CN119861305B - Lithium battery short circuit diagnosis and detection method and system - Google Patents
Lithium battery short circuit diagnosis and detection method and system Download PDFInfo
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Abstract
The invention belongs to the field of battery short circuit detection, relates to a data analysis technology, and is used for solving the problem that a short circuit detection result cannot be checked by combining risk analysis in the prior art, in particular to a lithium battery short circuit diagnosis detection method and a lithium battery short circuit diagnosis detection system, wherein the lithium battery short circuit diagnosis detection method comprises a diagnosis detection platform which is in communication connection with a short circuit detection module, a risk evaluation module, a factor investigation module, an optimization analysis module and a storage module; the short circuit detection module is used for carrying out short circuit detection analysis on the lithium battery, generating a detection period, dividing the detection period into a plurality of detection periods, marking the lithium battery as a detection object, marking the detection period as a short circuit normal period or a short circuit abnormal period through a short circuit coefficient DL.
Description
Technical Field
The invention belongs to the field of battery short circuit detection, relates to a data analysis technology, and particularly relates to a lithium battery short circuit diagnosis detection method and system.
Background
Lithium battery short circuit refers to the problems that positive and negative electrodes in a battery are in direct contact, electrolyte in the battery is short-circuited, current instantaneously bursts, the battery is out of control, overheating, ignition and the like are caused, and the lithium battery short circuit is a serious safety problem and easily causes risks such as fire and explosion.
The invention patent with the publication number of CN110376530B discloses a device and a method for detecting the short circuit in the battery, wherein in the detection process, data in each process cannot be influenced by working conditions, a good detection effect is achieved, the method can detect the internal short circuit before thermal runaway occurs, so that the damage caused by the thermal runaway is greatly reduced, but the method cannot be combined with risk analysis to verify the short circuit detection result, so that the accuracy of the short circuit detection result cannot be ensured, and meanwhile, lithium battery maintenance optimization cannot be performed according to the analysis result of the short circuit influencing factors.
The application provides a solution to the technical problem.
Disclosure of Invention
The invention aims to provide a short circuit diagnosis and detection method and system for a lithium battery, which are used for solving the problem that the short circuit detection result cannot be checked by combining risk analysis in the prior art;
the technical problem to be solved by the invention is how to provide a lithium battery short circuit diagnosis detection method and system capable of verifying a short circuit detection result by combining risk analysis.
The aim of the invention can be achieved by the following technical scheme:
The lithium battery short circuit diagnosis detection system comprises a diagnosis detection platform, wherein the diagnosis detection platform is in communication connection with a short circuit detection module, a risk assessment module, a factor investigation module, an optimization analysis module and a storage module;
The short circuit detection module is used for carrying out short circuit detection analysis on the lithium battery, generating a detection period, dividing the detection period into a plurality of detection periods, marking the lithium battery as a detection object, acquiring temperature difference data WC and pressure difference data YC of the detection object at the end time of the detection period, and carrying out numerical calculation to obtain a short circuit coefficient DL;
The risk evaluation module is used for performing risk evaluation analysis on the lithium battery, namely respectively acquiring the operation environment smoke concentration and the spark discharge voltage of a detection object through a smoke sensor and an electric spark sensor, respectively marking the maximum values of the operation environment smoke concentration and the spark discharge voltage in a detection period as smoke concentration data and spark data, and marking the detection period as a safe normal period or a safe abnormal period through the smoke concentration data and the spark data;
the factor investigation module is used for carrying out investigation and analysis on short circuit influence factors of the lithium battery;
the optimization analysis module is used for carrying out short circuit optimization analysis on the lithium battery, namely marking detection time periods marked as short circuit normal time periods and safety abnormal time periods as neglected time periods at the end time of the detection period, marking the ratio of the number of the neglected time periods to the total number of the detection time periods as neglected coefficients, and judging whether the detection link needs to be optimized or not through the neglected coefficients.
Further, the acquisition process of the temperature difference data WC and the pressure difference data YC comprises the steps of acquiring a surface temperature value and a circuit voltage value of a detection object in real time in a detection period, respectively marking a maximum value and a minimum value of the surface temperature value of the detection object in the detection period as Wen Gaozhi and a low temperature value, respectively marking a difference value of a high value and the temperature value as the temperature difference data WC of the detection object in the detection period, respectively marking a maximum value and a minimum value of the circuit voltage value of the detection object in the detection period as a high value and a low value, respectively marking a difference value of the high value and the low value as the pressure difference data YC of the detection object in the detection period.
Further, the specific process of marking the detection time period as a short-circuit normal time period or a short-circuit abnormal time period comprises the steps of acquiring a short-circuit threshold DLmax through a storage module, comparing a short-circuit coefficient DL of a detection object in the detection time period with the short-circuit threshold DLmax, judging that the detection object does not have a short-circuit characteristic in the detection time period if the short-circuit coefficient DL is smaller than the short-circuit threshold DLmax, marking the corresponding detection time period as the short-circuit normal time period, judging that the detection object has the short-circuit characteristic in the detection time period if the short-circuit coefficient DL is larger than or equal to the short-circuit threshold DLmax, marking the corresponding detection time period as the short-circuit abnormal time period, generating an inspection analysis signal and sending the inspection analysis signal to a factor inspection module through a diagnosis detection platform.
The specific process of marking the detection time period as the safe normal time period or the safe abnormal time period comprises the steps of acquiring a smoke concentration threshold value and a spark threshold value through a storage module, comparing smoke concentration data and spark data with the smoke concentration threshold value and the spark threshold value respectively, judging that the detection object does not have risk characteristics in the detection time period if the smoke concentration data is smaller than the smoke concentration threshold value and the spark data is smaller than the spark threshold value, marking the corresponding detection time period as the safe normal time period, otherwise, judging that the detection object has risk characteristics in the detection time period, marking the corresponding detection time period as the safe abnormal time period, generating a risk early warning signal and sending the risk early warning signal to a mobile phone terminal of a manager through a diagnosis detection platform.
The factor checking module is used for checking and analyzing short circuit influence factors of the lithium battery, and the specific process comprises the steps of analyzing whether a detection object is mechanically extruded in a short circuit abnormal period, marking the influence factors of the short circuit abnormal period as physical extrusion if the detection object is mechanically extruded in the short circuit abnormal period, acquiring the maximum value of the operation environment temperature value of the detection object in the short circuit abnormal period and marking the maximum value as a ring temperature value if the detection object is not extruded in the short circuit abnormal period, comparing the ring temperature value with a preset ring temperature threshold, marking the influence factors of the short circuit abnormal period as external temperature if the ring temperature value is larger than or equal to the ring temperature threshold, and marking the influence factors of the short circuit abnormal period as overcharge if the ring temperature value is not extruded in the short circuit abnormal period.
The specific process of judging whether the detection link needs to be optimized comprises the steps of comparing an neglect coefficient with a preset neglect threshold, judging that the short circuit detection link needs to be optimized if the neglect coefficient is larger than or equal to the neglect threshold, generating a detection optimizing signal and sending the detection optimizing signal to a mobile phone terminal of a manager, and judging that the short circuit detection link does not need to be optimized if the neglect coefficient is smaller than the neglect threshold, and carrying out deep analysis.
Further, the specific process of the depth analysis comprises the steps of marking a detection time period marked as a short-circuit abnormal time period and a safety abnormal time period as a deep-division time period, obtaining a marking result of an influence factor of the deep-division time period, marking the influence factor of the deep-division time period as physical extrusion, external temperature and the number of times of overcharging and discharging as extrusion data, external temperature data and charging and discharging data respectively, and comparing the extrusion data, the external temperature data and the charging and discharging data in numerical values, wherein if the numerical value of the extrusion data is maximum, a space optimization signal is generated and sent to a mobile phone terminal of a manager, if the numerical value of the external temperature data is maximum, an environment optimization signal is generated and sent to the mobile phone terminal of the manager, and if the numerical value of the charging and discharging data is maximum, a usage optimization signal is generated and sent to the mobile phone terminal of the manager.
A lithium battery short circuit diagnosis and detection method comprises the following steps:
The method comprises the steps of firstly, carrying out short circuit detection analysis on a lithium battery, namely, generating a detection period, dividing the detection period into a plurality of detection periods, and marking the detection periods as short circuit normal periods or short circuit abnormal periods;
step two, performing risk assessment analysis on the lithium battery, namely marking the detection time period as a safe normal time period or a safe abnormal time period;
Thirdly, checking and analyzing short circuit influencing factors of the lithium battery and marking the influencing factors of the short circuit abnormal period as physical extrusion, external temperature or overcharge and discharge;
And fourthly, carrying out short circuit optimization analysis on the lithium battery, namely generating a detection optimization signal, a space optimization signal, an environment optimization signal or a use optimization signal and sending the detection optimization signal, the space optimization signal and the environment optimization signal to a mobile phone terminal of a manager.
The invention has the following beneficial effects:
The short circuit detection module can be used for carrying out short circuit detection analysis on the lithium battery, the operation parameters of the lithium battery in each detection period are collected and calculated in a periodic detection mode to obtain short circuit coefficients, the short circuit probability of the lithium battery is fed back through the short circuit coefficients, and further the detection period is marked differently, so that data support is provided for optimizing the analysis process;
the risk assessment module can be used for carrying out risk assessment analysis on the lithium battery, and the operation safety of the lithium battery is assessed by combining the smoke concentration and the spark discharge voltage of the operation environment, so that an alarm is given out in time when the lithium battery has risk characteristics, and safety accidents caused by short circuits of the lithium battery are avoided;
The factor investigation module can be used for carrying out investigation analysis on short circuit influence factors of the lithium battery, carrying out gradual investigation analysis on external factors causing short circuit when the lithium battery has short circuit characteristics, improving the efficiency of short circuit abnormality treatment, and simultaneously providing data support for the optimization analysis process;
4. the short-circuit optimization analysis can be carried out on the lithium battery through the optimization analysis module, the short-circuit detection result and the safety evaluation result in the detection period are compared and analyzed, an neglect coefficient is generated according to the analysis result, and the optimization necessity of the detection link is evaluated through the neglect coefficient, so that the accuracy of the subsequent short-circuit detection result is improved.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a system block diagram of a first embodiment of the present invention;
Fig. 2 is a flowchart of a method according to a second embodiment of the invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the first embodiment, as shown in fig. 1, the lithium battery short circuit diagnosis and detection system comprises a diagnosis and detection platform, wherein the diagnosis and detection platform is in communication connection with a short circuit detection module, a risk assessment module, a factor investigation module, an optimization analysis module and a storage module.
The short circuit detection module is used for carrying out short circuit detection analysis on the lithium battery, wherein a detection period is generated and divided into a plurality of detection periods, the lithium battery is marked as a detection object, the surface temperature value and the circuit voltage value of the detection object are obtained in real time in the detection period, the maximum value and the minimum value of the surface temperature value of the detection object in the detection period are respectively marked as Wen Gaozhi and a low temperature value, the difference value of the high value and the temperature value is marked as temperature difference data WC of the detection object in the detection period, the maximum value and the minimum value of the circuit voltage value of the detection object in the detection period are respectively marked as a high value and a low voltage value, and the difference value of the high value and the low voltage value is marked as pressure difference data YC of the detection object in the detection period; obtaining a short-circuit coefficient DL of the detection object in a detection period through a formula DL=c1×WC+c2×YC, wherein c1 and c2 are both proportional coefficients, the process of taking the values of c1 and c2 comprises the steps of taking a normal temperature range and a normal voltage range of the detection object, marking a difference value between a maximum value and a minimum value of the normal temperature range as a temperature range value WF, marking a difference value between a maximum value and a minimum value of the normal voltage range as a voltage range value DF, marking a sum value of the temperature range value and the voltage range value as a range marking value FB, obtaining a value of c1 and c2 through the formulas c1=1-WF/FB and c2=1-DF/FB, obtaining a short-circuit threshold DLmax through a storage module, comparing the short-circuit coefficient DL of the detection object in the detection period with the short-circuit threshold DLmax, judging that the detection object does not have a short-circuit characteristic in the detection period if the short-circuit coefficient DL is smaller than the short-circuit threshold DLmax, the method comprises the steps of marking a corresponding detection period as a short-circuit normal period, judging that a detection object has a short-circuit characteristic in the detection period if a short-circuit coefficient DL is larger than or equal to a short-circuit threshold DLmax, marking the corresponding detection period as a short-circuit abnormal period, generating an inspection analysis signal, sending the inspection analysis signal to a factor inspection module through a diagnosis detection platform, acquiring and calculating lithium battery operation parameters in each detection period in a periodical detection mode to obtain a short-circuit coefficient, feeding back the short-circuit probability of the lithium battery through the short-circuit coefficient, and further marking the detection period in a differentiated mode to provide data support for the optimization analysis process.
The risk assessment module is used for carrying out risk assessment analysis on the lithium battery, wherein the smoke sensor and the electric spark sensor are used for respectively acquiring the smoke concentration and the spark discharge voltage of the operation environment of a detection object, the maximum values of the smoke concentration and the spark discharge voltage of the operation environment in the detection period are respectively marked as smoke concentration data and spark data, the smoke concentration threshold value and the spark threshold value are acquired through the storage module, the smoke concentration data and the spark data are respectively compared with the smoke concentration threshold value and the spark threshold value, if the smoke concentration data is smaller than the smoke concentration threshold value and the spark data is smaller than the spark threshold value, the detection object is judged to have no risk characteristic in the detection period, the detection object is marked as a safe normal period, otherwise, the detection object is judged to have the risk characteristic in the detection period, the detection object is marked as a safe abnormal period, a risk early warning signal is generated, and the risk early warning signal is sent to a mobile phone terminal of a manager through the diagnosis detection platform, and the operation safety of the lithium battery is assessed by combining the smoke concentration and the spark discharge voltage of the operation environment, so that an alarm is timely given when the lithium battery has the risk characteristic, and a safety accident due to a short circuit of the lithium battery is avoided.
The factor checking module is used for checking and analyzing short circuit influence factors of the lithium battery, wherein the factor checking module is used for analyzing whether a detection object is mechanically extruded in a short circuit abnormal period, if so, marking the influence factors of the short circuit abnormal period as physical extrusion, if not, acquiring the maximum value of the operation environment temperature value of the detection object in the short circuit abnormal period and marking the maximum value as a ring temperature value, comparing the ring temperature value with a preset ring temperature threshold, if the ring temperature value is greater than or equal to the ring temperature threshold, marking the influence factors of the short circuit abnormal period as external temperature, if not, marking the influence factors of the short circuit abnormal period as overcharging and discharging, and if the lithium battery has a short circuit characteristic, performing gradual checking analysis on the external factors causing short circuit, improving the efficiency of short circuit abnormal processing and simultaneously providing data support for the optimized analysis process.
The optimization analysis module is used for carrying out short-circuit optimization analysis on the lithium battery, namely marking the detection time period marked as a short-circuit normal time period and a safety abnormal time period as neglected time periods at the end time of a detection period, marking the ratio of the number of the neglected time periods to the total number of the detection time periods as neglected coefficients, comparing the neglected coefficients with preset neglected thresholds, judging that a short-circuit detection link needs to be optimized, generating a detection optimization signal and sending the detection optimization signal to a mobile phone terminal of a manager, judging that the short-circuit detection link does not need to be optimized if the neglected coefficients are smaller than the neglected thresholds, carrying out deep analysis, marking the detection time period marked as a short-circuit abnormal time period and the safety abnormal time period at the same time as a deep analysis time period, marking the influence factors of the deep analysis time period as physical extrusion, external temperature and over-charge amplification times, respectively marking the extrusion data, the external temperature data and the charge amplification times, carrying out numerical comparison on the extrusion data, the external temperature data and the charge amplification data, generating a space optimization signal and sending the detection optimization signal to the mobile phone terminal of the manager, judging that the detection optimization signal is needed to be optimized according to the maximum value of the mobile phone terminal, and sending the detection optimization signal to the optimal signal to the mobile phone terminal of the manager, and carrying out the detection signal and the optimal analysis result if the maximum analysis result is compared with the optimal by the mobile phone terminal, and carrying out the detection signal is generated by the optimal analysis terminal and the optimal analysis according to the detection result when the maximum value and the optimal analysis result is compared with the optimal detection terminal, thereby improving the accuracy of the subsequent short circuit detection result.
In a second embodiment, as shown in fig. 2, a method for detecting short circuit diagnosis of a lithium battery includes the following steps:
Generating a detection period, dividing the detection period into a plurality of detection periods, marking the lithium battery as a detection object, acquiring pressure difference data YC and temperature difference data WC of the detection object in the detection period, performing numerical calculation to obtain a short-circuit coefficient DL, and marking the detection period as a short-circuit normal period or a short-circuit abnormal period through the short-circuit coefficient DL;
step two, performing risk assessment analysis on the lithium battery, namely respectively acquiring the operation environment smoke concentration and the spark discharge voltage of a detection object through a smoke sensor and an electric spark sensor, and marking the detection time period as a safe normal time period or a safe abnormal time period through the operation environment smoke concentration and the spark discharge voltage;
Thirdly, checking and analyzing short circuit influencing factors of the lithium battery and marking the influencing factors of the short circuit abnormal period as physical extrusion, external temperature or overcharge and discharge;
And fourthly, carrying out short circuit optimization analysis on the lithium battery, namely generating a detection optimization signal, a space optimization signal, an environment optimization signal or a use optimization signal and sending the detection optimization signal, the space optimization signal and the environment optimization signal to a mobile phone terminal of a manager.
A detection method and a detection system for diagnosing short circuit of a lithium battery are characterized in that a detection period is generated and divided into a plurality of detection periods when the detection period works, the lithium battery is marked as a detection object, differential pressure data YC and differential temperature data WC of the detection object in the detection period are obtained, a short circuit coefficient DL is obtained through numerical calculation, the detection period is marked as a short circuit normal period or a short circuit abnormal period through the short circuit coefficient DL, the operation environment smoke concentration and the spark discharge voltage of the detection object are respectively collected through a smoke sensor and a spark sensor, the detection period is marked as a safe normal period or a safe abnormal period through the operation environment smoke concentration and the spark discharge voltage, short circuit influence factors of the lithium battery are analyzed through inspection, the influence factors of the short circuit abnormal period are marked as physical extrusion, external temperature or overcharge and discharge, and short circuit optimization analysis is conducted on the lithium battery. And generating a detection optimization signal, a space optimization signal, an environment optimization signal or a use optimization signal and sending the detection optimization signal, the space optimization signal, the environment optimization signal or the use optimization signal to a mobile phone terminal of the manager.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (8)
1. The lithium battery short circuit diagnosis and detection system is characterized by comprising a diagnosis and detection platform, wherein the diagnosis and detection platform is in communication connection with a short circuit detection module, a risk assessment module, a factor investigation module, an optimization analysis module and a storage module;
The short circuit detection module is used for carrying out short circuit detection analysis on the lithium battery, generating a detection period, dividing the detection period into a plurality of detection periods, marking the lithium battery as a detection object, acquiring temperature difference data WC and pressure difference data YC of the detection object at the end time of the detection period, and carrying out numerical calculation to obtain a short circuit coefficient DL;
The risk evaluation module is used for performing risk evaluation analysis on the lithium battery, namely respectively acquiring the operation environment smoke concentration and the spark discharge voltage of a detection object through a smoke sensor and an electric spark sensor, respectively marking the maximum values of the operation environment smoke concentration and the spark discharge voltage in a detection period as smoke concentration data and spark data, and marking the detection period as a safe normal period or a safe abnormal period through the smoke concentration data and the spark data;
the factor investigation module is used for carrying out investigation and analysis on short circuit influence factors of the lithium battery;
the optimization analysis module is used for carrying out short circuit optimization analysis on the lithium battery, namely marking detection time periods marked as short circuit normal time periods and safety abnormal time periods as neglected time periods at the end time of the detection period, marking the ratio of the number of the neglected time periods to the total number of the detection time periods as neglected coefficients, and judging whether the detection link needs to be optimized or not through the neglected coefficients.
2. The lithium battery short circuit diagnosis detection system according to claim 1, wherein the acquisition process of the temperature difference data WC and the pressure difference data YC comprises the steps of acquiring the surface temperature value and the circuit voltage value of the detection object in real time in a detection period, respectively marking the maximum value and the minimum value of the surface temperature value of the detection object in the detection period as Wen Gaozhi and the temperature low value, respectively marking the difference value of the high value and the temperature value as the temperature difference data WC of the detection object in the detection period, respectively marking the maximum value and the minimum value of the circuit voltage value of the detection object in the detection period as the high value and the low value, respectively marking the difference value of the high value and the low value as the pressure difference data YC of the detection object in the detection period.
3. The lithium battery short circuit diagnosis detection system according to claim 2, wherein the specific process of marking the detection period as a short circuit normal period or a short circuit abnormal period comprises the steps of acquiring a short circuit threshold DLmax through a storage module, comparing a short circuit coefficient DL of a detection object in the detection period with the short circuit threshold DLmax, judging that the detection object does not have a short circuit characteristic in the detection period if the short circuit coefficient DL is smaller than the short circuit threshold DLmax, marking the corresponding detection period as the short circuit normal period, judging that the detection object has a short circuit characteristic in the detection period if the short circuit coefficient DL is larger than or equal to the short circuit threshold DLmax, marking the corresponding detection period as the short circuit abnormal period, generating an inspection analysis signal and sending the inspection analysis signal to a factor inspection module through a diagnosis detection platform.
4. The lithium battery short circuit diagnosis and detection system according to claim 3 is characterized in that the specific process of marking the detection time period as a safe normal time period or a safe abnormal time period comprises the steps of acquiring a smoke concentration threshold value and a spark threshold value through a storage module, comparing smoke concentration data and spark data with the smoke concentration threshold value and the spark threshold value respectively, judging that a detection object does not have risk characteristics in the detection time period if the smoke concentration data is smaller than the smoke concentration threshold value and the spark data is smaller than the spark threshold value, marking the corresponding detection time period as the safe normal time period, otherwise, judging that the detection object has risk characteristics in the detection time period, marking the corresponding detection time period as the safe abnormal time period, generating a risk early warning signal and sending the risk early warning signal to a mobile phone terminal of a manager through a diagnosis and detection platform.
5. The lithium battery short circuit diagnosis detection system according to claim 4, wherein the factor checking module performs a specific process of checking and analyzing short circuit influence factors of the lithium battery, the specific process comprises the steps of analyzing whether a detection object is mechanically extruded in a short circuit abnormal period, marking the influence factors of the short circuit abnormal period as physical extrusion if the detection object is mechanically extruded in the short circuit abnormal period, acquiring the maximum value of the operation environment temperature value of the detection object in the short circuit abnormal period and marking the maximum value as a ring temperature value if the detection object is not mechanically extruded in the short circuit abnormal period, comparing the ring temperature value with a preset ring temperature threshold, marking the influence factors of the short circuit abnormal period as external temperature if the ring temperature value is greater than or equal to the ring temperature threshold, and marking the influence factors of the short circuit abnormal period as overcharging if the ring temperature value is not equal to the ring temperature threshold.
6. The lithium battery short-circuit diagnosis detection system according to claim 5, wherein the specific process of judging whether the detection link needs to be optimized comprises the steps of comparing an neglect coefficient with a preset neglect threshold, judging that the short-circuit detection link needs to be optimized if the neglect coefficient is larger than or equal to the neglect threshold, generating a detection optimization signal and sending the detection optimization signal to a mobile phone terminal of a manager, and judging that the short-circuit detection link does not need to be optimized if the neglect coefficient is smaller than the neglect threshold, and performing deep analysis.
7. The lithium battery short circuit diagnosis detection system according to claim 6, wherein the specific process of the depth analysis comprises the steps of marking a detection period marked as a short circuit abnormal period and a safety abnormal period as a deep time period, obtaining a result of marking an influence factor of the deep time period, marking the influence factor of the deep time period as physical extrusion, external temperature and the number of times of overcharging and discharging as extrusion data, external temperature data and charging and discharging data respectively, and performing numerical comparison on the extrusion data, the external temperature data and the charging and discharging data, wherein if the numerical value of the extrusion data is maximum, a space optimization signal is generated and sent to a mobile phone terminal of a manager, if the numerical value of the external temperature data is maximum, an environment optimization signal is generated and sent to the mobile phone terminal of the manager, and if the numerical value of the charging and discharging data is maximum, a usage optimization signal is generated and sent to the mobile phone terminal of the manager.
8. The lithium battery short circuit diagnosis and detection method is characterized by comprising the following steps of:
The method comprises the steps of firstly, carrying out short circuit detection analysis on a lithium battery, namely, generating a detection period, dividing the detection period into a plurality of detection periods, and marking the detection periods as short circuit normal periods or short circuit abnormal periods;
Acquiring the smoke concentration and the spark discharge voltage of the operation environment of a detection object through a smoke sensor and an electric spark sensor respectively, marking the maximum values of the smoke concentration and the spark discharge voltage of the operation environment in a detection period as smoke concentration data and spark data respectively, and marking the detection period as a safe normal period or a safe abnormal period through the smoke concentration data and the spark data;
Thirdly, checking and analyzing short circuit influencing factors of the lithium battery and marking the influencing factors of the short circuit abnormal period as physical extrusion, external temperature or overcharge and discharge;
And fourthly, carrying out short circuit optimization analysis on the lithium battery, namely marking the detection time period marked as the short circuit normal time period and the safety abnormal time period as neglect time periods at the end time of the detection period, marking the ratio of the number of the neglect time periods to the total number of the detection time periods as neglect coefficients, judging whether the detection link needs to be optimized through the neglect coefficients, and generating a detection optimization signal, a space optimization signal, an environment optimization signal or a usage optimization signal and sending the detection optimization signal to a mobile phone terminal of a manager.
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