CN111751731A - Method and device for determining battery activity, electronic equipment and storage medium - Google Patents

Method and device for determining battery activity, electronic equipment and storage medium Download PDF

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CN111751731A
CN111751731A CN202010695145.3A CN202010695145A CN111751731A CN 111751731 A CN111751731 A CN 111751731A CN 202010695145 A CN202010695145 A CN 202010695145A CN 111751731 A CN111751731 A CN 111751731A
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battery
determining
polarization
identification data
preset
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CN111751731B (en
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张利国
高静
刘彦昌
李占友
陈雷
韩春娟
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Northeast Petroleum University
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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]
    • G01R31/3644—Constructional arrangements
    • G01R31/3648—Constructional arrangements comprising digital calculation means, e.g. for performing an algorithm
    • 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]
    • G01R31/371—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] with remote indication, e.g. on external chargers
    • 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
    • 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
    • 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]
    • G01R31/389—Measuring internal impedance, internal conductance or related variables
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries

Abstract

The disclosure relates to a method and a device for determining battery activity, electronic equipment and a storage medium, and relates to the technical field of battery performance research experiments, wherein the method for determining the battery activity comprises the following steps: acquiring parameter identification data of the battery, wherein the parameter identification data at least comprises one of ohmic internal resistance, concentration polarization data and diffusion polarization data; and determining the activity level of the battery according to the parameter identification data and the set interval. Embodiments of the present disclosure may determine an activity level of a battery.

Description

Method and device for determining battery activity, electronic equipment and storage medium
Technical Field
The disclosure relates to the technical field of battery performance research experiments, and in particular, to a method and an apparatus for determining battery activity, an electronic device, and a storage medium.
Background
The power storage battery charging and discharging experiment is the basis for knowing and analyzing the performance of the storage battery, the storage battery capacity testing technology is mature, when the performances of different types (including lithium ion power storage batteries, lead-acid power storage batteries and the like) and different capacity storage batteries are researched, a storage battery model needs to be established, model parameter identification is carried out to obtain identified parameter data, a test bed suitable for various storage batteries is constructed, reliable basic guarantee can be provided for further analysis of the performance of the storage battery, and systematic research on the storage battery is more convenient.
However, the problem of determining the activity of the battery (storage battery) is lacked at present, so that the parameters of the battery cannot be comprehensively analyzed so as to avoid influencing the working condition of equipment using the battery (storage battery), causing the sudden shutdown of the equipment due to insufficient charge amount and the like.
Disclosure of Invention
The disclosure provides a method and a device for determining battery activity, an electronic device and a storage medium technical scheme, which are used for solving the problem that the battery activity cannot be determined at present, so that the parameters of a battery cannot be comprehensively analyzed.
According to an aspect of the present disclosure, there is provided a battery activity determination method including:
acquiring parameter identification data of the battery, wherein the parameter identification data at least comprises one of ohmic internal resistance, concentration polarization data and diffusion polarization data;
and determining the activity level of the battery according to the parameter identification data and the set interval.
Preferably, before acquiring parameter identification data of the battery, determining the parameter identification data;
the method for determining the parameter identification data comprises the following steps:
acquiring a preset fitting function of the state of charge with a first coefficient and the corresponding open-circuit voltage;
detecting the open-circuit voltage of the battery, and determining the state of charge corresponding to the open-circuit voltage according to the open-circuit voltage and the preset fitting function with a first coefficient;
converting the preset fitting function with the first coefficient into a preset fitting function with a second coefficient of the state of charge and corresponding parameter identification data thereof;
and determining parameter identification data of the battery according to the state of charge and the preset fitting function with a second coefficient.
Preferably, before the preset fitting function of the state of charge and the open-circuit voltage of the battery is obtained, the preset fitting function is determined;
the method for determining the preset fitting function comprises the following steps:
acquiring a preset dynamic parameter equivalent circuit model;
obtaining a first impedance expression of the preset dynamic parameter equivalent circuit model according to the preset dynamic parameter equivalent circuit model;
determining the number of alternating current signal frequencies input into the preset dynamic parameter equivalent circuit model according to the number of parameters of the preset dynamic parameter equivalent circuit model;
determining a plurality of second impedance expressions of the preset dynamic parameter equivalent circuit model according to a plurality of alternating current signals with different frequencies and corresponding voltage signals respectively;
determining a first coefficient and a second coefficient of the preset fitting function according to the first impedance expression and the two impedance expressions corresponding to the first impedance expression, and obtaining the preset fitting function according to the first coefficient and the second coefficient respectively;
and/or the presence of a gas in the interior of the container,
determining the range of the state of charge before determining the parameter identification data of the battery according to the state of charge and the preset fitting function with a second coefficient;
and determining parameter identification data of the battery according to the state of charge in the range and the preset fitting function with the second coefficient.
Preferably, before the obtaining of the preset dynamic parameter equivalent circuit model, the method for establishing the preset dynamic parameter equivalent circuit model includes:
determining an ideal voltage source, an adjustable ohmic internal resistance, an adjustable diffusion polarization characteristic circuit and an adjustable concentration polarization characteristic parameter circuit of a preset battery;
and obtaining the dynamic parameter equivalent circuit model according to the ideal voltage source, the adjustable ohmic internal resistance, the adjustable diffusion polarization characteristic circuit and the adjustable concentration polarization characteristic parameter circuit which are connected in series.
Preferably, the method for determining the activity of the battery according to the concentration polarization data and/or diffusion polarization data and a set interval comprises the following steps:
acquiring the set interval;
classifying the activity of the battery based on the set interval and the parameter identification data to obtain a classification result;
and determining the activity level of the battery according to the classification result.
Preferably, the concentration polarization data and the diffusion polarization data respectively include: concentration polarization resistance value, concentration polarization capacitance value, diffusion polarization resistance value and diffusion polarization capacitance value; the setting interval comprises: a plurality of resistance value setting intervals;
classifying based on the ohmic internal resistance, the concentration polarization resistance, the diffusion polarization resistance and the corresponding set resistance intervals respectively to obtain classification results of the ohmic internal resistance, the concentration polarization and the diffusion polarization respectively;
and determining the activity level of the internal resistance of the battery according to the classification result of the ohmic internal resistance, and determining the activity level of the concentration polarization and the diffusion polarization of the battery respectively according to the classification results of the concentration polarization and the diffusion polarization.
Preferably, the concentration polarization data and the diffusion polarization data further include: concentration polarization capacity value and diffusion polarization capacity value; the setting section further includes: a proportional interval;
determining the ratio of the concentration polarization capacity value to the diffusion polarization capacity value;
determining a classification result of the battery polarization ratio based on the ratio and the ratio interval;
and determining the activity level of the battery polarization ratio according to the classification result of the battery polarization ratio.
Preferably, before determining the activity level of the battery according to the parameter identification data and the set interval, the type of the parameter identification data is determined, and the activity level of the battery is determined according to the parameter identification data corresponding to the type and the set interval.
According to an aspect of the present disclosure, there is provided a battery activity determination apparatus including:
the device comprises an acquisition unit, a processing unit and a control unit, wherein the acquisition unit is used for acquiring parameter identification data of the battery, and the parameter identification data at least comprises one of ohmic internal resistance, concentration polarization data and diffusion polarization data;
and the determining unit is used for determining the activity level of the battery according to the parameter identification data and the set interval.
According to an aspect of the present disclosure, there is provided an electronic device including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to: the above-described method of determining the battery activity is performed.
According to an aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the above-described battery activity determination method.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure.
Fig. 1 shows a flow chart of a method of determining battery activity according to an embodiment of the present disclosure;
FIG. 2 illustrates a power battery DP-RC equivalent circuit model according to an embodiment of the present disclosure;
FIG. 3 illustrates a lithium power battery charge-discharge relationship curve according to an embodiment of the disclosure;
FIG. 4 illustrates a power battery variable parameter transient response in accordance with an embodiment of the present disclosure;
FIG. 5 shows an open circuit voltage fit curve according to an embodiment of the present disclosure;
FIG. 6 illustrates an ohmic internal resistance fit curve according to an embodiment of the disclosure;
FIG. 7 illustrates a short-time RC network fit curve according to an embodiment of the present disclosure;
FIG. 8 illustrates a long-term RC network fit curve according to an embodiment of the present disclosure;
FIG. 9 is a block diagram illustrating an electronic device 800 in accordance with an exemplary embodiment;
fig. 10 is a block diagram illustrating an electronic device 1900 according to an example embodiment.
Detailed Description
Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, and may mean including any one or more elements selected from the group consisting of A, B and C.
Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.
It is understood that the above-mentioned method embodiments of the present disclosure can be combined with each other to form a combined embodiment without departing from the logic of the principle, which is limited by the space, and the detailed description of the present disclosure is omitted.
In addition, the present disclosure also provides a device for determining battery activity, an electronic device, a computer-readable storage medium, and a program, which can be used to implement any method for determining battery activity provided by the present disclosure, and the corresponding technical solutions and descriptions and corresponding descriptions in the methods section are not repeated.
Fig. 1 shows a flowchart of a method for determining battery activity according to an embodiment of the present disclosure, as shown in fig. 1, the method for determining battery activity includes: step S101: acquiring parameter identification data of the battery, wherein the parameter identification data at least comprises one of ohmic internal resistance, concentration polarization data and diffusion polarization data; step S102: and determining the activity level of the battery according to the parameter identification data and the set interval. The method solves the problem that the activity of the battery cannot be determined at present so that the parameters of the battery cannot be comprehensively analyzed.
Step S101: and acquiring parameter identification data of the battery, wherein the parameter identification data at least comprises one of ohmic internal resistance, concentration polarization data and diffusion polarization data.
In the invention, before acquiring the parameter identification data of the battery, determining the parameter identification data; the method for determining the parameter identification data comprises the following steps: acquiring a preset fitting function of the state of charge with a first coefficient and the corresponding open-circuit voltage; detecting the open-circuit voltage of the battery, and determining the state of charge corresponding to the open-circuit voltage according to the open-circuit voltage and the preset fitting function with a first coefficient; converting the preset fitting function with the first coefficient into a preset fitting function with a second coefficient of the state of charge and corresponding parameter identification data thereof; and determining parameter identification data of the battery according to the state of charge and the preset fitting function with a second coefficient.
In a specific embodiment of the present invention, after the state of charge corresponding to the open circuit voltage is determined according to the open circuit voltage and the preset fitting function with a first coefficient, the first coefficient is adjusted to a second coefficient, and the state of charge and the preset fitting function of the open circuit voltage corresponding to the state of charge are converted into a preset fitting function of the state of charge and the parameter identification data of the battery. Therefore, in the invention, the coefficient of the preset fitting function only needs to be adjusted, the type conversion of the preset fitting function can be completed, and the type of the preset fitting function of the state of charge and the corresponding parameter identification data thereof does not need to be determined again.
In a specific embodiment of the present invention, the coefficient of the preset fitting function is a variable coefficient, and the variable coefficient can be obtained by presetting a battery. The preset battery and the battery needing to be determined to be the same type of battery, such as: a lithium power battery. In the application, preset fitting functions of different types and different capacities of storage batteries can be constructed.
In a specific embodiment of the present invention, a preset fitting function of a state of charge and an open-circuit voltage corresponding to the state of charge needs to be set first, then the open-circuit voltage of an active battery needs to be determined is detected, because the state of charge and the open-circuit voltage corresponding to the state of charge in the preset fitting function are in a one-to-one correspondence relationship, the state of charge corresponding to the open-circuit voltage of the active battery needs to be determined according to the open-circuit voltage and the preset fitting function with a first coefficient, after the state of charge is obtained, the first coefficient of the preset fitting function is replaced with a second coefficient, at this time, the preset fitting function is a preset fitting function of the state of charge and parameter identification data of the battery, and the parameter identification data of the battery is determined according to the state of charge and the preset fitting function with the second coefficient.
In the invention, before the preset fitting function of the state of charge and the open-circuit voltage of the battery is obtained, the preset fitting function is determined; the method for determining the preset fitting function comprises the following steps: acquiring a preset dynamic parameter equivalent circuit model; obtaining a first impedance expression of the preset dynamic parameter equivalent circuit model according to the preset dynamic parameter equivalent circuit model; determining the number of alternating current signal frequencies input into the preset dynamic parameter equivalent circuit model according to the number of parameters of the preset dynamic parameter equivalent circuit model; determining a plurality of second impedance expressions of the preset dynamic parameter equivalent circuit model according to a plurality of alternating current signals with different frequencies and corresponding voltage signals respectively; and determining a first coefficient and a second coefficient of the preset fitting function according to the first impedance expression and the two impedance expressions corresponding to the first impedance expression, and obtaining the preset fitting function according to the first coefficient and the second coefficient respectively.
In an embodiment of the present invention, a method for constructing a predetermined dynamic parameter equivalent circuit model can be seen in detail in the description of fig. 2.
And calculating a first impedance expression of the preset dynamic parameter equivalent circuit model according to the preset dynamic parameter equivalent circuit model. Establishing an impedance expression Z according to a preset dynamic parameter equivalent circuit model of the storage battery, wherein a real part: re (z) ═ f1(R, C), imaginary part: im (Z) ═ f2(R，C)，f1(R, C) represents the real part of the impedance expression Z, f2(R, C) represents the imaginary part of the impedance expression Z.
Specifically, the impedance expression Z:
Figure BDA0002590754670000071
according to said predetermined dynamic parameter equivalent circuit modelAnd the number of the parameters determines the number of the frequencies of the alternating current signals input into the preset dynamic parameter equivalent circuit model. Namely, the number m of the alternating current signal frequencies input into the preset dynamic parameter equivalent circuit model is determined according to the number n of the parameters of the preset dynamic parameter equivalent circuit model. Injecting m low-frequency sinusoidal AC signals u of different frequenciesk(t) the signal frequencies of the m low-frequency sinusoidal AC signals with different frequencies are respectively omega1，ω2，……，ωmWhere k is 1, 2, … …, m.
When the parameter number n is a base number, the number m of the alternating current signal frequencies is (n + 1)/2; when the number n of the parameters is an even number, the number m of the alternating current signal frequencies is equal to n/2.
And determining a plurality of second impedance expressions of the preset dynamic parameter equivalent circuit model according to the plurality of alternating current signals with different frequencies and the corresponding voltage signals. And (4) calculating impedance expressions under m different frequencies, and simplifying an imaginary part and a real part. Zk＝uk(t)/ik(t)(k＝1，2，……，m)，uk(t)/ik(t)＝Xk+jYkX is uk(t)/ik(t) real part, Y is uk(t)/ikThe imaginary part of (t).
In expression (3), Z is the impedance of the battery, and as can be seen from expression (3), 5 (n-5) parameters need to be identified, that is, the ohmic internal resistance Rohm(ohmic polarization), a first variable resistance RdpA second variable resistor RcpA first variable capacitor CdpAnd two variable capacitors Ccp. In this case, 3(m is 3) signals with different frequencies are required for identification. Respectively injecting low-frequency alternating current sinusoidal signals u into the battery under the same battery charge state1(t)、u2(t)、u3(t) the current flowing through the battery can be measured as i1(t)、i2(t)、i3(t) the frequencies of the three signals are respectively omega1、ω2、ω3Three signal frequencies satisfy omega2＝2ω1，ω3＝2ω2The battery impedance may be expressed as:
Figure BDA0002590754670000081
wherein
Figure BDA0002590754670000082
The phase difference between the voltage and the current on the storage battery can be obtained in real time through the online detection circuit and the MCU data processing.
From the formulas (3) and (4), let σ1＝ω1RcpCcp，σ2＝ω1RdpCdpWherein X is1、X2And X3Respectively injected with low-frequency AC sinusoidal signals u1(t)、u2(t)、u3(t) and measuring the current flowing through the battery as i1(t)、i2(t)、i3(t) the corresponding real part; wherein Y is1、Y2And Y3Respectively injected with low-frequency AC sinusoidal signals u1(t)、u2(t)、u3(t) and measuring the current flowing through the battery as i1(t)、i2(t)、i3(t) the corresponding imaginary part.
Figure BDA0002590754670000091
And determining a first coefficient and a second coefficient of the preset fitting function according to the first impedance expression and the two impedance expressions corresponding to the first impedance expression, and obtaining the preset fitting function according to the first coefficient and the second coefficient respectively. The parameter identification equation set can be obtained according to the formula (1) and the formula (5):
Figure BDA0002590754670000092
the solution of the equation (6) can be obtained,
Figure BDA0002590754670000093
can solve sigma1、σ2While R can be obtainedcp、Rdp、Rohm、Ccp(i.e., C)1)、Cdp(i.e., C)2)。
Figure BDA0002590754670000101
In the formula (8), 6 parameters X1，X2，X3，Y1，Y2，Y3For calculable data, it was obtained according to equation (5).
In the present invention, the battery model parameters (parameter identification data and/or open-circuit voltage) and the state of charge SOC adopt an approximate fitting function (preset fitting function):
Figure BDA0002590754670000102
in formula (9): f (S)c) -parameter identification data and/or open circuit voltage representative of a variation with state of charge, SOC; sc-represents the state of charge SOC; b0-exponential function term coefficients; b1-an exponential function term power coefficient; a is0-a polynomial constant term; a is1-polynomial first order coefficients; a is2-polynomial quadratic coefficients. That is, b0、b1、a0、a1、a2Together constitute a first coefficient of the preset fitting function or a second coefficient of the preset fitting function.
Wherein the preset fitting function is an exponential function
Figure BDA0002590754670000103
Describing the voltage rapid change characteristic after the state of charge SOC is less than 0.2; determining the characteristic of transition range 0.05 < SOC < 0.2, wherein the transition characteristic can be determined by the slope (f' (S)c) The inflection characteristic can be determined by the slope (f' (S)) of the curvec) Change determination, if the slope change is severe, it indicates that the battery voltage enters the turning region and the battery energy is not much left; the second order polynomial describes the voltage variation characteristic for SOC > 0.2.
In the invention, before determining the parameter identification data of the battery according to the state of charge and the preset fitting function with a second coefficient, determining the range of the state of charge; and determining parameter identification data of the battery according to the state of charge in the range and the preset fitting function with the second coefficient.
In an embodiment of the invention, the method for determining the parameter identification data of the battery according to the state of charge or the state of charge within the range and the preset fitting function with the second coefficient comprises: and setting parameter identification points according to the state of charge or the state of charge in the range, and determining the parameter identification data of the battery according to the parameter identification points and the preset fitting function with the second coefficient. Specifically, parameter identification points (abscissa SOC) are set according to the power storage battery SOC, the power storage battery SOC is judged through regression of a fitting curve function (fitting function), and parameter identification is carried out in a self-adaptive mode.
In an embodiment of the present invention, the method for setting the parameter identification point according to the state of charge or the state of charge within the range includes: determining a first interval and a second interval of the state of charge; measuring a first set of open circuit voltages within a first interval that sets the state of charge within the first interval; and measuring a second group of open-circuit voltages in a second interval of setting the state of charge in the second interval. The first interval can be SOC not less than 0 and not more than 0.1, the second interval can be SOC not less than 0.1 and not more than 1, the first interval is a state of charge SOC of 1 percent, and the second interval is a state of charge SOC of 5 percent. Specifically, according to the open circuit voltage UOCVThe characteristic of the relation curve with SOC is that SOC is more than or equal to 0 and less than or equal to 0.1, the open-circuit voltage is measured every 1% of SOC, SOC is more than 0.1 and less than or equal to 1, and the open-circuit voltage is measured every 5% of SOC.
Table 2 shows a table of the first coefficient and the fitting coefficient of the test and identification parameter of the first coefficient, wherein the open circuit voltage UOCVOne row corresponding to b0、b1、a0、a1、a2By a first factor, through an open circuit voltage UOCVAnd the first coefficient can be determined from UOCVA corresponding state of charge SOC; further determining 5 identification parameters, namely ohmic internal resistance R according to the determined state of charge SOC and the second coefficientohmA first variable resistor Rdp(concentration polarization resistance value), second variable resistor Rcp(diffusion polarization resistance value), first variable capacitance Cdp(concentration polarization capacitance value) and second variable capacitance Ccp(diffusion polarization capacity value). Wherein the second coefficients are respectively the coefficients corresponding to each identification parameter, such as the ohmic internal resistance R of the identification parameterohmThe corresponding coefficient is R in FIG. 2ohmOne row corresponding to b0、b1、a0、a1、a2Coefficient, i.e. identifying parameter first variable resistance RdpIs R in FIG. 2dpOne row corresponding to b0、b1、a0、a1、a2And (4) the coefficient.
TABLE 2 test and identification parameter fitting coefficient Table
Figure BDA0002590754670000111
Figure BDA0002590754670000121
In the specific embodiment of the invention, the range of the state of charge can be selected from a range of the state of charge SOC greater than or equal to 0.5, and when the range of the state of charge SOC is greater than or equal to 0.5, the parameter identification data of the battery is determined according to the state of charge in the range and the preset fitting function with the second coefficient, so that the activity of the battery can be reflected better.
In the present invention, before the obtaining of the preset dynamic parameter equivalent circuit model, the method for establishing the preset dynamic parameter equivalent circuit model includes: determining an ideal voltage source, an adjustable ohmic internal resistance, an adjustable diffusion polarization characteristic circuit and an adjustable concentration polarization characteristic parameter circuit of a preset battery; and obtaining the dynamic parameter equivalent circuit model according to the ideal voltage source, the adjustable ohmic internal resistance, the adjustable diffusion polarization characteristic circuit and the adjustable concentration polarization characteristic parameter circuit which are connected in series.
FIG. 2 illustrates a power battery DP-RC equivalent circuit model according to an embodiment of the present disclosure. In the embodiment of the present invention and fig. 2, a specific power storage battery DP-RC equivalent circuit model, that is, a Dynamic parameter second order RC (DP-RC) equivalent circuit model modeling of a power storage battery, is provided in this embodiment. The equivalent circuit model modeling can simulate the dynamic change of battery parameters in the chemical change process of the battery, so that the performance of the equivalent circuit model is closer to the actual dynamic characteristic of the battery, and the simulation precision of the equivalent circuit of the battery is improved.
a) The DP-RC equivalent circuit model adopts two variable parameter RC loops which are respectively used for simulating concentration polarization characteristics R of the batterycp、Ccp(tunable diffusion polarization characteristic circuit) and diffusion polarization characteristic Rdp、Cdp(adjustable concentration polarization characteristic parameter circuit); adjustable resistance RohmSimulating the ohmic internal resistance of the battery; capacitor ChHysteresis voltage U for simulating batteryhThe capacitance of (c). U shapetIs the load voltage, ItFor battery operating current, UOCVIs the open circuit voltage of the battery.
b) A first output terminal A and a first variable resistor RdpOne end of the first variable resistor R is connected with the other end of the second variable resistor RdpAnd the other end of the second resistance and a second variable resistor RcpIs connected to one end of a second variable resistor RcpThe other end of (2) and the individual resistor RohmAre connected at one end to a single resistor RohmAnother terminal and a capacitor ChIs connected to one terminal of a capacitor ChThe other end of the same and an ideal battery UavOne end of the ideal voltage source is connected with an ideal battery UavThe other end is connected with a second output end B, and a second variable capacitor CcpAnd a second variable resistor RcpParallel connection, a first variable capacitor CdpAnd a first variable resistor RdpParallel connection, UtIs the load voltage, ItIs the battery operating current. By measuring the voltage between the first output terminal a and the second output terminal BOpen circuit voltage U of batteryOCV。
Wherein the subscripts of each parameter mean: h represents hysteresis, av represents average, cp represents concentration polarization, dp represents diffusion polarization, and OCV represents Open Circuit Voltage.
The following description of the DP-RC equivalent circuit model from the power cell open circuit voltage and the variable parameter transient response network (polarization characteristics of the cell) is presented:
(1) open circuit voltage of power battery
Fig. 3 shows a lithium-powered battery charge-discharge relationship curve according to an embodiment of the disclosure. By presetting the state of charge of the battery and the corresponding open-circuit voltage, the charging and discharging relation curve or the preset fitting function of the state of charge and the corresponding open-circuit voltage of the battery can be obtained. For example: the preset battery adopts a certain polymer lithium battery (3.7V, nominal capacity 3.2Ah), charge and discharge data sampling is carried out on the certain polymer lithium battery (experiment temperature is 20 ℃), and open-circuit voltage is UOCVGenerally considered as a function of the battery SOC, the open circuit voltage UOCVThe measuring method comprises the steps of charging or discharging the battery to a preset SOC value, standing for 1 hour, and measuring the terminal voltage of the battery. According to the open circuit voltage UOCVThe characteristic of the relation curve with SOC is that SOC is more than or equal to 0 and less than or equal to 0.1, the measurement is carried out every 1% of SOC, SOC is more than 0.1 and less than or equal to 1, and the measurement is carried out every 5% of SOC.
Open circuit voltage U of power accumulatorOCVThe open-circuit voltage U is related to the current State of Charge (SOC) of the batteryOCVWith the change of the state of charge SOC, the DP-RC equivalent circuit model adopts a capacitor C as shown in FIG. 3 which has a nonlinear relationhAnd power battery voltage average value UavIt is shown in formula (2). The method can not only embody the relation between the open-circuit voltage and the SOC, but also distinguish the charge and discharge states under the same SOC.
Under the same state of charge SOC, the open-circuit voltage values are different at different temperatures, and FIG. 3 is a curve of the open-circuit voltage of the battery at normal temperature (20 ℃) measured in a lithium storage battery with a specific capacity. In fig. 3, the open-circuit voltage during charging and the open-circuit voltage during discharging are different under the same state of charge SOC, that is, the open-circuit voltage of the battery also depends on the charging and discharging state at the previous moment under the same state of charge SOC, which is the hysteresis characteristic of the battery.
Defining an average value U between the open circuit voltages of the charge/dischargeavAs shown by the solid line in FIG. 3, the difference between the open circuit voltage in the charged state and the open circuit voltage in the discharged state is maintained substantially constant at a state of charge SOC of 20% to 90%, and the hysteresis voltage at the time of discharge is set to Uh-dc(ii) a The hysteresis voltage during charging is set to Uh-cCan be regarded as Uh-dc＝Uh-cThe hysteresis voltage satisfies the differential equation as shown in equation (1).
Figure BDA0002590754670000141
In formula (1): u shapeh-a hysteresis voltage; i ist-a loop current; u shape0-an initial value of voltage; u shapeh-cMaximum hysteresis voltage value, whose value is constant, β time constant, which can be determined by experiment, sign (I)t) -sign function of current direction, discharge is negative and charge is positive.
Therefore, the open circuit voltage of the battery can be obtained: u shapeOCV＝Uav+Uh(2). In formula (2): u shapeOCV-an open circuit voltage; u shapeav-voltage average value.
(2) Variable parameter transient response network (polarization characteristic of battery)
There are three battery polarization phenomena in lithium batteries during use: ohmic polarization, concentration polarization, and diffusion polarization. Ohmic resistance RohmThe resistance value of the ohm is slightly larger than that of the charging process in the discharging process along with the change of the SOC (the SOC is more than or equal to 20 percent and less than or equal to 90 percent). While concentration polarization and diffusion polarization are severely affected by changes in the state of charge SOC of the battery.
FIG. 4 illustrates a power battery variable parameter transient response in accordance with an embodiment of the present disclosure. As shown in fig. 4, when the battery is loaded with current ItA step change occursChange time, voltage UtSlowly changing. The DP-RC equivalent circuit model comprises an ohmic internal resistance Rohm(ohmic polarization), two groups consisting of Ccp、Rcp(concentration polarization) and CdpAnd RdpThe RC parallel network (diffusion polarization) is formed, and the network can simulate transient response of the power battery. The two sets of RC networks reflect the concentration polarization characteristic of the shorter time constant and the diffusion polarization characteristic of the longer time constant, respectively, in the step response, as identified in fig. 4. The two RC network parameters change along with the SOC, a relation curve can be obtained through tests and DP-RC equivalent circuit model parameter identification, and the two RC networks are suitable for meeting the accuracy requirement and simultaneously preventing the circuit from being too complex.
The load voltage decreases with the decrease of the power storage battery SOC, three curves in FIG. 4 are voltage transient response curves under different power storage battery SOC, the concentration polarization is shown in a fast change stage of the open-circuit voltage on the transient response curve, and the diffusion polarization is a slow change process of the open-circuit voltage on the transient response curve. The ratio of the concentration polarization time constant to the diffusion polarization time constant is about 5%. The diffusion polarization is obvious at the initial stage of the discharge or the final stage of the charge of the power storage battery; under the condition of high-rate discharge, concentration polarization is obvious.
The preset dynamic parameter equivalent circuit model comprises two variable parameter RC (resistance-capacitance) loops, the two variable parameter RC loops are respectively used for simulating the concentration polarization characteristic and the diffusion polarization characteristic of the battery, the polarization characteristic of the battery can be refined, the variable parameters (the adjustable ohmic internal resistance, the adjustable diffusion polarization characteristic circuit and the adjustable concentration polarization characteristic parameter circuit) can simulate the real state of the battery, and the chemical change conditions of different stages (namely different states of charge SOC) in the charging/discharging process of the battery can be reflected. Capacitor ChConstant voltage source UhAs the introduction of the hysteresis voltage, the output voltage of a charge/discharge battery in the same charge state of the battery can be simulated at the same time. The preset dynamic parameter equivalent circuit model obtains better simulation performance, and can express transient response and steady-state voltage and current characteristics of the battery. Simultaneous capacitor ChAnd ideal battery UsocAt battery capacity, run time, open circuitThe simulation accuracy in terms of the nonlinear relationship between the voltage and the state of charge SOC of the battery is also improved. The preset dynamic parameter equivalent circuit model is suitable for collecting and analyzing the characteristic parameters of different types and different capacities of storage batteries.
Therefore, the DP-RC equivalent circuit model well simulates the application characteristics of the power battery in the electric automobile, and an ideal scheme is provided for simulation research and theoretical research of the electric automobile.
Fitting curves of the parameters can be drawn by table 2 and equation (9) as shown in fig. 5 to 8. FIG. 5 shows an open circuit voltage fit curve according to an embodiment of the present disclosure; FIG. 6 illustrates an ohmic internal resistance fit curve according to an embodiment of the disclosure; FIG. 7 illustrates a short-time RC network fit curve according to an embodiment of the present disclosure; fig. 8 illustrates a long-term rc network fitting curve according to an embodiment of the present disclosure. FIG. 5 shows a relationship curve between open-circuit voltage and SOC, and FIG. 6 shows SOC and ohmic internal resistance RohmFig. 7 shows the state of charge SOC and the first variable resistance RdpAnd a first variable capacitor CdpFig. 8 shows the state of charge SOC and the second variable resistance RcpAnd a second variable capacitor CcpThe relationship of (1).
Step S102: and determining the activity level of the battery according to the parameter identification data and the set interval.
In the present invention, the method for determining the activity of the battery according to the concentration polarization data and/or diffusion polarization data and a set interval includes: acquiring the set interval; classifying the activity of the battery based on the set interval and the parameter identification data to obtain a classification result; and determining the activity level of the battery according to the classification result.
If the battery needs to determine the activity levels of multiple parameters, the activity levels of the battery parameters can be determined according to each parameter of the multiple parameters. In an embodiment of the invention, before determining the activity level of the battery according to the parameter identification data and the setting interval, the type of the parameter identification data is determined, and the activity level of the battery is determined according to the parameter identification data corresponding to the type and the setting interval.
For example, the ohmic internal resistance of the battery needs to be determined, and the activity level of the ohmic internal resistance of the battery is determined according to the ohmic internal resistance and the set interval corresponding to the ohmic internal resistance only by acquiring the ohmic internal resistance of the parameter identification data. And if the concentration polarization of the battery needs to be determined, only the concentration polarization data of the parameter identification data needs to be acquired, and the activity level of the concentration polarization of the battery is determined according to the concentration polarization data and the set interval corresponding to the concentration polarization. Similarly, if the diffusion polarization of the battery needs to be determined, only the diffusion polarization data of the parameter identification data needs to be acquired, and the activity level of the diffusion polarization of the battery is determined according to the diffusion polarization data and the set interval corresponding to the diffusion polarization.
In an embodiment of the present invention, several of the ohmic internal resistance, the concentration polarization data and the diffusion polarization data of the battery may be determined at the same time, such as determining the ohmic internal resistance and the concentration polarization data of the battery or the concentration polarization data and the diffusion polarization data of the battery at the same time.
In an embodiment of the present invention, the method for determining the type of the parameter identification data and determining the activity level of the battery according to the parameter identification data corresponding to the type and the setting interval includes: inputting the type of the activity grade of the battery to be determined, wherein the type is one or more types of ohmic internal resistance, concentration polarization data and diffusion polarization data in the parameter identification data; and extracting the parameter identification data corresponding to the type from the parameter identification data according to the type, and determining the activity level of the battery according to the parameter identification data corresponding to the type and a set interval.
In an embodiment of the present invention, the set interval may be an interval range or an interval point. An activity grade of the battery comprising: and determining the normal level, the degradation starting level and the damage level of the battery according to the classification result.
In the present invention, the concentration polarization data and the diffusion polarization data respectively include: concentration polarization resistance value, concentration polarization capacitance value, diffusion polarization resistance value and diffusion polarization capacitance value; the setting interval comprises: a plurality of resistance value setting intervals; classifying based on the ohmic internal resistance, the concentration polarization resistance, the diffusion polarization resistance and the corresponding set resistance intervals respectively to obtain classification results of the ohmic internal resistance, the concentration polarization and the diffusion polarization respectively; and determining the activity level of the internal resistance of the battery according to the classification result of the ohmic internal resistance, and determining the activity level of the concentration polarization and the diffusion polarization of the battery respectively according to the classification results of the concentration polarization and the diffusion polarization.
In the present invention, the concentration polarization data and the diffusion polarization data further include: concentration polarization capacity value and diffusion polarization capacity value; the setting section further includes: a proportional interval; determining the ratio of the concentration polarization capacity value to the diffusion polarization capacity value; determining a classification result of the battery polarization ratio based on the ratio and the ratio interval; and determining the activity level of the battery polarization ratio according to the classification result of the battery polarization ratio.
In an embodiment of the present invention, an activity classification result of the battery internal resistances is obtained according to the ohmic internal resistances and a first set resistance interval of the plurality of set resistance intervals; and determining the activity grade of the internal resistance of the battery according to the activity classification result of the internal resistance of the battery. For example, the first set resistance interval may be configured to be 5m ohms to 20m ohms, where m ohms represents milliohms; when the ohm internal resistance is less than 5m ohm, the battery internal resistance is in a normal level; when the ohm internal resistance is between 5m ohm and 20m ohm, the battery internal resistance is at the degradation starting level; and when the ohm internal resistance is more than 20m ohm, the battery internal resistance is in a damage level.
In an embodiment of the present invention, the concentration polarization classification result of the battery is obtained according to the concentration polarization resistance value and a second set resistance value interval of the plurality of set resistance value intervals, respectively; and determining the activity level of the battery concentration polarization according to the concentration polarization classification result. For example, the second set resistance interval may be configured to be 5m ohm-20 m ohm, where m ohm represents milli ohm; when the concentration polarization resistance value is less than 5m ohm, the concentration polarization resistance value is in a normal level; when the concentration polarization resistance value is between 5m ohm and 20m ohm, the concentration polarization resistance value is at the initial degradation level; when the concentration polarization resistance value is greater than 20m ohm, the concentration polarization resistance value is in a damage level.
In an embodiment of the invention, the diffusion polarization classification result from the diffusion polarization resistance value and a third set resistance value interval of the plurality of set resistance value intervals to the battery internal resistance is respectively obtained; and determining the activity level of the diffusion polarization of the battery according to the diffusion polarization classification result. For example, the third set resistance interval may be configured to be 5m ohm-20 m ohm, where m ohm represents milli ohm; when the diffusion polarization resistance value is less than 5m ohm, the diffusion polarization resistance value is in a normal level; when the diffusion polarization resistance value is between 5m ohm and 20m ohm, the diffusion polarization resistance value is at the degradation starting level; when the diffusion polarization resistance value is larger than 20m ohm, the diffusion polarization resistance value is in a damage level.
In the present invention, the ratio interval may be a numerical value such as: cdp/C cp10 times of the relation and Cdp/C cp20 times higher; determining the ratio of the concentration polarization capacity value to the diffusion polarization capacity value; determining a classification result of the battery polarization ratio based on the relation between the ratio and the 10 times; and determining the activity level of the battery polarization ratio according to the classification result of the battery polarization ratio. For example, when the ratio is less than 10, the activity level of the cell polarization ratio is a normal level; when the ratio is greater than 10, the activity level of the battery polarization ratio is the initial degradation level; when the ratio is greater than 20, the activity level of the battery polarization ratio is a damage level.
In an embodiment of the invention, a prompt may be provided according to an activity level of the polarization ratio of the battery corresponding to the parameter identification data, so as to replace an original of the battery. The method comprises the following steps: sending the activity grade to a server or terminal equipment, and displaying or prompting the server or the terminal equipment according to the activity grade; if the activity level is a degradation starting level or a damage level, the server or the terminal equipment displays or prompts according to the activity level; and if the activity level is a normal level, the server or the terminal equipment does not display or prompt according to the activity level. For example, when the ohmic internal resistance is greater than 20m ohms, the internal resistance of the battery is a damaged level, at this time, the internal resistance of the battery needs to be replaced, and the server or the terminal device displays or prompts according to the activity level (damaged level).
In the present invention, the main body of the determination method of battery activity may be a determination apparatus of battery activity, for example, the determination method of battery activity may be performed by a terminal device or a server or other processing device, wherein the terminal device may be a User Equipment (UE), a mobile device, a User terminal, a cellular phone, a cordless phone, a Personal Digital Assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, or the like. In some possible implementations, the method for determining battery activity may be implemented by a processor calling computer readable instructions stored in a memory. "
It will be understood by those skilled in the art that in the method of the present invention, the order of writing the steps does not imply a strict order of execution and any limitations on the implementation, and the specific order of execution of the steps should be determined by their function and possible inherent logic.
The present disclosure also provides a device for determining battery activity, including: the device comprises an acquisition unit, a processing unit and a control unit, wherein the acquisition unit is used for acquiring parameter identification data of the battery, and the parameter identification data at least comprises one of ohmic internal resistance, concentration polarization data and diffusion polarization data; and the determining unit is used for determining the activity level of the battery according to the parameter identification data and the set interval. The specific implementation method can be seen in the detailed description of the determination method of the battery activity.
In some embodiments, functions of or modules included in the apparatus provided in the embodiments of the present disclosure may be used to execute the method described in the above method embodiments, and specific implementation thereof may refer to the description of the above method embodiments, and for brevity, will not be described again here.
Embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the above-mentioned method. The computer readable storage medium may be a non-volatile computer readable storage medium.
An embodiment of the present disclosure further provides an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured as the above method. The electronic device may be provided as a terminal, server, or other form of device.
Fig. 9 is a block diagram illustrating an electronic device 800 in accordance with an example embodiment. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, or the like terminal.
Referring to fig. 9, electronic device 800 may include one or more of the following components: processing component 802, memory 804, power component 806, multimedia component 808, audio component 810, input/output (I/O) interface 812, sensor component 814, and communication component 816.
The processing component 802 generally controls overall operation of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate prior interaction of the processing component 802 with other components. For example, the processing component 802 can include a multimedia module to facilitate previous interaction of the multimedia component 808 and the processing component 802.
The memory 804 is configured to store various types of data to support operations at the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The power supply component 806 provides power to the various components of the electronic device 800. The power components 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 800.
The multimedia component 808 includes a screen that provides an output interface in front of the electronic device 800 and the user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the electronic device 800 is in an operation mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.
The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the electronic device 800. For example, the sensor assembly 814 may detect an open/closed state of the electronic device 800, the relative positioning of components, such as a display and keypad of the electronic device 800, the sensor assembly 814 may also detect a change in the position of the electronic device 800 or a component of the electronic device 800, the presence or absence of user contact with the electronic device 800, orientation or acceleration/deceleration of the electronic device 800, and a change in the temperature of the electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 816 is configured to facilitate communication between the electronic device 800 and other devices in a previously wired or wireless manner. The electronic device 800 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the electronic device 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer-readable storage medium, such as the memory 804, is also provided that includes computer program instructions executable by the processor 820 of the electronic device 800 to perform the above-described methods.
Fig. 10 is a block diagram illustrating an electronic device 1900 according to an example embodiment. For example, the electronic device 1900 may be provided as a server. Referring to fig. 10, electronic device 1900 includes a processing component 1922 further including one or more processors and memory resources, represented by memory 1932, for storing instructions, e.g., applications, executable by processing component 1922. The application programs stored in memory 1932 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1922 is configured to execute instructions to perform the above-described method.
The electronic device 1900 may also include a power component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input/output (I/O) interface 1958. The electronic device 1900 may operate based on an operating system stored in memory 1932, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, or the like.
In an exemplary embodiment, a non-transitory computer readable storage medium, such as the memory 1932, is also provided that includes computer program instructions executable by the processing component 1922 of the electronic device 1900 to perform the above-described methods.
The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims (10)

1. A method for determining battery activity, comprising:
acquiring parameter identification data of the battery, wherein the parameter identification data at least comprises one of ohmic internal resistance, concentration polarization data and diffusion polarization data;
and determining the activity level of the battery according to the parameter identification data and the set interval.
2. The determination method according to claim 1, wherein the parameter identification data is determined before the parameter identification data of the battery is acquired;
the method for determining the parameter identification data comprises the following steps:
acquiring a preset fitting function of the state of charge with a first coefficient and the corresponding open-circuit voltage;
detecting the open-circuit voltage of the battery, and determining the state of charge corresponding to the open-circuit voltage according to the open-circuit voltage and the preset fitting function with a first coefficient;
converting the preset fitting function with the first coefficient into a preset fitting function with a second coefficient of the state of charge and corresponding parameter identification data thereof;
and determining parameter identification data of the battery according to the state of charge and the preset fitting function with a second coefficient.
3. The method according to claim 2, wherein the predetermined fitting function is determined before the predetermined fitting function of the state of charge and the open circuit voltage of the battery is obtained;
the method for determining the preset fitting function comprises the following steps:
acquiring a preset dynamic parameter equivalent circuit model;
obtaining a first impedance expression of the preset dynamic parameter equivalent circuit model according to the preset dynamic parameter equivalent circuit model;
determining the number of alternating current signal frequencies input into the preset dynamic parameter equivalent circuit model according to the number of parameters of the preset dynamic parameter equivalent circuit model;
determining a plurality of second impedance expressions of the preset dynamic parameter equivalent circuit model according to a plurality of alternating current signals with different frequencies and corresponding voltage signals respectively;
determining a first coefficient and a second coefficient of the preset fitting function according to the first impedance expression and the two impedance expressions corresponding to the first impedance expression, and obtaining the preset fitting function according to the first coefficient and the second coefficient respectively;
and/or the presence of a gas in the interior of the container,
determining the range of the state of charge before determining the parameter identification data of the battery according to the state of charge and the preset fitting function with a second coefficient;
and determining parameter identification data of the battery according to the state of charge in the range and the preset fitting function with the second coefficient.
4. The method according to claim 3, wherein before said obtaining a preset dynamic parameter equivalent circuit model, a method for establishing the preset dynamic parameter equivalent circuit model comprises:
determining an ideal voltage source, an adjustable ohmic internal resistance, an adjustable diffusion polarization characteristic circuit and an adjustable concentration polarization characteristic parameter circuit of a preset battery;
and obtaining the dynamic parameter equivalent circuit model according to the ideal voltage source, the adjustable ohmic internal resistance, the adjustable diffusion polarization characteristic circuit and the adjustable concentration polarization characteristic parameter circuit which are connected in series.
5. The method for determining according to any one of claims 1 to 4, wherein the method for determining the activity of the battery based on the concentration polarization data and/or diffusion polarization data and a set interval comprises:
acquiring the set interval;
classifying the activity of the battery based on the set interval and the parameter identification data to obtain a classification result;
and determining the activity level of the battery according to the classification result.
6. The method of claim 5, wherein the concentration polarization data and the diffusion polarization data respectively comprise: concentration polarization resistance and diffusion polarization resistance; the setting interval comprises: a plurality of resistance value setting intervals;
classifying based on the ohmic internal resistance, the concentration polarization resistance, the diffusion polarization resistance and the corresponding set resistance intervals respectively to obtain classification results of the ohmic internal resistance, the concentration polarization and the diffusion polarization respectively;
and determining the activity level of the internal resistance of the battery according to the classification result of the ohmic internal resistance, and determining the activity level of the concentration polarization and the diffusion polarization of the battery respectively according to the classification results of the concentration polarization and the diffusion polarization.
7. The method of determining according to claim 6, wherein the concentration polarization data and the diffusion polarization data further comprise: concentration polarization capacity value and diffusion polarization capacity value; the setting section further includes: a proportional interval;
determining the ratio of the concentration polarization capacity value to the diffusion polarization capacity value;
determining a classification result of the battery polarization ratio based on the ratio and the ratio interval;
and determining the activity level of the battery polarization ratio according to the classification result of the battery polarization ratio.
8. An apparatus for determining battery activity, comprising:
the device comprises an acquisition unit, a processing unit and a control unit, wherein the acquisition unit is used for acquiring parameter identification data of the battery, and the parameter identification data at least comprises one of ohmic internal resistance, concentration polarization data and diffusion polarization data;
and the determining unit is used for determining the activity level of the battery according to the parameter identification data and the set interval.
9. An electronic device, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the method of any of claims 1 to 7.
10. A computer readable storage medium having computer program instructions stored thereon, which when executed by a processor implement the method of any one of claims 1 to 7.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112364911A (en) * 2020-11-06 2021-02-12 东北石油大学 Resistivity imaging inversion method and device and storage medium
CN113093038A (en) * 2021-03-03 2021-07-09 同济大学 Power battery internal resistance composition analysis method based on pulse charge and discharge test
EP4198532A1 (en) * 2021-12-17 2023-06-21 Vito NV A computer implemented method and a system for determining electromotive force curves of a battery

Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FI20001876A0 (en) * 2000-08-25 2000-08-25 Nokia Mobile Phones Ltd Improved method and arrangement for data transmission in packet radio service
US20060161870A1 (en) * 2004-07-30 2006-07-20 Apple Computer, Inc. Proximity detector in handheld device
CN104007395A (en) * 2014-06-11 2014-08-27 北京交通大学 Lithium ion battery charge state and parameter adaptive joint estimation method
CN104392080A (en) * 2014-12-19 2015-03-04 山东大学 Lithium-battery variable fractional order and equivalent circuit model and identification method thereof
CN105334465A (en) * 2015-09-15 2016-02-17 重庆长安汽车股份有限公司 Method for online evaluating state of health of lithium ion battery
CN105912799A (en) * 2016-04-27 2016-08-31 华中科技大学 Modeling method of liquid state or semi-liquid state metal battery
CN106354962A (en) * 2016-08-02 2017-01-25 电子科技大学 Lithium-iron-phosphate-battery fractional-order equivalent circuit model establishing method based on frequency demultiplication representation
CN106908732A (en) * 2017-02-09 2017-06-30 北京长城华冠汽车科技股份有限公司 A kind of lithium ion battery equivalent circuit model parameter discrimination method and device
CN110275113A (en) * 2019-06-25 2019-09-24 内蒙古工业大学 A kind of lithium battery charge state estimation method
CN110673048A (en) * 2019-10-18 2020-01-10 清华大学 Method for identifying short circuit resistance in battery
US20200074297A1 (en) * 2018-09-03 2020-03-05 Lg Electronics Inc. Device including battery
CN111308358A (en) * 2019-11-28 2020-06-19 安徽江淮汽车集团股份有限公司 Method, device and equipment for estimating state of charge of battery of electric vehicle and storage medium
CN111426962A (en) * 2020-03-23 2020-07-17 华为技术有限公司 Battery parameter identification method and terminal

Patent Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FI20001876A0 (en) * 2000-08-25 2000-08-25 Nokia Mobile Phones Ltd Improved method and arrangement for data transmission in packet radio service
US20060161870A1 (en) * 2004-07-30 2006-07-20 Apple Computer, Inc. Proximity detector in handheld device
CN104007395A (en) * 2014-06-11 2014-08-27 北京交通大学 Lithium ion battery charge state and parameter adaptive joint estimation method
CN104392080A (en) * 2014-12-19 2015-03-04 山东大学 Lithium-battery variable fractional order and equivalent circuit model and identification method thereof
CN105334465A (en) * 2015-09-15 2016-02-17 重庆长安汽车股份有限公司 Method for online evaluating state of health of lithium ion battery
CN105912799A (en) * 2016-04-27 2016-08-31 华中科技大学 Modeling method of liquid state or semi-liquid state metal battery
CN106354962A (en) * 2016-08-02 2017-01-25 电子科技大学 Lithium-iron-phosphate-battery fractional-order equivalent circuit model establishing method based on frequency demultiplication representation
CN106908732A (en) * 2017-02-09 2017-06-30 北京长城华冠汽车科技股份有限公司 A kind of lithium ion battery equivalent circuit model parameter discrimination method and device
US20200074297A1 (en) * 2018-09-03 2020-03-05 Lg Electronics Inc. Device including battery
CN110275113A (en) * 2019-06-25 2019-09-24 内蒙古工业大学 A kind of lithium battery charge state estimation method
CN110673048A (en) * 2019-10-18 2020-01-10 清华大学 Method for identifying short circuit resistance in battery
CN111308358A (en) * 2019-11-28 2020-06-19 安徽江淮汽车集团股份有限公司 Method, device and equipment for estimating state of charge of battery of electric vehicle and storage medium
CN111426962A (en) * 2020-03-23 2020-07-17 华为技术有限公司 Battery parameter identification method and terminal

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
熊瑞等: "HEV用锂离子电池动态模型参数辨识方法研究", 《电力电子技术》 *
牛利勇等: "纯电动汽车用磷酸铁锂电池的模型参数分析", 《汽车工程》 *
皇甫海文等: "锂电池等效模型建立与参数辨识方法研究", 《电气开关》 *
郭毅锋: "铅酸动力电池等效电路模型参数辨识方法研究", 《科学技术与工程》 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112364911A (en) * 2020-11-06 2021-02-12 东北石油大学 Resistivity imaging inversion method and device and storage medium
CN113093038A (en) * 2021-03-03 2021-07-09 同济大学 Power battery internal resistance composition analysis method based on pulse charge and discharge test
CN113093038B (en) * 2021-03-03 2022-08-05 同济大学 Power battery internal resistance composition analysis method based on pulse charge and discharge test
EP4198532A1 (en) * 2021-12-17 2023-06-21 Vito NV A computer implemented method and a system for determining electromotive force curves of a battery
WO2023111298A1 (en) * 2021-12-17 2023-06-22 Vito Nv A computer implemented method and a system for determining electromotive force curves of a battery

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