CN112198433A - Battery capacity calibration method, calibration device and battery management system - Google Patents
Battery capacity calibration method, calibration device and battery management system Download PDFInfo
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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]
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- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
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Abstract
The present disclosure provides a battery capacity calibration method, including: s1, creating a composite parameter function at least based on a plurality of model parameter functions of the battery physical model; and S2, calibrating the battery capacity at least based on the composite parameter function, wherein the plurality of model parameter functions are all functions of the battery depth of discharge and the battery temperature, and the composite parameter function is a function of the battery depth of discharge and the battery temperature. The disclosure also discloses a battery capacity calibration device and a battery management system.
Description
Technical Field
The disclosure belongs to the technical field of batteries, and relates to a battery capacity calibration method, a calibration device and a battery management system.
Background
Generally, a capacity calibration method of a lithium battery is to establish a mapping table between Open Circuit battery OCV (Open Circuit Voltage) and DOD (Depth of discharge) of the lithium battery under different Temperature conditions to calibrate the real capacity of the battery, and refer to the following formula:
VOCV＝f(DOD，T)。
therefore, the accuracy of the electricity meter of the lithium battery is limited by the accuracy of the establishment of the OCV table, and only once calibration can be carried out at the beginning of the service life of the battery, and basically absolute battery capacity calibration is calibrated by the initially established mapping relation table of the OCV and the DOD in the whole service cycle of the battery.
However, as the battery ages, the chemical characteristics within the battery change, and the OCV of the battery and the true DOD of the battery correspondingly drift over time to some extent. Relying solely on OCV to calibrate DOD is not completely accurate.
In addition, the mapping relationship between OCV and DOD is usually expressed as a very complex high-order curve, and in digital implementation, the mapping relationship can only be implemented by a look-up table and interpolation method, which occupies a large amount of data space and has poor precision.
Disclosure of Invention
In order to solve at least one of the above technical problems, the present disclosure provides a battery capacity calibration method, a calibration apparatus, and a battery management system.
The battery capacity calibration method, the calibration device and the battery management system are realized through the following technical scheme.
According to an aspect of the present disclosure, there is provided a battery capacity calibration method, including: s1, creating a composite parameter function at least based on a plurality of model parameter functions of the battery physical model; and S2, calibrating the battery capacity at least based on the composite parameter function, wherein the plurality of model parameter functions are all functions of the battery depth of discharge and the battery temperature, and the composite parameter function is a function of the battery depth of discharge and the battery temperature.
According to the battery capacity calibration method of at least one embodiment of the present disclosure, the composite parameter function is obtained by data training and model parameter function modification, and the method includes the following steps: s11, training the composite parameter function by using different battery temperature data under the same battery depth of discharge (DOD) based on battery historical discharge data; training the composite parameter function by using different battery depth of discharge data under the same battery temperature (Temp); and S12, correcting the composite parameter function based on at least a plurality of the model parameter functions.
According to the battery capacity calibration method of at least one embodiment of the present disclosure, in step S11, the composite parameter function is trained using different battery temperature data at a plurality of battery depths of discharge, and the composite parameter function is trained using different battery depth of discharge data at a plurality of battery temperatures.
According to the battery capacity calibration method of at least one embodiment of the present disclosure, the plurality of model parameter functions include: a battery open circuit voltage function, a direct current resistance parameter function, a first high frequency impedance parameter function, and a second high frequency impedance parameter function.
According to the battery capacity calibration method of at least one embodiment of the present disclosure, the direct current resistance parameter function, the first high-frequency impedance parameter function, and the second high-frequency impedance parameter function are updated based on the measured output value of the battery during the use of the battery.
According to the battery capacity calibration method of at least one embodiment of the present disclosure, the battery open-circuit voltage function is obtained by a data fitting algorithm based on an actually measured output value in a battery open-circuit state.
According to the battery capacity calibration method of at least one embodiment of the present disclosure, the dc resistance parameter function, the first high-frequency impedance parameter function, and the second high-frequency impedance parameter function are updated by a data fitting algorithm based on an actually measured output value in a battery use process.
According to the battery capacity calibration method of at least one embodiment of the present disclosure, the composite parameter function may be a multi-order function.
According to the battery capacity calibration method of at least one embodiment of the disclosure, the battery physical model is established in real time by the following method: SS1, acquiring output values of the battery measured in real time in at least one state of a charging state, a discharging state and a non-charging and discharging state; SS2, obtaining a plurality of model parameter functions required by establishing a battery physical model in real time at least based on 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 SS3, real-time building battery physical model based on multiple said model parameter functions.
According to the battery capacity calibration method of at least one embodiment of the disclosure, when a battery is in a non-charging and 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 battery capacity calibration method 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 battery capacity calibration method of at least one embodiment of the disclosure, when a battery is in a non-charging and 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 high-frequency impedance parameter and a second high-frequency impedance parameter.
According to the battery capacity calibration method 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; obtaining a first high-frequency impedance parameter function and a second high-frequency impedance parameter function of the battery 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 of the plurality of non-charging and discharging states and the depth of discharge of the plurality of non-charging and discharging states, wherein the first high-frequency impedance parameter function and the second high-frequency impedance parameter function are at least functions of the depth of discharge and the temperature of the battery.
According to the battery capacity calibration method of at least one embodiment of the disclosure, 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.
According to the battery capacity calibration method of at least one embodiment of the disclosure, the battery historical discharge record data at least comprises mapping data of battery output voltage values and discharge depths.
According to the battery capacity calibration method, the initial discharge charge amount is lower than the product of the rated capacity of the battery and the preset percentage.
According to another aspect of the present disclosure, there is provided a battery capacity calibration apparatus including: a composite parametric function creation module that creates a composite parametric function based at least on a plurality of model parametric functions of a battery physical model; and the calibration module calibrates the battery capacity at least based on the composite parameter function, wherein the plurality of model parameter functions are functions of the battery discharge depth and the battery temperature, and the composite parameter function is a function of the battery discharge depth and the battery temperature.
The battery capacity calibration device according to at least one embodiment of the present disclosure further includes: the battery physical model building module builds a battery physical model by the following method:
SS1, acquiring output values of the battery measured in real time in at least one state of a charging state, a discharging state and a non-charging and discharging state;
SS2, obtaining a plurality of model parameter functions required by establishing a battery physical model in real time at least based on 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
and SS3, building a battery physical model in real time based on a plurality of model parameter functions.
According to still another aspect of the present disclosure, there is provided a battery management system including: the battery capacity calibration device of any one of the above.
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 battery capacity calibration method according to an embodiment of the present disclosure.
Fig. 2 is a schematic flow chart of a battery capacity calibration method according to still another embodiment of the present disclosure.
Fig. 3 is a schematic block diagram of a battery capacity calibration apparatus according to an embodiment of the present disclosure.
Fig. 4 is a block diagram schematically illustrating the structure of a battery capacity calibration apparatus according to still another embodiment of the present disclosure.
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 schematic flow chart of a battery capacity calibration method according to an embodiment of the present disclosure.
As shown in fig. 1, the battery capacity calibration method includes: s1, creating a composite parameter function at least based on a plurality of model parameter functions of the battery physical model; and S2, calibrating the battery capacity at least based on the composite parameter function, wherein the plurality of model parameter functions are all functions of the battery depth of discharge and the battery temperature, and the composite parameter function is a function of the battery depth of discharge and the battery temperature.
According to the method and the device, the DOD parameters of the lithium battery can be more truly and accurately mapped by creating the composite parameter function, the mathematical expression of the DOD is simplified through the mapping relation of the composite parameter function, and the influence of battery aging caused by long-term circulation of the battery can be reflected.
According to a preferred embodiment of the present disclosure, in the above embodiment, the obtaining of the composite parameter function through data training and model parameter function modification includes the following steps:
s11, training a composite parameter function by using different battery temperature data under the same battery depth of discharge (DOD) based on battery historical discharge data; training the composite parameter function by using different battery depth of discharge data under the same battery temperature (Temp); and S12, correcting the composite parameter function based on at least the plurality of model parameter functions.
For example, a composite parameter function (y)mix) As a function of the order of magnitude:
by the above method, each coefficient of the composite parameter function can be obtained.
Fig. 2 is a schematic flow chart of a battery capacity calibration method according to still another embodiment of the present disclosure. The battery capacity calibration method comprises the following steps:
s11, training a composite parameter function by using different battery temperature data under the same battery depth of discharge (DOD) based on battery historical discharge data; training the composite parameter function by using different battery depth of discharge data under the same battery temperature (Temp); s12, correcting the composite parameter function at least based on the plurality of model parameter functions; and S2, calibrating the battery capacity based on at least the composite parameter function.
Preferably, the plurality of model parameter functions in the above embodiment includes: a battery open circuit voltage function, a direct current resistance parameter function, a first high frequency impedance parameter function, and a second high frequency impedance parameter function.
According to a preferred embodiment of the present disclosure, in step S11, different battery temperature data are used for the composite parameter function (y) at a plurality of battery depths of discharge, respectivelymix) And training, namely training the composite parameter function by respectively using different battery depth of discharge data at a plurality of battery temperatures.
Composite parameter function, composite parameter function y obtained by the above embodimentmix(dod, Temp) at the same dod0 point, under different temperatures, the corresponding ymixThe values are different, and ymixTrend and true y of lithium batteriesmixThe trend of the change is the same. Composite parametric function ymixAt the same Temperature0 point, under different dod, corresponding ymixThe values are different, and ymixTrend and true y of lithium batteriesmixThe same trend of change.
Internal resistance R of lithium battery due to battery agingdc(do,dTem)p、Zhf1(dod,Temp)、Zhf2(dod, Temp) is changed, corresponding to ymixThe values also change synchronously, and ymixTrend and true y of lithium batteriesmixThe trend of the change is the same.
Wherein R isdc(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) isA first 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 a second high frequency impedance parameter of the battery, which is a non-linear function of the battery depth of discharge dod and the temperature Temp.
According to a preferred embodiment of the present disclosure, the direct current resistance parameter function, the first high frequency impedance parameter function and the second high frequency impedance parameter function are updated during use of the battery based on measured output values of the battery.
According to a preferred embodiment of the present disclosure, the battery open-circuit voltage function is obtained by a data fitting algorithm based on a measured output value in the battery open-circuit state.
According to a preferred embodiment of the present disclosure, the dc resistance parameter function, the first high frequency impedance parameter function, and the second high frequency impedance parameter function are updated by a data fitting algorithm based on the measured output values during the use of the battery.
Preferably, the physical model of the battery in the above embodiment is built in real time by the following method:
SS1, acquiring output values of the battery measured in real time in at least one state of a charging state, a discharging state and a non-charging and discharging state; SS2, obtaining a plurality of model parameter functions required by establishing a battery physical model in real time at least based on 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 SS3, real-time building a battery physical model based on a plurality of model parameter functions.
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.
According to the preferred embodiment of the present disclosure, when the physical model of the battery is established, the output current value (which may be constant current discharge or discharge of any load), the output voltage value and the battery temperature value of the battery from the full charge state to the discharge end state, i.e., the complete discharge process, of the battery 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.
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.
The non-charging and non-discharging state is a state that the battery is not charged and discharged and the voltage of the battery is stable for a long time, the measuring device measures the output voltage value and the temperature value of the battery at the moment, and the discharging depth of the battery at the moment is obtained based on historical discharging 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 preset time length can be reasonably set by a person skilled in the art.
Moreover, the initial discharge charge amount needs to be lower than the product of the rated capacity of the battery and a preset percentage, and the preset percentage can be set reasonably by a person skilled in the art.
More preferably, the direct-current resistance parameter function (i.e., one of the model 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 high-frequency impedance parameter and the second 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 high-frequency impedance parameter and a second high-frequency impedance parameter. Wherein, the second preset time length can be reasonably set by a person skilled in the art.
More preferably, the first high-frequency impedance parameter function and the second high-frequency impedance parameter function are 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 non-discharging state of the plurality of non-charging and non-discharging states, battery temperature values in the plurality of non-charging and non-discharging states and a depth of discharge in the plurality of non-charging and non-discharging states, a first high-frequency impedance parameter function and a second high-frequency impedance parameter function of the battery are obtained, and the first high-frequency impedance parameter function and the second high-frequency impedance parameter function are at least functions of the depth of discharge and the temperature of the battery.
The first high-frequency impedance parameter function and the second high-frequency impedance parameter function are obtained through a data fitting algorithm.
The physical model of the battery established by the method can be updated on line in real time, the physical model of the battery (particularly the lithium battery) can be directly established and optimized in practical application without performing modeling test on the battery in advance, the calculated lithium battery model gradually approaches to a theoretical actual value, the state parameter (such as an aging parameter) 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 judgment method.
The accuracy of the physical model of the battery established by the method is trained all the time along with the increase of the discharge times and the difference of the discharge current mode (constant current discharge or any load discharge) and the working temperature, and the accuracy is gradually improved.
Fig. 3 is a block diagram schematically illustrating the structure of a battery capacity calibration apparatus 1000 according to an embodiment of the present disclosure. The battery capacity calibration apparatus 1000 includes: a composite parametric function creation module 1001, the composite parametric function creation module 1001 creating a composite parametric function based on at least a plurality of model parametric functions of a battery physical model; and a calibration module 1002, where the calibration module 1002 calibrates the battery capacity based on at least a composite parameter function, where the plurality of model parameter functions are functions of battery depth of discharge and battery temperature, and the composite parameter function is a function of battery depth of discharge and battery temperature.
Fig. 4 is a block diagram schematically illustrating the structure of a battery capacity calibration apparatus 1000 according to still another embodiment of the present disclosure. The battery capacity calibration apparatus 1000 includes: a composite parametric function creation module 1001, the composite parametric function creation module 1001 creating a composite parametric function based on at least a plurality of model parametric functions of a battery physical model; the calibration module 1002 calibrates the battery capacity at least based on a composite parameter function, wherein the plurality of model parameter functions are functions of battery depth of discharge and battery temperature, and the composite parameter function is a function of battery depth of discharge and battery temperature; and a battery physical model establishing module 1003, wherein the battery physical model establishing module 1003 establishes the battery physical model by the following method: SS1, acquiring output values of the battery measured in real time in at least one state of a charging state, a discharging state and a non-charging and discharging state; SS2, obtaining a plurality of model parameter functions required by establishing a battery physical model in real time at least based on 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 SS3, real-time building a battery physical model based on a plurality of model parameter functions.
Fig. 3 to 4 are schematic block diagrams showing the structure of the battery capacity calibration apparatus 1000 implemented by hardware of the processing system.
The apparatus 1000 may include corresponding modules that perform each or several of the steps of the flowcharts described above. Thus, each step or several steps in the above-described flow charts may be performed by a respective module, and the apparatus may comprise one or more of these modules. The modules may be one or more hardware modules specifically configured to perform the respective steps, or implemented by a processor configured to perform the respective steps, or stored within a computer-readable medium for implementation by a processor, or by some combination.
The hardware architecture may be implemented using a bus architecture. The bus architecture may include any number of interconnecting buses and bridges depending on the specific application of the hardware and the overall design constraints. The bus 1100 couples various circuits including the one or more processors 1200, the memory 1300, and/or the hardware modules together. The bus 1100 may also connect various other circuits 1400, such as peripherals, voltage regulators, power management circuits, external antennas, and the like.
The bus 1100 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one connection line is shown, but no single bus or type of bus is shown.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present disclosure includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the implementations of the present disclosure. The processor performs the various methods and processes described above. For example, method embodiments in the present disclosure may be implemented as a software program tangibly embodied in a machine-readable medium, such as a memory. In some embodiments, some or all of the software program may be loaded and/or installed via memory and/or a communication interface. When the software program is loaded into memory and executed by a processor, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the processor may be configured to perform one of the methods described above by any other suitable means (e.g., by means of firmware).
The logic and/or steps represented in the flowcharts or otherwise described herein may be embodied in any readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions.
For the purposes of this description, a "readable storage medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the readable storage medium include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable read-only memory (CDROM). In addition, the readable storage medium may even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in the memory.
It should be understood that portions of the present disclosure may be implemented in hardware, software, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps of the method implementing the above embodiments may be implemented by hardware that is instructed to implement by a program, which may be stored in a readable storage medium, and when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present disclosure may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a readable storage medium. The storage medium may be a read-only memory, a magnetic or optical disk, or the like.
The battery management system of the present disclosure may include the battery capacity calibration apparatus 1000 of any of the above embodiments.
In the description herein, reference to the description of the terms "one embodiment/implementation," "some embodiments/implementations," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment/implementation or example is included in at least one embodiment/implementation or example of the present application. In this specification, the schematic representations of the terms described above are not necessarily 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 application, "plurality" means at least two, e.g., two, three, etc., unless 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 (10)
1. A battery capacity calibration method is characterized by comprising the following steps:
s1, creating a composite parameter function at least based on a plurality of model parameter functions of the battery physical model; and
s2, calibrating the battery capacity based on at least the composite parameter function,
the plurality of model parameter functions are all functions of battery discharge depth and battery temperature, and the composite parameter function is a function of the battery discharge depth and the battery temperature.
2. The battery capacity calibration method according to claim 1, wherein the composite parameter function is obtained by data training and model parameter function modification, and comprises the following steps:
s11, training the composite parameter function by using different battery temperature data under the same battery depth of discharge (DOD) based on battery historical discharge data; training the composite parameter function by using different battery depth of discharge data under the same battery temperature (Temp); and
s12, correcting the composite parameter function at least based on a plurality of model parameter functions.
3. The battery capacity calibration method according to claim 2, wherein in step S11, the composite parameter function is trained using different battery temperature data at a plurality of battery depths of discharge, and the composite parameter function is trained using different battery depth of discharge data at a plurality of battery temperatures.
4. The battery capacity calibration method of claim 1, wherein the plurality of model parameter functions comprises: a battery open circuit voltage function, a direct current resistance parameter function, a first high frequency impedance parameter function, and a second high frequency impedance parameter function.
5. The battery capacity calibration method according to claim 4, wherein the DC resistance parameter function, the first high-frequency impedance parameter function, and the second high-frequency impedance parameter function are updated based on a measured output value of the battery during the use of the battery.
6. The battery capacity calibration method according to claim 4, wherein the battery open-circuit voltage function is obtained by a data fitting algorithm based on a measured output value in a battery open-circuit state.
7. The battery capacity calibration method according to claim 5, wherein the DC resistance parameter function, the first high-frequency impedance parameter function, and the second high-frequency impedance parameter function are updated by a data fitting algorithm based on an actually measured output value during the use of the battery.
8. The battery capacity calibration method according to claim 1, wherein the composite parameter function is a multi-order function.
9. A battery capacity calibration device is characterized by comprising:
a composite parametric function creation module that creates a composite parametric function based at least on a plurality of model parametric functions of a battery physical model; and
a calibration module that calibrates battery capacity based at least on the composite parameter function,
the plurality of model parameter functions are all functions of battery discharge depth and battery temperature, and the composite parameter function is a function of the battery discharge depth and the battery temperature.
10. A battery management system, comprising: the battery capacity calibration apparatus of claim 9.
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