CN116027197A - Method, device, equipment and storage medium for evaluating self-discharge consistency of battery - Google Patents
Method, device, equipment and storage medium for evaluating self-discharge consistency of battery Download PDFInfo
- Publication number
- CN116027197A CN116027197A CN202211556122.XA CN202211556122A CN116027197A CN 116027197 A CN116027197 A CN 116027197A CN 202211556122 A CN202211556122 A CN 202211556122A CN 116027197 A CN116027197 A CN 116027197A
- Authority
- CN
- China
- Prior art keywords
- battery
- self
- soc
- difference value
- discharge consistency
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 46
- 230000005856 abnormality Effects 0.000 claims abstract description 12
- 238000004590 computer program Methods 0.000 claims description 17
- 238000005315 distribution function Methods 0.000 claims description 15
- 238000004891 communication Methods 0.000 description 8
- 238000004364 calculation method Methods 0.000 description 7
- 238000011156 evaluation Methods 0.000 description 6
- 230000006870 function Effects 0.000 description 6
- 238000012545 processing Methods 0.000 description 6
- 238000012360 testing method Methods 0.000 description 6
- 230000002159 abnormal effect Effects 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 5
- 238000005070 sampling Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 4
- 238000004422 calculation algorithm Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000003062 neural network model Methods 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000008707 rearrangement Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Landscapes
- Secondary Cells (AREA)
Abstract
The invention discloses a battery self-discharge consistency assessment method, device, equipment and storage medium. The battery self-discharge consistency assessment method comprises the following steps: determining the SOC values of all the single battery cells in the battery pack within a single day, and determining a single-day SOC difference value by adopting a first model through the SOC values; acquiring a single-day SOC difference value of a designated day, generating an SOC difference value sequence, and generating a date value sequence according to a date corresponding to the single-day SOC difference value; determining a self-discharge consistency difference value of the battery by adopting a second model through the SOC difference value sequence and the date value sequence; determining the self-discharge consistency confidence of the battery by adopting a third model based on the self-discharge consistency difference value, the SOC difference value sequence and the date value sequence of the battery; and determining whether the self-discharge consistency abnormality occurs to the battery according to the self-discharge consistency difference value of the battery and the self-discharge consistency confidence of the battery.
Description
Technical Field
The embodiment of the invention relates to a battery testing technology, in particular to a battery self-discharge consistency assessment method, device and equipment and a storage medium.
Background
The abnormal self-discharge consistency of the battery can be generally used as the basis for judging faults such as short circuit, battery leakage, capacity decay and the like of the battery, and the battery often has great potential safety hazards when the faults occur, so that the accurate identification of the self-discharge abnormal faults has great significance.
At present, a battery self-discharge abnormality identification method mainly comprises a voltage analysis method and an SOC analysis method, wherein the voltage analysis method mainly extracts the pressure difference characteristics of single voltage, and the self-discharge abnormality is identified based on the pressure difference change rate; the SOC analysis method is substantially identical to the voltage analysis method in principle, except that the evaluation data replaces the cell voltage with the cell SOC, and the method can quantitatively evaluate the influence of the self-discharge consistency, but the estimation accuracy is low.
In view of the foregoing, there is a need for a method that can effectively perform battery self-discharge consistency estimation and can provide a battery self-discharge consistency estimation result with higher accuracy.
Disclosure of Invention
The invention provides a method, a device, equipment and a storage medium for evaluating self-discharge consistency of a battery, so as to achieve the aim of accurately identifying the self-discharge consistency of the battery.
In a first aspect, an embodiment of the present invention provides a method for evaluating self-discharge consistency of a battery, including:
determining the SOC values of all the single battery cells in the battery pack within a single day, and determining a single-day SOC difference value by adopting a first model through the SOC values;
acquiring a single-day SOC difference value of a specified day, generating an SOC difference value sequence, and generating a date value sequence according to a date corresponding to the single-day SOC difference value;
determining a battery self-discharge consistency difference value by adopting a second model through the SOC difference value sequence and the date value sequence;
based on the battery self-discharge consistency difference value, the SOC difference value sequence and the date value sequence, determining the battery self-discharge consistency confidence by adopting a third model;
and determining whether the self-discharge consistency abnormality occurs to the battery according to the self-discharge consistency difference value of the battery and the self-discharge consistency confidence of the battery.
Optionally, the first model includes:
wherein S is u Representing the difference value of the SOC and the SOC of a single day i_max Representing the maximum value of SOC and SOC of all the single battery cells at the ith moment in a single day i_min The minimum value of the SOCs in all the individual cells at the i-th time in a single day is represented, and N represents the number of times in a single day.
Optionally, the second model includes:
wherein beta is 1 Represents the difference value of the self-discharge consistency of the battery, Y i Represents the ith element, d, in the SOC difference value sequence i Representing the ith element in the sequence of date values,
mean value of elements in the sequence of SOC difference values, < >>
Represents the average value of the elements in the date value sequence, and n represents the number of the elements in the SOC difference value sequence.
Optionally, the third model includes an F distribution function.
Optionally, the battery self-discharge consistency confidence is determined by:
wherein P is F For the confidence of the self-discharge consistency of the battery, F represents F distribution function, beta 1 Represents the difference value of the self-discharge consistency of the battery, Y i Represents the ith element, d, in the SOC difference value sequence i Representing the ith element in the sequence of date values,
mean value of elements in the sequence of SOC difference values, < >>
Represents the average value of the elements in the date value sequence, and n represents the number of the elements in the SOC difference value sequence.
Optionally, generating the date value sequence according to the date corresponding to the single-day SOC difference value includes:
and acquiring dates corresponding to the single-day SOC difference values, determining the number of days difference between the dates corresponding to the appointed two single-day SOC difference values, and subtracting each number of days difference from a set value to generate the date value sequence.
Optionally, determining the SOC value of the single battery cell by adopting a fourth model;
the fourth model inputs include at least cell voltage, battery pack state of charge, battery pack temperature, battery pack current, and vehicle accumulated range.
In a second aspect, an embodiment of the present invention further provides a battery self-discharge consistency assessment device, including a battery self-discharge consistency assessment unit, where the battery self-discharge consistency assessment unit is configured to:
determining the SOC values of all the single battery cells in the battery pack within a single day, and determining a single-day SOC difference value by adopting a first model through the SOC values;
acquiring a single-day SOC difference value of a specified day, generating an SOC difference value sequence, and generating a date value sequence according to a date corresponding to the single-day SOC difference value;
determining a battery self-discharge consistency difference value by adopting a second model through the SOC difference value sequence and the date value sequence;
based on the battery self-discharge consistency difference value, the SOC difference value sequence and the date value sequence, determining the battery self-discharge consistency confidence by adopting a third model;
and determining whether the self-discharge consistency abnormality occurs to the battery according to the self-discharge consistency difference value of the battery and the self-discharge consistency confidence of the battery.
In a third aspect, an embodiment of the present invention further provides an electronic device, including at least one processor, and a memory communicatively connected to the at least one processor;
the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor, so that the at least one processor can execute the battery self-discharge consistency assessment method according to the embodiment of the present invention.
In a fourth aspect, an embodiment of the present invention further provides a computer readable storage medium, where computer instructions are stored, where the computer instructions are configured to cause a processor to execute the method for evaluating self-discharge consistency of a battery according to the embodiment of the present invention.
Compared with the prior art, the invention has the beneficial effects that: the invention provides a battery self-discharge consistency assessment method, in the method, a single-day SOC difference value in a specified day and a date value corresponding to the single-day SOC difference value are adopted to quantify the battery self-discharge consistency, the single-day SOC difference value and the corresponding date value are used as the basis for calculating the battery self-discharge consistency, the number of parameter types required by calculation is small, the method can realize the estimation of the battery self-discharge consistency under any working condition, in addition, the linear variation of the SOC difference value in the specified day can be accurately reflected by the battery self-discharge consistency estimation value determined by the single-day SOC difference value in the specified day and the date value, further, the accurate estimation of the battery self-discharge consistency can be realized under most working conditions, a powerful data support is provided for battery fault early warning, and meanwhile, the reliability and the accuracy of a judgment result can be improved by judging whether the battery self-discharge abnormality occurs or not through the battery self-discharge consistency difference value and the battery self-discharge consistency confidence.
Drawings
FIG. 1 is a flow chart of a method of evaluating battery self-discharge consistency in an embodiment;
FIG. 2 is a graph of battery pack current test data in an embodiment;
FIG. 3 is a graph of battery pack voltage test data in an embodiment;
fig. 4 is a battery pack SOC data map in an embodiment;
FIG. 5 is a graph of maximum SOC versus minimum SOC data in an embodiment;
FIG. 6 is a graph of SOC differential value data in an embodiment;
FIG. 7 is a graph of single day SOC differential value data in an embodiment;
fig. 8 is a schematic diagram of the electronic device in the embodiment.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
Example 1
Fig. 1 is a flowchart of a battery self-discharge consistency evaluation method in an embodiment, and referring to fig. 1, the battery self-discharge consistency evaluation method includes:
s101, determining the SOC values of all the single battery cells in the battery pack in a single day, and determining a single-day SOC difference value by adopting a first model through the SOC values.
In this embodiment, the manner of determining the SOC value of the single cell is not particularly limited, and may be implemented by any SOC estimation method in the prior art.
For example, in this embodiment, the setting of the single-day SOC difference value may be used to represent at least: the difference between the maximum SOC value and the minimum SOC value in all the single battery cells in a single day.
Illustratively, in this embodiment, the first model may be a fitting function or a neural network model, where configuring the input of the first model includes at least: maximum SOC values and minimum SOC values in all single battery cells at a plurality of sampling moments;
the output of the first model is configured to include at least a single day SOC difference value.
S102, acquiring a single-day SOC difference value of a designated day, generating an SOC difference value sequence, and generating a date value sequence according to a date corresponding to the single-day SOC difference value.
Illustratively, after the single-day SOC difference value is determined in step S101, the single-day SOC difference value is stored, and at least a date corresponding to the single-day SOC difference value is stored in addition to the single-day SOC difference value.
For example, in this embodiment, the specified number of days may be set according to the requirement, and for example, the specified number of days may be a single month number of days, a single quarter number of days, or the like.
Illustratively, in this step, a sequence of SOC difference values is first generated, wherein the sequence of SOC difference values is made up of a number of single-day SOC difference values over a specified number of days.
For example, in the present embodiment, the number of single-day SOC difference values included in the SOC difference value sequence may be the same as or different from the specified number of days, and the length of the SOC difference value sequence is the same as the length of the date value sequence.
In this embodiment, after determining the specified number of days, the SOC difference value of a single day stored in the specified number of days before the current date is obtained, so as to generate a SOC difference value sequence;
if a single-day SOC difference value is stored daily in the above time period (a specified number of days before the current date), the number of single-day SOC difference values included in the SOC difference value sequence is the same as the specified number of days;
if the single-day SOC difference value is not stored on a certain day in the above-described period, the number of single-day SOC difference values included in the SOC difference value sequence is different from the number of specified days.
Illustratively, in the present embodiment, the elements in the date value series represent the positions of the respective single-day SOC difference values on the time axis in days when the consideration data (single-day SOC difference values) are absent;
for example, if the specified number of days is 3, the corresponding determined SOC difference value sequence includes three single-day SOC difference values, the date value sequence may be {1,2,3};
if the specified number of days is 3, the corresponding determined SOC difference value sequence includes two single-day SOC difference values, where the single-day SOC difference value on the next day is default, the date value sequence may be {1,3}.
Illustratively, in the present embodiment, the elements in the date value sequence are generated by date conversion corresponding to the single-day SOC difference value, and the specific manner thereof is not limited.
S103, determining the self-discharge consistency difference value of the battery by adopting a second model through the SOC difference value sequence and the date value sequence.
In this embodiment, the set battery self-discharge consistency difference value may be at least used to represent: the magnitude of the difference between individual day SOC difference values within the sequence of SOC difference values.
Illustratively, in this embodiment, the second model may be a fitting function or a neural network model, where configuring the input of the second model includes at least: a sequence of SOC differential values and a sequence of date values;
the output of the second model is configured to include at least the battery self-discharge consistency difference value.
S104, determining the self-discharge consistency confidence of the battery by adopting a third model based on the self-discharge consistency difference value, the SOC difference value sequence and the date value sequence of the battery.
Illustratively, in the present embodiment, the third model adopts a distribution function model, for example, a normal distribution function model, an F distribution function model, a poisson distribution function model, and the like;
correspondingly, in this embodiment, the confidence coefficient of the self-discharge consistency of the battery is the confidence coefficient calculated through the selected distribution function model.
Illustratively, in this embodiment, the battery self-discharge consistency difference value, the SOC difference value sequence, and the date value sequence are used to bring the probability density function of the selected distribution function, and the degree of freedom of the selected distribution function model is set according to the requirement (or experience).
S105, determining whether the self-discharge consistency abnormality occurs to the battery according to the self-discharge consistency difference value of the battery and the self-discharge consistency confidence coefficient of the battery.
In this embodiment, a self-discharge consistency difference interval and a confidence interval may be set, and if the self-discharge consistency difference value of the battery is in the self-discharge consistency difference interval and the self-discharge consistency confidence of the battery is in the confidence interval, the self-discharge consistency of the battery is determined to be normal, otherwise, the self-discharge consistency of the battery is determined to be abnormal.
The embodiment provides a battery self-discharge consistency assessment method, in the method, a single-day SOC difference value in a specified day and a date value corresponding to the single-day SOC difference value are adopted to quantify the battery self-discharge consistency, the single-day SOC difference value and the corresponding date value are used as the basis for calculating the battery self-discharge consistency, the number of parameter types required by calculation is small, the method can achieve the estimation of the battery self-discharge consistency under any working condition, in addition, the linear variation of the SOC difference value in the specified day can be accurately reflected by the single-day SOC difference value in the specified day and the estimated value of the battery self-discharge consistency determined by the date value, further, the accurate estimation of the battery self-discharge consistency can be achieved under most working conditions, powerful data support is provided for battery fault early warning, meanwhile, the reliability and the accuracy of a judgment result can be improved by judging whether the battery self-discharge abnormality occurs or not through the battery self-discharge consistency difference value and the battery self-discharge consistency confidence.
As an embodiment, on the basis of the content recorded in step S101, the single-day SOC difference value is determined by the following formula, that is, the first model is:
wherein S is u Representing the difference value of the SOC and the SOC of a single day i_max Representing the maximum value of SOC and SOC of all the single battery cells at the ith moment in a single day i_min The minimum value of the SOCs in all the individual cells at the i-th time in a single day is represented, and N represents the number of times in a single day.
By way of example, in the scheme, the first model is a linear model, and calculation of a single-day SOC difference value is realized through the linear model, so that algorithm complexity of battery self-discharge consistency estimation can be reduced, and calculation efficiency is improved.
As an embodiment, on the basis of the content of step S103, the battery self-discharge consistency difference value may be determined by the following formula, that is, the second model is:
wherein beta is 1 Represents the difference value of the self-discharge consistency of the battery, Y i Represents the ith element, d, in the SOC difference value sequence i Representing the ith element in the sequence of date values,
mean value of elements in the sequence of SOC difference values, < >>
Represents the average value of the elements in the date value sequence, and n represents the number of the elements in the SOC difference value sequence.
In the scheme, the second model is a linear model, and the calculation of the battery self-discharge consistency difference value is realized through the linear model, so that the algorithm complexity of the battery self-discharge consistency estimation can be reduced, and the calculation efficiency is improved.
As an embodiment, based on the description of step S104, the third model includes an F distribution function, specifically, the confidence of the self-discharge consistency of the battery is determined by the following formula:
in the above, adopt
As a probability density function of the F distribution function, 1 is a first degree of freedom of the F distribution function, n-2 is a second degree of freedom of the F distribution function, P F The confidence of the self-discharge consistency of the battery is expressed by adopting the following formula:
wherein beta is 1 Represents the difference value of the self-discharge consistency of the battery, Y i Represents the ith element, d, in the SOC difference value sequence i Representing the ith element in the sequence of date values,
mean value of elements in the sequence of SOC difference values, < >>
Represents the average value of the elements in the date value sequence, and n represents the number of the elements in the SOC difference value sequence.
As an embodiment, in addition to the content described in step S102, the generation of the date value sequence from the date corresponding to the single-day SOC difference value includes:
and acquiring dates corresponding to the single-day SOC difference values, determining the number of days difference between the appointed two dates, and subtracting each number of days difference from a set value to generate a date value sequence.
In the present embodiment, the number of specified days is set to 49, the number of single-day SOC difference values obtained in the corresponding period is set to n, and the date corresponding to the ith single-day SOC difference value is set to D i A date sequence D may be generated, namely:
D＝{D 1 ,D 2 …D n }
select D n For reference date, calculate D respectively 1 And D n ，D 2 And D n …D n-1 And D n The difference in days between the two days is subtracted by 49 to obtain d 1 ～d n-1 Simultaneously set d n 48, and further generates a date value sequence d, namely:
d＝{d 1 ,d 2 …d n }
as an embodiment, the SOC value of the cell is determined using the fourth model based on the content of step S101.
In this embodiment, the fourth model uses an XGBoost model, and the input of the XGBoost model is set to include at least a single cell voltage, a battery pack charging state, a battery pack temperature, a battery pack current, and a vehicle accumulated driving range, and the output of the XGBoost model is set to be an SOC value of the single cell.
For example, in the present solution, the training process of the XGBoost model is the same as that in the prior art, and the specific process thereof is not described in detail.
By adopting the XGBoost model to realize the SOC value estimation of the single battery cell, the accuracy of the SOC estimation value can be improved, and the accuracy of the self-discharge consistency estimation value of the battery can be improved.
In an exemplary embodiment of the present invention, the above-described determination method of SOC value, the first model, the second model, the third model, and the determination method of the date value sequence of any one of the individual cells may be arranged and combined to form an embodiment, for example, in an embodiment, the method for evaluating self-discharge consistency of a battery may be:
the method comprises the steps of taking single cell voltage, battery pack charging state, battery pack temperature, battery pack current and accumulated vehicle driving mileage as inputs, and determining SOC values of all single cells in a battery pack at each sampling time point by adopting an XGBoost model;
the single-day SOC difference value is determined by the SOC value of all the single cells by the following formula:
acquiring a single-day SOC difference value of a specified day, and generating an SOC difference value sequence Y;
acquiring a date corresponding to the single-day SOC difference value, determining the difference of days between two appointed dates, and subtracting each difference of days from a set value to generate a date value sequence d;
and determining the self-discharge consistency difference value of the battery through the following formula by using the SOC difference value sequence Y and the date value sequence d:
based on battery self-discharge consistency difference value beta 1 The SOC difference value sequence Y and the date value sequence d are determined by the following formulasDetermining the confidence coefficient of the self-discharge consistency of the battery:
determining whether the self-discharge consistency abnormality occurs to the battery according to the self-discharge consistency difference value of the battery and the self-discharge consistency confidence of the battery;
specifically, if the battery self-discharge consistency difference value beta 1 >0.1 and Battery self discharge consistency confidence P F <0.05, the battery is judged to have abnormal self-discharge consistency.
Fig. 2 is a battery pack current test data graph in the embodiment, fig. 3 is a battery pack voltage test data graph in the embodiment, fig. 4 is a battery pack SOC data graph in the embodiment, fig. 5 is a maximum SOC-minimum SOC data graph in the embodiment, fig. 6 is an SOC difference value data graph in the embodiment, and fig. 7 is a single day SOC difference value data graph in the embodiment;
referring to fig. 2 to 7, based on the selected XGBoost model, the current and voltage data in fig. 2 and 3 are used as the single cell voltage and the battery pack current required for calculating the SOC value by the XGBoost model;
the SOC value of the single battery cell can be obtained through the calculation of the XGBoost model, namely the SOC value shown in the figure 3 can be obtained;
based on the SOC values, a maximum SOC value and a minimum SOC value in all the individual cells at each sampling time may be determined, where the maximum SOC value and the minimum SOC value are shown in fig. 5;
based on the maximum SOC value and the minimum SOC value at each sampling time, the SOC difference values of all the single battery cells at each sampling time can be determined, and the SOC difference values are shown in FIG. 6;
based on the above-described SOC difference values, a single-day SOC difference value may be determined, which is shown in fig. 7;
if the battery self-discharge consistency difference value beta can be calculated based on the single-day SOC difference value and the corresponding date value sequence shown in FIG. 7 1 Confidence of battery self-discharge consistency P of 0.115 F Is 0;
difference value beta due to self-discharge consistency of battery 1 >0.1, while battery self-discharge uniformity confidence P F <0.05, and thus it is possible to determine that the self-discharge consistency of the battery corresponding to the test data shown in fig. 2 and 3 is abnormal.
Example two
The embodiment provides a battery self-discharge consistency assessment device, which comprises a battery self-discharge consistency assessment unit, wherein the battery self-discharge consistency assessment unit is used for:
determining the SOC values of all the single battery cells in the battery pack within a single day, and determining a single-day SOC difference value by adopting a first model through the SOC values;
acquiring a single-day SOC difference value of a designated day, generating an SOC difference value sequence, and generating a date value sequence according to a date corresponding to the single-day SOC difference value;
determining a self-discharge consistency difference value of the battery by adopting a second model through the SOC difference value sequence and the date value sequence;
determining the self-discharge consistency confidence of the battery by adopting a third model based on the self-discharge consistency difference value, the SOC difference value sequence and the date value sequence of the battery;
and determining whether the self-discharge consistency abnormality occurs to the battery according to the self-discharge consistency difference value of the battery and the self-discharge consistency confidence of the battery.
In this embodiment, the battery self-discharge consistency evaluation unit may be configured to implement any one of the battery self-discharge consistency evaluation methods described in the first embodiment, and the implementation process and the beneficial effects thereof are the same as those of the corresponding content described in the first embodiment, and are not described herein.
Example III
Fig. 8 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), 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 inventions described and/or claimed herein.
As shown in fig. 8, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the respective methods and processes described above, such as the battery self-discharge consistency evaluation method.
In some embodiments, the battery self-discharge consistency assessment method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the battery self-discharge consistency assessment method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the battery self-discharge consistency assessment method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (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.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program 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 the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage 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. Alternatively, the computer readable storage medium may be a machine readable signal medium. 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 an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. 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), blockchain networks, and the internet.
The computing system may include clients and servers. 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 can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (10)
1. A battery self-discharge consistency assessment method, comprising:
determining the SOC values of all the single battery cells in the battery pack within a single day, and determining a single-day SOC difference value by adopting a first model through the SOC values;
acquiring the single-day SOC difference value of a specified day, generating an SOC difference value sequence, and generating a date value sequence according to the date corresponding to the single-day SOC difference value;
determining a battery self-discharge consistency difference value by adopting a second model through the SOC difference value sequence and the date value sequence;
based on the battery self-discharge consistency difference value, the SOC difference value sequence and the date value sequence, determining the battery self-discharge consistency confidence by adopting a third model;
and determining whether the self-discharge consistency abnormality occurs to the battery according to the self-discharge consistency difference value of the battery and the self-discharge consistency confidence of the battery.
2. The battery self-discharge consistency assessment method according to claim 1, wherein the first model comprises:
wherein S is u Representing the difference value of the SOC and the SOC of a single day i_max Representing the maximum value of SOC and SOC of all the single battery cells at the ith moment in a single day i_min The minimum value of the SOCs in all the individual cells at the i-th time in a single day is represented, and N represents the number of times in a single day.
3. The battery self-discharge consistency assessment method according to claim 1, wherein the second model comprises:
wherein beta is 1 Represents the difference value of the self-discharge consistency of the battery, Y i Represents the ith element, d, in the SOC difference value sequence i Representing the ith element in the sequence of date values,
mean value of elements in the sequence of SOC difference values, < >>
Represents the average value of the elements in the date value sequence, and n represents the number of the elements in the SOC difference value sequence.
4. The battery self-discharge consistency assessment method according to claim 1, wherein the third model includes an F distribution function.
5. The battery self-discharge consistency assessment method according to claim 4, wherein the battery self-discharge consistency confidence is determined by:
wherein P is F For the confidence of the self-discharge consistency of the battery, F represents F distribution function, beta 1 Represents the difference value of the self-discharge consistency of the battery, Y i Represents the ith element, d, in the SOC difference value sequence i Representing the ith element in the sequence of date values,
mean value of elements in the sequence of SOC difference values, < >>
Represents the average value of the elements in the date value sequence, and n represents the number of the elements in the SOC difference value sequence.
6. The battery self-discharge consistency assessment method according to any one of claims 1 to 5, wherein generating a date value sequence from a date corresponding to the single-day SOC difference value comprises:
and acquiring dates corresponding to the single-day SOC difference values, determining the number of days difference between the dates corresponding to the appointed two single-day SOC difference values, and subtracting each number of days difference from a set value to generate the date value sequence.
7. The method for evaluating the self-discharge consistency of the battery according to any one of claims 1 to 5, wherein the SOC value of the individual cells is determined using a fourth model;
the fourth model inputs include at least cell voltage, battery pack state of charge, battery pack temperature, battery pack current, and vehicle accumulated range.
8. A battery self-discharge consistency assessment device, comprising a battery self-discharge consistency assessment unit for:
determining the SOC values of all the single battery cells in the battery pack within a single day, and determining a single-day SOC difference value by adopting a first model through the SOC values;
acquiring a single-day SOC difference value of a specified day, generating an SOC difference value sequence, and generating a date value sequence according to a date corresponding to the single-day SOC difference value;
determining a battery self-discharge consistency difference value by adopting a second model through the SOC difference value sequence and the date value sequence;
based on the battery self-discharge consistency difference value, the SOC difference value sequence and the date value sequence, determining the battery self-discharge consistency confidence by adopting a third model;
and determining whether the self-discharge consistency abnormality occurs to the battery according to the self-discharge consistency difference value of the battery and the self-discharge consistency confidence of the battery.
9. An electronic device comprising at least one processor, and a memory communicatively coupled to the at least one processor;
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the battery self-discharge consistency assessment method of any of claims 1-7.
10. A computer readable storage medium storing computer instructions for causing a processor to perform the method of battery self-discharge consistency assessment of any of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211556122.XA CN116027197A (en) | 2022-12-06 | 2022-12-06 | Method, device, equipment and storage medium for evaluating self-discharge consistency of battery |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211556122.XA CN116027197A (en) | 2022-12-06 | 2022-12-06 | Method, device, equipment and storage medium for evaluating self-discharge consistency of battery |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116027197A true CN116027197A (en) | 2023-04-28 |
Family
ID=86090313
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211556122.XA Pending CN116027197A (en) | 2022-12-06 | 2022-12-06 | Method, device, equipment and storage medium for evaluating self-discharge consistency of battery |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116027197A (en) |
-
2022
- 2022-12-06 CN CN202211556122.XA patent/CN116027197A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2021208079A1 (en) | Method and apparatus for obtaining power battery life data, computer device, and medium | |
| CN114386537B (en) | Lithium battery fault diagnosis method and device based on Catboost and electronic equipment | |
| CN115221795A (en) | Training method, prediction method, device, equipment and medium of capacity prediction model | |
| CN115932634A (en) | Method, device, equipment and storage medium for evaluating health state of battery | |
| CN115372841A (en) | Method and device for evaluating thermal runaway risk of lithium ion battery monomer | |
| CN116626524A (en) | Battery state evaluation method, battery state evaluation device, electronic equipment and storage medium | |
| CN115718265A (en) | Method for correcting battery DC resistance test value, electronic device and storage medium | |
| CN114325400B (en) | Method and device for determining remaining life of battery, electronic equipment and storage medium | |
| WO2024093963A1 (en) | Battery life determination method and apparatus, electronic device, and storage medium | |
| CN117629084A (en) | Film thickness determination method and device, computer equipment and storage medium | |
| CN115754772A (en) | Battery capacity attenuation processing method, device, equipment and storage medium | |
| CN116559693A (en) | Battery SOC evaluation method and device, electronic equipment and storage medium | |
| CN116027197A (en) | Method, device, equipment and storage medium for evaluating self-discharge consistency of battery | |
| CN114779109A (en) | Method and device for determining battery health state, electronic equipment and storage medium | |
| CN115291111B (en) | Training method of battery rest time prediction model and rest time prediction method | |
| CN117706390B (en) | Rolling optimization estimation method and device for battery state of charge | |
| CN115007503B (en) | Cell sorting method, device, equipment and storage medium | |
| CN116087797A (en) | Storage battery pack state determining method, device, equipment and storage medium | |
| CN115792628A (en) | Power battery safety evaluation method, device, equipment and storage medium | |
| CN115760074A (en) | Power equipment operation and maintenance method and device, computer equipment and storage medium | |
| CN116643186A (en) | Current detection method, capacity prediction method, screening method, device and medium for self-discharge of battery | |
| CN116859274A (en) | Capacity anomaly detection method, device, equipment and storage medium | |
| CN116990707A (en) | Battery life prediction method and device, electronic equipment and storage medium | |
| CN117054896A (en) | SOP function test method, device, equipment and storage medium | |
| CN117805621A (en) | Battery capacity estimation method, device, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |