CN117706371A - Method and device for evaluating running risk of energy storage battery and electronic equipment - Google Patents
Method and device for evaluating running risk of energy storage battery and electronic equipment Download PDFInfo
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Abstract
The application provides an operation risk evaluation method and device of an energy storage battery, electronic equipment and a storage medium, wherein the method comprises the following steps: according to the battery capacity data and the battery system efficiency anomaly scoring rule, determining a battery efficiency anomaly index scoring value, according to the battery temperature data and the battery temperature rise anomaly scoring rule, determining a battery temperature rise anomaly index scoring value, according to the battery voltage data and the battery voltage drop speed anomaly scoring rule, determining a battery voltage drop speed anomaly index scoring value, and according to the battery efficiency anomaly index scoring value, the first weight value, the battery temperature rise anomaly index scoring value, the second weight value, the battery voltage drop speed anomaly index scoring value and the third weight value, determining an operation risk coefficient, and according to the operation risk coefficient, determining a target operation risk grade and a target operation and maintenance mode, thereby solving the technical problems that the operation risk of the energy storage battery cannot be accurately predicted and evaluated and the proper operation and maintenance mode cannot be selected in the prior art.
Description
Technical Field
The present disclosure relates to the field of energy storage technologies, and in particular, to an operation risk evaluation method and apparatus for an energy storage battery, an electronic device, and a storage medium.
Background
Currently, electrochemical energy storage technologies represented by lithium ion batteries show an increasing potential in each link of an electric power system due to cost reduction and technology upgrading, in the electrochemical energy storage system, a battery system is a core energy storage carrier and is composed of a large number of battery cells in a serial-parallel connection mode, and in order to prevent safety accidents, the state of the battery system needs to be monitored and evaluated.
In the related art, power data in the operation process of the battery is generally collected, and is individually evaluated according to voltage, current and other data, so as to observe the operation state of the energy storage battery.
In this way, it is not possible to implement to find a safety risk possibly caused by current abnormality, voltage abnormality, temperature abnormality, etc., and a proper processing manner cannot be given, so that the battery operation risk of the energy storage system cannot be grasped.
Disclosure of Invention
The present application aims to solve, at least to some extent, one of the technical problems in the related art.
Therefore, a first object of the present application is to provide an operation risk evaluation method, apparatus, electronic device, storage medium and computer program product for an energy storage battery, so as to define risk factors according to battery operation data of an energy storage power station, and size of influence of the risk factors on safety risk, and grade the safety risk of the energy storage battery, so as to determine an optimal operation and maintenance scheme for the energy storage battery.
A second object of the present application is to provide an operation risk evaluation device for an energy storage battery.
A third object of the present application is to propose an electronic device.
A fourth object of the present application is to propose a computer readable storage medium.
A fifth object of the present application is to propose a computer programme product.
To achieve the above object, an embodiment of a first aspect of the present application provides a method for evaluating an operation risk of an energy storage battery, including: acquiring battery capacity data, battery temperature data and battery voltage data of an energy storage battery; acquiring a battery system efficiency abnormal scoring rule, a battery temperature rise abnormal scoring rule and a battery voltage drop speed abnormal scoring rule; determining a battery efficiency abnormality index scoring value according to the battery capacity data and the battery system efficiency abnormality scoring rule; determining a battery temperature rise abnormality index grading value according to the battery temperature data and the battery temperature rise abnormality grading rule; determining a battery voltage dropping speed abnormality index grading value according to the battery voltage data and the battery voltage dropping speed abnormality grading rule; respectively acquiring a first weight value corresponding to the battery efficiency abnormal index scoring value, a second weight value corresponding to the battery temperature rise abnormal index scoring value and a third weight value corresponding to the battery voltage falling speed abnormal index scoring value; determining an operation risk coefficient of the energy storage battery according to the battery efficiency abnormal index grading value, the first weight value, the battery temperature rise abnormal index grading value, the second weight value, the battery voltage falling speed abnormal index grading value and the third weight value; and determining a target operation risk level and a target operation and maintenance mode corresponding to the energy storage battery according to the operation risk coefficient.
To achieve the above object, an embodiment of a second aspect of the present application provides an operation risk evaluation device for an energy storage battery, including: the first acquisition module is used for acquiring battery capacity data, battery temperature data and battery voltage data of the energy storage battery; the second acquisition module is used for acquiring a battery system efficiency abnormal scoring rule, a battery temperature rise abnormal scoring rule and a battery voltage drop speed abnormal scoring rule; the first determining module is used for determining a battery efficiency abnormal index grading value according to the battery capacity data and the battery system efficiency abnormal grading rule; the second determining module is used for determining the abnormal index grading value of the battery temperature rise according to the battery temperature data and the abnormal grading rule of the battery temperature rise; the third determining module is used for determining the abnormal index grading value of the battery voltage dropping speed according to the battery voltage data and the abnormal grading rule of the battery voltage dropping speed; the third acquisition module is used for respectively acquiring a first weight value corresponding to the battery efficiency abnormal index grading value, a second weight value corresponding to the battery temperature rise abnormal index grading value and a third weight value corresponding to the battery voltage falling speed abnormal index grading value; the fourth determining module is used for determining an operation risk coefficient of the energy storage battery according to the battery efficiency abnormal index grading value, the first weight value, the battery temperature rise abnormal index grading value, the second weight value, the battery voltage falling speed abnormal index grading value and the third weight value; and the fifth determining module is used for determining a target operation risk level and a target operation and maintenance mode corresponding to the energy storage battery according to the operation risk coefficient.
To achieve the above object, an embodiment of a third aspect of the present application provides an electronic device, including: the system comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the running risk evaluation method of the energy storage battery as provided by the embodiment of the first aspect of the application when the processor executes the program.
To achieve the above object, an embodiment of a fourth aspect of the present application proposes a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method for evaluating an operation risk of an energy storage battery as proposed in the embodiment of the first aspect of the present application.
To achieve the above object, an embodiment of a fifth aspect of the present application proposes a computer program product, which when executed by a processor, performs a method for evaluating an operation risk of an energy storage battery as proposed in an embodiment of the first aspect of the present application.
According to the method, the device, the electronic equipment and the storage medium for evaluating the operation risk of the energy storage battery, the battery capacity data, the battery temperature data and the battery voltage data of the energy storage battery are obtained, the battery system efficiency abnormal score rule, the battery temperature rise abnormal score rule and the battery voltage drop speed abnormal score rule are obtained, the battery efficiency abnormal index score value is determined according to the battery capacity data and the battery system efficiency abnormal score rule, the battery temperature rise abnormal index score value is determined according to the battery temperature data and the battery temperature rise abnormal score rule, the battery voltage drop speed abnormal index score value is determined according to the battery voltage data and the battery voltage drop speed abnormal score rule, the first weight value corresponding to the battery efficiency abnormal index score value, the second weight value corresponding to the battery temperature rise abnormal index score value and the third weight value corresponding to the battery voltage drop speed abnormal index score value are respectively obtained, the operation risk coefficient of the energy storage battery is determined according to the battery efficiency abnormal index score value, the first weight value, the battery temperature rise abnormal index score value, the second weight value, the battery voltage drop speed abnormal index score value and the third weight value, and the operation risk of the energy storage battery is determined, and the operation risk is estimated according to the operation risk corresponding to the battery efficiency abnormal index score value and the corresponding to the battery voltage drop speed abnormal index score value, and the operation risk is not suitable for evaluating the operation risk.
Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
fig. 1 is a schematic flow chart of an operation risk evaluation method of an energy storage battery according to an embodiment of the present application;
FIG. 2 is a flow chart of the calculation of the running risk factor provided by an embodiment of the present application;
fig. 3 is a flow chart of another method for evaluating operation risk of an energy storage battery according to an embodiment of the present application;
fig. 4 is a schematic flow chart of operation and maintenance of the energy storage battery according to the embodiment of the present application;
fig. 5 is a schematic diagram of a mechanism of an energy storage battery operation risk evaluation system according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an operation risk evaluation device of an energy storage battery according to an embodiment of the present application; and
fig. 7 is a schematic structural diagram of another operation risk evaluation device for an energy storage battery according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and intended for the purpose of explaining the present application and are not to be construed as limiting the present application.
The following describes an operation risk evaluation method and apparatus for an energy storage battery according to embodiments of the present application with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of an operation risk evaluation method of an energy storage battery according to an embodiment of the present application.
In the related art, generally, power data in the battery operation process is collected, and is individually evaluated according to data such as voltage and current to observe the operation state of the energy storage battery, in this way, it is impossible to implement to find a safety risk possibly caused by reasons such as current abnormality, voltage abnormality, temperature abnormality, etc., and a proper processing manner cannot be given, so that the battery operation risk of the energy storage system cannot be mastered.
In view of this problem, the embodiment of the present application provides an operation risk evaluation method of an energy storage battery, so as to define risk factors according to battery operation data of an energy storage power station, and size of influence of the risk factors on safety risk, and grade the safety risk of the energy storage battery, so as to determine an optimal operation and maintenance scheme for the energy storage battery, as shown in fig. 1, where the operation risk evaluation method of the energy storage battery includes the following steps:
s101: and acquiring battery capacity data, battery temperature data and battery voltage data of the energy storage battery.
The energy storage battery can store energy electrochemically represented by a lithium ion battery, in the battery electrochemical energy storage system, the battery system is a core energy storage carrier, and the battery system is formed by a large number of energy storage battery monomers in a serial-parallel connection mode.
The battery capacity data refer to discharge capacity data and charge capacity data of the energy storage battery acquired in a period of time.
The battery temperature data refers to temperature change data of the energy storage battery acquired in a period of time.
The battery voltage data refers to voltage change data of the energy storage battery acquired in a period of time.
In this embodiment of the application, when obtaining battery capacity data, battery temperature data and battery voltage data of the energy storage battery, the discharge capacity data and the charge capacity data of the energy storage battery can be continuously collected as battery capacity data in a period of time through the battery management system where the energy storage battery is located, the battery temperature change data of the energy storage battery is continuously collected as battery temperature data of the energy storage battery, and the voltage change data of the energy storage battery is continuously collected as battery voltage data.
In other embodiments, the data acquisition device may be configured on the operation risk evaluation device of the energy storage battery, and the battery capacity data, the battery temperature data and the battery voltage data of the energy storage battery may be acquired based on the data acquisition device, or the battery capacity data, the battery temperature data and the battery voltage data of the energy storage battery may be acquired in any other manner, which is not limited.
S102: and acquiring a battery system efficiency abnormal scoring rule, a battery temperature rise abnormal scoring rule and a battery voltage drop speed abnormal scoring rule.
The battery system efficiency anomaly scoring rule is a scoring rule for scoring a risk value with anomaly of the battery system efficiency according to battery capacity data, and corresponds to a battery system efficiency anomaly index.
The abnormal grading rule of the battery temperature rise is used for grading a risk value of abnormal rise of the battery temperature according to the battery temperature data, and corresponds to an abnormal index of the battery temperature rise.
The abnormal grading rule of the battery voltage dropping speed is a grading rule used for grading a risk value of the abnormal voltage dropping speed of the battery voltage according to the battery voltage data, and corresponds to an abnormal index of the battery voltage dropping speed.
In this embodiment of the present application, when obtaining the abnormal scoring rule of the efficiency of the battery system, the abnormal scoring rule of the efficiency of the battery system may be obtained as follows: the method comprises the steps of setting an abnormal degree scoring range to be 0-100 minutes, setting a battery system efficiency abnormal index scoring to be 0 minutes at the lowest, setting a battery system efficiency abnormal index scoring to be 100 minutes at the highest, and setting a battery system efficiency abnormal scoring rule to be 0 minutes when a battery system efficiency value is more than 85%, setting a battery system efficiency value to be 100 minutes when the battery system efficiency value is less than 80%, setting a battery system efficiency value to be in a range of 85% -80%, setting a score to be increased by 2 minutes when the battery system efficiency value is reduced by 0.1%, and setting a value range to be 10%.
In this embodiment of the present application, when acquiring the abnormal score rule for increasing the battery temperature, the abnormal score rule for increasing the battery temperature may be acquired as follows: 30 minutes after the start of charging, the temperature of the battery surface rises to within 5 ℃ and is regarded as normal, the score is 0, the temperature rises to above 15 ℃, the score is 100 minutes, the score increases by 1 minute when the temperature rises to 0.1 ℃ within the range of 5-15 ℃, and the weight value range is 60%.
In this embodiment of the present application, when acquiring the abnormal scoring rule for the battery voltage decrease speed, the abnormal scoring rule for the battery voltage decrease speed may be acquired as follows: when the energy storage battery stops charging, when the State of Charge (SOC) of the battery is in a 15% -85% interval, the voltage is reduced to 0.030V after 10 minutes of self-stopping charging, the voltage is considered normal, the score is 0 and is higher than 0.050V, the score is 100 minutes, and when the voltage is increased by 0.001V within a range of 0.030-0.050V, the score is increased by 5 minutes, and the weight value is 30%.
S103: and determining the abnormal index grading value of the battery efficiency according to the battery capacity data and the abnormal grading rule of the battery system efficiency.
The battery efficiency abnormality index score value is a risk score value for indicating that the energy storage battery has abnormal battery system efficiency, and the higher the battery efficiency abnormality index score value is, the higher the risk degree is.
After the battery capacity data and the abnormal scoring rule of the battery system of the energy storage battery are obtained, the abnormal scoring value of the battery efficiency index can be determined according to the battery capacity data and the abnormal scoring rule of the battery system.
In this embodiment of the present application, when determining a score value of an abnormal indicator of battery efficiency according to battery capacity data and an abnormal score rule of battery system efficiency, the battery capacity data may be processed according to the abnormal score rule of battery system efficiency to determine a score of a risk degree of occurrence of abnormal battery system efficiency of an energy storage battery, and the score of the risk degree is used as the score value of the abnormal indicator of battery efficiency, where the abnormal score rule of battery system efficiency is specifically: setting the degree of abnormality scoring range to be 0-100 minutes, wherein the degree of abnormality scoring of the battery system efficiency abnormal index is 0 minutes at the lowest, the degree of abnormality scoring of the battery system efficiency abnormal index is 100 minutes at the highest, the degree of abnormality scoring of the battery system efficiency abnormal index is 0 minutes when the battery system efficiency value is more than 85%, the degree of abnormality scoring of the battery system efficiency index is 100 minutes when the battery system efficiency value is less than 80%, the degree of abnormality scoring of the battery system efficiency index is 0.1 percent when the battery system efficiency value is within the range of 85-80%, the degree of abnormality scoring of the battery system efficiency index is increased by 2 minutes, and the value of abnormality scoring of the battery system efficiency index is 10%.
S104: and determining the abnormal index grading value of the battery temperature rise according to the battery temperature data and the abnormal grading rule of the battery temperature rise.
The abnormal index grading value of the battery temperature rise is a risk grading value used for indicating that the battery temperature of the energy storage battery is abnormally raised, and the higher the abnormal index grading value of the battery temperature rise is, the higher the risk degree is.
After the battery temperature data and the battery temperature increase abnormality scoring rule are obtained, the embodiment of the application may determine the score value of the battery temperature increase abnormality index according to the battery temperature data and the battery temperature increase abnormality scoring rule.
In this embodiment of the present application, when determining a score value of an abnormal indicator of a battery temperature increase according to battery temperature data and an abnormal score rule of the battery temperature increase, the battery temperature data may be processed according to the abnormal score rule of the battery temperature increase to determine a risk score value of the energy storage battery with abnormal increase of the television temperature, and the risk score value is used as the score value of the abnormal indicator of the battery temperature increase, where the abnormal score rule of the battery temperature increase specifically includes: the temperature of the battery is calculated according to the temperature data of the battery, and is regarded as normal within 5 ℃ after the charging is started, the score is 0, the temperature is increased by more than 15 ℃, the score is 100, the score is increased by 1 minute when the temperature is increased by 0.1 ℃ within the range of 5-15 ℃, and the weight value is 60%.
S105: and determining the abnormal index grading value of the battery voltage dropping speed according to the battery voltage data and the abnormal grading rule of the battery voltage dropping speed.
The abnormal battery voltage reduction speed index grading value is a risk grading value used for indicating that the battery temperature of the energy storage battery is abnormally increased, and the higher the abnormal battery voltage reduction speed index grading value is, the higher the risk degree is.
After the battery voltage data and the abnormal battery voltage dropping speed scoring rule are obtained, the embodiment of the application can determine the abnormal battery voltage dropping speed index scoring value according to the battery voltage data and the abnormal battery voltage dropping speed scoring rule.
In this embodiment of the present application, when determining a score value of an abnormal indicator of a battery voltage decrease speed according to battery voltage data and an abnormal score rule of the battery voltage decrease speed, the battery voltage data may be processed according to the abnormal score rule of the battery voltage decrease speed to determine a risk score value of an energy storage battery having an abnormal battery voltage decrease speed, and the risk score value may be used as the abnormal score value of the battery voltage decrease speed, where the abnormal score rule of the battery voltage decrease speed may specifically be: abnormal scoring rule of the battery voltage drop rate.
S106: and respectively acquiring a first weight value corresponding to the battery efficiency abnormal index grading value, a second weight value corresponding to the battery temperature rise abnormal index grading value and a third weight value corresponding to the battery voltage falling speed abnormal index grading value.
The first weight value refers to a weight value when the battery efficiency abnormal index grading value participates in final operation risk evaluation of the energy storage battery.
The second weight value refers to a weight value when the battery temperature rise abnormality index scoring value participates in final operation risk evaluation of the energy storage battery.
The first weight value refers to a weight value when the battery voltage reduction speed abnormal index scoring value participates in final operation risk evaluation of the energy storage battery.
In this embodiment of the present application, when a first weight value corresponding to a battery efficiency abnormal indicator score value, a second weight value corresponding to a battery temperature increase abnormal indicator score value, and a third weight value corresponding to a battery voltage decrease speed abnormal indicator score value are respectively obtained, a weight value when the battery efficiency abnormal indicator score value participates in final running risk evaluation of the energy storage battery may be set as the first weight value, a weight value when the battery temperature increase abnormal indicator score value participates in final running risk evaluation of the energy storage battery may be set as the second weight value, and a weight value when the battery voltage decrease speed abnormal indicator score value participates in final running risk evaluation of the energy storage battery may be set as the third weight value.
S107: and determining an operation risk coefficient of the energy storage battery according to the battery efficiency abnormal index grading value, the first weight value, the battery temperature rise abnormal index grading value, the second weight value, the battery voltage falling speed abnormal index grading value and the third weight value.
The operation risk coefficient refers to a comprehensive grading value which can be used for evaluating the operation risk of the energy storage battery, the value range of the operation risk coefficient is 0-100 minutes, and the higher the score of the operation risk coefficient is, the greater the possibility of the operation risk is.
In this embodiment of the present application, when determining the running risk coefficient of the energy storage battery according to the battery efficiency anomaly index score value, the first weight value, the battery temperature rise anomaly index score value, the second weight value, the battery voltage drop rate anomaly index score value, and the third weight value, a calculation expression between the running risk coefficient of the energy storage battery and the battery efficiency anomaly index score value, the first weight value, the battery temperature rise anomaly index score value, the second weight value, the battery voltage drop rate anomaly index score value, and the third weight value may be introduced, where the expression is: r=a+b+b+c, wherein a is a first weight value, B is a second weight value, C is a third weight value, the sum of all weight values is 1, A is a battery efficiency abnormality index grading value, B is a battery temperature rise abnormality index grading value, C is a battery voltage drop speed abnormality index grading value, specific data are calculated in a calculation expression to obtain an operation risk coefficient of the energy storage battery, the value range of the operation risk coefficient is 0-100 points, and the higher the score is, the higher the risk is.
For example, as shown in fig. 2, fig. 2 is a flowchart of calculation of an operation risk coefficient provided in the embodiment of the present application, where an abnormal decrease in efficiency, an abnormal temperature rise, and an excessively fast decrease in cell voltage during standing of a battery system closely related to an energy storage battery safety risk are selected as calculation bases of the operation risk coefficient of the energy storage battery, and then a battery efficiency abnormal index score, a battery temperature rise abnormal index score, and a battery voltage decrease speed abnormal index score are calculated respectively, and the three scores are weighted and accumulated, and the accumulated result is used as the operation risk coefficient of the energy storage battery.
S108: and determining a target operation risk level and a target operation and maintenance mode corresponding to the energy storage battery according to the operation risk coefficient.
The target operation risk level is used for intuitively reflecting the corresponding risk emergency degree of the energy storage battery and is divided into four levels, wherein the level 1 is the lowest risk and the level 4 is the highest risk.
The target operation and maintenance mode is a specific battery processing measure adopted by the pointer to the target operation risk level of the energy storage battery, and may include, for example: the energy storage battery normally operates, real-time monitoring treatment is adopted for the energy storage battery, shutdown maintenance treatment is carried out for the energy storage battery, and the like, or a corresponding operation and maintenance mode can be set adaptively, so that the method is not limited.
After determining the operation risk coefficient of the energy storage battery according to the battery efficiency abnormal index grading value, the first weight value, the battery temperature rise abnormal index grading value, the second weight value, the battery voltage drop speed abnormal index grading value and the third weight value, the embodiment of the application can determine the target operation risk level and the target operation and maintenance mode corresponding to the energy storage battery according to the operation risk coefficient.
In the embodiment of the application, the target operation risk class is divided into four classes, the 1 class is the lowest risk, the 4 classes are the highest risk, different classes take different treatment measures, the treatment measures are corresponding target operation and maintenance modes, when the target operation risk class and the target operation and maintenance mode corresponding to the energy storage battery are determined according to the operation risk coefficient, the operation risk coefficient can be analyzed and processed, when the operation risk coefficient is lower than 40, the target operation risk class is determined to be 1 class, the target operation and maintenance mode is the energy storage battery can normally operate, when the operation risk coefficient is between 40 and 60, the target operation risk class is determined to be 2 classes, the energy storage battery of the target operation and maintenance mode can continue to operate, monitoring and observation are carried out at any time, when the operation risk coefficient is between 60 and 75, the target operation risk class is determined to be 3 classes, the target operation and maintenance mode is the power-down operation, and further troubleshooting problems are further examined, when the operation risk coefficient is above 75, the target operation risk class is determined to be 4 classes, and the energy storage battery is determined to be shut down.
In this embodiment, by acquiring battery capacity data, battery temperature data and battery voltage data of the energy storage battery, acquiring a battery system efficiency anomaly score rule, a battery temperature rise anomaly score rule and a battery voltage drop speed anomaly score rule, determining a battery efficiency anomaly index score value according to the battery capacity data and the battery system efficiency anomaly score rule, determining a battery temperature rise anomaly index score value according to the battery temperature data and the battery temperature rise anomaly score rule, determining a battery voltage drop speed anomaly index score value according to the battery voltage data and the battery voltage drop speed anomaly score rule, respectively acquiring a first weight value corresponding to the battery efficiency anomaly index score value, a second weight value corresponding to the battery temperature rise anomaly index score value and a third weight value corresponding to the battery voltage drop speed anomaly index score value, determining an operation risk coefficient of the energy storage battery according to the battery efficiency anomaly index score value, the first weight value, the battery temperature rise anomaly index score value, the second weight value, the battery voltage drop speed anomaly index score value and the third weight value, determining a target operation risk coefficient corresponding to the energy storage battery according to the battery voltage data and the battery voltage drop speed anomaly score value, and determining a risk factor for the energy storage battery, and implementing a risk factor-saving operation according to a risk-saving operation mode, and a risk-based on the energy storage battery energy storage system, and a risk-saving operation method.
The embodiment provides another method for evaluating the running risk of the energy storage battery, and fig. 3 is a schematic flow chart of another method for evaluating the running risk of the energy storage battery provided in the embodiment of the application.
As shown in fig. 3, the operation risk evaluation method of the energy storage battery may include the steps of:
s301: and acquiring battery capacity data, battery temperature data and battery voltage data of the energy storage battery.
S302: and acquiring a battery system efficiency abnormal scoring rule, a battery temperature rise abnormal scoring rule and a battery voltage drop speed abnormal scoring rule.
The specific description of S301 and S302 may be referred to the above embodiments, and will not be repeated here.
S303: and determining the abnormal index grading value of the battery efficiency according to the battery capacity data and the abnormal grading rule of the battery system efficiency.
Optionally, in some embodiments, the battery capacity data includes: when determining the abnormal battery efficiency index score according to the battery capacity data and the abnormal battery system efficiency score rule, the battery discharge capacity and the battery charge capacity can be subjected to ratio processing to obtain a battery system efficiency value, a first efficiency threshold and a first initial efficiency score corresponding to the first efficiency threshold are obtained, a second efficiency threshold and a second initial efficiency score corresponding to the second efficiency threshold are obtained, if the battery system efficiency value is smaller than or equal to the first efficiency threshold, the first initial efficiency score is used as the abnormal battery efficiency index score, if the battery system efficiency value is larger than or equal to the second efficiency threshold, the second initial efficiency score is used as the abnormal battery efficiency index score, and if the battery system efficiency value is larger than the first efficiency threshold and the battery system efficiency value is smaller than the second efficiency threshold, the efficiency difference between the second efficiency threshold and the battery system efficiency value is determined, and the product of the efficiency difference and the unit efficiency score is processed to obtain the abnormal battery efficiency index score.
In the embodiment of the application, the battery capacity data includes: battery discharge capacity Q discharge And battery charge capacity Q charge When determining the evaluation value of the abnormal battery efficiency index according to the battery capacity data and the abnormal battery system efficiency evaluation rule, the ratio processing can be performed on the battery discharge capacity and the battery charge capacity to obtain the battery system efficiency valueAnd then obtaining a first efficiency threshold value of 80%, a first initial efficiency score value of 100%, a second efficiency threshold value of 85%, and a second initial efficiency score value of 0%, wherein if the efficiency value of the battery system is less than or equal to 80%, the first initial efficiency score value of 100 is taken as a battery efficiency abnormality index score value, if the efficiency value of the battery system is greater than or equal to 85%, the second initial efficiency score value of 0 is taken as a battery efficiency abnormality index score value, if the efficiency value of the battery system is greater than the first efficiency threshold value of 80% and the efficiency value of the battery system is less than the second efficiency threshold value of 85%, the efficiency difference between the second efficiency threshold value of 85% and the efficiency value of the battery system is determined, and multiplying the efficiency difference by a unit efficiency score value, wherein the unit efficiency score value is set to be 0.1% each time, and the score is increased by 2 points, so as to calculate the battery efficiency abnormality index score value.
S304: and determining the abnormal index grading value of the battery temperature rise according to the battery temperature data and the abnormal grading rule of the battery temperature rise.
Alternatively, in some embodiments, when determining the value of the abnormal indicator score of the battery temperature increase according to the battery temperature data and the rule of the abnormal score of the battery temperature increase, the value of the battery temperature increase within the first preset time period after the charging process of the energy storage battery is started may be determined according to the battery temperature data, the first temperature threshold and the corresponding first initial value score thereof are obtained, the second temperature threshold and the corresponding second initial value score thereof are obtained, if the value of the battery temperature increase is less than or equal to the first temperature threshold, the first initial value of the temperature score is taken as the value of the abnormal indicator score of the battery temperature increase, if the value of the battery temperature increase is greater than or equal to the second temperature threshold, the second initial value of the temperature score is taken as the value of the abnormal indicator score of the battery temperature increase, and if the value of the battery temperature increase is greater than the first temperature threshold and the value of the battery temperature increase is less than the second temperature increase threshold, the temperature difference between the battery temperature increase and the first temperature threshold is determined, and the product of the temperature difference and the unit temperature score is processed, so as to obtain the value of the abnormal indicator score of the battery temperature increase.
In this embodiment of the present application, when determining the abnormal indicator score value of the battery temperature increase according to the battery temperature data and the abnormal score rule of the battery temperature increase, the battery temperature increase value within a first preset time period after the charging process of the energy storage battery is started may be determined according to the battery temperature data, where the first preset time period is set to 30 minutes, a first temperature threshold value of 5 ℃ and a corresponding first initial temperature score value of 0 score are obtained, and a second temperature threshold value of 15 ℃ and a corresponding second initial temperature score value of 100 scores are obtained, if the battery temperature increase value is less than or equal to 5 ℃, the first initial temperature score value of 0 score is used as the abnormal indicator score value of the battery temperature increase, if the battery temperature increase value is greater than or equal to 15 ℃, the second initial temperature score value of 100 score is used as the abnormal indicator score value of the battery temperature increase, if the battery temperature increase value is greater than or equal to 15 ℃, and if the battery temperature increase value is greater than 5 ℃ and the battery temperature increase value is less than 15 ℃, a temperature product of the first temperature threshold value and 5 ℃ is determined, and the temperature difference value and the unit temperature score value is processed, where the unit temperature score is set to be 1 score per 1 degree of the battery temperature increase value.
S305: and determining the abnormal index grading value of the battery voltage dropping speed according to the battery voltage data and the abnormal grading rule of the battery voltage dropping speed.
Optionally, in some embodiments, when determining the abnormal index score value of the battery voltage drop speed according to the battery voltage data and the abnormal score rule of the battery voltage drop speed, determining a battery voltage drop value corresponding to the energy storage battery in a second preset period after stopping charging the energy storage battery according to the battery voltage data, acquiring a first voltage threshold and a first initial voltage drop score value corresponding thereto, acquiring a second voltage threshold and a second initial voltage drop score value corresponding thereto, if the battery voltage drop value is less than or equal to the first voltage threshold, taking the first initial voltage drop score value as the abnormal index score value of the battery voltage drop speed, if the battery voltage drop value is greater than or equal to the second voltage threshold, taking the second initial voltage drop score value as the abnormal index score value of the battery voltage drop speed, if the battery voltage drop value is greater than the first voltage threshold and the battery voltage drop value is less than the second voltage threshold, determining an efficiency difference value between the battery voltage drop value and the first voltage score value, and performing product processing on the efficiency difference and the unit efficiency score value to obtain the abnormal index score value of the battery voltage drop speed.
In this embodiment of the present application, when determining the abnormal index score value of the battery voltage drop speed according to the battery voltage data and the abnormal score rule of the battery voltage drop speed, it may determine, according to the battery voltage data, a battery voltage drop value corresponding to the energy storage battery in a second preset time period after stopping charging the energy storage battery, where the second preset time is 10 minutes, obtain a first voltage threshold value of 0.030V and a first initial voltage drop score value of 0 score corresponding thereto, and obtain a second voltage threshold value of 0.050V and a second initial voltage drop score value of 100 score corresponding thereto, and if the battery voltage drop value is less than or equal to 0.030V, take the first initial voltage drop score value of 0 score as the abnormal index score value of the battery voltage drop speed, if the battery voltage drop value is greater than or equal to 0.050V, take the second initial voltage drop score of 100 score as the abnormal index score value of the battery voltage drop speed, and if the battery voltage drop value is greater than 0.030V and the battery voltage drop value is less than 0.050V, determine that the difference between the battery voltage and the first voltage drop efficiency is set as the unit, where the difference between the efficiency and the threshold value is set as the processing unit score value: in the range of 0.030-0.050V, every time 0.001V is increased, the score is increased by 5 minutes, and the weight value is 30 percent, so that the abnormal index grading value of the battery voltage dropping speed is calculated.
S306: and respectively acquiring a first weight value corresponding to the battery efficiency abnormal index grading value, a second weight value corresponding to the battery temperature rise abnormal index grading value and a third weight value corresponding to the battery voltage falling speed abnormal index grading value.
The specific description of S306 may be referred to the above embodiments, and will not be repeated here.
S307: and determining an operation risk coefficient of the energy storage battery according to the battery efficiency abnormal index grading value, the first weight value, the battery temperature rise abnormal index grading value, the second weight value, the battery voltage falling speed abnormal index grading value and the third weight value.
Optionally, in some embodiments, when determining the running risk coefficient of the energy storage battery according to the battery efficiency abnormal indicator score, the first weight value, the battery temperature increase abnormal indicator score, the second weight value, the battery voltage decrease speed abnormal indicator score and the third weight value, the product processing may be performed on the battery efficiency abnormal indicator score and the first weight value to obtain a first product result value, the product processing may be performed on the battery temperature increase abnormal indicator score and the second weight value to obtain a second product result value, the product processing may be performed on the battery voltage decrease speed abnormal indicator score and the third weight value to obtain a third product result value, and the accumulation processing may be performed on the first product result value, the second product result value and the third product result value to obtain the running risk coefficient.
In this embodiment, a product process may be performed on the battery efficiency abnormal indicator score a and the first weight value a to obtain a first product result value a, a product process may be performed on the battery temperature rising abnormal indicator score B and the second weight value B to obtain a second product result value B, a product process may be performed on the battery voltage falling speed abnormal indicator score C and the third weight value C to obtain a third product result value C, and then an accumulation process may be performed on the first product result value a×a, the second product result value b×b, and the third product result value C to obtain an operation risk coefficient r=a×a+b+c×c.
S308: and acquiring a plurality of candidate coefficient threshold intervals, wherein each candidate coefficient threshold interval has a corresponding candidate operation risk level and candidate operation and maintenance mode.
Wherein the candidate coefficient threshold interval may include: 0 to 40, 40 to 60, 60 to 75, and 75 to 100, wherein the candidate operation risk level corresponding to 0 to 40 is 1 level, the risk is lowest, the candidate operation risk level corresponding to 40 to 60 is 2 level, the energy storage battery can continue to operate, the candidate operation risk level corresponding to 60 to 75 is 3 level, the candidate operation risk level corresponding to 75 to 100 is 4 level, and the candidate operation risk level corresponding to 75 to 100 is energy storage battery shutdown maintenance.
S309: and determining a candidate coefficient threshold interval matched with the operation risk coefficient.
After the plurality of candidate coefficient threshold intervals and the corresponding candidate operation risk levels and the candidate operation and maintenance modes are obtained, the operation risk coefficients and the plurality of candidate coefficient threshold intervals can be subjected to matching processing to determine candidate coefficient threshold intervals matched with the operation risk coefficients.
S310: and taking the candidate operation risk level corresponding to the matched candidate coefficient threshold interval as a target operation risk level, and taking the candidate operation and maintenance mode corresponding to the matched candidate coefficient threshold interval as a target operation and maintenance mode.
After determining the candidate coefficient threshold interval matched with the operation risk coefficient, the embodiment of the application may use the candidate operation risk level corresponding to the matched candidate coefficient threshold interval as the target operation risk level and use the candidate operation and maintenance mode corresponding to the matched candidate coefficient threshold interval as the target operation and maintenance mode.
For example, as shown in fig. 4, fig. 4 is a schematic flow chart of an operation and maintenance method of an energy storage battery, where the abnormal decrease of efficiency, abnormal temperature rise and excessively fast decrease of single voltage during standing of the battery system closely related to the safety risk of the energy storage battery are selected as the calculation basis of risk coefficients of the energy storage battery, and the three risk factors are weighted by an energy storage battery risk coefficient calculation formula to obtain risk coefficient scores of the energy storage battery, and then the scores are classified into 4 grades, and corresponding operation and maintenance means are mapped according to different grades.
According to the technical scheme, the risk coefficient score in the operation period of the energy storage battery can be accurately calculated, the safety risk level is quantized, the operation is simple and easy, the operation can be performed after simple training, the energy storage battery maintenance planning strategy planning method is suitable for on-site monitoring and management of an energy storage power station, and the energy storage battery maintenance planning strategy planning method is further used for supporting energy storage battery maintenance planning strategy planning and guiding active rush repair.
For example, as shown in fig. 5, fig. 5 is a schematic diagram of a mechanism of an energy storage battery operation risk evaluation system provided in an embodiment of the present application, where the energy storage battery operation risk evaluation system includes: the system comprises a battery capacity acquisition module, a battery temperature acquisition module, a battery voltage acquisition module, an energy storage battery operation risk evaluation system, a database platform, an operation center and the like, wherein the acquisition module is used for acquiring and analyzing real-time data of battery monomers, modules and cluster levels, the database platform is used for storing data, the operation center is used for comprehensively summarizing and analyzing the data uploaded by each level of acquisition module, and the result is fed back to a visualization system, so that the energy storage battery operation risk evaluation method and system which are high in compatibility and capable of flexibly matching different project requirements and are very friendly in interface are provided.
In this embodiment, by acquiring the battery capacity data, the battery temperature data and the battery voltage data of the energy storage battery, acquiring the battery system efficiency anomaly score rule, the battery temperature rise anomaly score rule and the battery voltage drop speed anomaly score rule, determining the battery efficiency anomaly score value according to the battery capacity data and the battery system efficiency anomaly score rule, determining the battery temperature rise anomaly score value according to the battery temperature data and the battery temperature rise anomaly score rule, determining the battery voltage drop speed anomaly score value according to the battery voltage data and the battery voltage drop speed anomaly score rule, respectively acquiring a first weight value corresponding to the battery efficiency anomaly score value, a second weight value corresponding to the battery temperature rise anomaly score value and a third weight value corresponding to the battery voltage drop speed anomaly score value, determining the operating coefficient of the energy storage battery according to the operating coefficient, determining the target operating coefficient corresponding to the energy storage battery and the target operating coefficient, respectively acquiring a first weight value corresponding to the battery efficiency anomaly score value, a second weight value corresponding to the battery temperature rise anomaly score value and a third weight value corresponding to the battery voltage drop speed anomaly score value, and determining the risk factor corresponding to the battery temperature rise anomaly score value, and performing a risk factor, and performing a risk calculation to the energy storage battery in a risk level, and a risk level can be easily controlled according to the risk level, and a risk level can be easily calculated and accurately run in a risk level can be easily and accurately run by a risk level-controlled by a risk level, management is further used for supporting maintenance planning strategy formulation of the energy storage battery and guiding active rush repair.
In order to achieve the above embodiment, the present application further provides an operation risk evaluation device for an energy storage battery.
Fig. 6 is a schematic structural diagram of an operation risk evaluation device of an energy storage battery according to an embodiment of the present application.
As shown in fig. 6, the operation risk evaluation device 60 of the energy storage battery includes:
a first obtaining module 601, configured to obtain battery capacity data, battery temperature data, and battery voltage data of the energy storage battery;
a second obtaining module 602, configured to obtain a battery system efficiency anomaly score rule, a battery temperature rise anomaly score rule, and a battery voltage decrease speed anomaly score rule;
a first determining module 603, configured to determine a battery efficiency anomaly index score value according to the battery capacity data and a battery system efficiency anomaly score rule;
a second determining module 604, configured to determine a battery temperature increase abnormality index score value according to the battery temperature data and the battery temperature increase abnormality score rule;
a third determining module 605, configured to determine a score value of an abnormal indicator of the battery voltage decrease speed according to the battery voltage data and the abnormal score rule of the battery voltage decrease speed;
a third obtaining module 606, configured to obtain a first weight value corresponding to the battery efficiency abnormal indicator score, a second weight value corresponding to the battery temperature increase abnormal indicator score, and a third weight value corresponding to the battery voltage decrease speed abnormal indicator score, respectively;
A fourth determining module 607, configured to determine an operation risk coefficient of the energy storage battery according to the battery efficiency anomaly index score, the first weight value, the battery temperature increase anomaly index score, the second weight value, the battery voltage decrease speed anomaly index score, and the third weight value;
and a fifth determining module 608, configured to determine a target operation risk level and a target operation and maintenance mode corresponding to the energy storage battery according to the operation risk coefficient.
Further, in a possible implementation manner of the embodiment of the present application, as shown in fig. 7, fig. 7 is a schematic structural diagram of another operation risk evaluation device for an energy storage battery provided in the embodiment of the present application, where a fourth determining module 607 includes:
a first processing submodule 6071, configured to perform product processing on the battery efficiency anomaly index scoring value and the first weight value to obtain a first product result value;
a second processing submodule 6072, configured to perform product processing on the battery temperature increase abnormality index score value and the second weight value to obtain a second product result value;
a third processing submodule 6073, configured to perform product processing on the battery voltage drop speed abnormality index score and the third weight value to obtain a third product result value;
The fourth processing submodule 6074 is configured to perform accumulation processing on the first product result value, the second product result value, and the third product result value to obtain an operation risk coefficient.
Further, in one possible implementation manner of the embodiment of the present application, the fifth determining module 608 is specifically configured to:
acquiring a plurality of candidate coefficient threshold intervals, wherein each candidate coefficient threshold interval has a corresponding candidate operation risk level and candidate operation and maintenance mode;
determining a candidate coefficient threshold interval matched with the operation risk coefficient;
and taking the candidate operation risk level corresponding to the matched candidate coefficient threshold interval as a target operation risk level, and taking the candidate operation and maintenance mode corresponding to the matched candidate coefficient threshold interval as a target operation and maintenance mode.
Further, in one possible implementation of the embodiment of the present application, the battery capacity data includes: battery discharge capacity and battery charge capacity;
the first determining module 603 is specifically configured to:
performing ratio processing on the discharge capacity of the battery and the charge capacity of the battery to obtain the efficiency value of the battery system;
acquiring a first efficiency threshold and a first initial efficiency score value corresponding to the first efficiency threshold, and acquiring a second efficiency threshold and a second initial efficiency score value corresponding to the second efficiency threshold;
If the battery system efficiency value is less than or equal to the first efficiency threshold, taking the first initial efficiency score value as a battery efficiency anomaly index score value;
if the battery system efficiency value is greater than or equal to the second efficiency threshold, taking the second initial efficiency score value as a battery efficiency anomaly index score value;
if the efficiency value of the battery system is larger than the first efficiency threshold value and the efficiency value of the battery system is smaller than the second efficiency threshold value, determining an efficiency difference value between the second efficiency threshold value and the efficiency value of the battery system, and performing product processing on the efficiency difference value and the unit efficiency grading value to obtain a battery efficiency abnormality index grading value.
Further, in one possible implementation manner of the embodiment of the present application, the second determining module 604 is specifically configured to:
according to the battery temperature data, determining a battery temperature rise value in a first preset time period after the energy storage battery starts charging treatment;
acquiring a first temperature threshold and a first initial temperature grading value corresponding to the first temperature threshold, and acquiring a second temperature threshold and a second initial temperature grading value corresponding to the second temperature threshold;
if the battery temperature rise value is less than or equal to the first temperature threshold value, taking the first initial temperature score value as a battery temperature rise abnormality index score value;
If the battery temperature rise value is greater than or equal to the second temperature threshold value, taking the second initial temperature score value as the battery temperature rise abnormality index score value;
if the battery temperature rise value is larger than the first temperature threshold value and the battery temperature rise value is smaller than the second temperature rise threshold value, determining a temperature difference value between the battery temperature rise value and the first temperature threshold value, and performing product processing on the temperature difference value and the unit temperature grading value to obtain a battery temperature rise abnormality index grading value.
Further, in one possible implementation manner of the embodiment of the present application, the third determining module 605 is specifically configured to:
determining a battery voltage drop value corresponding to the energy storage battery in a second preset time period after the energy storage battery is stopped being charged according to the battery voltage data;
acquiring a first voltage threshold and a first initial voltage drop score corresponding to the first voltage threshold, and acquiring a second voltage threshold and a second initial voltage drop score corresponding to the second voltage threshold;
if the battery voltage drop value is less than or equal to the first voltage threshold value, taking the first initial voltage drop score value as a battery voltage drop speed abnormality index score value;
if the battery voltage drop value is greater than or equal to the second voltage threshold value, taking the second initial voltage drop score value as a battery voltage drop speed abnormality index score value;
If the battery voltage drop value is larger than the first voltage threshold value and the battery voltage drop value is smaller than the second voltage threshold value, determining an efficiency difference value between the battery voltage drop value and the first voltage threshold value, and performing product processing on the efficiency difference value and the unit efficiency grading value to obtain an abnormal index grading value of the battery voltage drop speed.
It should be noted that the foregoing explanation of the embodiment of the method for evaluating the running risk of the energy storage battery is also applicable to the device for evaluating the running risk of the energy storage battery of this embodiment, and will not be repeated here.
In this embodiment, by acquiring battery capacity data, battery temperature data and battery voltage data of the energy storage battery, acquiring a battery system efficiency anomaly score rule, a battery temperature rise anomaly score rule and a battery voltage drop speed anomaly score rule, determining a battery efficiency anomaly index score value according to the battery capacity data and the battery system efficiency anomaly score rule, determining a battery temperature rise anomaly index score value according to the battery temperature data and the battery temperature rise anomaly score rule, determining a battery voltage drop speed anomaly index score value according to the battery voltage data and the battery voltage drop speed anomaly score rule, respectively acquiring a first weight value corresponding to the battery efficiency anomaly index score value, a second weight value corresponding to the battery temperature rise anomaly index score value and a third weight value corresponding to the battery voltage drop speed anomaly index score value, determining an operation risk coefficient of the energy storage battery according to the battery efficiency anomaly index score value, the first weight value, the battery temperature rise anomaly index score value, the second weight value, the battery voltage drop speed anomaly index score value and the third weight value, determining a target operation risk coefficient corresponding to the energy storage battery according to the battery voltage data and the battery voltage drop speed anomaly score value, and determining a risk factor for the energy storage battery, and implementing a risk factor-saving operation according to a risk-saving operation mode, and a risk-based on the energy storage battery energy storage system, and a risk-saving operation method.
In order to achieve the above embodiments, the present application further proposes an electronic device including: a processor, and a memory communicatively coupled to the processor; the memory stores computer-executable instructions; the processor executes the computer-executed instructions stored in the memory to implement the method for evaluating the running risk of the energy storage battery provided in the foregoing embodiment.
In order to achieve the foregoing embodiments, the present application further proposes a computer-readable storage medium having stored therein computer-executable instructions that, when executed by a processor, are configured to implement the method for evaluating the running risk of the energy storage battery provided in the foregoing embodiments.
In order to implement the above embodiments, the present application further proposes a computer program product comprising a computer program which, when executed by a processor, implements the method for evaluating the running risk of the energy storage battery provided in the foregoing embodiments.
The processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the personal information of the user related in the application all accord with the regulations of related laws and regulations, and do not violate the popular public order.
It should be noted that personal information from users should be collected for legitimate and reasonable uses and not shared or sold outside of these legitimate uses. In addition, such collection/sharing should be performed after receiving user informed consent, including but not limited to informing the user to read user agreements/user notifications and signing agreements/authorizations including authorization-related user information before the user uses the functionality. In addition, any necessary steps are taken to safeguard and ensure access to such personal information data and to ensure that other persons having access to the personal information data adhere to their privacy policies and procedures.
The present application contemplates embodiments that may provide a user with selective prevention of use or access to personal information data. That is, the present application contemplates that hardware and/or software may be provided to prevent or block access to such personal information data. Once personal information data is no longer needed, risk can be minimized by limiting data collection and deleting data. In addition, personal identification is removed from such personal information, as applicable, to protect the privacy of the user.
In the foregoing descriptions of embodiments, descriptions of the terms "one embodiment," "some embodiments," "example," "particular example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "plurality" is at least two, such as two, three, etc., unless explicitly defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. Although embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.
Claims (10)
1. The operation risk evaluation method of the energy storage battery is characterized by comprising the following steps of:
acquiring battery capacity data, battery temperature data and battery voltage data of an energy storage battery;
acquiring a battery system efficiency abnormal scoring rule, a battery temperature rise abnormal scoring rule and a battery voltage drop speed abnormal scoring rule;
determining a battery efficiency abnormality index scoring value according to the battery capacity data and the battery system efficiency abnormality scoring rule;
determining a battery temperature rise abnormality index scoring value according to the battery temperature data and the battery temperature rise abnormality scoring rule;
determining a battery voltage dropping speed abnormality index grading value according to the battery voltage data and the battery voltage dropping speed abnormality grading rule;
Respectively obtaining a first weight value corresponding to the battery efficiency abnormal index grading value, a second weight value corresponding to the battery temperature rise abnormal index grading value and a third weight value corresponding to the battery voltage drop speed abnormal index grading value;
determining an operation risk coefficient of the energy storage battery according to the battery efficiency abnormal index grading value, the first weight value, the battery temperature rise abnormal index grading value, the second weight value, the battery voltage falling speed abnormal index grading value and the third weight value;
and determining a target operation risk level and a target operation and maintenance mode corresponding to the energy storage battery according to the operation risk coefficient.
2. The method of claim 1, wherein the determining the running risk factor of the energy storage battery based on the battery efficiency anomaly index score value, the first weight value, the battery temperature rise anomaly index score value, the second weight value, the battery voltage drop rate anomaly index score value, and the third weight value comprises:
performing product processing on the battery efficiency abnormal index grading value and the first weight value to obtain a first product result value;
Performing product processing on the battery temperature rise abnormality index grading value and the second weight value to obtain a second product result value;
performing product processing on the abnormal index grading value of the battery voltage dropping speed and the third weight value to obtain a third product result value;
and accumulating the first product result value, the second product result value and the third product result value to obtain the running risk coefficient.
3. The method of claim 1, wherein determining the target operation risk level and the target operation and maintenance mode corresponding to the energy storage battery according to the operation risk coefficient comprises:
acquiring a plurality of candidate coefficient threshold intervals, wherein each candidate coefficient threshold interval has a corresponding candidate operation risk level and candidate operation and maintenance mode;
determining a candidate coefficient threshold interval matched with the operation risk coefficient;
and taking the candidate operation risk level corresponding to the matched candidate coefficient threshold interval as the target operation risk level, and taking the candidate operation mode corresponding to the matched candidate coefficient threshold interval as the target operation mode.
4. The method of claim 1, wherein the battery capacity data comprises: battery discharge capacity and battery charge capacity;
wherein the determining a battery efficiency anomaly index score value according to the battery capacity data and the battery system efficiency anomaly score rule includes:
performing ratio processing on the discharge capacity of the battery and the charge capacity of the battery to obtain a battery system efficiency value;
acquiring a first efficiency threshold and a first initial efficiency score value corresponding to the first efficiency threshold, and acquiring a second efficiency threshold and a second initial efficiency score value corresponding to the second efficiency threshold;
if the battery system efficiency value is less than or equal to the first efficiency threshold, taking the first initial efficiency score value as the battery efficiency anomaly index score value;
if the battery system efficiency value is greater than or equal to the second efficiency threshold, taking the second initial efficiency score value as the battery efficiency anomaly indicator score value;
and if the efficiency value of the battery system is larger than the first efficiency threshold and the efficiency value of the battery system is smaller than the second efficiency threshold, determining an efficiency difference value between the second efficiency threshold and the efficiency value of the battery system, and performing product processing on the efficiency difference value and a unit efficiency grading value to obtain the abnormal index grading value of the battery efficiency.
5. The method of claim 1, wherein the determining a battery temperature increase abnormality index score value according to the battery temperature data and the battery temperature increase abnormality score rule comprises:
according to the battery temperature data, determining a battery temperature rise value in a first preset time period after the energy storage battery starts charging treatment;
acquiring a first temperature threshold and a first initial temperature grading value corresponding to the first temperature threshold, and acquiring a second temperature threshold and a second initial temperature grading value corresponding to the second temperature threshold;
if the battery temperature increase value is less than or equal to the first temperature threshold, taking the first initial temperature score value as the battery temperature increase abnormality index score value;
if the battery temperature increase value is greater than or equal to the second temperature threshold value, taking the second initial temperature score value as the battery temperature increase abnormality index score value;
and if the battery temperature rise value is larger than the first temperature threshold value and the battery temperature rise value is smaller than the second temperature rise threshold value, determining a temperature difference value between the battery temperature rise value and the first temperature threshold value, and performing product processing on the temperature difference value and a unit temperature grading value to obtain the battery temperature rise abnormality index grading value.
6. The method of claim 1, wherein determining a battery voltage rate of decrease abnormality indicator score value based on the battery voltage data and the battery voltage rate of decrease abnormality score rule comprises:
determining a battery voltage drop value corresponding to the energy storage battery in a second preset time period after the energy storage battery is stopped being charged according to the battery voltage data;
acquiring a first voltage threshold and a first initial voltage drop score corresponding to the first voltage threshold, and acquiring a second voltage threshold and a second initial voltage drop score corresponding to the second voltage threshold;
if the battery voltage drop value is less than or equal to the first voltage threshold value, taking the first initial voltage drop score value as the battery voltage drop speed abnormality index score value;
if the battery voltage drop value is greater than or equal to the second voltage threshold value, taking the second initial voltage drop score value as the battery voltage drop speed abnormality index score value;
and if the battery voltage drop value is larger than the first voltage threshold value and the battery voltage drop value is smaller than the second voltage threshold value, determining an efficiency difference value between the battery voltage drop value and the first voltage threshold value, and performing product processing on the efficiency difference value and a unit efficiency grading value to obtain the abnormal index grading value of the battery voltage drop speed.
7. An operation risk evaluation device of an energy storage battery, characterized by comprising:
the first acquisition module is used for acquiring battery capacity data, battery temperature data and battery voltage data of the energy storage battery;
the second acquisition module is used for acquiring a battery system efficiency abnormal scoring rule, a battery temperature rise abnormal scoring rule and a battery voltage drop speed abnormal scoring rule;
the first determining module is used for determining a battery efficiency abnormality index grading value according to the battery capacity data and the battery system efficiency abnormality grading rule;
the second determining module is used for determining a battery temperature rise abnormality index grading value according to the battery temperature data and the battery temperature rise abnormality grading rule;
the third determining module is used for determining a battery voltage dropping speed abnormality index grading value according to the battery voltage data and the battery voltage dropping speed abnormality grading rule;
the third acquisition module is used for respectively acquiring a first weight value corresponding to the battery efficiency abnormal index grading value, a second weight value corresponding to the battery temperature rise abnormal index grading value and a third weight value corresponding to the battery voltage drop speed abnormal index grading value;
A fourth determining module, configured to determine an operation risk coefficient of the energy storage battery according to the battery efficiency anomaly index score, the first weight value, the battery temperature increase anomaly index score, the second weight value, the battery voltage decrease speed anomaly index score, and the third weight value;
and a fifth determining module, configured to determine, according to the operation risk coefficient, a target operation risk level and a target operation and maintenance mode corresponding to the energy storage battery.
8. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored in the memory to implement the method of any one of claims 1-6.
9. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of any one of claims 1-6.
10. A computer program product comprising a computer program which, when executed by a processor, implements the method of any of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
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