CN111983479B - Real-time establishing method and updating method of battery physical model and battery monitoring equipment - Google Patents
Real-time establishing method and updating method of battery physical model and battery monitoring equipment Download PDFInfo
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- CN111983479B CN111983479B CN202010770101.2A CN202010770101A CN111983479B CN 111983479 B CN111983479 B CN 111983479B CN 202010770101 A CN202010770101 A CN 202010770101A CN 111983479 B CN111983479 B CN 111983479B
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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Abstract
The present disclosure provides a real-time online establishing method of a battery physical model, which includes: s1, measuring the output value of the battery in at least one state of a charging state, a discharging state and a non-charging and discharging state in real time; s2, obtaining parameter functions needed for building a battery physical model in real time at least based on output values of the battery in at least one state of a charging state, a discharging state and a non-charging and discharging state; and S3, building a battery physical model in real time based on the parameter function. The disclosure also provides a real-time online updating method of the battery physical model, a judging method of the battery state and/or the battery state change trend and a battery monitoring device.
Description
Technical Field
The disclosure belongs to the technical field of batteries, and relates to a real-time establishing method and updating method of a battery physical model and battery monitoring equipment.
Background
In the prior art, a battery model, such as a physical model of a lithium battery, is generally established in advance by a laboratory according to chemical characteristics of the battery.
A battery physical model which is established in advance by a laboratory according to the chemical performance and the like of the battery needs to be modeled and tested in advance, and the battery physical model is difficult to update along with the service condition of the battery.
Because the physical model of the battery in the prior art cannot be established and updated online in real time, the judgment of the state of charge (SOC), the state of health (SOH) and the like of the battery also has deviation, which is not beneficial to the management and use of the battery.
Disclosure of Invention
In order to solve at least one of the above technical problems, the present disclosure provides a real-time online establishing method and an updating method for a battery physical model, a method for judging a battery state and/or a battery state change trend, and a battery monitoring device.
The real-time online establishing method and the updating method of the battery physical model, the judging method of the battery state and/or the battery state change trend and the battery monitoring equipment are realized by the following technical scheme.
According to one aspect of the disclosure, a real-time online building method of a battery physical model is provided, which includes: s1, measuring the output value of the battery in at least one state of a charging state, a discharging state and a non-charging and discharging state in real time; s2, obtaining parameter functions needed for building a battery physical model in real time at least based on output values of the battery in at least one state of a charging state, a discharging state and a non-charging and discharging state; and S3, building a battery physical model in real time based on the parameter function.
According to the real-time online establishing method of the battery physical model, the parameter functions comprise a direct-current capacitance parameter function, a direct-current resistance parameter function, a first-order high-frequency impedance parameter function and a second-order high-frequency impedance parameter function.
According to the real-time online establishing method of the battery physical model of at least one embodiment of the disclosure, in the discharging state of the battery, the output current value (which may be constant current discharging or any load discharging), the output voltage value and the battery temperature value of the battery from the full charge state to the discharging end state, namely, the complete discharging process are measured in real time.
According to the real-time online establishing method of the battery physical model, the discharging charge quantity of the battery from the full charge state to the discharging end state is obtained based on the output current value (which can be constant current discharging or any load discharging) of the battery from the full charge state to the discharging end state measured in real time; the method comprises the steps of obtaining a direct current capacitance parameter function based on the discharge charge quantity of a battery from a full charge state to a discharge end state, a plurality of output voltage values of the battery from the full charge state to the discharge end state and a plurality of discharge depths corresponding to the output voltage values, wherein the plurality of output voltage values are measured in real time, and the direct current capacitance parameter function is at least a function of the discharge depths of the battery.
According to the real-time online establishing method of the battery physical model, the discharging charge quantity of a plurality of complete discharging processes is obtained based on the output current values (which can be constant current discharging or any load discharging) of the plurality of complete discharging processes measured in real time; the method comprises the steps of obtaining a direct current capacitance parameter function based on the discharge electric charge amount of a plurality of complete discharge processes, a plurality of temperature values of each discharge process of the plurality of complete discharge processes, a plurality of output voltage values of each complete discharge process of the plurality of complete discharge processes measured in real time and a plurality of discharge depths corresponding to the plurality of output voltage values of each discharge process of the plurality of complete discharge processes, wherein the direct current capacitance parameter function is at least a function of the discharge depth and the temperature of a battery.
According to the real-time online establishing method of the battery physical model, which is disclosed by the invention, based on the historical discharge record data of the battery, a plurality of discharge depths corresponding to a plurality of output voltage values in the complete discharge process are obtained.
According to the real-time online establishing method of the battery physical model, the direct current capacitance parameter function is obtained through a data fitting algorithm.
According to the real-time online building method of the battery physical model, the direct current capacitance parameter function comprises a coefficient of change of a direct current capacitance parameter relative to temperature.
According to the real-time online establishing method of the battery physical model of at least one embodiment of the disclosure, when a battery is in a non-charging and non-discharging state, an output voltage value and a battery temperature value of the battery are measured, and the current discharging depth of the battery is obtained based on historical discharging record data of the battery; when the battery starts to discharge from the non-charge-discharge state, synchronously measuring a voltage output value and a current output value in real time, and acquiring an initial discharge charge amount of the battery after a first preset time from the non-charge-discharge state and an output voltage value when the battery reaches the initial discharge charge amount; and obtaining the direct current resistance parameter of the battery based on the output voltage value when the battery is in a non-charging and non-discharging state, the output voltage value when the battery reaches the initial discharging charge quantity and the average value of the output current of the battery from the non-charging and non-discharging state to the initial discharging charge quantity.
According to the real-time online establishing method of the battery physical model of at least one embodiment of the disclosure, when a battery is in a plurality of non-charging and discharging states, the output voltage value and the battery temperature value of the battery are respectively measured, and the discharging depths of the plurality of non-charging and discharging states are obtained based on historical discharging record data of the battery, wherein the plurality of non-charging and discharging states are the non-charging and discharging states of the battery in a plurality of different discharging depths; obtaining initial discharge charge quantities of a plurality of non-charge and discharge states and output voltage values when the battery reaches the initial discharge charge quantities; the method comprises the steps of obtaining a direct current resistance parameter function of the battery based on output voltage values when the battery is in a plurality of non-charging and discharging states, the output voltage values when the battery reaches an initial discharging electric charge amount, an output current average value between the battery from the non-charging and discharging states to the initial discharging electric charge amount, battery temperature values when the battery is in the plurality of non-charging and discharging states and a discharging depth when the battery is in the plurality of non-charging and discharging states, wherein the direct current resistance parameter function is at least a function of the discharging depth and the temperature of the battery.
According to the real-time online building method of the battery physical model, the battery historical discharge record data at least comprises mapping data of the battery output voltage value and the discharge depth.
According to the real-time online building method of the battery physical model, the initial discharge charge amount is lower than the product of the rated capacity of the battery and the preset percentage.
According to the real-time online building method of the battery physical model, the preset percentage is 1% to 2%.
According to the real-time online building method of the battery physical model, the direct current resistance parameter function is obtained through a data fitting algorithm.
According to the real-time online building method of the battery physical model, the direct current resistance parameter function comprises a coefficient of variation of a direct current resistance parameter relative to temperature.
According to the real-time online establishing method of the battery physical model of at least one embodiment of the disclosure, when a battery is in a non-charging and non-discharging state, an output voltage value and a battery temperature value of the battery are measured, and the current discharging depth of the battery is obtained based on historical discharging record data of the battery; synchronously measuring a voltage output value and a current output value in real time within a second preset time length from the beginning of discharging of the battery from the non-charging and discharging state, and acquiring a voltage output value change curve and a current output value change curve within the second preset time length; and carrying out Fourier analysis on the voltage output value change curve and the current output value change curve to obtain a first-order high-frequency impedance parameter and a second-order high-frequency impedance parameter.
According to the real-time online establishing method of the battery physical model of at least one embodiment of the disclosure, when a battery is in a plurality of non-charging and discharging states, the output voltage value and the battery temperature value of the battery are respectively measured, and the discharging depths of the plurality of non-charging and discharging states are obtained based on historical discharging record data of the battery, wherein the plurality of non-charging and discharging states are the non-charging and discharging states of the battery in a plurality of different discharging depths; obtaining a voltage output value change curve and a current output value change curve of the battery within the second preset time length from the beginning of discharging of each non-charge-discharge state of the plurality of non-charge-discharge states; based on the Fourier analysis of each non-charging and discharging state of the plurality of non-charging and discharging states, the battery temperature values in the plurality of non-charging and discharging states and the discharging depth in the plurality of non-charging and discharging states, a first-order high-frequency impedance parameter function and a second-order high-frequency impedance parameter function of the battery are obtained, and the first-order high-frequency impedance parameter function and the second-order high-frequency impedance parameter function are at least functions of the discharging depth and the temperature of the battery.
According to the real-time online establishing method of the battery physical model, the first-order high-frequency impedance parameter and the second-order high-frequency impedance parameter are obtained through a data fitting algorithm.
According to another aspect of the disclosure, a real-time online updating method for a battery physical model is provided, and the battery physical model is updated in real time online based on a parameter function required for establishing the battery physical model, which is obtained by any one of the methods.
According to still another aspect of the present disclosure, a method for determining a battery state and/or a battery state change trend is provided, wherein the battery state and/or the battery state change trend is determined based on a change process of the parameter function of the battery physical model updated on line in real time by the above method for updating the battery physical model on line in real time.
According to the method for judging the state of the battery and/or the change trend of the state of the battery, the state of the battery comprises the state of charge (SOC) of the battery and/or the state of health (SOH) of the battery.
According to the method for judging the battery state and/or the battery state change trend, the parameter functions comprise a direct current capacitance parameter function, a direct current resistance parameter function, a first-order high-frequency impedance parameter function and a second-order high-frequency impedance parameter function.
According to yet another aspect of the present disclosure, there is provided a battery monitoring apparatus including: a measuring device that measures an output value of the battery in at least one of a charged state, a discharged state, and a non-charged and non-discharged state in real time; and the processing device is used for obtaining a parameter function required by establishing a battery physical model in real time at least on the basis of the output value of the battery in at least one state of a charging state, a discharging state and a non-charging and discharging state, and establishing the battery physical model in real time on the basis of the parameter function.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the disclosure and together with the description serve to explain the principles of the disclosure.
Fig. 1 is a schematic flow chart of a real-time online establishing method of a battery physical model according to an embodiment of the disclosure.
Fig. 2 is an example of the battery physical model established by the real-time online establishing method of the battery physical model according to an embodiment of the disclosure.
Fig. 3 is one of the flow diagrams of the method for acquiring the dc capacitance parameter function in the method for real-time online establishing a physical model of a battery according to an embodiment of the present disclosure.
Fig. 4 is a second schematic flow chart of a method for acquiring a dc capacitance parameter function in a real-time online establishing method of a battery physical model according to an embodiment of the present disclosure.
Fig. 5 is one of the flow diagrams of the method for acquiring the dc resistance parameter in the method for real-time online establishing the physical model of the battery according to the embodiment of the present disclosure.
Fig. 6 is a second schematic flow chart of the method for obtaining the dc resistance parameter function in the method for real-time online establishing a physical model of a battery according to an embodiment of the present disclosure.
Fig. 7 is a schematic flow diagram of a first-order high-frequency impedance parameter and a second-order high-frequency impedance parameter obtaining method in a real-time online battery physical model building method according to an embodiment of the disclosure.
Fig. 8 is a second schematic flow chart of a method for obtaining a first-order high-frequency impedance parameter function and a second-order high-frequency impedance parameter function in the real-time online establishing method of the battery physical model according to the embodiment of the disclosure.
Fig. 9 is a schematic structural diagram of a battery monitoring apparatus according to an embodiment of the present disclosure.
Description of the reference numerals
100 battery monitoring device
10 group battery
11 cell
12 negative temperature coefficient resistor
20 analog front end chip
21 analog switch
22 buffer
23 analog-to-digital converter
24 communication interface
25 coulometer
26 switch decoding circuit
27 controller
28 random volatile memory
29 nonvolatile memory
31 drive circuit
32 voltage regulator
41 sampling resistor
42 fuse
43 charging MOSFET
44 discharge MOSFET
50 microcontroller
61 data line
62 control line
63 control signal
64 control signal
65 measuring line
66A differential line
66B differential lines.
Detailed Description
The present disclosure will be described in further detail with reference to the drawings and embodiments. It is to be understood that the specific embodiments described herein are for purposes of illustration only and are not to be construed as limitations of the present disclosure. It should be further noted that, for the convenience of description, only the portions relevant to the present disclosure are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict. Technical solutions of the present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Unless otherwise indicated, the illustrated exemplary embodiments/examples are to be understood as providing exemplary features of various details of some ways in which the technical concepts of the present disclosure may be practiced. Accordingly, unless otherwise indicated, features of the various embodiments may be additionally combined, separated, interchanged, and/or rearranged without departing from the technical concept of the present disclosure.
The use of cross-hatching and/or shading in the drawings is generally used to clarify the boundaries between adjacent components. As such, unless otherwise noted, the presence or absence of cross-hatching or shading does not convey or indicate any preference or requirement for a particular material, material property, size, proportion, commonality between the illustrated components and/or any other characteristic, attribute, property, etc., of a component. Further, in the drawings, the size and relative sizes of components may be exaggerated for clarity and/or descriptive purposes. While example embodiments may be practiced differently, the specific process sequence may be performed in a different order than that described. For example, two processes described consecutively may be performed substantially simultaneously or in reverse order to that described. In addition, like reference numerals denote like parts.
When an element is referred to as being "on" or "on," "connected to" or "coupled to" another element, it can be directly on, connected or coupled to the other element or intervening elements may be present. However, when an element is referred to as being "directly on," "directly connected to" or "directly coupled to" another element, there are no intervening elements present. For purposes of this disclosure, the term "connected" may refer to physically, electrically, etc., and may or may not have intermediate components.
For descriptive purposes, the present disclosure may use spatially relative terms such as "below … …," below … …, "" below … …, "" below, "" above … …, "" above, "" … …, "" higher, "and" side (e.g., "in the sidewall") to describe one component's relationship to another (other) component as illustrated in the figures. Spatially relative terms are intended to encompass different orientations of the device in use, operation, and/or manufacture in addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, elements described as "below" or "beneath" other elements or features would then be oriented "above" the other elements or features. Thus, the exemplary term "below … …" can encompass both an orientation of "above" and "below". Further, the devices may be otherwise positioned (e.g., rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.
The terminology used herein is for the purpose of describing particular embodiments and is not intended to be limiting. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. Furthermore, when the terms "comprises" and/or "comprising" and variations thereof are used in this specification, the presence of stated features, integers, steps, operations, elements, components and/or groups thereof are stated but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof. It is also noted that, as used herein, the terms "substantially," "about," and other similar terms are used as approximate terms and not as degree terms, and as such, are used to interpret inherent deviations in measured values, calculated values, and/or provided values that would be recognized by one of ordinary skill in the art.
FIG. 1 is a flow chart of a method for real-time online building of a physical model of a battery according to one embodiment of the present disclosure.
As shown in fig. 1, the real-time online establishing method of the battery physical model includes: s1, measuring the output value of the battery in at least one state of a charging state, a discharging state and a non-charging and discharging state in real time; s2, obtaining parameter functions needed for building a battery physical model in real time at least based on output values of the battery in at least one state of a charging state, a discharging state and a non-charging and discharging state; and S3, building a battery physical model in real time based on the parameter function.
The output value may be a voltage output value and/or a current output value of the battery. The parameter functions to be obtained may differ based on the physical model of the battery to be built.
Fig. 2 exemplarily shows one of the battery physical models, i.e., a second-order lithium battery physical model.
C in FIG. 21(dod, Temp) is a direct current capacitance parameter of the battery, which is a non-linear function of the depth of discharge dod and the temperature Temp of the battery, Rdc(dod, Temp) is the direct current resistance parameter of the battery, which is a non-linear function of the depth of discharge dod and the temperature Temp of the battery, Zhf1(dod, Temp) is a first-order high-frequency impedance parameter of the battery, which is a non-linear function of the depth of discharge dod and the temperature Temp of the battery, Zhf2(dod, Temp) is the second-order high-frequency impedance parameter of the battery, which is a function of the depth of discharge dod and the temperature Temp of the batteryA non-linear function.
It should be noted that fig. 2 is only one of the battery physical models that can be built on line in real time by the real-time on-line building method for a battery physical model according to the present disclosure, and other types of battery physical models can also be built on line in real time by the real-time on-line building method for a battery physical model according to the present disclosure, and only the parameter functions required for building various battery physical models need to be obtained in step S2.
In the method for establishing a physical model of a battery in real time on line according to this embodiment, the parameter function may include one or more of a dc capacitance parameter function, a dc resistance parameter function, a first-order high-frequency impedance parameter function, and a second-order high-frequency impedance parameter function.
According to the method for establishing the physical model of the battery in real time on line, the output current value (which can be constant current discharge or any load discharge), the output voltage value and the battery temperature value of the battery from the full charge state to the discharge end state, namely the complete discharge process, are measured in real time in the discharge state of the battery.
It should be noted that the output current value, the output voltage value and the battery temperature value of the above-mentioned complete discharge process are synchronously measured. A plurality of sets of output current values, output voltage values and battery temperature values will be measured.
When measuring the output current value, the output voltage value and the battery temperature value in the complete discharge process, the measurement can be carried out at fixed time intervals, and the measurement can also be carried out at dynamic time intervals.
In the above embodiment, the dc capacitance parameter function (i.e., one of the parameter functions) is preferably obtained by:
obtaining the discharge charge quantity of the battery from the full charge state to the discharge end state based on the output current value (which can be constant current discharge or any load discharge) of the battery from the full charge state to the discharge end state measured in real time;
and obtaining a direct current capacitance parameter function based on the discharge charge quantity of the battery from the full charge state to the discharge end state, a plurality of output voltage values of the battery from the full charge state to the discharge end state and a plurality of discharge depths corresponding to the output voltage values, wherein the direct current capacitance parameter function is at least a function of the discharge depths of the battery.
Fig. 3 is a flowchart of the method for obtaining the dc capacitance parameter function.
The multiple discharge depths corresponding to the multiple output voltage values of the complete discharge process can be obtained based on the historical discharge record data of the battery.
The direct current capacitance parameter function is obtained through a data fitting algorithm, and the data fitting algorithm can adopt a data fitting algorithm in the prior art.
More preferably, the direct current capacitance parameter function (i.e. one of the parameter functions) is obtained by:
obtaining the discharge charge quantity of a plurality of complete discharge processes (namely obtaining the discharge charge quantity of each complete discharge process) based on the output current values (which can be constant current discharge or any load discharge) of the plurality of complete discharge processes measured in real time;
based on the discharge charge amount of each complete discharge process of the multiple complete discharge processes, the multiple temperature values of each discharge process of the multiple complete discharge processes, the multiple output voltage values of each complete discharge process of the multiple complete discharge processes measured in real time and the multiple discharge depths corresponding to the multiple output voltage values of each discharge process of the multiple complete discharge processes, a direct current capacitance parameter function is obtained, and the direct current capacitance parameter function is at least a function of the discharge depth and the temperature of the battery.
Fig. 4 is a flowchart of the method for obtaining the dc capacitance parameter function.
And obtaining a plurality of discharge depths corresponding to a plurality of output voltage values in the complete discharge process based on the historical discharge record data of the battery.
The direct current capacitance parameter function is obtained through a data fitting algorithm, and the data fitting algorithm can adopt a data fitting algorithm in the prior art.
The direct current capacitance parameter function obtained by the method comprises a coefficient of variation of the direct current capacitance parameter relative to the temperature.
According to a preferred embodiment of the present disclosure, the direct current resistance parameter of the battery is obtained by:
when the battery is in a non-charging and discharging state, measuring an output voltage value and a battery temperature value of the battery, and obtaining the current discharging depth of the battery based on historical discharging record data of the battery;
when the battery starts to discharge from a non-charging and discharging state, synchronously measuring a voltage output value and a current output value in real time, and acquiring an initial discharging charge amount of the battery after a first preset time from the non-charging and discharging state and an output voltage value when the battery reaches the initial discharging charge amount;
and obtaining the direct current resistance parameter of the battery based on the output voltage value when the battery is in a non-charging and non-discharging state, the output voltage value when the battery reaches the initial discharging charge quantity and the average value of the output current between the non-charging and non-discharging state and the initial discharging charge quantity.
Fig. 5 is a flow chart of the method for obtaining the dc resistance parameter.
The non-charge/discharge state is a state in which the battery is not charged or discharged and the voltage of the battery is stable for a long time, and for example, the cell voltage of the battery does not exceed 1mV within 1 hour. The measuring device measures the battery output voltage value and the battery temperature value at the moment, and obtains the discharge depth of the battery at the moment based on the historical discharge record data of the battery.
In the above embodiments, the battery history discharge record data at least includes mapping data of the battery output voltage value and the discharge depth.
Wherein the first predetermined time duration is preferably a first order high frequency capacitance parameter (C)hf1(dod, Temp)) and a first order high frequency resistance parameter (R)hf1(dod, Temp)), second-order high-frequency capacitance parameter (C)hf2(dod, Temp)) and a second-order high-frequency resistance parameter (R)hf2(dod, Temp)) is 4 times or more, preferably 4 to 5 times the maximum value of the product.
Moreover, the initial discharge charge amount needs to be lower than the product of the rated capacity of the battery and a preset percentage, wherein the preset percentage is 1% to 2%.
More preferably, as shown in fig. 6, the direct-current resistance parameter function (i.e., one of the parameter functions) is obtained by:
when the battery is in a plurality of non-charging and discharging states, respectively measuring an output voltage value and a battery temperature value of the battery, and obtaining the discharging depths of the plurality of non-charging and discharging states based on historical discharging record data of the battery, wherein the plurality of non-charging and discharging states are the non-charging and discharging states of the battery in a plurality of different discharging depths;
obtaining initial discharge charge quantities of a plurality of non-charge and discharge states and output voltage values when the battery reaches the initial discharge charge quantities;
the method comprises the steps of obtaining a direct current resistance parameter function of the battery based on output voltage values when the battery is in a plurality of non-charging and discharging states, output voltage values when the battery reaches an initial discharging charge amount, an output current average value between the battery from the non-charging and discharging state to the initial discharging charge amount, battery temperature values when the battery is in the plurality of non-charging and discharging states and discharging depths when the battery is in the plurality of non-charging and discharging states, wherein the direct current resistance parameter function is at least a function of the discharging depths and the temperatures of the battery.
Wherein the direct current resistance parameter function is obtained by a data fitting algorithm. The data fitting algorithm may use a data fitting algorithm in the prior art.
Wherein the direct resistance parameter function comprises a coefficient of variation of the direct resistance parameter with respect to temperature.
In the above embodiment, the first order high frequency impedance parameter and the second order high frequency impedance parameter are preferably obtained by:
when the battery is in a non-charging and discharging state, measuring an output voltage value and a battery temperature value of the battery, and obtaining the current discharging depth of the battery based on historical discharging record data of the battery;
synchronously measuring the voltage output value and the current output value in real time within a second preset time length from the beginning of discharging of the battery from a non-charging and discharging state, and acquiring a voltage output value change curve and a current output value change curve within the second preset time length;
and carrying out Fourier analysis on the voltage output value change curve and the current output value change curve to obtain a first-order high-frequency impedance parameter and a second-order high-frequency impedance parameter.
Wherein the second predetermined time period is preferably a first order high frequency capacitance parameter (C)hf1(dod, Temp)) and a first order high frequency resistance parameter (R)hf1(dod, Temp)), second-order high-frequency capacitance parameter (C)hf2(dod, Temp)) and a second-order high-frequency resistance parameter (R)hf2(dod, Temp)) is 5 times or less, preferably 4 to 5 times the maximum value of the product.
Fig. 7 is a flowchart of the first-order high-frequency impedance parameter and the second-order high-frequency impedance parameter obtaining method.
More preferably, as shown in fig. 8, the direct-current resistance parameter function (i.e., one of the parameter functions) is obtained by:
when the battery is in a plurality of non-charging and discharging states, respectively measuring an output voltage value and a battery temperature value of the battery, and obtaining the discharging depths of the plurality of non-charging and discharging states based on historical discharging record data of the battery, wherein the plurality of non-charging and discharging states are the non-charging and discharging states of the battery in a plurality of different discharging depths;
obtaining a voltage output value change curve and a current output value change curve of a second preset time length from the beginning of discharging of the battery from each non-charge-discharge state of a plurality of non-charge-discharge states;
based on Fourier analysis of each non-charging and discharging state of the plurality of non-charging and discharging states, battery temperature values in the plurality of non-charging and discharging states and discharging depth in the plurality of non-charging and discharging states, a first-order high-frequency impedance parameter function and a second-order high-frequency impedance parameter function of the battery are obtained, and the first-order high-frequency impedance parameter function and the second-order high-frequency impedance parameter function are at least functions of the discharging depth and the temperature of the battery.
The first-order high-frequency impedance parameter function and the second-order high-frequency impedance parameter function are obtained through a data fitting algorithm.
According to the real-time online updating method of the battery physical model of one embodiment of the disclosure, the battery physical model is updated in real time on line based on the parameter function of the battery physical model established by the real-time online establishing method of the battery physical model of any one of the embodiments.
According to the method for judging the battery state and/or the battery state change trend, the battery state and/or the battery state change trend is judged based on the change process of the parameter function of the battery physical model updated in real time on line by the real-time online updating method of the battery physical model.
The battery state includes a state of charge (SOC) of the battery and/or a state of health (SOH) of the battery.
The parameter function comprises a direct current capacitance parameter function, a direct current resistance parameter function, a first-order high-frequency impedance parameter function and a second-order high-frequency impedance parameter function.
The battery physical model established by the battery physical model real-time online establishing method can be updated online in real time, the physical model of the battery (particularly a lithium battery) can be directly established and optimized in practical application without performing modeling test on the battery in advance, so that the calculated lithium battery model gradually approaches to a theoretical actual value, the state parameters (such as aging parameters) of the battery can be updated in real time along with the aging of the battery, and the battery state and/or the battery state change trend (such as an aging predicted value of the battery) are obtained by the battery state and/or battery state change trend judging method.
The accuracy of the battery physical model established by the real-time online establishing method of the battery physical model is trained all the time along with the increase of the discharging times, the difference of the discharging current mode (constant current discharging or any load discharging) and the working temperature, and the accuracy is gradually improved.
Meanwhile, the real-time online updating method of the battery physical model can track the aging process of the battery in the using process in real time, continuously updates the battery physical model of the battery along with the aging of the battery, and keeps accurate estimation of the battery physical model along with the battery in the whole using period, so that the accurate calculated values of the SOC (State of Charge) and the SOH (State of health) of the battery (especially a lithium battery) are obtained.
According to the battery monitoring apparatus 100 of one embodiment of the present disclosure, as shown in fig. 9, the battery monitoring apparatus 100 includes: a measuring device that measures an output value of the battery in at least one of a charged state, a discharged state, and a non-charged and non-discharged state in real time; and the processing device is used for obtaining a parameter function required for establishing the battery physical model in real time at least on the basis of the output value of the battery in at least one state of a charging state, a discharging state and a non-charging and discharging state, and establishing the battery physical model in real time on the basis of the parameter function.
Among them, the battery monitoring apparatus 100 of the present embodiment preferably has the structure shown in fig. 9.
As shown in fig. 9, a processing device such as the microcontroller 50 may implement steps other than the measurement steps performed by the measurement apparatus in fig. 1 to 8 by executing a corresponding computer program, and the processing device may also be a single chip microcomputer or an FPGA device.
The battery monitoring apparatus 100 includes an analog front-end chip 20, a microcontroller 50, and the like. The analog front-end chip 20 is used to measure the output voltage of each cell of the battery pack 10 composed of the single cells 11 connected in series, and measure the voltage difference across the sampling resistor 41 through the differential lines 66A and 66B by the coulometer 25.
And measures the temperature of the battery pack 10 through a negative temperature coefficient resistor (NTC)12 or other means of temperature sensor provided inside the battery pack 10.
The analog front end chip 20 has the ability to open a circuit, such as driving MOSFETs or any form of circuit breakers or relays 43, 44 connected in series on the output circuit of the battery pack 10. The driver circuit 31 is integrated inside the analog front-end chip 20, and controls the external circuit breakers 43 and 44 by a control signal 63 and a control signal 64. The discharge MOSFET 44 is used to prevent the battery pack 10 from being discharged to the outside in the event of an accident. The charging MOSFET 43 is used to prevent an external charger from charging the battery pack 10 when an abnormal condition occurs. The fuse 42 is used for redundant protection in extreme cases to prevent irreversible damage to the equipment and the battery.
An analog switch 21 is provided inside the analog front-end chip 20, and a switching signal is controlled by a switching decoding circuit 26 for sequentially measuring the voltage of each cell of the battery pack 10 in a predetermined order. The voltage across the analog switch 21 is provided as an input to the buffer 22. The battery voltage through the buffer 22 serves as an input to an analog-to-digital converter 23. The digital result is stored in the random volatile memory 28 after conversion by the analog-to-digital converter 23. Meanwhile, the coulometer 25 is used to measure the voltage difference across the sampling resistor 41, and enters the measurement port of the coulometer 25 through the differential lines 66A and 66B.
The controller 27 inside the analog front-end chip 20 is, for example, a digital state machine, and is used for controlling the execution of the timing flow and other actions of the internal sampling conversion. In fig. 6, reference numeral 62 denotes a control line, reference numeral 65 denotes a measurement line, and reference numeral 61 denotes a data line.
The non-volatile memory 29 is used for storing configuration values and factory check values to improve measurement accuracy. The external microcontroller 50 writes or reads internal data of the analog front-end chip 20 via a public or private protocol of the communication interface 24, preferably a digital communication interface. Voltage regulator 32 draws power from battery pack 10.
As shown in fig. 9, the measuring device of the battery monitoring apparatus 100 of the present embodiment exemplarily includes a negative temperature coefficient resistor 12, an analog front-end chip 20, a sampling resistor 41, a fuse 42, a charging MOSFET 43, a discharging MOSFET 44, a data line 61, a control line 62, a control signal 63, a control signal 64, a measurement line 65, a differential line 66A, and a differential line 66B.
The configuration of the battery monitoring apparatus 100 shown in fig. 9 is only a preferred embodiment of the present disclosure, and is not particularly limited to the configurations of the battery monitoring apparatus 100, the measurement device of the battery monitoring apparatus 100, and the processing device of the battery monitoring apparatus 100 of the present disclosure.
In the description herein, reference to the description of the terms "one embodiment/mode," "some embodiments/modes," "example," "specific example" or "some examples" or the like means that a particular feature, structure, material, or characteristic described in connection with the embodiment/mode or example is included in at least one embodiment/mode or example of the present disclosure. In this specification, the schematic representations of the terms used above are not necessarily intended to be the same embodiment/mode or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments/modes or examples. Furthermore, the various embodiments/aspects or examples and features of the various embodiments/aspects or examples described in this specification can be combined and combined by one skilled in the art without conflicting therewith.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present disclosure, "a plurality" means at least two, e.g., two, three, etc., unless explicitly specifically limited otherwise.
It will be understood by those skilled in the art that the foregoing embodiments are merely for clarity of illustration of the disclosure and are not intended to limit the scope of the disclosure. Other variations or modifications may occur to those skilled in the art, based on the foregoing disclosure, and are still within the scope of the present disclosure.
Claims (21)
1. A real-time online building method of a battery physical model is characterized by comprising the following steps:
s1, measuring the output value of the battery in the discharge state in real time;
s2, obtaining parameter functions needed for building a battery physical model in real time at least based on the output value of the battery in the discharging state; and
s3, establishing a battery physical model in real time based on the parameter function;
the method comprises the following steps that when the battery is in a discharging state, the output current value, the output voltage value and the battery temperature value of the battery from a full-charge state to a discharging end state, namely a complete discharging process, are measured in real time;
acquiring the discharge charge quantity of the battery from the full charge state to the discharge end state based on the output current value of the battery from the full charge state to the discharge end state measured in real time;
the method comprises the steps of obtaining a direct current capacitance parameter function based on the discharge charge quantity of a battery from a full charge state to a discharge end state, a plurality of output voltage values of the battery from the full charge state to the discharge end state and a plurality of discharge depths corresponding to the output voltage values, wherein the plurality of output voltage values are measured in real time, and the direct current capacitance parameter function is at least a function of the discharge depths of the battery.
2. The method according to claim 1, wherein the parameter functions include a dc capacitance parameter function, a dc resistance parameter function, a first order high frequency impedance parameter function, and a second order high frequency impedance parameter function.
3. The real-time online establishing method of the battery physical model according to claim 1, characterized in that the discharge charge amount of a plurality of complete discharge processes is obtained based on the output current values of the plurality of complete discharge processes measured in real time;
the method comprises the steps of obtaining a direct current capacitance parameter function based on the discharge electric charge amount of a plurality of complete discharge processes, a plurality of temperature values of each discharge process of the plurality of complete discharge processes, a plurality of output voltage values of each complete discharge process of the plurality of complete discharge processes measured in real time and a plurality of discharge depths corresponding to the plurality of output voltage values of each discharge process of the plurality of complete discharge processes, wherein the direct current capacitance parameter function is at least a function of the discharge depth and the temperature of a battery.
4. The real-time online establishing method of the battery physical model according to claim 1 or 3, characterized in that a plurality of discharge depths corresponding to a plurality of output voltage values of a complete discharge process are obtained based on battery historical discharge record data.
5. The real-time online establishing method of the battery physical model according to claim 1 or 3, characterized in that the direct current capacitance parameter function is obtained by a data fitting algorithm.
6. The method according to claim 3, wherein the DC capacitance parameter function comprises a coefficient of variation of a DC capacitance parameter with respect to temperature.
7. A real-time online building method of a battery physical model is characterized by comprising the following steps:
s1, measuring the output value of the battery in a discharging state and a non-charging and discharging state in real time;
s2, obtaining parameter functions needed for building a battery physical model in real time at least based on output values of the battery in a discharging state and a non-charging and discharging state; and
s3, establishing a battery physical model in real time based on the parameter function;
when the battery is in a non-charging and discharging state, measuring an output voltage value and a battery temperature value of the battery, and obtaining the current discharging depth of the battery based on historical discharging record data of the battery;
when the battery starts to discharge from the non-charge-discharge state, synchronously measuring a voltage output value and a current output value in real time, and acquiring an initial discharge charge amount of the battery after a first preset time from the non-charge-discharge state and an output voltage value when the battery reaches the initial discharge charge amount;
and obtaining the direct current resistance parameter of the battery based on the output voltage value when the battery is in a non-charging and non-discharging state, the output voltage value when the battery reaches the initial discharging charge quantity and the average value of the output current of the battery from the non-charging and non-discharging state to the initial discharging charge quantity.
8. The method of real-time online building of a physical model of a battery according to claim 7,
when the battery is in a plurality of non-charging and discharging states, respectively measuring an output voltage value and a battery temperature value of the battery, and obtaining the discharging depths of the plurality of non-charging and discharging states based on historical discharging record data of the battery, wherein the plurality of non-charging and discharging states are the non-charging and discharging states of the battery in a plurality of different discharging depths;
obtaining initial discharge charge quantities of a plurality of non-charge and discharge states and output voltage values when the battery reaches the initial discharge charge quantities;
the method comprises the steps of obtaining a direct current resistance parameter function of the battery based on output voltage values when the battery is in a plurality of non-charging and discharging states, the output voltage values when the battery reaches an initial discharging electric charge amount, an output current average value between the battery from the non-charging and discharging states to the initial discharging electric charge amount, battery temperature values when the battery is in the plurality of non-charging and discharging states and a discharging depth when the battery is in the plurality of non-charging and discharging states, wherein the direct current resistance parameter function is at least a function of the discharging depth and the temperature of the battery.
9. The method for real-time online building of a physical model of a battery according to claim 7 or 8, wherein the historical discharge log data of the battery at least comprises mapping data of battery output voltage values and discharge depths.
10. The method for real-time on-line building of a physical model of a battery according to claim 7 or 8, wherein the initial discharge charge amount is lower than the product of the rated capacity of the battery and a preset percentage.
11. The method for real-time on-line building of a physical model of a battery according to claim 10, wherein the predetermined percentage is 1% to 2%.
12. The method according to claim 8, wherein the dc resistance parameter function is obtained by a data fitting algorithm.
13. The method according to claim 8, wherein the dc resistance parameter function comprises a coefficient of variation of the dc resistance parameter with respect to temperature.
14. A real-time online building method of a battery physical model is characterized by comprising the following steps:
s1, measuring the output value of the battery in a discharging state and a non-charging and discharging state in real time;
s2, obtaining parameter functions needed for building a battery physical model in real time at least based on output values of the battery in a discharging state and a non-charging and discharging state; and
s3, establishing a battery physical model in real time based on the parameter function; when the battery is in a non-charging and discharging state, measuring an output voltage value and a battery temperature value of the battery, and obtaining the current discharging depth of the battery based on historical discharging record data of the battery;
synchronously measuring a voltage output value and a current output value in real time within a second preset time length from the beginning of discharging of the battery from the non-charging and discharging state, and acquiring a voltage output value change curve and a current output value change curve within the second preset time length;
and carrying out Fourier analysis on the voltage output value change curve and the current output value change curve to obtain a first-order high-frequency impedance parameter and a second-order high-frequency impedance parameter.
15. The method of real-time online building of a physical model of a battery according to claim 14,
when the battery is in a plurality of non-charging and discharging states, respectively measuring an output voltage value and a battery temperature value of the battery, and obtaining the discharging depths of the plurality of non-charging and discharging states based on historical discharging record data of the battery, wherein the plurality of non-charging and discharging states are the non-charging and discharging states of the battery in a plurality of different discharging depths;
obtaining a voltage output value change curve and a current output value change curve of the battery within the second preset time length from the beginning of discharging of each non-charge-discharge state of the plurality of non-charge-discharge states;
based on the Fourier analysis of each non-charging and discharging state of the plurality of non-charging and discharging states, the battery temperature values in the plurality of non-charging and discharging states and the discharging depth in the plurality of non-charging and discharging states, a first-order high-frequency impedance parameter function and a second-order high-frequency impedance parameter function of the battery are obtained, and the first-order high-frequency impedance parameter function and the second-order high-frequency impedance parameter function are at least functions of the discharging depth and the temperature of the battery.
16. The method of real-time on-line building of a physical model of a battery according to claim 15,
the first-order high-frequency impedance parameter and the second-order high-frequency impedance parameter are obtained through a data fitting algorithm.
17. A real-time online updating method for a physical model of a battery, which is characterized in that the physical model of the battery is updated in real time online based on a parameter function required for establishing the physical model of the battery, which is obtained by the method in any one of claims 1 to 16.
18. A method for determining a battery state and/or a battery state change trend, characterized in that the battery state and/or the battery state change trend is determined based on the change process of the parameter function of the battery physical model updated on line in real time by the method of claim 17.
19. The determination method according to claim 18, wherein the battery state comprises a state of charge (SOC) of the battery and/or a state of health (SOH) of the battery.
20. The method according to claim 18, wherein the parameter function includes a dc capacitance parameter function, a dc resistance parameter function, a first-order high-frequency impedance parameter function, and a second-order high-frequency impedance parameter function.
21. A battery monitoring device, comprising:
the measuring device measures an output current value, an output voltage value and a battery temperature value of the battery from a full charge state to a discharge end state, namely a complete discharge process in real time, and obtains a discharge charge amount of the battery from the full charge state to the discharge end state based on the output current value of the battery from the full charge state to the discharge end state measured in real time; and
the processing device obtains a direct current capacitance parameter function based on the discharge charge quantity of the battery from the full charge state to the discharge end state, a plurality of output voltage values of the battery from the full charge state to the discharge end state measured in real time and a plurality of discharge depths corresponding to the output voltage values, wherein the direct current capacitance parameter function is at least a function of the discharge depth of the battery, and a battery physical model is established in real time based on the direct current capacitance parameter function.
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