CN116154900A - Active safety three-stage prevention and control system and method for battery energy storage power station - Google Patents
Active safety three-stage prevention and control system and method for battery energy storage power station Download PDFInfo
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Abstract
The invention provides an active safety three-level prevention and control method and system for a battery energy storage power station, and relates to the technical field of safety prevention and control of energy storage power stations. To overcome the inherent problems in the aspects of BMS, regular maintenance and the like of the battery management system in the prior art. The method comprises the following steps: performing real-time analysis based on battery operation data to identify a battery fault risk source on line; early warning is carried out on the micro short circuit fault and the abnormal failure fault of the battery; and early warning is carried out on the thermal runaway fault of the battery. The active safety three-level prevention and control system of the battery energy storage power station is applied to an active safety three-level prevention and control method of the battery energy storage power station.
Description
Technical Field
The invention relates to the technical field of safety prevention and control of energy storage power stations, in particular to an active safety three-stage prevention and control method and system of a battery energy storage power station.
Background
Aiming at the field of safety prevention and control of energy storage power stations, the safety problem has become a primary technical obstacle for large-scale application of battery energy storage power stations. In the aspect of operation monitoring, the battery is subjected to simple state evaluation by a Battery Management System (BMS) aiming at the put-in energy storage power station in the prior art, but due to the fact that the BMS is insufficient in calculation capacity, the storage space is limited, the energy storage safety state evaluation and early-stage fault early-warning capacity are not achieved, and the BMS is insufficient in failure risk and the safety operation of the power station is guaranteed. In terms of operation and maintenance, a passive periodic maintenance strategy is generally adopted at present in China, and the time from the occurrence of a fault symptom to the occurrence of thermal runaway of a battery is often only tens to hundreds of hours, so that a degraded battery is difficult to discover in time by adopting a periodic maintenance mode, and finally, safety accidents can be possibly caused.
Disclosure of Invention
In order to solve the technical problems, the invention provides an active safety three-level prevention and control method and system for a battery energy storage power station, which are based on the three-level prevention and control method and system, and overcome the inherent problems in aspects of battery management system BMS, regular maintenance and the like in the prior art.
The invention provides an active safety three-stage prevention and control method of a battery energy storage power station, which comprises the following steps:
step S1: performing real-time analysis based on battery operation data to identify a battery fault risk source on line;
step S2: early warning is carried out on the micro short circuit fault and the abnormal failure fault of the battery;
step S3: and early warning is carried out on the thermal runaway fault of the battery.
Preferably, the step S2 includes:
step S2.1: extracting features based on battery operation data, identifying battery micro-short circuit faults based on a method combining outlier detection and feature evolution analysis according to the extracted features, and carrying out grading early warning based on internal short circuit resistance of the micro-short circuit battery;
step S2.2: and extracting multidimensional feature quantity reflecting the state of health of the battery based on the battery operation data, constructing a battery abnormal failure recognition algorithm based on a mechanism model and a data model to recognize the abnormal failure battery on line, and performing engineering fault diagnosis based on the operation data of the abnormal failure battery.
Preferably, in the step S2.1, the method of combining outlier detection and feature evolution analysis identifies a micro-short circuit fault of the battery, and performs hierarchical early warning based on an internal short circuit resistance of the micro-short circuit battery, including:
step S2.11: taking a voltage curve of the single battery which reaches the charge cut-off voltage at first as a reference, and calculating the residual charge time of the single battery: Δt (delta t) n，j ＝t n，j -t n，0 Wherein t is n，0 Is the charging time of the first charge, t n，j Is the charging time of the jth charge;
step S2.12: according to the residual charging time of the single battery, calculating the relative charging time of two times of charging of adjacent single batteries, wherein the formula is as follows:
wherein Δt is n，j Is the remaining charge time of the nth cell, Δt n-1，j Is the remaining charge time of the n-1 th cell; />
Step S2.13: counting the relative charging time K of two times of charging of adjacent single batteries n-n-1，j The abnormal single battery is calculated, and the abnormal single battery direct current internal resistance is calculated according to the following formula:
wherein I is k For charging current, U z-k，j U and U 1-k，j The voltages at two sides of the abnormal single battery are respectively;
step S2.14: according to the internal resistance R of direct current k，j Micro-short circuit fault identification is carried out on abnormal single batteries, if the relative charging time K of two times of charging of adjacent single batteries is n-n-1，j Abnormal value and maximum repetition number without DC internal resistance R k，j If the battery is abnormal, judging that the abnormal single battery has micro short circuit fault;
step S2.15: the residual charge capacity of the micro-short circuit single battery is calculated, and the calculation formula is as follows:
wherein I is charging current, t 1 The charging end time of the battery pack is delta t, and the delta t is the residual charging time of the micro-short circuit single battery;
step S2.16: and calculating leakage current of the micro-short circuit single battery, wherein the calculation formula is as follows:
wherein C is RC，n When the nth charge is finished, the residual charge capacity of the micro-short circuit single battery is C RC，n-1 Is the end of the n-1 th chargeAt the time, the residual charge capacity of the micro-short circuit single battery, T n Is the time of the end of the nth charge, T n-1 Is the time when the n-1 th charge ends;
step S2.17: and calculating the internal short circuit resistance of the micro-short circuit single battery, wherein the calculation formula is as follows:
wherein U is M Is the average voltage, inf represents infinity;
step S2.18: judging the internal short circuit resistance of the micro-short circuit single battery,
if the internal short circuit resistance is larger than 10Ω, performing 1-level alarm;
if the internal short circuit resistance is more than 1 omega and less than 10 omega, 2-level alarming is carried out;
if the internal short circuit resistance is more than 0.1 omega and less than 1 omega, 3-level alarming is carried out;
if the internal short-circuit resistance is less than 0.1Ω, it is determined that the battery is in thermal runaway failure.
Preferably, the step S2.2 includes:
step S2.21: estimating the internal resistance change of the single battery on line by using a recursive least square method, calculating the internal resistance increase rate of the single battery and the open-circuit voltage change rate under a specific SOC, calculating an incremental capacity IC curve of the single battery by using constant-current charging data, and analyzing and extracting the change characteristic quantity of a main peak value;
s2.22, taking the internal resistance increase rate, the open circuit voltage change rate and the change characteristic quantity of a main peak value as a first joint characteristic parameter of abnormal failure of the battery, converting actual measurement data into a fault characteristic space, and extracting fault characteristics of the battery through simulation and analysis of experimental data under a specific fault mode of the battery;
step S2.23: and carrying out battery fault diagnosis by adopting a fuzzy reasoning method, combining the input fuzzy classification of the battery fault characteristics with a diagnosis decision rule base, making a decision through rules, and then mapping to an output variable of fault diagnosis so as to identify an abnormal failure battery on line, and carrying out engineering fault diagnosis based on the operation data of the abnormal failure battery.
Preferably, the step S2.23 includes:
a. the domain and membership function of the input and output variables are defined, and the connection between the fault characteristic vector and the fault mode is established according to the results of the simulation and experimental data fault mechanism analysis;
b. according to the actual operation data of the battery, calculating a fault feature vector on line, and determining membership degrees of various faults of battery characteristics under the current working condition by means of an input membership degree function;
c. defuzzification is carried out by adopting a fuzzy diagnosis method, so as to obtain probability values of various faults in the current state;
d. and judging whether the battery has abnormal failure fault or not based on a preset probability threshold value.
Preferably, the step S3 includes:
step S3.1: taking whether the battery short circuit occurs, whether the battery rapidly rises in temperature and whether thermal runaway side reaction gas production data are monitored as second coupling characteristic parameters;
step S3.2: training a machine learning model by using thermal runaway history data and experimental data to obtain a trained machine learning model;
step S3.3: and judging the second combined characteristic parameters by using the trained machine learning model so as to realize early warning of the thermal runaway fault of the battery.
Compared with the prior art, the active safety three-stage prevention and control method of the battery energy storage power station has the following beneficial effects: performing real-time analysis based on battery operation data to identify a battery fault risk source on line; early warning is carried out on the micro short circuit fault and the abnormal failure fault of the battery; and early warning is carried out on the thermal runaway fault of the battery. The invention evaluates the health state of the battery in real time and performs active safety three-level early warning on the power station system. The risk of serious explosion accidents of the energy storage power station is greatly reduced, and the operation and maintenance management efficiency of the energy storage power station is improved.
The invention also provides an active safety three-stage prevention and control system of the battery energy storage power station, which comprises the following components:
the first-stage prevention and control module is used for carrying out real-time analysis based on battery operation data so as to identify a battery fault risk source on line;
the second-stage prevention and control module is used for early warning of battery micro-short circuit faults and battery abnormal failure faults;
and the third-stage prevention and control module is used for early warning the thermal runaway fault of the battery in advance.
Preferably, the second-stage prevention and control module includes:
and a micro short circuit unit: the method is used for extracting the characteristics based on the battery operation data, identifying the micro-short circuit faults of the battery based on a method combining outlier detection and characteristic evolution analysis according to the extracted characteristics, and carrying out grading early warning based on the internal short circuit resistance of the micro-short circuit battery;
abnormal failure unit: the method is used for extracting multidimensional feature quantity reflecting the state of health of the battery based on the battery operation data, constructing a battery abnormal failure recognition algorithm based on a mechanism model and a data model to recognize the abnormal failure battery on line, and performing engineering fault diagnosis based on the operation data of the abnormal failure battery.
Compared with the prior art, the active safety three-stage prevention and control system of the battery energy storage power station has the same beneficial effects as the active safety three-stage prevention and control method of the battery energy storage power station in the technical scheme, and the description is omitted herein.
The invention also provides an electronic device comprising a bus, a transceiver, a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the transceiver, the memory and the processor are connected through the bus, and the computer program realizes the steps in the active safety three-level prevention and control method of the battery energy storage power station when being executed by the processor.
Compared with the prior art, the beneficial effects of the electronic equipment provided by the invention are the same as those of the active safety three-level prevention and control method of the battery energy storage power station in the technical scheme, and the detailed description is omitted.
The invention also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps in an active safety three-level prevention and control method of a battery energy storage power station as described in any of the preceding claims.
Compared with the prior art, the beneficial effects of the computer readable storage medium provided by the invention are the same as those of the active safety three-level prevention and control method of the battery energy storage power station in the technical scheme, and the detailed description is omitted.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an active safety three-level prevention and control method for a battery energy storage power station according to an embodiment;
fig. 2 is a schematic diagram showing state evaluation of a battery using a BMS according to the related art;
FIG. 3 is a schematic diagram showing outlier detection and feature evolution analysis according to an embodiment;
FIG. 4 is a schematic diagram showing battery micro-short and battery failure recognition provided in accordance with one embodiment;
FIG. 5 is a schematic diagram showing a gradual reduction of accident risk under tertiary prevention and control provided in the first embodiment;
FIG. 6 is a schematic diagram of a three-level early warning interface provided in the second embodiment;
fig. 7 shows a schematic diagram of a software architecture of a three-level control system according to the second embodiment;
FIG. 8 shows a schematic view of a smart service interface provided by the second embodiment;
fig. 9 shows a routine inspection flow chart of the device provided in the second embodiment;
fig. 10 shows a routing inspection path navigation schematic provided in the second embodiment;
fig. 11 shows a schematic structural diagram of an active safety three-level prevention and control system of a battery energy storage power station according to the third embodiment.
Detailed Description
In the description of the present invention, it should be understood that 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 number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
The term "plurality" as used in this embodiment means two or more. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a alone, a and B together, and B alone. The words "exemplary" or "such as" are used to mean serving as an example, instance, or illustration, intended to present concepts related in a specific manner, and should not be interpreted as being preferred or advantageous over other embodiments or designs.
Example 1
Fig. 1 shows a flow chart of an active safety three-level prevention and control method of a battery energy storage power station according to an embodiment of the present invention, as shown in fig. 1, the method includes:
step S1: and carrying out real-time analysis based on the battery operation data so as to identify the battery fault risk source on line.
It should be understood that as a first level of prevention and control, the battery is not damaged at this stage, but there are some safety hazards in the system, such as excessive battery pack inconsistency, excessive SOC error of the battery management system, insulation aging, etc., so that it is necessary to timely remove these safety hazards to avoid battery damage.
The existing energy storage power station generally manages the battery by means of a battery management system BMS, fig. 2 shows a schematic diagram of state evaluation of the battery by adopting the BMS in the prior art, and as shown in fig. 2, the BMS is not always reliable and a series of problems such as inaccurate SOC evaluation, relay failure, equalization failure, current detection failure, voltage detection failure and the like can exist.
The identification of the battery fault risk source is mainly realized based on mass operation data of the energy storage power station. Specifically, through real-time analysis of the operation data of the total station battery, a battery fault risk source is timely identified, and corresponding maintenance measure suggestions are pushed aiming at different fault hidden dangers.
Step S2: early warning is carried out on the micro short circuit fault and the abnormal failure fault of the battery.
It should be appreciated that as a second level of protection, battery micro-short circuit faults and battery abnormal failure faults fall into two categories of slow battery faults.
The step S2 includes:
step S2.1: and carrying out feature extraction based on battery operation data, identifying battery micro-short circuit faults based on a method combining outlier detection and feature evolution analysis according to the extracted features, and carrying out grading early warning based on internal short circuit resistance of the micro-short circuit battery.
It can be appreciated that the battery micro-short fault is an important cause for inducing thermal runaway of the battery. Micro-shorting is often initiated by dendrites inside the cell, which have little effect on cell performance at an early stage, but are likely to evolve into thermal runaway through slow development.
As shown in fig. 3, for a battery micro-short circuit fault, an embodiment of the present invention provides a method for extracting an internal short circuit feature of a battery, where the method performs feature extraction based on battery operation data, and identifies the battery micro-short circuit fault by adopting a method based on combination of outlier detection and feature evolution analysis according to the extracted features; meanwhile, an internal short circuit resistance estimation method based on leakage current is provided, quantitative evaluation of the severity of the internal short circuit is achieved, and grading early warning is carried out. The specific flow is as follows:
step S2.11: taking a voltage curve of the single battery which reaches the charge cut-off voltage at first as a reference, and acquiring and calculating the residual charge time of the single battery: Δt (delta t) n，j ＝t n，j -t n，0 Wherein t is n，0 Is the charging time of the first charge, t n，j Is the charging time of the jth charge;
step S2.12: according to the residual charging time of the single battery, calculating the relative charging time of two times of charging of adjacent single batteries, wherein the formula is as follows:
wherein Δt is n，j Is the remaining charge time of the nth cell, Δt n-1，j Is the remaining charge time of the n-1 th cell;
step S2.13: counting the relative charging time K of two times of charging of adjacent single batteries n-n-1，j The abnormal single battery is calculated, and the abnormal single battery direct current internal resistance is calculated according to the following formula:
wherein I is k For charging current, U z-k，j U and U 1-k，j The voltages at both sides of the abnormal single battery are respectively obtained.
It should be understood that the relative charging time K of two charges of adjacent single batteries n-n-1，j The cause of the abnormal value is generally a micro short circuit or a large internal resistance. So it is necessary to respond to the DC internal resistance R k，j Further analysis was performed.
Step S2.14: since the relative charging time of the micro-short circuit unit is prolonged with the increase of the charging times, the unit battery K with micro-short circuit in the battery pack n-n-1，j The value will be greater than other normal cells. Meanwhile, the single battery with larger internal resistance has fluctuation in each charging time, so that the voltage fluctuation is larger at the end of charging, thereby leading to the K of the single battery n-n-1，j The value is increased, and the direct current internal resistance R can be combined according to the consistency of the single batteries k，j For K n-n-1，j The influence of the value on the abnormalityAnd (3) carrying out micro short circuit fault identification on the single battery. If the relative charging time K of two times of charging of adjacent single batteries n-n-1，j Abnormal value and occurrence times of abnormal value are high, and at the same time, no DC internal resistance R exists k，j And if the relative charging time is abnormal, judging that the abnormal single battery has micro short circuit fault.
It should be understood that the DC internal resistance R herein k，j The abnormality may be the internal resistance R of DC k，j The consistency is better.
Step S2.15: the residual charge capacity of the micro-short circuit single battery is calculated, and the calculation formula is as follows:
wherein I is charging current, t 1 And delta t is the residual charging time of the micro-short circuit single battery as the charging end time of the battery pack.
Step S2.16: and calculating leakage current of the micro-short circuit single battery, wherein the calculation formula is as follows:
wherein C is RC，n When the nth charge is finished, the residual charge capacity of the micro-short circuit single battery is C RC，n-1 Is the residual charge capacity of the micro-short circuit single battery when the n-1 th charge is finished, T n Is the time of the end of the nth charge, T U-1 Is the time when the n-1 th charge ends.
Step S2.17: and calculating the internal short circuit resistance of the micro-short circuit single battery, wherein the calculation formula is as follows:
wherein U is M Is the average voltage and Inf represents infinity.
Step S2.18: judging the internal short circuit resistance of the micro-short circuit single battery,
if the internal short circuit resistance is larger than 10Ω, performing 1-level alarm;
if the internal short circuit resistance is more than 1 omega and less than 10 omega, 2-level alarming is carried out;
if the internal short circuit resistance is more than 0.1 omega and less than 1 omega, 3-level alarming is carried out;
if the internal short-circuit resistance is less than 0.1Ω, it is determined that the battery is in thermal runaway failure.
Step S2.2: and extracting multidimensional feature quantity reflecting the state of health of the battery based on the battery operation data, constructing a battery abnormal failure recognition algorithm based on a mechanism model and a data model to recognize the abnormal failure battery on line, and performing engineering fault diagnosis based on the operation data of the abnormal failure battery.
It will be appreciated that the aging process of the cell is very slow under normal conditions, but that certain degraded cells under certain conditions can accelerate decay, for reasons including non-uniform cell internal pressure distribution, negative electrode lithium evolution, etc.
As shown in fig. 4, the embodiment of the present invention extracts a multidimensional feature quantity reflecting the state of health of a battery based on battery operation data, constructs a battery failure recognition algorithm based on a mechanism model and a data model to recognize an abnormally failed battery on line, and performs an engineered failure diagnosis based on the operation data of the abnormally failed battery. The specific flow is as follows:
step S2.21: and (3) online estimating the internal resistance change of the single battery by using a recursive least square method, calculating the internal resistance increase rate of the single battery and the open-circuit voltage change rate under a specific SOC, calculating an incremental capacity IC curve of the single battery by using constant-current charging data, and analyzing and extracting the change characteristic quantity of the main peak value.
And S2.22, taking the internal resistance increase rate, the open circuit voltage change rate and the change characteristic quantity of the IC main peak value as a first joint characteristic parameter of abnormal failure of the battery, converting actual measurement data into a fault characteristic space, and carrying out mining and extraction of fault characteristics of the battery through analysis of simulation and experimental data under a specific fault mode of the battery.
Step S2.23: and carrying out battery fault diagnosis by adopting a fuzzy reasoning method, combining the input fuzzy classification of the battery fault characteristics with a diagnosis decision rule base, making a decision through rules, and then mapping to an output variable of fault diagnosis so as to identify an abnormal failure battery on line, and carrying out engineering fault diagnosis based on the operation data of the abnormal failure battery.
It should be understood that, due to the individual differences of the batteries, the variation of the first joint characteristic parameter is different and can float in a numerical range, so that it is difficult to define whether the abnormal failure of the battery occurs by adopting the binary logic based on the threshold value. In order to solve the problem, the embodiment of the invention adopts a fuzzy reasoning method to carry out battery fault diagnosis, the input fuzzy classification of the battery fault characteristics is directly combined with a diagnosis decision rule base, the decision is made through rules and then mapped to the output variable of the fault diagnosis so as to identify the abnormal failure battery on line, and the engineering fault diagnosis is carried out based on the operation data of the abnormal failure battery. The specific flow is as follows:
a. the domain and membership function of the input and output variables are defined, and the connection between the fault characteristic vector and the fault mode is established according to the results of the simulation and experimental data fault mechanism analysis;
b. according to the actual operation data of the battery, calculating a fault feature vector on line, and determining membership degrees of various faults of battery characteristics under the current working condition by means of an input membership function, namely evaluating the change degree of the current fault feature vector;
c. defuzzification is carried out by adopting a fuzzy diagnosis method, so as to obtain probability values of various faults in the current state;
d. based on a given probability threshold, e.g., 95%, it is determined whether an abnormal failure of the battery has occurred.
The second-stage prevention and control provided by the embodiment of the invention can early find the degraded battery and timely replace the degraded battery by early warning the micro-short circuit fault and the abnormal failure fault of the battery, so that the occurrence of thermal runaway can be avoided.
Step S3: and early warning is carried out on the thermal runaway fault of the battery.
It should be appreciated that the third level of protection is directed primarily to mutant battery failures. The method has the advantages that the combined characteristics of voltage and temperature of the battery core are extracted, the abnormal symptoms before the occurrence of the thermal runaway are identified by adopting a mode identification method, and further, the spread of the thermal runaway and the expansion of accidents can be avoided by adopting corresponding measures in advance.
Specifically, the step S3 includes:
step S3.1: and taking the data such as whether the short circuit in the battery occurs, whether the rapid temperature rise of the battery occurs, whether the thermal runaway side reaction gas production is monitored or not and the like as the second coupling characteristic parameters.
Step S3.2: and training a machine learning model by using thermal runaway history data, experimental data and the like to obtain a trained machine learning model.
Step S3.3: and judging the second combined characteristic parameters by using a trained machine learning model so as to realize early warning of the thermal runaway fault of the battery, wherein the time scale of the early warning is tens of seconds.
Compared with the existing BMS, regular overhaul and the like, the embodiment of the invention can identify and three-level early warning the early failure of the system by monitoring and evaluating the running state of the battery. Fig. 5 shows a schematic diagram of three-level prevention and control early warning, and as shown in fig. 5, the first-level prevention and control, the second-level prevention and control and the third-level prevention and control are sequentially performed from left to right. Specifically, as shown in fig. 5, the first-stage prevention and control is online identification of a fault risk source, and when a battery is not damaged, the damage of the battery can be avoided by timely removing the risk source, and the method belongs to the field of removing external risk hidden danger; the second-stage prevention and control is early warning of battery faults, namely on-line identification of micro short circuit and abnormal failure of the battery, and thermal runaway can be avoided by timely replacing a degraded battery, so that the early fault of the battery is eliminated; the third level prevention and control is early warning of thermal runaway, and the active thermal management system can be started in time by early warning before the thermal runaway occurs, so that the accident is further avoided. Through three-level prevention and control early warning, targeted maintenance measures can be adopted, and risk factors possibly causing serious accidents are killed in the cradle. The risk of serious explosion accidents of the energy storage power station is greatly reduced, the active safety of the energy storage power station is improved, and the operation and maintenance management efficiency of the energy storage power station is improved.
Example two
Taking an active safety and intelligent operation and maintenance system of a battery energy storage power station of a Qinghua Sichuan energy internet research institute as an example. At present, the intelligent operation and maintenance system is applied to the landing of a plurality of energy storage power stations in Beijing, hebei, jiangsu and other places, so that the risk of serious explosion accidents of the energy storage power stations is greatly reduced, and the operation and maintenance management efficiency of the energy storage power stations is improved.
Specifically, fig. 6 shows a schematic diagram of a three-level early warning interface provided in the second embodiment, and as shown in fig. 6, the operation and maintenance system adopts a real-time big data processing architecture, so that the health state of the battery can be evaluated in real time, and active safety three-level early warning can be performed on the power station system. On the basis, the system can provide intelligent auxiliary services for the operation and maintenance of the power station, including maintenance period optimization, degradation unit positioning, maintenance operation recommendation, maintenance record generation and the like, and supports the active operation and maintenance of the power station.
Fig. 7 shows a schematic diagram of a software architecture of a three-level control system according to the second embodiment. As shown in fig. 7, it can be seen from the software architecture diagram of the three-stage prevention and control system, the three-stage prevention and control system is based on massive big data acquired by the acquisition system and the external monitoring system, and a series of functions can be realized through the service center and the core algorithm.
Fig. 8 shows a schematic diagram of an intelligent maintenance interface provided in the second embodiment. As shown in fig. 8, the intelligent operation and inspection system is compatible with the national network PMS system, generates a specific maintenance task, and pushes the maintenance task to the intelligent operation and inspection terminal in a form of a table to assist operation and inspection personnel in implementing maintenance. For example, the two-dimension code label is adopted to realize the prevention of misoperation of battery replacement management, and the quick and accurate entry of replacement battery information is ensured.
Fig. 9 shows a routine inspection flow chart of the device provided in the second embodiment. As shown in fig. 9, the intelligent operation inspection system has a daily inspection function of equipment, the function is mainly used for interactive execution of paperless inspection tasks, is convenient for inspection formulators to make inspection plans, is convenient for inspection executives to execute inspection tasks in a mode of a mobile terminal and an equipment two-dimension code, records discovered equipment problems in time, and records the equipment problems in inspection records.
Fig. 10 shows a routing inspection path navigation schematic diagram according to the second embodiment. As shown in fig. 10, in the inspection process, not only can the inspection efficiency be greatly improved, but also misoperation can be avoided by making a scientific and reasonable inspection path. The energy storage power station has the characteristics of wider occupation area, large number of components and the like, so that equipment fault positioning is difficult. The battery modules in the container have consistent appearance sizes, and are easy to be confused. The intelligent operation detection system deploys two-dimensional code labels on equipment such as the total station battery module, the battery management system and the like, and realizes unified coding of equipment identification. The two-dimensional code label is used as an entrance, and information of the device can be quickly opened on the mobile terminal. The intelligent operation and detection system can be combined with the two-dimensional code equipment label in the process of applying the fault expert diagnosis function, so that equipment rapid positioning, historical information reading, fault root cause analysis, intelligent operation and detection decision and the like are realized.
Example III
Fig. 11 shows a schematic structural diagram of an active safety three-level prevention and control system of a battery energy storage power station according to a third embodiment of the present invention, as shown in fig. 11, the system includes:
the first-stage prevention and control module 1 is used for carrying out real-time analysis based on battery operation data so as to identify a battery fault risk source on line;
the second-stage prevention and control module 2 is used for early warning of battery micro-short circuit faults and battery abnormal failure faults;
and the third-stage prevention and control module 3 is used for early warning the thermal runaway fault of the battery in advance.
Preferably, the second-stage prevention and control module 2 includes:
and a micro short circuit unit: the method is used for extracting the characteristics based on the battery operation data, identifying the micro-short circuit faults of the battery based on a method combining outlier detection and characteristic evolution analysis according to the extracted characteristics, and carrying out grading early warning based on the internal short circuit resistance of the micro-short circuit battery;
abnormal failure unit: the method is used for extracting multidimensional feature quantity reflecting the state of health of the battery based on the battery operation data, constructing a battery abnormal failure recognition algorithm based on a mechanism model and a data model to recognize the abnormal failure battery on line, and performing engineering fault diagnosis based on the operation data of the abnormal failure battery.
Compared with the prior art, the beneficial effects of the active safety three-level prevention and control system of the battery energy storage power station provided by the embodiment of the invention are the same as those of the active safety three-level prevention and control method of the battery energy storage power station in the technical scheme, and are not repeated herein.
In addition, the embodiment of the invention also provides electronic equipment, which comprises a bus, a transceiver, a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the transceiver, the memory and the processor are respectively connected through the bus, and when the computer program is executed by the processor, the processes of the active safety three-level prevention and control method embodiment of the battery energy storage power station can be realized, and the same technical effects can be achieved, so that repetition is avoided and no repeated description is provided here.
In addition, the embodiment of the invention further provides a computer readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the processes of the active safety three-level prevention and control method embodiment of the battery energy storage power station are realized, and the same technical effects can be achieved, so that repetition is avoided, and the description is omitted here.
The computer-readable storage medium includes: persistent and non-persistent, removable and non-removable media are tangible devices that may retain and store instructions for use by an instruction execution device. The computer-readable storage medium includes: electronic storage, magnetic storage, optical storage, electromagnetic storage, semiconductor storage, and any suitable combination of the foregoing. The computer-readable storage medium includes: phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), non-volatile random access memory (NVRAM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disk read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassette storage, magnetic tape disk storage or other magnetic storage devices, memory sticks, mechanical coding (e.g., punch cards or bump structures in grooves with instructions recorded thereon), or any other non-transmission medium that may be used to store information that may be accessed by a computing device. In accordance with the definition in the present embodiments, the computer-readable storage medium does not include a transitory signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a pulse of light passing through a fiber optic cable), or an electrical signal transmitted through a wire.
In several embodiments provided herein, it should be understood that the disclosed apparatus, electronic device, and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, e.g., the division of the modules or units is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. In addition, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices, or elements, or may be an electrical, mechanical, or other form of connection.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one position, or may be distributed over a plurality of network units. Some or all of the units can be selected according to actual needs to solve the problem to be solved by the scheme of the embodiment of the invention.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the embodiments of the present invention is essentially or partly contributing to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (including: a personal computer, a server, a data center or other network device) to perform all or part of the steps of the method according to the embodiments of the present invention. And the storage medium includes various media as exemplified above that can store program codes.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art can easily think about variations or alternatives within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. An active safety three-level prevention and control method of a battery energy storage power station is characterized by comprising the following steps:
step S1: performing real-time analysis based on battery operation data to identify a battery fault risk source on line;
step S2: early warning is carried out on the micro short circuit fault and the abnormal failure fault of the battery;
step S3: and early warning is carried out on the thermal runaway fault of the battery.
2. The method of claim 1, wherein,
the step S2 includes:
step S2.1: extracting features based on battery operation data, identifying battery micro-short circuit faults based on a method combining outlier detection and feature evolution analysis according to the extracted features, and carrying out grading early warning based on internal short circuit resistance of the micro-short circuit battery;
step S2.2: and extracting multidimensional feature quantity reflecting the state of health of the battery based on the battery operation data, constructing a battery abnormal failure recognition algorithm based on a mechanism model and a data model to recognize the abnormal failure battery on line, performing engineering fault diagnosis based on the operation data of the abnormal failure battery, and judging the specific part and the fault type of the fault.
3. The method of claim 2, wherein,
in the step S2.1, a method based on the combination of outlier detection and feature evolution analysis identifies a battery micro-short circuit fault, and performs hierarchical early warning based on an internal short circuit resistance of the micro-short circuit battery, including:
step S2.11: taking a voltage curve of the single battery which reaches the charge cut-off voltage at first as a reference, and calculating the residual charge time of the single battery: Δt (delta t) n,j ＝t n,j -t n,0 Wherein t is n,0 Is the charging time of the first charge, t n,j Is the charging time of the jth charge;
step S2.12: according to the residual charging time of the single battery, calculating the relative charging time of two times of charging of adjacent single batteries, wherein the formula is as follows:
wherein Δt is n,j Is the remaining charge time of the nth cell, Δt n-1,j Is the remaining charge time of the n-1 th cell;
step S2.13: counting the relative charging time K of two times of charging of adjacent single batteries n-n-1,j The abnormal single battery is calculated, and the abnormal single battery direct current internal resistance is calculated according to the following formula:
wherein I is k For charging current, U z-k,j U and U 1-k,j The voltages at two sides of the abnormal single battery are respectively;
step S2.14: according to the internal resistance R of direct current k,j Micro-short circuit fault identification is carried out on abnormal single batteries, if the relative charging time K of two times of charging of adjacent single batteries is n-n-1,j Abnormal value and maximum repetition number without DC internal resistance R k,j If the battery is abnormal, judging that the abnormal single battery has micro short circuit fault;
step S2.15: the residual charge capacity of the micro-short circuit single battery is calculated, and the calculation formula is as follows:
wherein I is charging current, t 1 The charging end time of the battery pack is delta t, and the delta t is the residual charging time of the micro-short circuit single battery;
step S2.16: and calculating leakage current of the micro-short circuit single battery, wherein the calculation formula is as follows:
wherein C is RC,n When the nth charge is finished, the residual charge capacity of the micro-short circuit single battery is C RC,n-1 Is the residual charge capacity of the micro-short circuit single battery when the n-1 th charge is finished, T n Is the time of the end of the nth charge, T n-1 Is the time when the n-1 th charge ends;
step S2.17: and calculating the internal short circuit resistance of the micro short circuit battery, wherein the calculation formula is as follows:
wherein U is M Is the average voltage, inf represents infinity;
step S2.18: judging the internal short circuit resistance of the micro-short circuit battery,
if the internal short circuit resistance is larger than 10Ω, performing 1-level alarm;
if the internal short circuit resistance is more than 1 omega and less than 10 omega, 2-level alarming is carried out;
if the internal short circuit resistance is more than 0.1 omega and less than 1 omega, 3-level alarming is carried out;
if the internal short-circuit resistance is less than 0.1Ω, it is determined that the battery is in thermal runaway failure.
4. The method of claim 1, wherein,
the step S2.2 includes:
step S2.21: estimating the change of the internal resistance of the battery on line by using a recursive least square method, calculating the increase rate of the internal resistance of the battery and the change rate of the open circuit voltage under a specific SOC, calculating a battery increment capacity IC curve by using constant-current charging data, and analyzing and extracting the change characteristic quantity of a main peak value;
s2.22, taking the internal resistance increase rate, the open circuit voltage change rate and the change characteristic quantity of a main peak value as a first joint characteristic parameter of abnormal failure of the battery, converting actual measurement data into a fault characteristic space, and extracting fault characteristics of the battery through simulation and analysis of experimental data under a specific fault mode of the battery;
step S2.23: and carrying out battery fault diagnosis by adopting a fuzzy reasoning method, combining the input fuzzy classification of the battery fault characteristics with a diagnosis decision rule base, making a decision through rules, and then mapping to an output variable of fault diagnosis so as to identify an abnormal failure battery on line, and carrying out engineering fault diagnosis based on the operation data of the abnormal failure battery.
5. The method of claim 4, wherein,
the step S2.23 includes:
a. the domain and membership function of the input and output variables are defined, and the connection between the fault characteristic vector and the fault mode is established according to the results of the simulation and experimental data fault mechanism analysis;
b. according to the actual operation data of the battery, calculating a fault feature vector on line, and determining membership degrees of various faults of battery characteristics under the current working condition by means of an input membership degree function;
c. defuzzification is carried out by adopting a fuzzy diagnosis method, so as to obtain probability values of various faults in the current state;
d. and judging whether the battery has abnormal failure fault or not based on a preset probability threshold value.
6. The method of claim 1, wherein,
the step S3 includes:
step S3.1: taking whether the battery short circuit occurs, whether the battery rapidly rises in temperature and whether thermal runaway side reaction gas production data are monitored as second coupling characteristic parameters;
step S3.2: training a machine learning model by using thermal runaway history data and experimental data to obtain a trained machine learning model;
step S3.3: and judging the second combined characteristic parameters by using the trained machine learning model so as to realize early warning of the thermal runaway fault of the battery.
7. An active safety three-level prevention and control system of a battery energy storage power station is characterized by comprising:
the first-stage prevention and control module is used for carrying out real-time analysis based on battery operation data so as to identify a battery fault risk source on line;
the second-stage prevention and control module is used for early warning of battery micro-short circuit faults and battery abnormal failure faults;
and the third-stage prevention and control module is used for early warning the thermal runaway fault of the battery in advance.
8. The active safety tertiary prevention and control system of a battery energy storage power station of claim 7 wherein,
the second-stage prevention and control module comprises:
and a micro short circuit unit: the method is used for extracting the characteristics based on the battery operation data, identifying the micro-short circuit faults of the battery based on a method combining outlier detection and characteristic evolution analysis according to the extracted characteristics, and carrying out grading early warning based on the internal short circuit resistance of the micro-short circuit battery;
abnormal failure unit: the method is used for extracting multidimensional feature quantity reflecting the state of health of the battery based on the battery operation data, constructing a battery abnormal failure recognition algorithm based on a mechanism model and a data model to recognize the abnormal failure battery on line, and performing engineering fault diagnosis based on the operation data of the abnormal failure battery.
9. An electronic device comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected by the bus, characterized in that the computer program when executed by the processor implements the steps in an active safety three-level prevention and control method of a battery energy storage power station according to any one of claims 1-6.
10. A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps in an active safety three-level prevention and control method of a battery energy storage power station according to any one of claims 1-6.
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| CN116500459A (en) * | 2023-06-28 | 2023-07-28 | 中汽研汽车检验中心(常州)有限公司 | Lithium battery safety performance evaluation method and device, storage medium and electronic equipment |
| CN116500459B (en) * | 2023-06-28 | 2023-08-29 | 中汽研汽车检验中心(常州)有限公司 | Lithium battery safety performance evaluation method and device, storage medium and electronic equipment |
| CN117559016A (en) * | 2023-12-28 | 2024-02-13 | 黑龙江瑞兴科技股份有限公司 | Intelligent battery management system and battery management method |
| CN117559016B (en) * | 2023-12-28 | 2024-04-05 | 黑龙江瑞兴科技股份有限公司 | Intelligent battery management system and battery management method |
| CN117686921A (en) * | 2024-02-02 | 2024-03-12 | 江苏林洋亿纬储能科技有限公司 | Method and system for detecting short circuit in battery and computing device |
| CN117686921B (en) * | 2024-02-02 | 2024-05-31 | 江苏林洋亿纬储能科技有限公司 | Method and system for detecting short circuit in battery and computing device |
| CN117719345A (en) * | 2024-02-06 | 2024-03-19 | 湖北工业大学 | Battery micro-short circuit quantification method considering aging based on IC curve |
| CN117719345B (en) * | 2024-02-06 | 2024-05-17 | 湖北工业大学 | Battery micro-short circuit quantification method considering aging based on IC curve |
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