Industrial and commercial energy storage system intelligent control method and system

Abstract

The invention relates to the technical field of industrial and commercial energy storage system intelligent control, and discloses an industrial and commercial energy storage system intelligent control method and system. And estimating the amplitude and duration of the instantaneous power peak according to a preset equipment power characteristic file to obtain a pre-judged instantaneous power peak parameter. According to the method, the electric quantity state of the energy storage battery can be adjusted in advance, it is ensured that the battery has sufficient power response capacity during pulse load output, and the risks of voltage sag and equipment shutdown caused by insufficient electric quantity are effectively avoided.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for industrial and commercial energy storage systems, and in particular to an intelligent control method and system for industrial and commercial energy storage systems. Background Technology

[0002] Commercial and industrial energy storage systems play a crucial role in modern production operations. Their primary purpose is to ensure a stable power supply while helping users save on electricity costs through sophisticated charging and discharging schedules. Such a system typically consists of battery packs, a power conversion system, a battery management system, and an intelligent control center responsible for overall coordination. This control center determines the optimal charging and discharging scheme based on forecasts of user electricity consumption and real-time electricity price information. However, in actual operation, newly introduced industrial equipment, such as high-power laser cutting machines or multi-axis robotic workstations, exhibits periodic, short-duration, but extremely high-instantaneous power consumption characteristics. Existing energy storage system power consumption forecasting methods typically rely on learning from past power consumption records, making them better suited for handling relatively smooth, predictable power consumption curves, but unable to accurately capture these high-frequency, high-amplitude instantaneous power spikes.

[0003] This blind spot in forecasting means that when the intelligent control center executes economical dispatching schemes, such as instructing the energy storage battery to discharge during peak electricity price periods, the overall state of charge of the battery remains at a moderately low level, failing to prepare for potential sudden surges in power. When newly introduced equipment on the production line begins operation and its pulsed power spikes occur instantaneously, the combined power supply capacity of the grid and the estimated discharge power from the energy storage system is still insufficient to meet this sudden surge in power demand. More importantly, because the battery's state of charge is already at a moderately low level, its ability to output high current is significantly limited. This prevents the battery from providing the required ultra-high instantaneous power in a very short time, as the voltage drops rapidly, triggering protection mechanisms or causing the equipment to malfunction.

[0004] To ensure the normal operation of production equipment and avoid a plant-wide shutdown due to a sudden voltage drop, the power conversion system of the energy storage system is forced to discharge at a power exceeding its long-term stable operating rating for an extremely short period. While this emergency response avoids the risk of immediate production stoppage, it causes enormous electrical and thermal stress on critical power semiconductor devices within the power conversion system, such as insulated-gate bipolar transistor modules. The instantaneous high current passing through these devices generates Joule heating far exceeding the design threshold, causing the junction temperature to rise sharply within milliseconds. Simultaneously, the rapid switching between high voltage and high current also induces abnormal electric field and current density distributions within the devices, accelerating material fatigue and aging. The cumulative damage to the devices caused by this extreme operation is irreversible, potentially leading to performance degradation and even the risk of premature failure. Summary of the Invention

[0005] This invention provides an intelligent control method and system for industrial and commercial energy storage systems, aiming to solve the problems of existing industrial and commercial energy storage systems being unable to accurately predict instantaneous power spikes when facing high-power pulse loads from industrial equipment startup, resulting in poor energy storage battery charge status, excessive impact on the power conversion system, and damage to the battery itself.

[0006] Firstly, in order to solve the above-mentioned technical problems, the present invention provides an intelligent control method for industrial and commercial energy storage systems, comprising: Acquire the operating status signal of industrial equipment starting a high-power pulse load; The operating status signal is identified as precursor information, and the amplitude and duration of the instantaneous power spike are estimated based on the preset equipment power characteristic profile to obtain the predicted instantaneous power spike parameters. Based on the instantaneous power spike parameters, adjust the state of charge of the energy storage battery to ensure the power output of the energy storage battery under the pulse load; The operating parameters are adjusted according to the instantaneous power spike parameters to ensure that the power conversion system can withstand the impact under the pulse load.

[0007] Preferably, the step of identifying the operating status signal as precursor information and estimating the amplitude and duration of the instantaneous power spike based on a preset equipment power characteristic profile to obtain the predicted instantaneous power spike parameters includes: A communication link is established with the power regulation unit inside the industrial equipment to obtain the instantaneous power adjustment signal made by the industrial equipment during operation based on material inhomogeneity; Receive the instantaneous power adjustment signal and analyze the actual instantaneous power requirement of the industrial equipment; Based on the actual instantaneous power demand, the instantaneous power peak parameter is corrected to obtain the corrected instantaneous power peak parameter.

[0008] Preferably, adjusting the state of charge of the energy storage battery according to the instantaneous power spike parameters to ensure the power output of the energy storage battery under the pulsed load includes: Real-time acquisition of instantaneous power generation from the main power sources within the factory and real-time power consumption of auxiliary equipment provides information on internal power supply and demand. Based on the internal power supply and demand situation, determine whether the internal power supply is sufficient to support the charging power requirements of the energy storage battery; If not, identify non-core auxiliary equipment within the factory that has flexible adjustment capabilities; Send power reduction or delayed start commands to the auxiliary equipment to release power resources. This causes the battery management system to perform a short-term charging operation to adjust the state of charge of the energy storage battery.

[0009] Preferably, adjusting the operating parameters according to the instantaneous power peak parameters includes: Real-time acquisition of operating parameters of key components of the power conversion system; Assess the health status of the critical components based on the operating parameters; The operating parameters of the power conversion system are dynamically adjusted based on the health status.

[0010] Preferably, the real-time acquisition of operating parameters of key components of the power conversion system includes: Inside the package of the power conversion system, a piezoelectric thin film sensor and a thermocouple array are integrated. The piezoelectric thin-film sensor monitors the change in mechanical stress of the key power semiconductor device when a current pulse passes through it. The instantaneous junction temperature of the critical power semiconductor device is captured using the thermocouple array. The mechanical stress change and the instantaneous junction temperature are sampled synchronously, and preliminary filtering and timestamp marking are performed to obtain the synchronously sampled mechanical stress change and instantaneous junction temperature; the mechanical stress change is correlated with the amplitude and duration of the current pulse to obtain the correlation analysis results; By combining the instantaneous junction temperature and the correlation analysis results, the mechanical and thermal stresses borne by the key power semiconductor devices are identified, and operating parameters reflecting the health status of the components under specific operating conditions are obtained.

[0011] Preferably, assessing the health status of the critical components based on the operating parameters includes: Frequency domain analysis was performed on the mechanical stress variation to extract the dominant vibration mode and attenuation characteristics of the mechanical stress variation; The dominant vibration mode and the attenuation characteristics are matched with the mechanical fatigue characteristic spectrum of multiple preset pulse load modes to obtain the mechanical fatigue accumulation factor corresponding to the current pulse load mode. A fast Fourier transform is performed on the instantaneous junction temperature data to analyze the temperature fluctuation amplitude and thermal gradient change rate of the instantaneous junction temperature data within the pulse load cycle. The temperature fluctuation amplitude and the thermal gradient change rate are matched with the thermal aging characteristic spectra of multiple preset pulse load modes to obtain the thermal aging acceleration factor corresponding to the current pulse load mode. The mechanical fatigue accumulation factor and the thermal aging acceleration factor are weighted and fused together, and combined with the cumulative operating time of the key component, the current fatigue accumulation degree of the key component is calculated. The current fatigue accumulation is compared with a preset failure threshold to obtain the health status assessment result of the key component.

[0012] Preferably, the step of weightedly fusing the mechanical fatigue accumulation factor and the thermal aging acceleration factor, and combining them with the cumulative operating time of the key component to calculate the current fatigue accumulation degree of the key component includes: Receive the mechanical fatigue accumulation factor and the thermal aging acceleration factor; Monitor the instantaneous power output, operating temperature, and frequency and amplitude of the current pulse load of the key components; The instantaneous coupling strength between mechanical stress and thermal stress is calculated based on the instantaneous power output, the operating temperature, the frequency of the current pulse load, and the amplitude. The weighted fusion weights of the mechanical fatigue accumulation factor and the thermal aging acceleration factor are dynamically adjusted based on the instantaneous coupling strength. The adjusted weighted fusion weights are applied to the mechanical fatigue accumulation factor and the thermal aging acceleration factor, and combined with the cumulative operating time of the key component, the current fatigue accumulation degree of the key component is calculated.

[0013] Preferably, comparing the current fatigue accumulation with a preset failure threshold to obtain the health status assessment result of the critical component includes: The actual operating mode of the key components is monitored in real time to obtain the actual operating mode. Based on the actual operating mode, select a failure threshold that matches the actual operating mode from the preset dynamic failure threshold library; The current fatigue accumulation is compared with the failure threshold to obtain the health status assessment result of the key component.

[0014] Preferably, the step of dynamically adjusting the weighted fusion weights of the mechanical fatigue accumulation factor and the thermal aging acceleration factor based on the instantaneous coupling strength includes: Receive the mechanical fatigue accumulation factor and the thermal aging acceleration factor; Monitor the instantaneous power output of the key components, the operating temperature, and the frequency and amplitude of the current pulse load; Obtain the cumulative running time of the key components; The instantaneous coupling strength between mechanical stress and thermal stress is calculated based on the instantaneous power output, the operating temperature, the frequency and amplitude of the pulse load, and the cumulative operating time. The weighted fusion weights of the mechanical fatigue accumulation factor and the thermal aging acceleration factor are dynamically adjusted based on the instantaneous coupling strength.

[0015] Secondly, the present invention provides an industrial and commercial energy storage system, comprising: The input terminal is used to acquire the operating status signal of the high-power pulse load starting of industrial equipment; the operating status signal is identified as precursor information, and the amplitude and duration of the instantaneous power spike are estimated according to the preset equipment power characteristic profile to obtain the predicted instantaneous power spike parameters; The adjustment terminal is used to adjust the state of charge of the energy storage battery according to the instantaneous power peak parameter to ensure the power output of the energy storage battery under the pulse load; and to adjust the operating parameters according to the instantaneous power peak parameter to ensure that the power conversion system can withstand the impact under the pulse load.

[0016] This application discloses an intelligent control method and system for industrial and commercial energy storage systems. By acquiring the operating status signal of industrial equipment starting a high-power pulse load and identifying it as precursor information, the method estimates the amplitude and duration of the instantaneous power spike based on a preset equipment power characteristic profile, thus obtaining the predicted instantaneous power spike parameters. Accordingly, this method can adjust the state of charge of the energy storage battery in advance, ensuring that the battery has sufficient power response capability when the pulse load is output, effectively avoiding the risk of voltage drop and equipment shutdown due to insufficient charge. Simultaneously, this method can also adjust the operating parameters of the power conversion system based on the predicted instantaneous power spike parameters, thereby significantly reducing the electrical and thermal stress impacts caused by the pulse load on the key components of the power conversion system. This effectively solves the problem in existing technologies where energy storage systems cannot accurately capture high-frequency, high-amplitude instantaneous power spikes, leading to battery damage and power conversion system overload. Through this forward-looking intelligent control, this application not only ensures the stable operation of production equipment but also extends the service life of the energy storage battery and power conversion system, reducing long-term operation and maintenance costs, and has significant economic benefits and technical advantages. Attached Figure Description

[0017] Figure 1 This is a flowchart of an intelligent control method for an industrial and commercial energy storage system provided in an embodiment of the present invention; Figure 2 This is a flowchart of a method for identifying operating status signals as precursor information according to an embodiment of the present invention; Figure 3 This is a flowchart of a method for adjusting the state of charge of an energy storage battery according to an embodiment of the present invention; Figure 4 This is a schematic diagram of an industrial and commercial energy storage system provided in this application. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Reference Figure 1 , Figure 1 This is a flowchart of an intelligent control method for an industrial and commercial energy storage system provided by an embodiment of the present invention, including the following steps: S1, acquire the operating status signal of the industrial equipment starting a high-power pulse load; S2, the operating status signal is identified as precursor information, and the amplitude and duration of the instantaneous power spike are estimated according to the preset equipment power characteristic profile to obtain the predicted instantaneous power spike parameters; S3, adjust the state of charge of the energy storage battery according to the instantaneous power spike parameters to ensure the power output of the energy storage battery under the pulse load; S4, adjust the operating parameters according to the instantaneous power spike parameters to ensure that the power conversion system can withstand the impact under the pulse load.

[0020] The "commercial and industrial energy storage system" mentioned in this application typically consists of a battery pack, a power conversion system, a battery management system, and an intelligent control center. Its aim is to achieve electricity cost savings while ensuring a stable power supply through optimized charging and discharging strategies. "Pulse load" refers to the short-duration but extremely high instantaneous power demand generated by industrial equipment during startup or specific operating phases. For example, high-power laser cutting machines generate such pulse loads during the cutting process, and large stamping equipment generates such pulse loads during the stamping action. "Operating status signals" refer to various electrical or mechanical signals that reflect whether industrial equipment is about to enter or has already entered a high-power pulse load state, such as current, voltage, vibration, and temperature.

[0021] Embodiments of this application provide an intelligent control method for industrial and commercial energy storage systems. This method, through a series of coordinated steps, aims to effectively cope with instantaneous high-power pulse loads generated by industrial equipment.

[0022] First, the method involves acquiring operating status signals of industrial equipment starting high-power pulsed loads. In one implementation, high-precision current and voltage sensors can be installed at key electrical interfaces of the industrial equipment to monitor current and voltage changes in real time. When the equipment starts or performs specific operations, these sensors capture instantaneous fluctuations in current and voltage and transmit these electrical signals as operating status signals to the control system. In another implementation, the industrial equipment's own control system interface can be used to directly read the equipment's internal operating mode or status flags, such as whether the equipment is in "startup mode" or "high-power operation mode." These digital signals can also serve as operating status signals.

[0023] Secondly, the method identifies the operating status signal as precursor information and, based on a preset equipment power characteristic profile, estimates the amplitude and duration of the instantaneous power spike to obtain the predicted instantaneous power spike parameters. For example, the control system can receive an instantaneous current rise signal from a current sensor. This signal is identified as a precursor to a pulsed load. Subsequently, the system queries a pre-stored equipment power characteristic profile. This profile may contain typical power curves for a specific model of laser cutting machine at startup; for example, its instantaneous power spike typically reaches 100kW within 50 milliseconds and lasts for 200 milliseconds. Based on this profile, the system can estimate that the instantaneous power spike amplitude of this pulsed load is 100kW and the duration is 200 milliseconds. In another implementation, if the operating status signal is an internal "high-power mode activation" command, the system can directly retrieve the power spike parameters for the corresponding mode from the profile according to this command.

[0024] Furthermore, this method adjusts the state of charge (SOC) of the energy storage battery based on the instantaneous power spike parameters to ensure the battery can output power under the pulsed load. For example, if the predicted instantaneous power spike parameters indicate an impending large power demand, and the battery's SOC is low at this time, the battery management system can immediately initiate a short-term charging operation. This operation can draw power from the grid or other dispatchable power sources within the plant to rapidly increase the battery's SOC to a level capable of stable discharge under high current. In another implementation, if the battery has sufficient charge but its internal temperature or voltage state is unsuitable for high-power discharge, the system can adjust the battery's internal balancing strategy or preheating / precooling system to bring it to its optimal discharge state before the pulsed load arrives.

[0025] Finally, the method adjusts operating parameters based on the instantaneous power spike parameters to ensure the power conversion system can withstand the impact of the pulsed load. For example, when a high-power pulsed load is anticipated, the power conversion system can adjust its internal switching frequency, modulation strategy, or current limit. Specifically, the switching frequency can be temporarily increased to reduce output ripple, or the pulse width modulation (PWM) strategy can be adjusted to optimize the switching losses of the power semiconductor devices, thereby better distributing thermal stress during the pulsed load. In another implementation, the system can pre-activate redundant modules or backup cooling systems within the power conversion system to enhance its ability to withstand short-term high-power output.

[0026] The intelligent control method for industrial and commercial energy storage systems disclosed in this application acquires the operating status signal of industrial equipment starting a high-power pulse load and identifies it as precursor information. Based on a preset equipment power characteristic profile, it can accurately predict the amplitude and duration of the instantaneous power peak, obtaining the predicted instantaneous power peak parameters. Based on these predicted parameters, the system can adjust the state of charge of the energy storage battery in advance to ensure that the battery has sufficient energy and output capacity when the pulse load arrives. Simultaneously, the system can also adjust the operating parameters of the power conversion system to withstand the impact under pulse loads, avoiding equipment damage or shutdown due to overload.

[0027] For example, when a newly introduced high-power stamping press in a factory is about to start, the control method of this application first acquires the press's start-up signal or pre-operation status signal. These signals are identified as precursor information for an impending pulse load. The system then estimates the amplitude and duration of the instantaneous power spike that may be generated at the moment of startup based on the press's equipment power characteristic profile. Suppose the estimation shows that the press will generate a power spike lasting 100 milliseconds with an amplitude as high as 500kW. In traditional systems, due to the lack of such forward-looking estimation, the energy storage system may be in a low-to-medium power state under economical dispatch and unable to respond in time.

[0028] However, in the method of this application, the control system immediately instructs the battery management system to perform short-term rapid charging of the energy storage battery or adjust the parallel / series configuration of the battery pack according to the predicted power peak parameters, so that it reaches the optimal discharge state and output capacity before the pulse load arrives. At the same time, the power conversion system also dynamically adjusts its internal switching frequency and current limit according to the predicted parameters. For example, it temporarily increases the switching frequency to optimize the switching losses of the power semiconductor devices and activates additional heat dissipation modules to enhance its ability to withstand short-term high power output. When the press actually starts and generates an instantaneous power peak, the energy storage system and the power conversion system are fully prepared and can smoothly absorb and output the required power. In some of the above embodiments, the predicted instantaneous power peak parameters are obtained by identifying the operating status signal as precursor information and estimating the amplitude and duration of the instantaneous power peak according to the preset equipment power characteristic profile. However, in actual industrial production, due to factors such as material properties, process fluctuations, or dynamic adjustments within the equipment, the actual power demand of industrial equipment may deviate from the preset parameters. This is especially true when dealing with material inhomogeneities, where the power regulation unit within the equipment makes real-time instantaneous power adjustments. Relying solely on preset parameters for estimation may result in inaccurate predictions of instantaneous power spikes, thereby affecting the energy storage system's response efficiency to pulsed loads and the protection effectiveness of the power conversion system.

[0029] In this regard, refer to Figure 2 , Figure 2 A flowchart of a method for identifying operational status signals as precursor information is provided, wherein S2 includes: S21, establish a communication link with the power regulation unit inside the industrial equipment to obtain the instantaneous power adjustment signal made by the industrial equipment during operation based on material inhomogeneity; S22, Receive the instantaneous power adjustment signal and parse the actual instantaneous power requirement of the industrial equipment; S23, Based on the actual instantaneous power demand, the instantaneous power peak parameter is corrected to obtain the corrected instantaneous power peak parameter.

[0030] Specifically, the power regulation unit refers to a control module within industrial equipment responsible for adjusting power output in real time to adapt to changes in operating conditions. For example, in a large stamping press, its hydraulic system or servo motor drive system may include a power regulator for dynamically adjusting the stamping force based on changes in material hardness or thickness. The communication link can be a data transmission channel established via wired (e.g., Ethernet, CAN bus) or wireless (e.g., Wi-Fi, LoRa) methods to ensure real-time data interaction between the energy storage system and the industrial equipment. The instantaneous power adjustment signal is a real-time data stream of power output changes generated by the power regulation unit in response to internal factors such as material inhomogeneity.

[0031] The actual instantaneous power demand refers to the real power consumption or output demand of industrial equipment at the current moment or in the very short future, obtained by analyzing the instantaneous power adjustment signal. Correcting the instantaneous power spike parameters means adjusting the amplitude and duration of the initially estimated instantaneous power spike based on the actual instantaneous power demand, making it closer to the actual operating conditions of the industrial equipment, thereby obtaining more accurate corrected instantaneous power spike parameters.

[0032] The solution proposed in this application establishes a communication link with the power regulation unit inside industrial equipment, enabling real-time acquisition of instantaneous power adjustment signals made by the equipment during operation due to factors such as material inhomogeneity. These signals directly reflect the dynamic response of the equipment to actual operating conditions and contain more refined and real-time power demand information than preset profiles. By receiving and parsing these instantaneous power adjustment signals, the actual instantaneous power demand of the industrial equipment can be accurately identified. Based on this actual instantaneous power demand, the initially estimated instantaneous power peak parameters are corrected, thereby compensating for potential prediction biases that may exist when relying solely on preset profiles, resulting in more accurate predictions of the amplitude and duration of instantaneous power peaks.

[0033] Through the above technical solution, this application can significantly improve the prediction accuracy of instantaneous power peak parameters of industrial equipment. Compared with estimation based solely on preset data, introducing and correcting real-time instantaneous power adjustment signals allows the energy storage system to more accurately grasp the characteristics of upcoming pulse loads. Consequently, the state-of-charge adjustment of the energy storage battery and the operating parameters of the power conversion system will better match actual needs, effectively avoiding overcharging / over-discharging of the energy storage battery or unnecessary impacts on the power conversion system caused by inaccurate predictions, thereby extending equipment lifespan and improving system stability and economy.

[0034] In some preferred embodiments, a large electric arc furnace is used as an example. During the smelting process, due to the inhomogeneity of scrap steel composition and melting state, the electrodes of the electric arc furnace are frequently adjusted to maintain a stable arc, resulting in drastic fluctuations in instantaneous power demand. The control method of this application can establish a Modbus TCP communication link with the electrode adjustment system of the electric arc furnace. When the electrode adjustment system detects a change in furnace impedance and issues an instantaneous power adjustment command (e.g., increasing or decreasing electrode current), these commands are received by the energy storage system as instantaneous power adjustment signals. The energy storage system analyzes these signals to obtain the actual instantaneous power demand of the electric arc furnace, for example, identifying that the electric arc furnace will require an additional 5MW of power in the next few seconds. Based on this actual instantaneous power demand, the energy storage system corrects the instantaneous power spike parameter (e.g., amplitude 10MW, duration 5 seconds) originally estimated based on historical data, adjusting it to a more precise amplitude of 12MW and duration of 4 seconds. In this way, the energy storage system can more accurately predict and respond to the pulse load of the electric arc furnace, ensuring that the energy storage battery and power conversion system can cope with the situation in the best condition.

[0035] In some embodiments described above, this application proposes a method for adjusting the state of charge of an energy storage battery based on predicted instantaneous power peak parameters to ensure the battery's output power under pulsed loads. However, in real-world industrial and commercial applications, the power supply situation within a factory can be complex and variable. For example, when the energy storage battery needs rapid charging to cope with upcoming high-power pulsed loads, if the factory's main power source (such as self-generating equipment) has insufficient instantaneous power generation, or if auxiliary equipment has high real-time power consumption, the internal power supply may not be able to meet the charging needs of the energy storage battery. This could affect the battery's ability to adjust to its optimal state of charge in a timely manner to respond to pulsed loads. If these problems are not addressed, the energy storage system may not be able to fully utilize its peak-shaving and valley-filling functions, and may even be unable to effectively support the instantaneous high-power demands of industrial equipment due to insufficient power, thereby affecting production efficiency and grid stability.

[0036] In this regard, refer to Figure 3 , Figure 3 This is a flowchart of a method for adjusting the state of charge of an energy storage battery according to an embodiment of the present invention. S3 includes: S31: Real-time acquisition of the instantaneous power generation of the main power sources and the real-time power consumption of auxiliary equipment within the factory, to obtain the internal power supply and demand situation; S32, Based on the internal power supply and demand situation, determine whether the internal power supply is sufficient to support the charging power requirements of the energy storage battery; S33, if not, identify non-core auxiliary equipment within the factory that has flexible adjustment capabilities; S34, send a power reduction or delayed start command to the auxiliary device to release power resources, so that the battery management system performs a short-term charging operation to adjust the state of charge of the energy storage battery.

[0037] Specifically, in the step of obtaining real-time data on the instantaneous power generation of the main power sources and the real-time power consumption of auxiliary equipment within the factory to determine the internal power supply and demand situation, the main power sources may include, but are not limited to, the factory's own photovoltaic power generation system, wind power generation system, or small gas turbine, etc., whose instantaneous power generation is monitored in real time through corresponding sensors. The auxiliary equipment covers all electrical equipment within the factory except for core production equipment, such as lighting systems, ventilation systems, air conditioning systems, non-critical pump stations, office equipment, etc., whose real-time power consumption is collected through smart meters or power sensors. Through comprehensive analysis of this data, the total power supply and demand within the factory can be accurately determined, thus forming a real-time internal power supply and demand situation.

[0038] The step of determining whether the internal power supply is sufficient to support the charging power requirements of the energy storage battery, based on the internal power supply and demand situation, can be understood as comparing the current total internal power supply with the charging power required by the energy storage battery. The energy storage battery charging power requirement is calculated based on predicted instantaneous power peak parameters and the current and target charge levels of the energy storage battery. If the total internal power supply minus the power consumption of the core production equipment is still less than the charging power requirement of the energy storage battery, then the internal power supply is deemed insufficient.

[0039] In practical applications, if an internal power supply shortage is detected, non-core auxiliary equipment with flexible adjustment capabilities within the factory is identified. Non-core equipment refers to equipment whose operational status adjustments will not substantially affect the factory's core production processes. Flexible adjustment capability means that these devices can release some power resources in a short period through power reduction (e.g., lowering operating power or reducing operating frequency) or delayed startup (e.g., postponing the originally planned startup time). Examples include non-emergency lighting, some ventilation equipment, non-critical cooling pumps, or some intermittently operating heating equipment.

[0040] Furthermore, power reduction or delayed start-up commands are sent to the auxiliary equipment to free up power resources. These commands can be sent to the corresponding auxiliary equipment controllers via the factory's Energy Management System (EMS) or Building Automation System (BAS). For example, a dimming command can be sent for lighting equipment; a command to reduce fan speed can be sent for ventilation equipment; and a delayed start-up command can be sent for non-critical equipment scheduled to start. Through these operations, power originally allocated to these auxiliary equipment can be effectively diverted to meet more urgent energy storage battery charging needs.

[0041] This causes the battery management system (BMS) to perform a short-time charging operation to adjust the state of charge (SOC) of the energy storage battery. The short-time charging operation refers to the BMS, upon receiving released power, charging the energy storage battery at a high power level for a short period according to a preset charging strategy, rapidly raising its SOC to a level capable of effectively handling upcoming pulse loads. This process aims to ensure that the energy storage battery has sufficient energy reserves and power response capabilities before a pulse load occurs.

[0042] This application's solution intelligently manages internal power resources by monitoring the factory's power supply and demand in real time and predicting impending instantaneous power spikes. When it detects that the energy storage battery needs charging to cope with pulsed loads, and the internal power supply may be insufficient, the system no longer passively waits or relies on the external power grid. Instead, it proactively identifies and adjusts non-core flexible auxiliary equipment within the factory. This proactive load-side management releases the power resources previously consumed by auxiliary equipment, creating conditions for short-term rapid charging of the energy storage battery. This mechanism ensures that the energy storage battery can adjust its charge state in a timely manner at critical moments, effectively supporting the high-power pulsed loads of industrial equipment and avoiding system response delays or performance degradation due to insufficient battery charge.

[0043] Through the aforementioned technical solution, this application significantly enhances the flexibility and autonomy of commercial and industrial energy storage systems in handling pulsed loads from industrial equipment. Compared to basic solutions, this application, through refined internal power resource scheduling, effectively guarantees the charging needs of energy storage batteries, especially during periods of internal power shortage, thereby ensuring that the energy storage batteries are always in optimal standby condition. This not only reduces dependence on the external power grid and minimizes grid impact and additional electricity costs that may result from instantaneous high-power charging, but also optimizes the overall energy utilization efficiency within the factory, improving the adaptability and reliability of the entire energy storage system to complex operating conditions.

[0044] In some preferred embodiments, a specific example is given below. Suppose a large manufacturing plant whose core production line includes a large stamping press that generates instantaneous power spikes of up to several megawatts during startup or at specific stages of its work cycle. The plant is also equipped with a photovoltaic power generation system as the primary power source, as well as a large number of auxiliary devices, such as workshop lighting, ventilation systems, cooling water pumps, and air conditioning in office areas.

[0045] When the control system anticipates that the press is about to start a high-power pulse load based on the operating status signals, it calculates that the energy storage battery needs to be rapidly charged to reach the required state of charge based on the predicted instantaneous power peak parameters. At this time, the system will acquire the instantaneous power generation of the photovoltaic power generation system and the real-time power consumption of all auxiliary equipment in real time. It will find that the current photovoltaic power generation is low and the total power consumption of the auxiliary equipment is high, resulting in insufficient internal power supply to support the rapid charging needs of the energy storage battery.

[0046] The system then identified non-core auxiliary equipment within the factory that possessed flexible adjustment capabilities. For example, it reduced the lighting brightness in non-critical areas of the workshop by 20%, decreased the fan speed of some ventilation equipment by 15%, and delayed the start of the backup cooling water pump originally scheduled to start in the next 5 minutes. Through these operations, the system successfully released approximately 500kW of power. This released power was immediately directed to the battery management system for short-term charging of the energy storage battery. Within minutes, the energy storage battery's state of charge was adjusted to a level sufficient to fully handle the instantaneous power spikes that the press would generate. When the press started, the energy storage battery was able to smoothly output the required high power, effectively reducing the load impact on the grid and ensuring production continuity and grid stability.

[0047] In some embodiments described above, this application proposes a scheme to adjust operating parameters based on predicted instantaneous power spike parameters to ensure the power conversion system can withstand impacts under pulsed loads. However, in actual operation, critical components within the power conversion system may experience varying degrees of fatigue and aging due to long-term operation, environmental factors, or instantaneous high-power impacts. If adjustments are made uniformly based solely on predicted instantaneous power spike parameters, the actual health condition of the components may not be adequately considered, leading to inaccurate adjustment strategies and potentially accelerating component wear, thus affecting the long-term stability and reliability of the system. Therefore, this application further proposes a more refined method for adjusting operating parameters. By real-time monitoring and evaluation of the health condition of critical components in the power conversion system, dynamic optimization of operating parameters is achieved to more effectively ensure the power conversion system can withstand impacts under pulsed loads.

[0048] Specifically, the steps described above for adjusting operating parameters based on instantaneous power spike parameters to ensure the power conversion system can withstand the impact of pulsed loads include the following sub-steps: Real-time acquisition of operating parameters of key components of the power conversion system; Assess the health status of the critical components based on the operating parameters; The operating parameters of the power conversion system are dynamically adjusted based on the health status.

[0049] Real-time acquisition of operating parameters of key components in a power conversion system refers to the continuous collection of physical quantities related to the operating status of key components (such as power semiconductor devices, capacitors, inductors, etc.) through various sensors integrated inside or outside the power conversion system. These parameters include temperature, current, voltage, vibration, and stress. These parameters can directly or indirectly reflect the load condition and potential fatigue accumulation of the components under the current operating conditions.

[0050] Furthermore, assessing the health status of the critical components based on the operating parameters refers to using the collected real-time operating parameters, combined with preset component performance models, aging models, or fault diagnosis algorithms, to quantitatively evaluate the current performance degradation, remaining lifespan, or potential failure risk of the critical components. This assessment result can be a health index, fatigue accumulation, or failure probability, etc., and its purpose is to provide an objective basis for determining whether a component is in a sub-healthy state or nearing failure.

[0051] Therefore, dynamically adjusting the operating parameters of the power conversion system based on the health status refers to intelligently modifying the operating mode, control strategy, or protection threshold of the power conversion system based on the assessment results of the health status of key components. For example, when the health status assessment results of a key component show that its fatigue accumulation is high, the system can correspondingly reduce its maximum output power, limit its operating temperature range, or adjust its switching frequency to reduce the component load, extend its service life, and ensure the overall stability of the system under pulse load impacts.

[0052] This application's solution addresses the potential blindness of traditional solutions that rely solely on predicted instantaneous power spikes by introducing real-time health status assessments of key components in the power conversion system. Through this technical solution, the application enables more refined and intelligent management of the operating parameters of the power conversion system in commercial and industrial energy storage systems. Compared to solutions that adjust based solely on instantaneous power spikes, this application's real-time monitoring and assessment of the health status of key components makes parameter adjustments more targeted and adaptive. This not only effectively avoids unexpected failures caused by component aging or fatigue, significantly improving the reliability and safety of the power conversion system when dealing with high-power pulse loads, but also optimizes component lifespan and reduces maintenance costs, thereby bringing higher operating efficiency and longer service life to commercial and industrial energy storage systems. Furthermore, this dynamic adjustment mechanism helps the system maintain optimal performance under different operating conditions, avoiding overly conservative or overly aggressive operating strategies, and achieving a balance between performance and lifespan.

[0053] In some preferred embodiments, suppose an industrial or commercial energy storage system needs to handle the instantaneous power spike generated when a large industrial motor starts up. Upon receiving the predicted instantaneous power spike parameters, the system first acquires the IGBT junction temperature and current, among other operating parameters, in real time using temperature and current sensors integrated on the IGBT module of the power converter. Next, an embedded health assessment module calculates the current fatigue accumulation of the IGBT based on this real-time data, combined with the IGBT's cumulative operating time, historical load curves, and a preset fatigue model. If the assessment results indicate that the IGBT's fatigue accumulation has reached a warning level, the system dynamically adjusts the power converter's operating parameters. Specifically, it may slightly reduce the IGBT's switching frequency, or during pulsed loads, fine-tune the control algorithm to transfer part of the load to other parallel IGBT modules with better health, or, without affecting the system's response speed, slightly extend the rise time of the pulsed load to mitigate thermal shock and electrical stress on the IGBT. In this way, even under high-intensity pulsed loads, critical power semiconductor devices can be effectively protected, ensuring the power conversion system operates stably and reliably, and extending its overall service life.

[0054] In the above-described implementation of adjusting operating parameters based on instantaneous power peak parameters, this application proposes a specific implementation method in order to obtain the operating parameters of key components of the power conversion system in real time.

[0055] Specifically, the real-time acquisition of operating parameters of key components of the power conversion system includes: Inside the package of the power conversion system, a piezoelectric thin film sensor and a thermocouple array are integrated. The piezoelectric thin-film sensor monitors the change in mechanical stress of the key power semiconductor device when a current pulse passes through it. The instantaneous junction temperature of the critical power semiconductor device is captured using the thermocouple array. The mechanical stress change and the instantaneous junction temperature are sampled synchronously, and preliminary filtering and timestamp marking are performed to obtain the synchronously sampled mechanical stress change and instantaneous junction temperature; the mechanical stress change is correlated with the amplitude and duration of the current pulse to obtain the correlation analysis results; By combining the instantaneous junction temperature and the correlation analysis results, the mechanical and thermal stresses borne by the key power semiconductor devices are identified, and operating parameters reflecting the health status of the components under specific operating conditions are obtained.

[0056] The encapsulation of the power conversion system refers to the sealed space within which key power semiconductor devices and their auxiliary circuits are packaged together during the manufacturing process. Sensors are integrated within this space, enabling close-range, high-precision monitoring of the device's operating status. The piezoelectric thin-film sensor is a sensor that converts mechanical stress or deformation into electrical signals, aiming to accurately capture the minute deformations and mechanical vibrations generated by the key power semiconductor devices when subjected to current pulses. The thermocouple array is a sensor network composed of multiple thermocouples, used to measure the instantaneous junction temperature of the key power semiconductor devices in real time and at multiple points, reflecting the internal thermal state of the devices. The key power semiconductor devices refer to the core electronic components responsible for power conversion and control in the power conversion system, such as IGBTs and MOSFETs, which are susceptible to mechanical and thermal stress under pulsed loads.

[0057] In practical applications, by using piezoelectric thin-film sensors to monitor the changes in mechanical stress of critical power semiconductor devices when current pulses pass through, the physical pressure borne by the devices under the influence of factors such as electromagnetic force and uneven thermal expansion can be quantified.

[0058] Meanwhile, capturing the instantaneous junction temperature of critical power semiconductor devices using thermocouple arrays directly reflects the heat accumulation and dissipation during operation. Synchronously sampling these mechanical stress changes and instantaneous junction temperatures, performing preliminary filtering to remove noise, and adding timestamps to ensure data consistency are fundamental to obtaining accurate operating parameters.

[0059] Furthermore, correlation analysis between changes in mechanical stress and the amplitude and duration of current pulses can reveal the influence patterns of specific electrical pulse characteristics on the mechanical fatigue of devices. Finally, by combining the instantaneous junction temperature and correlation analysis results, the mechanical and thermal stresses experienced by critical power semiconductor devices can be comprehensively identified, thereby obtaining operating parameters reflecting the health status of components under specific operating conditions, providing a reliable basis for subsequent health assessments and adjustments to operating parameters.

[0060] This application's solution integrates piezoelectric thin-film sensors and thermocouple arrays within the power conversion system package, enabling direct, real-time monitoring of mechanical stress changes and instantaneous junction temperatures in critical power semiconductor devices. Synchronous sampling, filtering, and timestamping of these physical quantities ensure data accuracy and timeliness. Furthermore, correlation analysis between mechanical stress changes and the amplitude and duration of current pulses provides a deeper understanding of the impact of pulsed loads on device mechanical fatigue. Finally, combining instantaneous junction temperatures and correlation analysis results, the mechanical and thermal stresses experienced by the device can be comprehensively and accurately identified, thereby obtaining operating parameters reflecting the component's health status under specific operating conditions. This method overcomes potential errors associated with traditional indirect measurements or model estimations, providing data that more closely approximates the actual operating state of the device.

[0061] The above technical solution enables real-time and accurate acquisition of operating parameters for key components of a power conversion system. This method of directly measuring mechanical and thermal stress makes the health assessment of critical power semiconductor devices more accurate and reliable, avoiding errors caused by indirect measurements or empirical models. Therefore, it allows for more effective prediction of potential device failure risks, providing solid data support for subsequent adjustments to operating parameters, thereby ensuring that the power conversion system can withstand impacts under pulsed loads and extending its service life.

[0062] In some embodiments described above, while it is possible to monitor the mechanical stress changes and instantaneous junction temperature of critical power semiconductor devices by integrating piezoelectric thin-film sensors and thermocouple arrays, and obtain operating parameters reflecting the health status of components under specific operating conditions through correlation analysis, simply obtaining these operating parameters is insufficient for accurately predicting the long-term reliability and remaining lifespan of components. In actual industrial and commercial energy storage systems, the mechanical fatigue and thermal aging processes of critical components in power conversion systems are complex and interdependent under frequent pulse loads. Without a systematic assessment method, misjudgments of component health status may occur, thereby affecting the stable operation of the system and the formulation of maintenance strategies. Therefore, this application further proposes a more refined and comprehensive method for assessing the health status of critical components. This method quantifies the mechanical fatigue and thermal aging effects through in-depth analysis of the acquired operating parameters, thereby providing more instructive health status assessment results.

[0063] The above-mentioned assessment of the health status of the key components based on the operating parameters includes: Frequency domain analysis was performed on the mechanical stress variation to extract the dominant vibration mode and attenuation characteristics of the mechanical stress variation; The dominant vibration mode and the attenuation characteristics are matched with the mechanical fatigue characteristic spectrum of multiple preset pulse load modes to obtain the mechanical fatigue accumulation factor corresponding to the current pulse load mode. A fast Fourier transform is performed on the instantaneous junction temperature data to analyze the temperature fluctuation amplitude and thermal gradient change rate of the instantaneous junction temperature data within the pulse load cycle. The temperature fluctuation amplitude and the thermal gradient change rate are matched with the thermal aging characteristic spectra of multiple preset pulse load modes to obtain the thermal aging acceleration factor corresponding to the current pulse load mode. The mechanical fatigue accumulation factor and the thermal aging acceleration factor are weighted and fused together, and combined with the cumulative operating time of the key component, the current fatigue accumulation degree of the key component is calculated. The current fatigue accumulation is compared with a preset failure threshold to obtain the health status assessment result of the key component.

[0064] Specifically, frequency domain analysis of mechanical stress changes involves converting the mechanical stress change signal monitored by a piezoelectric thin-film sensor into the frequency domain using mathematical tools such as Fourier transform to reveal its inherent vibrational characteristics. By analyzing the frequency spectrum, the dominant vibrational modes exhibited by the internal structure of key power semiconductor devices under specific pulsed loads can be identified, such as resonance peaks at specific frequencies, and the attenuation characteristics of these vibrational modes over time or with varying loads. The aim is to extract structural response features directly related to mechanical fatigue from complex time-domain signals.

[0065] The matching of the dominant vibration mode and the attenuation characteristics with the mechanical fatigue characteristic spectrum under various preset pulsed load modes involves comparing the extracted frequency domain features with a feature database established in advance through experiments, simulations, or historical data, which identifies the mechanical fatigue evolution patterns of components under different pulsed load modes. This mechanical fatigue characteristic spectrum contains typical modes of component mechanical performance degradation under different stress amplitudes, frequencies, and durations. Through matching, the degree of fatigue accumulation caused by mechanical stress on the component under the current operating condition can be quantified, thereby obtaining the mechanical fatigue accumulation factor corresponding to the current pulsed load mode.

[0066] In practical applications, performing a Fast Fourier Transform (FFT) on the instantaneous junction temperature data refers to processing the instantaneous junction temperature data of key power semiconductor devices captured by the thermocouple array using FFT to analyze their periodic temperature fluctuation characteristics within the pulsed load cycle. Through FFT, the amplitude of temperature fluctuations can be accurately analyzed, reflecting the intensity of thermal shock and the rate of change of the thermal gradient, revealing the propagation and accumulation rate of thermal stress within the component. Its purpose is to extract thermodynamic characteristics directly related to thermal aging from the temperature data.

[0067] The matching of the temperature fluctuation amplitude and the thermal gradient change rate with preset thermal aging characteristic spectra under various pulsed load modes involves comparing the temperature fluctuation amplitude and thermal gradient change rate obtained from the above analysis with a pre-established characteristic database describing the thermal aging process of components under different thermal load modes. This thermal aging characteristic spectrum records typical patterns of component material performance degradation under different temperature cycles, thermal shock intensities, and durations. Through matching, the degree of accelerated aging caused by thermal stress on the component under the current operating condition can be assessed, thereby obtaining the thermal aging acceleration factor corresponding to the current pulsed load mode.

[0068] Furthermore, the mechanical fatigue accumulation factor and the thermal aging acceleration factor are weighted and fused together, and the current fatigue accumulation degree of the key component is calculated by combining them with the cumulative operating time of the key component. This is because mechanical fatigue and thermal aging often influence and interact with each other. This application comprehensively considers these two factors by setting reasonable weights. These weights can be dynamically adjusted according to the component material properties, packaging structure, and actual operating experience. At the same time, combining the cumulative operating time of the component can more comprehensively reflect the degradation state of the component throughout its entire life cycle, thereby calculating a more accurate current fatigue accumulation degree.

[0069] Therefore, comparing the current fatigue accumulation with a preset failure threshold to obtain the health status assessment result of the critical component involves comparing the calculated current fatigue accumulation with a pre-set threshold indicating that the component has reached a critical failure state. This failure threshold can be determined based on the component's design life, reliability requirements, and safety margin. By comparing, it can be determined whether the component is nearing or has reached the end of its service life, thus providing a clear health status assessment result, such as "healthy," "mildly fatigued," "moderately aged," or "about to fail."

[0070] The solution proposed in this application addresses the limitations and inaccuracies that traditional methods may have in assessing component health by deeply mining and analyzing the mechanical stress changes and instantaneous junction temperature data of key power semiconductor devices.

[0071] Through the aforementioned technical solutions, this application enables a precise and comprehensive assessment of the health status of key components in the power conversion system of commercial and industrial energy storage systems. Compared to methods relying solely on a single parameter or simple threshold, this application, by introducing frequency domain analysis, fast Fourier transform, and matching with multi-mode characteristic spectra, can more deeply reveal the mechanical fatigue and thermal aging mechanisms of components under complex pulsed loads. This quantitative and comprehensive assessment method not only improves the accuracy and reliability of health status assessment but also provides more refined data support for predictive maintenance of energy storage systems, effectively extending component lifespan, reducing operational risks and maintenance costs, thereby significantly improving the economic benefits and operational stability of the entire energy storage system.

[0072] In some preferred embodiments, a specific example is given below. Suppose a power conversion system in an industrial or commercial energy storage system, whose key power semiconductor devices need to withstand instantaneous high-power pulse loads generated when industrial equipment starts up. To assess the health of these devices, their mechanical stress changes and instantaneous junction temperature data are first collected in real time using piezoelectric thin-film sensors and thermocouple arrays integrated within the device package.

[0073] Specifically, when a current pulse with a duration of 100 microseconds and an amplitude of 500 amperes passes through, the piezoelectric thin-film sensor detects a specific mechanical vibration mode generated on the device surface, with a dominant frequency of 20 kHz and a vibration decay time constant of 50 microseconds. Simultaneously, the thermocouple array captures the device junction temperature rapidly rising from 30°C to 80°C during the pulse and slowly decreasing after the pulse ends, with a temperature fluctuation amplitude of 50°C and a thermal gradient change rate of 0.5°C / microsecond.

[0074] Subsequently, frequency domain analysis was performed on the mechanical stress variation data to extract the dominant vibration mode at 20 kHz and the attenuation characteristics at 50 microseconds. These characteristics were matched with the features of the "high-frequency short-time impact" mode in the preset mechanical fatigue characteristic spectrum, and the mechanical fatigue accumulation factor corresponding to the current pulse load mode was calculated to be 0.001.

[0075] Simultaneously, a fast Fourier transform was performed on the instantaneous junction temperature data to analyze the temperature fluctuation amplitude at 50°C and the thermal gradient change rate at 0.5°C / µs. These thermodynamic characteristics were matched with the characteristics of the "rapid thermal cycling" mode in the preset thermal aging characteristic spectrum, and the thermal aging acceleration factor corresponding to the current pulsed load mode was calculated to be 0.0008.

[0076] Assuming the critical component has accumulated 5000 hours of operation, and based on parameters such as instantaneous power output and operating temperature, the instantaneous coupling strength between mechanical and thermal stress is calculated to be high. Based on this coupling strength, the weighted fusion weights of the mechanical fatigue accumulation factor and the thermal aging acceleration factor are dynamically adjusted; for example, the mechanical fatigue weight is 0.6, and the thermal aging weight is 0.4. These two factors are then weighted and fused, and combined with the accumulated operating time, the current fatigue accumulation degree is calculated to be 0.05092 (this is an illustrative calculation result).

[0077] Finally, the calculated current fatigue accumulation of 0.05092 is compared with a preset failure threshold (e.g., 0.1). Since 0.05092 is less than 0.1, the assessment result indicates that the critical component is in a "healthy" state, but slight fatigue accumulation has already occurred, requiring continuous monitoring. In this way, the health status of critical components can be quantitatively and dynamically assessed, providing a scientific basis for subsequent operation, maintenance, and life prediction.

[0078] In some embodiments described above, this application proposes a weighted fusion of mechanical fatigue accumulation factors and thermal aging acceleration factors, combined with the cumulative operating time of key components, to calculate the current fatigue accumulation degree of the key components. However, in the actual operation of commercial and industrial energy storage systems, the coupling relationship between mechanical stress and thermal stress borne by key components is not constant, but dynamically adjusts with changes in operating parameters such as instantaneous power output, operating temperature, and the frequency and amplitude of pulse loads. If a fixed weighting fusion weight is used, it may not accurately reflect the true contribution of mechanical fatigue and thermal aging to the overall fatigue accumulation degree of components under different operating conditions, thus affecting the accuracy of health status assessment. To address this, this application further proposes a method for dynamically adjusting the weighted fusion weights of mechanical fatigue accumulation factors and thermal aging acceleration factors. By monitoring the operating status of key components in real time, calculating the instantaneous coupling strength, and then dynamically adjusting the weights, the current fatigue accumulation degree of key components can be more accurately assessed.

[0079] The aforementioned mechanical fatigue accumulation factor and thermal aging acceleration factor are weighted and fused together, and combined with the cumulative operating time of the aforementioned key components, to calculate the current fatigue accumulation degree of the aforementioned key components, including: Receive the above-mentioned mechanical fatigue accumulation factor and the above-mentioned thermal aging acceleration factor; Monitor the instantaneous power output, operating temperature, and frequency and amplitude of the current pulse load of the aforementioned key components; Based on the instantaneous power output, operating temperature, frequency of the current pulse load, and amplitude mentioned above, calculate the instantaneous coupling strength between mechanical stress and thermal stress. Based on the instantaneous coupling strength mentioned above, the weighted fusion weights of the mechanical fatigue accumulation factor and the thermal aging acceleration factor mentioned above are dynamically adjusted. The adjusted weighted fusion weights are applied to the mechanical fatigue accumulation factor and the thermal aging acceleration factor, and combined with the cumulative operating time of the key components, the current fatigue accumulation degree of the key components is calculated.

[0080] Specifically, receiving the mechanical fatigue accumulation factor and the thermal aging acceleration factor refers to obtaining the calculated quantitative indicators reflecting the degree of mechanical fatigue and thermal aging from the aforementioned steps. These factors are the basic data for assessing the health status of components.

[0081] Monitoring the instantaneous power output, operating temperature, and frequency and amplitude of the current pulsed load of key components aims to acquire critical operating parameters that affect the coupling relationship between mechanical and thermal stress in real time. Instantaneous power output reflects the immediate load condition of the components, operating temperature directly affects the thermal expansion and thermal stress of materials, and the frequency and amplitude of the pulsed load determine the characteristics of stress cycling. These parameters can be acquired in real time using sensors (such as current sensors, voltage sensors, and temperature sensors) integrated within the power conversion system.

[0082] The instantaneous coupling strength between mechanical and thermal stress is calculated based on instantaneous power output, operating temperature, and the frequency and amplitude of the current pulse load. This refers to quantifying the degree of interaction between mechanical and thermal stress using these real-time operating parameters and a pre-defined physical or machine learning model. For example, under high-temperature, high-power pulses, thermal stress may significantly enhance the influence of mechanical stress, and vice versa. The instantaneous coupling strength can be a dimensionless coefficient or an indicator reflecting the mutual promotion or inhibition of the two stresses.

[0083] Dynamically adjusting the weighted fusion weights of the mechanical fatigue accumulation factor and the thermal aging acceleration factor based on the instantaneous coupling strength means changing the weight coefficients assigned to the mechanical fatigue accumulation factor and the thermal aging acceleration factor in real time based on the calculated instantaneous coupling strength. For example, when the coupling strength is high, it may be necessary to increase the weight of a certain factor to more accurately reflect its dominant role under the current operating conditions. This adjustment can be based on lookup tables, empirical formulas, or adaptive algorithms.

[0084] The adjusted weighted fusion weights are applied to the mechanical fatigue accumulation factor and the thermal aging acceleration factor, and combined with the cumulative operating time of key components to calculate the current fatigue accumulation degree of the key components. This involves using dynamically adjusted weights to perform a weighted sum of the two fatigue factors and combining it with the component's cumulative operating time to obtain a comprehensive fatigue accumulation degree index. The cumulative operating time, as a benchmark for historical fatigue, combined with instantaneous fatigue accumulation, can more comprehensively reflect the overall health status of the component.

[0085] This application's solution achieves dynamic adjustment of the weighted fusion weights of the mechanical fatigue accumulation factor and the thermal aging acceleration factor by introducing the monitoring of instantaneous operating parameters of key components and calculating the instantaneous coupling strength between mechanical and thermal stresses based on these parameters. Specifically, when key components operate under different instantaneous power outputs, operating temperatures, and pulse load frequencies and amplitudes, the interaction mechanism between their internal mechanical and thermal stresses changes. For example, under extreme high temperatures or high-frequency pulse loads, thermal stress may become the dominant factor or produce a stronger synergistic effect with mechanical stress. By acquiring these operating parameters in real time, the system can accurately capture this dynamic coupling relationship and adjust the weights of the two fatigue factors accordingly. This dynamic adjustment ensures that, under any specific operating condition, the calculation of fatigue accumulation can more accurately reflect the actual impact of mechanical fatigue and thermal aging on component life, avoiding evaluation biases that may be caused by fixed weights.

[0086] Through the above technical solution, this application overcomes the problem of insufficient accuracy in evaluating dynamic operating conditions by traditional fixed-weight fusion methods. By monitoring key operating parameters in real time and dynamically adjusting the weighted fusion weights, the calculation of fatigue accumulation can more accurately reflect the true fatigue state of key components under different operating conditions. This not only improves the accuracy and reliability of key component health status assessment, but also provides a more solid data foundation for subsequent operating parameter adjustments and maintenance strategy formulation, helping to extend component life, reduce failure risk, and optimize the overall operating efficiency and safety of the energy storage system.

[0087] The above-mentioned comparison of the current fatigue accumulation with a preset failure threshold yields the health status assessment result of the key component, including: The actual operating mode of the key components is monitored in real time to obtain the actual operating mode. Based on the actual operating mode, select a failure threshold that matches the actual operating mode from the preset dynamic failure threshold library; The current fatigue accumulation is compared with the failure threshold to obtain the health status assessment result of the key component.

[0088] Specifically, real-time monitoring of the actual operating mode of the key components refers to continuously collecting electrical parameters (such as current, voltage, and power output), thermal parameters (such as junction temperature and case temperature), and environmental parameters (such as ambient temperature and humidity) of the key components through a sensor network integrated within the power conversion system. After processing and analysis, this data can identify the specific operating state of the key components, such as light load, heavy load, frequent start-stop, high temperature operation, or low temperature operation. The actual operating mode can be classified into several predefined modes, such as "continuous high power output mode," "intermittent pulse load mode," and "standby mode."

[0089] The preset dynamic failure threshold library is a dataset containing various failure thresholds, each associated with one or more specific actual operating modes. This threshold library can be established through analysis and training of a large amount of historical operating data, experimental data, and component failure mechanism models. For example, for modes operating in high-temperature and high-humidity environments, the failure threshold may be set lower to reflect the accelerated degradation rate; while for light-load, low-temperature operating modes, the failure threshold may be relatively higher.

[0090] In practical applications, selecting a failure threshold that matches the actual operating mode from a preset dynamic failure threshold library means that after identifying the current actual operating mode of a critical component, the system intelligently retrieves and selects the failure threshold that best matches that mode from the dynamic failure threshold library. This matching process can be based on pattern recognition algorithms, for example, by comparing the similarity between the feature vector of the current operating mode and the feature vectors of each mode in the library. Once a matching failure threshold is selected, it will be used for subsequent health status assessments.

[0091] This application's solution achieves adaptive adjustment of failure thresholds by introducing real-time monitoring of the actual operating modes of key components and combining it with a preset dynamic failure threshold library. Specifically, when the operating mode of a key component changes, the system can promptly identify and select the failure threshold that best matches the current operating mode from the dynamic failure threshold library. This dynamic matching mechanism ensures that the comparison between fatigue accumulation and failure thresholds under different operating conditions can more accurately reflect the true health status of the component. For example, under heavy load or high-frequency pulse load modes, the degradation rate of the component may accelerate. In this case, the system will select a more conservative (lower) failure threshold to identify potential risks earlier. Under light load or stable operating modes, a relatively lenient (higher) threshold may be selected to avoid unnecessary maintenance. Thus, health status assessment no longer relies on a single general threshold but makes personalized judgments based on the actual stress conditions experienced by the component, thereby improving the accuracy and reliability of the assessment.

[0092] Through the above technical solution, this application can significantly improve the accuracy and adaptability of health status assessment for key components in industrial and commercial energy storage systems. Because the failure threshold can be dynamically adjusted according to the actual operating mode of the key component, it can more accurately capture the degradation characteristics of the component under different operating conditions, avoiding misjudgments or omissions that may be caused by traditional static thresholds. This not only helps to extend the service life of key components, optimize maintenance cycles, and reduce unplanned downtime, but also provides more reliable health status information for the operation strategy of the energy storage system, thereby improving the overall system's operating efficiency and economic benefits. Furthermore, through refined health management, the safety risks caused by unexpected component failures can also be effectively reduced.

[0093] In some preferred embodiments, a specific example is given below. Assume that the critical power semiconductor devices in the power conversion system operate in two typical modes: Mode A is a "continuous high-power output mode," and Mode B is an "intermittent pulsed load mode." In Mode A, the devices are subjected to continuous high current and high junction temperature, primarily exhibiting thermal aging; in Mode B, the devices are subjected to frequent current pulses and temperature cycles, primarily exhibiting mechanical fatigue.

[0094] If a single static failure threshold is used, for example, setting a fatigue accumulation level of 0.8 as a high risk of failure, when the device operates in Mode A, its thermal aging acceleration factor may accumulate rapidly, but its mechanical fatigue accumulation factor increases more slowly. If the fatigue accumulation level reaches 0.75 at this time, the static threshold may consider the risk manageable. However, according to the solution of this application, the system monitors the device in Mode A in real time and selects a failure threshold matching Mode A from the dynamic failure threshold library, such as 0.70 (because thermal aging is accelerated in Mode A, requiring earlier warning). In this case, a fatigue accumulation level of 0.75 will be immediately judged as high risk, thereby triggering a maintenance warning in a timely manner.

[0095] Conversely, when the device operates in Mode B, the mechanical fatigue accumulation factor may increase rapidly, while the thermal aging acceleration factor is relatively slow. If the fatigue accumulation reaches 0.75 at this time, the static threshold is also judged as high risk. However, according to the scheme of this application, if the system detects that the device is in Mode B and selects a failure threshold that matches Mode B, such as 0.85 (because although mechanical fatigue is fast in Mode B, the overall lifespan may be slightly longer due to lower thermal stress), then a fatigue accumulation of 0.75 may be judged as medium risk, allowing the system to continue operating for a period of time and avoiding unnecessary premature shutdown for maintenance.

[0096] By dynamically adjusting the failure threshold in this way, this application can provide a more accurate health status assessment based on the actual operating mode of key components, thereby achieving smarter and more economical maintenance management.

[0097] In some embodiments described above, a weighted fusion method for dynamically adjusting the mechanical fatigue accumulation factor and the thermal aging acceleration factor based on instantaneous coupling strength is proposed. However, in practical applications, relying solely on instantaneous power output, operating temperature, and the frequency and amplitude of the current pulse load to calculate the instantaneous coupling strength may not fully reflect the long-term fatigue accumulation and aging process of critical components, resulting in limitations in the accuracy and adaptability of the weight adjustment. Therefore, this application further proposes a more refined weight adjustment method that comprehensively considers the cumulative operating time of critical components to more accurately assess their health status.

[0098] The above-mentioned dynamic adjustment of the weighted fusion weights of the mechanical fatigue accumulation factor and the thermal aging acceleration factor based on the instantaneous coupling strength specifically includes the following steps: Receive the mechanical fatigue accumulation factor and the thermal aging acceleration factor; Monitor the instantaneous power output of the key components, the operating temperature, and the frequency and amplitude of the current pulse load; Obtain the cumulative running time of the key components; The instantaneous coupling strength between mechanical stress and thermal stress is calculated based on the instantaneous power output, the operating temperature, the frequency and amplitude of the pulse load, and the cumulative operating time. The weighted fusion weights of the mechanical fatigue accumulation factor and the thermal aging acceleration factor are dynamically adjusted based on the instantaneous coupling strength.

[0099] Specifically, it receives mechanical fatigue accumulation factor and thermal aging acceleration factor. The mechanical fatigue accumulation factor and thermal aging acceleration factor are obtained by analyzing the mechanical stress changes and instantaneous junction temperature data of key components under pulsed load, and respectively reflect the degree of mechanical fatigue and thermal aging of the components.

[0100] Furthermore, the system monitors the instantaneous power output, operating temperature, and frequency and amplitude of the current pulsed load of key components. These parameters are acquired in real time and are used to characterize the operating status of key components and the external load characteristics they are subjected to at a specific moment. Instantaneous power output can be directly measured by a power sensor; operating temperature can be captured by a thermocouple array integrated within the power conversion system package; and the frequency and amplitude of the pulsed load can be obtained from the system control unit or load monitoring module.

[0101] In addition, the cumulative operating time of critical components is obtained. Cumulative operating time refers to the total working time a critical component has experienced since it was put into service, and its purpose is to reflect the component's long-term service history and potential cumulative damage. This data can be recorded and retrieved from system logs, equipment management databases, or through internal timers.

[0102] Therefore, based on the instantaneous power output, the operating temperature, the frequency and amplitude of the pulsed load, and the cumulative operating time, the instantaneous coupling strength between mechanical stress and thermal stress is calculated. Instantaneous coupling strength refers to the degree of interaction between mechanical stress and thermal stress under specific operating conditions. By incorporating the cumulative operating time into the calculation model, the impact of factors such as long-term wear and material aging on the interaction between mechanical stress and thermal stress can be more comprehensively assessed, thus obtaining a more representative coupling strength value. For example, a multivariate regression model or a machine learning-based algorithm can be used, taking the above parameters as input and outputting the instantaneous coupling strength.

[0103] Finally, based on the instantaneous coupling strength, the weighted fusion weights of the mechanical fatigue accumulation factor and the thermal aging acceleration factor are dynamically adjusted. These weighted fusion weights are used to balance the relative importance of mechanical fatigue and thermal aging in component health assessment. By dynamically adjusting these weights, it can be ensured that the health assessment model can more accurately reflect the actual damage mechanism under different operating conditions and component aging stages. For example, when the instantaneous coupling strength is high, it may mean that the synergistic effect between mechanical stress and thermal stress is more significant; in this case, the weight of the interaction between the two can be appropriately increased.

[0104] This application addresses the limitation that relying solely on instantaneous parameters may not fully reflect the long-term fatigue accumulation and aging process of components by incorporating the cumulative operating time of key components when calculating the instantaneous coupling strength between mechanical and thermal stresses. Specifically, cumulative operating time, as an important historical parameter, reflects the cumulative damage, material performance degradation, and potential microstructural changes experienced by components during long-term service. When combined with real-time operating parameters such as instantaneous power output, operating temperature, and the frequency and amplitude of pulsed loads, the calculated instantaneous coupling strength becomes more comprehensive and accurate. For example, a component with a long operating time may have a reduced material fatigue threshold and thermal stress tolerance even under the same instantaneous load, leading to a more significant coupling effect between mechanical and thermal stresses. In this way, this application can more accurately capture the dynamic interaction between mechanical fatigue and thermal aging, allowing the adjustment of the weighted fusion weights to better adapt to the actual aging state and damage accumulation history of the components.

[0105] Through the above technical solution, this application can more accurately assess the health status of critical components. Because the cumulative operating time of critical components is considered when calculating instantaneous coupling strength, the obtained coupling strength value not only reflects the current operating conditions but also incorporates the long-term service history and cumulative damage information of the components. Therefore, the dynamically adjusted weighted fusion weights can more accurately reflect the relative importance of mechanical fatigue and thermal aging at different aging stages and operating conditions, thereby improving the accuracy and reliability of fatigue accumulation calculation. This more refined health assessment method helps avoid misjudgments caused by relying solely on instantaneous data, extends the service life of critical components, optimizes the operation strategy of energy storage systems, and reduces maintenance costs.

[0106] refer to Figure 4 , Figure 4 This application provides a schematic diagram of an industrial and commercial energy storage system for intelligent control, the system comprising: The input terminal is used to acquire the operating status signal of the high-power pulse load starting of industrial equipment; the operating status signal is identified as precursor information, and the amplitude and duration of the instantaneous power spike are estimated according to the preset equipment power characteristic profile to obtain the predicted instantaneous power spike parameters; The adjustment terminal is used to adjust the state of charge of the energy storage battery according to the instantaneous power peak parameter to ensure the power output of the energy storage battery under the pulse load; and to adjust the operating parameters according to the instantaneous power peak parameter to ensure that the power conversion system can withstand the impact under the pulse load.

[0107] This system proactively acquires and identifies the operating status signals of industrial equipment through its input terminals, using these signals as precursors to pulsed loads. Combined with pre-defined equipment power characteristic profiles, it accurately predicts the amplitude and duration of instantaneous power spikes. Subsequently, the adjustment terminal adjusts the state of charge of the energy storage battery and the operating parameters of the power conversion system based on these predicted parameters. Therefore, this system can effectively handle instantaneous high-power pulsed loads generated by industrial equipment, avoiding the problems of response lag, equipment damage, and production interruptions caused by inaccurate predictions in traditional energy storage systems. This significantly improves the intelligence level, operational stability, and equipment lifespan of the energy storage system.

[0108] Embodiments of this application provide an industrial and commercial energy storage system that works in concert with its input and adjustment terminals to intelligently control the instantaneous high-power pulse loads generated by industrial equipment.

[0109] Specifically, the input terminal is configured to acquire the operating status signal of industrial equipment starting a high-power pulsed load, and identify the operating status signal as precursor information. Based on a preset equipment power characteristic profile, the amplitude and duration of the instantaneous power spike are estimated to obtain the predicted instantaneous power spike parameters. In some embodiments, the input terminal can be a hardware module integrating multiple sensors, such as current sensors, voltage sensors, vibration sensors, or temperature sensors, for real-time monitoring of the electrical and mechanical operating status of industrial equipment. The input terminal may also include a data acquisition and processing unit for receiving sensor data and performing signal processing and pattern recognition algorithms to identify the operating status signal as precursor information of the pulsed load.

[0110] Furthermore, the input terminal can be built into or connected to a storage unit containing preset device power characteristic profiles. These profiles are used to estimate the amplitude and duration of instantaneous power spikes based on identified precursor information, through table lookup or model calculation. For example, the input terminal can be an independent intelligent gateway that communicates with the industrial equipment PLC via industrial Ethernet or Modbus protocol to obtain the device's operating mode or internal status parameters, and uses these as operating status signals. The methods for obtaining operating status signals, identifying precursor information, and estimating instantaneous power spike parameters have already been described in the above embodiments and will not be repeated here. It is important to emphasize that the input terminal, as the core of the system's sensing and prediction capabilities, is designed to achieve early and accurate identification and parameter prediction of potential pulse loads.

[0111] Furthermore, the adjustment terminal is configured to adjust the state of charge of the energy storage battery according to the instantaneous power spike parameters to ensure the power output of the energy storage battery under the pulse load; and to adjust the operating parameters according to the instantaneous power spike parameters to ensure the power conversion system can withstand the impact under the pulse load. In some embodiments, the adjustment terminal can be a central control unit that integrates control logic and algorithms and communicates with the battery management system (BMS) and the power conversion system (PCS). The adjustment terminal can send instructions to the BMS based on the predicted instantaneous power spike parameters provided by the input terminal, for example, to initiate a short-term fast charging operation or adjust the parallel / series configuration of the battery pack to optimize its discharge capacity.

[0112] Simultaneously, the adjustment terminal can also send commands to the PCS, such as adjusting its internal switching frequency, modulation strategy, or current limit, or even activating the backup cooling system or redundant modules to enhance the PCS's ability to withstand short-term high-power output. For example, the adjustment terminal can be a programmable logic controller (PLC) or an industrial PC running dedicated control software, exchanging data and issuing commands to the BMS and PCS in real time via CAN bus or Ethernet. The methods for adjusting the energy storage battery's state of charge and the power conversion system's operating parameters have already been described in the above embodiments and will not be repeated here. It is important to emphasize that the adjustment terminal, as the core of the system's execution and optimization, is designed to achieve coordinated and dynamic control of the energy storage battery and the power conversion system to proactively adapt to and respond to pulse loads.

[0113] The commercial and industrial energy storage system proposed in this application represents a significant advancement over existing technologies. Traditional energy storage systems typically employ predictive models based on historical data analysis, and their system architecture lacks the ability to proactively detect and adjust for instantaneous high-power pulse loads from industrial equipment. For example, when faced with instantaneous power spikes generated during the startup of high-power laser cutting machines or large stamping equipment, existing systems often fail to anticipate and adjust battery capacity or power conversion system operating parameters in advance, leading to delayed response and potentially causing equipment overload, shutdown, or accelerated aging.

[0114] This application's system achieves intelligent and proactive control of pulsed loads by introducing dedicated input and adjustment terminals. The input terminal can acquire real-time operating status signals from industrial equipment and identify them as precursory information. Combined with preset equipment power characteristic profiles, it accurately predicts the parameters of instantaneous power spikes. This design allows the system to obtain accurate predictive information before the actual arrival of the pulsed load. Subsequently, the adjustment terminal actively and collaboratively adjusts the state of charge of the energy storage battery and the operating parameters of the power conversion system based on these predicted parameters. For example, before the arrival of the pulsed load, the system can instruct the battery to perform rapid charging or adjust the switching strategy of the power conversion system, thereby ensuring that the energy storage battery can provide sufficient power output during the pulsed load and that the power conversion system can smoothly withstand the impact. This system architecture fundamentally solves the blind spots and lag problems of traditional systems when dealing with instantaneous high-power pulsed loads, significantly improving the response speed, operational stability, and equipment reliability of industrial and commercial energy storage systems, effectively extending the service life of key components of the energy storage battery and power conversion system, and reducing overall operation and maintenance costs.

[0115] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A smart control method for an industrial and commercial energy storage system, characterized in that, The method includes: Acquire the operating status signal of industrial equipment starting a high-power pulse load; The operating status signal is identified as precursor information, and the amplitude and duration of the instantaneous power spike are estimated based on the preset equipment power characteristic profile to obtain the predicted instantaneous power spike parameters. Based on the instantaneous power spike parameters, adjust the state of charge of the energy storage battery to ensure the power output of the energy storage battery under the pulse load; The operating parameters are adjusted according to the instantaneous power spike parameters to ensure that the power conversion system can withstand the impact under the pulse load.

2. The intelligent control method for an industrial and commercial energy storage system according to claim 1, characterized in that, The step of identifying the operating status signal as precursor information and estimating the amplitude and duration of the instantaneous power spike based on a preset equipment power characteristic profile to obtain the predicted instantaneous power spike parameters includes: A communication link is established with the power regulation unit inside the industrial equipment to obtain the instantaneous power adjustment signal made by the industrial equipment during operation based on material inhomogeneity; Receive the instantaneous power adjustment signal and analyze the actual instantaneous power requirement of the industrial equipment; Based on the actual instantaneous power demand, the instantaneous power peak parameter is corrected to obtain the corrected instantaneous power peak parameter.

3. The intelligent control method for an industrial and commercial energy storage system according to claim 1, characterized in that, The step of adjusting the state of charge of the energy storage battery according to the instantaneous power spike parameters to ensure the power output of the energy storage battery under the pulse load includes: Real-time acquisition of instantaneous power generation from the main power sources within the factory and real-time power consumption of auxiliary equipment provides information on internal power supply and demand. Based on the internal power supply and demand situation, determine whether the internal power supply is sufficient to support the charging power requirements of the energy storage battery; If not, identify non-core auxiliary equipment within the factory that has flexible adjustment capabilities; Send a power reduction or delayed start command to the auxiliary equipment to release power resources, causing the battery management system to perform a short-term charging operation to adjust the state of charge of the energy storage battery.

4. The intelligent control method for an industrial and commercial energy storage system according to claim 1, characterized in that, The adjustment of operating parameters based on the instantaneous power peak parameters includes: Real-time acquisition of operating parameters of key components of the power conversion system; Assess the health status of the critical components based on the operating parameters; The operating parameters of the power conversion system are dynamically adjusted based on the health status.

5. The intelligent control method for an industrial and commercial energy storage system according to claim 4, characterized in that, The real-time acquisition of operating parameters of key components of the power conversion system includes: Inside the package of the power conversion system, a piezoelectric thin film sensor and a thermocouple array are integrated. The piezoelectric thin-film sensor monitors the change in mechanical stress of the key power semiconductor device when a current pulse passes through it. The instantaneous junction temperature of the critical power semiconductor device is captured using the thermocouple array. The mechanical stress change and the instantaneous junction temperature are sampled synchronously, and preliminary filtering and timestamp marking are performed to obtain the synchronously sampled mechanical stress change and instantaneous junction temperature; the mechanical stress change is correlated with the amplitude and duration of the current pulse to obtain the correlation analysis results; By combining the instantaneous junction temperature and the correlation analysis results, the mechanical and thermal stresses borne by the key power semiconductor devices are identified, and operating parameters reflecting the health status of the components under specific operating conditions are obtained.

6. The intelligent control method for an industrial and commercial energy storage system according to claim 5, characterized in that, The assessment of the health status of the key components based on the operating parameters includes: Frequency domain analysis was performed on the mechanical stress variation to extract the dominant vibration mode and attenuation characteristics of the mechanical stress variation; The dominant vibration mode and the attenuation characteristics are matched with the mechanical fatigue characteristic spectrum of multiple preset pulse load modes to obtain the mechanical fatigue accumulation factor corresponding to the current pulse load mode. A fast Fourier transform is performed on the instantaneous junction temperature data to analyze the temperature fluctuation amplitude and thermal gradient change rate of the instantaneous junction temperature data within the pulse load cycle. The temperature fluctuation amplitude and the thermal gradient change rate are matched with the thermal aging characteristic spectra of multiple preset pulse load modes to obtain the thermal aging acceleration factor corresponding to the current pulse load mode. The mechanical fatigue accumulation factor and the thermal aging acceleration factor are weighted and fused together, and combined with the cumulative operating time of the key component, the current fatigue accumulation degree of the key component is calculated. The current fatigue accumulation is compared with a preset failure threshold to obtain the health status assessment result of the key component.

7. The intelligent control method for an industrial and commercial energy storage system according to claim 6, characterized in that, The step of weightedly fusing the mechanical fatigue accumulation factor and the thermal aging acceleration factor, and combining them with the cumulative operating time of the key component to calculate the current fatigue accumulation degree of the key component includes: Receive the mechanical fatigue accumulation factor and the thermal aging acceleration factor; Monitor the instantaneous power output, operating temperature, and frequency and amplitude of the current pulse load of the key components; The instantaneous coupling strength between mechanical stress and thermal stress is calculated based on the instantaneous power output, the operating temperature, the frequency of the current pulse load, and the amplitude. The weighted fusion weights of the mechanical fatigue accumulation factor and the thermal aging acceleration factor are dynamically adjusted based on the instantaneous coupling strength. The adjusted weighted fusion weights are applied to the mechanical fatigue accumulation factor and the thermal aging acceleration factor, and combined with the cumulative operating time of the key component, the current fatigue accumulation degree of the key component is calculated.

8. The intelligent control method for an industrial and commercial energy storage system according to claim 6, characterized in that, The step of comparing the current fatigue accumulation with a preset failure threshold to obtain the health status assessment result of the key component includes: The actual operating mode of the key components is monitored in real time to obtain the actual operating mode. Based on the actual operating mode, select a failure threshold that matches the actual operating mode from the preset dynamic failure threshold library; The current fatigue accumulation is compared with the failure threshold to obtain the health status assessment result of the key component.

9. The intelligent control method for an industrial and commercial energy storage system according to claim 7, characterized in that, The step of dynamically adjusting the weighted fusion weights of the mechanical fatigue accumulation factor and the thermal aging acceleration factor based on the instantaneous coupling strength includes: Receive the mechanical fatigue accumulation factor and the thermal aging acceleration factor; Monitor the instantaneous power output of the key components, the operating temperature, and the frequency and amplitude of the current pulse load; Obtain the cumulative running time of the key components; The instantaneous coupling strength between mechanical stress and thermal stress is calculated based on the instantaneous power output, the operating temperature, the frequency and amplitude of the pulse load, and the cumulative operating time. The weighted fusion weights of the mechanical fatigue accumulation factor and the thermal aging acceleration factor are dynamically adjusted based on the instantaneous coupling strength.

10. An industrial and commercial energy storage system for intelligent control, characterized in that, The system includes: The input terminal is used to acquire the operating status signal of the high-power pulse load starting of industrial equipment; the operating status signal is identified as precursor information, and the amplitude and duration of the instantaneous power spike are estimated according to the preset equipment power characteristic profile to obtain the predicted instantaneous power spike parameters; The adjustment terminal is used to adjust the state of charge of the energy storage battery according to the instantaneous power peak parameter to ensure the power output of the energy storage battery under the pulse load; and to adjust the operating parameters according to the instantaneous power peak parameter to ensure that the power conversion system can withstand the impact under the pulse load.

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