CN113687255A - Method and device for diagnosing state of battery cell and storage medium - Google Patents
Method and device for diagnosing state of battery cell and storage medium Download PDFInfo
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Abstract
The invention provides a method, equipment and a storage medium for diagnosing the state of a single battery, which are used for maintaining an abnormal working circuit by detecting whether the working circuit of each single battery in a battery pack is normal or not; detecting whether the charge states of all the battery monomers in the battery pack are consistent, and adjusting the electric quantity of the battery monomers with inconsistent charge states; and diagnosing the states of the single batteries according to the voltage change rates of the single batteries in the charging process and the discharging process and the voltage median and the voltage standard deviation of the battery pack, and determining the single batteries with abnormal states. The available capacity of the energy storage system is improved, and the maintenance cost of the energy storage power station is reduced.
Description
Technical Field
The invention belongs to the technical field of battery energy storage power stations, and particularly relates to a method and equipment for diagnosing the state of a battery monomer and a storage medium.
Background
The energy storage technology has a very high strategic position, and all countries in the world continuously support the research and application of the energy storage technology. Battery energy storage power stations in developed countries such as japan and the usa have been developed earlier, and are now used, and china has been developed faster in recent years with the support of national policies. Practical engineering cases with great success exist in the aspects of operation control and application of large-scale battery energy storage power stations at home and abroad. For example, a 34MW/245MW & h sodium-sulfur battery energy storage power station of NGK company applied to wind power plants in Qingson county of Japan, an American SDG & E ESCondido 30MW/120MW & h lithium ion battery energy storage project, a China Zhang Bei wind and light energy storage and transmission demonstration project (first period) 20MW/84MW & h multi-type battery energy storage power station and the like. In these engineering applications, the battery type and the application scenario of the battery energy storage power station are different.
The cell voltage is a direct indication of the external characteristics of the cell, and therefore fault diagnosis of the cell voltage is most important, and over-high or under-low cell voltage is the most obvious fault, but should not occur as long as the battery management system can ensure that the cell is not abused. The battery pack is different from the single battery and is characterized in that the voltage of the single battery in the battery pack is inconsistent, which is caused by the inconsistency of the State of Charge (SOC), the capacity, the total internal resistance and the like of each single battery in the manufacturing and using processes, and the inconsistency cannot be avoided.
The lithium battery pack is in a floating charge and unattended state for a long time in the use of the communication base station, the irreversible capacity loss of the battery in the long-term floating charge state is far greater than the irreversible capacity loss of the battery in the standing state, the actual service life of the lithium battery pack is shortened, and the use cost of the battery is increased; and the maintainer can not judge the service life of the battery pack according to the state of the battery pack, so that the unexpected power failure phenomenon of the base station is often caused, and serious economic loss is caused. The main factor determining the service life of the lithium battery pack is that short-plate batteries with large self-discharge exist in the lithium battery pack, so that in order to prolong the service life of the battery pack, abnormal batteries in the lithium battery pack must be quickly identified and maintained or replaced, and the service life of the lithium battery pack is prolonged.
In the prior art, fault diagnosis and evaluation of a battery energy storage power station are generally carried out by diagnosing and analyzing monitoring information such as voltage, temperature and the like of a battery monomer, a protection threshold recommended by a manufacturer is set, when the conditions of over-high/over-low voltage and over-high/over-low temperature of the monomer occur, an alarm is given in time, and a graded alarm is set according to upper and lower limits of operation of the monomer voltage of an energy storage battery system. The alarm information of the single battery mainly comprises: monomer overpressure, monomer underpressure, monomer overtemperature, monomer low temperature. The voltage, current, temperature and SOC monitoring information of the battery pack and the battery system are diagnosed, analyzed and processed, related protection threshold values are set, and when the conditions of overhigh/overlow voltage, overlarge current, overhigh/overlow temperature and overhigh/overlow SOC of the battery pack and the battery system occur, an alarm is given in time. The warning information of the battery pack and the battery system mainly comprises: charging overcurrent, discharging overcurrent, large differential pressure, temperature difference overvoltage, insulation overvoltage, low SOC, high SOC, and cell temperature and voltage limit alarm. This prior art only employs threshold protection and fails to dynamically monitor and protect the system based on historical data.
Disclosure of Invention
The invention provides a method, equipment and a storage medium for diagnosing the state of a battery monomer, which can dynamically predict the state of the battery, reduce the maintenance cost and improve the availability ratio of an energy storage power station.
In a first aspect, the present invention provides a method for diagnosing the state of a battery cell, in which a battery pack includes N battery cells, N being an integer greater than or equal to 1, and the method includes:
detecting whether a measurement loop of each single battery in the battery pack is normal or not, and maintaining an abnormal working loop;
detecting whether the charge states of all the battery monomers in the battery pack are consistent, and adjusting the electric quantity of the battery monomers with inconsistent charge states;
and diagnosing the states of the single batteries according to the voltage change rates of the single batteries in the charging process and the discharging process and the voltage median and the voltage standard deviation of the battery pack, and determining the single batteries with abnormal states.
Further, detecting whether a measurement loop of each battery cell in the battery pack is normal includes:
acquiring voltage and temperature of a battery monomer in a charging and discharging process;
calculating the voltage change rate and the temperature change rate of the single battery and performing per unit;
calculating the deviation rate of the voltage change rate of the single battery relative to the voltage change rate mean value of the battery pack and the deviation rate of the temperature change rate of the single battery relative to the temperature change rate mean value of the battery pack;
and determining that the working circuit of the single battery is abnormal, wherein the deviation rate of the voltage change rate relative to the voltage change rate mean value of the battery pack is greater than a voltage change rate deviation rate threshold value, and the deviation rate of the temperature change rate relative to the temperature change rate mean value of the battery pack is greater than a temperature change rate deviation rate threshold value.
Furthermore, the voltage change rate and the temperature change rate of the battery cells are calculated in the following manner:
wherein, is Δ Vi(t)Is the voltage change rate, V, of the battery cell i at the moment ti(t)Is the voltage of the battery cell i at the time t, Vi(t-1)Is the voltage of cell i at time T-1, Δ Ti(t)Is the temperature change rate of the battery monomer i at the time T, Ti(t)Is the temperature of the cell i at time T, Ti(t-1)The voltage of a battery monomer i at the moment of t-1, delta t is a sampling interval, t is more than or equal to 1, and delta Vi(0)＝0，ΔTi(0)＝0；
The per unit method is as follows:
Vbase＝(VMax-VMin)/ttotal
ΔVmapi(t)＝ΔVi(t)/Vbase
Tbase＝(TMax-TMin)/ttotal
ΔTmapi(t)＝ΔTi(t)/Tbase
wherein, VbaseIs a voltage per unit reference, VMaxCut-off voltage, V, for charging of battery cells leaving factoryMinDischarge cutoff voltage, T, for the outgoing individual cellsMaxThe highest temperature T in the process of charging and discharging of the battery monomer leaving factoryMinIs the lowest temperature t in the charging and discharging process of the battery monomer leaving factorytotalThe charge and discharge time is shown.
Furthermore, the deviation ratio of the voltage change rate of the battery cell relative to the average value of the voltage change rate of the battery pack is calculated by:
wherein, is Δ ViTotalErrorIs the rate of deviation of the rate of change of voltage of cell i, Δ Vmapi(t)Is the voltage change rate per unit value, t, of the battery cell i at the moment ttotalIs the charging and discharging time;
the deviation rate of the temperature change rate of the single battery relative to the average value of the temperature change rate of the battery pack is calculated in the following mode:
wherein, Delta TiTotalErrorIs the deviation rate of the rate of temperature change of the cell i, Δ Tmapi(t)Is the temperature change rate per unit value of the battery monomer i at the time t, ttotalThe charge and discharge time is shown.
Further, detecting whether the states of charge of the battery cells are consistent comprises the following steps:
acquiring voltage of a battery monomer in a standing process;
calculating the voltage deviation of the voltage of the battery cell relative to the median of the voltage of the battery pack;
acquiring the charge cut-off voltage of the battery monomer when the charge is cut off;
acquiring the discharge cut-off voltage of the battery monomer when the discharge is cut off;
determining that the voltage deviation is greater than a voltage deviation threshold value and the voltage deviation is greater than a median of voltage deviations of the battery pack, and the charge states of the battery cells with the charge cut-off voltages greater than a first set value are inconsistent and the electric quantity needs to be released;
and determining that the voltage deviation is greater than a voltage deviation threshold value and less than or equal to a median of the voltage deviation of the battery pack, and the charge states of the battery cells with the discharge cut-off voltage less than a second set value are inconsistent and the electric quantity needs to be supplemented.
Further, the voltage deviation of the voltage of the battery cell with respect to the median value of the voltage of the battery pack is calculated in the following manner:
wherein, is Δ ViIs the voltage deviation, V, of the cell ii(t)For the voltage of cell i at time t, mean () is the median calculation sign, mean (V)i(t)) Is the median value of the voltage of the battery at time t, tstateStanding time.
Furthermore, the method for diagnosing the states of the battery cells according to the voltage change rates of the battery cells in the charging process and the discharging process and the voltage median and the voltage standard deviation of the battery pack and determining the battery cells with abnormal states comprises the following steps:
acquiring the voltage of a battery monomer in the charging process and the discharging process;
calculating the voltage change rate of the battery monomer in the charging process and the discharging process;
calculating a voltage median and a voltage standard deviation of the battery pack at each sampling moment;
normalizing outlier data formed on the basis of voltage median values and voltage standard deviations at all sampling moments;
performing cluster analysis on the result of the normalization processing to classify the battery monomer into a state doubt class and a state normal class;
and determining that the battery cells which have the voltage change rate greater than a third set value and belong to the state doubt class in the charging process are abnormal state cells, and determining that the battery cells which have the voltage change rate less than a fourth set value and belong to the state doubt class in the discharging process are abnormal state cells.
Further, the voltage change rates of the unit cells during the charge and the discharge are calculated using the following equations, respectively:
wherein, is Δ ViChargeIs the rate of change of voltage, Δ V, of cell i during chargingiDischaIs the rate of change of voltage, Δ V, of cell i during dischargei(t)The voltage change rate of the battery monomer i at the moment t is shown; t is tChargeFor the charging time, tDischaIs the discharge time, t is the sampling time;
ΔVi(t)the calculation is made using the following formula:
wherein, Vi(t)Is the voltage of the battery cell i at the time t, Vi(t-1)The voltage of the battery cell i at the moment t-1 is shown, and deltat is a sampling interval.
Further, the outlier data formed based on the voltage median and the voltage standard deviation at all sampling times is normalized using the following equation:
step_data＝1，(Vi(t)＞Xm(t)+kr×Xs(t))
step_data＝0，(Vi(t)＜Xm(t)-kr×Xs(t))
wherein step _ data is the result of normalization processing, Vi(t)Is the voltage of the cell i at time t, Xm(t)Is the median voltage, X, of the battery at time ts(t)Is the standard deviation of the voltage, k, of the battery pack at time trFor the scaling factor, a constant greater than 0 is taken.
Furthermore, each battery cell corresponds to a number thereof;
determining that the battery cell which has the voltage change rate larger than a third set value and belongs to the state doubt class in the charging process is an abnormal state cell, and determining that the battery cell which has the voltage change rate smaller than a fourth set value and belongs to the state doubt class in the discharging process is an abnormal state cell, including:
arranging the voltage change rates of the battery monomers in the charging process from large to small, taking the first m3 to obtain the numbers of the corresponding battery monomers to form an eighth number sequence, wherein 1< m3 is not more than N;
arranging the voltage change rates of the battery monomers in the discharging process from small to large, taking the first m4 to obtain the numbers of the corresponding battery monomers to form a ninth number sequence, wherein 1< m4 is not more than N;
acquiring the serial numbers of the battery monomers of the state doubt class to form a tenth serial number sequence;
and comparing the eighth numbered sequence and the ninth numbered sequence with a tenth numbered sequence respectively, and determining that the battery cells with numbers simultaneously appearing in the eighth numbered sequence and the tenth numbered sequence are abnormal state cells, and the battery cells with numbers simultaneously appearing in the ninth numbered sequence and the tenth numbered sequence are also abnormal state cells.
In a second aspect, the present invention also provides a storage medium storing a computer program, which is executable by one or more processors, and is operable to implement the method for diagnosing the state of a battery cell according to the first aspect.
In a third aspect, the invention further provides a wine battery cell state diagnosis device, which is characterized by comprising a collecting device, a memory and a processor; the acquisition device is used for acquiring the voltage and/or the temperature of the battery monomer; the memory stores thereon a computer program that, when executed by the processor, executes the method of diagnosing the state of the battery cell according to the first aspect.
The method, the device and the storage medium for diagnosing the state of the single battery dynamically predict the state of the battery according to the real-time operation data and the historical data of the single battery. And designing a dynamic battery state diagnosis method according to sampling drift caused by measurement loop abnormality, charge state deviation and internal resistance increase caused by battery life attenuation. The method is simple and easy to implement, the available capacity of the energy storage system and the availability ratio of the energy storage power station are improved, and the maintenance cost of the energy storage power station is reduced.
In addition, in a large energy storage power station, thousands of single batteries are usually arranged, and the method provided by the invention can select the single batteries with abnormal states and needing maintenance from the plurality of single batteries.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a flowchart of a battery cell state diagnosis method according to an embodiment of the present invention;
fig. 2 is a flowchart of a method for detecting an abnormality of a cell measurement circuit according to a second embodiment of the present invention;
fig. 3 is a flowchart of a battery cell state of charge detection method according to a third embodiment of the present invention;
fig. 4 is a flowchart of a battery cell state abnormality determination method according to a fourth embodiment of the present 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 only a part of the embodiments of the present invention, and not all of the embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
Example one
The present embodiment provides a method for diagnosing the state of a battery cell, as shown in fig. 1, including:
step S11, detecting whether the measurement loop of the battery cell is normal, if not, maintaining the abnormal measurement loop, if yes, entering step S12;
step S12, detecting whether the states of charge of the battery cells are consistent, if not, adjusting the electric quantity of the battery cells with inconsistent states of charge, and if so, entering step S13;
and step S13, obtaining and diagnosing the states of the single batteries according to the voltage change rates of the single batteries in the charging process and the discharging process and the voltage median and the voltage standard deviation of the battery pack, and determining the single batteries with abnormal states.
When the measurement loop of the battery cell is abnormal, the subsequent sampling drift of the battery parameters is caused, and the correctness of the state diagnosis of the battery cell is influenced, so that step S11 is executed to ensure that the measurement loop of the battery cell can work normally. Step S12 ensures that there is no deviation in the state of charge of the battery cells. Both of the above steps are the basis and precondition for implementing step S13. Step S13 diagnoses the state of the battery cell by acquiring parameters that reflect an increase in internal resistance due to a lifetime degradation of the battery cell, including a voltage change rate, a voltage median, and a voltage standard deviation.
Usually, in a large energy storage power station, there are thousands of battery cells, and the method provided by this embodiment can select the battery cell with abnormal state and needing maintenance from the numerous battery cells.
Example two
The present embodiment provides a method for detecting an abnormality of a cell measurement circuit, as shown in fig. 2, including:
step S21, acquiring the voltage and temperature of the battery monomer in the charging and discharging process;
step S22, calculating the voltage change rate and the temperature change rate of the single battery and performing per unit;
step S23, calculating the deviation rate of the voltage change rate of the single battery relative to the average value of the voltage change rate of the battery pack;
step S24, judging whether the deviation rate of the voltage change rate is larger than the voltage change rate deviation rate threshold value, if so, entering step S25, otherwise, returning to step S21;
step S25, calculating the deviation rate of the temperature change rate of the single battery relative to the average value of the temperature change rate of the battery pack;
and step S26, judging whether the deviation rate of the temperature change rate is larger than the temperature change rate deviation rate threshold value, if so, determining that the measurement loop of the battery cell is abnormal, otherwise, returning to the step S21.
The battery pack comprises N single batteries i, wherein N is an integer greater than or equal to 1.
In step S22, it is preferable that the voltage change rate and the temperature change rate of the unit cells are respectively calculated using the following formulas:
wherein, is Δ Vi(t)Is the voltage change rate, V, of the battery cell i at the moment ti(t)Is the voltage of the battery cell i at the time t, Vi(t-1)Is the voltage of cell i at time T-1, Δ Ti(t)Is the temperature change rate of the battery monomer i at the time T, Ti(t)Is the temperature of the cell i at time T, Ti(t-1)The voltage of a battery monomer i at the moment of t-1, delta t is a sampling interval, t is more than or equal to 1, and delta Vi(0)＝0，ΔTi(0)＝0。
The voltage change rate and the temperature change rate of the single battery are unified by the following formulas:
Vbase＝(VMax-VMin)/ttotal
ΔVmapi(t)＝ΔVi(t)/Vbase
Tbase＝(TMax-TMin)/ttotal
ΔTmapi(t)＝ΔTi(t)/Tbase
wherein, is Δ Vmapi(t)Is the voltage change rate per unit value, delta T, of the battery cell i at the moment Tmapi(t)Is the temperature change rate per unit value V of the battery monomer i at the time tbaseIs a voltage per unit reference, VMaxCut-off voltage, V, for charging of battery cells leaving factoryMinDischarge cutoff voltage, T, for the outgoing individual cellsbaseIs a temperature per unit basis, TMaxThe highest temperature T in the process of charging and discharging of the battery monomer leaving factoryMinIs the lowest temperature t in the charging and discharging process of the battery monomer leaving factorytotalThe charge and discharge time may be 2 hours.
In step S23, it is preferable to calculate the deviation ratio of the voltage change rate of the unit cells with respect to the average of the voltage change rates of the battery pack using the following formula:
wherein, is Δ ViTotalErrorIs the rate of deviation of the rate of change of voltage of cell i, Δ Vmapi(t)Is the voltage change rate per unit value, t, of the battery cell i at the moment ttotalAnd N is the number of the single batteries i in the battery pack for charging and discharging time.
In step S24, a cell whose voltage change rate deviation rate is greater than the voltage change rate deviation rate threshold value may be selected from the plurality of cells by using the cell number index (i). Specifically, the deviation rate of the voltage change rate of the plurality of battery cells is compared with a voltage change rate deviation rate threshold value, so as to obtain the serial number of the battery cell of which the deviation rate of the voltage change rate is greater than the voltage change rate deviation rate threshold value, and form a first serial number sequence, wherein the specific method comprises the following steps:
IndexErrorΔV[]＝kΔv×Index(i)
therein, IndexErrorΔV[]Is the first numbering sequence, Δ VErrorTholdIs a threshold value of the rate of change of voltage deviation, kΔvIs the first numbering coefficient.
In step S25, it is preferable to calculate the deviation ratio of the temperature change rate of the unit cells from the average value of the temperature change rate of the battery pack using the following formula:
wherein, Delta TiTotalErrorIs the deviation rate of the rate of temperature change of the cell i, Δ Tmapi(t)Is the temperature change rate per unit value of the battery monomer i at the time t, ttotalAnd N is the number of the single batteries i in the battery pack for charging and discharging time.
In step S26, when there are a plurality of battery cells, a battery cell whose temperature change rate deviation rate is greater than the temperature change rate deviation rate threshold value may be selected from the plurality of battery cells by using the battery number index (i). Specifically, the deviation rate of the temperature change rates of the plurality of battery cells is compared with a temperature change rate deviation rate threshold value, so as to obtain the serial numbers of the battery cells of which the deviation rates of the temperature change rates are greater than the temperature change rate deviation rate threshold value, and form a second serial number sequence, wherein the specific method comprises the following steps:
IndexErrorΔT[]＝kΔT×Index(i)
therein, IndexErrorΔT[]Is the second numbering sequence, Δ TErrorTholdIs a threshold value of the rate of temperature change deviation, kΔTIs the second numbered coefficient.
Step S26 is to finally screen out a battery cell having a deviation rate of voltage change greater than the threshold value of voltage change rate deviation rate and a deviation rate of temperature change greater than the threshold value of temperature change rate deviation rate from a plurality of battery cells. Specifically, the numbers appearing in the first numbered sequence and the second numbered sequence at the same time can be obtained by comparing the first numbered sequence with the second numbered sequence, and the measurement loop of the corresponding battery cell is in an abnormal state. The method can screen out a plurality of abnormal measuring loops at one time.
According to the method for detecting the abnormality of the single battery measurement loop, historical voltage/temperature data and current voltage/temperature data in the sampling process are utilized, the deviation of the change rate of the voltage/temperature is accumulated in time, the overall state of the single battery can be reflected, and the abnormality of the measurement loop can be detected more accurately.
EXAMPLE III
The present embodiment provides a method for detecting a state of charge of a battery cell, as shown in fig. 3, including:
step S31, acquiring voltage of the battery monomer in the standing process;
step S32, calculating a voltage deviation of the voltage of the battery cell with respect to a median value of the voltage of the battery pack;
step S33, judging whether the voltage deviation of the battery monomer is larger than a voltage deviation threshold value, if so, entering step S34, otherwise, returning to step S31;
step S34, judging whether the voltage deviation of the battery monomer is larger than the median of the voltage deviation of the battery pack, if so, entering step S35A, and if not, entering step S37A;
step S35A, acquiring the charge cut-off voltage of the battery cell when the charge is cut off;
step S36A, judging whether the charge cut-off voltage is larger than a first set value, if so, determining that the charge states of the battery monomers corresponding to the charge cut-off voltage are inconsistent;
step S37A, acquiring a discharge cutoff voltage of the battery cell at the time of discharge cutoff;
step S38A, determining whether the discharge cut-off voltage is smaller than a second set value, and if so, determining that the states of charge of the battery cells corresponding to the discharge cut-off voltage are inconsistent.
The battery pack comprises N single batteries i, wherein N is an integer greater than or equal to 1.
In step S32, the voltage deviation of the voltage of the battery cell with respect to the median value of the voltage of the battery pack is calculated using the following equation:
wherein, is Δ ViIs the voltage deviation, V, of the cell ii(t)For the voltage of cell i at time t, mean () is the median calculation sign, mean (V)i(t)) Is the median value of the voltage of the battery at time t, tstateStanding for a certain time;
in step S33, a battery cell with a voltage deviation greater than the voltage deviation threshold may be selected from the plurality of battery cells by using the battery number index (i), and the serial number of the battery cell forms a third serial number sequence. Specifically, the method is realized by the following steps:
IndexErrorV[]＝kV×Index(i)
therein, IndexErrorV[]Is the third numbering sequence, Δ VMeanTholdIs a voltage deviation threshold.
In steps S34 to S36A, the voltage deviation of the battery cells in the third numbered sequence obtained in step S33 is compared with the median value of the voltage deviation of the battery pack to obtain battery cells having a voltage deviation greater than the median value, the numbering thereof forms the fourth numbered sequence, and the battery cells having a voltage deviation equal to or less than the median value are obtained, and the numbering thereof forms the fifth numbered sequence. And acquiring the charge cut-off voltage of the battery monomer in the fourth serial number sequence, and determining that the charge states of the battery monomers of which the charge cut-off voltage is greater than the first set value are inconsistent and the electric quantity needs to be released. And acquiring the discharge cut-off voltage of the battery monomer in the fifth numbering sequence, and determining that the charge states of the battery monomers of which the discharge cut-off voltage is smaller than a second set value are inconsistent and the electric quantity needs to be supplemented.
Alternatively, after the fourth and fifth numbering sequences are obtained in step S34, the state of charge may be detected as follows.
Step S34, judging whether the voltage deviation is larger than the median value of the voltage deviation of the battery pack, if so, going to step S35B, and if not, going to step S37B;
step S35B, acquiring the charge cut-off voltage of all the battery monomers when the charging is cut off, and sequencing the battery monomers from large to small to obtain the corresponding battery monomer numbers and form a sixth number sequence;
step S36B, comparing the fourth serial number sequence with the sixth serial number sequence, and determining that the charge states of the battery cells with the serial numbers simultaneously appearing in the fourth serial number sequence and the sixth serial number sequence are inconsistent;
step S37B, acquiring the discharge cut-off voltage of all the battery monomers when the discharge is cut off, and sequencing the discharge cut-off voltage from small to large to obtain the corresponding battery monomer numbers and form a seventh number sequence;
and step S38B, comparing the fifth numbered sequence with the seventh numbered sequence, and determining that the states of charge of the battery cells with numbers simultaneously appearing in the fifth numbered sequence and the seventh numbered sequence are inconsistent.
The first m1 charging cut-off voltages after sorting can be taken to form a corresponding sixth number sequence, and the value of m1 can be determined according to the first set value. The first m2 discharge cutoff voltages after sorting can be taken to form a corresponding seventh number sequence, and the value of m2 can be determined according to the second set value.
According to the method for detecting the state of charge of the battery monomer, historical voltage data and current voltage data in the sampling process are utilized, the deviation of the voltage of the battery monomer relative to the median value of the voltage of the battery pack is accumulated in time, the overall state of the battery monomer can be reflected, the charging cut-off voltage and the discharging cut-off voltage are utilized for confirmation, and whether the state of charge is consistent or not is detected more accurately.
Example four
The present embodiment provides a method for determining a state abnormality of a battery cell, as shown in fig. 4, including:
step S41, acquiring the voltage of the battery monomer in the charging process and the discharging process;
step S42, calculating the voltage change rate of the battery monomer in the charging process and the discharging process;
step S43, calculating the voltage median and the voltage standard deviation of each sampling moment of the battery pack;
step S44, normalization processing is carried out based on outlier data formed by voltage median values and voltage standard deviations at all sampling moments;
step S45, performing cluster analysis on the result of the normalization processing to classify the battery monomer into a state in-doubt class and a state normal class, and entering the step S46 or S47;
step S46, judging whether the voltage change rate of the battery monomer in the charging process is larger than a third set value; if yes, go to step S48, otherwise, go back to step S41 or go to step S47;
step S47, judging whether the voltage change rate of the battery monomer in the discharging process is smaller than a fourth set value, if yes, entering step S48, otherwise, returning to step S41 or step S46;
and step S48, judging whether the battery cell belongs to the status doubtful class, if so, determining that the battery cell is an abnormal battery cell, otherwise, returning to the step S41.
Alternatively, in step S42, the voltage change rates of the unit cells during charging and discharging are calculated using the following equations, respectively:
wherein, is Δ ViChargeIs the rate of change of voltage, Δ V, of cell i during chargingiDischaIs the rate of change of voltage, Δ V, of cell i during dischargei(t)The voltage change rate of the battery monomer i at the moment t is shown; t is tChargeFor the charging time, tDischaAnd t is the sampling time, and is an integer which is greater than or equal to 0.
ΔVi(t)The calculation is made using the following formula:
wherein, Vi(t)Is the voltage of the battery cell i at the time t, Vi(t-1)The voltage of the battery cell i at the moment t-1 is shown, and deltat is a sampling interval.
Optionally, in step S43, the voltage median and the voltage standard deviation at each sampling time of the battery pack are calculated by using the following formula:
Xm(t)＝mean(Vi(t))
Xs(t)＝std(Vi(t))
wherein, Xm(t)Is the median voltage, X, of the battery at time ts(t)For the standard deviation of the voltage of the battery at time t, mean () is the median calculation sign, std () is the standard deviation calculation sign, Vi(t)Is the voltage of the battery cell i at the time t.
Optionally, in step S44, the normalization process is performed using the following equation:
step_data＝1，(Vi(t)＞Xm(t)+kr×Xs(t))
step_data＝0，(Vi(t)＜Xm(t)-kr×Xs(t))
wherein step _ data is the result of normalization, krFor the scaling factor, a constant greater than 0 is taken. step _ data is a matrix, and the size of the matrix is the number of the battery cells multiplied by the sampling time. The value in the matrix is whether all the battery cells deviate from the voltage median k at a sampling momentrStandard deviation, krMay be 2. Then, each row in the matrix is added to obtain the judgment standard of the battery cell at all sampling moments, and step _ data' is obtained, and is a matrix of the number of the battery cells multiplied by 1.
Optionally, in step S45, clustering analysis may be performed in MATLAB using the following parameters: hidx ═ clusterida (step _ data ', ' maxcclus ',3, ' distance ', ' spearman ', ' linkage ', ' weighted ')
Wherein maxclust refers to the construction of maximum 3 clusters, Spearman refers to the Spearman correlation coefficient between 1-samples, weighted refers to the clustering analysis by a weighted average method, and other parameters are conventional parameters in MATLAB. The final result classifies the battery cells into categories 1, 2 and 3, where categories 1 and 3 constitute the status questionable categories.
Optionally, in steps S45-S48, the battery number index (i) may be used to obtain an abnormal cell, specifically, the voltage change rates of the battery cells during the charging process are arranged in the order from large to small, and m3 first, the numbers of the corresponding battery cells are obtained, so as to form an eighth number sequence. The value of m3 may be determined according to the aforementioned third set value. Arranging the voltage change rates of the battery monomers in the discharging process from small to large, taking the first m4 to obtain the numbers of the corresponding battery monomers, and forming a ninth number sequence. The value of m4 may be determined according to the fourth set value described above. The numbers of the battery cells of the state doubt class form a tenth number sequence. And comparing the eighth numbered sequence and the ninth numbered sequence with the tenth numbered sequence, and determining that the battery cells with numbers simultaneously appearing in the eighth numbered sequence and the tenth numbered sequence are abnormal state cells, and the battery cells with numbers simultaneously appearing in the ninth numbered sequence and the tenth numbered sequence are also abnormal state cells.
The method for determining the state abnormality of the single battery according to the embodiment utilizes historical voltage data and current voltage data in the sampling process to accumulate the voltage change rate over time, and utilizes the voltage median and the voltage standard deviation of the battery pack to calculate, so that the overall state of the single battery can be reflected, and the single battery with increased internal resistance due to the service life attenuation can be diagnosed.
EXAMPLE five
The present embodiment provides a method for diagnosing the state of a battery cell, which includes steps S11-S13 described in the first embodiment, wherein step S11 is implemented by using the steps of the method for detecting an abnormality in a battery cell measurement circuit described in the second embodiment. For specific steps, reference is made to the foregoing first embodiment and second embodiment, which are not described herein again.
EXAMPLE six
The present embodiment provides a method for diagnosing a state of a battery cell, including steps S11-S13 described in the first embodiment, wherein step S12 is implemented by using the steps of the method for detecting a state of charge of a battery cell described in the third embodiment. For specific steps, reference is made to the first embodiment and the third embodiment, which are not described herein again.
EXAMPLE seven
The present embodiment provides a method for diagnosing the state of a battery cell, which includes steps S11-S13 described in the first embodiment, wherein step S13 is implemented by using the steps of the method for determining the abnormal state of the battery cell described in the fourth embodiment. For specific steps, reference is made to the first embodiment and the fourth embodiment, which are not described herein again.
Example eight
The present embodiment provides a method for diagnosing the state of a battery cell, which includes steps S11-S13 described in the first embodiment, wherein step S11 is implemented by using the steps of the method for detecting abnormality of the battery cell measurement circuit described in the second embodiment, and step S13 is implemented by using the steps of the method for determining abnormality of the state of the battery cell described in the fourth embodiment. For specific steps, reference is made to the foregoing first embodiment, second embodiment and fourth embodiment, which are not described herein again.
Example nine
The present embodiment provides a method for diagnosing the state of a battery cell, including steps S11-S13 described in the first embodiment, wherein step S12 is implemented by using the steps of the method for detecting the state of charge of the battery cell described in the third embodiment, and step S13 is implemented by using the steps of the method for determining the abnormal state of the battery cell described in the fourth embodiment. For specific steps, reference is made to the foregoing first embodiment, third embodiment and fourth embodiment, which are not described herein again.
Example ten
The present embodiment provides a method for diagnosing the state of a battery cell, including steps S11-S13 described in the first embodiment, wherein step S11 is implemented by using the steps of the method for detecting the abnormality of the battery cell measurement circuit described in the second embodiment, step S12 is implemented by using the steps of the method for detecting the state of charge of the battery cell described in the third embodiment, and step S13 is implemented by using the steps of the method for determining the abnormality of the state of the battery cell described in the fourth embodiment. For specific steps, reference is made to the foregoing first embodiment, second embodiment, third embodiment and fourth embodiment, which are not described herein again.
EXAMPLE eleven
The present embodiment provides a storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application mall, etc., on which a computer program is stored, which when executed by a processor, may implement the steps of the method of any one of the first to tenth embodiments.
Example twelve
The embodiment provides a battery cell state diagnosis device, which comprises a collecting device, a memory and a processor, wherein the collecting device is used for acquiring the voltage and/or the temperature of a battery cell. The memory has stored thereon a computer program which, when executed by the processor, may carry out the steps of the method of any of the preceding embodiments.
The memory is used to store various types of data to support the operation of the cell status diagnostic device, which may include, for example, instructions for any application or method operating on the cell status diagnostic device, as well as application-related data. The Memory may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk.
A processor may be used to perform all or part of the steps of the method as in any of the previous embodiments. The Processor may be an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components.
According to the state diagnosis method of the single battery, the battery state is dynamically predicted according to the real-time operation data and the historical data of the single battery. And designing a dynamic battery state diagnosis method according to sampling drift caused by measurement loop abnormality, charge state deviation and internal resistance increase caused by battery life attenuation. The method is simple and easy to implement, the available capacity of the energy storage system and the availability ratio of the energy storage power station are improved, and the maintenance cost of the energy storage power station is reduced.
The preferred embodiments of the present invention have been described in detail with reference to the accompanying drawings, however, the present invention is not limited to the specific details of the above embodiments, and various simple modifications can be made to the technical solution of the present invention within the technical idea of the present invention, and these simple modifications are within the protective scope of the present invention.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. The invention is not described in detail in order to avoid unnecessary repetition.
In addition, any combination of the various embodiments of the present invention is also possible, and the same should be considered as the disclosure of the present invention as long as it does not depart from the spirit of the present invention.
Claims (12)
1. A method for diagnosing the state of a battery cell, wherein a battery pack includes N battery cells, N being an integer of 1 or more, the method comprising:
detecting whether a measurement loop of each single battery in the battery pack is normal or not, and maintaining the abnormal measurement loop;
detecting whether the charge states of all the battery monomers in the battery pack are consistent, and adjusting the electric quantity of the battery monomers with inconsistent charge states;
and diagnosing the states of the single batteries according to the voltage change rates of the single batteries in the charging process and the discharging process and the voltage median and the voltage standard deviation of the battery pack, and determining the single batteries with abnormal states.
2. The method of claim 1, wherein detecting whether the measurement loop of each cell in the battery pack is normal comprises:
acquiring voltage and temperature of a battery monomer in a charging and discharging process;
calculating the voltage change rate and the temperature change rate of the single battery and performing per unit;
calculating the deviation rate of the voltage change rate of the single battery relative to the voltage change rate mean value of the battery pack and the deviation rate of the temperature change rate of the single battery relative to the temperature change rate mean value of the battery pack;
and determining that the measurement loop of the single battery is abnormal, wherein the deviation rate of the voltage change rate relative to the voltage change rate mean value of the battery pack is greater than a voltage change rate deviation rate threshold value, and the deviation rate of the temperature change rate relative to the temperature change rate mean value of the battery pack is greater than a temperature change rate deviation rate threshold value.
3. The method of claim 2, wherein the voltage change rate and the temperature change rate of the battery cell are calculated by:
wherein, is Δ Vi(t)Is the voltage change rate, V, of the battery cell i at the moment ti(t)Is the voltage of the battery cell i at the time t, Vi(t-1)Is the voltage of cell i at time T-1, Δ Ti(t)Is the temperature change rate of the battery monomer i at the time T, Ti(t)Is the temperature of the cell i at time T, Ti(t-1)The voltage of a battery monomer i at the moment of t-1, delta t is a sampling interval, t is more than or equal to 1, and delta Vi(0)＝0，ΔTi(0)＝0；
The per unit calculation formula is:
Vbase＝(VMax-VMin)/ttotal
ΔVmapi(t)＝ΔVi(t)/Vbase
Tbase＝(TMax-TMin)/ttotal
ΔTmapi(t)＝ΔTi(t)/Tbase
wherein, VbaseIs a voltage per unit reference, VMaxCut-off voltage, V, for charging of battery cells leaving factoryMinDischarge cutoff voltage, T, for the outgoing individual cellsMaxThe highest temperature T in the process of charging and discharging of the battery monomer leaving factoryMinIs the lowest temperature t in the charging and discharging process of the battery monomer leaving factorytotalThe charge and discharge time is shown.
4. The method according to claim 2, wherein the deviation ratio of the voltage change rate of the battery cell from the mean value of the voltage change rates of the battery pack is calculated by:
wherein, is Δ ViTotalErrorIs the rate of deviation of the rate of change of voltage of cell i, Δ Vmapi(t)Is the voltage change rate per unit value, t, of the battery cell i at the moment ttotalIs the charging and discharging time;
the calculation formula of the deviation rate of the temperature change rate of the single battery relative to the average value of the temperature change rate of the battery pack is as follows:
wherein, Delta TiTotalErrorIs the deviation rate of the rate of temperature change of the cell i, Δ Tmapi(t)Is the temperature change rate per unit value of the battery monomer i at the time t, ttotalThe charge and discharge time is shown.
5. The method of claim 1, wherein detecting whether the states of charge of the cells are consistent comprises:
acquiring voltage of a battery monomer in a standing process;
calculating the voltage deviation of the voltage of the battery cell relative to the median of the voltage of the battery pack;
acquiring the charge cut-off voltage of the battery monomer when the charge is cut off;
acquiring the discharge cut-off voltage of the battery monomer when the discharge is cut off;
determining that the voltage deviation is greater than a voltage deviation threshold value and the voltage deviation is greater than a median of voltage deviations of the battery pack, and the charge states of the battery cells with the charge cut-off voltages greater than a first set value are inconsistent and the electric quantity needs to be released;
and determining that the voltage deviation is greater than a voltage deviation threshold value and less than or equal to a median of the voltage deviation of the battery pack, and the charge states of the battery cells with the discharge cut-off voltage less than a second set value are inconsistent and the electric quantity needs to be supplemented.
6. The method of claim 5, wherein the voltage deviation of the cell voltage from the median value of the battery pack voltage is calculated by:
wherein, is Δ ViIs the voltage deviation, V, of the cell ii(t)For the voltage of cell i at time t, mean () is the median calculation sign, mean (V)i(t)) Is the median value of the voltage of the battery at time t, tstateStanding time.
7. The method of claim 1,
diagnosing the states of the single batteries according to the voltage change rates of the single batteries in the charging process and the discharging process and the voltage median and the voltage standard deviation of the battery pack, and determining the single batteries with abnormal states, wherein the method comprises the following steps:
acquiring the voltage of a battery monomer in the charging process and the discharging process;
calculating the voltage change rate of the battery monomer in the charging process and the discharging process;
calculating a voltage median and a voltage standard deviation of the battery pack at each sampling moment;
normalizing outlier data formed on the basis of voltage median values and voltage standard deviations at all sampling moments;
performing cluster analysis on the result of the normalization processing to classify the battery monomer into a state doubt class and a state normal class;
and determining that the battery cells which have the voltage change rate greater than a third set value and belong to the state doubt class in the charging process are abnormal state cells, and determining that the battery cells which have the voltage change rate less than a fourth set value and belong to the state doubt class in the discharging process are abnormal state cells.
8. The method of claim 7, wherein the voltage change rates of the unit cells during the charging and discharging are calculated using the following equations, respectively:
wherein, is Δ ViChargeIs the rate of change of voltage, Δ V, of cell i during chargingiDischaIs the rate of change of voltage, Δ V, of cell i during dischargei(t)The voltage change rate of the battery monomer i at the moment t is shown; t is tChargeFor the charging time, tDischaIs the discharge time, t is the sampling time;
ΔVi(t)the calculation is made using the following formula:
wherein, Vi(t)Is the voltage of the battery cell i at the time t, Vi(t-1)The voltage of the battery cell i at the moment t-1 is shown, and deltat is a sampling interval.
9. The method of claim 7, wherein the outlier data formed based on the voltage median and the voltage standard deviation for all sampling instants is normalized using the following equation:
step_data＝1，(Vi(t)＞Xm(t)+kr×Xs(t))
step_data＝0，(Vi(t)＜Xm(t)-kr×Xs(t))
wherein step _ data is the result of normalization processing, Vi(t)Is the voltage of the cell i at time t, Xm(t)Is the median voltage, X, of the battery at time ts(t)Is the standard deviation of the voltage, k, of the battery pack at time trFor the scaling factor, a constant greater than 0 is taken.
10. The method of claim 7, wherein each cell has its number associated therewith;
determining that the battery cell which has the voltage change rate larger than a third set value and belongs to the state doubt class in the charging process is an abnormal state cell, and determining that the battery cell which has the voltage change rate smaller than a fourth set value and belongs to the state doubt class in the discharging process is an abnormal state cell, including:
arranging the voltage change rates of the battery monomers in the charging process from large to small, taking the first m3 to obtain the numbers of the corresponding battery monomers to form an eighth number sequence, wherein 1< m3 is not more than N;
arranging the voltage change rates of the battery monomers in the discharging process from small to large, taking the first m4 to obtain the numbers of the corresponding battery monomers to form a ninth number sequence, wherein 1< m4 is not more than N;
acquiring the serial numbers of the battery monomers of the state doubt class to form a tenth serial number sequence;
and comparing the eighth numbered sequence and the ninth numbered sequence with a tenth numbered sequence respectively, and determining that the battery cells with numbers simultaneously appearing in the eighth numbered sequence and the tenth numbered sequence are abnormal state cells, and the battery cells with numbers simultaneously appearing in the ninth numbered sequence and the tenth numbered sequence are also abnormal state cells.
11. A storage medium storing a computer program executable by one or more processors to implement the method of diagnosing the state of a battery cell according to any one of claims 1 to 10.
12. The battery cell state diagnosis equipment is characterized by comprising a collecting device, a memory and a processor; the acquisition device is used for acquiring the voltage and/or the temperature of the battery monomer; the memory stores thereon a computer program that, when executed by the processor, performs the state diagnosis method of the battery cell according to any one of claims 1 to 10.
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