CN113759265A - Fault judgment method of power supply system and energy storage system - Google Patents
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- 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
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- 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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- 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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- 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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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0029—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with safety or protection devices or circuits
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract
The invention provides a fault judgment method of a power supply system and an energy storage system. The method for judging the fault of the power supply system comprises the steps of obtaining information of voltage, current, temperature and the like of a single battery, estimating real-time internal resistance of the single battery through an internal resistance estimation model of an OCV-SOC curve, ensuring that the detection of the resistance is carried out in real time under the full working condition, calibrating the internal resistance estimation model of the OCV-SOC curve based on a charging and discharging state overlapping internal resistance estimation model, calibrating the charging and discharging state overlapping internal resistance estimation model based on a standing internal resistance estimation model, and ensuring the monitoring precision of the real-time internal resistance. By comprehensive use and mutual calibration of the three internal resistance estimation models, the real-time high-precision monitoring of the full-working-condition internal resistance of the battery of the power supply system is ensured. And system fault diagnosis is carried out based on the obtained real-time high-precision internal resistance value, so that the safety accidents of the power supply system can be effectively avoided.
Description
Technical Field
The invention relates to the technical field of power supply system management, in particular to a fault judgment method of a power supply system and an energy storage system.
Background
The energy storage system comprises key components such as an energy storage battery, a Battery Management System (BMS), a monitoring system (EMS) and a PCS (Power System), wherein the health state of the energy storage battery with large quantity and the reliability of a connecting line bank in series-parallel connection of the battery are important bases for the safe and stable operation of the energy storage system. The equivalent internal resistance of the power supply system is the root of the heat generated by the system, wherein the impedance of the power supply system comprises the direct-current internal resistance (the sum of all heating equivalent internal resistances such as ohmic internal resistance, electrochemical internal resistance, concentration polarization internal resistance and the like) of the single battery, the connecting and discharging resistance and the contact resistance thereof, the cable resistance and the like. The change of the resistors can not only reflect the health state of the power supply system, but also early warn, identify and position battery faults, such as rapid attenuation, abnormal heating, electrical connection aging, oxidation and the like of the single battery according to the change trend of the resistors. Through acquiring the internal resistance of the single battery and the system connection internal resistance in real time, the sudden faults of the battery and the system can be diagnosed and early-warned, and the safety accidents of an energy storage system or a power station are effectively avoided.
Especially for a lithium ion power supply system, the early diagnosis difficulty of the sudden body fault of the lithium ion battery is high, the middle diagnosis time window is short, once the later stage is reached, the safety hazard is extremely high, and the early warning method is a difficult point for the diagnosis and early warning of the body fault of the lithium ion battery. In the key battery state variables for diagnosis, the direct current internal resistance of a battery core is a key characteristic variable of the health state and the heat production rate of the battery
In the prior art, large-scale internal resistance analysis or other advanced analysis functions of a battery are basically realized on an EMS (energy management system) or a cloud platform, and although the platforms have strong data analysis and processing capacity, the data updating period is long, the data synchronism is poor, and sudden abnormity cannot be accurately and timely identified and processed.
The prior art provides a method for calculating internal resistance of a power battery, which includes screening out a total current value and a total voltage value from data, calculating a series of total current difference values and a corresponding series of total voltage difference values, and calculating internal resistance R ═ k of the power battery. The method performs minimum variance fitting on uploaded data in half a year, and the result reflects the internal resistance state of the system for a long period and has no real-time characteristic at all.
The prior art also provides a method for detecting the internal resistance abnormality of the single battery in the battery pack on line, which feeds back the information of the single battery with abnormal internal resistance and the internal resistance abnormality information according to the continuous charging record. The method is mainly based on the internal resistance result after the charging time t, namely the internal resistance value of a certain specific SOC is evaluated, whether the battery with abnormal internal resistance exists in the battery pack is evaluated through the internal resistance value, although the internal resistance of each battery can be obtained, the real-time internal resistance estimation under the operation working condition still cannot be realized.
The prior art also provides a real-time online estimation method for the internal resistance of the secondary battery, which predicts the internal resistance of the battery by only depending on the terminal voltage and the terminal current of the battery through a Thevenin equivalent circuit model of the battery and a Kalman filter design observer. The estimation precision is influenced by the precision of a battery model and the inconsistency degree of the battery, the requirement on the real-time performance of the system is high, and the realization difficulty is high.
The prior art also provides an internal resistance estimation system and method for an energy storage system, which use a dc-dc converter (equalization) to charge and discharge a battery, and measure the voltage and current of the battery change, thereby estimating the internal resistance of the battery. According to the method, a relatively accurate measurement result can be obtained only when the energy storage system does not work (namely, the energy storage system is kept still for a relatively long time), meanwhile, a capacitor and a switching device are used for charging and discharging the battery, so that relatively large impact current is generated, the current change is difficult to control due to the fact that a loop does not have inductance, the requirement on the current acquisition speed is high, and the realization difficulty is high.
The prior art also provides a battery function state diagnosis method based on a constant-current mode impedance spectrum, which needs to acquire an electrochemical impedance spectrum of a battery system through an electrochemical station device, can realize high-precision battery model and parameter identification, but does not have the condition of long-term and continuous evaluation for a subsequent energy storage system which normally runs.
In summary, in the prior art, the adopted technical scheme for obtaining the internal resistance of the battery has the defects of no real-time performance, no realization of full-working-condition operation, limited estimation precision, no long-term continuous estimation and the like.
Disclosure of Invention
The invention mainly aims to provide a fault judgment method of a power supply system and an energy storage system, and aims to solve the problem that high-precision internal resistance cannot be obtained in real time under all working conditions in the prior art.
In order to achieve the above object, according to an aspect of the present invention, there is provided a fault determination method of a power supply system, the method including the steps of: acquiring at least one piece of single battery information, wherein the single battery information comprises at least one of voltage, current and temperature of a single battery; determining the real-time internal resistance of the single battery according to an internal resistance estimation model of an OCV-SOC curve; acquiring a first internal resistance of a single battery according to the charging and discharging state overlapping internal resistance estimation model, judging whether the first internal resistance and the real-time internal resistance meet a first preset condition, and if the first internal resistance and the real-time internal resistance do not meet the preset condition, performing at least one calibration on the internal resistance estimation model of the OCV-SOC to generate a new internal resistance estimation model of the OCV-SOC curve; acquiring a second internal resistance of the single battery according to the standing internal resistance estimation model, judging whether the second internal resistance and the real-time internal resistance meet a second preset condition, and if the second internal resistance and the real-time internal resistance do not meet the preset condition, performing at least one calibration on the charging and discharging state overlapping internal resistance estimation model; calibrating the new internal resistance estimation model of the OCV-SOC curve at least once based on the calibrated charge-discharge state overlapped internal resistance estimation model to obtain a final internal resistance estimation model of the OCV-SOC curve; determining a real-time third internal resistance of the single batteries based on the final OCV-SOC internal resistance estimation model, wherein the third internal resistance comprises the internal resistance of the single batteries and the internal resistance of a battery cluster formed by connecting a plurality of single batteries; and generating display information based on the third internal resistance, wherein the display information is used for judging the real-time fault information of the single battery and the real-time fault information of the battery cluster.
Further, the method also comprises a method for acquiring the internal resistance of the battery cluster, and the method for acquiring the internal resistance of the battery cluster comprises the following steps: acquiring cluster voltage and cluster current of a battery cluster, and determining cluster connection resistance of the battery cluster based on the cluster voltage, the cluster current and the voltage of a single battery; and determining the internal resistance of the battery cluster based on the cluster connection resistance and the real-time internal resistance.
Further, the method further comprises: after the real-time internal resistance of the single battery is determined by adopting the internal resistance estimation model of the OCV-SOC curve, the first internal resistance of the single battery is obtained according to the charging and discharging state overlapping internal resistance estimation model after the first preset time.
Further, the method further comprises: and after the first internal resistance of the single battery is obtained according to the charging and discharging state overlapping internal resistance estimation model, after a second preset time, obtaining a second internal resistance of the single battery according to the standing internal resistance estimation model.
Further, the charge time and the discharge time of the unit cell are greater than or equal to 3 times the cell polarization time constant.
Further, the standing time of the single battery is greater than or equal to 3 times of the battery polarization time constant.
Further, the method for obtaining the first internal resistance of the single battery by adopting the charging and discharging state overlapping internal resistance estimation model comprises the following steps: and determining a charging curve and a discharging curve of SOC superposition, and carrying out SOC difference on the superposed effective part to obtain an estimated value of the internal resistance of the battery of the SOC superposition line segment.
Further, the method further comprises: the real-time fault information comprises serious abnormal information and non-serious abnormal information, and when the display information corresponding to the third internal resistance is the serious abnormal information, a preset action is executed, wherein the preset action comprises a direct action contactor; and when the display information corresponding to the third internal resistance is non-serious abnormal information, storing the data information before and after the single battery is abnormal.
Further, the method further comprises: and based on the data information, adopting the charging and discharging state overlapping internal resistance estimation model after the last calibration to carry out fault judgment on the power supply system.
Further, the method further comprises: and based on the data information, adopting a static internal resistance estimation model to judge the fault of the power supply system.
According to another aspect of the present invention, an energy storage system is provided, where the energy storage system includes a battery management system, and the battery management system adopts the above fault determination method for a power supply system.
By applying the technical scheme of the invention, the real-time internal resistance of the single battery is calculated by acquiring the information of the voltage, the current, the temperature and the like of the single battery and based on the internal resistance estimation model of the OCV-SOC curve, calculating and obtaining the first internal resistance and the real-time internal resistance of the single battery according to the charging and discharging state overlapping internal resistance estimation model for judgment, calibrating the internal resistance estimation model of the OCV-SOC curve, calculating to obtain the second internal resistance and the real-time internal resistance of the single battery according to the static internal resistance estimation model, calibrating the charge-discharge state overlapped internal resistance estimation model, calibrating a new internal resistance estimation model of the OCV-SOC curve based on the calibrated charge-discharge state overlapped internal resistance estimation model, and obtaining an internal resistance estimation model of the final OCV-SOC curve, determining a third internal resistance of the single battery, and establishing display information based on the third internal resistance. By calibrating the internal resistance estimation model of the OCV-SOC curve based on the charging and discharging state overlapping internal resistance estimation model and calibrating the charging and discharging state overlapping internal resistance estimation model based on the standing internal resistance estimation model, the real-time internal resistance value of the battery obtained by calculation of the internal resistance estimation model of the OCV-SOC curve keeps high precision, so that the power supply system realizes real-time high-precision internal resistance monitoring under all working conditions, and meanwhile, the power supply system carries out system fault diagnosis based on the real-time resistance, thereby effectively avoiding the occurrence of safety accidents of the power supply system.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 shows a schematic diagram of an embodiment of a three-stage energy storage battery management system according to the invention;
FIG. 2 shows a schematic diagram of an embodiment of an internal resistance estimation algorithm according to the present invention;
FIG. 3 is a schematic diagram illustrating an embodiment of a stationary internal resistance estimation process curve according to the present invention;
fig. 4 shows a schematic diagram of an embodiment of an internal resistance estimation model based on charge-discharge state overlap according to the present invention;
FIG. 5 is a diagram illustrating an embodiment of an internal resistance estimation model of an OCV-SOC curve according to the present invention;
FIG. 6 is a diagram illustrating an example of an internal resistance estimation result based on an OCV-SOC curve according to the present invention;
FIG. 7 shows a schematic diagram of an embodiment of resistance versus voltage distribution of a battery cluster according to the present invention;
fig. 8 shows a schematic diagram of an embodiment of an energy storage system diagnostic implementation architecture according to the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Exemplary embodiments according to the present application will now be described in more detail with reference to the accompanying drawings. These exemplary embodiments may, however, be embodied in many different forms and should not be construed as limited to only the embodiments set forth herein. It is to be understood that these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art, in the drawings, it is possible to enlarge the thicknesses of layers and regions for clarity, and the same devices are denoted by the same reference numerals, and thus the description thereof will be omitted.
With reference to fig. 1 to 8, according to an embodiment of the present application, a method for determining a fault of a power supply system is provided.
The method comprises the following steps: acquiring at least one piece of single battery information, wherein the single battery information comprises at least one of voltage, current and temperature of a single battery; determining the real-time internal resistance of the single battery according to an internal resistance estimation model of an OCV-SOC curve; acquiring a first internal resistance of a single battery according to the charging and discharging state overlapping internal resistance estimation model, judging whether the first internal resistance and the real-time internal resistance meet a first preset condition, and if the first internal resistance and the real-time internal resistance do not meet the preset condition, performing at least one calibration on the internal resistance estimation model of the OCV-SOC to generate a new internal resistance estimation model of the OCV-SOC curve; acquiring a second internal resistance of the single battery according to the standing internal resistance estimation model, judging whether the second internal resistance and the real-time internal resistance meet a second preset condition, and if the second internal resistance and the real-time internal resistance do not meet the preset condition, performing at least one calibration on the charging and discharging state overlapping internal resistance estimation model; calibrating the new internal resistance estimation model of the OCV-SOC curve at least once based on the calibrated charge-discharge state overlapped internal resistance estimation model to obtain a final internal resistance estimation model of the OCV-SOC curve; determining a real-time third internal resistance of the single batteries based on the final OCV-SOC internal resistance estimation model, wherein the third internal resistance comprises the internal resistance of the single batteries and the internal resistance of a battery cluster formed by connecting a plurality of single batteries; and generating display information based on the third internal resistance, wherein the display information is used for judging the real-time fault information of the single battery and the real-time fault information of the battery cluster.
As shown in fig. 2, the static internal resistance estimation model and the charge-discharge state overlapping internal resistance estimation model have higher estimation accuracy, and the internal resistance estimation model based on the OCV-SOC curve has a wider estimation range and a higher estimation speed, the static internal resistance estimation model in fig. 2 is specifically shown in fig. 3, the charge-discharge state overlapping internal resistance estimation model in fig. 2 is specifically shown in fig. 4, and the internal resistance estimation model based on the OCV-SOC curve in fig. 2 is specifically shown in fig. 5. The real-time internal resistance of the single battery is estimated through the internal resistance estimation model of the OCV-SOC curve, real-time full working condition of resistance detection is guaranteed, meanwhile, the internal resistance estimation model of the OCV-SOC curve is calibrated based on the charging and discharging state overlapping internal resistance estimation model, the charging and discharging state overlapping internal resistance estimation model is calibrated based on the standing internal resistance estimation model, and the precision of real-time internal resistance detection is guaranteed. By comprehensive use and mutual calibration of the three internal resistance estimation models, the real-time high-precision monitoring of the full-working-condition internal resistance of the battery of the power supply system is ensured. And based on the obtained real-time high-precision internal resistance value, system fault diagnosis is carried out, and the occurrence of safety accidents of the power supply system can be effectively avoided.
Further, the method also comprises a method for acquiring the internal resistance of the battery cluster, and the method for acquiring the internal resistance of the battery cluster comprises the following steps: acquiring cluster voltage and cluster current of a battery cluster, and determining cluster connection resistance of the battery cluster based on the cluster voltage, the cluster current and the voltage of a single battery; and determining the internal resistance of the battery cluster based on the cluster connection resistance and the real-time internal resistance. The resistance and voltage distribution of the battery cluster system are shown in fig. 7, the battery cluster system is formed by connecting a plurality of single batteries in series and in parallel, the internal resistance of the battery cluster comprises internal resistance of the battery and cluster connection resistance, wherein the internal resistance of the battery comprises internal resistance of the battery, resistance from a lug to a connection bar and resistance from the connection bar to an acquisition line, the voltage of the part of resistance can be acquired when the voltage of the single batteries is acquired, the cluster connection resistance mainly comprises the battery series connection resistance, contact resistance, cable resistance and the like, and the part of voltage cannot be acquired when the voltage of the single batteries is acquired. Alternatively, the cluster connection resistance and the battery cluster internal resistance may be calculated using the following equations:
wherein, VtotIs a cluster voltage, I is a cluster current, RciIs a single connection impedance, RcIs the cluster resistance (sum of all connection impedances), VbiIs the voltage of the cell, RbiIs the calculated internal resistance value, R, of the single batterytotIs the internal resistance of the battery cluster. Vtot、VbiThe measurement unit of (A) and (R) is V and the measurement unit of I is A and Rci、Rc、Rbi、RtotThe unit of measurement of (a) is Ω.
Preferably, the method further comprises the step of obtaining the first internal resistance of the single battery according to the charging and discharging state overlapping internal resistance estimation model after the first preset time after the real-time internal resistance of the single battery is determined by the internal resistance estimation model of the OCV-SOC curve. Neglecting the Rint model of polarization effects, the terminal voltage V (in V) of the battery satisfies the following formula:
V＝VOC(SOC)+IR(SOC)
wherein, I is the current of the single battery (unit: A), R (SOC) is the internal resistance of the battery (unit: omega) corresponding to the current SOC point, and VOC(SOC) is the voltage (unit: V) corresponding to the current SOC point on the OCV-SOC curve.
As shown in fig. 4, during charge and discharge of the battery, the SOC overlaps in an effective region indicating that the start and end values of the charging process SOC are just the end and start values of the discharging process SOC. Because the internal resistance R is a function of the SOC and the open-circuit voltages of the battery are different under different SOC conditions, if the internal resistance of the battery is calculated based on the above formula, the condition that the terminal voltage must be the voltage of the same SOC point must be satisfied, i.e., an effective area needs to be selected. The first preset time can be used for ensuring that the selected data are located in the effective area and ensuring the accuracy of the calculation result.
Further, the method comprises the steps of obtaining a first internal resistance of the single battery according to the charging and discharging state overlapping internal resistance estimation model, obtaining a second internal resistance of the single battery according to the standing internal resistance estimation model after a second preset time. As shown in FIG. 3, during the charging and discharging process, when the current passes through the resistor, the voltage of the battery will change instantly, as shown by U0To U1，U5To U6However, due to the polarized capacitance and resistance of the system, the battery voltage will decrease or increase slowly and continuously during the charging and discharging process, such as U in fig. 31To U4，U6To U8. When the charging and discharging or standing time is long enough, the voltage hardly changes any more, so the influence of polarization effect can be eliminated through the long enough charging and discharging or standing process, the internal resistance value of the battery is obtained through the difference value of the voltage and the current, and the standing internal resistance estimation model is a method for calculating the internal resistance of the battery through the long enough standing link of the power supply system in the operation process by using the voltage and the current difference. The setting of the second preset time can ensure the charging and discharging time and the standing time which are long enough, so that the calculation result is moreAnd (3) accuracy.
Preferably, the charge time and the discharge time of the unit cell are greater than or equal to 3 times the cell polarization time constant. The arrangement can avoid the influence of the polarization effect on the accuracy of the calculation result of the static internal resistance estimation model and the charge-discharge state overlapping internal resistance estimation model.
Likewise, it is preferable that the standing time of the unit cell is greater than or equal to 3 times the cell polarization time constant. The arrangement can avoid the influence of the polarization effect on the accuracy of the calculation result of the static internal resistance estimation model and the charge-discharge state overlapping internal resistance estimation model.
The method for acquiring the first internal resistance of the single battery by adopting the charge-discharge state overlapping internal resistance estimation model comprises the following steps: and determining a charging curve and a discharging curve of SOC superposition, and carrying out SOC difference on the superposed effective part to obtain an estimated value of the internal resistance of the battery of the SOC superposition line segment. In practical applications, it is desirable to have a minimum current limit to ensure measurement accuracy. The method can also carry out estimation in a constant power (current change) and variable current mode (ensuring synchronism), simultaneously carries out SOC overlapping matching in a certain temperature interval, can ensure that the influence of temperature on internal resistance is considered as much as possible under the condition of a certain measurement range, strictly selects charging and discharging data time pairs matching the SOC and the temperature, reduces the density of the internal resistance value obtained by measurement, namely the obtained internal resistance data is changed from a line segment to a scattered point, and then difference calculation needs to be carried out on the points of an effective part, thereby being convenient for calculating the internal resistance of the battery.
Further, the method further comprises: the real-time fault information comprises serious abnormal information and non-serious abnormal information, and when the display information corresponding to the third internal resistance is the serious abnormal information, a preset action is executed, wherein the preset action comprises a direct action contactor; and when the display information corresponding to the third internal resistance is non-serious abnormal information, storing the data information before and after the single battery is abnormal. The third resistor comprises the internal resistance of the single battery obtained by calculating the final OCV-SOC curve, and the preset action is directly executed after the serious abnormity is judged, so that the system can process in time when the serious abnormity occurs, the occurrence of serious system accidents is avoided, meanwhile, if the serious abnormity does not occur, the data information before the abnormity occurs and after the abnormity occurs of the battery is stored, and the subsequent abnormity processing and judgment of the system can be facilitated.
In order to more accurately detect the condition of the system, the method further comprises the step of judging the fault of the power supply system by adopting the charging and discharging state overlapping internal resistance estimation model after the last calibration based on the data information. When the charging and discharging state overlapping internal resistance estimation model is adopted to carry out fault judgment on the internal resistance of the battery, optionally, if the judgment is serious heritage, a preset action is executed, the preset action comprises a direct action contactor, and the power supply system is prevented from being damaged due to serious abnormity of the system.
Preferably, the method further comprises the step of performing fault judgment on the power supply system by adopting a static internal resistance estimation model based on the data information. The static internal resistance estimation model has higher precision, and can make different response actions according to the judged abnormal level of the system when being used for fault judgment.
In the method adopted by the embodiment, the internal resistance estimation model based on the OCV-SOC curve has the advantages of wide measurement range and high calculation speed in calculation, but the OCV-SOC curve is influenced by temperature and cycle life, and the characteristic difference between the single batteries further influences the accuracy of the calculation of the internal resistance of the battery. The standing internal resistance estimation model and the charge-discharge state overlapping internal resistance estimation model are not influenced by battery characteristic attenuation and have a high precision effect, and the estimation precision of the battery internal resistance based on the OCV-SOC can be ensured by calibrating the OCV-SOC curve by the two internal resistance estimation methods. Meanwhile, the abnormal judgment of the internal resistance of the battery is carried out based on each internal resistance estimation model, so that the resolution of fault diagnosis and early warning can be improved. By adopting the calibration method, optionally, other models except the charging and discharging state overlapping internal resistance estimation model and the standing internal resistance estimation model can be adopted to calibrate the real-time internal resistance and perform fault judgment based on the calibrated internal resistance.
By adopting the method, the application provides a three-level energy storage battery management system. As shown in fig. 1, optionally, the three-level energy storage management system includes a BMS slave acquisition balancing module (BSU), a BMS master control module (BMU), and a battery stack control module (BDU). In practical application, the method can be adopted on other system architectures and different controller modules to achieve the final effect according to different application scenes.
The BMS slave acquisition equalization module (BSU) is a terminal module for realizing voltage and temperature acquisition of a single battery in a three-level energy storage battery management system, real-time information acquisition CAN be carried out on the single battery, acquired information comprises voltage, current, temperature and the like of the single battery, meanwhile, the BMS slave acquisition equalization module (BSU) has heat management and bidirectional active equalization capacity, and CAN be interconnected and intercommunicated with the BMS master control module (BMU) through a CAN bus.
The BMS master control module (BMU) is a controller module integrating the functions of battery cluster total voltage and battery cluster total current acquisition, charge and discharge management, insulation detection, slave control management and the like, and is mainly used for realizing key functions of battery SOC, SOH, balance strategy, insulation resistance detection, data exchange, fault diagnosis and the like.
The BMS stack control module (BDU) is positioned on the uppermost layer in the three-layer framework of the three-level energy storage battery management system and is mainly used for realizing the functions of real-time data acquisition, real-time calculation, performance analysis, alarm processing, protection processing, record storage and the like of the BMS master control module (BMU) so that each cluster of batteries can work stably. The BMS stack control module (BDU) is also responsible for communicating with an energy storage converter (PCS) and an energy storage dispatching monitoring system (EMU) to realize linkage.
Specifically, the three-level energy storage battery management system can be used for implementing real-time fault diagnosis and early warning of the energy storage system, as shown in fig. 8, in this embodiment, the energy storage system diagnosis implementation architecture mainly includes a BMS primary slave control module (BSU), a BMS secondary master control module (BMU), a BMS tertiary stack controller module (BDU), and an EMS energy management system.
The BMS primary slave control module (BSU) is used for realizing real-time acquisition of voltage, current, temperature and the like of the single battery.
And the BMS secondary main control module (BMU) determines the real-time internal resistance of the single battery according to the internal resistance estimation model of the OCV-SOC curve, has the update rate of the full battery data of 0.2-0.5 s, and can realize the real-time acquisition of the full working condition of the internal resistance of the battery. In the BMS secondary master control module (BMU), based on the Rint model, the internal resistance estimation (as shown in fig. 6) can be realized by only acquiring the OCV-SOC curve (such as the volt25 — OCV curve shown in fig. 5) of the current condition (which may be a plurality of temperature conditions). That is, the internal resistance is estimated according to the internal resistance estimation model of the OCV-SOC curve, and the internal resistance (unit: Ω) of the unit cell is calculated by the following formula, ignoring the polarization effect, in the case where the OCV-SOC curve is known:
R(SOC)＝(V(SOC)-VOC(SOC))/I
wherein, I is the current (unit: A) of the single battery at the current SOC point, V (SOC) is the voltage (unit: V) of the single battery obtained at the current SOC point, VOCThe (SOC) is a voltage value (unit: V) corresponding to the current SOC point on the OCV-SOC curve, and the R (SOC is an internal resistance value (unit: omega) of the single battery at the current SOC point).
Meanwhile, a BMS secondary master control module (BMU) collects the cluster voltage and the cluster current of the battery cluster, and calculates the internal resistance (unit: omega) of the battery cluster based on the cluster voltage, the cluster current, the voltage of each single battery and the internal resistance of the single battery obtained by calculation, wherein the specific calculation formula can be as follows:
wherein, VtotIs the cluster voltage (unit: V), I is the cluster current (unit: A), RcIs a cluster connection resistance (unit: omega), VbiIs the voltage (unit: V), R of the single cellbiThe calculated internal resistance value (unit: omega) of the single battery is RtotIs the internal resistance (unit: omega) of the battery cluster.
Based on the calculated internal resistance value of the single battery, the BMS secondary main control module (BMU) may determine the real-time internal resistance of the single battery, optionally, the determination criterion may be a threshold determination criterion, i.e., the real-time internal resistance value is determined to be normal within a certain range of a preset normal operation resistance value, and the battery is abnormal within another range, e.g., 30% exceeding the normal operation resistance value threshold may be set as serious abnormality, 15% to 20% exceeding the normal operation resistance value threshold is set as slight abnormality, etc., when the determination result is serious abnormality, the BMS secondary main control module (BMU) may directly operate a contactor, e.g., may separate a power supply system from a power grid or a load, etc., when the determination is other level abnormality, the BMS secondary main control module (BMU) stores data information (including parameters of voltage, current, internal resistance, etc.) before and after the abnormality of the single battery, and generates an abnormality flag, the abnormality flag contains at least a level of abnormality, and feeds back the abnormality flag to a BMS tertiary stack controller module (BDU) in time. And after the BMS secondary main control module (BMU) receives the instruction of the superior controller, uploading data information before and after the battery is abnormal.
And the BMS three-level stack controller module (BDU) calculates the resistance of the single battery according to the charge-discharge state overlapping internal resistance estimation model to obtain a first internal resistance. Specifically, by the Rint model which neglects the polarization effect of the resistance, the terminal voltage V (unit: V) of the battery:
V＝VOC(SOC)+IR(SOC)
wherein, I is the current of the single battery (unit: A), R (SOC) is the internal resistance of the battery (unit: omega) corresponding to the current SOC point, and VOC(SOC) is the voltage (unit: V) corresponding to the current SOC point on the OCV-SOC curve.
The internal resistance R is a function of the SOC, so that the internal resistance of the battery in an effective interval can be estimated by utilizing charge-discharge data of an SOC overlapping part in the charge-discharge process. As shown in fig. 4, in the effective interval of SOC overlap, the starting point and the end point of the charging process SOC are exactly the end point and the starting point of the discharging process SOC, i.e. to calculate the internal resistance of the battery by the above formula of Rint model, the terminal voltage must be the voltage of the same SOC point, and after the condition is satisfied, the internal resistance (unit: Ω) can be estimated by the following formula:
R(SOC)＝(Vc(SOC)-Vd(SOC))/(I1+I2)
wherein, Vc(SOC) is the charging terminal voltage at the current SOC point, Vd(SOC) is the discharge end voltage at the current SOC point, I1Is the discharge current at the present SOC point, I2Is the charging current (I) of the current SOC point1And I2Both positive values), and R (SOC) is the internal resistance of the battery at the current SOC point. Vc(SOC)、VdThe measurement units of (SOC) are V, I1、I2The measurement unit of (A) and the measurement unit of R (SOC) is omega.
Meanwhile, the BMS tertiary stack controller module (BDU) judges the real-time internal resistance value obtained by calculation of the BMS secondary main control module (BMU) based on the first internal resistance, and when the real-time internal resistance value does not meet the judgment condition, the BMS tertiary stack controller module (BDU) calibrates an internal resistance estimation model of an OCV-SOC curve used by the BMS secondary main control module (BMU). Alternatively, the calibration method is to perform weight distribution on the original resistance and the first internal resistance of each battery, for example, the original resistance accounts for 40%, and the first internal resistance accounts for 60%, so as to determine the resistance value corresponding to the calibrated OCV-SOC curve, and the first internal resistance accounts for 100%, and the original resistance accounts for 0%. The BMS three-level stack controller module (BDU) has a full battery data update rate of 0.5-2 s. And the calibrated OCV-SOC curve is issued to a BMS secondary master control module (BMU) as an internal resistance estimation model of the latest OCV-SOC curve. The BMS three-level stack controller module (BDU) uploads a normal operation data EMS system at regular time as historical curve data of normal operation of a power supply system
Meanwhile, when the BMS tertiary stack controller module (BDU) detects an abnormal mark sent by the BMS secondary master control module (BMU), data information stored by the BMS secondary master control module (BMU) before and after the battery is abnormal is extracted. Based on data information before and after the battery is abnormal, the BMS three-level stack controller module (BDU) judges the abnormal level, if the abnormal level is judged to be serious abnormality, the BMS three-level stack controller module (BDU) can directly act a contactor to respond, if a power supply system is separated from a power grid or a load and the like, and when other levels are judged to be abnormal, the BMS three-level stack controller module (BDU) generates a new abnormal mark and feeds back the abnormal mark to the EMS in time.
The EMS calculates the resistance of the single battery according to the static internal resistance estimation model to obtain a second internal resistance, the static internal resistance estimation model is mainly a method for calculating the internal resistance by using voltage and current difference through a static link of the battery system in the operation process, as shown in figure 3, the influence of polarization effect is eliminated through a sufficiently long charging and discharging or static process, and the calculation formula of the second internal resistance is as follows:
Rput＝(U4-U5)/I
RCharging device＝(U8-U9)/I
Wherein R isPutIs the internal resistance (unit: omega) of the single battery during the discharging processCharging deviceIs the internal resistance (unit: omega) of the single battery during the charging process, I is the current (unit: A) of the single battery, U4、U5、U8、U9Is the voltage value (unit: V) of the unit cell.
Meanwhile, the EMS judges the real-time internal resistance value obtained by calculation of the BMS secondary master control module (BMU) based on the second internal resistance, and when the real-time internal resistance value does not meet the judgment condition, the EMS calibrates a charge-discharge state overlapping internal resistance estimation model used by the BMS tertiary stack controller module (BDU). Alternatively, the calibration method is to perform weight distribution on the original resistance and the second internal resistance of each battery, for example, the original resistance accounts for 40%, and the second internal resistance accounts for 60%, so as to determine the resistance value corresponding to the calibrated OCV-SOC curve, and the second internal resistance accounts for 100%, and the original resistance accounts for 0%. And sending the calibrated charge-discharge state overlapping internal resistance estimation model to a BMS three-level stack controller module (BDU) as a latest charge-discharge state overlapping internal resistance estimation model.
The EMS can be used for storing various internal resistance data, including real-time internal resistance and battery cluster internal resistance obtained by BMS secondary master control module (BMU) calculation, first internal resistance obtained by BMS tertiary stack controller module (BDU) calculation, second internal resistance obtained by EMS calculation and the like, and outputting various internal resistance charts according to various internal resistance data, wherein the internal resistance chart data are in different time scales due to different update times, and the EMS comprehensively analyzes and evaluates the health state of the power supply system based on the various internal resistance charts.
Meanwhile, the EMS receives the abnormal mark uploaded by the BMS three-level stack controller module (BDU), extracts data information before and after the battery is abnormal, comprehensively compares the data information with historical normal operation data of the battery, analyzes and evaluates the abnormal level, and makes corresponding prompt or action.
Based on the division of the control modules at all levels, the BMS secondary master control module (BMU) can quickly detect the abnormal internal resistance of the battery, directly act a contactor when the abnormality is serious, store the data information before the battery is abnormal and after the battery is abnormal when the abnormality is not serious, and upload the abnormal mark to a BMS tertiary stack controller module (BDU). And comparing data information before and after the battery is abnormal with the internal resistance data of the BMS three-level stack controller module (BDU), if the data information is determined to be non-serious abnormal, continuing to report, and if the data information is determined to be serious abnormal, directly actuating the contactor to separate the power supply system from a power grid or a load. After receiving the abnormal mark, the EMS extracts the data information before and after the battery is abnormal, comprehensively compares and analyzes the data information with the historical normal operation data, evaluates the abnormal level and gives corresponding warning and action.
According to another embodiment of the present application, an energy storage system is provided, where the energy storage system includes a battery management system, and the battery management system adopts the method for determining a fault of a power supply system.
Spatially relative terms, such as "above … …," "above … …," "above … …," "above," and the like, may be used herein for ease of description to describe one device or feature's spatial relationship to another device or feature as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. For example, if a device in the figures is turned over, devices described as "above" or "on" other devices or configurations would then be oriented "below" or "under" the other devices or configurations. Thus, the exemplary term "above … …" can include both an orientation of "above … …" and "below … …". The device may be otherwise variously oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.
In addition to the foregoing, it should be noted that reference throughout this specification to "one embodiment," "another embodiment," "an embodiment," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment described generally throughout this application. The appearances of the same phrase in various places in the specification are not necessarily all referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with any embodiment, it is submitted that it is within the scope of the invention to effect such feature, structure, or characteristic in connection with other embodiments.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (11)
1. A fault judgment method of a power supply system is characterized by comprising the following steps:
acquiring at least one piece of single battery information, wherein the single battery information comprises at least one of voltage, current and temperature of a single battery;
determining the real-time internal resistance of the single battery according to an internal resistance estimation model of an OCV-SOC curve;
acquiring a first internal resistance of the single battery according to a charging and discharging state overlapping internal resistance estimation model, judging whether the first internal resistance and the real-time internal resistance meet a first preset condition, and if the first internal resistance and the real-time internal resistance do not meet the preset condition, performing at least one calibration on the internal resistance estimation model of the OCV-SOC to generate a new internal resistance estimation model of an OCV-SOC curve;
acquiring a second internal resistance of the single battery according to the static internal resistance estimation model, judging whether the second internal resistance and the real-time internal resistance meet a second preset condition, and if the second internal resistance and the real-time internal resistance do not meet the preset condition, performing at least one calibration on the charge-discharge state overlapping internal resistance estimation model;
calibrating the new internal resistance estimation model of the OCV-SOC curve at least once based on the calibrated overlapping internal resistance estimation model of the charging and discharging states to obtain a final internal resistance estimation model of the OCV-SOC curve;
determining a real-time third internal resistance of the single batteries based on a final OCV-SOC internal resistance estimation model, wherein the third internal resistance comprises the internal resistance of the single batteries and the internal resistance of a battery cluster formed by connecting a plurality of single batteries;
and generating display information based on the third internal resistance, wherein the display information is used for judging the real-time fault information of the single battery and the real-time fault information of the battery cluster.
2. The method according to claim 1, wherein the method further comprises a method for obtaining an internal resistance of a battery cluster of the battery cluster, and the method for obtaining the internal resistance of the battery cluster comprises the following steps:
acquiring cluster voltage and cluster current of a battery cluster, and determining cluster connection resistance of the battery cluster based on the cluster voltage, the cluster current and the voltage of the single battery;
and determining the internal resistance of the battery cluster based on the cluster connection resistance and the real-time internal resistance.
3. The method for determining a fault in a power supply system according to claim 1, further comprising:
after the real-time internal resistance of the single battery is determined by adopting the internal resistance estimation model of the OCV-SOC curve, the first internal resistance of the single battery is obtained according to the charging and discharging state overlapping internal resistance estimation model after a first preset time.
4. The method for determining a fault in a power supply system according to claim 3, further comprising:
and after the first internal resistance of the single battery is obtained according to the charging and discharging state overlapping internal resistance estimation model, after a second preset time, obtaining the second internal resistance of the single battery according to the standing internal resistance estimation model.
5. The method according to claim 1, wherein the charge time and the discharge time of the unit battery are greater than or equal to 3 times a battery polarization time constant.
6. The method according to claim 1, wherein a standing time of the unit battery is 3 times or more a battery polarization time constant.
7. The power supply system fault judgment method according to claim 1, wherein the method for obtaining the first internal resistance of the unit battery by using the charge-discharge state overlapping internal resistance estimation model comprises the steps of:
and determining a charging curve and a discharging curve of SOC superposition, and carrying out SOC difference on the superposed effective part to obtain an estimated value of the internal resistance of the battery of the SOC superposition line segment.
8. The method for determining a fault in a power supply system according to claim 1, further comprising:
the real-time fault information comprises serious abnormal information and non-serious abnormal information, and when the display information corresponding to the third internal resistance is the serious abnormal information, a preset action is executed, wherein the preset action comprises a direct action contactor;
and when the display information corresponding to the third internal resistance is the non-serious abnormal information, storing the data information before and after the single battery is abnormal.
9. The method for determining a fault in a power supply system according to claim 8, further comprising:
and based on the data information, adopting the charging and discharging state overlapping internal resistance estimation model after the last calibration to carry out fault judgment on the power supply system.
10. The method for determining a fault in a power supply system according to claim 9, further comprising:
and based on the data information, adopting the static internal resistance estimation model to judge the fault of the power supply system.
11. An energy storage system comprising a battery management system, characterized in that the battery management system employs the method of fault diagnosis of the power supply system of any one of claims 1-10.
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| WO2023206660A1 (en) * | 2022-04-27 | 2023-11-02 | 石家庄科林电气股份有限公司 | Fire-proof and explosion-proof method for lithium-battery-based energy storage power station |
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| WO2023206660A1 (en) * | 2022-04-27 | 2023-11-02 | 石家庄科林电气股份有限公司 | Fire-proof and explosion-proof method for lithium-battery-based energy storage power station |
| CN115469226A (en) * | 2022-08-01 | 2022-12-13 | 哈尔滨工业大学（威海） | Real-time safety early warning method for electric vehicle power battery based on operation big data |
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