CN115201681A - Lithium battery safety performance detection method and system - Google Patents
Lithium battery safety performance detection method and system Download PDFInfo
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- CN115201681A CN115201681A CN202210736034.1A CN202210736034A CN115201681A CN 115201681 A CN115201681 A CN 115201681A CN 202210736034 A CN202210736034 A CN 202210736034A CN 115201681 A CN115201681 A CN 115201681A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
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Abstract
The invention discloses a method and a system for detecting the safety performance of a lithium battery, which relate to the technical field of battery safety management, wherein the method comprises the following steps: s1: acquiring production index data of a lithium battery to be detected, detecting performance index data of the lithium battery to be detected, and establishing a corresponding relation between the production index and the performance index; s2: constructing a battery performance state evaluation model, and evaluating the performance index by using the evaluation model to obtain an evaluation result; s3: when the evaluation result does not meet the working requirement, detecting the fault index of the lithium battery to be tested, and establishing the corresponding relation among the production index, the performance index and the fault index; according to the invention, the relevant performance indexes of the lithium battery are classified and detected, and when a certain index does not meet the requirement, the following index detection is not carried out, so that the detection efficiency is improved.
Description
Technical Field
The invention relates to the technical field of battery safety management, in particular to a method and a system for detecting the safety performance of a lithium battery.
Background
Currently, a lithium battery is a battery using a nonaqueous electrolyte solution with lithium metal or a lithium alloy as a positive/negative electrode material. Because the chemical characteristics of lithium metal are very active, the lithium metal has very high requirements on the environment in processing, storage and use. With the development of science and technology, lithium batteries have become the mainstream.
However, in the prior art, a plurality of data are generally required to be detected simultaneously in the lithium battery safety performance detection process, the data required to be detected and recorded are not classified, if some important parameters are found to be unqualified in the detection process, all the parameters are required to be detected, the whole detection process is complex, and meanwhile, the comprehensiveness and continuity of the post-processing and utilization of the relevant parameters of the lithium battery are lacked.
Therefore, how to provide a method for detecting the safety performance of a lithium battery, which can solve the above problems, is a problem that needs to be solved urgently by those skilled in the art.
Disclosure of Invention
In view of the above, the invention provides a method and a system for detecting the safety performance of a lithium battery, which perform classification detection on relevant performance indexes of the lithium battery, and when a certain index does not meet the requirement, do not perform the next index detection, thereby improving the detection efficiency; meanwhile, the performance of the lithium battery and whether the lithium battery breaks down or not can be detected respectively, fault information is output, and the lithium battery testing device has higher flexibility and reliability.
In order to achieve the purpose, the invention adopts the following technical scheme:
a lithium battery safety performance detection method comprises the following steps:
s1: acquiring production index data of a lithium battery to be detected, detecting performance index data of the lithium battery to be detected, and establishing a corresponding relation between the production index and the performance index;
s2: constructing a battery performance state evaluation model, and evaluating the performance index by using the evaluation model to obtain an evaluation result;
s3: when the evaluation result does not meet the working requirement, detecting the fault index of the lithium battery to be tested, and establishing the corresponding relation among the production index, the performance index and the fault index;
s4: a battery fault diagnosis network is constructed, and the fault indexes are processed by using the fault diagnosis model to obtain a fault diagnosis result;
s5: and analyzing the fault diagnosis result by using an expert diagnosis model to obtain a maintenance suggestion, and completing performance detection.
Preferably, the specific process of S2 includes:
s21: extracting a characteristic vector from the performance index data, taking the characteristic vector as a fuzzy input set, and taking the evaluation result as a fuzzy output set;
s22: obtaining the relation between the fuzzy output set and the fuzzy input set according to a fuzzy relation matrix, wherein the specific expression is as follows:
B＝A·R
in the formula, B represents a fuzzy output set, a represents a fuzzy input set, and R represents a fuzzy relation matrix.
Preferably, the specific process of S4 includes:
s41: acquiring historical lithium battery fault index data, preprocessing the historical lithium battery fault index data to form a data set, and dividing the data set into a training set and a test set;
s42: building a battery fault diagnosis network, training the battery fault diagnosis model by using the training set, and performing verification by using the verification set to obtain a network corresponding to the minimum loss of the verification set as an optimal battery fault diagnosis network;
s43: and detecting the fault index data of the lithium battery to be detected, preprocessing the fault index data, and detecting and extracting the preprocessed fault index data by using the optimal battery fault diagnosis network to obtain a fault diagnosis result.
Preferably, the production indicators include, but are not limited to: manufacturer, date of manufacture, the performance indicators include, but are not limited to, total voltage, temperature, battery consistency, and entropy heat coefficient, and the failure indicators include, but are not limited to: battery alternating current-direct current internal resistance, discharge overcurrent, insulating property, battery leakage, interface property and charging precision.
Further, the invention also provides a lithium battery safety performance detection system, which comprises:
the data acquisition module is used for acquiring production index data and performance index data of the lithium battery to be detected and establishing an association relationship between the production index data and the performance index data;
the performance evaluation module is used for constructing a battery performance state evaluation model and evaluating the performance indexes by using the evaluation model to obtain an evaluation result;
the judging module is used for detecting the fault index of the lithium battery to be detected when the evaluation result does not meet the working requirement, and establishing the corresponding relation among the production index, the performance index and the fault index;
the fault diagnosis module is used for constructing a battery fault diagnosis network and processing the fault indexes by using the fault diagnosis model to obtain a fault diagnosis result;
and the expert diagnosis module is used for analyzing the fault diagnosis result by utilizing an expert diagnosis model to obtain a maintenance suggestion and finish the performance detection.
According to the technical scheme, compared with the prior art, the method and the system for detecting the safety performance of the lithium battery are provided, the related performance indexes of the lithium battery are classified and detected, and when a certain index does not meet the requirement, the next index detection is not carried out, so that the detection efficiency is improved; meanwhile, the performance of the lithium battery and whether the lithium battery breaks down can be detected respectively, fault information is output, and the flexibility and the reliability are higher.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is an overall flowchart of a lithium battery safety performance detection method according to the present invention;
fig. 2 is a structural schematic block diagram of a lithium battery safety performance detection system provided by the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Referring to the attached drawing 1, the embodiment of the invention discloses a method for detecting the safety performance of a lithium battery, which comprises the following steps:
s1: acquiring production index data of a lithium battery to be detected, detecting performance index data of the lithium battery to be detected, and establishing a corresponding relation between the production index and the performance index;
s2: constructing a battery performance state evaluation model, and evaluating the performance index by using the evaluation model to obtain an evaluation result;
s3: when the evaluation result does not meet the working requirement, detecting the fault index of the lithium battery to be tested, and establishing the corresponding relation among the production index, the performance index and the fault index;
s4: a battery fault diagnosis network is constructed, and fault indexes are processed by using a fault diagnosis model to obtain a fault diagnosis result;
s5: and analyzing the fault diagnosis result by using an expert diagnosis model to obtain a maintenance suggestion, and completing performance detection.
In a specific embodiment, the specific process of S2 includes:
s21: extracting a characteristic vector from the performance index data, taking the characteristic vector as a fuzzy input set, and taking an evaluation result as a fuzzy output set;
s22: obtaining the relation between the fuzzy output set and the fuzzy input set according to the fuzzy relation matrix, wherein the specific expression is as follows:
B＝A·R
in the formula, B represents a fuzzy output set, a represents a fuzzy input set, and R represents a fuzzy relation matrix.
Specifically, R is a fuzzy relation matrix of 5 × 4 orders and represents the influence degree of fuzzy input quantity on an output result, wherein each row of the fuzzy relation matrix is from the fuzzy input set A of various factors of the lithium battery to the fuzzy relation matrix of the fuzzy output set.
In a specific embodiment, the specific process of S4 includes:
s41: acquiring historical lithium battery fault index data, preprocessing the historical lithium battery fault index data to form a data set, and dividing the data set into a training set and a test set;
s42: building a battery fault diagnosis network, training a battery fault diagnosis model by using a training set, and performing verification by using a verification set to obtain a corresponding network as an optimal battery fault diagnosis network when the loss of the verification set is minimum;
s43: and detecting the fault index data of the lithium battery to be detected, preprocessing the fault index data, and detecting and extracting the preprocessed fault index data by using an optimal battery fault diagnosis network to obtain a fault diagnosis result.
Specifically, the battery fault diagnosis network model may be a composite model of a BP neural network and a CNN convolutional neural network.
In a particular embodiment, production metrics include, but are not limited to: manufacturer, date of manufacture, performance indicators including but not limited to total voltage, temperature, battery consistency, and entropy heat coefficient, and failure indicators including but not limited to: battery alternating current-direct current internal resistance, discharge overcurrent, insulating property, battery leakage, interface property and charging precision.
Referring to fig. 2, an embodiment of the present invention further provides a lithium battery safety performance detection system, including:
the data acquisition module is used for acquiring production index data and performance index data of the lithium battery to be detected and establishing an association relation between the production index data and the performance index data;
the performance evaluation module is used for constructing a battery performance state evaluation model and evaluating the performance indexes by using the evaluation model to obtain an evaluation result;
the judging module is used for detecting the fault index of the lithium battery to be detected when the evaluation result does not meet the working requirement, and establishing the corresponding relation among the production index, the performance index and the fault index;
the fault diagnosis module is used for constructing a battery fault diagnosis network and processing fault indexes by using the fault diagnosis model to obtain a fault diagnosis result;
and the expert diagnosis module is used for analyzing the fault diagnosis result by utilizing the expert diagnosis model to obtain a maintenance suggestion and finish the performance detection.
In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (5)
1. A lithium battery safety performance detection method is characterized by comprising the following steps:
s1: acquiring production index data of a lithium battery to be detected, detecting performance index data of the lithium battery to be detected, and establishing a corresponding relation between the production index and the performance index;
s2: constructing a battery performance state evaluation model, and evaluating the performance index by using the evaluation model to obtain an evaluation result;
s3: when the evaluation result does not meet the working requirement, detecting the fault index of the lithium battery to be tested, and establishing the corresponding relation among the production index, the performance index and the fault index;
s4: a battery fault diagnosis network is constructed, and the fault indexes are processed by using the fault diagnosis model to obtain a fault diagnosis result;
s5: and analyzing the fault diagnosis result by using an expert diagnosis model to obtain a maintenance suggestion, and completing performance detection.
2. The lithium battery safety performance detection method according to claim 1, wherein the specific process of S2 includes:
s21: extracting a characteristic vector from the performance index data, taking the characteristic vector as a fuzzy input set, and taking the evaluation result as a fuzzy output set;
s22: obtaining the relation between the fuzzy output set and the fuzzy input set according to a fuzzy relation matrix, wherein the specific expression is as follows:
B＝A·R
in the formula, B represents a fuzzy output set, a represents a fuzzy input set, and R represents a fuzzy relation matrix.
3. The lithium battery safety performance detection method according to claim 2, wherein the specific process of S4 includes:
s41: acquiring historical lithium battery fault index data, preprocessing the historical lithium battery fault index data to form a data set, and dividing the data set into a training set and a test set;
s42: constructing a battery fault diagnosis network, training the battery fault diagnosis model by using the training set, and performing verification by using the verification set to obtain the network corresponding to the minimum loss of the verification set as an optimal battery fault diagnosis network;
s43: and detecting the fault index data of the lithium battery to be detected, preprocessing the fault index data, and detecting and extracting the preprocessed fault index data by using the optimal battery fault diagnosis network to obtain a fault diagnosis result.
4. The lithium battery safety performance detection method of claim 3, wherein the production indexes include but are not limited to: manufacturer, date of manufacture, performance indicators including but not limited to total voltage, temperature, battery consistency, and entropy thermal coefficient, and fault indicators including but not limited to: battery alternating current-direct current internal resistance, discharge overcurrent, insulating property, battery leakage, interface property and charging precision.
5. A lithium battery safety performance detection system is characterized by comprising:
the data acquisition module is used for acquiring production index data and performance index data of the lithium battery to be detected and establishing an association relationship between the production index data and the performance index data;
the performance evaluation module is used for constructing a battery performance state evaluation model and evaluating the performance indexes by using the evaluation model to obtain an evaluation result;
the judging module is used for detecting the fault index of the lithium battery to be detected when the evaluation result does not meet the working requirement, and establishing the corresponding relation among the production index, the performance index and the fault index;
the fault diagnosis module is used for constructing a battery fault diagnosis network and processing the fault indexes by using the fault diagnosis model to obtain a fault diagnosis result;
and the expert diagnosis module is used for analyzing the fault diagnosis result by utilizing an expert diagnosis model to obtain a maintenance suggestion and finish the performance detection.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116404186A (en) * | 2023-06-08 | 2023-07-07 | 西安黄河电子技术有限公司 | Power lithium-manganese battery production system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116404186A (en) * | 2023-06-08 | 2023-07-07 | 西安黄河电子技术有限公司 | Power lithium-manganese battery production system |
| CN116404186B (en) * | 2023-06-08 | 2023-09-19 | 西安黄河电子技术有限公司 | Power lithium-manganese battery production system |
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