CN116632386A - Battery auxiliary device and battery working method - Google Patents
Battery auxiliary device and battery working method Download PDFInfo
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- 239000004973 liquid crystal related substance Substances 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 1
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
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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]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
-
- 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]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
- G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/30—Circuit design
- G06F30/36—Circuit design at the analogue level
- G06F30/367—Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/4285—Testing apparatus
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
- H02J7/0048—Detection of remaining charge capacity or state of charge [SOC]
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Abstract
The invention discloses a battery auxiliary device and a battery working method, and belongs to the technical field of methods or devices for directly converting chemical energy into electric energy. The auxiliary device comprises an equation building module, a data acquisition module and an online solving module; the method comprises the following steps: carrying out parameter identification; judging the parameters and executing different actions; according to the invention, through an equation building module, a lithium battery RC model is built, and parameters to be identified and the input-output relation of the model are determined; performing charge and discharge operation test on the battery to obtain battery current and voltage data, sampling the data in real time, obtaining battery terminal voltage and battery output current and obtaining residual electric quantity, and determining the relation between the open-circuit voltage and the residual electric quantity of the battery; and establishing an optimization equation based on a battery model through an online solving module, setting the size of a rolling time domain window, and applying extended Kalman filtering to realize online estimation of unknown parameters according to the battery current and voltage data sampled in real time and the open circuit voltage of the battery.
Description
Technical Field
The invention belongs to the technical field of methods or devices for directly converting chemical energy into electric energy, and particularly relates to a battery auxiliary device and a battery working method.
Background
For a power battery, the electrochemical reaction process is complex, the influence factors are many and the uncertainty exists, the mathematical modeling is a multi-field and multi-disciplinary problem, and the mathematical modeling is also the key point and the difficulty of research in academia and industry all the time. The external characteristics of the power battery are described more accurately, and the method is indispensable for developing an optimal new energy automobile energy management system and an accurate battery model aiming at a novel reliable battery power battery state estimation algorithm. In order to overcome the shortcomings of the equivalent circuit model and the electrochemical model, an RC model is established through electrochemical impedance spectrum data.
The existing parameter identification method for the RC model is listed as follows: classical least squares: according to the method, input and output data of a battery model are fitted in a batch processing mode, and optimization is performed based on a minimum variance index, so that a parameter estimated value is obtained. The method is characterized in that: the fitting precision is high, but a large amount of data needs to be batched, a large amount of storage space needs to be occupied, and the fitting method is not suitable for online application.
Recursive least squares algorithm with forgetting factor: the method is based on the minimum variance index thought, constructs a recursive form and is suitable for online application. And a forgetting factor is introduced, so that the algorithm has the capability of continuously tracking the change parameters. The disadvantage of this approach is that in the case of low system excitations, there is not enough innovation to enter the system, and the covariance matrix is continuously divided by the forgetting factor, resulting in a large recognition error.
Multiple/vector forgetting factor recursive least squares method: the method has different time constants, namely different dynamic characteristics, aiming at different links in the battery model, and adopts a plurality of forgetting factors to avoid the mutual coupling among all coefficients in the gain matrix. The method also cannot suppress the influence of data fluctuation and data abnormality on the identification result.
The inventors found that the above battery model identification methods all have common disadvantages: in a battery, the polynomial coefficient in the circuit element analysis model has the characteristic of sensitivity, and the coefficient of a battery original element changes along with the environmental change, so that the model mismatch problem is caused. Through practical tests, the voltage acquisition deviation of a general battery management system can reach 5mV under the high and low temperature and electromagnetic interference environment, and the voltage acquisition fluctuation can reach 10mV at maximum. Sensitivity analysis experiments show that acquisition noise of 5mV will cause identification errors of 6% -11% of different model parameters.
In a battery management system, the phenomenon of asynchronous voltage and current collection is seriously affected. The phenomenon of current and voltage acquisition asynchronism means that voltage and current data for an identification algorithm are not measured at the same time. This can have a significant impact on the recognition results.
The above two problems are that the battery management system is widely present and unavoidable in practical applications. For the existing identification algorithm of the linear minimum variance class, the problem can introduce larger prediction errors in each iterative process of the algorithm, namely, the residual error in the parameter identification process is overlarge.
In summary, the existing least square recognition algorithm has the advantages of iterative operation, small calculation amount and the like, but the existing least square recognition algorithm cannot effectively solve the two problems of circuit element sensitivity and asynchronous acquisition in practical application.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides a battery auxiliary device and a battery working method, which are reasonable in design, overcome the defects in the prior art and have good effects.
In order to achieve the above purpose, the present invention adopts the following technical scheme: the battery auxiliary device comprises an equation establishment module, a data acquisition module and an online solving module; the equation establishment module: the method comprises the steps of being configured for establishing a battery model, and determining parameters to be identified and a model input-output relationship; and a data acquisition module: the method comprises the steps of performing charge and discharge operation test on a battery, obtaining battery current and voltage data, sampling the data in real time, obtaining battery terminal voltage and battery output current, obtaining residual electric quantity, and determining the relation between open-circuit voltage and the residual electric quantity of the battery; and an online solving module: the method is configured for establishing an optimization equation based on a battery model based on a rolling time domain optimization idea, setting the size of a rolling time domain window, and applying extended Kalman filtering to realize online estimation of unknown parameters according to battery current and voltage data sampled in real time and open circuit voltage of a battery.
In addition, the invention also relates to a battery working method, which adopts the battery auxiliary device, and specifically comprises the following steps: step 1: carrying out parameter identification; step 2: and (3) judging the parameters in the step (1) and executing different actions.
Preferably, in step 1, the method specifically comprises the following steps: step 1.1: establishing a lithium battery RC model through an equation establishment module, and determining parameters to be identified and the input-output relation of the model; step 1.2: the method comprises the steps of performing charge and discharge operation test on a battery through a data acquisition module, obtaining battery current and voltage data, sampling the data in real time, obtaining battery terminal voltage and battery output current, obtaining residual electric quantity, and determining the relation between open-circuit voltage and the residual electric quantity of the battery; step 1.3: and establishing an optimization equation based on a battery model through an online solving module, setting the size of a rolling time domain window, and applying extended Kalman filtering to realize online estimation of unknown parameters according to the battery current and voltage data sampled in real time and the open circuit voltage of the battery.
The invention has the beneficial technical effects that: according to the invention, the sensitivity analysis is implemented on polynomial coefficients in the circuit element analysis model, constant term coefficients are selected as model updating parameters, and an amplified nonlinear state space model is constructed so as to synchronously perform state estimation and parameter updating, thereby effectively improving the accuracy of parameter identification; the effectiveness of the method is verified and evaluated under the condition of non-uniformity of the battery and the condition of difference of working condition characteristics; the method comprises the following steps: 1. applying the rolling time domain idea to a parameter estimation algorithm to optimize the parameter estimation error, namely minimizing the error; 2. the application of the rolling time domain effectively solves the problem of polynomial coefficient sensitivity in the battery model; 3. the provided parameter estimation identification algorithm can realize the parameter identification of the model on line, and effectively improve the parameter identification rate.
Drawings
FIG. 1 is a flow chart of a parameter identification algorithm; FIG. 2 is a state update flow diagram; FIG. 3 is a schematic diagram of a first-order model of a battery; FIG. 4 is a graph of Uocv-SOC fit under HPPC conditions; FIG. 5 is a schematic diagram of the terminal voltage identification result; fig. 6 is a schematic diagram of model accuracy.
Detailed Description
The invention is described in further detail below with reference to the attached drawings and detailed description: example 1: the embodiment provides a lithium battery parameter identification system based on a rolling time domain, which comprises an equation establishment module, a data acquisition module and an online solving module; the equation establishment module: the method comprises the steps of being configured for establishing a lithium battery model, and determining parameters to be identified and a model input-output relationship; and a data acquisition module: the method comprises the steps of performing charge and discharge operation test on a battery, obtaining battery current and voltage data, sampling the data in real time, obtaining battery terminal voltage and battery output current, obtaining residual electric quantity, and determining the relation between open-circuit voltage and the residual electric quantity of the battery; and an online solving module: the method is configured for establishing an optimization equation based on a battery model based on a rolling time domain optimization idea, setting the size of a rolling time domain window, and applying extended Kalman filtering to realize online estimation of unknown parameters according to battery current and voltage data sampled in real time and open circuit voltage of a battery.
Example 2: on the basis of the above embodiment, the present invention also refers to a method for identifying parameters of a lithium battery based on a rolling time domain (as shown in fig. 1 and 2), which includes the following steps: step 1: carrying out parameter identification; the method specifically comprises the following steps: step 1.1: establishing a lithium battery RC model through an equation establishment module, and determining parameters to be identified and the input-output relation of the model; step 1.2: the method comprises the steps of performing charge and discharge operation test on a battery through a data acquisition module, obtaining battery current and voltage data, sampling the data in real time, obtaining battery terminal voltage and battery output current, obtaining residual electric quantity, and determining the relation between open-circuit voltage and the residual electric quantity of the battery; step 1.3: establishing an optimization equation based on a battery model through an online solving module, setting the size of a rolling time domain window, and realizing online estimation of unknown parameters by applying extended Kalman filtering according to battery current and voltage data sampled in real time and open circuit voltage of a battery; step 2: and (3) judging the parameters in the step (1) and executing different actions.
The embodiment provides a battery model parameter identification method based on a rolling time domain, which selects constant term coefficients as model updating parameters by carrying out sensitivity analysis on polynomial coefficients in a circuit element analysis model; then, an augmented nonlinear state space model is constructed to synchronously perform state estimation and parameter updating, so that the accuracy of parameter identification is effectively improved.
The following describes the above steps in detail: the step 1 specifically comprises the following steps: establishing a lithium battery model dynamic equation, a defect discrete time domain system equation, determining an equation linear regression form according to the discrete time domain system equation, and extracting the identified parameters.
As shown in fig. 3, the present example is a first order model. The voltage source represents the open circuit voltage Uocv of the battery, which is related to the battery charge SOC, as shown in fig. 4 in Uocv-SOC relation. Ohmic internal resistanceRepresenting the transient response of the battery voltage to current. Parallel resistors->And capacitance->For describing the polarization effect inside the cell. />For voltage drop, & lt & gt>And->Respectively representing the terminal voltage and the load current of the battery.
And establishing a battery model according to the circuit model:
；
wherein, the liquid crystal display device comprises a liquid crystal display device,，/>，/>polynomial functions of SOC, respectively +.>Is a polynomial coefficient; establishing a discrete equation: />The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>For the system state->For the output voltage>，/>For model uncertainty, the variance is +.>Gaussian of (2)White noise; />Representing measurement uncertainty, variance is +.>Is a gaussian white noise of (c).
Equation of stateAnd->The method comprises the following steps of:
；
defining an augmentation state:；
augmentation system:；
for sampling time interval, +.>For battery capacity>Is a time constant->Is a polynomial coefficient.
In the step 2, a data acquisition module is used for carrying out charge and discharge operation test on the battery to obtain battery current and voltage data, sampling the data in real time to obtain battery terminal voltage and battery output current and obtain residual electric quantity, and determining the relation between the open-circuit voltage and the residual electric quantity of the battery; the method specifically comprises the following steps: obtained in an off-line mannerTaking the open circuit voltage of the batteryRelationship with the battery remaining capacity SOC; determining open circuit voltage +.>After the relation with the battery remaining power SOC, the battery remaining power SOC is determined according to the detected current voltage data in the discharging parameter identification of the battery, and the battery remaining power SOC is determined according to the open circuit voltage +.>Relation with the remaining battery power SOC, determining an open circuit voltage +.>Is a numerical value of (2).
The battery is tested by adopting an HPPC test method, namely (Hybrid Pulse Power Characteristic) (hybrid power pulse capability characteristic), wherein HPPC is a characteristic for reflecting the pulse charge and discharge performance of the power battery, and the HPPC test is generally completed by adopting special battery detection equipment.
In this example, the OCV at this SOC point was obtained for 1 hour at a time of battery rest for each 2% discharge until the battery discharge was cut off.
The SOC value State of Charge, which is the State of Charge of the battery, also called the residual capacity, is a quantity related to the current and voltage of the output terminal, and can be obtained by calculation using an ampere-hour integration method.
As a possible way, the correspondence relationship between the battery SOC value and the open circuit voltage OCV of the battery may be variously employed, and the OCV-SOC curve obtained in the present embodiment is shown in fig. 4.
Step 3: an optimization equation based on a battery model is established based on a rolling time domain optimization idea, the size of a rolling time domain window is set, and on-line estimation of unknown parameters is realized by applying extended Kalman filtering according to battery current and voltage data sampled in real time and open circuit voltage of a battery, and the method specifically comprises the following steps: step 3.1: according to a rolling time domain optimization strategy, constructing an on-line joint estimation model of the following states and parameters:
the method comprises the steps of carrying out a first treatment on the surface of the The object is:
；
；
in the method, in the process of the invention,representing the size of the rolling time domain window and the arrival costCalculating by using EKF (Extended Kalman filter) approximate estimation method;representation ofA priori state estimation value of time, wherein the estimated value contains information of all measured variables outside a window; step 3.2: initializing: given a givenInitial state estimationAnd scrolling time domain window small The method comprises the steps of carrying out a first treatment on the surface of the Step 3.3: when (when)When the state estimation value is calculated by using the EKF update formulaSum covarianceThe method comprises the steps of carrying out a first treatment on the surface of the Step 3.4: when (when)Solving a nonlinear programming problem formula (10) by using a sequential quadratic programming method; step 3.5: application of EKF update reach agentPrice; step 3.6: at the position ofTime of day, obtain measurementsConstructing a new measurement datasetAnd returning to the step 3.4.
To illustrate the effect of the method of this example, the verification was performed experimentally. The voltage and current data of the battery are collected by using the battery management system through a discharging experiment on the battery, and then the method proposed by the embodiment is applied and compared with other methods, and the results are shown in fig. 5 and 6.
Fig. 5 and fig. 6 are experimental results comparing the accuracy of the identification of the battery terminal voltage by the method and the adaptive genetic algorithm according to the present embodiment.
From experimental results, it can be seen that the proposed method is closer to the reference value than other methods. With the networking development of new energy automobiles, the problem of measurement dyssynchrony is more remarkable in the application scenes of wireless transmission such as 5G, cloud computing, cloud storage and the like, and the method has more obvious advantages in improving the accuracy of identification results.
Example 3
A computer readable storage medium configured to store computer instructions which, when executed by a processor, perform the steps of the method described above.
It should be understood that the above description is not intended to limit the invention to the particular embodiments disclosed, but to limit the invention to the particular embodiments disclosed, and that the invention is not limited to the particular embodiments disclosed, but is intended to cover modifications, adaptations, additions and alternatives falling within the spirit and scope of the invention.
Claims (3)
1. A battery assist apparatus characterized in that: the system comprises an equation building module, a data acquisition module and an online solving module; the equation establishment module: the method comprises the steps of being configured for establishing a battery model, and determining parameters to be identified and a model input-output relationship; and a data acquisition module: the method comprises the steps of performing charge and discharge operation test on a battery, obtaining battery current and voltage data, sampling the data in real time, obtaining battery terminal voltage and battery output current, obtaining residual electric quantity, and determining the relation between open-circuit voltage and the residual electric quantity of the battery; and an online solving module: the method is configured for establishing an optimization equation based on a battery model based on a rolling time domain optimization idea, setting the size of a rolling time domain window, and applying extended Kalman filtering to realize online estimation of unknown parameters according to battery current and voltage data sampled in real time and open circuit voltage of a battery.
2. A method of operating a battery, characterized by: a battery assist device according to claim 1, comprising the steps of: step 1: carrying out parameter identification; step 2: and (3) judging the parameters in the step (1) and executing different actions.
3. The battery operation method according to claim 2, wherein: in step 1, the method specifically comprises the following steps: step 1.1: establishing a lithium battery RC model through an equation establishment module, and determining parameters to be identified and the input-output relation of the model; step 1.2: the method comprises the steps of performing charge and discharge operation test on a battery through a data acquisition module, obtaining battery current and voltage data, sampling the data in real time, obtaining battery terminal voltage and battery output current, obtaining residual electric quantity, and determining the relation between open-circuit voltage and the residual electric quantity of the battery; step 1.3: and establishing an optimization equation based on a battery model through an online solving module, setting the size of a rolling time domain window, and applying extended Kalman filtering to realize online estimation of unknown parameters according to the battery current and voltage data sampled in real time and the open circuit voltage of the battery.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108008320A (en) * | 2017-12-28 | 2018-05-08 | 上海交通大学 | A kind of charge states of lithium ion battery and the adaptive combined method of estimation of model parameter |
| CN108072847A (en) * | 2018-01-29 | 2018-05-25 | 西南交通大学 | A kind of method of estimation of dynamic lithium battery identification of Model Parameters and remaining capacity |
| CN112858928A (en) * | 2021-03-08 | 2021-05-28 | 安徽理工大学 | Lithium battery SOC estimation method based on online parameter identification |
| US20210364574A1 (en) * | 2020-11-06 | 2021-11-25 | Beijing Institute Of Technology | Intelligent battery and state-of-charge online estimation method and applications thereof |
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| CN108008320A (en) * | 2017-12-28 | 2018-05-08 | 上海交通大学 | A kind of charge states of lithium ion battery and the adaptive combined method of estimation of model parameter |
| CN108072847A (en) * | 2018-01-29 | 2018-05-25 | 西南交通大学 | A kind of method of estimation of dynamic lithium battery identification of Model Parameters and remaining capacity |
| US20210364574A1 (en) * | 2020-11-06 | 2021-11-25 | Beijing Institute Of Technology | Intelligent battery and state-of-charge online estimation method and applications thereof |
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| Title |
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