CN112557905B - Battery pack, data processing method thereof, computer device, medium, and vehicle - Google Patents

Battery pack, data processing method thereof, computer device, medium, and vehicle Download PDF

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CN112557905B
CN112557905B CN201910912509.6A CN201910912509A CN112557905B CN 112557905 B CN112557905 B CN 112557905B CN 201910912509 A CN201910912509 A CN 201910912509A CN 112557905 B CN112557905 B CN 112557905B
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soc
ocv
battery pack
curve
value
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CN112557905A (en
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冯天宇
刘思佳
李晓倩
康斌
宋旬
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BYD Co Ltd
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BYD Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/385—Arrangements for measuring battery or accumulator variables
    • G01R31/387—Determining ampere-hour charge capacity or SoC
    • G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery

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Abstract

The invention discloses a battery pack, a data processing method thereof, a computer device, a medium and a vehicle. The method comprises the following steps: preprocessing an initial OCV-SOC curve of each single battery to generate a first OCV-SOC curve, and converting the first OCV-SOC curve into a first OCV-SOC curve cluster according to the balance difference of each single battery; intercepting a common SOC section in the first OCV-SOC curve cluster and carrying out normalization processing on the common SOC section; determining an OCV-SOC curve of the battery pack according to a preset OCV weight model and the normalized common SOC section; and determining the state parameters of the battery pack according to the OCV-SOC curve of the battery pack. According to the method, the accuracy of the state parameters of the battery pack obtained according to the OCV-SOC curve of the battery pack is high, the calculated amount is small, and the load of a server is reduced; the calculation efficiency and the accuracy of the battery pack state parameters are improved.

Description

Battery pack, data processing method thereof, computer device, medium, and vehicle
Technical Field
The invention relates to the technical field of battery packs, in particular to a battery pack, a data processing method thereof, computer equipment, a medium and a vehicle.
Background
At present, the battery pack may be monitored through State parameters (including parameters for characterizing the State of the battery pack, such as a State of Charge (SOC) value, a State of Health (SOH) value, a power State value, and the like, or other parameters that may characterize the performance of the battery, such as an equivalent internal resistance, and the like) to improve user experience, and therefore, it is important to know the State parameters of the battery pack. In the prior art, the voltage and the current of each single battery in the battery pack are measured respectively, and the state parameters of each single battery are estimated according to an algorithm model, so that the state parameters of the whole battery pack are obtained finally. The disadvantages of the scheme are that: firstly, because the voltage, current and other data of each single battery are respectively and independently sampled and obtained, the calculation logic is simple, and in the process of calculating the state parameters, the used OCV-SOC curve cannot change along with the SOH value and the balance difference, so when the SOH value and the balance difference actually influence the OCV-SOC curve, the state parameters cannot be calculated, and the finally obtained OCV-SOC curve and the state parameters calculated according to the OCV-SOC curve are not accurate; secondly, because the state parameters of each single battery are estimated firstly and then the state parameters of the battery pack are obtained through calculation, the calculation amount is large, the load of a server is increased, the consumed time is long, and the efficiency is low; thirdly, since the voltages of the single batteries are independently sampled, and the total voltage of the battery pack is not verified redundantly according to the total voltage of the battery pack, which is finally calculated in the prior art, the accuracy of the calculated state parameters cannot be guaranteed.
Disclosure of Invention
The embodiment of the invention provides a battery pack, a data processing method of the battery pack, computer equipment, a medium and a vehicle, wherein the state parameters of the battery pack are obtained according to the OCV-SOC curve of the battery pack determined in the data processing method of the battery pack, the finally obtained state parameters are high in accuracy and small in calculated amount, and the load of a server is reduced; the calculation efficiency and the accuracy of the battery pack state parameters are improved.
In order to achieve the above object, the present invention provides a data processing method of a battery pack, comprising:
acquiring an initial OCV-SOC curve of each single battery in the battery pack;
preprocessing the initial OCV-SOC curves of the single batteries, and recording the preprocessed initial OCV-SOC curves as first OCV-SOC curves;
converting the first OCV-SOC curve into a first OCV-SOC curve cluster according to the balance difference of each single battery;
acquiring a common SOC section in the first OCV-SOC curve cluster according to a preset intercepting rule;
carrying out normalization processing on the public SOC section;
equivalently obtaining an OCV-SOC curve of the battery pack according to a preset OCV weight model and the normalized common SOC section;
and determining the state parameters of the battery pack according to the OCV-SOC curve of the battery pack.
Further, the preprocessing the initial OCV-SOC curve of each of the single batteries includes:
acquiring SOH values and aging rule data of the single batteries;
acquiring a preprocessing type matched with the aging rule data of the single battery;
and preprocessing the initial OCV-SOC curve of each single battery according to the SOH value of each single battery and a preprocessing type matched with the aging rule data of each single battery.
Further, the converting the first OCV-SOC curve into a first OCV-SOC curve cluster according to the balance difference of each single battery includes:
integrating all the first OCV-SOC curves into a second OCV-SOC curve cluster;
and converting the first OCV-SOC curves in the second OCV-SOC curve cluster into second OCV-SOC curves according to the balance difference of each single battery, and recording the second OCV-SOC curve cluster only containing the second OCV-SOC curves as the first OCV-SOC curve cluster.
Further, the converting the first OCV-SOC curve into a first OCV-SOC curve cluster according to the balance difference of each single battery includes:
acquiring a voltage peak value of each first OCV-SOC curve;
acquiring a maximum voltage value in voltage vertex values of the first OCV-SOC curves;
acquiring a voltage difference value between the maximum voltage value and other voltage vertex values except the maximum voltage value;
and according to the voltage difference, a second OCV-SOC curve with the voltage vertex value translated into the first OCV-SOC curve aligned with the maximum voltage value is recorded as a first OCV-SOC curve cluster after the translation is finished.
Further, the obtaining a common SOC segment in the first OCV-SOC curve cluster according to a preset intercept rule includes:
acquiring an SOC starting point value and an SOC end point value of the second OCV-SOC curve from the first OCV-SOC curve cluster;
acquiring a maximum starting point value of all the SOC starting point values and a minimum end point value of all the SOC end point values;
intercepting the second OCV-SOC curve into a common SOC curve according to the maximum starting point value and the minimum end point value; wherein the SOC starting point value of the common SOC curve is the maximum starting point value, and the SOC end point value of the common SOC curve is the minimum end point value;
recording the first OCV-SOC curve cluster as the common SOC segment when only the common SOC curve is included in the first OCV-SOC curve cluster.
Further, the normalizing the common SOC segment includes:
extending all the common SOC curves in the common SOC section according to a preset proportion along an SOC axis, wherein the preset proportion is a ratio between 1 and an SOC range difference of the common SOC curves, and the SOC range difference refers to a difference between an SOC terminal point value of the common SOC curve and an SOC starting point value of the common SOC curve; the SOC starting point values of all the common SOC curves after extension are 0, and the SOC end point values of all the common SOC curves after extension are 100%.
Further, the equivalent battery pack OCV-SOC curve according to the preset OCV weight model and the common SOC segment after normalization includes:
inquiring the second OCV-SOC curve corresponding to the maximum starting point value in the first OCV-SOC curve cluster, and recording a common SOC curve corresponding to the second OCV-SOC curve in the common SOC section after normalization as an M curve;
inquiring the second OCV-SOC curve corresponding to the minimum terminal point value in the first OCV-SOC curve cluster, and recording a common SOC curve corresponding to the second OCV-SOC curve in the common SOC section after normalization as an N curve;
determining an OCV value of a battery pack OCV-SOC curve by a preset OCV weight model:
Figure BDA0002215129040000041
wherein:
SOCithe SOC value is the coordinate of the point i on the SOC axis;
OCVithe OCV value is an OCV value corresponding to an OCV-SOC curve of the battery pack and an i-point coordinate on an SOC axis;
OCVMthe OCV value corresponding to the i point coordinate of the M curve on the SOC axis;
OCVNthe OCV value corresponding to the i point coordinate of the N curve on the SOC axis;
and generating a battery pack OCV-SOC curve according to the OCV value of the battery pack OCV-SOC curve.
Further, the determining the state parameter of the battery pack according to the OCV-SOC curve of the battery pack comprises:
and acquiring the total voltage and/or the total current of the battery pack, and determining the state parameters of the battery pack according to the total voltage and/or the total current of the battery pack and the OCV-SOC curve of the battery pack.
The invention also provides computer equipment comprising a memory, a processor and computer readable instructions stored in the memory and executable on the processor, wherein the processor executes the computer readable instructions to realize the data processing method of the battery pack.
The present invention also provides a computer-readable storage medium storing computer-readable instructions, which when executed by a processor implement the data processing method of the battery pack.
The invention also provides a battery pack, and the battery pack acquires the state parameters of the battery pack through the data processing method of the battery pack.
The invention also provides a vehicle which comprises the battery pack and a control module which is in communication connection with the battery pack and is used for executing the data processing method of the battery pack.
The data processing method of the battery pack comprises the steps of firstly preprocessing an initial OCV-SOC curve of each single battery to generate a first OCV-SOC curve, and then converting the first OCV-SOC curve into a first OCV-SOC curve cluster according to the balance difference of each single battery; then, a public SOC section in the first OCV-SOC curve cluster is intercepted and normalized; determining an OCV-SOC curve of the battery pack according to a preset OCV weight model and the normalized common SOC section; and finally, determining the state parameters of the battery pack according to the OCV-SOC curve of the battery pack. According to the data processing method of the battery pack, the OCV-SOC curve of the battery pack is finally determined by combining the SOH value (in the preprocessing process) and the balance difference, the state parameters of the battery pack obtained according to the OCV-SOC curve of the battery pack are high in accuracy and small in calculated amount, and the load of a server is reduced; the calculation efficiency and the accuracy of the battery pack state parameters are improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without inventive labor.
Fig. 1 is a flowchart of a data processing method of a battery pack according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a computer device in one embodiment of the invention;
FIG. 3 is a schematic diagram of a first OCV-SOC curve cluster in an embodiment of the present invention;
FIG. 4 is a schematic diagram of a common SOC segment in an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The data processing method of the battery pack can be applied to an application environment in which a client (computer equipment) communicates with a server through a network. The client (computer device) includes, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, cameras, and portable wearable devices. The server may be implemented as a stand-alone server or as a server cluster consisting of a plurality of servers.
In one embodiment, as shown in fig. 1, there is provided a data processing method of a battery pack, including the following steps S10-S70:
s10, acquiring an initial OCV-SOC curve of each single battery in the battery pack; the OCV (open Circuit voltage) is a Battery open Circuit voltage, and it is understood that an initial OCV-SOC curve of each single Battery is stored in a BMS (Battery Management System) and can be directly obtained from the BMS or another database (the initial OCV-SOC curve of each single Battery is stored in the BMS or another database in advance). Understandably, the battery pack is preferably a series battery pack.
S20, preprocessing the initial OCV-SOC curves of the single batteries, and recording the preprocessed initial OCV-SOC curves as first OCV-SOC curves; the preprocessing process is that the preprocessing type (including scaling, intercepting, translating and the like) of a single battery can be determined according to the SOH value of the single battery and the aging rule data of the single battery (one aging rule data corresponds to one preprocessing type), so that the preprocessing type can be determined according to the aging rule data (the change rule between an OCV-SOC curve after the single battery is aged and an OCV-SOC curve before the single battery is not aged); for example, when a single battery is an N battery, the aging rule data thereof refers to data that can characterize an OCV-SOC curve of the aged N battery is an OCV-SOC curve after scaling and before non-aging, so that the type of pretreatment of the N battery is scaling; when a single battery is a P battery, the aging rule data of the P battery refers to data which can represent that an OCV-SOC curve of the P battery after aging is an OCV-SOC curve before non-aging after intercepting, and therefore the pretreatment type of the P battery is intercepting. And the preprocessing amplitude (such as scaling and clipping ranges) can be determined according to the SOH value of the single battery. Understandably, the SOH value of a single battery and the aging law data of the single battery can be directly obtained from the BMS or other databases (initial OCV-SOC curve, SOH value, aging law data, etc. of each single battery are pre-stored in the BMS or other databases).
S30, converting the first OCV-SOC curve into a first OCV-SOC curve cluster according to the balance difference of each single battery; understandably, the balancing difference of each battery can be directly obtained from the BMS or other databases (the balancing difference of each battery is stored in the BMS or other databases in advance, etc.). In this step, it is necessary to align the voltage peaks according to the equalization difference of the single battery cells (at least, the first OCV-SOC curves are shifted on the OCV axis according to the principle of "the peak voltage value is aligned with the maximum voltage value" to align the voltage peaks on the OCV axis), and it may also be necessary to shift the first OCV-SOC curves on the SOC axis according to the conditions for forming the curve clusters, such as the interval requirement between the first OCV-SOC curves or/. and the alignment criteria of the SOCs on the curve clusters, that is, to shift the first OCV-SOC curves (for example, the first OCV-SOC curves are shifted in the second OCV-SOC curve clusters to align the peak voltage values of the first OCV-SOC curves), to keep the peak voltage value of each first OCV-SOC curve aligned with the maximum voltage value among the voltage peak values of the first OCV-SOC curves, to convert the first OCV-SOC curve cluster to a first OCV-SOC curve cluster.
Understandably, in an embodiment, after the initial OCV-SOC is preprocessed according to the SOH value in step S20, a first OCV-SOC curve is obtained, and then the process directly proceeds to step S30, and each first OCV-SOC curve is converted into a first OCV-SOC curve cluster according to the equalization difference of a single battery.
In another embodiment, after obtaining the first OCV-SOC curve in step S20, the step S30 of converting the first OCV-SOC curve into a first OCV-SOC curve cluster according to the balance difference of each single battery includes:
integrating all the first OCV-SOC curves into a second OCV-SOC curve cluster;
and converting the first OCV-SOC curves in the second OCV-SOC curve cluster into second OCV-SOC curves according to the balance difference of each single battery, and recording the second OCV-SOC curve cluster only containing the second OCV-SOC curves as the first OCV-SOC curve cluster.
That is, in the present embodiment, it is necessary to first integrate the first OCV-SOC curve into the second OCV-SOC curve cluster according to the requirement (in the process, the alignment on the SOC axis is already achieved), and then shift the first OCV-SOC curve in the second OCV-SOC curve cluster according to the equalization difference (voltage peak alignment) in the second OCV-SOC curve cluster, so that during the shift, it is only necessary to shift the first OCV-SOC curve in the OCV axis direction so that the peak voltage value thereof is flush with the maximum voltage value.
S40, acquiring a common SOC section in the first OCV-SOC curve cluster according to a preset intercepting rule; that is, in this step, from the first OCV-SOC curve cluster, a maximum starting value among SOC starting values of all second OCV-SOC curves and a minimum ending value among SOC ending values of all second OCV-SOC curves are obtained; intercepting a common SOC section according to the maximum starting point value and the minimum end point value; wherein the SOC starting point value of the common SOC section is the maximum starting point value, and the SOC end point value of the common SOC section is the minimum end point value.
S50, carrying out normalization processing on the public SOC section; that is, in this embodiment, the partial SOC section originally in the aged battery pack (i.e., the common SOC section) is normalized to the actual full SOC section of the battery pack (i.e., the common SOC section after normalization), so that the OCV-SOC curve of the battery pack is more accurately determined. Specifically, extending a public SOC section along an SOC axis according to a preset proportion, wherein the preset proportion is a ratio between 1 and an SOC range difference of the public SOC section, and the SOC range difference refers to a difference value between an SOC end point value of the public SOC section and an SOC start point value of the public SOC section; the SOC starting point values of the common SOC sections after extension are all 0, and the SOC end point values of all the common SOC curves after extension are all 100%.
S60, equivalently obtaining an OCV-SOC curve of the battery pack according to a preset OCV weight model and the normalized common SOC section; that is, in the preset OCV weight model, the weight of the OCV value of the common SOC curve in the common SOC section is set according to the SOC value in the common SOC section, and then the OVC value in the OCV-SOC curve of the battery pack is calculated according to the OCV value of the common SOC curve and the weight thereof; the OVC values in the battery pack OCV-SOC curves are then used along with the SOC values of the common SOC segment to determine the battery pack OCV-SOC curves.
And S70, determining the state parameters of the battery pack according to the OCV-SOC curve of the battery pack.
That is, the total voltage of the battery pack is collected by the voltage collector, the total current of the battery pack can also be collected by the current collector, and then the state parameters of the battery pack are determined according to the total voltage and/or the total current of the battery pack and the OCV-SOC curve of the battery pack. The state parameter of the battery pack may include a battery pack SOC value, a battery pack SOH value, a battery pack power state value, and other parameters that are used to represent the state of the battery pack, and may also include other parameters that may represent the performance of the battery, such as the equivalent internal resistance of the battery pack. It should be understood that the formulas for calculating the above state parameters are all in the prior art, and are not repeated herein, and in calculating one of the state parameters, only one or more of the total voltage and total current of the battery pack and the OCV-SOC curve of the battery pack may be used.
The data processing method of the battery pack comprises the steps of firstly preprocessing an initial OCV-SOC curve of each single battery to generate a first OCV-SOC curve, and then converting the first OCV-SOC curve into a first OCV-SOC curve cluster according to the balance difference of each single battery; then, a public SOC section in the first OCV-SOC curve cluster is intercepted and normalized; determining an OCV-SOC curve of the battery pack according to a preset OCV weight model and the normalized common SOC section; and finally, determining the state parameters of the battery pack according to the OCV-SOC curve of the battery pack. The current and voltage synchronism of the battery pack is high, so that the updating speed is high; therefore, in the present invention, the OCV-SOC curve of the battery pack is finally determined in combination with the SOH value (during the preprocessing) and the equalization difference, and the accuracy of the state parameters of the battery pack obtained from the OCV-SOC curve of the battery pack and from the OCV-SOC curve of the battery pack is high; in addition, in the invention, the OCV-SOC curves are merged and then subjected to a series of processing to form an overall battery pack OCV-SOC curve, and then the state parameters of the battery pack can be estimated only by performing primary calculation according to the battery pack OCV-SOC curve (in the prior art, the voltage and the current of each single battery or the highest and lowest node voltage and the current are required to be introduced into a state parameter estimation algorithm to be respectively calculated to obtain the state parameters of each single node, and then the state parameters of the whole battery pack are calculated by weighted average according to the SOH, the balance error, the highest and lowest voltage node number and the like, so that the calculation amount is extremely large), the calculation amount is small, the server load is reduced, and the calculation efficiency is improved.
Further, in step S20, the preprocessing the initial OCV-SOC curve of each single battery includes:
acquiring SOH values and aging rule data of the single batteries; the aging rule data refers to a change rule between an OCV-SOC curve after the single battery is aged and an OCV-SOC curve before the single battery is not aged; understandably, the SOH value of a single battery and the aging law data of the single battery can be directly obtained from the BMS or other databases (the SOH value and the aging law data of each single battery are stored in the BMS or other databases in advance).
Acquiring a preprocessing type matched with the aging rule data of the single battery; wherein the preprocessing types comprise zooming, intercepting, translating and the like; for example, when a single battery is an N battery, the aging rule data thereof refers to data that can characterize an OCV-SOC curve of the aged N battery is an OCV-SOC curve after scaling and before non-aging, so that the type of pretreatment of the N battery is scaling; when a single battery is a P battery, the aging rule data of the P battery refers to data which can represent that an OCV-SOC curve of the P battery after aging is an OCV-SOC curve before non-aging after intercepting, and therefore the pretreatment type of the P battery is intercepting.
And preprocessing the initial OCV-SOC curve of each single battery according to the SOH value of each single battery and a preprocessing type matched with the aging rule data of each single battery. That is, the magnitude of the preprocessing (such as the range of scaling and clipping, the distance of translation, etc.) according to the type of preprocessing can be determined according to the SOH value of the single battery. After pre-processing the initial OCV-SOC according to the SOH value, a first OCV-SOC curve is obtained.
Further, the step S30, namely, the converting the first OCV-SOC curve into a first OCV-SOC curve cluster according to the equalization difference of each single battery, includes:
acquiring a voltage peak value of each first OCV-SOC curve;
acquiring a maximum voltage value in voltage vertex values of the first OCV-SOC curves;
acquiring a voltage difference value between the maximum voltage value and other voltage vertex values except the maximum voltage value;
and according to the voltage difference, a second OCV-SOC curve with the voltage vertex value translated into the first OCV-SOC curve aligned with the maximum voltage value is recorded as a first OCV-SOC curve cluster after the translation is finished.
That is, in the above embodiment, the balancing difference of the single battery refers to SOC balancing difference or capacity balancing difference, and the balancing difference may be directly obtained from the BMS or other databases (the balancing difference of each single battery is stored in advance in the BMS or other databases). The equalization difference may be converted into the voltage difference value, and thus, converting the first OCV-SOC curve into a first OCV-SOC curve cluster according to the equalization difference may be finally embodied as adjusting all the first OCV-SOC curves to achieve voltage vertex alignment of all the first OCV-SOC curves, that is, as an preference, when the first OCV-SOC curves have been integrated into a second OCV-SOC curve cluster according to a requirement, the first OCV-SOC curves in the second OCV-SOC curve cluster may be directly translated, the vertex voltage value of each first OCV-SOC curve may be maintained aligned with the maximum voltage value in the voltage vertex values of the respective first OCV-SOC curves, and finally, the first OCV-SOC curves may be converted into the first OCV-SOC curve cluster. In this embodiment, each of the first OCV-SOC curves corresponds to a voltage difference value, and understandably, the voltage difference value corresponding to the first OCV-SOC curve corresponding to the maximum voltage value is zero, so that when the second OCV-SOC curve in which the voltage vertex value corresponding to the first OCV-SOC curve is shifted to be aligned with the maximum voltage value according to the voltage difference value, since the voltage difference value of the first OCV-SOC curve corresponding to the maximum voltage value is zero, the moving distance of the first OCV-SOC curve corresponding to the maximum voltage value is zero, and thus, the second OCV-SOC curve can be recorded as long as the voltage vertex value is aligned with the maximum voltage value regardless of whether the first OCV-SOC curve is finally moving upward. In the above-described embodiment, completion of the shift may be confirmed when all of the first OCV-SOC curves have been shifted to the second OCV-SOC curves whose voltage peak values are aligned with the maximum voltage values, at which time all of the second OCV-SOC curves after completion of the shift are recorded as the first OCV-SOC curve cluster. As shown in fig. 3, which is a first cluster of OCV-SOC curves where the voltage vertex values are all aligned with the maximum voltage values.
Further, the step S40, that is, obtaining the common SOC segment in the first OCV-SOC curve cluster according to a preset clipping rule, includes:
acquiring an SOC starting point value and an SOC end point value of the second OCV-SOC curve from the first OCV-SOC curve cluster;
acquiring a maximum starting point value of all the SOC starting point values and a minimum end point value of all the SOC end point values;
intercepting the second OCV-SOC curve into a common SOC curve according to the maximum starting point value and the minimum end point value; wherein the SOC starting point value of the common SOC curve is the maximum starting point value, and the SOC end point value of the common SOC curve is the minimum end point value;
recording the first OCV-SOC curve cluster as the common SOC segment when only the common SOC curve is included in the first OCV-SOC curve cluster.
That is, in this embodiment, from the first OCV-SOC curve cluster, the maximum starting point value among the SOC starting point values of all the second OCV-SOC curves and the minimum ending point value among the SOC ending point values of all the second OCV-SOC curves are obtained; intercepting a common SOC section which can be used for finally determining an OCV-SOC curve of the battery pack according to the maximum starting point value and the minimum end point value; wherein the SOC start point value of the common SOC segment (i.e. the SOC start point values of all common SOC curves) is the maximum start point value, and the SOC end point value of the common SOC segment (i.e. the SOC end point values of all common SOC curves) is the minimum end point value. In the first OCV-SOC curve cluster shown in FIG. 3, SOC P0 is the maximum starting point value (5% SOC shown in FIG. 3)P100 is the minimum endpoint value (80% shown in fig. 3); while SOC P0 and SOCPThe middle curve segment of 100 is the common SOC curve.
Further, the step S50, namely the normalizing the common SOC segment, includes:
extending all the common SOC curves in the common SOC section according to a preset proportion along an SOC axis, wherein the preset proportion is a ratio between 1 and an SOC range difference of the common SOC curves, and the SOC range difference refers to a difference between an SOC terminal point value of the common SOC curve and an SOC starting point value of the common SOC curve; the SOC starting point values of all the common SOC curves after extension are 0, and the SOC end point values of all the common SOC curves after extension are 100%. That is, in this embodiment, a part of the SOC sections originally in the aged battery pack (i.e., the common SOC section) is normalized to the actual SOC sectionThe full SOC segment of the battery pack (i.e., the common SOC segment after normalization) in order to more accurately determine the OCV-SOC curve of the battery pack. Understandably, in the embodiment shown in FIG. 3, the SOC range difference is equal to SOCP100-SOC P0 due to SOC P100 is 80% in FIG. 3, SOC P0 is 5% in FIG. 3, therefore, the SOC range difference is 75%; therefore, it is necessary to stretch the 75% length common SOC segment to 100% length, and SOC in the common SOC segment before stretchingPA coordinate point corresponding to 0 is aligned with a position on the SOC axis where the SOC value is 0 after stretching, and the SOC in the common SOC section before stretchingPThe coordinate point corresponding to 100 is aligned with the position on the SOC axis where the SOC value is 100% after stretching.
Further, the step S60, namely, the equalizing the OCV-SOC curve of the battery pack according to the preset OCV weight model and the common SOC segment after the normalization, includes:
inquiring the second OCV-SOC curve corresponding to the maximum starting point value in the first OCV-SOC curve cluster, and recording a common SOC curve corresponding to the second OCV-SOC curve in the common SOC section after normalization as an M curve; in fig. 4, the M curve is curve 3.
Inquiring the second OCV-SOC curve corresponding to the minimum terminal point value in the first OCV-SOC curve cluster, and recording a common SOC curve corresponding to the second OCV-SOC curve in the common SOC section after normalization as an N curve; in fig. 4, the N curve is curve 2.
Determining an OCV value of a battery pack OCV-SOC curve by a preset OCV weight model:
Figure BDA0002215129040000151
wherein:
SOCithe SOC value is the coordinate of the point i on the SOC axis;
OCVithe OCV value is an OCV value corresponding to an OCV-SOC curve of the battery pack and an i-point coordinate on an SOC axis;
OCVMis the i point of the M curve on the SOC axisOCV value corresponding to the coordinate;
OCVNthe OCV value corresponding to the i point coordinate of the N curve on the SOC axis;
and generating a battery pack OCV-SOC curve according to the OCV value of the battery pack OCV-SOC curve. In fig. 4, the OCV-SOC curve of the battery pack is curve 4.
That is, in the preset OCV weight model, according to the SOC value (i.e., SOC) in the common SOC sectioni) The OCV value (i.e., OCV) of the common SOC curve in the common SOC segment is setMAnd OCVN) Weight of (1), (b)
Figure BDA0002215129040000152
And
Figure BDA0002215129040000153
) Then, an OVC value (i.e., OCV) in the OCV-SOC curve of the battery pack is calculated based on the OCV value of the common SOC curve and its weighti) (ii) a And then determining the OCV-SOC curve of the battery pack by using the OCV value in the OCV-SOC curve of the battery pack obtained in the preset OCV weight model and the SOC value of the common SOC section, namely determining the i-point coordinate (SOC) of the OCV-SOC curve of the battery packi，OCVi) That is, the OCV value corresponding to the i-point coordinate of the OCV-SOC curve of the battery pack is OCViThe SOC value corresponding to the i point coordinate of the OCV-SOC curve of the battery pack is the SOCi。
Further, the step S70, namely, the determining the state parameter of the battery pack according to the OCV-SOC curve of the battery pack, includes:
and acquiring the total voltage and/or the total current of the battery pack, and determining the state parameters of the battery pack according to the total voltage and/or the total current of the battery pack and the OCV-SOC curve of the battery pack. The state parameters of the battery pack include parameters for characterizing the state of the battery pack (such as a state of charge (SOC) value, a state of health (SOH) value, a power state value, and the like), or other parameters that may characterize the performance of the battery (such as an equivalent internal resistance, and the like). It should be understood that, in this embodiment, in the step S70, the voltage collector is directly used to collect the total voltage of the battery pack, or the current collector may be used to collect the total current of the battery pack, and then, the state parameters of the battery pack are determined according to the total voltage and/or the total current of the battery pack and the OCV-SOC curve of the battery pack (in the prior art, since each single battery independently samples the voltage and the current, and introduces them into the state parameter estimation algorithm, the state parameters of each single battery are respectively calculated, and therefore, the collected total voltage and total current cannot be used); therefore, the voltage of each single battery does not need to be independently sampled, and the sampling process is simplified. The invention can be used for directly determining the state parameters of the battery pack. Understandably, the method determines the state parameters of the battery pack according to the total voltage and/or the total current of the battery pack and the OCV-SOC curve of the battery pack, and the process can also be used for performing redundancy verification on the state parameters finally calculated in the prior art, so that the accuracy of the state parameters calculated in the prior art is ensured.
The state parameter of the battery pack may include a battery pack SOC value, a battery pack SOH value, a battery pack power state value, and the like, and may also include other parameters that may represent battery performance, such as an equivalent internal resistance of the battery pack, and the like. It should be understood that the formulas for calculating the above state parameters are all in the prior art, and are not repeated herein, and in calculating one of the state parameters, only one or more of the total voltage and total current of the battery pack and the OCV-SOC curve of the battery pack may be used.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 2. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer readable instructions, and a database. The internal memory provides an environment for the operating system and execution of computer-readable instructions in the non-volatile storage medium. The computer readable instructions, when executed by a processor, implement a data processing method for a battery pack.
In one embodiment, a computer device is provided, which includes a memory, a processor, and computer readable instructions stored in the memory and executable on the processor, wherein the processor executes the computer readable instructions to implement the data processing method of the battery pack.
The present invention also provides a computer-readable storage medium storing computer-readable instructions, which when executed by a processor implement the data processing method of the battery pack described above.
In an embodiment, the invention further provides a battery pack, and the battery pack acquires the state parameters of the battery pack through the data processing method of the battery pack. For the specific limitations of the battery pack, reference may be made to the limitations of the data processing method for the battery pack, and details are not repeated here.
The invention also provides a vehicle which comprises the battery pack and a control module which is in communication connection with the battery pack and is used for executing the data processing method of the battery pack.
For the specific definition of the control module, reference may be made to the above definition of the data processing method of the battery pack, and details are not described herein again. Each of the above control modules may be implemented in whole or in part by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
It will be understood by those of ordinary skill in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware associated with computer readable instructions, which can be stored in a non-volatile computer readable storage medium, and when executed, can include processes of the embodiments of the methods described above. Any reference to memory, storage, databases, or other media used in embodiments provided herein may include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), Direct Rambus Dynamic RAM (DRDRAM), and Rambus Dynamic RAM (RDRAM).
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of each functional unit or module is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units or modules according to requirements, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.

Claims (12)

1. A data processing method of a battery pack, comprising:
acquiring an initial OCV-SOC curve of each single battery in the battery pack;
preprocessing the initial OCV-SOC curves of the single batteries, and recording the preprocessed initial OCV-SOC curves as first OCV-SOC curves;
converting the first OCV-SOC curve into a first OCV-SOC curve cluster according to the balance difference of each single battery;
acquiring a common SOC section in the first OCV-SOC curve cluster according to a preset intercepting rule;
carrying out normalization processing on the public SOC section;
equivalently obtaining an OCV-SOC curve of the battery pack according to a preset OCV weight model and the normalized common SOC section; the preset OCV weight model is as follows:
Figure FDA0003249542180000011
wherein:
SOCithe SOC value is the coordinate of the point i on the SOC axis;
OCVithe OCV value is an OCV value corresponding to an OCV-SOC curve of the battery pack and an i-point coordinate on an SOC axis;
OCVMthe OCV value corresponding to the i point coordinate of the M curve on the SOC axis;
OCVNthe OCV value corresponding to the i point coordinate of the N curve on the SOC axis;
and determining the state parameters of the battery pack according to the OCV-SOC curve of the battery pack.
2. The data processing method of the battery pack according to claim 1, wherein the preprocessing of the initial OCV-SOC curve of each of the single batteries includes:
acquiring SOH values and aging rule data of the single batteries;
acquiring a preprocessing type matched with the aging rule data of the single battery;
and preprocessing the initial OCV-SOC curve of each single battery according to the SOH value of each single battery and a preprocessing type matched with the aging rule data of each single battery.
3. The data processing method of the battery pack according to claim 1, wherein the converting the first OCV-SOC curve into a first OCV-SOC curve cluster according to the equalization difference of each of the single batteries comprises:
integrating all the first OCV-SOC curves into a second OCV-SOC curve cluster;
and converting the first OCV-SOC curves in the second OCV-SOC curve cluster into second OCV-SOC curves according to the balance difference of each single battery, and recording the second OCV-SOC curve cluster only containing the second OCV-SOC curves as the first OCV-SOC curve cluster.
4. The data processing method of the battery pack according to claim 1, wherein the converting the first OCV-SOC curve into a first OCV-SOC curve cluster according to the equalization difference of each of the single batteries comprises:
acquiring a voltage peak value of each first OCV-SOC curve;
acquiring a maximum voltage value in voltage vertex values of the first OCV-SOC curves;
acquiring a voltage difference value between the maximum voltage value and other voltage vertex values except the maximum voltage value;
and according to the voltage difference, a second OCV-SOC curve with the voltage vertex value translated into the first OCV-SOC curve aligned with the maximum voltage value is recorded as a first OCV-SOC curve cluster after the translation is finished.
5. The data processing method of the battery pack according to claim 4, wherein the obtaining the common SOC segment in the first OCV-SOC curve cluster according to a preset truncation rule comprises:
acquiring an SOC starting point value and an SOC end point value of the second OCV-SOC curve from the first OCV-SOC curve cluster;
acquiring a maximum starting point value of all the SOC starting point values and a minimum end point value of all the SOC end point values;
intercepting the second OCV-SOC curve into a common SOC curve according to the maximum starting point value and the minimum end point value; wherein the SOC starting point value of the common SOC curve is the maximum starting point value, and the SOC end point value of the common SOC curve is the minimum end point value;
recording the first OCV-SOC curve cluster as the common SOC segment when only the common SOC curve is included in the first OCV-SOC curve cluster.
6. The data processing method of a battery pack according to claim 5, wherein the normalizing the common SOC segment includes:
extending all the common SOC curves in the common SOC section according to a preset proportion along an SOC axis, wherein the preset proportion is a ratio between 1 and an SOC range difference of the common SOC curves, and the SOC range difference refers to a difference between an SOC terminal point value of the common SOC curve and an SOC starting point value of the common SOC curve; the SOC starting point values of all the common SOC curves after extension are 0, and the SOC end point values of all the common SOC curves after extension are 100%.
7. The data processing method of the battery pack according to claim 6, wherein the equating an OCV-SOC curve of the battery pack according to a preset OCV weight model and the common SOC segment after normalization comprises:
inquiring the second OCV-SOC curve corresponding to the maximum starting point value in the first OCV-SOC curve cluster, and recording a common SOC curve corresponding to the second OCV-SOC curve in the common SOC section after normalization as an M curve;
inquiring the second OCV-SOC curve corresponding to the minimum terminal point value in the first OCV-SOC curve cluster, and recording a common SOC curve corresponding to the second OCV-SOC curve in the common SOC section after normalization as an N curve;
determining an OCV value of an OCV-SOC curve of the battery pack through the preset OCV weight model;
and generating a battery pack OCV-SOC curve according to the OCV value of the battery pack OCV-SOC curve.
8. The data processing method of the battery pack according to any one of claims 1 to 7, wherein the determining of the state parameter of the battery pack from the OCV-SOC curve of the battery pack comprises:
and acquiring the total voltage and/or the total current of the battery pack, and determining the state parameters of the battery pack according to the total voltage and/or the total current of the battery pack and the OCV-SOC curve of the battery pack.
9. A computer device comprising a memory, a processor and computer readable instructions stored in the memory and executable on the processor, characterized in that the processor implements a data processing method of a battery pack according to any one of claims 1 to 8 when executing the computer readable instructions.
10. A computer-readable storage medium storing computer-readable instructions, wherein the computer-readable instructions, when executed by a processor, implement a data processing method of a battery pack according to any one of claims 1 to 8.
11. A battery pack characterized in that the battery pack acquires a state parameter of the battery pack by the data processing method of the battery pack according to any one of claims 1 to 8.
12. A vehicle, characterized in that the vehicle comprises a battery pack and a control module communicatively connected to the battery pack for performing the data processing method of the battery pack according to any one of claims 1 to 8.
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