Fault online monitoring and early warning method of energy storage system

Abstract

The invention discloses a fault online monitoring and early warning method for an energy storage system. The monitoring method comprises the following steps: acquiring real-time state parameters of each battery in a plurality of energy storage battery assemblies in a current detection period and a previous detection period; calculating the internal resistance change rate of the battery based on the real-time state parameters of the battery; calculating a fault risk index of the battery by combining the real-time state parameter and the aging coefficient of the battery based on the internal resistance change rate; and judging whether the fault risk index of the battery is greater than or equal to a fault risk threshold, if so, controlling the battery to stop running, and sending fault warning information to a control end of the energy storage system, and if not, controlling the battery to run normally. Through the multi-dimensional state parameters of two continuous detection periods, the fault risk index of each battery is comprehensively calculated to judge the operation state of a single battery, the problems that an existing scheme is single in detection parameter, lacks dynamic monitoring capability and does not consider battery aging factors are solved, and comprehensive, dynamic and accurate fault monitoring and early warning are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage device monitoring, and in particular to an online fault monitoring and early warning method for an energy storage system. Background Art

[0002] With the global emphasis on renewable energy and the growing demand for energy storage, energy storage systems have been widely used in many fields such as power systems, new energy vehicles, and distributed energy. The performance and reliability of the core components of the energy storage system (i.e., energy storage battery components) directly affect the operating stability and safety of the entire energy storage system. During the operation of the energy storage system, the battery may face various complex operating conditions and environmental conditions. For example, in large-scale energy storage power stations, batteries need to be frequently charged and discharged to balance the supply and demand fluctuations of the power grid; in electric vehicle applications, batteries must adapt to different driving conditions and ambient temperature changes. These factors may cause battery performance degradation, failure, and even safety accidents.

[0003] At present, the following problems are common in the schemes for monitoring batteries in energy storage systems: First, the detection parameters are single: Traditional energy storage system fault detection methods often only focus on one or several parameters of the battery, such as voltage, temperature, etc. For example, some early detection systems mainly rely on setting a fixed voltage threshold to determine whether the battery is overcharged or over-discharged. However, relying on a single parameter alone cannot fully reflect the true health status of the battery, because the battery failure may be caused by the combined action of multiple parameters. For example, even if the voltage is within the normal range, if the internal resistance of the battery increases, the current distribution is uneven, and the temperature rises abnormally, it may also indicate that the battery has a potential fault, and a single parameter detection method is difficult to detect such complex situations. Second, there is a lack of dynamic monitoring capabilities: Many existing detection schemes focus on judging the parameters of the battery at a specific moment, while ignoring the dynamic changes of the battery parameters in different operating cycles. During long-term use, the performance of the battery gradually changes. For example, the internal resistance will increase with the increase in the number of charge and discharge cycles, and the change law of the state of charge will also change due to battery aging. Without monitoring the dynamic change rate of these parameters, it is difficult to effectively warn in the early stages of battery failure. For example, when the internal resistance of the battery begins to rise slowly, if this dynamic change is not captured in time, the best time to take maintenance measures when the battery performance slightly declines may be missed. By the time the battery failure is obvious, it may have a significant impact on the operation of the entire energy storage system, and even cause system shutdown or safety accidents. Third, the battery aging factor is not fully considered: battery aging is an inevitable process, which will cause changes in various performance indicators of the battery, thereby affecting the probability and characteristics of its failure. However, some existing fault detection methods do not reasonably incorporate the battery aging coefficient into the fault prediction model. For example, for energy storage batteries that have been used for many years and have undergone a large number of charge and discharge cycles, their tolerance for changes in parameters such as voltage and current is different from that of new batteries, but some detection schemes still use a unified standard to judge, which is prone to misjudgment or missed judgment. For aging batteries, there may be a high risk of failure before the parameters exceed the conventional threshold, and the existing schemes are difficult to accurately assess this potential risk caused by aging, thereby reducing the accuracy and reliability of fault detection of the entire energy storage system. Summary of the invention

[0004] The purpose of the embodiment of the present invention is to provide an online fault monitoring and early warning method for an energy storage system, which detects the multi-dimensional real-time status parameters of each battery in the energy storage battery assembly in two consecutive detection cycles in real time, comprehensively calculates the battery internal resistance change rate and the fault risk index obtained by combining the real-time status parameters and the aging coefficient, and then judges the operating status of the single battery based on the comparison between the index and the fault risk threshold. The existing scheme overcomes the problems of single detection parameters, lack of dynamic monitoring capabilities, and failure to fully consider battery aging factors, and realizes more comprehensive, dynamic and accurate fault monitoring of energy storage batteries, which helps to warn of battery failures in advance, ensure the stable and safe operation of the energy storage system, and improve the reliability and safety of the entire energy storage system.

[0005] To solve the above technical problems, a first aspect of an embodiment of the present invention provides an online fault monitoring method for an energy storage system, wherein the energy storage system includes a plurality of energy storage battery assemblies, each of which includes a plurality of batteries, and the monitoring method includes the following steps:

[0006] Acquire the real-time status parameters of each battery in a current detection cycle and a previous detection cycle of a plurality of energy storage battery assemblies, wherein the real-time status parameters include: the voltage value, current value, temperature value, state of charge value and charge and discharge rate of the battery;

[0007] Calculating the internal resistance change rate of the battery based on the real-time state parameters of the battery;

[0008] Calculating a failure risk index of the battery based on the internal resistance change rate and in combination with the real-time state parameters and aging coefficient of the battery;

[0009] Determine whether the fault risk index of the battery is greater than or equal to the fault risk threshold, if so, control the battery to stop running, and send fault alarm information to the control end of the energy storage system, if not, control the battery to run normally.

[0010] Furthermore, the internal resistance change rate of the battery The calculation formula is:

[0011]

[0012] R i =R ohm_i +R p_i ;

[0013]

[0014] R p_i =R p_0 ×(1-SOC i ) n ;

[0015] Among them, ΔR i is the difference between the internal resistance of the battery in the current detection cycle and the previous detection cycle, ΔV i is the voltage difference between the battery in the current detection cycle and the previous detection cycle, ΔI i is the difference between the current value of the battery in the current detection cycle and the previous detection cycle, C rate_i is the charge and discharge rate of the battery, C rate_0 is the reference charge and discharge rate of the battery, α1, β1 and λ1 are the correlation coefficients of the battery charge and discharge rate, T i is the temperature value of the battery in the current detection cycle, T0 is the battery reference temperature value, α2 and λ2 are the correlation coefficients of the battery temperature, SOC i is the state of charge value of the battery in the current detection cycle, SOC0 is the battery reference state of charge, α3, β3 and λ3 are the correlation coefficients of the battery state of charge, R p_i is the polarization resistance value of the battery in the current detection cycle, R p_0 is the polarization resistance value corresponding to the battery state of charge of 0, n is the empirical constant related to the polarization resistance and the battery state of charge, Δt is the time difference between the current detection cycle and the previous detection cycle, R ohm_i is the ohmic resistance value of the battery in the current detection cycle, R i is the internal resistance of the battery in the current detection cycle, R i-1 is the internal resistance value of the battery in the previous detection cycle, and i is the serial number of the current detection cycle.

[0016] Furthermore, the aging coefficient is related to the battery life and the number of charge and discharge cycles, and its calculation formula is:

[0017]

[0018] Among them, SOH i is the aging coefficient of the battery, Cycle count_i Cycle is the number of charge and discharge cycles of the battery. max is the maximum number of charge and discharge cycles of the battery, Age count_i Age is the age of the battery. max is the maximum service life of the battery, σ is a constant term related to the number of battery charge and discharge cycles, and η is a constant term related to the service life of the battery.

[0019] Furthermore, a first temperature sensor is disposed on the outer side of the side wall of each battery, and a second temperature sensor is also disposed inside the battery;

[0020] The calculation formula of the ohmic resistance value of the battery in the current detection cycle is:

[0021]

[0022] Among them, T i1 is the side wall temperature value of the battery obtained by the first temperature sensor, T i2 is the internal temperature value of the battery acquired by the second temperature sensor, and α2, λ2 and m are correlation coefficients of battery temperature respectively.

[0023] Furthermore, the calculation formula of the failure risk index of the battery is:

[0024]

[0025] Among them, RI i is the failure risk index of the battery in the current detection cycle, R initial is the initial internal resistance of the battery, V0 is the open circuit voltage of the battery, I0 is the nominal current of the current, T max is the maximum safe operating temperature of the battery, R max is the maximum internal resistance of the battery, and θ1, θ2, θ3, θ4, θ5, θ6 and θ7 are constant terms related to the aging factor, internal resistance change rate, battery voltage, battery current, battery temperature, state of charge, charge and discharge rate and battery internal resistance, respectively.

[0026] Furthermore, the controlling the battery to stop running and sending fault alarm information to the control end of the energy storage system includes:

[0027] Sending a battery operation stop instruction to the battery management system of the energy storage system;

[0028] Cutting off the corresponding solid-state switch of the battery based on the battery management system;

[0029] Sending fault warning information to the control end of the energy storage system.

[0030] Accordingly, a second aspect of an embodiment of the present invention provides an online fault monitoring device for an energy storage system, wherein the energy storage system includes a plurality of energy storage battery assemblies, each of which includes a plurality of batteries, including:

[0031] A first data acquisition module, which is used to obtain real-time status parameters of each battery in a current detection cycle and a previous detection cycle in a plurality of energy storage battery assemblies, wherein the real-time status parameters include: voltage value, current value, temperature value, state of charge value and charge and discharge rate of the battery;

[0032] A first monitoring and calculating module, which is used to calculate the internal resistance change rate of the battery based on the real-time state parameters of the battery;

[0033] A second monitoring and calculation module, which is used to calculate the failure risk index of the battery based on the internal resistance change rate in combination with the real-time state parameters and aging coefficient of the battery;

[0034] A fault judgment alarm module is used to determine whether the fault risk index of the battery is greater than or equal to the fault risk threshold. If so, the battery is controlled to stop running and a fault alarm message is sent to the control end of the energy storage system. Otherwise, the battery is controlled to run normally.

[0035] Accordingly, a third aspect of an embodiment of the present invention provides an online fault warning method for an energy storage system. After the battery is controlled to stop running by the above-mentioned online fault monitoring method for the energy storage system, the warning method comprises the following steps:

[0036] Obtain the open circuit voltage and real-time temperature value of the battery in a stopped state in real time;

[0037] Determining whether the open circuit voltage and the real-time temperature value of the battery are greater than or equal to respective corresponding thresholds;

[0038] When the open circuit voltage and / or the real-time temperature value exceeds a corresponding threshold value, it is determined that the battery is in a dangerous state, and a danger warning message is sent to a control terminal of the energy storage system.

[0039] Furthermore, after sending the danger warning information to the control end of the energy storage system, the method further includes:

[0040] A plurality of batteries adjacent to the battery in a dangerous state are controlled to stop operating.

[0041] Accordingly, a fourth aspect of an embodiment of the present invention provides an online fault warning device for an energy storage system, the warning device comprising:

[0042] A second data acquisition module, which is used to obtain the open circuit voltage and real-time temperature value of the battery in a stopped state in real time;

[0043] A second data judgment module, which is used to judge whether the open circuit voltage and the real-time temperature value of the battery are greater than or equal to their respective corresponding thresholds;

[0044] The danger determination and warning module is used to determine that the battery is in a dangerous state when the open circuit voltage and / or the real-time temperature value exceeds a corresponding threshold value, and to send danger warning information to the control end of the energy storage system.

[0045] The above technical solution of the embodiment of the present invention has the following beneficial technical effects:

[0046] 1. By simultaneously collecting the battery's voltage, current, temperature, state of charge, charge and discharge rate and other real-time status parameters, the battery's operating status can be comprehensively considered from multiple dimensions. The multi-parameter comprehensive analysis method breaks through the limitations of traditional detection methods and is no longer limited to a single parameter evaluation. It can more comprehensively reflect the complex electrochemical processes and physical changes inside the battery, avoiding the masking of other potential problems due to a single parameter being normal, thereby accurately locating various potential fault hazards that may exist in the battery, providing a rich and complete information basis for a comprehensive understanding of the battery's health status;

[0047] 2. It can keenly capture the subtle change trends of the battery at different operating stages and promptly detect the dynamic evolution of battery performance. Whether it is the gradual increase of internal resistance, the change of the charge state change law, or the abnormal fluctuation of other parameters over time, it can be effectively monitored and quantified as a fault risk index at an early stage. The dynamic early warning mechanism can issue an alarm in time when the battery fault first appears, significantly advancing the time node of fault discovery, buying sufficient time for taking corresponding maintenance measures, and greatly reducing the possibility of serious impact on the energy storage system due to sudden battery failure;

[0048] 3. Incorporating the aging coefficient into the calculation process of the fault risk index effectively improves the accuracy and reliability of detection. Since battery aging has a profound impact on its performance and fault characteristics, batteries with different aging degrees respond completely differently to the same parameter changes. Taking this factor into consideration, differentiated evaluation standards are adopted for aged batteries and new batteries, thereby avoiding misjudgments or missed judgments caused by unified threshold judgments. By accurately quantifying the interaction between the battery aging degree and other real-time status parameters, the calculated fault risk index can more accurately reflect the actual fault risk level of the battery, providing a more reliable basis for fault judgment of the energy storage system, and effectively ensuring that the entire energy storage system can maintain a stable, efficient and safe operating state at different life cycle stages. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a flow chart of a method for online fault monitoring of an energy storage system provided by an embodiment of the present invention;

[0050] Figure 2 It is a module block diagram of an online fault monitoring device for an energy storage system provided by an embodiment of the present invention;

[0051] Figure 3 is a flow chart of an online fault warning method for an energy storage system provided by an embodiment of the present invention;

[0052] Figure 4 It is a module block diagram of an online fault warning device for an energy storage system provided by an embodiment of the present invention.

[0053] Reference numerals:

[0054] 1. First data acquisition module, 2. First monitoring and calculation module, 3. Second monitoring and calculation module, 4. Fault judgment and alarm module, 5. Second data acquisition module, 6. Second data judgment module, 7. Danger judgment and warning module. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the description of well-known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present invention.

[0056] Please refer to Figure 1 A first aspect of an embodiment of the present invention provides a method for online fault monitoring of an energy storage system, wherein the energy storage system includes a plurality of energy storage battery assemblies, each of which includes a plurality of batteries, and the method includes the following steps:

[0057] Step S100, obtaining real-time status parameters of each battery in a current detection cycle and a previous detection cycle of a plurality of energy storage battery assemblies, wherein the real-time status parameters include: battery voltage value, current value, temperature value, state of charge value and charge and discharge rate.

[0058] Step S200, calculating the internal resistance change rate of the battery based on the real-time state parameters of the battery.

[0059] Step S300, calculating the battery failure risk index based on the internal resistance change rate in combination with the real-time state parameters and aging coefficient of the battery.

[0060] Step S400, determining whether the fault risk index of the battery is greater than or equal to the fault risk threshold, if so, controlling the battery to stop running, and sending fault alarm information to the control end of the energy storage system, if not, controlling the battery to run normally.

[0061] By real-time detection of the multi-dimensional real-time status parameters of each battery in the energy storage battery assembly in two consecutive detection cycles, the battery internal resistance change rate and the fault risk index obtained by combining the real-time status parameters and the aging coefficient are comprehensively calculated, and then the operating status of the single battery is judged based on the comparison between the index and the fault risk threshold. The problems of the existing scheme such as single detection parameters, lack of dynamic monitoring capabilities and failure to fully consider battery aging factors are overcome, and more comprehensive, dynamic and accurate fault monitoring of energy storage batteries is achieved, which helps to warn of battery failures in advance, ensure the stable and safe operation of the energy storage system, and improve the reliability and safety of the entire energy storage system.

[0062] Furthermore, the battery internal resistance change rate The calculation formula is:

[0063]

[0064] R i =R ohm_i +R p_i ;

[0065]

[0066] R p_i =R p_0 ×(1-SOC i ) n ;

[0067] Among them, ΔR i is the difference between the internal resistance of the battery in the current detection cycle and the previous detection cycle, ΔV i ΔI is the voltage difference between the battery in the current detection cycle and the previous detection cycle. i is the difference between the battery current value in the current detection cycle and the previous detection cycle, C rate_i is the charge and discharge rate of the battery, C rate_0 is the battery's benchmark charge and discharge rate, α1, β1 and λ1 are the correlation coefficients of the battery's charge and discharge rate, T i is the temperature value of the battery in the current detection cycle, T0 is the battery reference temperature value, α2 and λ2 are the correlation coefficients of the battery temperature, SOC i is the state of charge value of the battery in the current detection cycle, SOC0 is the battery reference state of charge, α3, β3 and λ3 are the correlation coefficients of the battery state of charge, R p_i is the polarization resistance value of the battery in the current detection cycle, R p_0 is the polarization resistance value corresponding to the battery state of charge of 0, n is the empirical constant related to the polarization resistance and the battery state of charge, Δt is the time difference between the current detection cycle and the previous detection cycle, R ohm_i is the ohmic resistance of the battery in the current detection cycle, R i is the internal resistance of the battery in the current detection cycle, R i-1 is the internal resistance of the battery in the previous detection cycle, and i is the serial number of the current detection cycle.

[0068] Specifically, the charge and discharge rate is a key factor in the formula, indicating that the charge and discharge rate can reflect the change of the internal resistance of the battery under different working intensities. For example, when the charge and discharge rate is higher than the benchmark charge and discharge rate, it means that the battery is in a high-power working state. At this time, the chemical reaction rate inside the battery is accelerated, and the polarization phenomenon may be more serious. According to the formula, this will have a corresponding impact on the internal resistance change rate, because the ohmic resistance is an important component of the battery's internal resistance. This association enables the calculation of the internal resistance change rate to take into account the impact of changes in the actual working conditions of the battery on the internal resistance. Furthermore, the various correlation coefficients α1, β1, and λ1 can be adjusted according to the characteristics of the battery and experimental data, so as to more accurately reflect the law of internal resistance changes under different charge and discharge rates. Different types of batteries have different sensitivities to the charge and discharge rate. By reasonably setting the correlation coefficients α1, β1, and λ1, the calculation of the internal resistance change rate can be made more in line with the actual performance of the battery. For example, for batteries with high power density, the internal resistance change may be more significant at high charge and discharge rates. By appropriately increasing the value of the corresponding correlation coefficient, this change can be captured more accurately, and then when calculating the internal resistance change rate, the health status of the battery under different operating conditions can be more accurately reflected.

[0069] Specifically, the battery temperature value and the reference temperature value participate in the calculation of the internal resistance change rate through the related parameters α2 and λ2. Temperature is one of the important factors affecting the internal resistance of the battery, because the chemical reaction rate and ion conduction speed inside the battery are closely related to the temperature. When the temperature rises, the ion activity of the battery electrolyte increases and the ion conduction resistance decreases, but at the same time, high temperature may also accelerate the side reactions inside the battery, affecting the performance and internal resistance of the battery. By introducing temperature-related terms, the formula can directly consider the effect of temperature change on the internal resistance change rate. For example, in a low temperature environment, the internal resistance of the battery usually increases, which can be reflected in the formula by the difference between the temperature value T and the reference temperature value and the coefficients α2 and λ2, so that the calculated internal resistance change rate is more consistent with the performance change of the battery under actual temperature conditions. Furthermore, the existence of the α2 and λ2 coefficients makes it possible to adjust the weight of the temperature on the calculation of the internal resistance change rate according to the characteristics of the battery in different temperature ranges. Different battery materials and electrolyte systems have different internal resistance change rules in different temperature ranges. By optimizing these coefficients, the evaluation of internal resistance changes caused by temperature changes can be more accurately corrected. For example, for some batteries with relatively stable performance at high temperatures, the value of α2 can be set relatively small to avoid overestimating the impact of temperature increase on the internal resistance change rate; and for batteries that are sensitive to low temperatures, the value of λ2 can be appropriately increased in the low temperature range so that the calculated internal resistance change rate can more truly reflect the abnormal changes in internal resistance that may occur in the battery under low temperature conditions.

[0070] Specifically, the state of charge value and the reference state of charge participate in the calculation of the internal resistance change rate through the coefficients α3, β3 and λ3. The state of charge reflects the power storage situation inside the battery, and there is an intrinsic connection between it and the internal resistance of the battery. As the battery charges and discharges, the state of charge changes, and the electrochemical balance inside the battery will also change accordingly, which will affect the internal resistance of the battery. For example, when the battery is close to being fully charged or discharged, the internal resistance of the battery may change due to factors such as changes in the reaction on the electrode surface and changes in ion concentration. By introducing items related to the state of charge into the formula, the impact of battery charge changes on the internal resistance can be taken into account, thereby more comprehensively evaluating the internal resistance change rate. Furthermore, the polarization resistance R in the formula p_i Empirical constants related to the battery state of charge and the corresponding polarization resistance value R when the state of charge is 0 p_0 The combination of further considers the change of polarization resistance of the battery at different states of charge. As the battery is used and aged, the polarization resistance usually changes, and the polarization resistance is an important component of the internal resistance. This setting enables the calculation of the internal resistance change rate to reflect the impact of battery aging and performance degradation on the internal resistance to a certain extent. For example, when the battery aging causes the change pattern of polarization resistance at different states of charge to change, these terms in the formula can more accurately calculate the internal resistance change rate, so as to promptly discover the downward trend of battery performance.

[0071] Furthermore, the aging coefficient is related to the battery life and the number of charge and discharge cycles, and its calculation formula is:

[0072]

[0073] Among them, SOH i is the battery aging coefficient, Cycle count_i Cycle is the number of charge and discharge cycles of the battery. max is the maximum charge and discharge cycle number of the battery, Age count_i Age is the age of the battery. max is the maximum service life of the battery, σ is a constant term related to the number of battery charge and discharge cycles, and η is a constant term related to the service life of the battery.

[0074] Specifically, the ratio of the battery's service life to its maximum service life is part of the calculation of the aging coefficient. The service life directly reflects the battery's use in the time dimension. As time goes by, the various materials and structures inside the battery will gradually change. For example, the active substances of the electrode material may slowly deplete, the electrolyte may gradually dry up or deteriorate, and components such as the battery casing may also be corroded by environmental factors. These time-related aging processes will affect the performance of the battery, causing its performance to gradually decline. By incorporating the service life into the calculation of the aging coefficient, the contribution of the time factor to the degree of battery aging can be intuitively reflected. Dividing the service life by the maximum service life gives the proportion of time that the battery has been used during its life cycle. This ratio can be used as a comprehensive indicator to measure the degree of battery aging in the time dimension. For example, if a battery's service life reaches half of its maximum service life, then from a time perspective, the battery has experienced a considerable degree of aging. This approach enables the aging coefficient to reflect the time loss of the battery over its entire service life to a certain extent, thereby providing a time-based quantitative standard for evaluating the battery's aging status.

[0075] Specifically, the ratio of the number of charge and discharge cycles to the maximum number of charge and discharge cycles is also important in the calculation of the aging coefficient. It is an important indicator for measuring the actual working intensity of the battery. Each charge and discharge process will cause a certain degree of change in the electrochemical structure inside the battery. For example, the lattice structure of the electrode material may be damaged during the insertion and extraction of lithium ions, and the ion concentration distribution in the electrolyte will also change. Frequent charge and discharge cycles will accelerate these aging processes, resulting in a decrease in battery performance. Therefore, the number of charge and discharge cycles can reflect the impact of the intensity of use of the battery in the actual working process on its aging degree. Similar to the service life, dividing the number of charge and discharge cycles by the maximum number of charge and discharge cycles gives the proportion of the battery that has been used in the cycle life dimension. This ratio can accurately quantify the degree of aging of the battery during operation. For example, if the number of charge and discharge cycles of a battery is close to the maximum number of charge and discharge cycles, it means that the battery has undergone a large number of working cycles and the degree of aging may be high. In this way, the aging coefficient comprehensively considers the aging of the battery in terms of charge and discharge cycles, making the evaluation of the battery aging degree more comprehensive and accurate, and can better reflect the performance degradation state of the battery in actual work.

[0076] Furthermore, a first temperature sensor is arranged on the outside of the side wall of each battery, and a second temperature sensor is arranged inside the battery. Since the temperature distribution inside and outside the battery may be uneven during the charging and discharging process, there is a temperature gradient. This temperature gradient will affect the ohmic resistance of the battery. For example, the chemical reaction and ion conduction process inside the battery have different rates at different temperatures. The temperature difference obtained by two temperature sensors can help to more accurately calculate the effect of temperature on ohmic resistance. Because Ohm's law changes the resistance value at different temperatures when considering the temperature factor, multiple temperature detection values ​​can describe this change more carefully, thereby improving the accuracy of ohmic resistance value calculation. If only a single temperature sensor is relied on to estimate the battery temperature, errors may occur due to the limitations of the sensor position. For example, the external temperature sensor may not accurately reflect the actual temperature inside the battery, especially when the internal heating of the battery is more serious. By using the temperature values ​​obtained by the first temperature sensor and the second temperature sensor at the same time, this error can be corrected. When calculating the ohmic resistance value, the influence of temperature factors on resistance can be more accurately considered based on the comprehensive situation of internal and external temperatures, avoiding the deviation of ohmic resistance calculation caused by inaccurate temperature estimation.

[0077] Accordingly, based on the two temperature detection values ​​of the battery outer side wall and the battery interior acquired by the first temperature sensor and the second temperature sensor, the calculation formula of the ohmic resistance value of the battery in the current detection cycle is:

[0078]

[0079] Among them, T i1 is the side wall temperature value of the battery obtained by the first temperature sensor, T i2 is the internal temperature value of the battery acquired by the second temperature sensor, and α2, λ2 and m are the correlation coefficients of the battery temperature respectively.

[0080] In addition, multiple temperature detection values ​​can be used to analyze the contribution of temperature changes at different locations inside the battery to the ohmic resistance. For example, when the temperature inside the battery rises rapidly and the external temperature changes relatively little, this may mean that the electrochemical processes inside the battery (such as ion conduction and electrode reactions) are more intense, thus having a greater impact on the ohmic resistance. By comparing the temperature data of the first temperature sensor and the second temperature sensor, we can gain an in-depth understanding of the changes in physical processes at different locations inside the battery, and then more accurately evaluate the specific impact of these changes on the ohmic resistance. This helps to build a more accurate battery model for calculating the ohmic resistance value and other parameters related to battery performance.

[0081] Finally, the calculation formula for the battery failure risk index is:

[0082]

[0083] Among them, RI i is the failure risk index of the battery in the current detection cycle, R initial is the initial internal resistance of the battery, V0 is the open circuit voltage of the battery, I0 is the nominal current, T max is the maximum safe operating temperature of the battery, R max is the maximum internal resistance of the battery, and θ1, θ2, θ3, θ4, θ5, θ6 and θ7 are constant terms related to the aging factor, internal resistance change rate, battery voltage, battery current, battery temperature, state of charge, charge and discharge rate, and battery internal resistance, respectively.

[0084] In the calculation process of the above-mentioned failure risk index, the first is the aging factor of the battery, which comprehensively considers the aging of the battery during use. As the battery's service life increases and the number of charge and discharge cycles increases, the battery's performance will gradually decline and the internal structure will change, which makes the battery more prone to failure; through the aging factor, this increase in failure risk caused by aging can be quantified. The aging factor can combine the two aging factors of battery usage time and charge and discharge cycle number. Different battery aging processes may vary depending on usage. Some batteries may age quickly due to frequent charge and discharge, while others may age naturally over time. The aging factor takes these factors into account, allowing the failure risk index to more comprehensively reflect the impact of battery aging on failure risk during actual use.

[0085] The second is the internal resistance change rate, which is an important indicator to measure the change of the internal state of the battery. The change of the internal resistance of the battery is usually closely related to the physical and chemical changes inside the battery, such as the loss of electrode materials, the drying up of the electrolyte or the internal short circuit. The increase in the internal resistance change rate may mean that there is a problem with the internal structure of the battery, such as plate corrosion or the shedding of active substances. In the calculation of the fault risk index, the greater the internal resistance change rate, the higher the fault risk index, which shows that the internal resistance change rate can effectively reflect the impact of changes in the internal structure of the battery on the fault risk and is an important parameter for evaluating the health status of the battery. The internal resistance change rate can send out an early warning signal before the battery has obvious faults. Even if other parameters of the battery (such as voltage, current, etc.) are temporarily within the normal range, abnormal changes in the internal resistance change rate may also indicate that potential problems are occurring inside the battery. By incorporating the internal resistance change rate into the calculation of the fault risk index, the hidden dangers of battery failure can be discovered in advance, providing a basis for timely maintenance measures.

[0086] The third is the battery voltage, which is an important representation of the battery's energy state. Under normal circumstances, the battery voltage will vary within a certain range as the charge and discharge process progresses. When the battery voltage exceeds the normal range (such as overcharging or over-discharging), the battery's chemical reaction may get out of control, causing battery damage or failure. The battery voltage combined with other parameters (such as current, state of charge, etc.) can help determine the type of battery failure that may occur. For example, when the battery voltage drops too quickly and the current increases abnormally, it may indicate a short circuit inside the battery; and when the battery voltage is too high and the state of charge is close to full charge, it may be due to a problem in the charging control link that causes overcharging. In the calculation of the fault risk index, the battery voltage can cooperate with other parameters to more comprehensively assess the battery's failure risk and possible fault types.

[0087] Fourth, the battery current, which represents the working load of the battery. In different application scenarios, the battery may be subjected to current loads of different sizes. Excessive current load may cause battery heating, increased polarization, and even cause faults such as internal short circuits. Abnormal current changes (such as a sudden increase or decrease in current) are often an important signal of battery failure. By incorporating the battery current into the calculation of the fault risk index, the risk of failure caused by abnormal current can be discovered in a timely manner. For example, when a short circuit occurs inside the battery, the current will increase sharply, which will cause the fault risk index to increase significantly, thereby reminding the user that the battery may have a serious fault.

[0088] The fifth is battery temperature, which is one of the key factors affecting battery performance and life. Too high or too low temperature will have an adverse effect on the battery's chemical reaction rate, ion conduction efficiency, and internal structure. During the operation of the battery, if the temperature is not properly controlled or abnormal internal heating occurs, it may cause a series of failures, such as thermal runaway and other serious problems. By incorporating battery temperature into the calculation of the fault risk index, the risk of failure caused by abnormal temperature can be monitored in real time.

[0089] Sixth, the state of charge (SOC) reflects the amount of power remaining in the battery. Under different states of charge, the performance and failure risk of the battery are also different. For example, when the battery power is too low (SOC is close to 0) or too high (SOC is close to 1), the battery may be more prone to failure. The state of charge changes continuously during the charging and discharging process of the battery, and its changing pattern is closely related to the health status and failure risk of the battery. During the charging process, if the state of charge does not rise normally, or the state of charge drops too quickly during the discharge process, this may indicate that there is a problem with the battery. By incorporating the state of charge into the calculation of the failure risk index, the battery's charging and discharging process can be combined to more comprehensively evaluate the failure risk of the battery at different power states.

[0090] The seventh is the charge and discharge rate, which indicates the working intensity of the battery during the charge and discharge process. A high charge and discharge rate means that the battery needs to transfer a large amount of charge in a short period of time, which will put greater pressure on the electrochemical process inside the battery. The physical and chemical processes inside the battery are different at different charge and discharge rates, and the failure mode and risk will also change accordingly. By incorporating the charge and discharge rate into the calculation of the failure risk index, the difference in failure risk of the battery in different working modes (such as fast charging, slow discharge, etc.) can be considered.

[0091] Eighth, the internal resistance of the battery is one of the important indicators of battery performance. It is closely related to the battery's energy loss, heat generation, and internal structural integrity. The battery internal resistance and the internal resistance change rate complement each other when assessing the risk of battery failure. The internal resistance change rate focuses on reflecting the dynamic changes of the internal resistance, while the battery internal resistance itself reflects the current state of the internal resistance. The combination of the two can more comprehensively assess the integrity and health of the battery's internal structure. In the calculation of the failure risk index, by simultaneously considering the battery internal resistance and the internal resistance change rate, it is possible to more accurately determine whether the battery has a failure risk and the magnitude of the risk.

[0092] In addition, the step S400 of the present invention of controlling the battery to stop running and sending fault alarm information to the control end of the energy storage system includes:

[0093] S401, sending a battery operation stop instruction to a battery management system of an energy storage system.

[0094] By sending a stop command to the battery management system (BMS) that is responsible for monitoring and managing the operating status of each battery cell in the battery pack, the subsequent stop operation process is accurately initiated. As a key component in the energy storage system that directly interacts with the battery, the BMS can receive data from various sensors, process and analyze the data, and determine whether the battery status is normal. After receiving the stop operation command, the BMS can prepare to perform operations such as disconnecting the battery according to its built-in control logic and preset programs to ensure the safety of the battery and the entire energy storage system.

[0095] S402, cutting off the corresponding solid-state switch of the battery based on the battery management system.

[0096] After receiving the stop operation command, the battery management system will trigger the action of cutting off the corresponding solid-state switch of the battery. Solid-state switches have the advantages of fast response, high reliability, and no arc generation. They are widely used in energy storage systems to control the connection between battery cells and external circuits. By cutting off the solid-state switch, the battery at risk of failure can be effectively isolated, preventing current from continuing to flow into or out of the battery. This can not only prevent the faulty battery from further deteriorating, such as preventing the overcharged battery from continuing to receive electrical energy and causing more serious dangerous situations such as thermal runaway, or preventing batteries with internal short circuit risks from causing adverse effects on other normal batteries (such as causing overdischarge or abnormal heating of other batteries), but also maintain the normal operation of the rest of the energy storage system, minimize the impact of a single battery failure on the function of the entire energy storage system, and ensure the overall stability and safety of the system.

[0097] S403, sending fault alarm information to the control end of the energy storage system.

[0098] Sending fault alarm information to the control end allows system managers or related automated monitoring systems to quickly know that there is a problem with the battery. The control end can record detailed information such as the time and location of the fault (specifically which battery) and possible fault type based on the alarm information, so as to facilitate subsequent troubleshooting, analysis and maintenance arrangements.

[0099] Accordingly, a second aspect of an embodiment of the present invention provides an online fault monitoring device for an energy storage system, wherein the energy storage system includes a plurality of energy storage battery assemblies, each of which includes a plurality of batteries, including:

[0100] Data acquisition module 1, which is used to obtain the real-time status parameters of each battery in the current detection cycle and the previous detection cycle of several energy storage battery assemblies, and the real-time status parameters include: battery voltage value, current value, temperature value, state of charge value and charge and discharge rate;

[0101] A first calculation module 2, which is used to calculate the internal resistance change rate of the battery based on the real-time state parameters of the battery;

[0102] A second calculation module 3, which is used to calculate the failure risk index of the battery based on the internal resistance change rate combined with the real-time state parameters and aging coefficient of the battery;

[0103] The fault judgment module 4 is used to judge whether the fault risk index of the battery is greater than or equal to the fault risk threshold. If so, the battery is controlled to stop running and a fault alarm message is sent to the control end of the energy storage system. Otherwise, the battery is controlled to run normally.

[0104] Accordingly, please refer to Figure 3A third aspect of an embodiment of the present invention provides an online fault warning method for an energy storage system. After the battery is controlled to stop running by the above-mentioned online fault monitoring method for the energy storage system, the warning method comprises the following steps:

[0105] Step S510, obtaining in real time the open circuit voltage and real-time temperature value of the battery in the stopped state.

[0106] When the battery is controlled to stop running, its internal electrochemical state may still be unstable or continuously changing. Obtaining the open circuit voltage can reflect the potential difference of the battery under no-load conditions, which is of great significance for further judging the equilibrium state of the electrochemical reaction inside the battery and whether there are potential dangerous situations (such as abnormal voltage caused by internal short circuit of the battery). The acquisition of real-time temperature values ​​is equally critical, because even if the battery stops running, the heat accumulated during the previous operation may still be dissipated or the internal heat may continue to be generated due to certain faults (such as abnormal self-discharge, internal micro-short circuit, etc.). By monitoring the temperature, it is possible to promptly detect whether the battery has a risk of thermal runaway or other temperature-related abnormalities, providing an important basis for subsequent risk assessment and decision-making.

[0107] Step S520, determining whether the open circuit voltage and the real-time temperature value of the battery are greater than or equal to their respective corresponding thresholds.

[0108] The thresholds for setting the open circuit voltage and real-time temperature values ​​are determined based on research and practical experience on the safe operating range of the battery. When the open circuit voltage exceeds the threshold, it may indicate that there is serious structural damage or abnormal electrochemical reaction inside the battery. For example, the battery plate may have been severely corroded or deformed, causing the open circuit voltage of the battery to deviate from the normal range. This may cause further performance deterioration of the battery or even dangerous situations such as explosion. When the real-time temperature value exceeds the threshold, it indicates that there is a serious problem with the thermal management of the battery. It may be due to a previous fault that the heat cannot be dissipated normally or a large amount of heat is still being generated inside. This is very likely to cause thermal runaway. Once thermal runaway occurs, the battery temperature will rise sharply, generating a large amount of flammable gas and may cause catastrophic consequences such as explosion or fire.

[0109] Step S530: When the open circuit voltage and / or the real-time temperature value exceeds the corresponding threshold value, it is determined that the battery is in a dangerous state, and a danger warning message is sent to the control end of the energy storage system.

[0110] Sending danger warning information enables system managers or automated monitoring systems to immediately know that the battery is in an extremely dangerous state, so that they can quickly take measures such as emergency evacuation of surrounding personnel, activation of fire extinguishing equipment, and further isolation of batteries, thereby minimizing the harm caused by possible safety accidents to personnel, equipment and the environment.

[0111] Further, after sending the danger warning information to the control terminal of the energy storage system in step S530, the method further includes:

[0112] Step S540, controlling a plurality of batteries adjacent to the battery in a dangerous state to stop operating.

[0113] When a battery is in a dangerous state, there may be a variety of potential risk factors, such as high temperature caused by internal short circuits, corrosive substances or flammable gases produced by electrolyte leakage, etc. Adjacent batteries are physically close to each other and are easily affected by these adverse factors. For example, high temperature may be transmitted to adjacent batteries, causing their temperatures to gradually rise, which in turn triggers the intensification of chemical reactions inside them, causing the originally normal adjacent batteries to fail; or corrosive substances leaked from dangerous batteries may corrode the outer shell or electrodes of adjacent batteries, destroying their structural integrity, and ultimately leading to a chain reaction of failures in the entire battery pack. By stopping the operation of several adjacent batteries, an "isolation zone" can be formed in space, effectively blocking the fault propagation path, preventing the fault range from further expanding, protecting more battery cells, and maintaining the relative integrity and stability of the overall structure of the energy storage system.

[0114] Accordingly, please refer to Figure 4 A fourth aspect of an embodiment of the present invention provides an online fault warning device for an energy storage system, the warning device comprising:

[0115] A second data acquisition module 5, which is used to obtain the open circuit voltage and real-time temperature value of the battery in a stopped state in real time;

[0116] A second data judgment module 6, which is used to judge whether the open circuit voltage and the real-time temperature value of the battery are greater than or equal to their respective corresponding thresholds;

[0117] The danger determination and warning module 7 is used to determine that the battery is in a dangerous state when the open circuit voltage and / or the real-time temperature value exceeds a corresponding threshold value, and to send danger warning information to the control end of the energy storage system.

[0118] Accordingly, the fifth aspect of an embodiment of the present invention provides an electronic device, comprising: at least one processor; and a memory connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor executes the online fault monitoring method of the energy storage system as described above.

[0119] Accordingly, a sixth aspect of an embodiment of the present invention provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implements the above-mentioned method for online fault monitoring of an energy storage system.

[0120] The embodiment of the present invention aims to protect an online fault monitoring and early warning method for an energy storage system, wherein the energy storage system includes a plurality of energy storage battery assemblies, each of which includes a plurality of batteries, and the monitoring method includes the following steps: obtaining the real-time status parameters of each battery in the plurality of energy storage battery assemblies during the current detection cycle and the previous detection cycle, the real-time status parameters including: the voltage value, current value, temperature value, state of charge value, and charge and discharge ratio of the battery; calculating the internal resistance change rate of the battery based on the real-time status parameters of the battery; calculating the fault risk index of the battery based on the internal resistance change rate, in combination with the real-time status parameters and aging coefficient of the battery; determining whether the fault risk index of the battery is greater than or equal to the fault risk threshold, if so, controlling the battery to stop running, and sending fault alarm information to the control end of the energy storage system, otherwise controlling the battery to operate normally. The above technical scheme has the following effects:

[0121] 1. By simultaneously collecting the battery's voltage, current, temperature, state of charge, charge and discharge rate and other real-time status parameters, the battery's operating status can be comprehensively considered from multiple dimensions. The multi-parameter comprehensive analysis method breaks through the limitations of traditional detection methods and is no longer limited to a single parameter evaluation. It can more comprehensively reflect the complex electrochemical processes and physical changes inside the battery, avoiding the masking of other potential problems due to a single parameter being normal, thereby accurately locating various potential fault hazards that may exist in the battery, providing a rich and complete information basis for a comprehensive understanding of the battery's health status;

[0122] 2. It can keenly capture the subtle change trends of the battery at different operating stages and promptly detect the dynamic evolution of battery performance. Whether it is the gradual increase of internal resistance, the change of the charge state change law, or the abnormal fluctuation of other parameters over time, it can be effectively monitored and quantified as a fault risk index at an early stage. The dynamic early warning mechanism can issue an alarm in time when the battery fault first appears, significantly advancing the time node of fault discovery, buying sufficient time for taking corresponding maintenance measures, and greatly reducing the possibility of serious impact on the energy storage system due to sudden battery failure;

[0123] 3. Incorporating the aging coefficient into the calculation process of the fault risk index effectively improves the accuracy and reliability of detection. Since battery aging has a profound impact on its performance and fault characteristics, batteries with different aging degrees respond completely differently to the same parameter changes. Taking this factor into consideration, differentiated evaluation standards are adopted for aged batteries and new batteries, thereby avoiding misjudgments or missed judgments caused by unified threshold judgments. By accurately quantifying the interaction between the battery aging degree and other real-time status parameters, the calculated fault risk index can more accurately reflect the actual fault risk level of the battery, providing a more reliable basis for fault judgment of the energy storage system, and effectively ensuring that the entire energy storage system can maintain a stable, efficient and safe operating state at different life cycle stages.

[0124] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0125] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0126] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0127] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0128] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for online fault monitoring of an energy storage system, characterized in that: The energy storage system includes a plurality of energy storage battery assemblies, each of which includes a plurality of batteries, and the monitoring method includes the following steps: Acquire the real-time status parameters of each battery in a current detection cycle and a previous detection cycle of a plurality of energy storage battery assemblies, wherein the real-time status parameters include: the voltage value, current value, temperature value, state of charge value and charge and discharge rate of the battery; Calculating the internal resistance change rate of the battery based on the real-time state parameters of the battery; Calculating a failure risk index of the battery based on the internal resistance change rate and in combination with the real-time state parameters and aging coefficient of the battery; Determine whether the fault risk index of the battery is greater than or equal to the fault risk threshold, if so, control the battery to stop running, and send fault alarm information to the control end of the energy storage system, if not, control the battery to run normally.

2. The method for online fault monitoring of an energy storage system according to claim 1, characterized in that: The internal resistance change rate of the battery The calculation formula is: R i =R ohm_i +R p_i ; R p_i =R p_0 ×(1-SOC i ) n ; Among them, ΔR i is the difference between the internal resistance of the battery in the current detection cycle and the previous detection cycle, ΔV i is the voltage difference between the battery in the current detection cycle and the previous detection cycle, ΔI i is the difference between the current value of the battery in the current detection cycle and the previous detection cycle, C rate_i is the charge and discharge rate of the battery, C rate_0 is the reference charge and discharge rate of the battery, α1, β1 and λ1 are the correlation coefficients of the battery charge and discharge rate, T i is the temperature value of the battery in the current detection cycle, T0 is the battery reference temperature value, α2 and λ2 are the correlation coefficients of the battery temperature, SOC i is the state of charge value of the battery in the current detection cycle, SOC0 is the battery reference state of charge, α3, β3 and λ3 are the correlation coefficients of the battery state of charge, R p_i is the polarization resistance value of the battery in the current detection cycle, R p_0 is the polarization resistance value corresponding to the battery state of charge of 0, n is the empirical constant related to the polarization resistance and the battery state of charge, Δt is the time difference between the current detection cycle and the previous detection cycle, R ohm_i is the ohmic resistance value of the battery in the current detection cycle, R i is the internal resistance of the battery in the current detection cycle, R i-1 is the internal resistance value of the battery in the previous detection cycle, and i is the serial number of the current detection cycle.

3. The method for online fault monitoring of an energy storage system according to claim 2, characterized in that: The aging coefficient is related to the battery life and the number of charge and discharge cycles, and its calculation formula is: Among them, SOH i is the aging coefficient of the battery, Cycle count_i Cycle is the number of charge and discharge cycles of the battery. max is the maximum number of charge and discharge cycles of the battery, Age count_i Age is the age of the battery. max is the maximum service life of the battery, σ is a constant term related to the number of battery charge and discharge cycles, and η is a constant term related to the service life of the battery.

4. The method for online fault monitoring of an energy storage system according to claim 2, characterized in that: A first temperature sensor is disposed on the outer side of the side wall of each battery, and a second temperature sensor is also disposed inside the battery; The calculation formula of the ohmic resistance value of the battery in the current detection cycle is: Among them, T i1 is the side wall temperature value of the battery obtained by the first temperature sensor, T i2 is the internal temperature value of the battery acquired by the second temperature sensor, and α2, λ2 and m are correlation coefficients of battery temperature respectively.

5. The method for online fault monitoring of an energy storage system according to any one of claims 1 to 4, characterized in that: The calculation formula of the failure risk index of the battery is: Among them, RI i is the failure risk index of the battery in the current detection cycle, R initial is the initial internal resistance of the battery, V0 is the open circuit voltage of the battery, I0 is the nominal current of the current, T max is the maximum safe operating temperature of the battery, R max is the maximum internal resistance of the battery, and θ1, θ2, θ3, θ4, θ5, θ6 and θ7 are constant terms related to the aging factor, internal resistance change rate, battery voltage, battery current, battery temperature, state of charge, charge and discharge rate and battery internal resistance, respectively.

6. The method for online fault monitoring of an energy storage system according to claim 5, characterized in that: The controlling the battery to stop running and sending fault alarm information to the control terminal of the energy storage system includes: Sending a battery operation stop instruction to the battery management system of the energy storage system; Cutting off the corresponding solid-state switch of the battery based on the battery management system; Sending fault warning information to the control end of the energy storage system.

7. An online fault monitoring device for an energy storage system, characterized in that: The energy storage system includes a plurality of energy storage battery assemblies, each of which includes a plurality of batteries, and the monitoring device includes: A first data acquisition module, which is used to obtain real-time status parameters of each battery in a current detection cycle and a previous detection cycle in a plurality of energy storage battery assemblies, wherein the real-time status parameters include: voltage value, current value, temperature value, state of charge value and charge and discharge rate of the battery; A first monitoring and calculating module, which is used to calculate the internal resistance change rate of the battery based on the real-time state parameters of the battery; A second monitoring and calculation module, which is used to calculate the failure risk index of the battery based on the internal resistance change rate in combination with the real-time state parameters and aging coefficient of the battery; A fault judgment alarm module is used to determine whether the fault risk index of the battery is greater than or equal to the fault risk threshold. If so, the battery is controlled to stop running and a fault alarm message is sent to the control end of the energy storage system. Otherwise, the battery is controlled to run normally.

8. An online fault warning method for an energy storage system, characterized in that: After the battery is controlled to stop running by the online fault monitoring method for the energy storage system according to any one of claims 1 to 6, the early warning method comprises the following steps: Obtain the open circuit voltage and real-time temperature value of the battery in a stopped state in real time; Determining whether the open circuit voltage and the real-time temperature value of the battery are greater than or equal to respective corresponding thresholds; When the open circuit voltage and / or the real-time temperature value exceeds a corresponding threshold value, it is determined that the battery is in a dangerous state, and a danger warning message is sent to a control terminal of the energy storage system.

9. The method for online fault warning of energy storage system according to claim 7, characterized in that: After sending the danger warning information to the control end of the energy storage system, the method further includes: A plurality of batteries adjacent to the battery in a dangerous state are controlled to stop operating.

10. An online fault warning device for an energy storage system, characterized in that: include: A second data acquisition module, which is used to obtain the open circuit voltage and real-time temperature value of the battery in a stopped state in real time; A second data judgment module, which is used to judge whether the open circuit voltage and the real-time temperature value of the battery are greater than or equal to their respective corresponding thresholds; The danger determination and warning module is used to determine that the battery is in a dangerous state when the open circuit voltage and / or the real-time temperature value exceeds a corresponding threshold value, and to send danger warning information to the control end of the energy storage system.

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