a first moment of actual operation of the vehicle;
inputting the first battery surface temperature, the first joint vector prior value, the first joint vector covariance prior value and the first measurement correction matrix into a preset estimation model, and determining a first joint vector posterior value and a first joint vector covariance posterior value of the battery of the vehicle at a first moment of actual operation of the vehicle;
and determining a first battery internal temperature estimated value and a first model parameter estimated value of the battery of the vehicle at a first moment when the vehicle actually operates according to the first joint vector posterior value and the first joint vector covariance posterior value.
9. The method for processing the AUKF-based battery internal temperature information as recited in claim 8, wherein said determining a first battery internal temperature estimate and a first model parameter estimate of the battery of the vehicle at a first time instant when the vehicle is actually operating, based on the initial AUKF combined vector value, the first operation data and the equivalent thermal network model including the initial values of the optimal model parameters, further comprises:
acquiring a first theoretical noise variance of the equivalent thermal network model at a first moment of actual operation of a vehicle according to the estimated value of the internal temperature of the first battery and the estimated value of the parameter of the first model;
and according to a preset noise updating rule, correcting a first theoretical noise variance of the equivalent heat network model at a first moment when the vehicle actually runs through an AUKF (autonomous Kalman Filter).
10. The method for processing the AUKF-based battery internal temperature information of claim 9, wherein said obtaining a first theoretical noise variance of said equivalent thermal network model at a first instant of actual vehicle operation based on said first battery internal temperature estimate and said first model parameter estimate comprises:
acquiring a preset time sequence length and a first battery surface temperature of a battery of the vehicle at a first time of actual operation;
determining a first model output residual error of the equivalent thermal network model at a first moment when the vehicle actually runs according to the first battery internal temperature estimated value, the first model parameter estimated value and the first battery surface temperature;
and determining a first theoretical noise variance of the equivalent thermal network model at the first moment when the vehicle actually runs according to the historical model output residual within the time sequence length before the first moment and the first model output residual.
11. The method of claim 9, wherein the first theoretical noise variance includes a first theoretical measurement noise variance and a first theoretical process noise variance;
the method for correcting the first theoretical noise variance of the equivalent thermal network model at the first moment of actual operation of the vehicle through the AUKF according to the preset noise updating rule comprises the following steps:
when the first theoretical measurement noise variance is smaller than or equal to a preset noise boundary value, keeping the first theoretical measurement noise variance and the first theoretical process noise variance unchanged;
when the first theoretical measurement noise variance is larger than a preset noise boundary value, updating the first theoretical measurement noise variance to the larger value of the measurement noise initial value in the first theoretical measurement noise variance and the preset noise variance initial value through the AUKF filter, and simultaneously updating the first theoretical process noise variance to the larger value of the matrix trace of the process noise initial value in the first theoretical process noise variance and the preset noise variance initial value through the AUKF filter.
12. The method for processing the temperature information in the battery based on the AUKF according to claim 9, wherein the modifying the first theoretical noise variance of the equivalent thermal network model at the first time when the vehicle is actually operated by the AUKF filter according to a preset noise update rule comprises:
when the theoretical measurement noise variance at all the time when the vehicle actually runs is smaller than or equal to the noise boundary value, keeping the first theoretical measurement noise variance and the first theoretical process noise variance unchanged, and degrading the AUKF filter into a UKF filter; the theoretical measured noise variance includes a first theoretical measured noise variance at a first time when the vehicle is actually operating.
13. The method for processing the temperature information inside the battery based on the AUKF according to claim 9, wherein the modifying the equivalent thermal network model by the AUKF filter according to a preset noise update rule after the first theoretical noise variance at the first time when the vehicle is actually operated further comprises:
acquiring second operation data of a battery of the vehicle at the Nth moment of actual operation of the vehicle, a second joint vector posterior value at the Nth-1 moment and a second joint vector covariance posterior value at the Nth-1 moment; n is a positive integer greater than or equal to 2;
and determining a second battery internal temperature estimated value and a second model parameter estimated value of the battery of the vehicle at the Nth moment when the vehicle actually operates according to the second combination vector posterior value, the second combination vector covariance posterior value, the second operation data and the equivalent thermal network model containing the second model parameter estimated value at the Nth moment when the vehicle actually operates.
14. The AUKF-based battery internal temperature information processing method according to claim 13, wherein the second operation data includes a second battery surface temperature, a second cooling plate temperature, a second ambient temperature, a second voltage value, and a second current value; the second battery surface temperature is measured by a fourth temperature sensing device provided on a battery surface of the vehicle, and the second cooling plate temperature is measured by a fifth temperature sensing device provided on a cooling plate of a vehicle cooling system connected to a battery of the vehicle; the second environment temperature is the temperature of the environment of the actual operation of the vehicle at the Nth moment, 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 the current value of the battery of the vehicle at the Nth moment of the actual operation.
15. The method for processing the AUKF-based battery internal temperature information of claim 14, wherein said determining a second battery internal temperature estimate and a second model parameter estimate of the battery of the vehicle at the Nth time point at which the vehicle is actually operating based on the second coupling vector posterior value, the second coupling vector covariance posterior value, the second operation data, and the ESN including the second model parameter estimate at the Nth time point, comprises:
inputting the second joint vector posterior value and the second joint vector covariance posterior value into the symmetric sampling model to generate a first AUKF joint vector feature point set;
inputting the second voltage value, the second current value and the second battery surface temperature into the second heat generation rate model, and acquiring a third battery heat generation rate of the battery of the vehicle at an N-th time of actual operation, which is output by the second heat generation rate model;
inputting the first AUKF joint vector feature point set, the third battery heat generation rate, the second cooling plate temperature and the second ambient temperature into the equivalent heat network model containing a second model parameter estimation value at the N-1 th moment, and acquiring a second feature point state value of the battery of the vehicle at the N th moment of actual operation, which is output by the equivalent heat network model;
acquiring a corrected second theoretical noise variance at the N-1 th moment, inputting the state value of the second characteristic point and the second theoretical noise variance into the state space model, and determining a second joint vector prior value, a second joint vector covariance prior value and a second measurement correction matrix of a battery of the vehicle at the N th moment when the vehicle actually runs;
inputting the second battery surface temperature, the second combination vector prior value, the second combination vector covariance prior value and the second measurement correction matrix into the estimation model, and determining a third combination vector posterior value and a third combination vector covariance posterior value of the battery of the vehicle at the Nth moment of actual operation of the vehicle;
and determining a second battery internal temperature estimated value and a third model parameter estimated value of the battery of the vehicle at the Nth moment of actual operation of the vehicle according to the third combination vector posterior value and the third combination vector covariance posterior value.
16. The method for processing the temperature information in the battery based on the AUKF of claim 9, wherein the second heat generation rate model is:
Figure FDA0002293520830000091
wherein:
k is the kth moment of actual operation of the battery of the vehicle, k is more than or equal to 0 and less than or equal to N, and k is an integer; at the initial moment when the vehicle actually runs, k is 0; at a first moment when the vehicle is actually running, k is 1; at the Nth moment when the vehicle actually runs, k is N;
Qheat2ka battery heat generation rate at a kth time for a battery of the vehicle;
OCV(SOC2k) A second open circuit voltage of a battery of the vehicle;
U2kthe voltage value of a battery of the vehicle at the k moment;
I2ka current value of a battery of the vehicle at a k-th moment;
T2kis a battery surface temperature of a battery of the vehicle at a kth time;
Figure FDA0002293520830000101
is a second temperature coefficient of a battery of the vehicle.
17. The method for processing the temperature information in the battery based on the AUKF of claim 9, wherein the symmetric sampling model is:
Figure FDA0002293520830000102
Figure FDA0002293520830000103
Figure FDA0002293520830000104
wherein the content of the first and second substances,
k is the kth moment of actual operation of the battery of the vehicle, k is more than or equal to 0 and less than or equal to N, and k is an integer; at a first moment when the vehicle is actually running, k is 1; at the Nth moment when the vehicle actually runs, k is N;
Figure FDA0002293520830000105
the value is a combined vector posterior value of the ith characteristic point at the k-1 moment;
m is a combined vector posterior value XjointLength of (d);
mu is a preset scaling coefficient;
Pjoint(k-1) is a combined vector covariance posterior value at the k-1 time;
Figure FDA0002293520830000106
and the AUKF joint vector feature point set at the k-1 moment.
18. The method of claim 9, wherein the estimation model is:
Figure FDA0002293520830000111
wherein the content of the first and second substances,
Xjoint(k) the posterior value of the combined vector at the k moment of the actual running of the vehicle is obtained;
Pjoint(k) the combined vector covariance posterior value at the k moment of actual running of the vehicle is obtained;
Figure FDA0002293520830000112
the joint vector prior value at the k moment is taken as a joint vector prior value;
Kjoint(k) correcting a matrix for the measurement at the kth moment when the vehicle actually runs;
Ts measure(k) is the battery surface temperature of the vehicle at time k;
Figure FDA0002293520830000113
updating the expected value of the output value for the measurement of the AUKF joint vector feature point set at the kth moment;
Figure FDA0002293520830000114
the prior value of the covariance of the joint vector at the kth moment;
Figure FDA0002293520830000115
and updating the variance between the output values for the measurement of the characteristic point state value and the characteristic point state value of the AUKF joint vector characteristic point set at the Kth moment.
19. A computer device comprising a memory, a processor and computer readable instructions stored in the memory and executable on the processor, wherein the processor implements the method for processing the AUKF-based internal temperature information of the battery according to any one of claims 1 to 18 when executing the computer readable instructions.
20. A computer-readable storage medium storing computer-readable instructions, wherein the computer-readable instructions, when executed by a processor, implement the method for processing the AUKF-based internal battery temperature information according to any one of claims 1 to 18.
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