Method and system for monitoring short circuit in battery

Abstract

The invention relates to a method and a system for monitoring short circuit in a battery, which realize real-time monitoring of the short circuit degree in the battery by establishing a battery equivalent model and collecting current and voltage data in operation in real time, improve the timeliness of judgment, and ensure the accuracy of a monitoring result through continuous repeated monitoring. In addition, the monitoring data of the temperature and the battery end voltage are further integrated, the internal power consumption degree of the battery is combined, the severity of the short-circuit fault is accurately evaluated, and a solid data support is provided for formulating a targeted processing plan. The cloud server is adopted to carry out optimization selection on the battery equivalent model, and the criteria of the internal short circuit degree and the internal power consumption degree are flexibly adjusted according to the actual operation condition, so that the accuracy and reliability of judgment are improved. Through the innovative monitoring method and technical means, efficient and accurate monitoring of the short circuit fault in the battery is achieved, and powerful technical guarantee is provided for safe operation and fault processing of the battery.

Description

Technical Field

[0001] The present application belongs to the field of battery management technology, and in particular, relates to a method and system for monitoring short circuits within a battery. Background Art

[0002] With the popularization of mobile electronic devices and the rapid development of new energy vehicles, lithium batteries have been widely used as an efficient and portable energy storage device. However, during the use of lithium batteries, internal short circuits may sometimes occur. Internal short circuits not only lead to a decline in battery performance, but also due to their low predictability, once they occur and are not discovered and handled in time, they may cause thermal runaway of the battery, leading to serious accidents such as fire and explosion.

[0003] At present, most of the detection methods for internal short circuits of lithium batteries use a single parameter for fault judgment. For example, by monitoring the changes in parameters such as battery voltage, current or temperature to determine whether the battery has an internal short circuit, this method is simple and easy to use, but because the occurrence of internal short circuits in batteries is often accompanied by complex changes in multiple parameters, single parameter detection often has the risk of misjudgment. In addition, there are some methods that use multiple characteristic parameters for comprehensive judgment, but because the causes and manifestations of internal short circuits in lithium batteries are diverse, and the performance differences between different batteries are also large, the setting of thresholds often needs to be adjusted according to specific circumstances, which undoubtedly increases the complexity and uncertainty of detection.

[0004] In summary, the existing lithium battery internal short circuit detection methods have the risk of misjudgment or delayed diagnosis, and cannot meet the requirements of accuracy and real-time performance in practical applications. Therefore, developing a new technology that can accurately and quickly determine whether a lithium battery has an internal short circuit is of great significance for improving the safety and reliability of lithium batteries. Summary of the invention

[0005] The purpose of this application is to provide a method for timely and accurately judging a short circuit in a battery, thereby improving the safety and reliability of a lithium battery. The method is implemented by the following technical solutions. The method for monitoring a short circuit in a battery of this application includes: Establish a battery equivalent model and obtain model parameters of the battery equivalent model; Obtain the current parameters and voltage parameters of the battery during operation, and calculate the internal resistance of the battery according to the obtained current parameters and voltage parameters and the battery equivalent model; Determine whether the calculated battery internal resistance exceeds a preset threshold and determine the degree of internal short circuit; The steps of obtaining current parameters and voltage parameters and the judging step are repeated to monitor the degree of short circuit in the battery in real time.

[0006] In one embodiment, the specific steps of calculating the internal resistance of the battery based on the acquired current parameters and voltage parameters include calculating the SOC change and the ampere-hour increment at adjacent moments, and obtaining the short-circuit current and resistance in the battery based on the SOC change and the ampere-hour increment at adjacent moments.

[0007] In one of the embodiments, the method further includes obtaining temperature parameters and comparing the obtained temperature parameters with temperature parameters of adjacent batteries, and / or obtaining battery terminal voltage and comparing the obtained battery terminal voltage with the average of other battery terminal voltages, so as to determine the internal power consumption of the battery, and determine the severity of the internal short circuit fault of the battery according to the degree of internal short circuit and the internal power consumption.

[0008] In one embodiment, a plurality of different types of battery equivalent models are established simultaneously, and each time the degree of internal short circuit exceeding the limit is determined, a determination is performed using each battery equivalent model.

[0009] In one embodiment, different types of battery equivalent models are sorted according to their relevance corresponding to different battery usage conditions, and the battery equivalent model with the highest relevance under different battery usage conditions is selected as a basis for determination.

[0010] In one of the embodiments, a battery equivalent model is established and an internal short circuit is determined through a cloud server, and the determination criteria are adjusted in the cloud server according to different operating conditions of the battery.

[0011] In one of the embodiments, it also includes a graded alarm for the degree of internal short circuit and / or the severity of the internal short circuit fault of the battery.

[0012] The present application further provides a battery internal short circuit monitoring system, which is used in the aforementioned battery internal short circuit monitoring method, comprising: Model management module, used to establish and manage battery equivalent models; A parameter acquisition module is used to obtain the current parameters and voltage parameters of the battery during operation; The determination module is used to calculate the battery internal resistance based on the acquired current parameters and voltage parameters and the battery equivalent model, determine whether the calculated battery internal resistance exceeds a preset threshold, and determine the degree of internal short circuit exceeding the limit.

[0013] In one of the embodiments, the parameter acquisition module also acquires temperature parameters and battery terminal voltage during battery operation.

[0014] In one of the embodiments, the determination module is disposed on a cloud server.

[0015] Compared with the prior art, the present application has the following beneficial effects: the present application establishes a battery equivalent model and obtains the battery internal resistance according to the operating current and voltage, so as to monitor the degree of short circuit in the battery in real time, improve the timeliness of judgment through real-time monitoring, and improve the accuracy of monitoring through uninterrupted repeated execution. The technical solution of the present application further obtains the temperature and the battery terminal voltage, and judges the severity of the short circuit fault based on the internal short circuit and the internal power consumption, so as to provide a basis for accurately providing a treatment plan. The present application also optimizes the model through the cloud server, and can timely adjust the criteria for the degree of internal short circuit and the degree of internal power consumption according to the specific operating conditions, so as to improve the accuracy of judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the method for detecting short circuit in a battery of the present application.

[0017] Figure 2 It is a structural schematic diagram of the battery internal short circuit monitoring system of the present application.

[0018] Description of the accompanying drawings in the specification: 100, battery internal short circuit monitoring system; 110, model management module; 120, parameter acquisition module; 130, determination module. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are described in detail below in conjunction with the accompanying drawings. It is to be understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for ease of description, only some structures related to the present application are shown in the accompanying drawings, rather than all structures. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0020] The terms "including" and "having" and any variations thereof in this application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products or devices.

[0021] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0022] Internal short circuit monitoring provides control over battery performance, health status, and safety. Battery internal short circuit monitoring is of great significance in battery performance evaluation, life prediction, safety assurance, system optimization, and R&D improvements. It is an indispensable part of the battery management field. During the battery internal short circuit monitoring process, the monitoring results need to be accurate and reliable, and the monitoring system needs to work continuously. Battery internal resistance monitoring requires not only the ability to obtain data, but also the intelligent analysis and processing of data. Through the application of algorithms and models, functions such as prediction of battery internal short circuit conditions, fault warning, and optimization suggestions can be realized, thereby improving the intelligence level of battery management. This application provides a battery internal short circuit monitoring method based on relevant needs. Please refer to Figure 1 , Figure 1 Schematic diagram of a method for detecting a short circuit in a battery. The method for detecting a short circuit in a battery includes: Establish a battery equivalent model, obtain the model parameters of the battery equivalent model, first build a mathematical model that can accurately reflect the battery operating status, and then select a specific battery model to understand and predict the battery behavior by establishing the battery equivalent model, providing a theoretical basis for the subsequent internal resistance calculation. The experimental data is obtained through battery experiments, and then the relevant parameters of the battery model are identified based on the experimental data. These parameters may include battery capacity, internal resistance, open circuit voltage, etc. according to different models. The accuracy of these parameters directly determines the accuracy and reliability of the model. Through accurate experimental data, the exact values ​​of the model parameters can be obtained.

[0023] The current parameters and voltage parameters of the battery during operation are obtained, and the internal resistance of the battery is calculated based on the obtained current parameters and voltage parameters and the battery equivalent model. The current parameters and voltage parameters obtained during battery operation are important indicators reflecting the working state of the battery. These parameters are obtained in real time as a basis for calculating the internal resistance of the battery. After the current parameters and voltage parameters are obtained, the battery equivalent model and these parameters are used to calculate the internal resistance of the battery. After the internal resistance is calculated, it is determined whether the battery has an internal short circuit by the internal resistance.

[0024] Determine whether the calculated battery internal resistance exceeds the preset threshold, and determine the degree of internal short circuit. Compare the calculated battery internal resistance with the preset threshold to determine whether the battery has an internal short circuit and the degree of the internal short circuit. By setting a reasonable threshold, accurate identification and judgment of the battery internal short circuit fault can be achieved. When the internal resistance value exceeds the threshold, it can be determined that the battery has an internal short circuit, and the severity of the internal short circuit can be evaluated based on the size of the internal resistance value.

[0025] Repeat the steps of obtaining current parameters and voltage parameters and judging the steps to monitor the degree of internal short circuit of the battery in real time. In order to monitor the degree of internal short circuit of the battery in real time, it is necessary to repeatedly perform the steps of obtaining current parameters and voltage parameters and judging the steps. By continuously acquiring new current and voltage data and calculating the internal resistance of the battery in real time, continuous monitoring of the degree of internal short circuit of the battery can be achieved. The real-time monitoring method can timely discover the changing trend of the internal short circuit of the battery, and provide a timely basis for taking corresponding treatment measures.

[0026] In order to acquire and process data in a timely manner, the battery current, voltage and other parameters can be collected at a high frequency and processed immediately. By increasing the frequency of data collection, the changes in battery status can be captured more accurately, and data can be continuously collected during operation to form a continuous data stream. Since the system can automatically and quickly perform related operations, the interval time for collecting data can be set, and related processing can be set at different times or corresponding trigger conditions.

[0027] It should be noted that the battery model mentioned in the above method can be a known battery model, such as the currently known models such as the second-order RC model, PNGV model, GNL model, P2D model, or a new model developed later that can accurately reflect the internal state of the battery. The specific model is not specifically limited in this application. This application can use any model, and the model can be added, deleted, and selected as needed. The resistance accuracy of the internal short circuit depends on the accuracy of the construction of the battery equivalent model. This application also optimizes how to select the model and how to apply it in further solutions.

[0028] How to accurately obtain the internal short-circuit resistance through the acquired parameters is also an important part of the technical solution. The solution of this application further defines the relevant solution. This application adopts the following method. The specific steps of calculating the internal resistance of the battery according to the acquired current parameters and voltage parameters include calculating the SOC change and ampere-hour increment at adjacent moments, and obtaining the internal short-circuit current and resistance of the battery according to the SOC change and ampere-hour increment at adjacent moments. The specific method is to obtain the current parameters and voltage parameters of the battery during operation from the battery management system. The parameters reflect the actual operating state of the battery under different working conditions. Next, calculate the battery state of charge (SOC) change at adjacent moments, and compare the SOC values ​​at two adjacent moments to obtain the SOC change of the battery during this period. At the same time, calculate the ampere-hour increment at adjacent moments. The ampere-hour increment refers to the amount of electricity charged or discharged by the battery during a period of time. It can be obtained by integrating the current parameters. By calculating the ampere-hour increment at adjacent moments, the change in the amount of electricity of the battery during this period is determined. After obtaining the SOC change and ampere-hour increment at adjacent moments, these data are used to estimate the internal short-circuit current of the battery. By analyzing the relationship between the SOC change and the ampere-hour increment, the magnitude and direction of the internal short-circuit current of the battery are deduced. Using the estimated internal short-circuit current and the known battery voltage parameters, the internal resistance of the battery is calculated according to the battery model and Ohm's law.

[0029] In practical applications, since the internal characteristics of the battery are dynamically affected by multiple factors such as current and temperature, it is difficult for the battery equivalent model to fully cover all situations. Therefore, in order to improve the accuracy of battery internal short circuit detection and reduce the possibility of false alarms, the present application further improves the technical solution. It not only relies on the calculation of the equivalent model, but also combines the real-time voltage and temperature data of the battery for a re-determination process. Through this method, the working state of the battery can be more comprehensively evaluated, thereby more accurately identifying internal short circuit faults. The specific steps include obtaining temperature parameters and comparing the obtained temperature parameters with the temperature parameters of adjacent batteries, and / or obtaining the battery terminal voltage and comparing the obtained battery terminal voltage with the average of other battery terminal voltages, so as to judge the internal power consumption of the battery, and determine the severity of the internal short circuit fault of the battery according to the degree of internal short circuit and the internal power consumption.

[0030] By obtaining temperature parameters and comparing the obtained temperature parameters with the temperature parameters of adjacent batteries, the temperature difference between batteries can be accurately captured. During the operation of the battery, if an internal short circuit occurs, it is usually accompanied by a local temperature increase. By comparing the temperatures of adjacent batteries, it is possible to quickly identify whether there are batteries with abnormal temperatures, thereby preliminarily judging that there may be power consumption or short circuit faults inside the battery. The present application scheme can also be further combined with the monitoring of the battery terminal voltage. By obtaining the battery terminal voltage and comparing it with the mean of other battery terminal voltages, the power consumption of the battery can be further judged. A short circuit inside the battery can cause abnormal internal current of the battery, thereby affecting the stability of the battery terminal voltage. Therefore, by comparing the terminal voltage with the mean, the power consumption state of the battery can be more accurately evaluated, thereby assisting in judging the severity of the short circuit inside the battery.

[0031] The technical solution combining temperature detection and voltage monitoring comprehensively considers the two factors of internal short circuit degree and internal power consumption degree to determine the severity of the battery internal short circuit fault, and uses a comprehensive judgment method to improve the accuracy and reliability of diagnosis, avoiding the misjudgment that may be caused by a single indicator. At the same time, according to the severity of the fault, corresponding treatment measures can be taken, such as timely replacement of the faulty battery or maintenance, so as to ensure the overall safety and stable operation of the battery system.

[0032] Since the accuracy of the battery equivalent model directly affects the accuracy of the judgment, but usually a battery equivalent model is not suitable for all situations, the present application has made further designs for the technical solutions related to the battery equivalent model, aiming to ensure the accuracy of the internal short circuit detection by further optimizing the technical solutions. In the further technical solutions of the present application, multiple different types of battery equivalent models are established at the same time. Each time the degree of internal short circuit exceeds the limit, a judgment is made through each battery equivalent model. By establishing multiple different types of battery equivalent models at the same time, different types of battery equivalent models are used to estimate the internal resistance under different working conditions, which can avoid the influence of different factors such as the internal structure of the battery, material properties, and working environment on the model. The internal resistance obtained by different models can be cross-validated, so that the battery internal short circuit fault can be more accurately identified, and the possibility of false alarms and omissions can be reduced. Each time the degree of internal short circuit exceeds the limit, a judgment can be made through each battery equivalent model. Since different models may be based on different assumptions and theories, their judgment results for internal short circuit faults may be different. By combining the judgment results of multiple models, they can be verified and supplemented with each other, so as to obtain more accurate and comprehensive judgment results.

[0033] In combination with the present application, the steps are repeatedly executed, and the current parameters and voltage parameters are obtained in sequence each time, and the internal resistance is calculated and obtained through a battery equivalent model. Different battery equivalent models are cycled in sequence, and each model will make a judgment based on the new current parameters and voltage parameters. The above calculation process improves the accuracy of the battery internal resistance calculation and improves the accuracy of the internal short circuit fault judgment. At the same time, by continuously optimizing the calculation method and model, the timeliness and accuracy of the judgment are further improved.

[0034] The battery equivalent model of a battery usually cannot completely and accurately cover all situations. The battery may operate at different temperatures and different charge and discharge states, and the battery also has its corresponding life cycle. The model applicable to the battery in the initial state and the battery that has been used for a period of time will also change. Therefore, on the basis of the above technical solution, different types of battery equivalent models are sorted by relevance corresponding to different battery usage conditions, and the battery equivalent model that best suits the current battery usage state is found through data analysis. The usage conditions include but are not limited to the following conditions: different temperatures, different charge and discharge states, and different life cycles. The battery equivalent model with the highest relevance under different battery usage conditions is selected as the basis for judgment.

[0035] This solution can select the most appropriate equivalent model according to the actual use of the battery. Each battery equivalent model has its specific scope of application and conditions, such as temperature range, charge and discharge status, aging degree, etc. Through relevance sorting, it can ensure that the model that best matches the actual situation is selected under different use conditions, thereby improving the accuracy and reliability of the judgment. This flexible way of selecting the basis for judgment enables the solution to adapt to a variety of complex battery usage scenarios. In actual applications, the battery usage conditions may change at any time, such as temperature changes, load changes, etc. By selecting the most appropriate equivalent model according to the changes in conditions, in addition, the solution also embodies the concept of refined management. By subdividing the battery usage conditions and selecting the most matching equivalent model for each condition, more accurate control of the battery status can be achieved, which helps to timely discover potential internal short-circuit faults and take corresponding measures to prevent or deal with them, thereby improving the efficiency and safety of battery management.

[0036] In order to further improve the efficiency of resource utilization and continuously optimize the judgment criteria through big data, this application further establishes the battery equivalent model and internal short circuit judgment through the cloud server, and adjusts the judgment criteria in the cloud server according to the different operating conditions of the battery. Through the cloud server to establish the battery equivalent model and internal short circuit judgment, the centralization and efficiency of data processing are realized. The cloud server has powerful computing power and storage space, which can quickly process a large amount of battery data and establish and optimize the equivalent model in real time, which can not only improve the speed and accuracy of data processing, but also reduce the computing burden of local equipment and improve the overall performance of the system. Secondly, according to the different operating conditions of the battery, the judgment criteria are adjusted in the cloud server, which reflects the flexibility and adaptability of the scheme. The operating conditions of the battery will change with the changes of time, environment and usage, such as temperature, humidity, charge and discharge status, etc. Through the cloud server, the operating data of the battery can be obtained in real time, and the judgment criteria can be adjusted according to different conditions. This dynamic adjustment method can better adapt to the changes in the actual operating conditions of the battery and improve the accuracy and timeliness of fault detection.

[0037] In a preferred technical solution, the battery life cycle is managed in combination, and the large amount of data obtained is used to determine at which stage of the battery life cycle or in which usage state failure is likely to occur. For life cycle stages and usage states with low probability, the judgment frequency is automatically reduced, while for stages or usage states that are prone to internal short circuits, the judgment frequency is automatically increased, and at the same time, the battery equivalent model of the current stage or usage state is adapted. By adopting the above technical solution, it is possible to reduce the resource configuration requirements at the user end on the basis of accurately detecting internal short circuits in the battery, and the model deployment and optimization criteria based on conditions can be carried out flexibly.

[0038] In addition, the use of cloud servers for the establishment of battery equivalent models and internal short circuit determination also facilitates data sharing and collaboration. Cloud servers can connect multiple battery management systems to achieve centralized storage and sharing of data, and can achieve resource utilization through the same system. There is no need to configure the corresponding modules at a single user end, avoiding idle resources at the user end. In addition, data between different systems can be mutually borrowed and supplemented to improve the intelligence level of the entire battery management system. Cloud servers can also provide remote monitoring and management functions, which facilitate users to monitor and troubleshoot battery status in real time. Since cloud servers can simultaneously obtain relevant data of batteries, when a certain influencing factor causes the data to be judged to change, it can be simultaneously known whether other batteries have undergone corresponding changes. If consistent changes occur, the cloud server can determine that the relevant batteries have not undergone abnormalities, thereby reducing the possibility of false alarms. Specifically, changes in temperature may cause changes in the internal resistance of the acquired battery. If the cloud server finds that the same or similar batteries have also undergone the same changes, it can be shown that the change in the internal resistance of the battery is normal, which can greatly improve the accuracy of the system.

[0039] Specifically, it also includes graded alarms for the degree of internal short circuit and / or the severity of the battery internal short circuit fault. The graded alarm function enables the battery management system to issue different levels of alarms according to different fault levels. This means that when the system detects a minor internal short circuit or fault, a low-level alarm will be issued to remind the operator to pay attention and conduct a preliminary inspection. When a serious internal short circuit or fault is detected, the system will issue a high-level alarm and even trigger an emergency handling mechanism to ensure the safety of the battery system. The graded alarm also helps the operator to take corresponding handling measures according to the level of the alarm. For low-level alarms, the operator may only need to perform simple inspections and maintenance; for high-level alarms, emergency measures need to be taken immediately, such as cutting off the power supply, replacing the faulty battery, etc., to prevent the fault from further expanding or causing more serious consequences. The graded alarm function can also be linked with other systems or equipment to achieve more comprehensive safety management. For example, when the battery management system issues a high-level alarm, it can automatically trigger the fire protection system or emergency shutdown system to minimize the impact of the fault on equipment and personnel.

[0040] The present application further provides a battery internal short circuit monitoring system 100, see Figure 2 , Figure 2 : is a schematic diagram of the structure of the battery internal short circuit monitoring system 100 of the present application. The battery internal short circuit monitoring system 100 is used for the aforementioned battery internal short circuit monitoring method, including: The model management module 110 is used to establish and manage the battery equivalent model; the model management module 110 is responsible for the establishment and management of the battery equivalent model. According to the characteristics of the battery structure, material, working environment, etc., the battery equivalent model stored or acquired autonomously is used to construct an equivalent model that can accurately reflect the battery performance, taking into account the static characteristics of the battery and the dynamic change process. In addition to model establishment, the model management module 110 is also responsible for maintaining and updating the existing model. With the continuous advancement of battery technology and changes in battery usage conditions, the original model may need to be adjusted or optimized. This module can automatically or manually update the model to ensure that it is always consistent with the actual situation.

[0041] The parameter acquisition module 120 is used to obtain the current parameters and voltage parameters of the battery during operation; the parameter acquisition module 120 is responsible for real-time acquisition of the current parameters and voltage parameters of the battery during operation, and can obtain real-time data of the battery during operation by connecting to the battery management system or sensor. The module has high sensitivity and accuracy, and can quickly capture changes in the battery status. It also needs to have strong data processing capabilities, and can pre-process and screen the collected data to remove noise and interference, and ensure the reliability and validity of the data.

[0042] The determination module 130 is used to calculate the internal resistance of the battery according to the acquired current parameters and voltage parameters and the battery equivalent model, determine whether the calculated internal resistance of the battery exceeds the preset threshold, and determine the degree of internal short circuit exceeding the limit. The determination module 130 calculates the internal resistance of the battery according to the acquired current parameters, voltage parameters and battery equivalent model. By comparing with the preset threshold, the module can accurately determine whether the battery has an internal short circuit fault and evaluate its degree of exceeding the limit. The determination module 130 can use advanced algorithms and logical judgment mechanisms, comprehensively consider multiple factors such as temperature, charge and discharge status, etc., and accurately determine the internal short circuit fault of the battery. At the same time, it can also give corresponding alarm levels and processing suggestions according to the severity of the fault, and further connect with related equipment to automatically perform related operations to avoid impact.

[0043] The battery internal short-circuit monitoring system 100 realizes real-time monitoring and accurate judgment of battery internal short-circuit faults through the coordinated work of the model management module 110, the parameter acquisition module 120 and the judgment module 130. It has high sensitivity, accuracy and reliability, and can provide strong technical support for the safe operation and fault handling of the battery.

[0044] Specifically, the parameter acquisition module 120 also acquires the temperature parameters and battery terminal voltage when the battery is running. The parameter acquisition module 120 not only collects the current parameters and voltage parameters of the battery in real time, but also integrates the monitoring of temperature parameters and battery terminal voltage, enriching the dimensions of the battery status data, so that the monitoring system can have a more comprehensive and in-depth understanding of the working condition of the battery. By accurately acquiring the temperature parameters of the battery, the thermal state of the battery can be grasped in real time, and then the heat distribution and heat dissipation inside the battery can be evaluated, providing strong data support for preventing battery overheating and internal short circuits. At the same time, real-time monitoring of the battery terminal voltage is also crucial, which directly reflects the operating voltage and energy state of the battery, and is an important indicator for judging battery performance and safety.

[0045] Through efficient data acquisition and processing technology, various parameters of the battery during operation can be quickly and accurately obtained, providing a reliable data basis for subsequent judgment and analysis, making the battery internal short circuit monitoring system 100 more intelligent and efficient, and can better ensure the safe operation of the battery and extend the service life of the battery. The parameter acquisition module 120 provides more comprehensive and in-depth data support for battery internal short circuit monitoring by acquiring temperature parameters and battery terminal voltage during battery operation, further improving the accuracy and reliability of the monitoring system.

[0046] Specifically, the determination module 130 is arranged on a cloud server. The determination module 130 is arranged on a cloud server. Deploying the determination module 130 on the cloud can make full use of the powerful computing power and efficient data processing capability of the cloud server, which also means that the system can receive and process the battery operation data from the parameter acquisition module 120 in real time, including key parameters such as current, voltage, temperature and battery terminal voltage, so as to quickly and accurately calculate the internal resistance of the battery. The application of the cloud server not only improves the real-time performance of the determination, but also through the centralized management and optimization algorithm of the cloud, the determination module 130 can flexibly adjust the determination criteria according to different battery use conditions, and select the most suitable battery equivalent model for calculation, thereby greatly improving the accuracy of the determination. In addition, the cloud server also has powerful data storage and sharing capabilities, which can easily realize the centralized storage and cross-device sharing of data, and provide strong support for the intelligence and collaborative work of the battery management system. At the same time, through cloud monitoring and management, the battery status can be understood at any time, potential faults can be discovered in time, and corresponding measures can be taken to prevent or deal with them. Setting the determination module 130 on the cloud server not only improves the real-time performance and accuracy of the short circuit monitoring in the battery, but also enhances the intelligence and collaborative work ability of the system.

[0047] Specific embodiments, a specific embodiment is provided below to further illustrate the method of the present application: taking the second-order RC model as an example, firstly, a second-order RC model of a battery is established, experimental data is obtained from a battery experiment, and then the relevant parameters of the battery model are identified according to the experimental data. In the second-order RC model, experimental data is obtained by performing a battery HPPC experiment, and then specific model parameters are identified through parameter identification; Then the battery SOC is estimated in real time according to the Kalman filter algorithm, and then the SOC change ΔSOCi within ΔT time, ΔSOCi = SOCi K -SOCi K-1 ; According to the actual time integration algorithm, calculate the SOC change ΔSOCL within ΔT time, ΔSOCL = I L * ΔT / Q, where Q is the battery capacity; Calculate I from ΔSOCi i , ΔSOCL = I i * ΔT / Q; By simplifying the formula ΔSOCi / ΔSOCL, we can get I i = ΔSOCi / ΔSOCL*I L ; According to Kirchhoff's current law, I S = I L -I i = (1-ΔSOCi / ΔSOCL)* I L ; According to R S = U L / I s The internal short circuit resistance can be calculated; R S Whether it exceeds the preset threshold value determines whether the internal short circuit exceeds the limit; Repeat the above steps to detect the internal short-circuit resistance exceeding limit fault in real time online.

[0048] The above embodiment is a specific implementation method. Other embodiments can be improved according to the further technical solutions described above and as needed, such as introducing multiple models, performing unified judgment and management through a cloud server, etc. Other embodiments with corresponding functions can be enriched and expanded on the basis of the above embodiment.

[0049] As can be seen from the foregoing, the battery internal short circuit monitoring method and system proposed in this application, by establishing and selecting a battery equivalent model, collects the current and voltage data in operation in real time, and monitors the degree of short circuit in the battery in real time, thereby greatly improving the timeliness of judgment, ensuring that any potential internal short circuit problem can be quickly captured, and effectively preventing the further deterioration of the fault. The system ensures the accuracy of the monitoring results through continuous and repeated monitoring, and adopts a continuous monitoring method to ensure that the battery status is accurately captured, thereby providing a reliable basis for judgment. In addition, the present application also innovatively integrates the monitoring data of temperature and battery terminal voltage, and accurately evaluates the severity of the short circuit fault by combining the internal power consumption of the battery. In addition, the present application uses a cloud server to optimize the selection of battery equivalent models. According to the actual operating conditions of the battery, the criteria for the degree of internal short circuit and the degree of internal power consumption can be flexibly adjusted to improve the accuracy and reliability of the judgment, so that the system can adapt to various complex battery usage scenarios. This application achieves efficient and accurate monitoring of short-circuit faults in batteries through innovative monitoring methods and technical means, which not only provides strong technical guarantees for the safe operation of batteries, but also provides reliable data support for fault handling.

[0050] The above is only a specific implementation of the present application, and any other improvements made based on the concept of the present application are deemed to be within the protection scope of the present application.

Claims

1. A method for monitoring short circuit in a battery, characterized in that: include: Establish a battery equivalent model and obtain model parameters of the battery equivalent model; Obtain the current parameters and voltage parameters of the battery during operation, and calculate the internal resistance of the battery according to the obtained current parameters and voltage parameters and the battery equivalent model; Determine whether the calculated battery internal resistance exceeds a preset threshold and determine the degree of internal short circuit; The steps of obtaining current parameters and voltage parameters and the judging step are repeated to monitor the degree of short circuit in the battery in real time.

2. The method for monitoring short circuit in a battery according to claim 1, characterized in that: The specific steps of calculating the internal resistance of the battery according to the acquired current parameters and voltage parameters include calculating the SOC change and the ampere-hour increment at adjacent moments, and obtaining the short-circuit current and resistance in the battery according to the SOC change and the ampere-hour increment at adjacent moments.

3. The method for monitoring short circuit in a battery according to claim 2, characterized in that: It also includes obtaining temperature parameters and comparing the obtained temperature parameters with temperature parameters of adjacent batteries, and / or obtaining battery terminal voltage and comparing the obtained battery terminal voltage with the average of other battery terminal voltages, so as to determine the internal power consumption of the battery, and determine the severity of the internal short circuit fault of the battery according to the degree of internal short circuit and the internal power consumption.

4. The method for monitoring short circuit in a battery according to claim 1, characterized in that: At the same time, multiple different types of battery equivalent models are established. Each time the degree of internal short circuit exceeding the limit is determined, a determination is made through each battery equivalent model.

5. The method for monitoring short circuit in a battery according to claim 4, characterized in that: Different types of battery equivalent models are ranked according to their relevance to different battery usage conditions, and the battery equivalent model with the highest relevance under different battery usage conditions is selected as the basis for determination.

6. The method for monitoring short circuit in a battery according to claim 1, characterized in that: The battery equivalent model is established and the internal short circuit is determined through the cloud server, and the determination criteria are adjusted in the cloud server according to different operating conditions of the battery.

7. The method for monitoring short circuit in a battery according to claim 3, characterized in that: It also includes graded warnings for the degree of internal short circuit and / or the severity of the battery internal short circuit fault.

8. A battery internal short circuit monitoring system, used for performing the battery internal short circuit monitoring method according to any one of claims 1 to 7, characterized in that: include: Model management module, used to establish and manage battery equivalent models; A parameter acquisition module is used to obtain the current parameters and voltage parameters of the battery during operation; The determination module is used to calculate the battery internal resistance based on the acquired current parameters and voltage parameters and the battery equivalent model, determine whether the calculated battery internal resistance exceeds a preset threshold, and determine the degree of internal short circuit exceeding the limit.

9. The battery internal short circuit monitoring system according to claim 8, characterized in that: The parameter acquisition module also acquires temperature parameters and battery terminal voltage during battery operation.

10. The battery internal short circuit monitoring system according to claim 8, characterized in that: The determination module is arranged on the cloud server.

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