CN116466168B - New energy management platform anomaly monitoring system and method based on cloud computing - Google Patents
New energy management platform anomaly monitoring system and method based on cloud computing Download PDFInfo
- Publication number
- CN116466168B CN116466168B CN202310447640.6A CN202310447640A CN116466168B CN 116466168 B CN116466168 B CN 116466168B CN 202310447640 A CN202310447640 A CN 202310447640A CN 116466168 B CN116466168 B CN 116466168B
- Authority
- CN
- China
- Prior art keywords
- evaluation
- charging
- evaluated
- electrical equipment
- energy
- 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.)
- Active
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 118
- 238000000034 method Methods 0.000 title claims abstract description 63
- 238000011156 evaluation Methods 0.000 claims abstract description 210
- 238000004146 energy storage Methods 0.000 claims abstract description 67
- 230000002159 abnormal effect Effects 0.000 claims abstract description 62
- 238000004458 analytical method Methods 0.000 claims abstract description 48
- 230000032683 aging Effects 0.000 claims abstract description 41
- 230000008569 process Effects 0.000 claims abstract description 39
- 238000007726 management method Methods 0.000 claims abstract description 25
- 230000005856 abnormality Effects 0.000 claims abstract description 18
- 238000000605 extraction Methods 0.000 claims abstract description 17
- 230000004044 response Effects 0.000 claims abstract description 17
- 238000006243 chemical reaction Methods 0.000 claims abstract description 12
- 230000001939 inductive effect Effects 0.000 claims abstract description 4
- 238000004364 calculation method Methods 0.000 claims description 24
- 230000008859 change Effects 0.000 claims description 22
- 238000007599 discharging Methods 0.000 claims description 12
- 238000010276 construction Methods 0.000 claims description 6
- 238000013075 data extraction Methods 0.000 claims description 6
- 238000012795 verification Methods 0.000 claims description 6
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 238000013523 data management Methods 0.000 abstract 1
- 230000009471 action Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013524 data verification Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The application relates to the technical field of new energy data management, in particular to a new energy management platform abnormality monitoring system and method based on cloud computing, comprising an energy management platform analysis module, an evaluation unit determination module, a fluctuation evaluation value analysis module, an effective evaluation electric appliance analysis module, an abnormality feature extraction module and an early warning response module; the energy management platform analysis module is used for acquiring an energy database recorded by energy storage equipment which utilizes a new energy device to perform energy conversion behavior; the evaluation unit determining module is used for inducing events with the same charging process into an evaluation unit; the fluctuation evaluation value analysis module is used for analyzing a fluctuation evaluation value corresponding to the electric appliance to be evaluated; the effective evaluation electric appliance analysis module is used for analyzing the error aging index of the energy storage equipment, calibrating the electric appliance to be evaluated and outputting an effective evaluation electric appliance; the abnormal characteristic extraction module is used for analyzing and effectively evaluating abnormal characteristics existing in the corresponding evaluation unit of the electrical appliance.
Description
Technical Field
The application relates to the technical field of new energy management, in particular to a new energy management platform abnormality monitoring system and method based on cloud computing.
Background
Along with the innovation and development of energy science and technology, new energy is gradually applied to aspects of our daily life, such as a mobile power supply assisted by new energy, the solar mobile power supply has good expressive force from the aspects of functionality, the field of application range and safety, and the solar mobile power supply is more suitable for being used in the outdoor emergency power utilization condition; the solar mobile power supply can convert solar energy into electric energy to be stored in the storage battery only in a scene with illumination intensity; however, in the actual use process, the user often ignores the magnitude relation between the discharge power when using the mobile power supply to supply power to other devices and the charging power obtained by the solar device, and the charging power of the energy storage device is far less than the discharge power generally due to the problems of insufficient weather light intensity, so that the charging efficiency of the device which causes real-time charging is poor, and damage is brought to the energy storage device.
Disclosure of Invention
The application aims to provide a new energy management platform abnormality monitoring system and method based on cloud computing, which are used for solving the problems in the background technology.
In order to solve the technical problems, the application provides the following technical scheme: a new energy management platform anomaly monitoring method based on cloud computing comprises the following analysis steps:
step S1: the method comprises the steps of obtaining an energy database recorded by energy storage equipment which utilizes a new energy device to perform energy conversion behavior, wherein the energy conversion behavior refers to conversion of new energy into electric energy for storage, and the energy database comprises use records of the energy storage equipment in a single charge-discharge cycle process and use records corresponding to the new energy device;
step S2: based on an energy database, extracting usage records of the same energy storage equipment in a plurality of charge and discharge cycle processes, inducing events with the same charge process as an evaluation unit, marking electrical equipment involved in the charge process as an electrical equipment to be evaluated, and analyzing a fluctuation evaluation value corresponding to the electrical equipment to be evaluated;
step S3: extracting use records contained under different evaluation units in an energy database, analyzing error aging indexes of energy storage equipment, calibrating an electric appliance to be evaluated based on the error aging indexes and fluctuation evaluation values, and outputting an effective evaluation electric appliance;
step S4: determining an evaluation unit in an energy database to which the effective evaluation electric appliance belongs, extracting a usage record corresponding to a new energy device in the evaluation unit, and analyzing abnormal characteristics in the evaluation unit corresponding to the effective evaluation electric appliance;
step S5: based on the abnormal characteristics, acquiring a use record under real-time monitoring, analyzing whether the current electrical equipment is in an abnormal charging state according to the use record, and performing early warning response.
Further, step S2 includes the following analysis steps:
step S21: extracting electrical equipment contained in the charge-discharge cycle process, and sequencing the electrical equipment according to the time sequence of the charge behavior generated by the connection of the electrical equipment with the energy storage equipment to obtain an initial course corresponding to each charge-discharge cycle process; comparing initial histories corresponding to different charge-discharge cycle processes, and extracting the initial histories which contain electrical equipment and have identical electrical equipment sequences as target monitoring histories;
step S22: summarizing the target monitoring history into an evaluation unit, wherein the evaluation unit at least comprises two target monitoring histories;
step S23: acquiring the charging time length h of the ith electric appliance to be evaluated in the target monitoring process i And the electric quantity variation w i The electric quantity variation refers to a variation difference value from the initial electric quantity when the electric appliance to be evaluated is connected to the energy storage equipment to the ending electric quantity when the charging is completed; using the formula:
X i ＝(w i /h i )/max[w i /h i ]
calculating the fluctuation evaluation value X of the ith electric appliance to be evaluated i Wherein max [ w ] i /h i ]And the maximum value of the ratio of the electric quantity change quantity corresponding to different target monitoring histories and the charging duration in the evaluation unit to which the ith electric appliance to be evaluated belongs is represented. The larger the maximum value of the ratio is, the higher the charging efficiency is, and the state of the electrical equipment when the energy storage equipment is used for charging can be reflected; meanwhile, the smaller the fluctuation evaluation value is, the greater the possibility of an abnormal state existing in the electrical equipment when the electrical equipment is suitable for charging the energy storage equipment is indicated.
Further, step S3 includes the following analysis steps:
step S31: obtaining average charging efficiency E of electrical equipment to be evaluated in kth evaluation unit of energy database k0 ，E k0 ＝(1/m k )(∑E k ) Wherein E is k Representing the charging efficiency of the electrical equipment to be evaluated in the kth evaluation unit, wherein the charging efficiency represents the ratio of the electric quantity change amount to the charging duration, m k Representing the corresponding electrical equipment to be evaluated in the kth evaluation unitThe number of the target monitoring courses;
step S32: extracting the charging efficiency of the electrical equipment to be evaluated corresponding to the initial target monitoring process in the evaluation unit, wherein the marking difference value is smaller than or equal to the error threshold value, and the charging efficiency is larger than or equal to the average charging efficiency E k0 The target monitoring process corresponding to the affiliated evaluation unit is an ending monitoring process, the period duration corresponding to the initial target monitoring process to the ending monitoring process is used as a target monitoring period, and the formula is utilized: f= { Σ [ (E) k1 -E k2 )/T k ]}/n；
Calculating an error aging index F of the energy storage device; wherein n represents the total number of evaluation units in the energy database; e (E) k1 Indicating the charging efficiency of the electrical equipment to be evaluated corresponding to the initial target monitoring process in the kth evaluation unit, E k2 Indicating the charging efficiency, T, of the electrical equipment to be evaluated corresponding to the ending monitoring process in the kth evaluation unit k Representing a target monitoring period corresponding to the kth evaluation unit;
step S33: setting an error aging index threshold F 0 Extracting F to be less than or equal to F 0 Maximum value max [ E ] of the corresponding efficiency difference k1 -E k2 ]The method comprises the steps of carrying out a first treatment on the surface of the Then the check fluctuation evaluation value Yi, yi= (w) of the i-th electrical appliance to be evaluated is calculated i /h i +max[E k1 -E k2 ])/max[w i /h i ]The method comprises the steps of carrying out a first treatment on the surface of the And if the difference value of Yi-Xi is greater than or equal to the fluctuation evaluation threshold value, outputting the ith electric appliance to be evaluated as an effective evaluation electric appliance.
Further, step S4 includes the following analysis steps:
step S41: marking an evaluation unit in an energy database to which the effective evaluation electric appliance belongs as a target evaluation unit, and determining a target monitoring process corresponding to the effective evaluation electric appliance in the target evaluation unit as an effective monitoring process; extracting a usage record corresponding to the new energy device before the effective monitoring process data record, wherein the usage record comprises the power change condition when the new energy device is connected with the energy storage equipment to charge the energy storage equipment and the measured intensity data when the new energy device is used;
step S42: simultaneous charging power and energy storage device presence in case of extracted power variationUse of an electrical device P at discharge power and charging power Q 1 And discharge power Q 2 The method comprises the steps of carrying out a first treatment on the surface of the Marking the presence of Q prior to effective monitoring of history 1 ≤Q 2 Electrical equipment P for use in time 1 And calculates a charging abnormality frequency value v, v=d { Q } 1 ≤Q 2 ,P 1 }/d{Q 1 >Q 2 ,P 2 }, where d { Q } 1 ≤Q 2 ,P 1 ' represent Q 1 ≤Q 2 Electrical equipment P for use in time 1 D { Q }, d 1 >Q 2 ,P 2 ' represent Q 1 >Q 2 Electrical equipment P for use in time 2 Is the number of (3);
step S43: setting a charging abnormality threshold v 0 At v 0 When v is less than or equal to v, Q is obtained 1 ≤Q 2 Intensity data Z corresponding to time 1 Then construct and use the electrical equipment P 1 Abnormal characteristics A, A { P when connecting energy storage devices 1 ,Z 1 }。
Further, step S5 includes the following analysis steps:
acquiring real-time electrical equipment when charging power and discharging power exist in use records under real-time monitoring, and extracting a use electrical appliance P identical to the real-time electrical equipment 1 Intensity data Z corresponding to the abnormal feature A 1 ，
Acquiring intensity data Z in real-time 0 When Z is 0 >Z 1 Continuing monitoring when the monitoring is performed;
when Z is 0 ≤Z 1 And when the output electrical equipment is in an abnormal charging state, transmitting an early warning response.
The new energy management platform abnormality monitoring system is characterized by comprising an energy management platform analysis module, an evaluation unit determination module, a fluctuation evaluation value analysis module, an effective evaluation electric appliance analysis module, an abnormality feature extraction module and an early warning response module;
the energy management platform analysis module is used for acquiring an energy database recorded by energy storage equipment which utilizes a new energy device to perform energy conversion behavior;
the evaluation unit determining module is used for extracting the use records of the same energy storage equipment in a plurality of charge and discharge cycle processes and summarizing the events with the same charge and discharge process as an evaluation unit;
the fluctuation evaluation value analysis module is used for marking the electrical equipment involved in the charging process as the electrical equipment to be evaluated and analyzing the fluctuation evaluation value corresponding to the electrical equipment to be evaluated;
the effective evaluation electrical appliance analysis module is used for extracting use records contained under different evaluation units in the energy database, analyzing error aging indexes of the energy storage equipment, calibrating the electrical appliance to be evaluated based on the error aging indexes and the fluctuation evaluation values and outputting the effective evaluation electrical appliance;
the abnormal characteristic extraction module is used for analyzing and effectively evaluating abnormal characteristics existing in the corresponding evaluation unit of the electrical appliance;
the early warning response module is used for acquiring the use record under the real-time monitoring, analyzing whether the current electrical equipment is in an abnormal charging state or not according to the use record, and performing early warning response.
Further, the fluctuation evaluation value analysis module comprises a charging time length acquisition unit, an electric quantity variation acquisition unit and a fluctuation evaluation value calculation unit;
the charging time length acquisition unit is used for acquiring the charging time length of the electric appliance to be evaluated in the target monitoring process;
the electric quantity change amount acquisition unit is used for acquiring the electric quantity change amount of the electric appliance to be evaluated in the target monitoring process;
the fluctuation evaluation value calculation unit is configured to calculate a fluctuation evaluation value based on the amount of change in the electric quantity and the charging period.
Further, the effective evaluation electric appliance analysis module comprises an error aging index calculation unit, a check fluctuation evaluation value calculation unit and an effective evaluation electric appliance output unit;
the error aging index calculation unit is used for calculating an error aging index in the target monitoring period based on the charging efficiency;
the verification fluctuation evaluation value calculation unit is used for setting an error aging index threshold value, extracting the maximum value of the corresponding efficiency difference value when the error aging index is smaller than or equal to the error aging index threshold value, and calculating a verification fluctuation evaluation value;
the effective evaluation electric appliance output unit is used for outputting an electric appliance to be evaluated, which corresponds to the fluctuation evaluation threshold value and is used for verifying that the difference value between the fluctuation evaluation value and the fluctuation evaluation value is greater than or equal to the fluctuation evaluation threshold value, as an effective evaluation electric appliance.
Further, the abnormal characteristic extraction module comprises an effective monitoring process extraction unit, a usage data extraction unit, a charging abnormal frequency value calculation unit and an abnormal characteristic construction unit;
the effective monitoring process extraction unit is used for marking an evaluation unit in an energy database to which the effective evaluation electric appliance belongs as a target evaluation unit, and determining a target monitoring process corresponding to the effective evaluation electric appliance in the target evaluation unit as an effective monitoring process;
the usage data extraction unit is used for extracting usage records corresponding to the new energy devices before the effective monitoring process data records;
the charging abnormal frequency value calculation unit is used for extracting the electrical equipment used under the condition that the charging power and the discharging power exist in the energy storage equipment simultaneously in the power change condition and calculating the charging abnormal frequency value;
the abnormal characteristic construction unit is used for setting a charging abnormal threshold value, acquiring corresponding intensity data when the charging power is smaller than or equal to the discharging power when the charging abnormal frequency value is larger than or equal to the charging abnormal threshold value, and constructing abnormal characteristics when the electrical equipment is connected with the energy storage equipment.
Compared with the prior art, the application has the following beneficial effects: the application analyzes based on the usage record corresponding to the new energy type energy storage power supply, takes the charge-discharge circulation process with the same monitoring process as an evaluation unit, controls the variation factors of different equipment electric appliances caused by the difference of usage modes in the use process, on the basis, analyzes the electric appliances possibly having abnormality in each evaluation unit, analyzes the aging data corresponding to the whole to verify the effective evaluation electric appliances, and finally analyzes the abnormal relation of the charge power and the discharge power of the effective evaluation electric appliances, which are reflected in the new energy device corresponding to the energy storage equipment, in the history data, and extracts the corresponding characteristics as retrieval examples; according to the application, in actual use, the problem that a user cannot reasonably monitor external environmental influence factors possibly causing damage to electrical equipment or energy storage equipment in the use process can be effectively solved, the intelligent reminding of the system is utilized to remind whether the energy storage equipment is properly charged and discharged at the same time in the current state, and the safety of the equipment is ensured on the premise of ensuring efficient charging.
Drawings
The accompanying drawings are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate the application and together with the embodiments of the application, serve to explain the application. In the drawings:
fig. 1 is a schematic structural diagram of a new energy management platform anomaly monitoring system based on cloud computing.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1, the present application provides the following technical solutions: a new energy management platform anomaly monitoring method based on cloud computing comprises the following analysis steps:
step S1: the method comprises the steps of obtaining an energy database recorded by energy storage equipment which utilizes a new energy device to perform energy conversion behavior, wherein the energy conversion behavior refers to conversion of new energy into electric energy for storage, and the energy database comprises use records of the energy storage equipment in a single charge-discharge cycle process and use records corresponding to the new energy device;
in the application, the new energy device and the energy storage equipment can be separated independently, and the energy storage equipment can store electric quantity through the new energy device and can be externally connected with other power supply equipment for charging; the single charge-discharge cycle process corresponds to one complete charge-discharge of the energy storage device, namely, a cycle process from full charge of the energy storage device to empty use of the electric quantity;
step S2: based on an energy database, extracting usage records of the same energy storage equipment in a plurality of charge and discharge cycle processes, inducing events with the same charge process as an evaluation unit, marking electrical equipment involved in the charge process as an electrical equipment to be evaluated, and analyzing a fluctuation evaluation value corresponding to the electrical equipment to be evaluated;
step S2 comprises the following analysis steps:
step S21: extracting electrical equipment contained in the charge-discharge cycle process, and sequencing the electrical equipment according to the time sequence of the charge behavior generated by the connection of the electrical equipment with the energy storage equipment to obtain an initial course corresponding to each charge-discharge cycle process; comparing initial histories corresponding to different charge-discharge cycle processes, and extracting the initial histories which contain electrical equipment and have identical electrical equipment sequences as target monitoring histories;
step S22: summarizing the target monitoring history into an evaluation unit, wherein the evaluation unit at least comprises two target monitoring histories; because there are at least two data records to have the electrical device and the rank compared; the evaluation unit is only based on the analysis that the equipment is the same and the use sequence is the same, and the actual use data of the corresponding electrical equipment in each target monitoring process can be the same or different;
step S23: acquiring the charging time length h of the ith electric appliance to be evaluated in the target monitoring process i And the electric quantity variation w i The electric quantity variation refers to a variation difference value from the initial electric quantity when the electric appliance to be evaluated is connected to the energy storage equipment to the ending electric quantity when the charging is completed; using the formula:
X i ＝(w i /h i )/max[w i /h i ]
calculating the fluctuation evaluation value X of the ith electric appliance to be evaluated i Wherein max [ w ] i /h i ]And the maximum value of the ratio of the electric quantity change quantity corresponding to different target monitoring histories and the charging duration in the evaluation unit to which the ith electric appliance to be evaluated belongs is represented. The larger the maximum value of the ratio is, the higher the charging efficiency is, and the state of the electrical equipment when the energy storage equipment is used for charging can be reflected; the smaller the simultaneous fluctuation evaluation value is, sayThe greater the likelihood of an abnormal condition of the light electrical device when the light electrical device is charged in response to the energy storage device.
Because the electrical devices included in different target monitoring histories in the same evaluation unit are identical and in the same order, the i-th electrical device to be evaluated in different target monitoring histories refers to the same electrical device.
Step S3: extracting use records contained under different evaluation units in an energy database, analyzing error aging indexes of energy storage equipment, calibrating an electric appliance to be evaluated based on the error aging indexes and fluctuation evaluation values, and outputting an effective evaluation electric appliance;
step S3 comprises the following analysis steps:
step S31: obtaining average charging efficiency E of electrical equipment to be evaluated in kth evaluation unit of energy database k0 ，E k0 ＝(1/m k )(∑E k ) Wherein E is k Representing the charging efficiency of the electrical equipment to be evaluated in the kth evaluation unit, wherein the charging efficiency represents the ratio of the electric quantity change amount to the charging duration, m k Representing the number of target monitoring courses of the electrical equipment to be evaluated in the kth evaluation unit;
step S32: extracting the charging efficiency of the electrical equipment to be evaluated corresponding to the initial target monitoring process in the evaluation unit, wherein the marking difference value is smaller than or equal to the error threshold value, and the charging efficiency is larger than or equal to the average charging efficiency E k0 The target monitoring process corresponding to the affiliated evaluation unit is an ending monitoring process, the period duration corresponding to the initial target monitoring process to the ending monitoring process is used as a target monitoring period, and the formula is utilized: f= { Σ [ (E) k1 -E k2 )/T k ]}/n；
Calculating an error aging index F of the energy storage device; wherein n represents the total number of evaluation units in the energy database; e (E) k1 Indicating the charging efficiency of the electrical equipment to be evaluated corresponding to the initial target monitoring process in the kth evaluation unit, E k2 Indicating the charging efficiency, T, of the electrical equipment to be evaluated corresponding to the ending monitoring process in the kth evaluation unit k Representing a target monitoring period corresponding to the kth evaluation unit;
step S33: setting an error aging index threshold F 0 Extracting F to be less than or equal to F 0 Maximum value max [ E ] of the corresponding efficiency difference k1 -E k2 ]The method comprises the steps of carrying out a first treatment on the surface of the Then the check fluctuation evaluation value Yi, yi= (w) of the i-th electrical appliance to be evaluated is calculated i /h i +max[E k1 -E k2 ])/max[w i /h i ]The method comprises the steps of carrying out a first treatment on the surface of the And if the difference value of Yi-Xi is greater than or equal to the fluctuation evaluation threshold value, outputting the ith electric appliance to be evaluated as an effective evaluation electric appliance. The effective evaluation of the electrical appliance means that after the aging data verification is carried out, the charging efficiency is still reduced beyond the threshold range, and the analysis of the aging data is based on the overall analysis of all evaluation units, because the aging problem of the energy storage equipment is reflected to the charging states or the efficiency of different connection equipment and has homogeneity; and the number of the evaluation appliances can be one or a plurality of the monitoring histories.
Step S4: determining an evaluation unit in an energy database to which the effective evaluation electric appliance belongs, extracting a usage record corresponding to a new energy device in the evaluation unit, and analyzing abnormal characteristics in the evaluation unit corresponding to the effective evaluation electric appliance;
step S4 comprises the following analysis steps:
step S41: marking an evaluation unit in an energy database to which the effective evaluation electric appliance belongs as a target evaluation unit, and determining a target monitoring process corresponding to the effective evaluation electric appliance in the target evaluation unit as an effective monitoring process; extracting a usage record corresponding to the new energy device before the effective monitoring process data record, wherein the usage record comprises the power change condition when the new energy device is connected with the energy storage equipment to charge the energy storage equipment and the measured intensity data when the new energy device is used; when the new energy device is a solar device, the intensity data at the position is light intensity data;
step S42: electrical equipment P used under condition of extracting power change and simultaneously existence of charging power and discharging power of energy storage equipment and charging power Q 1 And discharge power Q 2 The method comprises the steps of carrying out a first treatment on the surface of the Marking the presence of Q prior to effective monitoring of history 1 ≤Q 2 Electrical equipment P for use in time 1 And calculates a charging abnormality frequency value v, v=d { Q } 1 ≤Q 2 ,P 1 }/d{Q 1 >Q 2 ,P 2 }, where d { Q } 1 ≤Q 2 ,P 1 ' represent Q 1 ≤Q 2 Electrical equipment P for use in time 1 D { Q }, d 1 >Q 2 ,P 2 ' represent Q 1 >Q 2 Electrical equipment P for use in time 2 Is the number of (3);
step S43: setting a charging abnormality threshold v 0 At v 0 When v is less than or equal to v, Q is obtained 1 ≤Q 2 Intensity data Z corresponding to time 1 Then construct and use the electrical equipment P 1 Abnormal characteristics A, A { P when connecting energy storage devices 1 ,Z 1 }. According to the application, when the input power affecting the energy storage equipment is analyzed, all influence factors which cause the change of the input power on the new energy hardware device are defined as zero, and only the external environment influence is considered. And the analyzed charging condition of the energy storage equipment is that the new energy device is utilized for charging.
Step S5: based on the abnormal characteristics, acquiring a use record under real-time monitoring, analyzing whether the current electrical equipment is in an abnormal charging state according to the use record, and performing early warning response.
Step S5 comprises the following analysis steps:
acquiring real-time electrical equipment when charging power and discharging power exist in use records under real-time monitoring, and extracting a use electrical appliance P identical to the real-time electrical equipment 1 Intensity data Z corresponding to the abnormal feature A 1 ，
Acquiring intensity data Z in real-time 0 When Z is 0 >Z 1 Continuing monitoring when the monitoring is performed;
when Z is 0 ≤Z 1 And when the output electrical equipment is in an abnormal charging state, transmitting an early warning response.
The new energy management platform abnormality monitoring system is characterized by comprising an energy management platform analysis module, an evaluation unit determination module, a fluctuation evaluation value analysis module, an effective evaluation electric appliance analysis module, an abnormality feature extraction module and an early warning response module;
the energy management platform analysis module is used for acquiring an energy database recorded by energy storage equipment which utilizes a new energy device to perform energy conversion behavior;
the evaluation unit determining module is used for extracting the use records of the same energy storage equipment in a plurality of charge and discharge cycle processes and summarizing the events with the same charge and discharge process as an evaluation unit;
the fluctuation evaluation value analysis module is used for marking the electrical equipment involved in the charging process as the electrical equipment to be evaluated and analyzing the fluctuation evaluation value corresponding to the electrical equipment to be evaluated;
the effective evaluation electrical appliance analysis module is used for extracting use records contained under different evaluation units in the energy database, analyzing error aging indexes of the energy storage equipment, calibrating the electrical appliance to be evaluated based on the error aging indexes and the fluctuation evaluation values and outputting the effective evaluation electrical appliance;
the abnormal characteristic extraction module is used for analyzing and effectively evaluating abnormal characteristics existing in the corresponding evaluation unit of the electrical appliance;
the early warning response module is used for acquiring the use record under the real-time monitoring, analyzing whether the current electrical equipment is in an abnormal charging state or not according to the use record, and performing early warning response.
The fluctuation evaluation value analysis module comprises a charging time length acquisition unit, an electric quantity variation acquisition unit and a fluctuation evaluation value calculation unit;
the charging time length acquisition unit is used for acquiring the charging time length of the electric appliance to be evaluated in the target monitoring process;
the electric quantity change amount acquisition unit is used for acquiring the electric quantity change amount of the electric appliance to be evaluated in the target monitoring process;
the fluctuation evaluation value calculation unit is configured to calculate a fluctuation evaluation value based on the amount of change in the electric quantity and the charging period.
Further, the effective evaluation electric appliance analysis module comprises an error aging index calculation unit, a check fluctuation evaluation value calculation unit and an effective evaluation electric appliance output unit;
the error aging index calculation unit is used for calculating an error aging index in the target monitoring period based on the charging efficiency;
the verification fluctuation evaluation value calculation unit is used for setting an error aging index threshold value, extracting the maximum value of the corresponding efficiency difference value when the error aging index is smaller than or equal to the error aging index threshold value, and calculating a verification fluctuation evaluation value;
the effective evaluation electric appliance output unit is used for outputting an electric appliance to be evaluated, which corresponds to the fluctuation evaluation threshold value and is used for verifying that the difference value between the fluctuation evaluation value and the fluctuation evaluation value is greater than or equal to the fluctuation evaluation threshold value, as an effective evaluation electric appliance.
The abnormal characteristic extraction module comprises an effective monitoring process extraction unit, a usage data extraction unit, a charging abnormal frequency value calculation unit and an abnormal characteristic construction unit;
the effective monitoring process extraction unit is used for marking an evaluation unit in an energy database to which the effective evaluation electric appliance belongs as a target evaluation unit, and determining a target monitoring process corresponding to the effective evaluation electric appliance in the target evaluation unit as an effective monitoring process;
the usage data extraction unit is used for extracting usage records corresponding to the new energy devices before the effective monitoring process data records;
the charging abnormal frequency value calculation unit is used for extracting the electrical equipment used under the condition that the charging power and the discharging power exist in the energy storage equipment simultaneously in the power change condition and calculating the charging abnormal frequency value;
the abnormal characteristic construction unit is used for setting a charging abnormal threshold value, acquiring corresponding intensity data when the charging power is smaller than or equal to the discharging power when the charging abnormal frequency value is larger than or equal to the charging abnormal threshold value, and constructing abnormal characteristics when the electrical equipment is connected with the energy storage equipment.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present application, and the present application is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present application has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (6)
1. The new energy management platform anomaly monitoring method based on cloud computing is characterized by comprising the following analysis steps:
step S1: the method comprises the steps of obtaining an energy database recorded by energy storage equipment which utilizes a new energy device to perform energy conversion behavior, wherein the energy conversion behavior is to convert new energy into electric energy for storage, and the energy database comprises use records of the energy storage equipment in a single charge-discharge cycle process and use records corresponding to the new energy device;
step S2: based on an energy database, extracting usage records of the same energy storage equipment in a plurality of charge and discharge cycle processes, inducing events with the same charge process as an evaluation unit, marking electrical equipment involved in the charge process as an electrical equipment to be evaluated, and analyzing a fluctuation evaluation value corresponding to the electrical equipment to be evaluated;
the step S2 includes the following analysis steps:
step S21: extracting electrical equipment contained in the charge-discharge cycle process, and sequencing the electrical equipment according to the time sequence of the charge behavior generated by the connection of the electrical equipment with the energy storage equipment to obtain an initial course corresponding to each charge-discharge cycle process; comparing initial histories corresponding to different charge-discharge cycle processes, and extracting the initial histories which contain electrical equipment and have identical electrical equipment sequences as target monitoring histories;
step S22: summarizing the target monitoring histories into an evaluation unit, wherein the evaluation unit at least comprises two target monitoring histories;
step S23: acquiring the charging time length h of the ith electric appliance to be evaluated in the target monitoring process i And the electric quantity variation w i The electric quantity variation refers to a variation difference value from an initial electric quantity when an electric appliance to be evaluated is connected to the energy storage equipment to an ending electric quantity when charging is completed; using the formula:
X i ＝(w i /h i )/max[w i /h i ]
calculating the fluctuation evaluation value X of the ith electric appliance to be evaluated i Wherein max [ w ] i /h i ]Representing the maximum value of the ratio of the electric quantity change quantity corresponding to different target monitoring histories and the charging duration in the evaluation unit to which the ith electric appliance to be evaluated belongs;
step S3: extracting use records contained under different evaluation units in an energy database, analyzing error aging indexes of energy storage equipment, calibrating an electric appliance to be evaluated based on the error aging indexes and fluctuation evaluation values, and outputting an effective evaluation electric appliance;
the step S3 includes the following analysis steps:
step S31: obtaining average charging efficiency E of electrical equipment to be evaluated in kth evaluation unit of energy database k0 ，E k0 ＝(1/m k )(∑E k ) Wherein E is k Representing the charging efficiency of the electrical equipment to be evaluated in the kth evaluation unit, wherein the charging efficiency represents the ratio of the electric quantity change amount to the charging duration, m k Representing the number of target monitoring courses of the electrical equipment to be evaluated in the kth evaluation unit;
step S32: extracting the charging efficiency of the electrical equipment to be evaluated corresponding to the initial target monitoring process in the evaluation unit, wherein the marking difference value is smaller than or equal to the error threshold value, and the charging efficiency is larger than or equal to the average charging efficiency E k0 The target monitoring process corresponding to the affiliated evaluation unit is an ending monitoring process, the period duration corresponding to the initial target monitoring process to the ending monitoring process is used as a target monitoring period, and the formula is utilized: f= { Σ [ (E) k1 -E k2 )/T k ]}/n；
Computing energy storage deviceAn error aging index F of (2); wherein n represents the total number of evaluation units in the energy database; e (E) k1 Indicating the charging efficiency of the electrical equipment to be evaluated corresponding to the initial target monitoring process in the kth evaluation unit, E k2 Indicating the charging efficiency, T, of the electrical equipment to be evaluated corresponding to the ending monitoring process in the kth evaluation unit k Representing a target monitoring period corresponding to the kth evaluation unit;
step S33: setting an error aging index threshold F 0 Extracting F to be less than or equal to F 0 Maximum value max [ E ] of the corresponding efficiency difference k1 -E k2 ]The method comprises the steps of carrying out a first treatment on the surface of the Then the check fluctuation evaluation value Yi, yi= (w) of the i-th electrical appliance to be evaluated is calculated i /h i +max[E k1 -E k2 ])/max[w i /h i ]The method comprises the steps of carrying out a first treatment on the surface of the If the difference value of Yi-Xi is larger than or equal to the fluctuation evaluation threshold value, outputting an ith electric appliance to be evaluated as an effective evaluation electric appliance;
step S4: determining an evaluation unit in an energy database to which the effective evaluation electric appliance belongs, extracting a usage record corresponding to a new energy device in the evaluation unit, and analyzing abnormal characteristics in the evaluation unit corresponding to the effective evaluation electric appliance;
the step S4 includes the following analysis steps:
step S41: marking an evaluation unit in an energy database to which the effective evaluation electric appliance belongs as a target evaluation unit, and determining a target monitoring process corresponding to the effective evaluation electric appliance in the target evaluation unit as an effective monitoring process; extracting a usage record corresponding to a new energy device before the effective monitoring process data record, wherein the usage record comprises power change conditions when the new energy device is connected with energy storage equipment to charge the energy storage equipment and measured intensity data when the new energy device is used;
step S42: electrical equipment P used under condition of extracting power change and simultaneously existence of charging power and discharging power of energy storage equipment and charging power Q 1 And discharge power Q 2 The method comprises the steps of carrying out a first treatment on the surface of the Marking the presence of Q prior to effective monitoring of history 1 ≤Q 2 Electrical equipment P for use in time 1 And calculates a charging abnormality frequency value v, v=d { Q } 1 ≤Q 2 ,P 1 }/d{Q 1 >Q 2 ,P 2 }, where d { Q } 1 ≤Q 2 ,P 1 ' represent Q 1 ≤Q 2 Electrical equipment P for use in time 1 D { Q }, d 1 >Q 2 ,P 2 ' represent Q 1 >Q 2 Electrical equipment P for use in time 2 Is the number of (3);
step S43: setting a charging abnormality threshold v 0 At v 0 When v is less than or equal to v, Q is obtained 1 ≤Q 2 Intensity data Z corresponding to time 1 Then construct and use the electrical equipment P 1 Abnormal characteristics A, A { P when connecting energy storage devices 1 ,Z 1 }；
Step S5: based on the abnormal characteristics, acquiring a use record under real-time monitoring, analyzing whether the current electrical equipment is in an abnormal charging state according to the use record, and performing early warning response.
2. The cloud computing-based new energy management platform anomaly monitoring method is characterized by comprising the following steps of: the step S5 includes the following analysis steps:
acquiring real-time electrical equipment when charging power and discharging power exist in use records under real-time monitoring, and extracting a use electrical appliance P identical to the real-time electrical equipment 1 Intensity data Z corresponding to the abnormal feature A 1 ，
Acquiring intensity data Z in real-time 0 When Z is 0 >Z 1 Continuing monitoring when the monitoring is performed;
when Z is 0 ≤Z 1 And when the output electrical equipment is in an abnormal charging state, transmitting an early warning response.
3. The new energy management platform abnormality monitoring system applying the new energy management platform abnormality monitoring method based on cloud computing as claimed in any one of claims 1-2 is characterized by comprising an energy management platform analysis module, an evaluation unit determination module, a fluctuation evaluation value analysis module, an effective evaluation electric appliance analysis module, an abnormality feature extraction module and an early warning response module;
the energy management platform analysis module is used for acquiring an energy database recorded by energy storage equipment which utilizes a new energy device to perform energy conversion behavior;
the evaluation unit determining module is used for extracting the use records of the same energy storage equipment in a plurality of charge and discharge cycle processes and summarizing the events with the same charge and discharge cycle process into an evaluation unit;
the fluctuation evaluation value analysis module is used for marking electric equipment involved in the charging process as electric equipment to be evaluated and analyzing a fluctuation evaluation value corresponding to the electric equipment to be evaluated;
the effective evaluation electrical appliance analysis module is used for extracting use records contained under different evaluation units in the energy database, analyzing error aging indexes of the energy storage equipment, calibrating the electrical appliance to be evaluated based on the error aging indexes and the fluctuation evaluation values and outputting the effective evaluation electrical appliance;
the abnormal characteristic extraction module is used for analyzing and effectively evaluating abnormal characteristics existing in the corresponding evaluation unit of the electrical appliance;
the early warning response module is used for acquiring the use record under the real-time monitoring, analyzing whether the current electrical equipment is in an abnormal charging state or not according to the use record, and performing early warning response.
4. The new energy management platform anomaly monitoring system of claim 3, wherein: the fluctuation evaluation value analysis module comprises a charging time length acquisition unit, an electric quantity variation acquisition unit and a fluctuation evaluation value calculation unit;
the charging duration acquisition unit is used for acquiring the charging duration of the electric appliance to be evaluated in the target monitoring process;
the electric quantity variation acquisition unit is used for acquiring the electric quantity variation of the electric appliance to be evaluated in the target monitoring process;
the fluctuation evaluation value calculation unit is configured to calculate a fluctuation evaluation value based on the amount of change in the electric quantity and the charging period.
5. The anomaly monitoring system of the new energy management platform according to claim 4, wherein: the effective evaluation electrical appliance analysis module comprises an error aging index calculation unit, a check fluctuation evaluation value calculation unit and an effective evaluation electrical appliance output unit;
the error aging index calculation unit is used for calculating an error aging index in a target monitoring period based on the charging efficiency;
the verification fluctuation evaluation value calculation unit is used for setting an error aging index threshold value, extracting the maximum value of the corresponding efficiency difference value when the error aging index is smaller than or equal to the error aging index threshold value, and calculating a verification fluctuation evaluation value;
the effective evaluation electric appliance output unit is used for outputting an electric appliance to be evaluated, which corresponds to the fluctuation evaluation threshold value and is used for verifying that the difference value between the fluctuation evaluation value and the fluctuation evaluation value is greater than or equal to the fluctuation evaluation threshold value, as an effective evaluation electric appliance.
6. The anomaly monitoring system of the new energy management platform according to claim 5, wherein: the abnormal characteristic extraction module comprises an effective monitoring process extraction unit, a usage data extraction unit, a charging abnormal frequency value calculation unit and an abnormal characteristic construction unit;
the effective monitoring process extraction unit is used for marking an evaluation unit in an energy database to which the effective evaluation electric appliance belongs as a target evaluation unit, and determining that a target monitoring process corresponding to the effective evaluation electric appliance in the target evaluation unit is an effective monitoring process;
the usage data extraction unit is used for extracting usage records corresponding to the new energy devices before the effective monitoring process data records;
the abnormal charging frequency value calculation unit is used for extracting the electric equipment used under the condition that the energy storage equipment simultaneously has charging power and discharging power and the charging power and the discharging power in the power change condition and calculating the abnormal charging frequency value;
the abnormal characteristic construction unit is used for setting a charging abnormal threshold value, and when the charging abnormal frequency value is greater than or equal to the charging abnormal threshold value, acquiring corresponding intensity data when the charging power is smaller than or equal to the discharging power, and constructing the abnormal characteristic when the electrical equipment is connected with the energy storage equipment.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310447640.6A CN116466168B (en) | 2023-04-24 | 2023-04-24 | New energy management platform anomaly monitoring system and method based on cloud computing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310447640.6A CN116466168B (en) | 2023-04-24 | 2023-04-24 | New energy management platform anomaly monitoring system and method based on cloud computing |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116466168A CN116466168A (en) | 2023-07-21 |
| CN116466168B true CN116466168B (en) | 2023-11-24 |
Family
ID=87175087
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310447640.6A Active CN116466168B (en) | 2023-04-24 | 2023-04-24 | New energy management platform anomaly monitoring system and method based on cloud computing |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116466168B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117250898A (en) * | 2023-10-24 | 2023-12-19 | 杭州梵迪智能科技有限公司 | Building equipment monitoring system and method based on cloud computing |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN203707806U (en) * | 2013-09-17 | 2014-07-09 | 苏州经贸职业技术学院 | Wind-solar complementary power supply with fault traceability |
| WO2015106691A1 (en) * | 2014-01-17 | 2015-07-23 | 宁波吉利罗佑发动机零部件有限公司 | Soc estimation method for power battery for hybrid electric vehicle |
| CN204855672U (en) * | 2015-07-31 | 2015-12-09 | 辽宁道纪天力电力工程有限公司 | Electric energy quality evaluation system based on intelligence photovoltaic power generation system |
| CN106020154A (en) * | 2016-07-12 | 2016-10-12 | 中国石油化工股份有限公司 | Safe dynamic health assessment method and assessment system for ethylene production |
| CN110503238A (en) * | 2019-07-16 | 2019-11-26 | 北京科诺伟业科技股份有限公司 | A kind of wisdom energy is provided multiple forms of energy to complement each other evaluation visualization real example platform |
| CN112345952A (en) * | 2020-09-23 | 2021-02-09 | 上海电享信息科技有限公司 | Power battery aging degree judging method |
| CN112526347A (en) * | 2020-11-13 | 2021-03-19 | 瑞萨科林(上海)新能源有限公司 | Evaluation model of lithium ion power battery for retired vehicle |
| CN112836174A (en) * | 2020-12-31 | 2021-05-25 | 深圳市加码能源科技有限公司 | AHP-based real-time charging safety evaluation method and storage medium |
| CN112986851A (en) * | 2021-02-05 | 2021-06-18 | 合肥徽韵光电有限公司 | Energy storage discharge system test platform for power supply detection |
| CN114518539A (en) * | 2022-01-14 | 2022-05-20 | 北京交通大学 | SOC abnormity analysis method for power battery |
| WO2022136098A1 (en) * | 2020-12-21 | 2022-06-30 | Commissariat A L'energie Atomique Et Aux Energies Alternatives | Method for estimating the lifespan of an energy storage system |
| CN115170000A (en) * | 2022-09-06 | 2022-10-11 | 浙江万胜智能科技股份有限公司 | Remote monitoring method and system based on electric energy meter communication module |
| CN115249972A (en) * | 2022-09-22 | 2022-10-28 | 南京江行联加智能科技有限公司 | Performance evaluation system and method for wind generating set of wind power plant under big data |
| CN115310567A (en) * | 2022-10-12 | 2022-11-08 | 合肥凯泉电机电泵有限公司 | Sewage treatment equipment operation monitoring system and method based on big data |
| CN115327422A (en) * | 2022-09-05 | 2022-11-11 | 大连理工大学 | Electric bus power battery health degree evaluation method based on charging and discharging behaviors |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP6413311B2 (en) * | 2014-04-11 | 2018-10-31 | 株式会社村田製作所 | Power storage device, control method, control device, power storage system, electric vehicle, and electronic device |
-
2023
- 2023-04-24 CN CN202310447640.6A patent/CN116466168B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN203707806U (en) * | 2013-09-17 | 2014-07-09 | 苏州经贸职业技术学院 | Wind-solar complementary power supply with fault traceability |
| WO2015106691A1 (en) * | 2014-01-17 | 2015-07-23 | 宁波吉利罗佑发动机零部件有限公司 | Soc estimation method for power battery for hybrid electric vehicle |
| CN204855672U (en) * | 2015-07-31 | 2015-12-09 | 辽宁道纪天力电力工程有限公司 | Electric energy quality evaluation system based on intelligence photovoltaic power generation system |
| CN106020154A (en) * | 2016-07-12 | 2016-10-12 | 中国石油化工股份有限公司 | Safe dynamic health assessment method and assessment system for ethylene production |
| CN110503238A (en) * | 2019-07-16 | 2019-11-26 | 北京科诺伟业科技股份有限公司 | A kind of wisdom energy is provided multiple forms of energy to complement each other evaluation visualization real example platform |
| CN112345952A (en) * | 2020-09-23 | 2021-02-09 | 上海电享信息科技有限公司 | Power battery aging degree judging method |
| CN112526347A (en) * | 2020-11-13 | 2021-03-19 | 瑞萨科林(上海)新能源有限公司 | Evaluation model of lithium ion power battery for retired vehicle |
| WO2022136098A1 (en) * | 2020-12-21 | 2022-06-30 | Commissariat A L'energie Atomique Et Aux Energies Alternatives | Method for estimating the lifespan of an energy storage system |
| CN112836174A (en) * | 2020-12-31 | 2021-05-25 | 深圳市加码能源科技有限公司 | AHP-based real-time charging safety evaluation method and storage medium |
| CN112986851A (en) * | 2021-02-05 | 2021-06-18 | 合肥徽韵光电有限公司 | Energy storage discharge system test platform for power supply detection |
| CN114518539A (en) * | 2022-01-14 | 2022-05-20 | 北京交通大学 | SOC abnormity analysis method for power battery |
| CN115327422A (en) * | 2022-09-05 | 2022-11-11 | 大连理工大学 | Electric bus power battery health degree evaluation method based on charging and discharging behaviors |
| CN115170000A (en) * | 2022-09-06 | 2022-10-11 | 浙江万胜智能科技股份有限公司 | Remote monitoring method and system based on electric energy meter communication module |
| CN115249972A (en) * | 2022-09-22 | 2022-10-28 | 南京江行联加智能科技有限公司 | Performance evaluation system and method for wind generating set of wind power plant under big data |
| CN115310567A (en) * | 2022-10-12 | 2022-11-08 | 合肥凯泉电机电泵有限公司 | Sewage treatment equipment operation monitoring system and method based on big data |
Non-Patent Citations (1)
| Title |
|---|
| 湖南电网电能质量智能化监测分析系统;王灿;宋永;宁志毫;张可人;张斌;罗潇;;湖南电力(02);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116466168A (en) | 2023-07-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2022253038A1 (en) | Method and system for predicting state of health of lithium battery on basis of elastic network, and device and medium | |
| CN110658462B (en) | Lithium battery online service life prediction method based on data fusion and ARIMA model | |
| CN114371409B (en) | Training method of battery state prediction model, battery state prediction method and device | |
| CN110133503B (en) | Battery cell detection method and device | |
| CN116466168B (en) | New energy management platform anomaly monitoring system and method based on cloud computing | |
| CN110133533A (en) | The method and cell managing device of estimating state of health of battery | |
| Lu et al. | Modeling discharge characteristics for predicting battery remaining life | |
| CN116401585B (en) | Energy storage battery failure risk assessment method based on big data | |
| CN112765149A (en) | System and method for calculating capacity of energy storage system | |
| CN114609523A (en) | Online battery capacity detection method, electronic equipment and storage medium | |
| CN114865668A (en) | Energy storage scheduling support evaluation method | |
| CN115469229A (en) | Method for estimating state of charge of lithium battery of uninterruptible power supply | |
| CN115480180A (en) | New energy battery health diagnosis and analysis method | |
| CN204030697U (en) | Based on the battery management system of dynamic SOC estimating system | |
| CN115598557A (en) | Lithium battery SOH estimation method based on constant voltage charging current | |
| CN111711209A (en) | Optical storage and charging combined operation method and system based on energy storage life and frequency modulation performance | |
| CN110794319A (en) | Method and device for predicting parameters of lithium battery impedance model and readable storage medium | |
| Li et al. | Remaining useful life prediction of lithium-ion batteries using multi-model gaussian process | |
| Yu et al. | SOH estimation method for lithium-ion battery based on discharge characteristics | |
| Li et al. | A high-fidelity hybrid lithium-ion battery model for SOE and runtime prediction | |
| CN117893059A (en) | Energy storage data acquisition and analysis method and system based on sensor | |
| CN117169761A (en) | Battery state evaluation method, apparatus, device, storage medium, and program product | |
| CN114594380A (en) | Battery SOC prediction method based on BP neural network | |
| CN117686935B (en) | Battery RUL prediction method based on voltage probability density | |
| CN117828408B (en) | Energy storage capacity data processing method and system |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |