CN115980601A - Energy storage battery safety detection method and device - Google Patents
Energy storage battery safety detection method and device Download PDFInfo
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Abstract
The application relates to a safety detection method and device for an energy storage battery, which are used for acquiring management data and working characteristic values of each battery and searching matched preset management data according to the management data of the batteries. Acquiring a management deviation value according to the difference between the battery management data and preset management data, and acquiring a safety mark value according to the sum of the management deviation value and the working characteristic value; and finally, according to the marking threshold interval where the safety marking value is positioned, determining a safety detection result corresponding to the marking threshold interval. Based on this, compare traditional safety inspection mode, the information that the safety inspection result of this application was based on is more comprehensive, can reflect the physical condition of battery effectively and improve the detection accuracy.
Description
Technical Field
The present disclosure relates to energy storage battery technologies, and in particular, to a method and an apparatus for detecting safety of an energy storage battery.
Background
The green low-carbon of energy becomes an important subject in the current energy field, and the energy storage technology is used for breaking the space-time limitation of the energy, can realize the power generation and power utilization balance across time periods and seasons, and is the key for supporting the large-scale development of the green energy. Wind power, photovoltaic and other green renewable energy modeling need to be matched with energy storage systems in a certain proportion. In an energy storage system, electrochemical energy storage (such as a lithium ion battery, a lead-acid battery and a sodium ion battery) is not influenced by external climate and geographical factors, is the most valuable energy storage technology, can be flexibly applied to various demand configurations, and has a very high proportion in the energy storage system at present. However, at the back of the vigorous development of the energy storage industry, there still exists a small challenge in terms of energy storage safety, and the lithium ion battery with the largest scale has a fire hazard on the power generation side, the power grid side and the user side. Unlike conventionally used electronic product batteries, the battery capacity and power provided in the energy storage system are greater, and once a safety problem occurs, the impact is greater and the consequences are more serious. Currently, the main causes include: the BATTERY MANAGEMENT SYSTEM includes a BATTERY body (defects in the manufacturing process, BATTERY aging, etc.), an external excitation source (external short circuit, electrical shock, thermal shock, etc.), a MANAGEMENT SYSTEM (including human factors such as BMS (BATTERY MANAGEMENT SYSTEM), PCS (Power Control SYSTEM, energy storage converter SYSTEM), EMS (Energy MANAGEMENT SYSTEM), and MANAGEMENT specifications).
The lithium ion battery energy storage power station used at present can be divided into four levels: battery monomer, module, battery cluster and battery cabin. The battery single bodies are arranged into an integrated module, the module is electrically connected to form a battery cluster, and a plurality of battery clusters and the converter and other equipment form a battery cabin. According to the size of the energy storage capacity and the design of the physical space, the number of the batteries is increased, the batteries are arranged relatively densely, and if the single batteries are abnormal due to faults, the chain reaction of the peripheral batteries is easily caused. Therefore, the risk of detecting each energy storage cell is required.
The current risk evaluation of the energy storage battery usually takes SOH (State of health) as an evaluation standard, but SOH is an evaluation value and cannot completely reflect the actual risk condition of the battery, and in reality, there are many cases that the SOH of the battery is normal but the battery bulges to generate a potential risk. For example, a patent with application number "CN201910237132.9" and name "a method and an apparatus for thermal runaway simulation based on lithium ion battery thermal runaway prediction model" discloses a scheme for risk calculation through SOC (State of Charge) and SOH. The SOC and the SOH are obtained by calculation according to a certain electricity mathematical scheme, the actual condition of the battery cannot be truly reflected, and early warning cannot be achieved before the incident only by means of thermal runaway inspection and hardware. Secondly, energy storage cells are often obscured by external covers in use, making it difficult to see changes in shape and properties by inspection. The existing battery safety related technologies, such as the patent with application number "CN202111127963.4" and named "a battery thermal runaway warning method and device", mainly focus on the battery thermal runaway problem, and focus on hardware detection and explosion protection. However, through hardware detection, due to the fact that the processing capability of hardware operation is weak, the cost and technical difficulty of operation from the hardware side are relatively high, the battery use environment conditions are very different and complicated, the accuracy may be affected, once thermal runaway occurs, the effect is already generated, and at the moment, protection is performed again, and loss is already caused.
In summary, the conventional battery safety detection related technology has the above disadvantages under the limitation of software and hardware.
Disclosure of Invention
Therefore, it is necessary to provide a method and an apparatus for detecting the safety of an energy storage battery to overcome the shortcomings of the related conventional battery safety detection technologies.
At least one embodiment of the present disclosure provides a method for detecting safety of an energy storage battery, including the steps of:
acquiring battery management data and a working characteristic value;
searching matched preset management data according to the battery management data;
acquiring a management deviation value according to the difference between the battery management data and the preset management data;
obtaining a safety mark value according to the sum of the management deviation value and the working characteristic value;
and determining a safety detection result corresponding to the marking threshold interval according to the marking threshold interval where the safety marking value is positioned.
According to the safety detection method for the energy storage battery, the management data and the working characteristic value of each battery are obtained, and the matched preset management data is searched according to the management data of the batteries. Acquiring a management deviation value according to the difference between the battery management data and preset management data, and acquiring a safety mark value according to the sum of the management deviation value and the working characteristic value; and finally, according to the marking threshold interval where the safety marking value is positioned, determining a safety detection result corresponding to the marking threshold interval. Based on this, compare traditional safety inspection mode, the information that the safety inspection result of this application was based on is more comprehensive, can reflect the physical condition of battery effectively and improve the detection accuracy.
In one embodiment, the battery management data comprises system data, battery pack data and battery cell data;
the system data comprises time data, model data, characteristic data, positioning data, SOC, SOH and/or environment temperature data;
the battery pack data comprises battery pack charging data, battery pack discharging data and/or battery pack temperature data;
the cell data includes cell charge data, cell discharge data, and/or cell temperature data.
In one embodiment, the operating characteristic value comprises a characteristic value acquired by a sensor.
In one embodiment, the battery management data includes cell voltage, cell voltage maximum difference, cell temperature maximum difference, cell temperature rise rate, and battery pack critical alarm times.
In one embodiment, the working characteristic value includes a characteristic value collected by a displacement sensor, a characteristic value collected by a gas sensor and/or a characteristic value collected by a sound sensor.
In one embodiment, the process of obtaining the management deviation value according to the difference between the battery management data and the preset management data includes the steps of:
correcting the difference value between the battery management data and the preset management data through the correction quantity; wherein, the correction quantity is in one-to-one correspondence with the battery management data;
and obtaining a management deviation value according to the product of the difference value and the correction weight.
In one embodiment, the method further comprises the following steps:
and executing corresponding security processing according to the security detection result.
An energy storage battery safety detection device, comprising:
the information acquisition module is used for acquiring battery management data and working characteristic values;
the information matching module is used for searching matched preset management data according to the battery management data;
the deviation calculation module is used for obtaining a management deviation value according to the difference between the battery management data and the preset management data;
the mark calculation module is used for obtaining a safety mark value according to the sum of the management deviation value and the working characteristic value;
and the result output module is used for determining a safety detection result corresponding to the marking threshold interval according to the marking threshold interval where the safety marking value is positioned.
According to the energy storage battery safety detection device, the information acquisition module acquires the battery management data and the working characteristic value, and the information matching module searches the matched preset management data according to the battery management data. The deviation calculation module obtains a management deviation value according to the difference between the battery management data and preset management data, and the mark calculation module obtains a safety mark value according to the sum of the management deviation value and the working characteristic value; and finally, the mark calculation module determines a safety detection result corresponding to the mark threshold interval according to the mark threshold interval in which the safety mark value is positioned. Based on this, compare traditional safety inspection mode, the information that the safety inspection result of this application was based on is more comprehensive, can reflect the physical condition of battery effectively and improve the detection accuracy.
At least one embodiment of the present disclosure further provides a data detection apparatus, including:
one or more memories non-transitory storing computer-executable instructions;
one or more processors configured to execute computer-executable instructions, wherein the computer-executable instructions, when executed by the one or more processors, implement a method for safety detection of an energy storage battery according to any embodiment of the present disclosure.
The data detection device acquires the battery management data and the working characteristic value, and searches the matched preset management data according to the battery management data. Acquiring a management deviation value according to the difference between the battery management data and preset management data, and acquiring a safety mark value according to the sum of the management deviation value and the working characteristic value; and finally, according to the marking threshold interval where the safety marking value is positioned, determining a safety detection result corresponding to the marking threshold interval. Based on this, compare traditional safety inspection mode, the information that the safety inspection result of this application was based on is more comprehensive, can reflect the physical condition of battery effectively and improve the detection accuracy.
At least one embodiment of the present disclosure also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer-executable instructions, and when executed by a processor, the computer-executable instructions implement the method for detecting safety of an energy storage battery according to any one of the embodiments of the present disclosure.
The non-transitory computer readable storage medium obtains the battery management data and the working characteristic value, and searches for the matched preset management data according to the battery management data. Acquiring a management deviation value according to the difference between the battery management data and preset management data, and acquiring a safety mark value according to the sum of the management deviation value and the working characteristic value; and finally, according to the marking threshold interval where the safety marking value is positioned, determining a safety detection result corresponding to the marking threshold interval. Based on this, compare traditional safety inspection mode, the information that the safety inspection result of this application was based on is more comprehensive, can reflect the physical condition of battery effectively and improve the detection accuracy.
Drawings
Fig. 1 is a schematic view of an application scenario of an embodiment of the present disclosure;
fig. 2 is a flowchart of a method for detecting the safety of an energy storage battery according to an embodiment;
fig. 3 is a flowchart of a method for detecting the safety of an energy storage battery according to another embodiment;
FIG. 4 is a schematic block diagram of an energy storage battery safety detection apparatus according to an embodiment;
fig. 5 is a schematic block diagram of a data detection apparatus provided in at least one embodiment of the present disclosure;
fig. 6 is a schematic diagram of a non-transitory computer-readable storage medium according to at least one embodiment of the disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions of the embodiments of the present disclosure will be described below clearly and completely in conjunction with the accompanying drawings of the embodiments of the present disclosure. It is to be understood that the described embodiments are only a few embodiments of the present disclosure, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the disclosure without any inventive step, are within the scope of protection of the disclosure.
Unless defined otherwise, technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this disclosure belongs. The use of "first," "second," and similar terms in this disclosure is not intended to indicate any order, quantity, or importance, but rather is used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", and the like are used merely to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships may also be changed accordingly.
To keep the following description of the embodiments of the present disclosure clear and concise, a detailed description of some known functions and known components of the disclosure are omitted.
At least one embodiment of the present disclosure provides a method for detecting safety of an energy storage battery. In order to better explain the effect of the embodiment of the present disclosure compared with the conventional security detection method, a scene environment to which the embodiment of the present disclosure is applied is first developed below.
Fig. 1 is a schematic view of an application scenario of an embodiment of the present disclosure, and as shown in fig. 1, an energy storage battery of the embodiment of the present disclosure generally refers to a battery system composed of single battery cells 10. In the embodiment of the present disclosure, a unit battery refers to a unit cell 10 as shown in fig. 1. The plurality of unit cells 10 constitute a battery pack 11 (battery module), the plurality of battery packs 11 constitute a battery cluster 12 (cabinet), the plurality of battery clusters 12 constitute a battery stack 13, and the plurality of battery stacks 13 constitute a battery compartment 14. The battery pack 11, the battery cluster 12, the battery stack 13 and the battery compartment 14 form an integral battery system, and it should be noted that the battery system can be any grade of the battery pack 11, the battery cluster 12, the battery stack 13 and the battery compartment 14.
As shown in fig. 1, a corresponding battery management system 20 is configured for the operation of the overall battery system. The battery management system 20 performs information acquisition and battery management processing on the battery system based on hardware such as sensors. The data collected by the battery management system 20 includes battery management data and operating characteristic values.
The disclosed embodiment is mainly based on the hardware level of the existing battery management system 20, and reduces the hardware dependence. On the premise of not increasing the hardware cost, the detection accuracy and granularity of the safety of the energy storage battery are improved. The traditional safety detection mode mainly detects information such as macroscopic SOC, SOH or thermal runaway, and the detection result reflects the battery problem of the whole battery system. The method for detecting the safety of the energy storage battery according to the embodiment of the disclosure can select the data collected by the battery management system 20 to adjust the safety detection level according to the detected granularity.
As shown in fig. 1, the cloud server 30 is connected to each battery management system 20 in a unified manner, and performs corresponding data processing. In the energy storage battery safety detection method according to the embodiment of the present disclosure, the cloud server 30 may serve as an execution main body, and safety detection is uniformly performed on each battery system. The energy storage battery safety detection method according to the embodiment of the present disclosure may also use the battery management system 20 as an execution subject to perform safety detection on a local battery system. As a preferred implementation manner, the energy storage battery safety detection method according to the embodiment of the present disclosure is deployed in the cloud server 30, so as to perform corresponding result representation of the safety detection result. It should be noted that the safety detection method for the energy storage battery according to the embodiment of the present disclosure obtains a safety detection result, and may perform different characterizations according to different hardware and platform processing capabilities. Therefore, the form of the security detection result is not unique.
Based on the above system structure, fig. 2 is a flowchart of a method for detecting the safety of an energy storage battery according to an embodiment, and as shown in fig. 2, the method for detecting the safety of an energy storage battery according to an embodiment includes steps S100 to S104:
s100, acquiring battery management data and a working characteristic value;
s101, searching matched preset management data according to the battery management data;
s102, acquiring a management deviation value according to the difference between the battery management data and preset management data;
s103, obtaining a safety mark value according to the sum of the management deviation value and the working characteristic value;
and S104, determining a safety detection result corresponding to the marking threshold interval according to the marking threshold interval in which the safety marking value is positioned.
According to the target granularity of safety detection, battery management data and working characteristic values of different levels can be obtained, wherein the battery management data and the working characteristic values comprise battery management data and working characteristic values of a single battery, a battery pack, a battery cluster, a battery stack or a battery cabin. As a preferred implementation manner, the embodiment of the present disclosure uses battery management data and working characteristic values of the single battery to improve the accuracy of safety detection of the energy storage battery with the most refined granularity. Meanwhile, the single battery is used for detection, so that the physical condition of the bottom battery can be reflected more directly.
The battery management data is battery management system execution algorithm, and the acquired battery related data reflects the physical state of an acquired object in the battery system. The working characteristic value is data reflecting the working environment of the battery system, and is generally acquired by external hardware aiming at the overall working environment of the battery system.
In one embodiment, the battery management data comprises system data, battery pack data and battery cell data;
the system data comprises time data, model data, characteristic data, positioning data, SOC, SOH and/or ambient temperature data;
the battery pack data comprises battery pack charging data, battery pack discharging data and/or battery pack temperature data;
the cell data includes cell charge data, cell discharge data, and/or cell temperature data.
The system data, the battery pack data and the battery monomer data are all based on a battery management system. The time data includes real-time data and/or historical data, and the battery management system is typically accurate to the order of seconds. The model data includes a battery model and/or a cell model. The characteristic data comprises chemical materials of the battery system, the number of the single batteries, the shapes of the single batteries, the cycle times of the single batteries and/or the series-parallel connection mode.
Wherein, the positioning data comprises GPS positioning data or Bluetooth positioning data. Preferably characterized by latitude and longitude.
The battery pack charging data comprises battery pack charging current data and/or battery pack charging voltage data of the battery pack, and the battery pack discharging data comprises battery pack discharging current data and battery pack discharging voltage data of the battery pack.
The battery management data further comprises discharge total current data, discharge total voltage data, charge total current data and charge total voltage data of the battery system.
The single battery charging data comprises single charging current data and/or single charging voltage data of the single battery, and the single battery discharging data comprises single discharging current data and single discharging voltage data of the single battery.
The temperature data of the battery pack represents the temperature of the whole battery pack, and the temperature data of the single battery represents the temperature of the single battery.
The battery management data is original acquisition data, and the battery management data further comprises data obtained by performing further operation according to the original acquisition data. Based on this, it is preferable to use battery management data obtained by performing further operations on the raw collected data to reduce the computational complexity of the subsequent management offset value. And performing further operation according to the original acquired data to obtain battery management data which is convenient to compare, so that the difference between the battery management data and the preset management data is determined conveniently.
As a preferred embodiment, the battery management data includes a cell voltage, a cell voltage maximum difference value, a cell temperature, a cell maximum temperature difference value, a cell temperature rise rate value, and a battery pack key alarm frequency.
The battery management data in the preferred embodiment is data obtained by performing further operations on the raw collected data, and the data dimension is on the single battery level.
The main body for collecting the working characteristic value is external hardware, and the external hardware comprises measuring equipment such as a sensor and a measuring instrument.
Among them, the battery management system requires a plurality of external hardware for cooperation when performing the operation. On the premise of not increasing the burden and cost of external hardware, the characteristic values acquired by the sensors are preferably adopted in the embodiment of the disclosure.
In one embodiment, the operating characteristic value includes a characteristic value collected by a displacement sensor, a characteristic value collected by a gas sensor and/or a characteristic value collected by a sound sensor.
The displacement sensor is used for acquiring deformation data of the battery cell packaging body and the pressure release valve, and the acquired data is used as the distance (nanometer and millimeter) of an object to judge whether bulges exist or not; the gas sensor is used for acquiring whether the battery produces methane, carbon monoxide, carbon dioxide and other gases, and acquiring data that the gases reach a certain concentration (ppm) and are possibly triggered to fire or explode at a certain temperature together with oxygen in the air; the sound sensor is used for acquiring whether the battery core generates noise outside normal work or not, and the acquired data are the sound intensity value (dB) and the frequency (Hz) of the object. And converting the data acquired by the three sensors into values with consistent characterization standards as working characteristic values.
The system adopts three sensors, namely a displacement sensor, a gas sensor and a sound sensor, and belongs to hardware equipment which is configured in advance in a battery management system. The embodiment of the disclosure adopts the data collected by the three sensors to calculate the working characteristic value, does not need to additionally configure more hardware, and is beneficial to controlling the hardware cost.
Based on the method, the matched preset management data is searched according to the battery management data. The matching of the preset management data and the battery management data can be based on the pairing of inherent information such as a battery model, a battery cell model or a battery identification code. Management data is preset as empirical data, and is determined by pre-training acquisition. The pre-training determines battery management data when the normal battery operates as preset management data.
As a preferred embodiment, for N (N is more than or equal to 3) batteries of the same type, acquiring battery pack data and battery monomer data through a battery terminal BMS (battery management system) in laboratories at different temperatures, different SOCs (system on chip) and different discharging depths, wherein the battery pack data acquisition comprises the following steps: time data (real-time and historical, accurate to seconds), model data (comprising a battery model and a cell model), characteristic data (comprising chemical materials, the number of single batteries, the shapes of the single batteries, the cycle times of the single batteries and a series-parallel connection mode), positioning data (comprising longitude and latitude), SOC, SOH, charging current data and voltage data (comprising total voltage and current data of a battery pack, total current data and voltage and current data of each single battery), discharging current data and voltage data (comprising total voltage and current data of the battery pack, total current data and voltage and current data of each single battery), and temperature data (comprising environment data, total temperature data of the battery pack and each single battery). Collecting data collected by auxiliary sensors (a displacement sensor, a gas sensor and a sound sensor), wherein the displacement sensor is used for collecting deformation data of the battery cell packaging body and the pressure release valve, and the collected data is the distance (nanometer and millimeter) of an object to judge whether bulges exist or not; the gas sensor is used for acquiring whether the battery generates methane, carbon monoxide, carbon dioxide and other gases, and acquiring data which is the concentration (ppm) of the object, wherein the gases reach a certain concentration and are combined with oxygen in the air, and a fire or explosion can be triggered at a certain temperature; the sound sensor is used for acquiring whether the battery core generates noise outside normal work or not, and the acquired data are the sound intensity value (dB) and the frequency (Hz) of the object.
The charge and discharge data of the above N cells at different SOC from 0% to 100% and different cycle number in the laboratory were modeled, for example: and establishing a model corresponding to the SOC by taking different cycle times as coordinate axes, wherein the model comprises each single battery voltage Vig, the maximum difference Vg of all single battery voltages, each single battery temperature Tig, the maximum temperature difference Tg of all single batteries, the temperature rise rate value Tir of each single battery and the key alarm time value Ab of the battery pack. In the application process, the laboratory data as a training set accounts for 80% of all data, the verified battery data as a testing set accounts for 20% of all data, and algorithms corresponding to the testing set comprise random forests and decision trees. Based on the preset management data, the preset management data of each type of battery is obtained and used as a basis for matching the battery management data. Based on the preset management data, the preset management data of each type of battery is obtained and used as a basis for matching the battery management data.
The deviation values are managed in step S102 to reflect the difference between the battery system and the battery in the ideal situation of the laboratory, and the greater the difference, the greater the safety risk that represents the occurrence of the battery.
And summing the management deviation value of the BMS characteristics representing the battery and the working characteristic value representing the working environment of the battery, and overlapping the BMS characteristics and the working characteristics to be used as the expression of safety detection to obtain a safety mark value.
Different safety mark values correspond to different safety detection results, and the safety detection results are classified according to the mark threshold interval in order to facilitate the detection expression of the safety mark values.
In one embodiment, fig. 3 is a flowchart of a method for detecting safety of an energy storage battery according to another embodiment, and as shown in fig. 3, a process of obtaining a management deviation value according to a difference between battery management data and preset management data in step S102 includes steps S200 and S201:
s200, correcting the difference value between the battery management data and the preset management data through correction; wherein, the correction quantity is in one-to-one correspondence with the battery management data;
and S201, acquiring a management deviation value according to the product of the difference value and the correction weight.
To better explain step S200 and step S201, battery estimation criteria are established with different model specifications and characteristic data. The difference value of the battery management data and the preset management data is combined with the correction amount and the correction weight to calculate a management deviation value, and the management deviation value is as follows:
acquiring a difference value between the battery management data and preset management data: each single cell voltage delta Vig (1-n), the maximum difference value delta Vg (1-n) of all single cell voltages, each single cell temperature delta Tig (1-n), the maximum temperature difference value delta Tg (1-n) of all single cells, the temperature rise rate value delta Tir (1-n) of each single cell, the key alarm times value delta Ab of the battery pack, the correction weight W (1-n) and the correction quantity D (1-n) are combined to obtain the following formula:
S＝(△V ig1 +D 1 )*W1+(△V ig2 +D 1 )*W 1 +……(△V ign +D 1 )*W 1 +(△
V g +D 2 )*W 2 +(△T ig1 +D 3 )*W 3 +(△T ig2 +D 3 )*W 3 +……(△T ign +D 3 )*W 3 +(△T g +D s4 )*W 4 +(△T ir1 +D s5 )*W5+(△T ir2 +D s5 )*W5……(△T irn +D 5 )*W5+(△A b +D6)*W6
where S represents a security marker value.
Meanwhile, combining the working characteristic values: and (3) assisting the sensor data displacement value M, the gas induction value G and the sound induction value V to obtain a final safety mark value S, which is as follows:
S＝(△Vig1+D1)*W1+(△Vig2+D1)*W1+……(△Vign+D1)*W1+(△Vg+D2)*W2+(△Tig1+D3)*W3+(△Tig2+D3)*W3+……(△Tign+D3)*W3+(△Tg+Ds4)*W4+(△Tir1+Ds5)*W5+(△Tir2+Ds5)*W5……(△Tirn+D5)*W5+(△Ab+D6)*W6+(M+D7)*W7+(G+D8)*W8+(V+D9)*W9
in one embodiment, the correction weight is 0.01-0.03, and the correction amount is-0.02- + 0.02. According to actual calculation, the setting of the correction weight and the correction quantity can distribute the safety mark values of various battery systems in four mark threshold value intervals, and effectively distinguish the safe operation of the battery.
Based on this, the cloud server completes one inference of the security token value. In one embodiment, the cloud server compares the data of the current battery with the instance model according to the cloud computing performance, computing power and network condition conditions to obtain a comparison result. According to the condition that the battery BMS data depends on the network and the acquisition and uploading period, the task time interval of comparing the cloud battery data with the model is dynamically regulated and controlled so as to reduce the server pressure of cloud computing. Example (c): the data acquisition time of the group A battery is 10S/packet, the data acquisition time of the group B battery is 30S/packet, the time interval between the group A cloud battery data and the model comparison task is more than 10S, and the time interval between the group B cloud battery data and the model comparison task is more than 30S, so that the cloud computing power is not wasted, the occupation of the computing power resource of the server is reduced, and the safety detection of the deployment of multiple battery systems is facilitated.
And after the safety mark value is determined, determining a corresponding safety detection result according to the mark threshold interval where the safety mark value is located.
In one embodiment, corresponding to the preferred implementation of step S200 and step S201, the marking threshold interval includes four intervals: 0 to 5;5-10;10-20 parts of; is greater than 20. And defining the corresponding battery system as a corresponding safety detection result according to the marked threshold interval where the safety mark value is located. It is essential that the setting of the flag threshold interval is adaptively adjusted according to the selected battery management data. In the marked threshold interval of the embodiment of the present disclosure, the corresponding battery management data includes a cell voltage, a cell voltage maximum difference, a cell temperature, a cell maximum temperature difference, a cell temperature rise rate value, and a battery pack critical alarm frequency, and the correction weight and the correction amount range correspond to those of the preferred embodiment.
In one embodiment, as shown in fig. 3, a method for detecting safety of an energy storage battery in another embodiment includes step S202:
and S202, executing corresponding safety processing according to the safety detection result.
Different security processes are executed according to the directionality of the security detection result. The security processing includes information processing or control processing, and may depend on the processing capability of the execution subject of the embodiment of the present disclosure, or depend on the processing capability of a third party execution subject.
In one embodiment, the marking threshold interval comprises four intervals (Y0-Y3): y0 is 0 to 5; y1:5-10; y2 is 10-20; y3 > 20. Example (c): when the security mark value S belongs to Y0, no processing is carried out; when the safety mark value S belongs to Y1, the cloud server sends a mark value and detection data to a battery manufacturer corresponding to the battery system, and sends a use suggestion to a battery owner of the battery system; and when the safety mark value S belongs to Y3, the cloud server sends a mark value and detection data to a battery manufacturer corresponding to the battery system, sends a warning and use suggestion to a battery owner, and sends a warning and service suggestion to technical and operation and maintenance personnel. Each user can see the full life cycle record corresponding to the battery system, including information such as battery delivery time, SOH, battery abnormity, alarm, processing result and the like, so as to assist the user in judging. Based on the method, the safety detection result is visualized, and a corresponding safety detection function is realized.
In the safety detection method for the energy storage battery in any embodiment, the battery management data and the working characteristic value are acquired, and the matched preset management data is searched according to the battery management data. Acquiring a management deviation value according to the difference between the battery management data and preset management data, and acquiring a safety mark value according to the sum of the management deviation value and the working characteristic value; and finally, according to the marking threshold interval where the safety marking value is positioned, determining a safety detection result corresponding to the marking threshold interval. Based on this, compare traditional safety inspection mode, the information that the safety inspection result of this application was based on is more comprehensive, can reflect the physical condition of battery effectively and improve the detection accuracy.
At least one embodiment of this disclosure still provides an energy storage battery safety inspection device.
Fig. 4 is a schematic block diagram of an embodiment of a safety detection device for an energy storage battery, and as shown in fig. 4, the safety detection device for an energy storage battery according to an embodiment includes:
an information acquisition module 100, configured to acquire battery management data and a working characteristic value;
the information matching module 101 is used for searching matched preset management data according to the battery management data;
the deviation calculation module 102 is configured to obtain a management deviation value according to a difference between the battery management data and preset management data;
the mark calculation module 103 is used for obtaining a safety mark value according to the sum of the management deviation value and the working characteristic value;
and the result output module 104 is configured to determine a security detection result corresponding to the marked threshold interval according to the marked threshold interval where the security mark value is located.
According to the energy storage battery safety detection device, the information acquisition module acquires the battery management data and the working characteristic value, and the information matching module searches the matched preset management data according to the battery management data. The deviation calculation module obtains a management deviation value according to the difference between the battery management data and preset management data, and the mark calculation module obtains a safety mark value according to the sum of the management deviation value and the working characteristic value; and finally, the mark calculation module determines a safety detection result corresponding to the mark threshold interval according to the mark threshold interval in which the safety mark value is positioned. Based on this, compare traditional safety inspection mode, the information that the safety inspection result of this application was based on is more comprehensive, can reflect the physical condition of battery effectively and improve the detection accuracy.
At least one embodiment of the present disclosure also provides a data detection apparatus. Fig. 5 is a schematic block diagram of a data detection apparatus according to at least one embodiment of the present disclosure. For example, as shown in fig. 5, the data detection device 20 may include one or more memories 200 and one or more processors 201. The memory 200 is used to store computer-executable instructions non-transiently; the processor 201 is configured to execute computer-executable instructions, and when the computer-executable instructions are executed by the processor 201, the processor 201 may be configured to execute one or more steps of the energy storage battery safety detection method according to any embodiment of the present disclosure.
For specific implementation and related explanation of each step of the energy storage battery safety detection method, reference may be made to related contents in the above embodiment of the energy storage battery safety detection method, which is not described herein again. It should be noted that the components of the data detection device 20 shown in fig. 5 are only exemplary and not limiting, and the data detection device 20 may have other components according to the actual application.
In one embodiment, the processor 201 and the memory 200 may be in direct or indirect communication with each other. For example, the processor 201 and the memory 200 may communicate over a network connection. The network may include a wireless network, a wired network, and/or any combination of wireless and wired networks, and the present disclosure is not limited as to the type and functionality of the network. For another example, the processor 201 and the memory 200 may communicate via a bus connection. The bus may be a peripheral component interconnect standard (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. For example, the processor 201 and the memory 200 may be disposed on a remote data server side (cloud side) or a distributed energy system side (local side), or may be disposed on a client side (e.g., a mobile device such as a mobile phone). For example, the processor 201 may be a device having data processing capability and/or instruction execution capability, such as a Central Processing Unit (CPU), tensor Processor (TPU), or graphics processor GPU, and may control other components in the data detection apparatus 20 to perform desired functions. The Central Processing Unit (CPU) may be an X86 or ARM architecture, etc.
In one embodiment, memory 200 may comprise any combination of one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. Volatile memory can include, for example, random Access Memory (RAM), cache memory (or the like). The non-volatile memory may include, for example, read Only Memory (ROM), a hard disk, an Erasable Programmable Read Only Memory (EPROM), a portable compact disc read only memory (CD-ROM), USB memory, flash memory, and the like. One or more computer-executable instructions may be stored on the computer-readable storage medium and executed by the processor 201 to implement the various functions of the data detection apparatus 20. Various applications and various data may also be stored in the memory 200, as well as various data used and/or generated by the applications, and the like.
It should be noted that the data detection apparatus 20 can achieve the similar technical effects to the foregoing method for detecting the safety of the energy storage battery, and the repeated descriptions are omitted.
At least one embodiment of the present disclosure also provides a non-transitory computer-readable storage medium. Fig. 6 is a schematic diagram of a non-transitory computer-readable storage medium according to at least one embodiment of the disclosure. For example, as shown in FIG. 6, one or more computer-executable instructions 301 may be stored non-transitory on the non-transitory computer-readable storage medium 30. For example, the computer-executable instructions 301, when executed by a computer, may cause the computer to perform one or more steps of a method of energy storage battery safety detection according to any embodiment of the present disclosure.
In one embodiment, the non-transitory computer readable storage medium 30 may be applied to the data detection apparatus 20, for example, it may be the memory 200 in the data detection apparatus 20.
In one embodiment, the description of the non-transitory computer readable storage medium 30 may refer to the description of the memory 200 in the embodiment of the data detection apparatus 20, and the repeated description is omitted.
For the present disclosure, there are also the following points to be explained:
(1) The drawings of the embodiments of the present disclosure only relate to the structures related to the embodiments of the present disclosure, and other structures may refer to general designs.
(2) Thicknesses and dimensions of layers or structures may be exaggerated in the drawings used to describe embodiments of the present invention for clarity. It will be understood that when an element such as a layer, film, region, or substrate is referred to as being "on" or "under" another element, it can be "directly on" or "under" the other element or intervening elements may be present.
(3) Without conflict, embodiments of the present disclosure and features of the embodiments may be combined with each other to arrive at new embodiments. The above are only specific embodiments of the present disclosure, but the scope of the present disclosure is not limited thereto, and the scope of the present disclosure should be determined by the scope of the claims
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above examples only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the claims. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, and these are all within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A safety detection method for an energy storage battery is characterized by comprising the following steps:
acquiring battery management data and a working characteristic value;
searching matched preset management data according to the battery management data;
acquiring a management deviation value according to the difference between the battery management data and the preset management data;
obtaining a safety mark value according to the sum of the management deviation value and the working characteristic value;
and determining a safety detection result corresponding to the marking threshold interval according to the marking threshold interval in which the safety marking value is positioned.
2. The safety detection method for the energy storage battery according to claim 1, wherein the battery management data comprises system data, battery pack data and battery cell data;
the system data comprises time data, model data, characteristic data, positioning data, SOC, SOH and/or environment temperature data;
the battery pack data comprises battery pack charging data, battery pack discharging data and/or battery pack temperature data;
the battery cell data comprises battery cell charging data, battery cell discharging data and/or battery cell temperature data.
3. The energy storage battery safety detection method according to claim 1, wherein the working characteristic value comprises a characteristic value acquired by a sensor.
4. The safety detection method for the energy storage battery according to claim 2, wherein the battery management data comprises a single battery voltage, a single battery voltage maximum difference value, a single battery temperature, a single battery maximum temperature difference value, a single battery temperature rise rate value and a battery pack key alarm frequency.
5. The safety detection method for the energy storage battery according to claim 3, wherein the working characteristic value comprises a characteristic value acquired by a displacement sensor, a characteristic value acquired by a gas sensor and/or a characteristic value acquired by a sound sensor.
6. The method for detecting the safety of the energy storage battery as claimed in claim 1, wherein the process of obtaining the management deviation value according to the difference between the battery management data and the preset management data comprises the following steps:
correcting the difference value between the battery management data and the preset management data through correction quantity; wherein the correction amount corresponds to the battery management data one to one;
and obtaining the management deviation value according to the product of the difference value and the correction weight.
7. The safety detection method for the energy storage battery according to claim 1, characterized by further comprising the steps of:
and executing corresponding safety processing according to the safety detection result.
8. An energy storage battery safety detection device, characterized by includes:
the information acquisition module is used for acquiring battery management data and working characteristic values;
the information matching module is used for searching matched preset management data according to the battery management data;
the deviation calculation module is used for obtaining a management deviation value according to the difference between the battery management data and the preset management data;
the mark calculation module is used for obtaining a safety mark value according to the sum of the management deviation value and the working characteristic value;
and the result output module is used for determining a safety detection result corresponding to the marking threshold interval according to the marking threshold interval in which the safety marking value is positioned.
9. A data detection apparatus comprising:
one or more memories non-transitory to store computer-executable instructions;
one or more processors configured to execute the computer-executable instructions, wherein the computer-executable instructions, when executed by the one or more processors, implement the energy storage battery safety detection method according to any one of claims 1 to 7.
10. A non-transitory computer-readable storage medium having stored thereon computer-executable instructions which, when executed by a processor, implement the energy storage battery safety detection method of any one of claims 1 to 7.
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