CN106557637B - Safety performance evaluation method of energy storage product - Google Patents
Safety performance evaluation method of energy storage product Download PDFInfo
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- CN106557637B CN106557637B CN201611091455.4A CN201611091455A CN106557637B CN 106557637 B CN106557637 B CN 106557637B CN 201611091455 A CN201611091455 A CN 201611091455A CN 106557637 B CN106557637 B CN 106557637B
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Abstract
The invention provides a safety performance evaluation method of energy storage products, which is based on an intelligent sensing technology and is combined with a modern data analysis method, and different sensing technologies are developed to acquire key factor safety data according to the self characteristics of various energy storage products; carrying out sensing tests on related products aiming at different safety factors by means of classification detection; establishing a data model of the influence of a safety factor on the safety of an energy storage product by using modern data analysis methods such as an artificial neural network system, a sparse nuclear correlation vector machine, a hidden Markov model, a linear dynamic system and the like; the safety level of the energy storage product is formulated through simulation analysis, and a safety identification and risk prevention system is formed; and objectively evaluating the safety of the energy storage product.
Description
Technical Field
The invention belongs to the field of energy storage product safety, and particularly relates to a safety performance evaluation method of an energy storage product.
Background
China is the first producing country of the first large energy storage products in the world, and according to statistics, more than 50% of the energy storage products are produced in China all over the world at present, and the energy storage products account for half of the world output. Research and study of the high-industry lithium battery industry institute (GBII) show that market scale of finished lithium battery packs in the fields of new energy automobiles, power grid energy storage, communication base stations and the like in 2012 is increased by 34.6 percent on a same scale; wherein, the market scale of lithium batteries applied to domestic and foreign communication base stations by Chinese enterprises is increased by 100%. Energy storage products are frequently used as electromechanical products in daily work and life of people, in recent years, the chemical structure of the energy storage products is continuously improved, and the chemical performance is continuously improved, for example, the capacity, the discharge capacity and the like of the energy storage products are improved, so that the requirements of technical development of novel power-driven appliances taking the energy storage products as power sources are met, new types and specifications of the energy storage products are continuously generated, and the safety problem of the energy storage products is increasingly highlighted. Due to the characteristics of personal, health, environmental hazard and the like, accidents such as fire explosion, blood lead poisoning, water and soil environmental pollution and the like occur endlessly every year.
Disclosure of Invention
In view of the above, the present invention is directed to a method for evaluating the safety performance of an energy storage product, which is implemented by investigating and screening key safety factors of the energy storage product, acquiring data information of different key safety factors in real time with high sensitivity by using various novel sensing detection technologies, combining self characteristics of various energy storage products, establishing a performance safety identification model for a typical energy storage product by analyzing and fitting existing result information and sensing detection information, and formulating the safety level of the energy storage product by simulation analysis to form a performance safety identification and risk prevention evaluation system.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a safety performance evaluation method of an energy storage product comprises the following steps:
the first step is the screening of key safety factors and the information acquisition of the existing safety detection results:
the method mainly focuses on collecting, sorting and acquiring safety information of various equipment related to energy storage;
the second step is key safety factor sensing data acquisition:
aiming at key safety factors of different energy storage products, different sensing technologies are adopted to collect key factor safety data, and a classified detection means is adopted to carry out sensing tests on related products aiming at different safety factors;
the third step is the establishment of a data analysis and safety identification model:
the method comprises the steps of firstly processing and denoising an experimental result and sensing data of the existing safety detection, then establishing a data model of the influence of a safety factor on the safety of an energy storage product by using modern data analysis methods such as an artificial neural network system, a sparse nuclear correlation vector machine, a hidden Markov model, a linear dynamic system and the like, verifying the reliability of the model by using a k-fold cross verification method, simulating, detecting and analyzing the safety performance of the energy storage product by using the established model, comparing a test result with the existing experimental method, and verifying the efficacy of the model;
the fourth step is simulation detection and risk prevention based on the safety identification model:
the method mainly comprises the steps of establishing a simulation safety experiment database by collecting a large amount of sensing data in the safety test process of the energy storage product, applying a safety identification model to the safety test process of the energy storage product, and early warning and preventing possible risks.
Further, the source of the information in the first step is mainly focused on the following three aspects:
a. classifying the possible safety hazards according to the types of the energy storage products;
b. analyzing and deducing the principle of safety hazard of the energy storage product, and prejudging and evaluating the result and degree of possible hazard;
c. by summarizing the safety test standards of various existing energy storage products, the key attention information and data acquisition method in the experimental process is determined; energy storage products include primary batteries, secondary batteries, and lead-acid batteries.
Further, the safety hazards of the energy storage product in the step a in the first step include deformation, leakage, breakdown, fire and explosion.
Further, the sensing test in the second step comprises the following three tests:
a. current detection: carrying out non-contact measurement through a Hall current sensor;
b. and (3) temperature detection: the detection items related to the temperature change of the battery are subjected to visual sensing analysis through an infrared thermal imaging sensing device, the heat change of the battery is tested, the heat changes of different parts of the battery are visually distinguished through an infrared thermal imaging graph, and the difference of detection results caused by different temperature measuring points is avoided;
c. structural form change: aiming at various items of detection of battery core, such as impact, vibration, collision, free falling and extrusion of heavy objects related to the physical form change of the battery, the detection is carried out by adopting X-ray and ultrasonic sensing, sensing signals are converted into the appearance of the internal and external physical structures of the battery through imaging equipment, and the appearance change of the physical structure of the battery in the process of various detection items is analyzed through continuous sensing data acquisition and conversion, so that the safety performance of the battery is judged.
Furthermore, a first-stage amplifying circuit is added in front of the Hall sensor in the step a in the second step, so that the sensing detection of batteries with different types and capacities is realized.
Further, the data processing method in the third step mainly includes the following steps:
a. by taking the characteristics and parameters of the energy storage product and safety factors in the safety experiment process as model input vectors, training and verifying a large amount of battery safety detection test data of an electromechanical laboratory of the Tianjin office industrial product center in 2007-2012 by using a 3-layer back propagation artificial neural network method, and performing a large amount of iterative operations through SAS software, a data model of the material, technical parameters and various safety factors of the energy storage product influencing the safety performance is established;
b. introducing a correlation vector machine, and establishing a corresponding safety identification model based on a continuous sparse Bayesian learning algorithm;
c. in order to process sensing data which is real-time dynamic measurement data, a hidden Markov model and a linear dynamic system are introduced for the time correlation in the data, and an intelligent model of the influence of the real-time sensing data on the safety is established by using a Viterbi algorithm;
d. in order to further verify the reliability of the three models, the reliability of the models is verified by using a traditional k-fold cross verification method, and the model with the minimum error is selected as a data model of the safety factor influencing the safety of the energy storage product;
e. through a large number of experimental verifications, the consistency of the model prediction result and the actual result is further compared, the model efficacy is evaluated, and the accuracy of the model is improved.
Compared with the prior art, the method for evaluating the safety performance of the energy storage product has the following advantages:
the method is characterized in that key safety factors of energy storage products such as lead-acid storage batteries, secondary power lithium batteries and fuel cells are investigated and screened, various novel sensing detection technologies are utilized, data information of different key safety factors is acquired in real time in a high-sensitivity mode, the characteristics of various energy storage products are combined, a performance safety identification model for typical energy storage products is established through analysis and fitting of existing result information and sensing detection information, the safety level of the energy storage products is formulated through simulation analysis, a performance safety identification and risk prevention evaluation system is formed, the domestic blank is filled, and the method has the advantages of being capable of being applied to all links of production, storage and transportation and management of the energy storage products, and promoting development and industrial upgrading of the energy storage products in China comprehensively.
Detailed Description
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art through specific situations.
The invention mainly aims at the research of battery energy storage products, and the main research objects are as follows
(1) Primary battery: mainly comprises common zinc-manganese batteries, alkaline zinc-manganese batteries, zinc-mercuric oxide batteries, zinc-silver oxide batteries, zinc-air batteries, lithium batteries (lithium-manganese dioxide, lithium-copper sulfide, lithium-carbon fluoride, lithium-lithium sulfur dioxide-thionyl chloride and the like) and the like,
(2) secondary battery: mainly comprises a lithium ion battery (a liquid lithium ion battery and a polymer lithium ion battery), a cadmium-nickel battery, a hydrogen-nickel battery and the like.
(3) Lead-acid storage battery: mainly comprises a lead-acid storage battery for starting; a lead-acid battery for power; fixed valve-regulated sealed lead-acid batteries and others including lead-acid batteries and the like
The invention mainly comprises the following four steps:
the first step is mainly screening safety factors and acquiring the information of the existing safety detection result:
mainly focuses on collecting, sorting and acquiring the safety information of various equipment related to energy storage. The information source is mainly focused on the following aspects:
(1) according to the types of energy storage products, the potential safety hazards (such as deformation, leakage, breakdown, fire, explosion and the like) of the energy storage products are classified respectively;
(2) analyzing and deducing the principle of safety hazard of the energy storage product, and prejudging and evaluating the result and degree of possible hazard;
(3) by summarizing the safety test standards of various conventional energy storage products, the key attention information and data acquisition method in the experimental process is determined.
The second step is key safety factor sensing data acquisition:
aiming at key safety factors of different batteries, different sensing technologies are adopted to collect key factor safety data; and (4) carrying out sensing test on related products aiming at different safety factors by means of classification detection.
Aiming at the existing test modes such as falling, extrusion, needling, excessive charging and discharging and the like, a novel sensing technology (such as Hall element electromagnetic sensing detection, infrared remote sensing temperature test, X-ray physical morphology analysis, ultrasonic damage real-time detection and the like) is adopted to carry out sensing test on factors such as current, voltage, temperature, deformation, damage and the like of a detection target, so that the test accuracy is improved, the test result is quantized, and the consistency of different sensing technologies on the battery safety detection result is researched.
Aiming at different safety factors, a sensing detection method is to be established by different sensing detection modes:
(1) current detection: contactless measurement is carried out by means of a hall current sensor. A primary amplifying circuit can be added to the Hall sensor according to the requirement, and the sensing detection of batteries with different types and capacities is realized.
(2) And (3) temperature detection: through infrared thermal imaging sensing device, carry out visual sensory analysis to the testing item (such as charge-discharge test) that relates to the battery temperature change, not only test battery heat change, through infrared thermal imaging picture, the heat change of the different positions of battery is directly perceived to distinguish moreover, avoids because of the different testing result differences that arouse of temperature measurement point.
(3) Structural form change: the method is characterized in that sensing detection is performed by adopting X rays, ultrasonic waves and the like aiming at various detection items such as weight impact, vibration, collision, free falling, extrusion (electric core) and the like related to the physical form change of the battery, sensing signals are converted into the internal and external physical structure appearances (such as the positions of the anode and the cathode of the lithium battery) of the battery through imaging equipment, and the physical structure appearance change of the battery in the process of various detection items is analyzed through continuous sensing data acquisition and conversion, so that the safety performance of the battery is judged.
The third step is the establishment of a data analysis and safety identification model:
in the stage, firstly, the experimental result and the sensing data of the existing safety detection are processed and denoised. And then establishing a data Model of the influence of the safety factor on the safety of the energy storage product by using modern data analysis methods such as an artificial neural network system (artificial neural network), "a Sparse Kernel Relevance Vector Machine (Sparse Kernel Machine)," a Hidden Markov Model (HMM), "a Linear dynamic system (Linear dynamic system)" and the like. The reliability of the model is verified by a method such as a k-fold cross-validation method. And (3) simulating, detecting and analyzing the safety performance of the energy storage product by using the established model, comparing the test result with the conventional test method, and verifying the efficacy of the model. The main contents comprise:
(1) by taking the characteristics, parameters and safety factors in the safety experiment process of the energy storage product as model input vectors, training and verifying a large amount of battery safety detection test data of an electromechanical laboratory of the Tianjin office industrial product center in 2007-2012 by using a 3-layer (22 x 5 x 1) back-propagation artificial neural network method, and carrying out a large amount of iterative operations through SAS software, a data model of the influence of the material, technical parameters and various safety factors of the energy storage product on the safety performance of the energy storage product is established;
(2) the artificial neural network system method has the problems of 'over learning' and difficult model interpretation in the model establishing process, and has large sample demand. In order to solve the problem of long application time in the training and prediction processes, a relevant vector machine (Relevance vector machine) is introduced, and a corresponding safety recognition model is established based on a continuous Sparse Bayesian learning Algorithm (Sequential Sparse Bayesian learning Algorithm);
(3) as sensing data are mostly real-time dynamic measurement data, a hidden Markov model and a Linear dynamic System (Linear dynamic System) are introduced for the time correlation in the data, and a Viterbi algorithm is used for establishing an intelligent model of the influence of the real-time sensing data on the safety;
(4) in order to further verify the reliability of the three models, the reliability of the models is verified by using a traditional verification method such as k-fold cross-validation and the like, and the model with the minimum error is selected as a data model of which the safety factor influences the safety of the energy storage product;
(5) through a large number of experimental verifications, the consistency of the model prediction result and the actual result is further compared, the model efficacy is evaluated, and the accuracy of the model is improved.
The fourth step is simulation detection and risk prevention based on the safety identification model:
in the stage, a simulation safety experiment database is established mainly by collecting a large amount of sensing data in the safety test process of the energy storage product, and a safety identification model is applied to the safety test process of the energy storage product to early warn and prevent possible risks.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (5)
1. A safety performance evaluation method of an energy storage product is characterized by comprising the following steps: the method comprises the following steps:
the first step is the screening of key safety factors and the information acquisition of the existing safety detection results:
the method comprises the steps of collecting, sorting and obtaining safety information of various devices related to energy storage;
the second step is key safety factor sensing data acquisition:
aiming at key safety factors of different energy storage products, different sensing technologies are adopted to collect key factor safety data, and a classified detection means is adopted to carry out sensing tests on related products aiming at different safety factors;
the third step is the establishment of a data analysis and safety identification model:
firstly, processing and denoising an experimental result and sensing data of the existing safety detection, and then establishing a data model of safety influence by using a modern data analysis method;
the fourth step is simulation detection and risk prevention based on the safety identification model:
establishing a simulation safety experiment database by acquiring a large amount of sensing data in the safety experiment process of the energy storage product, and applying a safety identification model to the safety experiment process of the energy storage product to early warn and prevent possible risks;
the data analysis in the third step includes the contents:
a. training and verifying detection test data by using the characteristics, parameters and safety factors in the safety experiment process of the energy storage product as model input vectors and applying a 3-layer back propagation artificial neural network method, and performing a large amount of iterative operations through SAS software to establish a data model of the influence of the material, technical parameters and various safety factors of the energy storage product on the safety performance of the energy storage product;
b. introducing a relevant vector machine, and establishing a corresponding safety recognition model based on a continuous sparse Bayesian learning algorithm;
c. in order to process sensing data which is real-time dynamic measurement data, a hidden Markov model and a linear dynamic system are introduced for the time correlation in the data, and an intelligent model of the influence of the real-time sensing data on the safety is established by using a Viterbi algorithm;
d. in order to further verify the reliability of the data model, the safety identification model and the hidden Markov model, the reliability of the data model, the safety identification model and the hidden Markov model is verified by using a traditional k-folding cross verification method, and the model with the minimum error is selected as the data model with the influence of a safety factor on the safety of the energy storage product;
e. through a large number of experimental verifications, the consistency of the model prediction result and the actual result is further compared, the model efficacy is evaluated, and the accuracy of the model is improved.
2. The method for evaluating the safety performance of an energy storage product according to claim 1, characterized in that: the information in the first step is mainly focused on the following three aspects:
a. classifying the possible safety hazards according to the types of the energy storage products;
b. analyzing and deducing the principle of safety hazard of the energy storage product, and prejudging and evaluating the result and degree of possible hazard;
c. by summarizing the safety test standards of various conventional energy storage products, the key attention information and data acquisition method in the experimental process is determined.
3. The safety performance evaluation method of an energy storage product according to claim 2, characterized in that: the safety hazards of the energy storage product in the step a in the first step comprise deformation, leakage, breakdown, fire and explosion.
4. The safety performance evaluation method of an energy storage product according to claim 1, characterized in that: the sensing test in the second step comprises the following three detections:
a. current detection: carrying out non-contact measurement through a Hall current sensor;
b. and (3) temperature detection: the method comprises the steps of carrying out visual sensing analysis on a detection project related to the temperature change of an energy storage product through an infrared thermal imaging sensing device, testing the heat change of the energy storage product, visually distinguishing the heat change of different parts of the energy storage product through an infrared thermal imaging graph, and avoiding detection result difference caused by different temperature measuring points;
c. structural morphology change: aiming at various items of weight impact, vibration, collision, free falling and extrusion battery core detection related to the physical form change of the energy storage product, X-ray and ultrasonic sensing detection is adopted, sensing signals are converted into the internal and external physical structure appearance of the energy storage product through imaging equipment, the physical structure appearance change of the energy storage product in the process of various items of detection is analyzed through continuous sensing data acquisition and conversion, and therefore the safety performance of the energy storage product is judged.
5. The safety performance evaluation method of an energy storage product according to claim 4, characterized in that: in the second step, a first-stage amplifying circuit is added in front of the Hall current sensor in the step a, so that the sensing detection of different types and capacity energy storage products is realized.
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| CN110228450B (en) * | 2019-06-06 | 2020-11-13 | 浙江吉利控股集团有限公司 | Electric automobile safety management system and method |
| CN112729700B (en) * | 2019-10-28 | 2023-09-12 | 无锡新迪新能源车业有限公司 | Quick detection device of lithium battery pack |
| CN111006834A (en) * | 2019-12-24 | 2020-04-14 | 汕头大学 | Method for real-time monitoring and evaluation of battery collision damage based on sensor signals |
| CN116150566B (en) * | 2023-04-20 | 2023-07-07 | 浙江浙能迈领环境科技有限公司 | Ship fuel supply safety monitoring system and method thereof |
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