CN118858968B

CN118858968B - Battery equivalent circuit parameter identification method and system

Info

Publication number: CN118858968B
Application number: CN202411327903.0A
Authority: CN
Inventors: 王芳; 高妍; 石晓磊; 刘仕强; 刘磊; 马天翼; 闫鹏飞; 韩策
Original assignee: China Automotive Research New Energy Vehicle Inspection Center Tianjin Co ltd; China Automotive Technology and Research Center Co Ltd
Current assignee: China Automotive Research New Energy Vehicle Inspection Center Tianjin Co ltd; China Automotive Technology and Research Center Co Ltd
Priority date: 2024-09-24
Filing date: 2024-09-24
Publication date: 2025-01-14
Anticipated expiration: 2044-09-24
Also published as: CN118858968A

Abstract

The invention provides a battery equivalent circuit parameter identification method and a system, wherein the method comprises the steps of receiving the identification requirement of the battery equivalent circuit parameter; and testing the battery sample according to the test scheme to obtain the equivalent circuit parameters of the battery sample. The system comprises a battery sample, an environment box, a charging and discharging device, a control device and a model building device, wherein the environment box is used for providing corresponding environment temperature for testing the battery sample, the charging and discharging device is used for controlling the battery sample to charge and discharge, the control device is used for controlling the environment box and the charging and discharging device to test the battery sample so as to obtain battery equivalent circuit parameters of the battery sample, and the model building device is used for importing the equivalent circuit parameters into a battery equivalent circuit model of the battery sample so as to obtain a simulation model of a battery equivalent circuit of the battery sample. According to the invention, the parameter information of the battery equivalent circuit model can be identified based on a small amount of actual battery tests, so that the simulation model of the battery equivalent circuit is built.

Description

Battery equivalent circuit parameter identification method and system

Technical Field

The invention relates to the field of battery modeling, in particular to a battery equivalent circuit parameter identification method and system.

Background

The lithium ion battery (lithium battery for short) has the advantages of high energy density, long cycle life, no memory effect, green environmental protection and the like, and rapidly develops into a new generation of energy storage power supply. In order to ensure safe and reliable operation of the battery energy storage system, a perfect battery management system must be established based on an accurate battery model to estimate and predict the battery state.

There are 2 kinds of current digital twin models of battery, namely electrochemical model and equivalent circuit model. The electrochemical model expresses basic electrochemical reaction through a complex nonlinear partial differential algebraic equation, and can accurately capture various characteristics of a battery, but a large amount of complex calculation is needed to obtain a solution of the equation, and the calculation capacity of a processor is high, so that the model is generally suitable for battery design rather than system level simulation. In contrast, the equivalent circuit model describes the operating characteristics of a battery based on the operating principle of the battery by using a circuit network composed of electronic elements such as resistors, capacitors, and the like. The physical meaning is clear, so that mathematical analysis and parameter identification are convenient, and the method is widely applied to new energy automobile simulation. The accuracy of the equivalent circuit model is largely dependent on the accuracy of the model's structure and model parameter identification. In theory, a higher-order equivalent circuit model can represent more kinds of batteries and generate more accurate voltage estimation values, but has the defects of increasing calculation load and reducing stability of the estimated value of the state parameter of the battery at the later stage, so that 3 aspects of model precision, calculation load and numerical stability are comprehensively considered.

For a second-order RC equivalent circuit model, the reliability and the accuracy of parameters in the model are directly determined by the quality of the model parameter identification method. The least square fitting method is simple in calculation and large in error, the Kalman filtering method can obtain the optimal estimation of the state variable, but the system noise value can directly influence the identification accuracy, and the genetic algorithm has good identification accuracy on the whole world and poor local searching capability. Meanwhile, the parameter on-line identification technology is mainly used for battery state estimation and is not applied to battery model building, and the parameter off-line identification technology is mainly used for battery model building, but has a certain difference in accuracy and granularity compared with on-line identification. Therefore, only by combining the advantages of several algorithms and combining on-line and off-line identification, the model parameters can be accurately identified. As the second-order RC equivalent circuit model belongs to a nonlinear system, for the nonlinear system, nonlinear parameter linearization is usually adopted in the current research, and then a linear least square fitting method is used for realizing parameter identification, and nonlinear parameters which cannot be linearized exist in the second-order RC equivalent circuit model, so that parameters in the model cannot be accurately identified.

The foregoing description is provided for general background information and does not necessarily constitute prior art.

Disclosure of Invention

In view of the above drawbacks of the prior art, the present invention is directed to a method and a system for identifying parameters of a battery equivalent circuit, which are used for solving the problem that in the prior art, nonlinear parameters which cannot be linearized exist in a second-order RC equivalent circuit model, so that parameters in the model cannot be accurately identified.

To achieve the above and other related objects, the present invention provides a battery equivalent circuit parameter identification method, including:

receiving the identification requirement of the battery equivalent circuit parameters;

determining a corresponding test scheme according to the identification requirement;

and testing the battery sample according to the test scheme to obtain the equivalent circuit parameters of the battery sample.

Preferably, the test protocol includes at least an SOC-OCV test protocol and an HPPC test protocol.

Preferably, when the test scheme is an SOC-OCV test scheme, the step of testing the battery sample according to the test scheme to obtain the equivalent circuit parameters of the battery sample includes:

s11, controlling the battery sample to run for the first time according to an SOC-OCV pre-grinding test scheme;

s12, optimizing the SOC-OCV formal test scheme based on the first operation result;

S13, controlling the battery sample to run for the second time according to the optimized SOC-OCV formal test scheme;

s14, processing the second operation result to obtain an open-circuit voltage two-dimensional graph of the battery sample.

Preferably, the step of controlling the battery sample to run for the first time according to the SOC-OCV pre-grinding test scheme includes:

s111, controlling the battery sample to be processed according to a first rule to obtain the actual capacity of the battery sample;

and S112, controlling the battery sample to be processed according to a second rule based on the actual capacity to obtain the first operation result.

Preferably, the step of controlling the battery sample to run for the second time according to the optimized SOC-OCV formal test scheme includes:

s131, controlling the battery sample to be processed according to a first rule to obtain the actual capacity of the battery sample;

And S132, controlling the battery sample to be processed according to a third rule based on the actual capacity to obtain the second operation result.

Preferably, the step of controlling the battery sample to be processed according to the first rule to obtain the actual capacity of the battery sample comprises the following steps:

controlling the battery sample to repeatedly perform charge and discharge treatment to a first preset number of times based on a first preset temperature;

determining an average value of discharge capacities of the battery samples based on a second preset number of times as an actual capacity of the battery samples;

wherein the second preset times are smaller than the first preset times.

Preferably, the step of controlling the battery sample to repeatedly perform the charge and discharge process to the first preset number of times based on the first preset temperature includes:

Controlling the battery sample to be subjected to standing treatment at a first preset temperature;

when the standing treatment time length meets a first preset time length, controlling the battery sample to perform constant-current discharge at the first preset temperature according to a first preset current;

when the voltage of the battery sample is smaller than the discharge cut-off voltage, controlling the battery sample to perform standing treatment at the first preset temperature;

when the standing treatment duration meets a first preset duration, controlling the battery sample to perform constant-current charging at the first preset temperature according to the first preset current;

when the voltage of the battery sample is larger than the charge cut-off voltage, the battery sample is controlled to be charged at a constant voltage;

and when the charging current of the battery sample is smaller than a preset current, controlling the battery sample to repeatedly execute the steps to the first preset times.

Preferably, the step S112 of controlling the battery sample to process according to a second rule based on the actual capacity to obtain the voltage stabilization time of the battery sample under different conditions includes:

S1121, controlling the battery sample to perform standing treatment at a first preset temperature based on the actual capacity, and detecting battery terminal voltage at the first preset temperature;

s1122, when the standing treatment duration at the first preset temperature meets a second preset duration, adjusting the first preset temperature to a second preset temperature, controlling the battery sample to perform the standing treatment at the second preset temperature, and detecting the battery terminal voltage at the second preset temperature;

S1123, when the standing treatment duration at the second preset temperature meets a third preset duration, adjusting the second preset temperature to a third preset temperature, controlling the battery sample to perform the standing treatment at the third preset temperature, and detecting the battery terminal voltage at the third preset temperature;

S1124, when the standing treatment duration at the third preset temperature meets the fourth preset duration, adjusting the third preset temperature to the fourth preset temperature, controlling the battery sample to perform the standing treatment at the fourth preset temperature, and detecting the battery terminal voltage at the fourth preset temperature;

S1125, when the standing treatment duration at the fourth preset temperature meets the fourth preset duration, adjusting the fourth preset temperature to a fifth preset temperature, controlling the battery sample to perform the standing treatment at the fifth preset temperature, and detecting the battery terminal voltage at the fifth preset temperature;

s1126, when the standing treatment duration at the fifth preset temperature meets the fourth preset duration, controlling the battery sample to perform constant current discharge according to a first preset current;

and S1127, when the discharge capacity reaches a first preset capacity, controlling the battery sample to repeatedly execute the steps S1121-S1127 to a third preset number of times.

Preferably, the step of controlling the battery sample to be processed according to a third rule based on the actual capacity S132 includes:

s1321, controlling the battery sample to perform standing treatment at a first preset temperature based on the actual capacity;

s1322, when the standing treatment duration at the first preset temperature meets a fifth preset duration, adjusting the first preset temperature to a second preset temperature, and controlling the battery sample to perform the standing treatment at the second preset temperature;

S1323, when the standing treatment duration at the second preset temperature meets a sixth preset duration, adjusting the second preset temperature to a third preset temperature, and controlling the battery sample to perform the standing treatment at the third preset temperature;

s1324, when the standing treatment duration at the third preset temperature meets the seventh preset duration, adjusting the third preset temperature to a fourth preset temperature, and controlling the battery sample to perform the standing treatment at the fourth preset temperature;

S1325, when the standing treatment duration at the fourth preset temperature meets a seventh preset duration, adjusting the fourth preset temperature to the first preset temperature, and controlling the battery sample to perform the standing treatment at the first preset temperature;

S1326, when the standing treatment duration at the first preset temperature meets a seventh preset duration, controlling the battery sample to perform constant current discharge according to a first preset current;

S1327, when the discharge capacity reaches a first preset capacity, adjusting the first preset temperature to the second preset temperature, controlling the battery sample to perform standing treatment at the second preset temperature, and detecting the battery terminal voltage at the second preset temperature;

S1328, when the standing treatment duration at the second preset temperature meets an eighth preset duration, adjusting the second preset temperature to a third preset temperature, controlling the battery sample to perform the standing treatment at the third preset temperature, and detecting the battery terminal voltage at the third preset temperature;

S1329, when the standing treatment duration at the third preset temperature meets the eighth preset duration, adjusting the third preset temperature to a fourth preset temperature, controlling the battery sample to perform the standing treatment at the fourth preset temperature, and detecting the battery terminal voltage at the fourth preset temperature;

S1330, when the standing treatment duration at the fourth preset temperature meets an eighth preset duration, adjusting the fourth preset temperature to the first preset temperature, controlling the battery sample to perform the standing treatment at the first preset temperature, and detecting the battery terminal voltage at the first preset temperature;

S1331, when the standing processing time at the first preset temperature meets an eighth preset time, controlling the battery sample to repeatedly execute the steps S1326-S1330 to fourth preset times.

Preferably, when the test scheme is an HPPC test scheme, the step of testing the battery sample according to the test scheme to obtain the equivalent circuit parameters of the battery sample includes:

S21, controlling the battery sample to be processed according to a fourth rule so as to meet a first preset condition;

S22, processing the battery sample after control processing according to a fifth rule to obtain HPPC test data;

s23, obtaining equivalent circuit parameters of the battery sample according to a preset method based on the HPPC test data.

Preferably, the step of processing the battery sample after the step S22 of controlling the processing according to a fifth rule includes:

s221, performing constant-current discharge on the battery sample after control processing according to a second preset current;

s222, when the electric quantity discharged by the battery sample reaches a second preset capacity, controlling the battery sample to perform standing treatment until a ninth preset time length;

S223, controlling the battery sample to perform pulse discharge according to a third preset current;

s224, when the pulse discharge time length meets a tenth preset time length, controlling the battery sample to be subjected to standing treatment until the eleventh preset time length;

s225, controlling the battery sample to be subjected to pulse charging according to a third preset current;

s226, when the pulse charging time length meets the tenth preset time length, controlling the battery sample to be subjected to standing treatment until the eleventh preset time length;

and controlling the battery sample to repeatedly execute the steps S221-S226, and stopping when the electric quantity of the battery sample is smaller than the second preset capacity.

Preferably, the step S23 of obtaining the equivalent circuit parameters of the battery sample according to a preset method based on the HPPC test data includes:

the equivalent circuit parameters are obtained by identification according to a least square method on-line parameter identification method based on HPPC test data of a first preset standing time stage and/or,

The equivalent circuit parameters are obtained by identification according to a neural network offline parameter identification method based on HPPC test data of a second preset standing time stage and/or,

Based on HPPC test data of a first preset charge-discharge time stage, the equivalent circuit parameters are obtained through identification according to a least square method on-line parameter identification method, and/or,

And identifying and obtaining the equivalent circuit parameters according to a neural network offline parameter identification method based on HPPC test data of a second preset charge-discharge time stage.

The invention also provides a construction method of the battery equivalent circuit simulation model, which comprises the following steps:

Constructing a battery equivalent circuit model of a battery sample;

And importing the parameters obtained by adopting the battery equivalent circuit parameter identification method into the circuit model to obtain a battery equivalent circuit simulation model of the battery sample.

Preferably, after the step of obtaining the battery equivalent circuit simulation model of the battery sample, the method further includes:

Operating the simulation model according to a second preset condition to obtain a battery terminal voltage value of the simulation model;

Comparing and analyzing the battery terminal voltage value and the actual terminal voltage value of the simulation model;

and determining the accuracy of the simulation model according to the result of the comparison analysis.

Preferably, the step of constructing a battery equivalent circuit model of the battery sample includes:

determining a circuit equation of a battery equivalent circuit model of the battery sample based on kirchhoff's law:

Wherein V1 is electrochemical polarization voltage, and V2 is concentration polarization voltage;

Simultaneous equation type And equation (1), using SOC, V ₁ and V ₂ as state variables, I as input quantity, V as output quantity, to obtain a state space equation of the battery equivalent circuit model, and performing discrete processing on the state space equation to obtain a system state equation:

Where T is the sampling time, τ ₁=R₁C₁,τ₂=R₂C₂, c=battery nominal capacity x nominal voltage;

Obtaining a mathematical equation of the battery sample:

the battery equivalent circuit model is generated based on the mathematical equation.

The invention also provides a battery equivalent circuit parameter identification system, which comprises:

A battery sample;

the environment box is used for providing corresponding environment temperature when the battery sample is tested;

The charge-discharge equipment is used for controlling the battery sample to charge and discharge;

The control device is used for controlling the environment box and the charging and discharging equipment to test the battery sample so as to obtain battery equivalent circuit parameters of the battery sample;

the model building device is used for importing the battery equivalent circuit parameters into a battery equivalent circuit model of the battery sample to obtain a simulation model of the battery equivalent circuit of the battery sample.

Preferably, the control device includes:

The SOC-OCV testing unit is used for controlling the environment box and the charging and discharging equipment to complete an SOC-OCV test on the battery sample and obtaining an open-circuit voltage two-dimensional graph based on the SOC-OCV test data;

and the HPPC testing unit is used for controlling the environment box and the charge-discharge equipment to finish HPPC testing on the battery sample and obtaining equivalent circuit parameters of the battery sample based on the HPPC testing data.

Preferably, the model building device includes:

the construction unit is used for establishing a mathematical equation of a battery equivalent circuit model of the battery sample;

an importing unit for importing the test result into the mathematical equation;

and the building unit is used for building the battery equivalent circuit simulation model based on a mathematical equation for importing the test result.

As described above, the method and the system for building the battery equivalent circuit simulation model can identify the parameter information of the battery equivalent circuit model based on a small amount of actual battery tests, thereby building the battery equivalent circuit simulation model, reducing the test cost, helping the research and development of batteries and promoting the development of the battery virtual test technology.

Drawings

Fig. 1 is a flow chart of a battery equivalent circuit parameter identification method according to the present invention.

Fig. 2 is a schematic flow chart of a method for constructing a simulation model of a battery equivalent circuit according to the present invention.

Fig. 3 is a schematic flow chart of testing a battery sample according to the SOC-OCV test scheme of the present invention to obtain equivalent circuit parameters.

FIG. 4 is a schematic flow chart of the invention for testing battery samples according to the HPPC test scheme to obtain equivalent circuit parameters;

FIG. 5 is a schematic flow chart of a control battery sample execution SOC-OCV pre-test scheme according to the present invention;

FIG. 6 is a flow chart of the present invention for controlling the actual capacity of a battery sample;

FIG. 7 is a flow chart of the step of obtaining the actual capacity of the battery sample according to the present invention;

FIG. 8 is a flow chart of the control battery sample performing the SOC-OCV pre-test procedure according to the present invention;

FIGS. 9A-9J are schematic diagrams showing the effect of stabilizing the battery terminal voltage of the battery sample according to the present invention at different amounts of electricity;

FIG. 10 is a flow chart of a control battery sample execution SOC-OCV formal test protocol of the present invention;

FIG. 11 is a flow chart of the test steps for controlling a battery sample to perform a SOC-OCV formal test according to the present invention;

Fig. 12 is a flow chart of a method for constructing a simulation model of a battery equivalent circuit according to the present invention.

FIG. 13 is a schematic diagram of a simulation model of a battery equivalent circuit of the present invention;

FIG. 14 is a schematic diagram showing the effect of the nearest neighbor interpolation method of the present invention;

FIG. 15 is a schematic diagram of a process for verifying a battery equivalent circuit simulation model according to the present invention;

FIG. 16 is a schematic diagram of a construction system of a battery equivalent circuit simulation model of the present invention;

FIG. 17 is a schematic diagram of a control device according to the present invention;

FIG. 18 is a schematic view of the construction of the model building apparatus of the present invention;

FIG. 19 is a schematic diagram showing the effect of a system for constructing a simulation model of a battery equivalent circuit according to an embodiment of the present invention;

FIG. 20 is a schematic diagram showing the two-dimensional effect of the open circuit voltage according to an embodiment of the present invention;

FIGS. 21A-21G are schematic diagrams illustrating the results of parameter identification and voltage simulation according to embodiments of the present invention;

FIG. 22 is a graph showing the simulation results of the battery terminal voltage under the HPPC operating mode according to the embodiment of the invention;

Fig. 23 is a schematic diagram of a simulation model of a battery equivalent circuit according to an embodiment of the present invention.

Detailed Description

Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.

It should be noted that the illustrations provided in the following embodiments merely illustrate the basic concept of the present invention by way of illustration, and only the components related to the present invention are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.

Referring to fig. 1, the method for identifying parameters of a battery equivalent circuit according to the present invention includes:

In the embodiment of the application, when the corresponding battery equivalent circuit simulation model is constructed for different battery samples, different parameters of the different battery samples need to be acquired, and in order to facilitate operation, a plurality of test schemes are arranged in the scheme of the application for acquiring the different parameters of the battery samples through testing. In this embodiment, before acquiring parameters of a battery sample, an identification requirement of the battery sample parameter is required to be received, and a corresponding test scheme is determined according to the identification requirement, for example, when the received parameter identification requirement of the battery sample is to acquire an open circuit voltage value of the battery sample under different conditions, such as different temperatures and different SOCs, the corresponding test scheme is determined to be an SOC-OCV test scheme, and when the received parameter identification requirement of the battery sample is to acquire an ohmic internal resistance value, an electrochemical polarization capacitance value, a concentration polarization internal resistance value, a concentration polarization capacitance value, and the like of the battery sample, the corresponding test scheme is determined to be an HPPC test scheme, and the battery sample is tested by executing the test scheme, thereby obtaining equivalent circuit parameters of the battery sample.

In the embodiment of the application, before a battery equivalent circuit simulation model is built on a battery sample, the performance of the battery sample on battery terminal voltages at different temperatures and different SOCs is required to be obtained, and an open-circuit voltage two-dimensional graph of the battery sample is obtained through the temperatures, SOCs and corresponding terminal voltages. In the example of the application, when the terminal voltage of the battery sample under different temperatures and different SOCs is obtained, the battery sample is tested by adopting an SOC-OCV test scheme, and different temperatures, SOCs, test cycle times, test periods and the like can be set according to actual test requirements by using the test scheme.

Referring to fig. 2, the step of testing the battery sample according to the SOC-OCV test scheme to obtain the equivalent circuit parameters of the battery sample includes:

s13, controlling the battery to run for the second time according to the optimized SOC-OCV formal test scheme;

s14, processing the second operation result to obtain an open-circuit voltage two-dimensional graph of the battery.

In the embodiment of the application, when the SOC-OCV test data of the battery sample is obtained through the SOC-OCV test scheme, firstly, the SOC-OCV test scheme is pre-researched, namely, the battery sample is controlled to be tested according to the SOC-OCV pre-research test scheme, so that battery terminal voltage information of the battery sample is obtained, for example, when 10% SOC, 20% SOC, 30% SOC, 40% SOC, 60% SOC, 80% SOC and 90% SOC are all in the condition that the battery terminal voltage is not stabilized in a preset standing time period, and when 100% SOC is in the condition that the battery terminal voltage is reduced in the standing time period. And then optimizing the SOC-OCV pre-grinding test scheme according to the test data of the SOC-OCV pre-grinding test scheme, for example, adjusting the standing time of the battery with the SOC of 90% and below to be longer, adjusting the standing time of the battery with the SOC of 100% to be shorter, and the like. And executing a formal test flow on the battery sample according to the optimized SOC-OCV formal test scheme to obtain open-circuit voltage values of the battery samples with different temperatures and different SOCs under the SOC-OCV formal test scheme. By adopting the battery SOC-OCV test schemes with different standing durations, the battery open-circuit voltage identification precision under different working condition points is effectively improved.

In an example, through the above test scheme, after the open-circuit voltage value of the test working point is determined, the open-circuit voltage is subjected to two-dimensional 8-order polynomial fitting by taking the identified open-circuit voltage values as target points and taking the temperature and the SOC as dimensions, so that the open-circuit voltage two-dimensional map of the battery sample is obtained.

In the embodiment of the application, before the battery equivalent circuit simulation model is built on the battery sample, the values of preset parameters of the battery sample, such as an ohmic internal resistance value, an electrochemical polarized capacitance value, a concentration polarized internal resistance value and a concentration polarized capacitance value, are required to be obtained. Under an exemplary scenario, when the number of preset parameters is obtained, HPPC test data of the battery sample is required to be obtained through HPPC test, and then the HPPC test data of the battery sample is processed based on a least square method combined with an online and offline identification algorithm to obtain the number of the preset parameters.

For example, HPPC test data of a battery sample in a first preset standing time stage is obtained as a data segment to be identified, for example, HPPC test data of the first 50% standing time stage of the total standing process is taken as a data segment to be identified, because the polarization influence of the battery in the stage is large, the influence is difficult to be accurately simulated by an offline algorithm, the voltage error simulated by a battery equivalent model built based on the offline parameter identification algorithm is large, therefore, the parameter identification precision needs to be improved by means of a related algorithm of online identification, and the least square method is simple in calculation logic and high in calculation speed, so that the application preferably adopts an online parameter identification algorithm of a least square method to identify the preset parameters, wherein the input of the least square method is real-time current and real-time voltage of a working condition segment to be identified, and the output is calculated numerical value of the 5 preset impedance parameters at each moment, namely an impedance parameter array based on a time axis.

For example, HPPC test data of a battery sample in a second preset standing time stage is obtained as a data segment to be identified, for example, HPPC test data of a later 50% standing time stage of the total standing process is taken as a data segment to be identified, and because the polarization influence of the battery in this stage is relatively small, the voltage of the battery terminal is relatively stable, the precision of an offline parameter identification algorithm can meet the requirement, and meanwhile, because the condition that i=0 and dv=0 can occur in this stage, the condition that the denominator is 0 can occur in the parameter decoupling process of the online identification algorithm, and therefore, the parameter value cannot be obtained, and an offline identification mode is necessary. The neural network parameter identification method based on genetic optimization has slower calculation speed but higher identification precision of parameters, so that the neural network off-line parameter identification algorithm based on genetic optimization is preferably adopted to identify the preset parameters. The input of the neural network offline parameter identification algorithm based on genetic optimization is the rated capacity of a battery, the actual time, the actual current and the actual voltage of a working condition section to be identified, and the open-circuit voltage value with smaller granularity obtained in the SOC-OCV test, and the value of the 5 preset impedance parameters identified by the working condition section is output.

For example, HPPC test data of the battery sample during a first preset charge-discharge time period is obtained as the data segment to be identified, e.g., HPPC test data of the battery sample during the first 50% of the total charge or total discharge process is taken as the data segment to be identified. According to the method, the input of the algorithm is real-time current and real-time voltage of a working condition section to be identified, and the output is calculated values of the 5 preset impedance parameters at each moment, namely the impedance parameter array based on the time axis.

In the second preset charge-discharge time period, for example, HPPC test data of the battery sample is obtained as the data segment to be identified, for example, HPPC test data of the battery sample in the last 50% of the total charge or total discharge process is taken as the data segment to be identified. According to the method, the value of a series of impedance parameter sets based on a time axis is obtained based on an online identification method, the fluctuation of each parameter is gentle in the later stage of charge and discharge, the state of a battery sample is stable, and the error of the parameter obtained through offline algorithm identification can meet the precision requirement, so that the method preferably adopts a neural network offline parameter identification algorithm based on genetic optimization to identify the preset parameters, wherein the input of the algorithm is the rated capacity of the battery, the actual time, the actual current and the actual voltage of a working condition section to be identified, and the open-circuit voltage value with smaller granularity obtained through SOC-OCV test is output as the value of the 5 preset impedance parameters identified through the working condition section.

Referring to fig. 3, the step of testing a battery sample according to an HPPC test protocol to obtain equivalent circuit parameters of the battery sample includes:

In the embodiment of the application, when HPPC test data is acquired, a battery sample is first processed according to a fourth rule so that the battery sample meets a first preset condition. In an exemplary scenario, before starting the HPPC test, the battery sample is fully charged by adopting a constant voltage and current limiting mode, and then the battery sample with the SOC of 100% is subjected to a standing process until the voltage is in a stable state, i.e., the electric quantity in the battery sample at this time is the actual capacity of the battery sample. Further, the battery is controlled to process according to a fifth rule, and HPPC test data are obtained.

Referring to fig. 4, the step of controlling the battery after the processing to perform the processing according to the fifth rule includes:

S221, performing constant current discharge on the battery after the control processing according to a second preset current;

S222, when the electric quantity discharged by the battery reaches a second preset capacity, controlling the battery to perform standing treatment until a ninth preset duration is reached;

S223, controlling the battery to perform pulse discharge according to a third preset current;

s224, when the pulse discharge duration meets a tenth preset duration, controlling the battery to perform standing treatment until the eleventh preset duration;

s225, controlling the battery to be charged in a pulse mode according to a third preset current;

s226, when the pulse charging duration meets the tenth preset duration, controlling the battery to perform standing treatment until the eleventh preset duration;

And controlling the battery sample to repeatedly execute the steps S221-S226, and stopping when the capacity of the battery sample is smaller than the second preset capacity.

In the embodiment of the application, when HPPC test data of a battery sample is acquired, after constant current discharge is performed on the battery sample with full capacity and stable voltage, standing treatment is performed to enable the voltage of the battery sample to be in a stable state, pulse discharge is continuously performed on the battery sample, because the pulse discharge is usually large in current and can be suddenly reduced in the electric quantity of the battery in a short time, further standing treatment is required until the voltage of a battery terminal of the battery sample is stable, and after the battery sample is charged by the same pulse current, the standing treatment is performed on the battery sample after charging until the voltage of the battery terminal is stable. And repeatedly executing the steps until the electric quantity of the battery is lower than the preset capacity.

In an exemplary scenario, in a state where the battery is full and the voltage is stable, the battery is controlled to discharge at a constant current of 1C, when the amount of electricity discharged by the battery sample is 5% of the actual capacity of the battery sample, that is, the SOC of the battery is reduced by 5%, the battery sample is subjected to a rest treatment for 10 hours to make the battery terminal voltage of the battery sample be in a stable state, after the battery is controlled to pulse-discharge at a current of 3C for 10S, the battery is controlled to pulse-charge at a current of 3C for 10S, the rest treatment is continued for 5 hours, and the above steps are repeatedly performed until the current of the battery sample is 5% SOC. In this embodiment, the current of the constant current discharge, the pulse discharge and the pulse charge may be adjusted according to the actual test requirement, and the discharge interval of each 5% soc may also be adjusted according to the actual application scenario, which is only exemplary and not intended to limit the protection scope of the present application.

Referring to fig. 5, when the control battery sample is tested according to the SOC-OCV pre-grinding test scheme, the following steps are performed:

In the embodiment of the application, the chemical reaction inside the battery is unstable when the battery is just fully charged, particularly in the overcharged state, for example, the lithium battery is overcharged, so that the chemical reaction inside the lithium battery is out of control or even explodes, and the battery needs to be kept for a period of time after being fully charged to start working, so that the out of control or explosion cannot occur. In this embodiment, in order to accurately obtain the actual capacitance of the battery sample, after the battery is fully charged, the battery sample needs to be processed according to a preset rule, so that the battery sample can be tested under the conditions of full charge and stable battery terminal voltage.

Referring to fig. 6, the step of controlling the battery sample to be processed according to a first rule to obtain the actual capacity of the battery sample includes:

s1111, controlling the battery sample to repeatedly perform charge and discharge treatment to a first preset number of times based on a first preset temperature;

s1112, determining an average value of discharge capacities of the battery samples based on a second preset number of times as an actual capacity of the battery samples;

wherein the second preset times are smaller than the first preset times.

In the embodiment of the application, in order to ensure more accurate acquisition of the actual capacity of the battery sample, the battery sample is controlled to be subjected to repeated charge and discharge treatment for a plurality of times in a preset temperature environment, for example, the battery sample is subjected to repeated charge and discharge operation for a plurality of times in an environment of 25 ℃ at normal temperature, the battery capacity of the battery sample in a full charge and voltage stable state after each charge and discharge is acquired, and the actual capacity of the battery sample is determined by averaging the plurality of times of battery capacities. For example, the battery sample is controlled to repeatedly perform charge and discharge processing 4 times in an environment of 25 ℃ at normal temperature, and the charge and discharge capacity of the battery sample is recorded three times after that, and the average value of the charge and discharge capacities of the three times is calculated to be taken as the actual capacity of the battery sample.

Referring to fig. 7, the step of controlling the battery to repeatedly perform charge and discharge processes to a first preset number of times includes:

s11111, controlling the battery sample to be subjected to standing treatment at a first preset temperature;

S11112, when the standing treatment duration meets a first preset duration, controlling the battery to perform constant current discharge at the first preset temperature according to a first preset current;

S11113, when the voltage of the battery is smaller than the discharge cut-off voltage, controlling the battery to perform standing treatment at the first preset temperature;

S11114, when the standing processing time length meets a first preset time length, controlling the battery to perform constant current charging according to the first preset current at the first preset temperature;

S11115, when the voltage of the battery is larger than the charge cut-off voltage, controlling the battery to be changed into constant voltage charge;

And S11116, when the charging current is smaller than a preset current, controlling the battery to repeatedly execute the steps to the first preset times.

In an exemplary scenario, referring to table 1, in an environment of 25 ℃, a battery is controlled to stand for 1 hour in a fully charged state, in a state where a battery terminal voltage of a battery sample is stable, the battery sample is controlled to perform a discharge process at a constant current of 0.33C, when it is detected that the voltage of the battery sample is less than a discharge cut-off voltage of the battery, i.e., a stage where the discharge cannot be performed, the battery is controlled to stand for 1 hour so that a chemical reaction inside the battery tends to be stable, after the battery sample is charged by the constant current of 0.33C, when it is detected that the battery terminal voltage of the battery sample is greater than the charge cut-off voltage of the battery, the battery sample is controlled to be subjected to constant voltage charging, and after it is detected that a charge current is less than 0.05C, the battery sample is controlled to repeatedly perform the above steps. The foregoing is merely illustrative, and in the actual operation process of measuring the actual capacity of the battery sample, the ambient temperature, the charge-discharge current, the length of the rest time, etc. may be adjusted according to the requirements, which will not be described herein.

TABLE 1

In the embodiment of the application, based on the mode, the actual capacity of the battery sample is obtained, and the battery sample is controlled to be processed according to the second rule, so that the stabilizing time of the battery terminal voltage of the battery sample under the working condition environments of different temperatures and different electric quantity SOCs is obtained.

Referring to fig. 8, the step of controlling the battery sample to be processed according to the second rule includes:

S1121, controlling the battery to perform standing treatment at a first preset temperature based on the actual capacity, and detecting battery terminal voltage at the first preset temperature;

s1122, when the standing treatment duration at the first preset temperature meets a second preset duration, adjusting the first preset temperature to a second preset temperature, controlling the battery to perform the standing treatment at the second preset temperature, and detecting the battery terminal voltage at the second preset temperature;

S1123, when the standing treatment duration at the second preset temperature meets a third preset duration, adjusting the second preset temperature to a third preset temperature, controlling the battery to perform the standing treatment at the third preset temperature, and detecting the battery terminal voltage at the third preset temperature;

s1124, when the standing treatment duration at the third preset temperature meets the fourth preset duration, adjusting the third preset temperature to the fourth preset temperature, controlling the battery to perform the standing treatment at the fourth preset temperature, and detecting the battery terminal voltage at the fourth preset temperature;

S1125, when the standing treatment duration at the fourth preset temperature meets the fourth preset duration, adjusting the fourth preset temperature to a fifth preset temperature, controlling the battery to perform the standing treatment at the fifth preset temperature, and detecting the battery terminal voltage at the fifth preset temperature;

S1126, when the standing treatment duration at the fifth preset temperature meets the fourth preset duration, controlling the battery to perform constant current discharge according to the first preset current;

And S1127, when the discharged capacity reaches a first preset capacity, controlling the battery to repeatedly execute the steps S1121-S1127 to a third preset number of times.

In an exemplary scenario, referring to table 2, the battery terminal voltage of the battery sample is first controlled to be in a state of an actual capacity in the battery sample, the battery terminal voltage of the battery sample is detected and stored in the 3-hour stationary period in the 25 ℃ environment for 3 hours, then the battery terminal voltage of the battery sample is detected and stored in the 12-hour stationary period in the 0 ℃ environment for 12 hours, then the battery sample is controlled to be in a stationary state in the 15 ℃ environment for 8 hours, the battery terminal voltage of the battery sample is detected and recorded in the 8-hour stationary period in the 15 ℃ environment for 8 hours, then the battery terminal voltage of the battery sample is detected and stored in the 8-hour stationary period in the 35 ℃ environment for 8 hours, then the battery terminal voltage of the battery sample is detected and stored in the 8-hour stationary period in the 25 ℃ environment, then the battery is controlled to be in a constant current of 0.33C, when the battery terminal voltage of the battery sample is detected to be in a constant current state, the actual capacity of the battery is 10% of the actual capacity is reached, and then the actual capacity of the battery sample is measured for 10% by repeating the steps of 10 times, and the actual capacity of the battery is discharged for 10% of the actual battery sample, and the actual capacity is measured for 10% of the actual battery sample. In this embodiment, the foregoing is merely illustrative, and in the actual testing process, the user may adjust different rest temperatures according to the requirements of the actual scene, and may also perform processing according to different discharge amounts, which is not described herein.

TABLE 2

In the embodiment of the present application, based on the SOC-OCV pre-grinding test scheme, it is possible to obtain the case that the battery sample is not stable in the preset rest period of the battery terminal voltage at 10%, 20%, 30%, 40%, 60%, 80% and 90% SOC, respectively, as shown in fig. 9A to 9I, and at 100% SOC, the battery terminal voltage has a tendency to decrease during rest, and it is possible to determine that the battery is self-discharged in this state, as shown in fig. 9J.

Referring to fig. 10, the step of controlling the battery sample to perform the second operation according to the SOC-OCV formal test scheme after the optimization process includes:

s131, controlling the battery to process according to a first rule to obtain the actual capacity of the battery;

In the embodiment of the present application, when the battery sample is tested based on the SOC-OCV formal test scheme, the battery sample is also required to be fully charged, and the battery sample is processed according to the steps S11111 to S11116, as shown in table 3, and the average value of the discharge capacities detected for a plurality of times is taken as the actual capacity of the battery sample, which is not described herein.

TABLE 3 Table 3

And S132, controlling the battery to process according to a third rule based on the actual capacity to obtain the second operation result.

In the embodiment of the application, based on the mode, the actual capacity of the battery sample is obtained, and the battery sample is controlled to be processed according to the third rule, so that the stabilizing time of the battery terminal voltage of the battery sample under the working condition environments of different temperatures and different electric quantity SOCs is obtained.

Referring to fig. 11, the step of controlling the battery to perform processing according to a third rule based on the actual capacity includes:

S1321, controlling the battery to perform standing treatment at a first preset temperature based on the actual capacity;

s1322, when the standing treatment duration at the first preset temperature meets a fifth preset duration, adjusting the first preset temperature to a second preset temperature, and controlling the battery to perform the standing treatment at the second preset temperature;

s1323, when the standing treatment duration at the second preset temperature meets a sixth preset duration, adjusting the second preset temperature to a third preset temperature, and controlling the battery to perform the standing treatment at the third preset temperature;

S1324, when the standing treatment duration at the third preset temperature meets the seventh preset duration, adjusting the third preset temperature to a fourth preset temperature, and controlling the battery to perform the standing treatment at the fourth preset temperature;

S1325, when the standing treatment duration at the fourth preset temperature meets a seventh preset duration, adjusting the fourth preset temperature to the first preset temperature, and controlling the battery to perform the standing treatment at the first preset temperature;

S1326, when the standing treatment duration at the first preset temperature meets a seventh preset duration, controlling the battery to perform constant current discharge according to a first preset current;

s1327, when the discharge capacity reaches a first preset capacity, adjusting the first preset temperature to the second preset temperature, controlling the battery to perform standing treatment at the second preset temperature, and detecting the battery terminal voltage at the second preset temperature;

S1328, when the standing treatment duration at the second preset temperature meets an eighth preset duration, adjusting the second preset temperature to a third preset temperature, controlling the battery to perform the standing treatment at the third preset temperature, and detecting the battery terminal voltage at the third preset temperature;

S1329, when the standing treatment duration at the third preset temperature meets the eighth preset duration, adjusting the third preset temperature to a fourth preset temperature, controlling the battery to perform the standing treatment at the fourth preset temperature, and detecting the battery terminal voltage at the fourth preset temperature;

s1330, when the standing treatment duration at the fourth preset temperature meets an eighth preset duration, adjusting the fourth preset temperature to the first preset temperature, controlling the battery to perform the standing treatment at the first preset temperature, and detecting the battery terminal voltage at the first preset temperature;

S1331, when the standing processing time at the first preset temperature meets an eighth preset time, controlling the battery to repeatedly execute the steps S1326-S1330 to fourth preset times.

In an exemplary scenario, referring to Table 4, the battery sample was first controlled to be in a state of actual capacity, and was then sequentially controlled to be subjected to rest processing at 25 ℃,0 ℃, 15 ℃, 35 ℃ and 25 ℃ for 24 hours at the five temperatures, respectively, and then discharged at a constant current of 0.33 ℃, after the discharge capacity was detected to be 10% of the actual capacity, i.e., the battery remaining capacity was 90% SOC, the battery was sequentially controlled to be respectively kept rest for 24 hours in environments of 0 ℃, 15 ℃, 35 ℃ and 25 ℃, and the battery terminal voltage of the last step of each rest stage was detected and stored, after which the tests were repeatedly performed according to the steps of 11-15 of Table 4, to obtain battery terminal voltages of 80% SOC, 70% SOC, 60% SOC, 50% SOC, 40% SOC, 30% SOC, 20% SOC, 10% SOC, and after each rest stage, respectively. In this embodiment, the foregoing is merely illustrative, and in the actual testing process, the user may adjust different rest temperatures according to the requirements of the actual scene, and may also perform processing according to different discharge amounts, which is not described herein.

TABLE 4 Table 4

Referring to fig. 12, the method for constructing a battery equivalent circuit simulation model provided by the present invention includes:

Constructing a battery equivalent circuit model of a battery sample;

And importing the parameters obtained by the battery equivalent circuit parameter identification method into the circuit model to obtain a battery equivalent circuit simulation model of the battery sample.

In the embodiment of the application, after the parameters of the equivalent circuit of the battery sample are obtained, further, after a corresponding battery equivalent circuit model is constructed for the battery sample, the equivalent circuit parameters of the battery sample obtained through the SOC-OCV test and the HPPC test are imported into the battery equivalent circuit model, so that a battery equivalent circuit simulation model of the battery sample is obtained.

In the embodiment of the present application, before the two-dimensional plot of open-circuit voltage obtained by the SOC-OCV test and the parameters obtained by the HPPC test are introduced into the battery equivalent circuit model, first, a circuit equation of the corresponding equivalent circuit model needs to be established according to the battery sample, and in one example, the circuit equation of the battery equivalent circuit model is arranged according to kirchhoff's law, as follows:

Wherein V ₁ is electrochemical polarization voltage, V ₂ is concentration polarization voltage;

Where T is the sampling time, τ ₁=R₁C₁,τ₂=R₂C₂, c=battery nominal capacity (mAh) x nominal voltage;

Obtaining a mathematical equation of the battery:

In the embodiment of the application, the obtained open-circuit voltage two-dimensional graph of the battery sample and the values of preset parameters, such as the ohmic internal resistance value, the electrochemical polarization capacitance value, the concentration polarization internal resistance value, the concentration polarization capacitance value and the like, are imported into a battery equivalent circuit model of the battery sample, and a one-dimensional dynamic battery equivalent circuit simulation model is generated through a visual simulation tool, and is shown with reference to fig. 13.

In the embodiment of the application, because 5 preset impedance parameters, namely an ohmic internal resistance value, an electrochemical polarization capacitance value, a concentration polarization internal resistance value and a concentration polarization capacitance value, which are obtained through HPPC test are imported into the battery equivalent circuit model, other algorithms are needed to be imported. Because the application passes the HPPC test, only the test is completed aiming at the determined working point, and the obtained values of the five preset parameters are only the impedance parameter values at the determined working point, the values of the impedance parameters of the non-determined working point also need to be determined. In the embodiment of the application, the impedance parameter of any other working point in the test working point interval is obtained by adopting a nearest neighbor interpolation method.

By way of example, 5 impedance parameters of the non-test working point, namely an ohmic internal resistance value, an electrochemical polarization capacitance value, a concentration polarization internal resistance value and a concentration polarization capacitance value, are obtained through a nearest neighbor point interpolation method. In the present invention, the dimensions of the nearest neighbor interpolation method are 3, namely, SOC, temperature and charge-discharge multiplying power, and two dimensions of SOC and temperature are selected for development explanation, and reference is made to fig. 14:

For each impedance parameter, each determined working point covers a working four grid, all working points falling in the four grids adopt the parameter identification result of the determined working point, the four grids cover all working points outside the range and the self, for example, the red working point is [ SOC=37.5% and the temperature is = -5 ℃, then the 5 impedance parameters of the red working point adopt the identification result of the working point of [ SOC=40% and the temperature is=0 ℃, the dark green working point is [ SOC=53% and the temperature is = -30 ℃, and the 5 impedance parameters of the dark green working point adopt the identification result of the working point of [ SOC=50% and the temperature is = -20 ℃. The above is merely an exemplary explanation of expansion through two dimensions of SOC and temperature, and when the charge-discharge magnification needs to be added, the charge-discharge magnification needs to be taken as the third dimension, and then only the two-dimensional map shown in fig. 14 needs to be converted into the three-dimensional map, that is, the planar four-grid of the working condition is classified as the working condition cube in the three-dimensional space. In this example, when the density of the test operating point increases, the accuracy of the 5 impedance parameters of the non-test operating point also increases substantially, and at the same time, the influence of the abrupt parameter change on the simulation voltage also decreases.

The foregoing is merely illustrative, and in the actual testing process, 5 impedance parameters of the non-test operating point may be obtained by other manners, where other manners of obtaining the non-test operating point based on the foregoing thought are all within the scope of the present application, and are not described herein in detail.

Referring to fig. 15, the step of verifying the battery equivalent circuit simulation model includes:

In the embodiment of the application, after an open-circuit voltage two-dimensional graph and a preset parameter value are imported into a battery equivalent circuit model of a battery sample to obtain a simulation model of the battery equivalent circuit, the battery equivalent circuit simulation model is controlled to operate according to the same current working condition as that of a battery in an actual test to obtain the battery terminal voltage of the simulation model, the battery terminal voltage of the simulation model is compared with the actual terminal voltage of the battery sample, when the difference value between the battery terminal voltage of the simulation model and the actual terminal voltage of the battery sample is smaller than the preset difference value, the accuracy of the battery equivalent circuit simulation model can be verified to be high, and when the difference value is larger than the preset value, the accuracy of the battery equivalent circuit simulation model can be verified to be low.

In another exemplary scenario, different difference ranges are set, and different accuracy levels are determined according to the different difference ranges, namely, the accuracy level of the battery equivalent circuit simulation model is determined by judging the difference range corresponding to the difference between the battery terminal voltage of the simulation model and the actual terminal voltage of the battery sample.

The invention further provides a battery equivalent circuit parameter identification system which comprises a battery sample, an environment box, charge and discharge equipment, a control device and a model building device.

In the embodiment of the application, the battery can be a lithium iron phosphate battery, a lead-acid battery, other energy storage batteries, a battery sample is required to be placed in an environment box before a simulation model of the battery is built, namely, the environment temperature in the battery test process is provided through the environment box, meanwhile, the battery sample is controlled to be subjected to constant-current discharge, constant-current charge, pulse discharge, pulse charge and the like through a charge and discharge equipment device, in the test process, the control device is used for controlling the environment box to regulate the temperature according to the test requirement and the test rule, and controlling the charge and discharge device to regulate the charge and discharge current, the charge and discharge time and the like, such as developing an SOC-OCV test, an HPPC test and the like, and the model building device is used for introducing relevant data obtained by the test into a battery equivalent circuit model of the battery sample to obtain the simulation model of the battery equivalent circuit.

Referring to FIG. 17, the control apparatus includes an SOC-OCV test unit and an HPPC test unit.

In the embodiment of the application, the control device performs an SOC-OCV test on the battery sample through the SOC-OCV test unit, specifically, firstly, the battery sample is controlled to be tested according to an SOC-OCV pre-grinding test mode, and the SOC-OCV pre-grinding test scheme is optimized according to the test result, for example, the standing time of the battery with the SOC of 90% and below is prolonged, the standing time of the battery with the SOC of 100% is shortened, and the like. And then testing the battery sample according to the optimized SOC-OCV formal test scheme, and processing the test result, for example, performing two-dimensional 8-order polynomial fitting on the open circuit voltage by taking a plurality of identified open circuit voltage values as target points and taking the temperature and the SOC as dimensions, thereby obtaining an open circuit voltage two-dimensional graph of the battery sample. The SOC-OCV test for this battery sample is referred to the foregoing test procedure and will not be described in detail herein.

In the embodiment of the application, the control device also carries out HPPC test on the battery sample through the HPPC test unit, specifically, firstly charges the battery sample, then carries out standing treatment, carries out HPPC test on the battery sample when the battery sample is in a full-charge and voltage-stable state, and obtains preset impedance parameters such as an ohmic internal resistance value, an electrochemical polarization capacitance value, a concentration polarization internal resistance value and a concentration polarization capacitance value after carrying out treatment on HPPC test data based on a least square method and an online and offline identification algorithm.

Referring to fig. 18, the model construction device includes a construction unit, an introduction unit, and a construction unit.

In the embodiment of the present application, before the battery equivalent circuit simulation model of the battery is built, a building unit needs to build a circuit equation of the corresponding battery equivalent circuit model according to the relevant information of the battery and based on kirchhoff's law, for example:

Obtaining a mathematical equation of the battery:

Further, the open circuit voltage or the two-dimensional graph of the open circuit voltage obtained by the SOC-OCV test unit and the value of the preset impedance parameter obtained by the HPPC test unit are imported into the mathematical equation of the battery equivalent circuit model by the import unit, and the construction unit constructs the battery equivalent circuit simulation model of the battery sample according to the mathematical equation of the imported test result, as shown in fig. 13.

In an embodiment of the application, the model building device further comprises a checking unit, wherein the checking unit is used for controlling the battery equivalent circuit simulation model to operate according to the same current working condition as that of the battery in actual test so as to obtain the battery terminal voltage of the simulation model, comparing the battery terminal voltage of the simulation model with the actual terminal voltage of the battery sample, and when the difference value between the battery terminal voltage of the simulation model and the actual terminal voltage of the battery sample is smaller than a preset difference value, verifying that the accuracy of the battery equivalent circuit simulation model is high and greater than the preset value, and verifying that the accuracy of the battery equivalent circuit simulation model is low.

Referring to fig. 19, the system for constructing a battery equivalent circuit simulation model is disclosed, wherein a maximum discharge circuit is 300A, a maximum discharge voltage is 5V, the accuracy is 0.02% of single charge-discharge equipment is controlled to perform constant current discharge, constant current charge, pulse discharge, pulse charge and other operations on a battery, a high-low temperature test box with an adjustable temperature range of-40 ℃ to 150 ℃ is selected to provide an environment temperature required by the test on the battery to be tested, in the test process, a user controls the single charge-discharge equipment to complete the test on the battery according to an SOC-OCV test flow and an HPPC test flow through a control computer, and the control computer controls the high-low temperature test box to adjust the environment temperature of the battery sample in the test process and generates the battery equivalent circuit simulation model according to the test result and the battery equivalent circuit model of the battery sample. In an exemplary case, during the test, a user can also directly control and adjust the environmental temperature of the high-low temperature test chamber according to the test requirement.

In an embodiment of the application, a battery sample is selected from a lithium iron phosphate battery for SOC-OCV test, wherein the basic parameters of the battery sample are shown in Table 5:

TABLE 5

The ternary lithium battery is placed in the high-low temperature test box, and is tested through a single charging and discharging device according to the SOC-OCV test working conditions shown in table 6, so that SOC-OCV test data are obtained.

TABLE 6

And after testing according to the SOC-0CV test working condition, obtaining battery open-circuit voltage identification results under a plurality of fixed working condition points, and obtaining the battery open-circuit voltage value under all working conditions.

After following the SOC-OCV test described above, open circuit voltages were obtained at 11 defined operating points at 25C, as shown in Table 7:

TABLE 7

And carrying out two-dimensional 8-order polynomial fitting processing according to the obtained open-circuit voltage values of the 11 determined working condition points to obtain a fitting curve shown in figure 20.

The ternary lithium battery was placed in the high and low temperature test chamber and tested by the monomer charge and discharge equipment according to the HPPC test conditions shown in table 8, resulting in HPPC test data.

TABLE 8

After HPPC test data are identified by means of multi-algorithm fusion, offline combination and split working condition identification, an ohmic internal resistance value R0, an electrochemical polarization internal resistance value R1, an electrochemical polarization capacitance value C1, a concentration polarization internal resistance value R2 and a concentration polarization capacitance value C2 of the battery equivalent circuit model are obtained.

According to the online parameter identification method of the least square method, online parameter identification is performed on a charging pulse stage (the charging multiplying power is 1C) of 90% of SOC in a 25 ℃ environment, and as shown in the parameter identification and voltage simulation results shown in the following figures 21A-21G, the online parameter identification method has small error and can meet the precision requirement.

According to the above-mentioned neural network offline parameter identification method, in an environment of 25 ℃, the offline parameter identification is performed on the pulse charging (charging multiplying power 1C), pulse discharging (discharging multiplying power 1C) and SOC adjusting (discharging multiplying power 1/3C) stages in each SOC state, as shown in the parameter identification results in table 9, the battery terminal voltage simulation results under the HPPC working condition are shown in fig. 22, and the result shows that the offline identification method has higher precision in the second half of charging and discharging and the second half of standing, so as to meet the precision requirement.

TABLE 9

The voltage fitting curve shown in fig. 20 is directly introduced into the Simulink model as a simulation value of the battery open-circuit voltage, and after 5 impedance parameters (an ohmic internal resistance value R0, an electrochemical polarization internal resistance value R1, an electrochemical polarization capacitance value C1, a concentration polarization internal resistance value R2, and a concentration polarization capacitance value C2) are introduced into the Simulink model by a nearest neighbor interpolation method, a battery equivalent circuit simulation model equivalent to an actual battery is obtained, as shown in fig. 23.

In summary, the method and the system for building the battery equivalent circuit simulation model can greatly improve the parameter identification precision of the battery model by means of a series of intelligent algorithms based on a small amount of actual battery tests, thereby building the battery equivalent circuit simulation model with higher precision. The method reduces the test cost, assists the research and development of the battery, and promotes the development of the virtual test technology of the battery. Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.

The above embodiments are merely illustrative of the principles of the present invention and its effectiveness, and are not intended to limit the invention. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the invention. Accordingly, it is intended that all equivalent modifications and variations of the invention be covered by the claims, which are within the ordinary skill of the art, be within the spirit and scope of the present disclosure.

Claims

1. The battery equivalent circuit parameter identification method is characterized by comprising the following steps of:

determining a corresponding test scheme according to the identification requirement, wherein the test scheme at least comprises an SOC-OCV test scheme and an HPPC test scheme;

Testing the battery sample according to the test scheme to obtain equivalent circuit parameters of the battery sample;

When the test scheme is an SOC-OCV test scheme, testing a battery sample according to the test scheme to obtain an equivalent circuit parameter of the battery sample, including:

S14, processing the second operation result to obtain an open-circuit voltage two-dimensional graph of the battery;

When the test scheme is an HPPC test scheme, testing a battery sample according to the test scheme to obtain an equivalent circuit parameter of the battery sample, including:

S23, obtaining equivalent circuit parameters of the battery sample according to a preset method based on the HPPC test data;

The step S23 includes:

The equivalent circuit parameters are obtained by identification according to a least squares online parameter identification method based on HPPC test data of the first 50% of the rest time period of the total rest process, and/or,

The equivalent circuit parameters are obtained by identification according to a neural network offline parameter identification method based on HPPC test data of the last 50% of the standing time period of the total standing process, and/or,

The equivalent circuit parameters are identified and obtained according to a least squares method on-line parameter identification method based on HPPC test data of the first 50% time period of the total charge or total discharge process, and/or,

And identifying and obtaining the equivalent circuit parameters according to a neural network offline parameter identification method based on HPPC test data of the last 50% time period of the total charging or total discharging process.

2. The battery equivalent circuit parameter identification method according to claim 1, wherein the step of controlling the battery sample to perform the first operation according to the SOC-OCV pre-grinding test scheme comprises:

3. The battery equivalent circuit parameter identification method according to claim 1, wherein the step of controlling the battery sample to perform the second operation according to the optimized SOC-OCV formal test scheme includes:

4. A battery equivalent circuit parameter identification method according to claim 2 or 3, characterized in that the step of controlling the battery sample to be processed according to a first rule to obtain the actual capacity of the battery sample comprises:

Determining an average value of discharge capacities of the battery samples based on a second preset number of times as an actual capacity of the battery;

wherein the second preset times are smaller than the first preset times.

5. The battery equivalent circuit parameter identification method according to claim 4, wherein the step of controlling the battery sample to repeatedly perform charge and discharge processes to a first preset number of times based on a first preset temperature comprises:

6. The battery equivalent circuit parameter identification method according to claim 2, wherein the step of controlling the battery sample to be processed according to a second rule based on the actual capacity at S112 includes:

S1124, when the standing treatment duration at the third preset temperature meets a fourth preset duration, adjusting the third preset temperature to a fourth preset temperature, controlling the battery sample to perform the standing treatment at the fourth preset temperature, and detecting the battery terminal voltage at the fourth preset temperature;

7. The battery equivalent circuit parameter identification method according to claim 3, wherein the step of controlling the battery sample to be processed according to a third rule based on the actual capacity at S132 comprises:

8. The battery equivalent circuit parameter identification method according to claim 1, wherein the step of processing the battery sample after the control processing according to a fifth rule includes:

9. The construction method of the battery equivalent circuit simulation model is characterized by comprising the following steps of:

Constructing a battery equivalent circuit model of a battery sample;

introducing parameters obtained by the battery equivalent circuit parameter identification method according to any one of claims 1 to 8 into the circuit model to obtain a battery equivalent circuit simulation model of the battery sample.

10. The method for constructing a battery equivalent circuit simulation model according to claim 9, wherein after the step of obtaining the battery equivalent circuit simulation model of the battery sample, further comprises:

11. The method for constructing a battery equivalent circuit simulation model according to claim 9, wherein the step of constructing a battery equivalent circuit model of a battery sample comprises:

Obtaining a mathematical equation of the battery:

12. The battery equivalent circuit parameter identification system is characterized by comprising:

A battery sample;

the model building device is used for importing the equivalent circuit parameters into a battery equivalent circuit model of the battery sample to obtain a simulation model of the battery equivalent circuit of the battery sample;

wherein the control device includes:

The HPPC testing unit is used for controlling the environment box and the charging and discharging equipment to complete HPPC testing on the battery sample and obtaining equivalent circuit parameters of the battery sample based on the HPPC testing data, and comprises the following steps:

13. The battery equivalent circuit parameter identification system according to claim 12, wherein the model building device comprises:

an importing unit for importing the test result into the mathematical equation;