Battery safety monitoring method and system

Abstract

Embodiments of the present disclosure relate to the technical field of battery monitoring, and more particularly, to a battery safety monitoring method and system. The surface morphology of a soft-packaged battery is sensed by a resistive film pressure sensor arranged on the surface of the soft-packaged battery, and the resistance value of the resistive film pressure sensor is changed synchronously according to the sensing result. The resistance value of the resistive film pressure sensor is analyzed according to a preset threshold value. When the analysis result shows that the resistance value exceeds the preset threshold value, the charging and discharging path of the soft-packaged battery is cut off. After the charging and discharging path is cut off, an alarm information is sent, and the charging and discharging path is locked until the battery is replaced and the locking state of the charging and discharging path is released.

Description

Technical Field

[0001] The embodiments of this disclosure relate to the technical field of battery monitoring, and more specifically, the embodiments of this disclosure relate to a battery safety monitoring method and system. Background Technology

[0002] In energy storage systems, pouch batteries are increasingly being used. These systems are used to balance peak and valley loads on the power grid and improve the utilization rate of renewable energy. The safety performance of pouch batteries directly affects the stability and reliability of the energy storage system. During use, pouch batteries can bulge due to internal chemical reactions, overcharging, over-discharging, short circuits, and other reasons. This bulging not only affects the battery's performance and lifespan but can also lead to serious safety accidents such as thermal runaway, fire, or even explosion. Traditional battery monitoring methods often focus only on single indicators such as battery voltage, current, and temperature. While these indicators can reflect some basic battery conditions, they cannot effectively monitor information such as changes in the battery's surface morphology. However, changes in the battery's surface morphology, such as bulging, are often important external manifestations of internal battery problems. Single monitoring indicators cannot detect these potential safety hazards in a timely manner. Summary of the Invention

[0003] In view of this, the present disclosure provides a battery safety monitoring method and system for more accurate safety monitoring of batteries.

[0004] According to a first aspect of this disclosure, a battery safety monitoring method is provided, comprising: The surface morphology of the soft-pack battery is sensed by a resistive thin-film pressure sensor placed on the surface of the soft-pack battery, and the resistance value of the resistive thin-film pressure sensor is changed synchronously according to the sensing result. The resistance value of the resistive thin-film pressure sensor is analyzed according to a preset threshold. When the analysis result shows that the resistance value exceeds the preset threshold, the charging and discharging path of the soft-pack battery is cut off. After the charging and discharging path is cut off, an alarm message is issued and the charging and discharging path is locked until the battery is detected to be replaced and the locked state of the charging and discharging path is released.

[0005] According to a second aspect of this disclosure, a battery safety monitoring system is provided for implementing a battery safety monitoring method as described in any one of the first aspects, comprising: A resistive thin-film pressure sensor is used to be placed on the surface of a pouch battery to sense the surface morphology of the pouch battery and to synchronously change its own resistance value based on the sensing result. The comparison circuit is electrically connected to the resistive thin-film pressure sensor and the charge / discharge path, respectively, and is used to judge the resistance value of the resistive thin-film pressure sensor according to a preset threshold to determine whether to cut off the charge / discharge path.

[0006] The technical solution disclosed herein has the following beneficial effects: This invention utilizes a resistive thin-film pressure sensor to sense the surface morphology of the battery, enabling timely detection of abnormalities such as bulging. It reacts sensitively and analyzes the resistance value through a preset threshold. If the resistance value exceeds the threshold, the charging and discharging path is cut off to prevent further damage to the battery and expansion of danger. An alarm message is issued to remind the user to handle the situation in time. The path is locked to prevent misoperation and is only unlocked when the battery is replaced. Overall, this invention effectively ensures the safety of soft-pack batteries, reduces the risk of accidents such as thermal runaway and fire, and improves the reliability of battery applications. Attached Figure Description

[0007] Figure 1 This illustration shows a schematic diagram of the steps of a battery safety monitoring method in this exemplary embodiment; Figure 2 A schematic diagram of the structure of a battery safety monitoring system in this exemplary embodiment is shown. Detailed Implementation

[0008] To make the objectives, technical solutions, and advantages of this disclosure clearer, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure. Unless otherwise specified, the embodiments and features in the embodiments of this disclosure can be arbitrarily combined with each other. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0009] The term "comprising" and any variations thereof in the specification and claims of this disclosure are intended to cover non-exclusive protection. For example, a process, method, system, product, or apparatus that comprises a series of steps or units is not limited to the steps or units listed, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0010] In this disclosure, there are one or more embodiments; "multiple" refers to two or more. "And / or" describes the relationship between the associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following associated objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0011] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order, sequence, size, or priority. For example, the terms "first dialogue information" and "second dialogue information" in the embodiments of this disclosure are merely used to distinguish different dialogue information. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0012] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, which are schematic illustrations of this disclosure and are not necessarily drawn to scale. Some block diagrams shown in the drawings may be functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in hardware modules or integrated circuits, or in networks, processors, or microcontrollers. Implementations can be carried out in various forms and should not be construed as limited to the examples set forth herein. The features, structures, or characteristics described in this disclosure can be combined in any suitable manner in one or more implementations. Numerous specific details are provided in the following description to give a thorough description of the embodiments of this disclosure. However, those skilled in the art will recognize that one or more specific details may be omitted when implementing the technical solutions of this disclosure, or other methods, components, apparatuses, steps, etc., may be used to replace one or more specific details. It should be noted that in the embodiments of this disclosure, the dissemination and use of data comply with relevant national laws and regulations.

[0013] Please see Figure 1 As shown in the embodiments of this disclosure, a battery safety monitoring method is provided, including: S1: The surface morphology of the soft-pack battery is sensed by a resistive thin-film pressure sensor set on the surface of the soft-pack battery, and the resistance value of the resistive thin-film pressure sensor is changed synchronously according to the sensing result. S2: Analyze the resistance value of the resistive thin-film pressure sensor according to the preset threshold. If the analysis result shows that the resistance value exceeds the preset threshold, the charging and discharging path of the soft-pack battery is cut off. S3: After cutting off the charging and discharging path, an alarm message is issued and the charging and discharging path is locked until the battery is detected to be replaced and the locked state of the charging and discharging path is released.

[0014] In step S1 of the embodiment provided in this disclosure, the central area on the surface of the soft-pack battery is used as the setting area. The resistive thin-film pressure sensor is set in the setting area in a flat and fitted manner, eliminating air bubbles in the middle. The sensor can be fixed by tape or glue, so that the sensor is fixed in the setting area in a non-deformed posture.

[0015] When soft-pack batteries exhibit morphological changes such as bulging, the deformation in the central area is usually more obvious and representative. Placing the sensor in the central area allows for more accurate detection of surface morphological changes in the battery, improving monitoring sensitivity and accuracy. This ensures close contact between the sensor and the battery surface, preventing the sensor from accurately detecting pressure changes due to air bubbles or uneven adhesion. This ensures the sensor can truly reflect the battery's morphological changes. The non-deformable, fixed posture also helps ensure the stability of sensor performance, avoiding errors caused by sensor deformation itself.

[0016] Read the ADC (analog-to-digital converter) value of the soft-pack battery under normal conditions, use the ADC value as a reference value, and reduce the reference value by 15% - 20% to obtain the initial preset threshold. The preset threshold is then adjusted in subsequent press simulation tests.

[0017] The ADC value reflects the sensor's output signal under normal battery conditions. Using this as a benchmark can provide a reference for subsequent judgment of whether the battery is abnormal. Different soft-pack batteries will have different ADC values ​​under normal conditions. By reading the benchmark value of the actual battery, the threshold suitable for the battery can be set more accurately.

[0018] The initial preset threshold is obtained by reducing the benchmark value by a certain percentage. This takes into account that some slight normal fluctuations may occur during battery use. However, when the resistance value drops beyond a certain range, it means that the battery has undergone abnormal morphological changes, such as bulging. The reduction ratio of 15% to 20% is a reasonable range derived from actual testing and experience. It can avoid failing to detect battery abnormalities in time due to setting the threshold too high, and also prevent misjudgment due to setting the threshold too low.

[0019] The press simulation test can simulate abnormal conditions such as battery bulging. The actual test verifies the rationality of the preset threshold, and the threshold is adjusted according to the test results to make the threshold more in line with the actual situation and improve the accuracy of monitoring.

[0020] Resistive thin-film pressure sensors detect changes in the surface morphology of soft-pack batteries in real time. When the battery surface morphology changes, the pressure on the sensor also changes accordingly, resulting in a synchronous change in the sensor's resistance value. The working principle of resistive thin-film pressure sensors is based on the relationship between pressure and resistance. When the battery surface morphology changes, such as bulging, additional pressure is applied to the sensor, causing a change in the structure of the resistive material inside the sensor, which in turn leads to a change in the resistance value. By sensing this change in resistance value in real time, the surface morphology changes of the battery can be indirectly understood, providing a basis for subsequent judgment of whether the battery is abnormal.

[0021] The resistance value of the resistive thin-film pressure sensor is analyzed according to a preset threshold to determine whether the resistance value exceeds the preset threshold. When the analysis result shows that the resistance value exceeds the preset threshold, the charging and discharging path of the soft-pack battery is cut off. After the charging and discharging path is cut off, an alarm message is issued and the charging and discharging path is locked until the battery is detected to be replaced and the locked state of the charging and discharging path is released.

[0022] Preset thresholds are crucial for determining whether a battery is malfunctioning. By comparing real-time resistance values ​​with preset thresholds, it's possible to promptly detect any abnormal changes in the battery that could lead to safety issues. When the resistance value exceeds the preset threshold, it indicates that the battery has developed abnormalities such as bulging. Continuing to charge or discharge the battery will cause further damage or even lead to a safety accident. Disconnecting the charging and discharging path can prevent the battery from continuing to operate in an abnormal state, reducing safety risks. Issuing alarm messages can promptly notify the user of battery malfunctions and remind them to take appropriate measures. Locking the charging and discharging path can prevent the user from restarting the battery without addressing the malfunction, ensuring that the battery will not be used again before replacement and guaranteeing safe use.

[0023] In steps S2 and S3 of the embodiments provided in this disclosure, the resistive thin-film pressure sensor continuously senses changes in the surface morphology of the soft-pack battery and converts these changes into changes in resistance. The monitoring system reads the resistance value data of the resistive thin-film pressure sensor in real time to prepare for subsequent analysis.

[0024] During battery use, if problems such as internal short circuits or thermal runaway occur, it often leads to battery bulging and changes in the battery's surface morphology. Resistive thin-film pressure sensors can convert these surface morphology changes into changes in resistance values. By comparing these changes with preset thresholds, abnormal battery conditions can be detected in a timely manner. When the resistance value exceeds the preset threshold, it indicates that the changes in the battery's surface morphology have exceeded the normal range and pose a safety hazard.

[0025] The monitoring system compares and analyzes the real-time resistance value with a pre-set threshold. This process can be achieved through a comparison circuit, which is electrically connected to the resistive thin-film pressure sensor and the charging / discharging path, and can accurately determine whether the resistance value exceeds the preset threshold.

[0026] Once the resistance value is detected to exceed the preset threshold, immediately cutting off the charging and discharging path can prevent the battery from continuing to charge and discharge under abnormal conditions, prevent further damage to the battery, and reduce the possibility of safety accidents such as thermal runaway, fire, and explosion. For example, when the battery is severely bulging, if charging continues, it will cause the internal pressure of the battery to increase further, accelerating battery damage and the occurrence of dangerous situations. Cutting off the charging and discharging path can effectively prevent this situation from deteriorating and ensure battery safety.

[0027] If the analysis results show that the resistance value exceeds the preset threshold, the comparison circuit will send a control signal to trigger the switching element (such as a relay) in the charging and discharging path, thereby cutting off the charging and discharging path of the soft-pack battery, preventing further current flow, and issuing an alarm message to promptly notify the user that the battery is abnormal, so that the user can take timely measures, such as checking the battery or replacing the battery, to avoid more serious consequences due to failure to detect the problem in time.

[0028] After the charging and discharging path is cut off, the system will activate the alarm unit to issue an alarm message to remind the user that there is a safety risk to the battery. At the same time, the locking unit will lock the charging and discharging path to prevent the soft-pack battery from being restarted without resolving the battery abnormality. The locking state of the charging and discharging path will not be released until the battery is detected to have been replaced.

[0029] Locking the charging and discharging path can prevent users from restarting the battery before resolving the battery malfunction, ensuring that the battery will not be used again before replacement and preventing safety risks caused by continued use of malfunctioning batteries. The lock is only released after the battery is detected to be replaced, ensuring that the new battery can be used normally.

[0030] In one possible implementation, the step of placing the resistive thin-film pressure sensor on the surface of the pouch battery includes: S11: The central area on the surface of the soft-pack battery is used as the setting area, and the resistive thin-film pressure sensor is set in the setting area in a flat and attached manner; S12: Read the ADC value of the soft-pack battery under normal conditions, and set a preset threshold for the resistive thin-film pressure sensor based on the ADC value; S13: Perform a battery bulging pressure simulation test on the resistive thin-film pressure sensor to verify the alarm function.

[0031] In one possible implementation, when the resistive thin-film pressure sensor is placed in the setting area in a flat and fitted manner, eliminating air bubbles, the resistive thin-film pressure sensor is fixed with tape or glue to fix it in the setting area in a non-deformed position.

[0032] In one possible implementation, the ADC value is used as a reference value, and the reference value is reduced by 15%-20% to obtain an initial preset threshold, which is then adjusted in subsequent press simulation tests.

[0033] Measure the dimensions of the soft-pack battery and find the geometric center of the battery surface by calculation or using tools (such as rulers, positioning templates, etc.). Use this as the core to determine a suitable area as the setting area. The size of this area needs to be determined according to the size of the resistive thin-film pressure sensor. Ensure that the sensor can be completely placed within this area. Clean the surface of the setting area to remove dust, oil, and other impurities, ensuring that the surface is clean and flat. Then, carefully place the resistive thin-film pressure sensor on the setting area and fix it with tape or glue. During the fixing process, pay attention to eliminating air bubbles between the sensor and the battery surface to ensure that the sensor is tightly and flatly attached to the battery surface and that the sensor is in a non-deformed position.

[0034] When soft-pack batteries exhibit abnormalities such as bulging, the central area is usually where the deformation is most pronounced. This is because changes in internal pressure diffuse outwards, with the center being most affected. Therefore, placing the sensor in the central area allows for more sensitive and accurate detection of changes in the battery's surface morphology, improving monitoring effectiveness. The degree of contact between the sensor and the battery surface directly affects the accuracy of its detection of surface morphology changes. Uneven contact or the presence of air bubbles can lead to uneven stress on the sensor, causing changes in resistance to not accurately reflect the actual pressure changes on the battery surface, thus affecting the accuracy of the monitoring results. A fixed, non-deformable posture ensures the stability of the sensor's performance, preventing additional resistance changes caused by sensor deformation that could interfere with the assessment of the battery's condition.

[0035] When the soft-pack battery is in a normal state (e.g., normal charge, normal temperature, no charging or discharging operations), an ADC converter is used to connect to a resistive thin-film pressure sensor to convert the analog resistance signal output by the sensor into a digital signal. The corresponding ADC value is read, and the read ADC value is used as a reference value. This value is then reduced by 15% to 20% to obtain an initial preset threshold. For example, if the reference ADC value is 1000, the initial preset threshold obtained after a 15% reduction is 850. In subsequent actual use, the preset threshold can be further fine-tuned according to the specific condition of the battery and the monitoring effect.

[0036] The ADC value under normal conditions reflects the sensor's output when the battery is working normally. Setting a threshold based on this ensures accurate detection of resistance changes when the battery undergoes abnormal deformation. Different soft-pack batteries have different ADC values ​​under normal conditions due to their own characteristics and manufacturing processes. Therefore, using the normal ADC value of an actual battery as a benchmark can improve the accuracy of the threshold setting. Lowering the benchmark value by 15% - 20% is to ensure timely detection of battery abnormalities while avoiding misjudgments due to normal slight fluctuations. During normal use, the surface pressure of the battery may change slightly due to factors such as ambient temperature and slight vibration, causing minor fluctuations in the resistance value. Setting the threshold below the benchmark value by a certain percentage can filter out these normal fluctuations. Only when the resistance value drops below this range is the battery considered abnormal.

[0037] Apply pressure to the resistive thin-film pressure sensor using a suitable tool (such as a simulated bulging mold) similar to that generated when a battery bulges. Gradually increase the pressure to simulate the gradual worsening of the bulge. During the pressure application, closely observe the monitoring system's response to see if an alarm is triggered at the pressure corresponding to the preset threshold. If the alarm function is activated normally, it indicates that the sensor and the entire monitoring system can accurately detect the abnormal condition of the battery. If no alarm is triggered or the alarm timing is not as expected, the preset threshold or the sensor's installation needs to be adjusted.

[0038] The pressure simulation test is a practical test of the entire battery safety monitoring system. By simulating a bulging battery, it verifies whether the resistive thin-film pressure sensor can normally sense pressure changes and convert them into corresponding resistance changes, and whether the monitoring system can accurately determine the battery status and trigger the alarm function based on the preset threshold. If problems with the alarm function are found during the test, such as delayed alarms or false alarms, the preset threshold can be adjusted based on the test results, or the sensor installation can be checked to further optimize the performance of the monitoring system and improve its reliability and accuracy.

[0039] In one possible implementation, the method further includes: sensing the surface temperature of the soft-pack battery using an additional temperature sensor, generating temperature detection information based on the sensing result, converting the resistance value of the resistive thin-film pressure sensor into morphological detection information, and combining the temperature detection information with the morphological detection information to assess the potential risks of the soft-pack battery, so as to dynamically adjust the preset threshold based on the assessment result.

[0040] Select a suitable type of temperature sensor (such as a thermocouple or thermistor) and install it on the surface of the soft-pack battery. The location should be chosen in an area that can better reflect the overall temperature of the battery, such as the center of the battery surface or a corner where heat tends to accumulate. During installation, ensure good contact with the battery surface. Thermal conductive adhesive or other auxiliary tools can be used. The temperature sensor measures the battery surface temperature in real time, converts the temperature signal into an electrical signal (such as voltage or resistance changes), and then uses an ADC (analog-to-digital converter) to convert the analog electrical signal into a digital signal, accurately recording the battery surface temperature at each moment in digital form to form temperature detection information.

[0041] Relying solely on resistive thin-film pressure sensors to monitor battery surface morphology cannot fully reflect the potential risks of the battery. Battery temperature changes are also an important indicator of its health status. For example, internal short circuits, overcharging, and over-discharging can all cause abnormal increases in battery temperature. By adding an additional temperature sensor and combining information from both temperature and morphology, we can gain a more comprehensive and accurate understanding of the battery's actual condition and promptly identify potential safety hazards.

[0042] A resistive thin-film pressure sensor continuously senses changes in the battery surface morphology and outputs corresponding resistance values. The monitoring system periodically collects these resistance data and, based on the characteristic curve of the resistive thin-film pressure sensor (i.e., the pressure-resistance relationship curve), converts the collected resistance values ​​into corresponding pressure values. Then, combining the correlation between battery surface morphology and pressure, the pressure values ​​are further converted into information reflecting the battery surface morphology characteristics, such as whether there are slight bulges and the degree of bulging, thereby generating morphology detection information.

[0043] The temperature and morphology detection information at each time point are arranged chronologically to obtain a temperature detection sequence and a morphology detection sequence, respectively. Each sequence corresponds one-to-one with the battery temperature and morphology at the same point in time. Based on the temperature detection sequence, a pre-established temperature-battery internal condition prediction model is used to predict the battery's internal conditions that might lead to changes in the current temperature, forming a first set of predicted scenarios. Similarly, based on the morphology detection sequence, a second set of predicted scenarios is obtained using a pressure-battery internal condition prediction model.

[0044] Cross-analysis is performed on the first set of hypothetical scenarios and the second set of hypothetical scenarios. For example, if temperature rise and battery bulging occur simultaneously, several comprehensive hypothetical scenarios are generated by analyzing the degree of correlation and possible causal relationship between the two. Based on existing data and experience, each comprehensive hypothetical scenario is assigned a corresponding hypothetical weight. Based on the comprehensive hypothetical scenarios and their hypothetical weights, and with reference to pre-set risk assessment standards (such as the hazard levels corresponding to different scenarios), the potential risks of soft-pack batteries are quantitatively assessed to determine the current risk level of the battery.

[0045] Different detection information is subject to errors or uncertainties due to various factors. By cross-validating temperature detection information and morphology detection information, we can combine the advantages of both and reduce misjudgments caused by a single information source. For example, when the temperature rises but the morphology does not change significantly, morphology detection information can help determine whether this temperature rise is a normal fluctuation. Conversely, when the morphology changes but the temperature is normal, temperature information can also help determine the severity of the morphology change and potential risks.

[0046] Based on different risk levels, corresponding preset threshold correction rules are formulated. For example, when the risk level is high, the preset threshold is appropriately lowered so that the system can detect battery anomalies more sensitively; when the risk level is low, the preset threshold can be appropriately increased to reduce false judgments. According to the battery risk level obtained from the assessment, the preset threshold of the resistive thin-film pressure sensor is numerically corrected by a specified amount according to the correction rules. The corrected threshold will be used for subsequent resistance value analysis and battery status judgment.

[0047] During use, the performance and state of a battery will change continuously. Different operating conditions and usage stages lead to different types and degrees of potential risks. Static preset thresholds cannot adapt to these changes, resulting in failure to detect anomalies in a timely manner in some cases and easy misjudgment in other cases. By dynamically adjusting the preset thresholds based on the results of potential risk assessment, the monitoring system can always maintain high sensitivity and accuracy to battery anomalies, respond to changes in battery state in a timely manner, and improve the safety of battery use.

[0048] In one possible implementation, the step of assessing the potential risks of the soft-pack battery by combining the temperature detection information and the morphology detection information, and dynamically adjusting the preset threshold based on the assessment results, includes: S41: Arrange the temperature detection information and morphology detection information at each time point according to the temporal relationship to obtain the temperature detection sequence and the morphology detection sequence; S42: Based on the temperature detection sequence and the morphology detection sequence, the internal condition of the soft-pack battery is inferred to obtain a first set of inferred conditions and a second set of inferred conditions; S43: Based on the first set of inferred scenarios and the second set of inferred scenarios, cross-validate the internal battery situation of the soft-pack battery to generate several comprehensive inferred scenarios and corresponding inferred weights; S44: Based on the comprehensive inference scenarios and corresponding inference weights, the risk level of the soft-pack battery is assessed to obtain the risk level of the soft-pack battery; S45: Adjust the preset threshold by a specified amount based on the risk level.

[0049] The data acquisition system records the temperature detection information obtained by the temperature sensor and the morphological detection information converted by the resistive thin-film pressure sensor at fixed time intervals (such as per second, per minute). According to the chronological order, these discrete temperature detection information and morphological detection information are arranged into ordered temperature detection sequences and morphological detection sequences, respectively. Data structures such as databases or arrays can be used to store and manage these sequences for convenient subsequent processing.

[0050] The temporal arrangement helps to clearly present the temperature and morphological changes of the battery at different times. Through the ordered sequence, the evolution trend of the battery state over time can be observed intuitively, providing a basis for subsequent analysis and inference. It also facilitates the synchronous analysis and correlation of the two sequences in subsequent steps, ensuring that temperature and morphological information are comprehensively considered at the same point in time, thereby improving the accuracy of the analysis.

[0051] Establish a temperature-battery internal condition prediction model. This model can be built based on historical data, physical principles, or machine learning algorithms. Input the temperature detection sequence into the model, and the model will predict the internal conditions of the battery that may cause these temperature changes, such as internal short circuit, overcharging, poor heat dissipation, etc., based on the temperature change trend (such as sudden rise, sustained high temperature, etc.) and the change magnitude, forming a first set of predicted conditions.

[0052] Similarly, a morphology-battery internal condition prediction model is constructed. The morphology detection sequence is input into the model, and possible internal conditions of the battery, such as electrolyte decomposition and gas production, are predicted based on changes in the battery surface morphology (such as the degree of bulging and the speed of bulging). This yields a second set of predicted conditions.

[0053] By speculating on the internal condition of the battery from different perspectives and using two key indicators, temperature and morphology, we can gain a more comprehensive understanding of what is happening inside the battery. Different battery problems manifest differently in terms of temperature and morphology. Speculating on each indicator can fully explore the information contained in each indicator, providing independent speculative results for subsequent cross-validation. This helps to discover the correlation and mutual verification between different indicators and improves the accuracy of judging the internal condition of the battery.

[0054] The first and second sets of hypothetical scenarios are compared and analyzed to identify cases that are related or corroborate each other. For example, if the temperature detection sequence indicates an internal short circuit in the battery and the morphological detection sequence also shows that the battery has obvious bulging, then these two scenarios can be considered together to generate a more accurate comprehensive hypothetical scenario, such as the battery bulging caused by an internal short circuit. Based on historical data, expert experience, or statistical analysis, each comprehensive hypothetical scenario is assigned a corresponding hypothetical weight. The weight reflects the probability of the scenario occurring. For example, if a certain comprehensive hypothetical scenario occurs frequently in a large number of similar battery failure cases, it can be assigned a higher weight.

[0055] Inferences based on a single indicator have limitations and uncertainties. Cross-validation can combine information from two indicators to reduce the possibility of misjudgment and omission. By comprehensively considering temperature and morphology information, the actual internal condition of the battery can be judged more accurately. Assigning weights to inferences helps to quantify the credibility of each comprehensive inference scenario, providing a more scientific basis for subsequent risk assessment and making the assessment results more objective and reliable.

[0056] Develop risk assessment standards and classify the potential risks of soft-pack batteries into different levels, such as low risk, medium risk, and high risk, based on different comprehensive inference scenarios and their inference weights. Some quantitative indicators, such as the sum of inference weights and the severity of comprehensive inference scenarios, can be set to determine the boundaries of each level. Calculate the risk score of the battery based on the comprehensive inference scenarios and corresponding inference weights, and compare the score with the risk assessment standards to determine the risk level of the soft-pack battery.

[0057] Risk level assessment can intuitively reflect the current safety status of the battery, helping users quickly understand the potential risk level of the battery so as to take corresponding measures. It provides a clear basis for subsequent dynamic correction of preset thresholds. Different risk levels correspond to different correction strategies, making the correction more targeted and reasonable.

[0058] Based on the risk level, preset threshold correction rules are established. For example, when the risk level is high, the preset threshold of the resistive thin-film pressure sensor is appropriately lowered so that the system can detect abnormal changes in the battery more sensitively. When the risk level is low, the preset threshold is appropriately increased to reduce the possibility of false judgment. The current preset threshold is numerically corrected by a specified amount according to the correction rules. After correction, the new preset threshold is applied to subsequent resistance value analysis to determine whether the battery is abnormal.

[0059] The potential risks of a battery can change with usage and internal conditions. Static preset thresholds cannot adapt to these changes. Dynamically adjusting preset thresholds can ensure that the monitoring system always maintains high sensitivity and accuracy to battery anomalies, promptly detects potential risks, and adjusts them according to the risk level. This ensures that the monitoring system can effectively detect anomalies at different risk levels while avoiding frequent false alarms caused by oversensitivity, thus improving the reliability and practicality of the monitoring system.

[0060] In one possible implementation, the total operating time of the pouch battery is recorded to predict the expected lifespan of the pouch battery, and the parameters of a pre-deployed battery intrinsic condition inference algorithm are adaptively adjusted based on the expected lifespan, so as to perform a comprehensive inference of the first inference set and the second inference set through the adjusted battery intrinsic condition inference algorithm.

[0061] Set a timer in the battery management system (BMS) to start timing from the first time the battery is put into use. This timer can be a hardware timer or a software-implemented timing function. It continuously accumulates time during each charging and discharging process of the battery and stores the recorded working time data in the memory of the battery management system to ensure the integrity and traceability of the data. At the same time, the working time data is backed up regularly to prevent data loss.

[0062] Battery performance and condition change with increasing usage time, i.e., batteries undergo aging. The aging process affects the battery's internal structure and chemical reactions, making the battery more prone to various problems. Recording the total operating time and predicting the expected lifespan can incorporate the aging factors into the prediction of the battery's internal condition, making the prediction results more accurate.

[0063] Based on extensive battery test data and practical usage experience, a battery expected lifespan prediction model is established. This model can consider various factors, such as the number of charge and discharge cycles, depth of charge and discharge, and operating temperature. It predicts the expected lifespan of the battery through mathematical formulas or machine learning algorithms. The recorded total operating time and other relevant parameters (such as historical charge and discharge data of the battery, ambient temperature, etc.) are input into the lifespan prediction model, and the model calculates the expected lifespan of the soft-pack battery based on these input data.

[0064] Batteries at different stages of their lifespan will exhibit different failure modes and behaviors. For example, in the early stages of battery use, problems caused by manufacturing defects are more likely to occur; while near the end of their lifespan, issues such as electrode material wear and electrolyte drying become more prominent. By adjusting the parameters of the prediction algorithm according to the expected lifespan, the algorithm can better adapt to the characteristics of the battery at different stages, thereby improving the accuracy and reliability of the prediction.

[0065] Based on different expected lifespan stages, corresponding battery intrinsic condition inference algorithm parameter adjustment rules are formulated. For example, when the battery is close to its expected lifespan, the sensitivity to abnormal battery conditions is increased, and the threshold, weight and other parameters in the inference algorithm are adjusted accordingly. Based on the predicted expected lifespan, the parameters of the pre-deployed battery intrinsic condition inference algorithm are adjusted according to the parameter adjustment rules. The adjusted algorithm can better adapt to the characteristics of the battery at different lifespan stages.

[0066] As batteries age, potential risks gradually increase. The adjusted inference algorithm can more accurately detect abnormal situations at different stages of battery life, promptly identify potential risks, provide a basis for taking corresponding measures, and ensure the safe use of batteries.

[0067] Using the first and second sets of hypothetical scenarios as input, the adjusted battery intrinsic condition prediction algorithm processes this data. Based on the adjusted parameters, the algorithm analyzes and merges the first and second sets of hypothetical scenarios to generate a comprehensive prediction scenario. During the prediction process, the current expected lifespan stage of the battery is considered to more accurately determine its intrinsic condition.

[0068] Accurate comprehensive predictions help in developing more reasonable battery management strategies. For example, when a high risk is predicted for a battery, battery replacement or maintenance can be scheduled in advance to avoid damage to the equipment due to battery failure and improve the reliability and stability of the equipment.

[0069] Please see Figure 2 As shown, this disclosure provides a battery safety monitoring system for implementing a battery safety monitoring method as described in any one of the first aspects, comprising: A resistive thin-film pressure sensor is used to be placed on the surface of a pouch battery to sense the surface morphology of the pouch battery and to synchronously change its own resistance value based on the sensing result. The comparison circuit is electrically connected to the resistive thin-film pressure sensor and the charge / discharge path, respectively, and is used to judge the resistance value of the resistive thin-film pressure sensor according to a preset threshold to determine whether to cut off the charge / discharge path.

[0070] In one possible implementation, an alarm unit is also included to perform alarm processing after the charging and discharging path is cut off.

[0071] In one possible implementation, a locking unit is also included to lock the battery after the charge / discharge path is cut off, so as to prevent restarting the pouch battery.

[0072] In this embodiment, the specific implementation of each module in the above system embodiment is described in the above method embodiment, and will not be repeated here.

[0073] As can be seen from the above, the technical solutions disclosed herein can be implemented as methods, apparatus, systems, computer program products, storage media, electronic devices, etc. Those skilled in the art will understand that various aspects of this disclosure can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be referred to as "circuit," "module," or "system," respectively.

[0074] It should be understood that this disclosure is not limited to the specific methods, steps, or structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. Those skilled in the art will readily conceive of other embodiments based on the specific implementations provided in this disclosure. Therefore, the specific implementations provided in this disclosure are merely exemplary, and the scope and spirit of this disclosure are indicated by the claims, and should cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary technical means in the art not disclosed in this disclosure.

Claims

1. A battery safety monitoring method, characterized in that, include: The surface morphology of the soft-pack battery is sensed by a resistive thin-film pressure sensor placed on the surface of the soft-pack battery, and the resistance value of the resistive thin-film pressure sensor is changed synchronously according to the sensing result. The resistance value of the resistive thin-film pressure sensor is analyzed according to a preset threshold. When the analysis result shows that the resistance value exceeds the preset threshold, the charging and discharging path of the soft-pack battery is cut off. After the charging and discharging path is cut off, an alarm message is issued and the charging and discharging path is locked until the battery is detected to be replaced and the locked state of the charging and discharging path is released.

2. The battery safety monitoring method as described in claim 1, characterized in that, The step of mounting the resistive thin-film pressure sensor on the surface of the pouch battery includes: The central area on the surface of the soft-pack battery is used as the setting area, and a resistive thin-film pressure sensor is set in the setting area in a flat and attached manner. Read the ADC value of the soft-pack battery under normal conditions, and set a preset threshold for the resistive thin-film pressure sensor based on the ADC value; The resistive thin-film pressure sensor was subjected to a battery bulging-induced pressure simulation test to verify the alarm function.

3. The battery safety monitoring method as described in claim 2, characterized in that, When the resistive thin-film pressure sensor is placed in the setting area in a flat and fitted manner, eliminating air bubbles in the middle, the resistive thin-film pressure sensor is fixed with tape or glue to fix the resistive thin-film pressure sensor in the setting area in a non-deformed position.

4. The battery safety monitoring method as described in claim 2, characterized in that, The ADC value is used as a reference value, and the reference value is reduced by 15%-20% to obtain an initial preset threshold. The preset threshold is then adjusted in subsequent press simulation tests.

5. The battery safety monitoring method as described in claim 1, characterized in that, Also includes: The surface temperature of the soft-pack battery is sensed by an additional temperature sensor, and temperature detection information is generated based on the sensing results. At the same time, the resistance value of the resistive thin-film pressure sensor is converted into shape detection information. The potential risks of the soft-pack battery are assessed by combining the temperature detection information and the shape detection information, and the preset threshold is dynamically corrected according to the assessment results.

6. The battery safety monitoring method as described in claim 5, characterized in that, The step of assessing the potential risks of the soft-pack battery by combining the temperature detection information and the morphology detection information, and dynamically adjusting the preset threshold based on the assessment results, includes: The temperature detection information and morphology detection information at each time point are arranged according to their temporal relationship to obtain the temperature detection sequence and the morphology detection sequence. Based on the temperature detection sequence and the morphology detection sequence, the internal condition of the soft-pack battery is inferred to obtain a first set of inferred conditions and a second set of inferred conditions. Based on the first set of inferred scenarios and the second set of inferred scenarios, the internal battery conditions of the soft-pack battery are cross-validated to generate several comprehensive inferred scenarios and corresponding inferred weights. The risk level of the soft-pack battery is assessed based on the comprehensive inference scenarios and corresponding inference weights described above, and the risk level of the soft-pack battery is obtained. The preset threshold is numerically adjusted by a specified amount based on the risk level.

7. The battery safety monitoring method as described in claim 6, characterized in that, The total operating time of the soft-pack battery is recorded to predict its expected lifespan. Based on the expected lifespan, the parameters of the pre-deployed battery intrinsic condition inference algorithm are adaptively adjusted to perform a comprehensive inference of the first and second inference case sets using the adjusted battery intrinsic condition inference algorithm.

8. A battery safety monitoring system, characterized in that, A battery safety monitoring method according to any one of claims 1-6 includes: A resistive thin-film pressure sensor is used to be placed on the surface of a pouch battery to sense the surface morphology of the pouch battery and to synchronously change its own resistance value based on the sensing result. The comparison circuit is electrically connected to the resistive thin-film pressure sensor and the charge / discharge path, respectively, and is used to judge the resistance value of the resistive thin-film pressure sensor according to a preset threshold to determine whether to cut off the charge / discharge path.

9. The battery safety monitoring system as described in claim 8, characterized in that, It also includes an alarm unit to trigger an alarm after the charging / discharging path is cut off.

10. The battery safety monitoring system as described in claim 8, characterized in that, It also includes a locking unit to lock the battery after the charge / discharge path is cut off, so as to prevent the pouch battery from being restarted.

Images