CN117406125B - Battery health state confirmation method, device, equipment and storage medium - Google Patents
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Abstract
The embodiment of the disclosure provides a battery state of health confirmation method, device, equipment and storage medium, which are applied to the technical field of battery state detection, wherein the method comprises the following steps: if the differential current at the time t meets a preset threshold, a first state value is obtained according to the voltage and current data of the battery cells at the time t and the time t+1; obtaining a second state value according to the SOC difference value and the accumulated charge/discharge amount of the battery monomer during two standing periods; acquiring the current cycle charge/discharge times of the battery cell, and calculating a third state value according to the cycle charge/discharge times; and calculating an SOH value according to the first state value, the second state value and the third state value to confirm the state of health of the battery. In this way, the accuracy of SOH value estimation can be improved.
Description
Technical Field
The disclosure relates to the technical field of battery management, and further relates to the technical field of battery state detection, in particular to a method, a device, equipment and a storage medium for confirming the state of health of a battery.
Background
SOH (State of Health) is one of core parameters in a BMS (Battery Management System ), which is significant for the gradient utilization and fault detection of a battery, and accurate SOH can improve the estimation accuracy of other modules, so that it is necessary to perform estimation analysis on SOH.
Current methods for SOH estimation analysis are quite numerous, for example: discharge test method, resistance folding algorithm, impedance analysis method, big data estimation method, etc. The discharge experiment method is simple to operate, but can not estimate the SOH of the battery on line; the resistance folding algorithm only takes the resistance as the basis for evaluating the SOH of the battery, but the change range of the resistance is smaller when the battery ages, so that the error is larger; the impedance analysis method can intuitively analyze the change of the SOH of the battery according to the impedance spectrum, but the method has higher cost and higher precision only when the SOH value of the battery is lower; big data estimation can predict battery SOH, but model training requires a lot of data and time and prediction accuracy is limited.
Disclosure of Invention
In view of this, the present disclosure provides a method, apparatus, device and storage medium for confirming a battery state of health.
According to a first aspect of the present disclosure, a battery state of health confirmation method is provided. The method comprises the following steps:
collecting voltage and current data of the battery cell, and carrying out real-time operation on the current data to obtain differential current corresponding to each moment; if the differential current at the time t meets a preset threshold, a first state value is obtained according to the voltage and current data of the battery cells at the time t and the time t+1; acquiring an SOC value read by the OCV after the battery monomer is stood for two times until the battery monomer exceeds a standing threshold value, and calculating an SOC difference value; obtaining a second state value according to the SOC difference value and the accumulated charge/discharge amount of the battery monomer during two standing periods; acquiring the current cycle charge/discharge times of the battery cell, and calculating a third state value according to the cycle charge/discharge times; and calculating an SOH value according to the first state value, the second state value and the third state value to confirm the state of health of the battery.
In the method, the first state value, the second state value and the third state value are fused by adopting a multi-data fusion method to calculate the SOH value, so that the precision of on-line estimation of the SOH can be effectively improved within the cost controllable range, and the more accurate battery health state can be obtained.
In some implementations of the first aspect, if the differential current at the time t meets a preset threshold, obtaining a first state value according to the voltage and current data of the battery cell at the time t and at the time t+1 includes:
if the differential current at the time t meets a preset threshold, acquiring voltage and current data of the battery cell at the time t and at the time t+1, and calculating a difference ratio of the two groups of voltage and current data to obtain the internal resistance of the battery cell;
and obtaining a first state value according to the relation between the internal resistance of the battery cell and the SOH value.
In some implementations of the first aspect, obtaining the second state value according to the SOC difference value and the accumulated charge/discharge amount during two standstill of the battery cell includes:
inputting the SOC difference value and the accumulated charge/discharge amount of the battery cell during two standing periods into a second state value calculation formula to obtain a second state value; wherein,
the second state value calculation formula is:
，
wherein delta SOC is the SOC difference, Q is the accumulated charge/discharge amount of the battery cell during two standing periods, and C Rated for Is the rated capacity of the battery cell.
In some implementations of the first aspect, calculating the SOH value to confirm the battery state of health from the first state value, the second state value, and the third state value includes:
according to the first state value, the second state value and the third state value, calculating an SOH value by adopting a Kalman filtering method so as to confirm the health state of the battery;
further comprises:
and calculating an SOH value by adopting a weighted summation method according to the first state value, the second state value and the third state value so as to confirm the state of health of the battery.
In some implementations of the first aspect, calculating the SOH value to confirm the battery state of health using kalman filtering based on the first state value, the second state value, and the third state value includes:
substituting the first state value, the second state value and the third state value into a state equation;
based on an observation equation and a state equation, continuously correcting the SOH value according to a Kalman recursion principle to enable the SOH value to gradually approach a true value; ending the recursion process until the error is smaller than a preset threshold value, and outputting an SOH value;
and confirming the state of health of the battery according to the SOH value.
In the method, the first state value, the second state value and the third state value are fused by adopting a Kalman filtering method to obtain the SOH value, and the SOH value is continuously corrected, so that the calculation accuracy of the SOH value can be effectively improved.
In some implementations of the first aspect, calculating the SOH value to confirm the battery state of health using a weighted summation method based on the first state value, the second state value, and the third state value includes:
corresponding weight values are distributed for the first state value, the second state value and the third state value according to a preset weight table;
inputting the first state value, the second state value and the third state value and the corresponding weight values into a weighted summation calculation formula, and calculating an SOH value to confirm the health state of the battery; wherein,
the weight table is a dynamic weight table;
and when the weight coefficient of the third state value is 0, reassigning the cyclic charge/discharge times according to the SOH value of the previous cycle, so that the SOH value is calculated according to the reassigned cyclic charge/discharge times when the SOH value is calculated in the subsequent cycle.
In the method, based on a weighted summation method, the first state value, the second state value and the third state value are flexibly assigned by adopting a dynamic weight table, and different weights can be assigned according to different conditions, so that the calculation result of the SOH value is more accurate.
In some implementations of the first aspect, the method further includes:
uploading the collected voltage and current data, the differential current corresponding to each moment, the data used for calculating the SOH value and the obtained SOH value to a cloud server; the cloud server builds a battery health state analysis model by adopting a machine learning algorithm according to the voltage and current data, the differential current corresponding to each moment, the data used for calculating the SOH value and the obtained SOH value.
In the method, the cloud server is used for storing daily calculation data to train the battery health state analysis model step by step, so that the accurate battery health state analysis model can be trained while calculating the SOH value.
According to a second aspect of the present disclosure, a battery state of health confirmation device is provided. The device comprises:
the first processing module is used for collecting voltage and current data of the battery cell, and carrying out real-time operation on the current data to obtain differential current corresponding to each moment;
the second processing module is used for obtaining a first state value according to the voltage and current data of the battery cells at the time t and the time t+1 if the differential current at the time t meets a preset threshold value;
the third processing module is used for obtaining the SOC value read by the OCV after the battery monomer is stood for two times until the battery monomer exceeds a standing threshold value, and calculating an SOC difference value;
the fourth processing module is used for obtaining a second state value according to the SOC difference value and the accumulated charge/discharge amount of the battery cell during two standing periods;
the fifth processing module is used for obtaining the current cycle charge/discharge times of the battery cell and calculating a third state value according to the cycle charge/discharge times;
and the sixth processing module is used for calculating the SOH value according to the first state value, the second state value and the third state value so as to confirm the state of health of the battery.
According to a third aspect of the present disclosure, an electronic device is provided. The electronic device includes: at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method as described above.
According to a fourth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform a method as described above.
In the method, based on multi-data fusion, a dynamic weight distribution method is adopted, so that a more accurate SOH value can be obtained, and the health state of the battery can be confirmed more accurately.
It should be understood that what is described in this summary is not intended to limit the critical or essential features of the embodiments of the disclosure nor to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following description.
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The above and other features, advantages and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. For a better understanding of the present disclosure, and without limiting the disclosure thereto, the same or similar reference numerals denote the same or similar elements, wherein:
Fig. 1 shows a flowchart of a battery state of health confirmation method provided by an embodiment of the present disclosure;
fig. 2 is a schematic diagram illustrating a method for confirming a battery state of health according to an embodiment of the present disclosure;
fig. 3 shows a block diagram of a battery state of health confirmation device provided by an embodiment of the present disclosure;
fig. 4 illustrates a block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are some embodiments of the present disclosure, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments in this disclosure without inventive faculty, are intended to be within the scope of this disclosure.
In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
In view of the problems mentioned in the background art, the present disclosure provides a method, an apparatus, a device, and a storage medium for confirming a battery state of health.
Specifically, collecting voltage and current data of a battery cell, and carrying out real-time operation on the current data to obtain differential current corresponding to each moment; if the differential current at the time t meets a preset threshold, a first state value is obtained according to the voltage and current data of the battery cells at the time t and the time t+1; acquiring an SOC value read by the OCV after the battery monomer is stood for two times until the battery monomer exceeds a standing threshold value, and calculating an SOC difference value; obtaining a second state value according to the SOC difference value and the accumulated charge/discharge amount of the battery monomer during two standing periods; acquiring the current cycle charge/discharge times of the battery cell, and calculating a third state value according to the cycle charge/discharge times; and calculating an SOH value according to the first state value, the second state value and the third state value to confirm the state of health of the battery.
In this way, the accuracy of SOH online estimation can be effectively improved within the cost-controllable range, and the method is strong in universality and easy to implement.
The battery state of health confirmation method provided by the present disclosure is described in detail below by way of specific examples with reference to the accompanying drawings.
Fig. 1 shows a flowchart of a battery state of health confirmation method provided by an embodiment of the present disclosure; as shown in fig. 1, the battery state of health confirmation method 100 may include the steps of:
s110, collecting voltage and current data of the battery cell, and carrying out real-time operation on the current data to obtain differential current corresponding to each moment.
It should be noted that, the real-time operation may be a differential integral operation or other operations, the operation result includes a plurality of results, and the differential current is set as a judgment basis, so that the differential current corresponding to each time is obtained from the operation result.
And S120, if the differential current at the time t meets a preset threshold, obtaining a first state value according to the voltage and current data of the battery cells at the time t and the time t+1.
Specifically, if the differential current at the time t meets a preset threshold, acquiring voltage and current data of the battery cells at the time t and the time t+1, and calculating a difference ratio of the two groups of voltage and current data to obtain the internal resistance of the battery cells; and obtaining a first state value according to the relation between the internal resistance of the battery cell and the SOH value through a table look-up method.
It can be understood that the steps of collecting and recording the bus voltage and current data of the battery cell and performing real-time operation are performed synchronously, if the differential current in the operation result meets the preset threshold, further calculating the internal resistance of the battery cell, and if the differential current in the operation result does not meet the preset threshold, continuously recording the bus voltage and current data of the battery cell and uploading the data to the cloud server.
S130, obtaining the SOC value read by the OCV after the battery monomer is stood for two times to exceed the standing threshold value, and calculating the SOC difference value.
Illustratively, the rest threshold may be set to rest for 1h or more.
And S140, obtaining a second state value according to the SOC difference value and the accumulated charge/discharge amount of the battery cell during two standing periods.
Specifically, obtaining an SOC value read out by OCV (Open circuit voltage ) of a battery cell after standing for more than 1h for two times, and calculating an SOC difference value; inputting the SOC difference value and the accumulated charge/discharge amount of the battery cell in the period of standing twice until the SOC difference value and the battery cell exceed a standing threshold value into a second state value calculation formula to obtain a second state value; wherein,
the accumulated charge/discharge amount during which the battery cell is allowed to stand twice to exceed the standing threshold is calculated from the charge/discharge current and time during which the battery cell is allowed to stand.
The second state value calculation formula is:
，
wherein delta SOC is the SOC difference, Q is the accumulated charge/discharge amount of the battery cell during two standing periods, and C Rated for Is the rated capacity of the battery cell.
S150, obtaining the current cycle charge/discharge times of the battery cell, and calculating a third state value according to the cycle charge/discharge times.
Specifically, the current cycle charge/discharge times of the battery cell are obtained, and a third state value is calculated according to a cycle charge time accumulation method.
S160, calculating SOH value according to the first state value, the second state value and the third state value to confirm the state of health of the battery.
Specifically, according to the first state value, the second state value and the third state value, a Kalman filtering method is adopted to calculate an SOH value so as to confirm the health state of the battery.
Further, substituting the first state value, the second state value and the third state value into a state equation; based on an observation equation and a state equation, continuously correcting the SOH value according to a Kalman recursion principle to enable the SOH value to gradually approach a true value; ending the recursion process until the error is smaller than a preset threshold value, and outputting an SOH value; and confirming the state of health of the battery according to the SOH value.
According to the embodiment of the disclosure, the Kalman filtering method is adopted to fuse the first state value, the second state value and the third state value to obtain the SOH value and continuously correct the SOH value, so that the calculation accuracy of the SOH value can be effectively improved.
In some embodiments, calculating the SOH value to confirm the battery state of health based on the first state value, the second state value, and the third state value may further include:
and calculating an SOH value by adopting a weighted summation method according to the first state value, the second state value and the third state value so as to confirm the state of health of the battery.
Further, corresponding weight values are distributed for the first state value, the second state value and the third state value according to a preset weight table; inputting the first state value, the second state value and the third state value and the corresponding weight values into a weighted summation calculation formula, and calculating an SOH value to confirm the health state of the battery; wherein,
the weight table is a dynamic weight table;
and when the weight coefficient of the third state value is 0, reassigning the cyclic charge/discharge times according to the SOH value of the previous cycle, so that the SOH value is calculated according to the reassigned cyclic charge/discharge times when the SOH value is calculated in the subsequent cycle.
It can be understood that the first state value, the second state value and the third state value have weight coefficients respectively, and the weight coefficients corresponding to the first state value, the second state value and the third state value are self-adjusted according to the dynamic weight table.
Specifically, the weight coefficient of the first state value is positively correlated with the magnitude of the voltage difference, i.e., the larger the voltage difference is, the larger the weight coefficient is; the weight coefficient of the second state value is determined by the SOC difference value between two standing times and the charge/discharge current, when the SOC difference value is larger, the charge/discharge current is more stable, and when the SOC difference value is larger, the weight coefficient is larger, otherwise, the weight coefficient is smaller; the weight coefficient of the third state value is related to the battery usage, and the weight coefficient may be set to 0 when overcharging/overdischarging as the battery usage is more severe.
Illustratively, the dynamic weight table may be as follows:
wherein,is the minimum threshold, ++>Is the maximum threshold.
Further, the dynamic weight table may be implemented by a state machine, polling weights, or other manners that achieve the same effect.
According to the embodiment of the disclosure, based on a weighted summation method, the first state value, the second state value and the third state value are flexibly assigned by adopting the dynamic weight table, and different weights can be assigned according to different conditions, so that the calculation result of the SOH value is more accurate.
In some embodiments, the battery state of health confirmation method 100 may further include:
uploading the collected voltage and current data, the differential current corresponding to each moment, the data used for calculating the SOH value and the obtained SOH value to a cloud server; the cloud server builds a battery health state analysis model by adopting a machine learning algorithm according to the voltage and current data, the differential current corresponding to each moment, the data used for calculating the SOH value and the obtained SOH value.
According to the embodiment of the disclosure, the cloud server is utilized to store daily calculation data so as to train the battery health state analysis model step by step, and the accurate battery health state analysis model can be trained while calculating the SOH value.
Fig. 2 is a schematic diagram illustrating a battery state of health confirmation method according to an embodiment of the present disclosure.
As shown in fig. 2, the BMS collects voltage and current data, performs real-time operation on the current data to obtain an operation result, further judges a differential current value in the operation result, if the differential current at the time t is greater than a preset threshold, obtains bus voltage and current data of the battery cell at the time t and the time t+1, obtains internal resistance of the battery cell through calculation according to the bus voltage and current data of the battery cell at the time t and the time t+1, and obtains a first state value through a relation between the internal resistance of the battery cell and an SOH value; calculating to obtain a second state value according to the SOC difference value when the battery monomer read by the OCV is stood for two times and exceeds a standing threshold value and the charge/discharge quantity of the battery monomer during the two times of standing; obtaining a third state value according to the current cycle charge/discharge times of the battery cell; and calculating the first state value, the second state value and the third state value by adopting a Kalman filtering method or a weighted summation method to obtain an SOH value, and confirming the health state of the battery through the SOH value.
Further, the BMS uploads the collected voltage and current data, the differential current corresponding to each moment, the data used for calculating the SOH value and the obtained SOH value to the cloud server; the cloud server builds a battery health state analysis model by adopting a machine learning algorithm according to the voltage and current data, the differential current corresponding to each moment, the data used for calculating the SOH value and the obtained SOH value.
The foregoing is explained in more detail in connection with a specific embodiment.
Assuming that the collected voltage data is V, the current data is I, the sampling period is I, and the preset maximum differential current threshold value isThe minimum differential current threshold is +.>Then:
the calculation formula defining the first state value is:
,
after the R value is obtained, SOH is obtained by inquiring an R-SOH table 1 。
The calculation formula defining the second state value is:
，
wherein delta SOC is the SOC difference, Q is the accumulated charge/discharge amount of the battery cell during two standing periods, and C Rated for Is the rated capacity of the battery cell.
The calculation formula defining the third state value is:
，
wherein n is 0 The total cycle charge/discharge times of the battery cell are theoretically; n is n k The number of charge/discharge cycles is the current cycle.
Further, SOH i-1 The resulting SOH value is calculated for the last cycle.
Defining a weighted sum calculation formula as:
，
wherein X is 1 Is SOH 1 Weight coefficient, X of (2) 2 Is SOH 2 Weight coefficient, X of (2) 3 Is SOH 3 Weight coefficient of (c) in the above-mentioned formula (c).
After the above formulas are defined, specific steps are described next.
Battery to be collectedSubstituting the current data of the single body into a real-time operation formula to obtain differential current。
Further assume that the differential current at time t increases rapidly from 0 to a preset threshold Within the range, and falling back to 0 quickly remains stable, then:
for a first state value:
according to the voltage and current data of the battery cell at the time t and the time t+1, calculating the internal resistance R of the battery cell, and according to the R value, inquiring an R-SOH table to obtain SOH 1 。
Corresponding weight coefficient X is distributed for the first state value according to the differential current of the dynamic weight table and the time t 1 。
For the second state value:
acquiring an SOC value read by OCV of a battery monomer after standing for more than 1h for two times, and calculating delta SOC; if 20% < delta SOC is less than or equal to 80%, SOH is calculated according to the charge/discharge amount Q during the standing period of the battery cell 2 。
Distributing corresponding weight coefficient X for the second state value according to the dynamic weight table 2 Illustratively X 2 The allocation of (c) may be as follows:
negative values are discarded.
If delta SOC<20, let X 2 Is 0.
For the third state value:
assume SOC>20%, obtaining the current accumulated cycle charge/discharge times n of the battery cell k ，Wherein Q is the current residual charge of the battery cell, Q e The electric charge is rated for the battery cell.
Will n k Substitution formulaIn (3) calculating a third state value.
Distributing corresponding weight coefficient X for the third state value according to the dynamic weight table 3 。
For example, if SOC is less than or equal to 20%, the third state value weight coefficient is 0, and the calculation of the SOH value in the present period is not involved, but the calculation of reassigning the number of times of cyclic charge/discharge is needed, which may be specifically calculated as follows:
，
Wherein SOH i-1 The resulting SOH value is calculated for the last sampling period.
If the next period SOC>20%, thenWill n k Substitution formula->A third state value is obtained.
Finally, the first state value, the second state value, the third state value and the corresponding weight values are input into a weighted summation formula to calculate SOH i According to SOH i And confirming the health state of the battery in the ith period.
In this embodiment, in order to ensure SOH value accuracy, when the weight coefficients of the state values are defined to be all 0, the SOH value calculation is not performed in the present period, and the next period is waited.
It will be appreciated that the above weight coefficient assignment is merely an example, and the weight coefficients may be finely divided, and the smaller weight coefficient may be used to replace the "0" coefficient in the weight coefficient, so that each state value participates in the SOH value calculation in each period.
Further, on the basis of the above embodiment, the fusion weight coefficient may be added on the basis of weighting each state value, and the dynamic weight table to which the fusion weight coefficient is added may be as follows, for example:
as shown in the above table, when the weight coefficient of each state value is 0, the fusion weight coefficient is 0, and when the weight coefficient of each state value is not 0, the fusion weight coefficient is a preset weight value.
Further, each state value is input into a fusion formula to obtain an SOH value, wherein the fusion formula can be as follows:
，
it will be appreciated that the disclosure is presented in terms of the foregoing simplified examples for convenience of description, and that in practical applications, the dynamic weighting coefficients may be divided in more detail to accommodate various situations.
According to the embodiment of the disclosure, the following technical effects are achieved:
1. the first state value, the second state value and the third state value are fused to calculate the SOH value, and compared with the prior art, the method can effectively improve the accuracy of on-line estimation of the SOH value in a cost controllable range and acquire more accurate battery health state.
2. On the basis of data fusion calculation, dynamic weights are added, and a dynamic weight table is adopted to flexibly assign the first state value, the second state value and the third state value, so that different weights can be assigned according to different conditions, and the calculation result of the SOH value is more accurate.
3. Compared with the prior art, the method does not need a large amount of data and time, and has high estimation accuracy.
4. The cloud server is used for storing daily calculation data to train the battery health state analysis model step by step, so that the SOH value can be calculated and the accurate battery health state analysis model can be trained.
5. Compared with the prior art, the method has strong universality and easy implementation, and can be applied to a plurality of fields such as mobile phones, computers, new energy automobiles and the like.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present disclosure is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present disclosure. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all alternative embodiments, and that the acts and modules referred to are not necessarily required by the present disclosure.
The foregoing is a description of embodiments of the method, and the following further describes embodiments of the present disclosure through examples of apparatus.
Fig. 3 shows a block diagram of a battery state of health confirmation device provided by an embodiment of the present disclosure; as shown in fig. 3, the battery state of health confirmation device 300 may include:
the first processing module 310 is configured to collect voltage and current data of the battery cell, and perform real-time operation on the current data to obtain differential current corresponding to each moment.
The second processing module 320 is configured to obtain a first state value according to the voltage and current data of the battery cell at the time t and the time t+1 if the differential current at the time t meets a preset threshold.
And a third processing module 330, configured to obtain the SOC value read out by the OCV after the battery cell is allowed to stand twice to exceed the standing threshold, and calculate the SOC difference value.
The fourth processing module 340 is configured to obtain a second state value according to the SOC difference value and the accumulated charge/discharge amount during the two standing periods of the battery cells.
Specifically, the fourth processing module 340 is configured to input the SOC difference value and the accumulated charge/discharge amount during two standing periods of the battery cell into a second state value calculation formula, to obtain a second state value; wherein,
the second state value calculation formula is:
，
wherein the method comprises the steps ofDelta SOC is the SOC difference, Q is the accumulated charge/discharge amount of the battery cell during two standing periods, C Rated for Is the rated capacity of the battery cell.
The fifth processing module 350 is configured to obtain the current cycle charge/discharge number of the battery cell, and calculate the third state value according to the cycle charge/discharge number.
The sixth processing module 360 is configured to calculate an SOH value according to the first state value, the second state value and the third state value to confirm the state of health of the battery.
Specifically, the sixth processing module 360 is configured to calculate an SOH value according to the first state value, the second state value, and the third state value by using a kalman filtering method to confirm the state of health of the battery; also used for: and calculating an SOH value by adopting a weighted summation method according to the first state value, the second state value and the third state value so as to confirm the state of health of the battery.
Further, the sixth processing module 360 is configured to calculate an SOH value according to the first state value, the second state value and the third state value by using a kalman filter method to confirm the state of health of the battery, and includes:
substituting the first state value, the second state value and the third state value into the state equation.
Based on an observation equation and a state equation, continuously correcting the SOH value according to a Kalman recursion principle to enable the SOH value to gradually approach a true value; ending the recursion process until the error is smaller than a preset threshold value, and outputting an SOH value; and confirming the state of health of the battery according to the SOH value.
Further, the sixth processing module 360 is further configured to calculate an SOH value according to the first state value, the second state value and the third state value by using a weighted summation method to confirm the health status of the battery, and includes:
and distributing corresponding weight values for the first state value, the second state value and the third state value according to a preset weight table.
Inputting the first state value, the second state value and the third state value and the corresponding weight values into a weighted summation calculation formula, and calculating an SOH value to confirm the health state of the battery; wherein the weight table is a dynamic weight table; and when the weight coefficient of the third state value is 0, reassigning the cycle charge/discharge times according to the SOH value of the previous cycle, so that when the SOH value is calculated in the subsequent cycle, the SOH value is calculated according to the reassigned data.
In some embodiments, the battery state of health confirmation device 300 may further include:
the seventh processing module is used for uploading the acquired voltage and current data, the corresponding operation result, the data used for calculating the SOH value and the obtained SOH value to the cloud server; the cloud server builds a battery health state analysis model by adopting a machine learning algorithm according to the voltage and current data, the corresponding operation result, the data used for calculating the SOH value and the obtained SOH value.
It can be clearly understood by those skilled in the art that each module/unit in the block diagram 300 of the battery health status confirmation device shown in fig. 3 has a function of implementing each step in the battery health status confirmation method 100 provided in the embodiment of the present disclosure, and can achieve the corresponding technical effects thereof, and specific working processes may refer to corresponding processes in the foregoing method embodiments, which are not repeated herein for convenience and brevity of description.
According to an embodiment of the disclosure, the disclosure further provides an electronic device, a readable storage medium.
Fig. 4 illustrates a block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure.
As shown in FIG. 4, electronic device 400 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
The electronic device 400 includes a computing unit 401 that can perform various suitable actions and processes according to a computer program stored in a ROM402 or a computer program loaded from a storage unit 408 into a RAM 403. In the RAM403, various programs and data required for the operation of the electronic device 400 may also be stored. The computing unit 401, ROM402, and RAM403 are connected to each other by a bus 404. An I/O interface 405 is also connected to bus 404.
Various components in electronic device 400 are connected to I/O interface 405, including: an input unit 406 such as a keyboard, a mouse, etc.; an output unit 407 such as various types of displays, speakers, and the like; a storage unit 408, such as a magnetic disk, optical disk, etc.; and a communication unit 409 such as a network card, modem, wireless communication transceiver, etc. The communication unit 409 allows the electronic device 400 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The computing unit 401 may be a variety of general purpose and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 401 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above, such as method 100. For example, in some embodiments, the method 100 may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 408. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 400 via the ROM402 and/or the communication unit 409. One or more of the steps of the method 100 described above may be performed when a computer program is loaded into RAM403 and executed by the computing unit 401. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the method 100 by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems-on-chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: display means for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (6)
1. A battery state of health determination method applied to a BMS, the method comprising:
collecting voltage and current data of the battery cell, and carrying out real-time operation on the current data to obtain differential current corresponding to each moment;
if the differential current at the time t meets a preset threshold, a first state value is obtained according to the voltage and current data of the battery cells at the time t and the time t+1;
Acquiring an SOC value read by the OCV after the battery monomer is stood for two times until the battery monomer exceeds a standing threshold value, and calculating an SOC difference delta SOC;
obtaining a second state value according to the SOC difference value and the accumulated charge/discharge amount of the battery cell during two standing periods;
acquiring the current cycle charge/discharge times of the battery cell, and calculating a third state value according to the cycle charge/discharge times;
calculating SOH value according to the first state value, the second state value and the third state value to determine the state of health SOH of the battery i ；
If the differential current at the time t meets a preset threshold, a first state value is obtained according to the voltage and current data of the battery cell at the time t and the time t+1, including:
if the differential current at the time t meets a preset threshold, acquiring voltage and current data of the battery cell at the time t and at the time t+1, and calculating a difference ratio of the two groups of voltage and current data to obtain the internal resistance of the battery cell; obtaining the first state value according to the relation between the internal resistance of the battery cell and the SOH value;
the obtaining a second state value according to the SOC difference value and the accumulated charge/discharge amount during the two standing periods of the battery cell includes:
inputting the SOC difference value and the accumulated charge/discharge amount of the battery cell during two standing periods into a second state value calculation formula to obtain a second state value; wherein,
The second state value calculation formula is:
，
wherein delta SOC is the SOC difference, the accumulated charge/discharge amount of the Q battery monomer during two standing periods, C Rated for Is the rated capacity of the battery cell;
the method comprises the steps of obtaining the current cycle charge/discharge times of the battery cell, and calculating a third state value according to the cycle charge/discharge times, wherein the third state value comprises the following steps:
，
wherein n is 0 The total cycle charge/discharge times of the battery cell are theoretically; n is n k Charge/discharge times for the current cycle; and is also provided withWherein Q is the current residual charge of the battery cell, Q e Rated charge for the battery cell;
calculating SOH value according to the first state value, the second state value and the third state value to determine the SOH of the battery i Comprising:
calculating SO by adopting a Kalman filtering method according to the first state value, the second state value and the third state valueH value to determine battery state of health SOH i ；
Further comprises:
calculating SOH value according to the first state value, the second state value and the third state value by adopting a weighted summation method to determine the state of health SOH of the battery i ；
The SOH value is calculated according to the first state value, the second state value and the third state value by adopting a weighted summation method to determine the SOH of the battery state of health i Comprising:
corresponding weight values are distributed to the first state value, the second state value and the third state value according to a preset weight table;
for a first state value:
corresponding weight coefficient X is distributed for the first state value according to the differential current of the dynamic weight table and the time t 1 ；
For the second state value:
if the first threshold value is smaller than delta SOC and smaller than or equal to the second threshold value, corresponding weight coefficient X is distributed for the second state value according to the dynamic weight table 2 The method comprises the steps of carrying out a first treatment on the surface of the If delta SOC<First threshold value, let X 2 Is 0;
for the third state value:
if SOC is>A third threshold value is obtained to obtain the current accumulated cycle charge/discharge times n of the battery cell k Will n k Substituting the third state value calculation formula to calculate a third state value; distributing corresponding weight coefficient X for the third state value according to the dynamic weight table 3 ；
If SOC is less than or equal to the third threshold value, the third state value weight coefficient is 0, and the cyclic charge/discharge times are reassigned according to the SOH value of the previous period, so that n k =n 0 (1-SOH i-1 ) So that the SOH value is calculated according to the number of times of cyclic charge/discharge after reassignment in the subsequent period calculation; wherein SOH i-1 Calculating the obtained SOH value for the last sampling period;
inputting the first state value, the second state value and the third state value and the corresponding weight values into a weighted sum calculation formula, and calculating an SOH value to determine the state of health SOH of the battery i ；
The weighted summation calculation formula is as follows:
。
2. the method of claim 1, wherein calculating SOH values to determine battery state of health using kalman filtering based on the first state value, the second state value, and the third state value comprises:
substituting the first state value, the second state value and the third state value into a state equation;
based on an observation equation and the state equation, continuously correcting the SOH value according to a Kalman recursion principle to enable the SOH value to gradually approach to a true value; ending the recursion process until the error is smaller than a preset error threshold value, and outputting an SOH value;
and determining the state of health of the battery according to the SOH value.
3. The method according to claim 1, wherein the method further comprises:
uploading the collected voltage and current data, the differential current corresponding to each moment, the data used for calculating the SOH value and the obtained SOH value to a cloud server; and the cloud server builds a battery health state analysis model by adopting a machine learning algorithm according to the voltage and current data, the differential current corresponding to each moment, the data used for calculating the SOH value and the obtained SOH value.
4. A battery state of health determination apparatus adapted for use in the battery state of health determination method as set forth in any one of claims 1 to 3, characterized in that the apparatus comprises:
The first processing module is used for collecting voltage and current data of the battery cell, and carrying out real-time operation on the current data to obtain differential current corresponding to each moment;
the second processing module is used for obtaining a first state value according to the voltage and current data of the battery cells at the time t and the time t+1 if the differential current at the time t meets a preset threshold value;
the third processing module is used for obtaining the SOC value read by the OCV after the battery monomer is placed for two times until the battery monomer exceeds a standing threshold value, and calculating an SOC difference value delta SOC;
the fourth processing module is used for obtaining a second state value according to the SOC difference value and the accumulated charge/discharge amount of the battery cell during two standing periods;
the fifth processing module is used for obtaining the current cycle charge/discharge times of the battery monomer and calculating a third state value according to the cycle charge/discharge times;
a sixth processing module for calculating SOH value according to the first state value, the second state value and the third state value to determine the state of health SOH of the battery i ；
If the differential current at the time t meets a preset threshold, a first state value is obtained according to the voltage and current data of the battery cell at the time t and the time t+1, including:
if the differential current at the time t meets a preset threshold, acquiring voltage and current data of the battery cell at the time t and at the time t+1, and calculating a difference ratio of the two groups of voltage and current data to obtain the internal resistance of the battery cell; obtaining the first state value according to the relation between the internal resistance of the battery cell and the SOH value;
The obtaining a second state value according to the SOC difference value and the accumulated charge/discharge amount during the two standing periods of the battery cell includes:
inputting the SOC difference value and the accumulated charge/discharge amount of the battery cell during two standing periods into a second state value calculation formula to obtain a second state value; wherein,
the second state value calculation formula is:
，
wherein delta SOC is the SOC difference, the accumulated charge/discharge amount of the Q battery monomer during two standing periods, C Rated for Is the rated capacity of the battery cell;
the method comprises the steps of obtaining the current cycle charge/discharge times of the battery cell, and calculating a third state value according to the cycle charge/discharge times, wherein the third state value comprises the following steps:
，
wherein n is 0 The total cycle charge/discharge times of the battery cell are theoretically; n is n k Charge/discharge times for the current cycle; and is also provided withWherein Q is the current residual charge of the battery cell, Q e Rated charge for the battery cell;
calculating SOH value according to the first state value, the second state value and the third state value to determine the SOH of the battery i Comprising:
calculating SOH value to determine the state of health SOH of the battery by adopting Kalman filtering method according to the first state value, the second state value and the third state value i ；
Further comprises:
Calculating SOH value according to the first state value, the second state value and the third state value by adopting a weighted summation method to determine the state of health SOH of the battery i ；
The SOH value is calculated according to the first state value, the second state value and the third state value by adopting a weighted summation method to determine the SOH of the battery state of health i Comprising:
corresponding weight values are distributed to the first state value, the second state value and the third state value according to a preset weight table;
for a first state value:
corresponding weight coefficient X is distributed for the first state value according to the differential current of the dynamic weight table and the time t 1 ；
For the second state value:
if the first threshold value is smaller than delta SOC and smaller than or equal to the second threshold value, corresponding weight coefficient X is distributed for the second state value according to the dynamic weight table 2 The method comprises the steps of carrying out a first treatment on the surface of the If it isΔSOC<First threshold value, let X 2 Is 0;
for the third state value:
if SOC is>A third threshold value is obtained to obtain the current accumulated cycle charge/discharge times n of the battery cell k Will n k Substituting the third state value calculation formula to calculate a third state value; distributing corresponding weight coefficient X for the third state value according to the dynamic weight table 3 ；
If SOC is less than or equal to the third threshold value, the third state value weight coefficient is 0, and the cyclic charge/discharge times are reassigned according to the SOH value of the previous period, so that n k =n 0 (1-SOH i-1 ) So that the SOH value is calculated according to the number of times of cyclic charge/discharge after reassignment in the subsequent period calculation; wherein SOH i-1 Calculating the obtained SOH value for the last sampling period;
inputting the first state value, the second state value and the third state value and the corresponding weight values into a weighted sum calculation formula, and calculating an SOH value to determine the state of health SOH of the battery i ；
The weighted summation calculation formula is as follows:
。
5. an electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-3.
6. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-3.
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