Energy storage converter and energy storage device

Abstract

The invention provides an energy storage converter and an energy storage device, and belongs to the technical field of energy storage. The converter adopts a hybrid multi-level topological structure and is formed by connecting a three-level neutral point clamping circuit and a modular multi-level sub-module in parallel, and further comprises a hierarchical coordination control strategy, an upper decision-making layer calculates the priority weight of a charging and discharging mode, and a lower execution layer adopts model prediction control to generate a switch sequence. The heat dissipation system is accurately regulated and controlled based on an IGBT junction temperature prediction model, and cooperates with a battery management system (BMS) for protection, so that rapid data interaction and rapid emergency response are realized. The energy storage device comprises the converter and a battery pack, the battery pack is divided into at least two independent clusters, and the SOC of the standby battery cluster is maintained within the range of + / -5% of the main battery cluster. Through an innovative topological structure, an intelligent control strategy and an efficient heat dissipation system, the problems of wide voltage input adaptation, dynamic performance optimization, heat dissipation management and the like are solved, the efficiency and reliability of an energy storage system are improved, and the service life of the energy storage system is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage, and more particularly to an energy storage converter and an energy storage device. Background Art

[0002] With the large-scale access of renewable energy and the increasing demand for flexible energy storage in the power system, as the core equipment of the energy storage system, the performance of the energy storage converter directly affects the power conversion efficiency, system reliability and applicable scenarios. Traditional energy storage converters mostly adopt fixed topology structures (such as two-level or three-level topologies), and there are problems of efficiency decline and high harmonic content in a wide voltage input range (such as 300V - 800V). In addition, the DC side of existing converters usually adopts a static configuration and is difficult to dynamically adapt to voltage fluctuations, resulting in the need to rely on additional boosting or bucking devices when the voltage changes greatly, increasing the system complexity and cost.

[0003] In terms of control strategies, traditional methods mostly rely on PI control or hysteresis control with fixed parameters, and it is difficult to balance the grid dynamic response and multi-objective optimization (such as battery charge and discharge priority, temperature management). Especially when the load suddenly changes or the grid frequency fluctuates, control delay may cause risks of overcurrent or overheating. In addition, existing heat dissipation systems mostly rely on passive heat dissipation or single temperature control strategies, and the heat dissipation efficiency is insufficient in the face of instantaneous thermal shocks of power devices (such as IGBT switching losses), easily leading to shortened device life or even failure.

[0004] The coordinated protection between the battery management system (BMS) and the converter is also a technical difficulty. Existing systems mostly adopt low-speed communication interfaces (such as CAN bus), and the data interaction delay is relatively high (usually in the ms level), making it difficult to respond to faults such as sudden increase in internal resistance of the battery cluster and abnormal temperature in a timely manner. In addition, the switching mechanism of the standby battery cluster often relies on simple threshold judgment and lacks dynamic matching of the state of charge (SOC), which may lead to a decrease in system efficiency or secondary faults after switching.

[0005] In view of the above problems, although modular multilevel converters (MMCs) and dynamic reconfiguration schemes have been proposed in the prior art, their sub-module switching logic mostly relies on fixed voltage thresholds and lacks flexibility; the intelligent level of the heat dissipation system is limited, and there is a lack of temperature prediction methods combining physical models and data-driven; control strategies also rarely hierarchically coordinate multi-dimensional parameters such as grid state, battery state and device temperature.

[0006] Therefore, there is an urgent need for an energy storage converter and an energy storage device with wide voltage adaptability, efficient thermal management, intelligent cooperative control and fast fault response to meet the requirements of high-reliability and high-flexibility energy storage systems. Summary of the Invention

[0007] The objective of the present invention is to provide an energy storage converter and an energy storage device, which solve problems such as wide voltage input adaptation, dynamic performance optimization, and heat dissipation management through innovative topological structures, intelligent control strategies, and efficient heat dissipation systems, and improve the efficiency, reliability, and lifespan of the energy storage system.

[0008] To achieve the above objective, the present invention provides an energy storage converter, which includes a hybrid multilevel topological structure. The topological structure is composed of a three-level neutral point clamped circuit and at least two modular multilevel sub-modules connected in parallel. A dynamic reconstruction module is configured on the DC side of the modular multilevel sub-module. The dynamic reconstruction module is used to dynamically switch the number of sub-modules according to the comparison result between the real-time DC bus voltage and a preset threshold to adapt to a wide voltage input range of 300V - 800V. The dynamic reconstruction module includes multiple switchable sub-module groups composed of SiC MOSFETs and fast recovery diodes connected in parallel. Each switchable sub-module group is connected in series with a bidirectional DC / DC converter, and the switching logic is as follows: when the DC voltage is lower than 500V, only the three-level neutral point clamped circuit is enabled; when the DC voltage ≥ 500V, one additional modular multilevel sub-module is connected for every 100V increase.

[0009] Preferably, a magnetic integrated inductor is configured on the AC side of the hybrid multilevel topology, and its inductance value is dynamically adjusted through the real-time switching frequency, with an adjustment range of 20μH - 50μH, to suppress high-frequency harmonics and reduce losses.

[0010] Preferably, it also includes a hierarchical coordination control strategy as follows: Upper decision-making layer: Based on the grid voltage frequency deviation, battery SOC, and converter temperature, calculate the priority weight of the charge and discharge modes. Lower execution layer: Use model predictive control to generate a switching sequence, and dynamically adjust the efficiency weight coefficient and response weight coefficient of the model predictive control based on the load change rate.

[0011] Preferably, the rolling optimization period of the model predictive control is 2ms - 5ms, and within each period, the top 3 schemes with the lowest losses in the switching state combinations are preferentially calculated, and the scheme with the highest comprehensive score is selected through fuzzy rules.

[0012] Preferably, the heat dissipation system of the converter includes: A paraffin-based phase change material layer embedded in the power device substrate, with a phase change temperature of 45℃ - 60℃, used to absorb instantaneous thermal shocks. Adjustable guide vanes and variable frequency fans, whose control parameters are generated based on the IGBT junction temperature prediction model. The input of the IGBT junction temperature prediction model includes real-time current, ambient temperature, and historical temperature rise curves. The IGBT junction temperature prediction model combines a physical model and a deep learning model, and the prediction process is as follows: Collect the working current, working voltage, ambient temperature, radiator temperature, and historical temperature rise data of the IGBT in real time; Predict the initial junction temperature based on the physical model according to the following formula; ; Among them, represents the initial junction temperature, represents the ambient temperature, represents the thermal resistance, represents the working current, represents the resistance value, represents the historical temperature rise data, represents the historical data correction coefficient, represents the working voltage, represents the duty cycle, represents the number of modular multilevel sub-modules; Take the initial junction temperature, radiator temperature, and working temperature as the input features of the deep learning model; Use the LSTM model to process the input features, and the output of the LSTM model is the final IGBT junction temperature prediction value; Quantify the uncertainty of the prediction result of the LSTM model through the Monte Carlo dropout method, calculate the standard deviation of the prediction value, and if the standard deviation exceeds the preset threshold, trigger the adaptive update mechanism of the model and retrain the LSTM model.

[0013] Preferably, the adjustment of the deflector angle and the fan speed satisfies: ; ; Among them, represents the deflector angle, represents the basic angle of the deflector, , represents the adjustment coefficient, represents the IGBT junction temperature prediction value, represents the reference temperature, represents the fan speed, represents the basic speed of the fan.

[0014] Preferably, it also includes a cooperative protection mechanism with the battery management system BMS, specifically: Implement a μs-level data interaction channel between the battery management system BMS and the converter through FPGA to transmit the internal resistance, temperature, and voltage data of the battery cluster in real time; Calculate the battery health state indicator based on the transmitted data : ; Among them, , , respectively represent the average values of the internal resistance, temperature, and voltage within the main battery cluster, , , respectively represent the maximum allowable values of the internal resistance, temperature, and voltage within the main battery cluster; If it is detected that the internal resistance of a single battery suddenly increases by more than 10% within 10 ms, the converter is immediately triggered to limit the current to 50% of the rated value, and the standby battery cluster is switched within 100 ms; The selection of the standby battery cluster is based on the SOC matching degree and satisfies: ; Among them, represents the state of charge of the main battery cluster, represents the state of charge of the standby battery cluster.

[0015] The present invention also provides an energy storage device, including the energy storage converter described above, and a battery pack connected to the converter; the battery pack is divided into at least two independent clusters, each cluster is connected in parallel through a solid-state relay, and the SOC of the standby battery cluster is always maintained within the range of ±5% of the main battery cluster.

[0016] Preferably, the thermal management pipeline of the battery pack is coupled with the converter cooling system. When the temperature of the converter exceeds 50 °C, the flow rate of the coolant pump of the battery pack is increased to 1.5 times, and the fan speed of the converter is synchronously increased by 20%.

[0017] Therefore, the present invention adopts the above-mentioned energy storage converter and energy storage device, and the beneficial technical effects are as follows: (1) Wide voltage adaptability and high-efficiency power conversion: Adopting a hybrid multilevel topology structure (a three-level neutral point clamped circuit and a modular multilevel sub-module in parallel) and a dynamic reconfiguration module, the number of sub-modules can be switched in real time according to the DC bus voltage, realizing adaptive matching for a wide voltage input range of 300V - 800V. Compared with traditional fixed topology structures (such as two-level or three-level), the dynamic reconfiguration module can adapt to voltage fluctuations without additional boost / buck devices through a preset logic (such as adding one sub-module for every 100V increase when the voltage ≥ 500V), reducing the system complexity and cost. At the same time, a switchable sub-module group with SiC MOSFETs and fast recovery diodes in parallel, combined with a bidirectional DC / DC converter, significantly reduces the switching loss and improves the conversion efficiency (especially in the high-voltage section).

[0018] (2) Harmonic suppression and dynamic inductance optimization: On the AC side, a magnetic integrated inductor is configured, and its inductance value is dynamically adjusted through the real-time switching frequency (20 μH - 50 μH). Compared with traditional fixed inductors, dynamic adjustment can effectively suppress high-frequency harmonics, reduce core losses, and improve the overall system efficiency and power quality.

[0019] (3) Intelligent hierarchical coordination control strategy: Through the hierarchical control architecture of the upper decision-making layer and the lower execution layer, multi-dimensional parameters such as the grid voltage frequency deviation, battery SOC, and converter temperature are comprehensively considered to dynamically optimize the priority of the charge and discharge modes. The lower execution layer uses model predictive control (MPC) to generate the switching sequence and dynamically adjusts the efficiency and response weight coefficients in combination with the load change rate. Compared with traditional PI control or hysteresis control, the rolling optimization period of MPC (2 ms - 5 ms) and the fuzzy rule optimization mechanism (selecting the top 3 schemes with the lowest losses) significantly improve the dynamic response speed and control accuracy, reducing the risks of overcurrent and overheating.

[0020] (4) Efficient thermal management and reliability improvement: The cooling system innovatively integrates a paraffin-based phase change material layer with active cooling control based on the IGBT junction temperature prediction model. The phase change material (phase change temperature 45°C - 60°C) can quickly absorb the instantaneous thermal shock of power devices, and the junction temperature prediction model combines physical formulas and LSTM deep learning algorithms to predict the junction temperature through multi-parameters such as real-time current, voltage, and ambient temperature, and quantifies the prediction uncertainty using Monte Carlo dropout. When the predicted standard deviation exceeds the limit, the model is triggered to adaptively update to ensure the accuracy of the cooling control.

[0021] (5) μs-level collaborative protection and intelligent fault switching: Through the FPGA, μs-level data interaction between the BMS and the converter is achieved, and the internal resistance, temperature, and voltage data of the battery cluster are transmitted in real-time. The system risk is dynamically evaluated based on the battery health state indicator (SOH). When it is detected that the internal resistance of a single battery suddenly increases by 10% within 10 ms, the converter is immediately triggered to limit the current (50% of the rated value) and switch to the standby battery cluster within 100 ms. The selection of the standby cluster is based on the SOC matching degree (the difference between the main and standby SOCs ≤ 5%), avoiding the efficiency loss caused by traditional threshold switching.

[0022] (6) Thermal management coupling and energy efficiency linkage optimization: The thermal management pipeline of the battery pack is coupled with the cooling system of the converter. When the converter temperature exceeds 50°C, the coolant flow rate (1.5 times) and the fan speed (+20%) are synchronously increased to achieve dynamic allocation of the overall cooling resources. Description of the Drawings

[0023] Figure 1 It is the architecture diagram of the energy storage converter; Figure 2It is a schematic diagram of a hybrid multilevel topology structure; Figure 3 It is a schematic diagram of a hierarchical coordination control strategy; Figure 4 It is a schematic diagram of the architecture of the heat dissipation system; Figure 5 It is a schematic diagram of the cooperative protection mechanism between the energy storage converter and the BMS. Specific implementation manners

[0024] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0025] Unless otherwise defined, the technical terms or scientific terms used in the present invention shall have the ordinary meanings understood by those of ordinary skill in the field to which the present invention belongs.

[0026] Embodiment 1 As Figures 2 - 5 , the present invention provides an energy storage converter, including a hybrid multilevel topology structure, which is composed of a three-level neutral point clamped (NPC) circuit and at least two modular multilevel sub-modules (MMC) connected in parallel; a dynamic reconstruction module is configured on the DC side of the modular multilevel sub-module, and the dynamic reconstruction module is used to dynamically switch the number of sub-modules according to the comparison result between the real-time DC bus voltage and a preset threshold value to adapt to the wide voltage input range of 300V - 800V. The dynamic reconstruction module includes a plurality of switchable sub-module groups composed of SiC MOSFETs and fast recovery diodes connected in parallel. Each switchable sub-module group is connected in series with a bidirectional DC / DC converter, and the switching logic satisfies that when the DC voltage is lower than 500V, only the three-level neutral point clamped circuit is enabled, and when the DC voltage ≥ 500V, an additional modular multilevel sub-module is connected for every 100V increase. For example, when the DC bus voltage is 500V - 600V, one modular multilevel sub-module is enabled and connected in parallel with the three-level NPC circuit; when the DC bus voltage is 600V - 700V, two modular multilevel sub-modules are enabled and connected in parallel with the three-level NPC circuit; and so on until the maximum set number of sub-modules is reached. This dynamic adjustment method can achieve the adaptive matching of the 300V - 800V wide voltage input range, effectively reduce the switching loss, and improve the conversion efficiency. Especially in the high-voltage section, compared with the traditional fixed topology structure, the conversion efficiency can be increased by about 10%.

[0027] The AC side of the hybrid multilevel topology is configured with a magnetically integrated inductor, whose inductance value is dynamically adjusted through the real-time switching frequency, with an adjustment range of 20 μH - 50 μH, to suppress high-frequency harmonics and reduce losses. During actual operation, when the converter operates in the high-frequency switching mode, the inductance value is automatically adjusted to a higher value (such as 50 μH) to enhance the harmonic suppression ability; while in the low-frequency mode, the inductance value is reduced to 20 μH to reduce core losses, improve the overall efficiency and power quality of the system. Compared with the traditional fixed inductor scheme, the core losses are reduced by about 30%.

[0028] The converter adopts a hierarchical coordinated control strategy, including an upper-layer decision-making layer and a lower-layer execution layer.

[0029] The upper-layer decision-making layer calculates the priority weight of the charge and discharge modes based on the grid voltage frequency deviation, battery SOC, and converter temperature. Assuming that the calculated charge priority weight is 0.6 and the discharge priority weight is 0.4, according to this weight, the lower-layer execution layer uses model predictive control (MPC) to generate the switching sequence and dynamically adjusts the efficiency weight coefficient and response weight coefficient of the model predictive control based on the load change rate. To implement this strategy, parameters such as the grid voltage frequency deviation, battery SOC, and converter temperature are converted into numerical inputs. After calculation, the charge priority weight of 0.6 and the discharge priority weight of 0.4 are obtained, and this weight will guide the converter to preferentially charge or discharge under different operating conditions.

[0030] The rolling optimization period of the model predictive control is 2 ms - 5 ms. In each period, the loss values of all possible switching state combinations are calculated according to the formula: ; Among them, represents the loss value, represents the loss when the switch is on, represents the loss when the switch is off, represents the conduction loss of the switch in the on state; After selecting the top 3 schemes with the lowest losses, a comprehensive score is calculated according to the formula through fuzzy rules: ; Among them, represents the comprehensive score, , represent the weight coefficients, represents the system stability score, represents the dynamic response speed score; Select the scheme with the highest comprehensive score as the optimal switching state combination in the current period to control the on and off of the power devices of the energy storage converter to achieve efficient and stable operation.

[0031] The heat dissipation system of the converter includes: A paraffin - based phase - change material layer embedded in the power device substrate, with a phase - change temperature of 45°C - 60°C, is used to absorb instantaneous thermal shock. During the operation of the converter, when the power device generates heat and the temperature rises to 45°C, the paraffin - based phase - change material begins to undergo a phase change, absorbing a large amount of heat, rapidly reducing the device temperature, slowing down the temperature rise rate, providing a buffer time for subsequent active heat - dissipation control, and effectively protecting the power device from damage caused by instantaneous thermal shock.

[0032] An adjustable deflector and a variable - frequency fan, whose control parameters are generated based on an IGBT junction - temperature prediction model. The inputs of the IGBT junction - temperature prediction model include real - time current, ambient temperature, and historical temperature - rise curves. The IGBT junction - temperature prediction model combines a physical model and a deep - learning model. The prediction process is as follows: Real - time collect the operating current, operating voltage, ambient temperature, radiator temperature, and historical temperature - rise data of the IGBT. Based on the physical model, perform an initial junction - temperature prediction according to the following formula; ; Where, represents the initial junction temperature, represents the ambient temperature, represents the thermal resistance, represents the operating current, represents the resistance value, represents the historical temperature - rise data, represents the historical - data correction coefficient, represents the operating voltage, represents the duty cycle, represents the number of modular multilevel sub - modules; Take the initial junction temperature, radiator temperature, and operating temperature as the input features of the deep - learning model; Use an LSTM model to process the input features. The output of the LSTM model is the final predicted value of the IGBT junction temperature; Through the Monte Carlo dropout method, quantify the uncertainty of the prediction result of the LSTM model, calculate the standard deviation of the predicted value. If the standard deviation exceeds the preset threshold, trigger the adaptive update mechanism of the model and retrain the LSTM model.

[0033] The adjustment of the deflector angle and the fan speed satisfies: ; ; Where, represents the deflector angle, represents the basic angle of the deflector, 、 represents the regulation coefficient, represents the predicted value of the IGBT junction temperature, represents the reference temperature, represents the fan speed, represents the basic speed of the fan.

[0034] An FPGA is used to implement a μs-level data interaction channel between the battery management system BMS and the converter, and the internal resistance, temperature, and voltage data of the battery cluster are transmitted in real time; Based on the transmitted data, calculate the battery health status indicator : ; Among them, , , respectively represent the average values of the internal resistance, temperature, and voltage in the main battery cluster, , , respectively represent the maximum allowable values of the internal resistance, temperature, and voltage in the main battery cluster; If it is detected that the internal resistance of a single battery suddenly increases by more than 10% within 10 ms, the converter is immediately limited to 50% of the rated value, and the standby battery cluster is switched within 100 ms; The selection of the standby battery cluster is based on the SOC matching degree and satisfies: ; Among them, represents the state of charge of the main battery cluster, represents the state of charge of the standby battery cluster.

[0035] Embodiment 2 As Figure 1 shown, the present invention also provides an energy storage device, which includes the energy storage converter in Embodiment 1 and a battery pack connected to the converter; the battery pack is divided into at least two independent clusters, and each cluster is connected in parallel through a solid-state relay, and the SOC of the standby battery cluster is always maintained within ±5% of the main battery cluster.

[0036] The thermal management pipeline of the battery pack is coupled with the converter cooling system. When the temperature of the converter exceeds 50 °C, the flow rate of the battery pack coolant pump is increased from the original 1 L / min to 1.5 L / min, and the fan speed of the converter is synchronously increased by 20% from 1000 rpm to 1200 rpm, realizing the dynamic allocation of the overall heat dissipation resources, improving the thermal management efficiency of the energy storage device, and ensuring the stable operation of the system under high-temperature conditions.

[0037] It should be noted that the content not elaborated in detail in the present invention is all prior art and is well known to those skilled in the art.

[0038] Therefore, the present invention adopts the above-mentioned energy storage converter and energy storage device, and solves the problems of wide voltage input adaptation, dynamic performance optimization and heat dissipation management through innovative topological structures, intelligent control strategies and efficient heat dissipation systems, thereby improving the efficiency, reliability and lifespan of the energy storage system.

[0039] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify or equivalently replace the technical solutions of the present invention, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A energy storage converter, characterized in that, It includes a hybrid multilevel topology structure, which is composed of a three-level neutral point clamped circuit in parallel with at least two modular multilevel sub-modules; a dynamic reconstruction module is configured on the DC side of the modular multilevel sub-module, and the dynamic reconstruction module is used to dynamically switch the number of sub-modules according to the comparison result of the real-time DC bus voltage and the preset threshold to adapt to the wide voltage input range of 300V - 800V. The dynamic reconstruction module includes multiple switchable sub-module groups composed of SiC MOSFETs and fast recovery diodes in parallel. Each switchable sub-module group is connected in series with a bidirectional DC / DC converter, and the switching logic is as follows: when the DC voltage is lower than 500V, only the three-level neutral point clamped circuit is enabled; when the DC voltage ≥ 500V, an additional modular multilevel sub-module is connected for every 100V increase.

2. The energy storage converter according to claim 1, wherein A magnetic integrated inductor is configured on the AC side of the hybrid multilevel topology, and its inductance value is dynamically adjusted through the real-time switching frequency, and the adjustment range is 20μH - 50μH.

3. A power storage converter according to claim 1, characterized in that, It also includes a hierarchical coordinated control strategy as follows: Upper decision-making layer: Based on the grid voltage frequency deviation, battery SOC, and converter temperature, calculate the priority weight of the charge and discharge modes; Lower execution layer: Adopt model predictive control to generate a switching sequence, and dynamically adjust the efficiency weight coefficient and response weight coefficient of the model predictive control based on the load change rate.

4. The energy storage converter according to claim 3, characterized in that, The rolling optimization period of the model predictive control is 2ms - 5ms, and within each period, the top 3 schemes with the lowest losses in the switching state combinations are preferentially calculated, and the scheme with the highest comprehensive score is selected through fuzzy rules.

5. An energy storage converter according to claim 1, characterized in that, The heat dissipation system of the converter includes: A paraffin-based phase change material layer embedded in the power device substrate, whose phase change temperature is 45℃ - 60℃, is used to absorb instantaneous thermal shocks; An adjustable deflector and a variable frequency fan, whose control parameters are generated based on the IGBT junction temperature prediction model. The input of the IGBT junction temperature prediction model includes real-time current, ambient temperature, and historical temperature rise curves; The IGBT junction temperature prediction model combines a physical model and a deep learning model, and the prediction process is as follows: Real-time collect the working current, working voltage, ambient temperature, radiator temperature, and historical temperature rise data of the IGBT; Based on the physical model, perform initial junction temperature prediction according to the following formula; ； Among them, represents the initial junction temperature, represents the ambient temperature, represents the thermal resistance, represents the operating current, represents the resistance value, represents the historical temperature rise data, represents the historical data correction coefficient, represents the operating voltage, represents the duty cycle, represents the number of modular multilevel sub-modules; Use the initial junction temperature, radiator temperature, and working temperature as the input features of the deep learning model; Use the LSTM model to process the input features, and the output of the LSTM model is the final IGBT junction temperature prediction value; Quantify the uncertainty of the prediction result of the LSTM model through the Monte Carlo dropout method, calculate the standard deviation of the prediction value, and if the standard deviation exceeds the preset threshold, trigger the adaptive update mechanism of the model and retrain the LSTM model.

6. The energy storage converter according to claim 5, wherein, The adjustment of the deflector angle and the fan speed satisfies: ； ； Among them, represents the angle of the deflector,[[]]END]] represents the basic angle of the deflector,[[]]END]] and represent the adjustment coefficient,[[]]END]] represents the predicted value of the IGBT junction temperature,[[]]END]] represents the reference temperature,[[]]END]] represents the fan speed,[[]]END]] represents the basic speed of the fan.[[]]END]] 7. An energy storage converter according to claim 1, characterized in that, It also includes a cooperative protection mechanism with the battery management system BMS, specifically: Implement a μs-level data interaction channel between the battery management system BMS and the converter through FPGA to transmit the internal resistance, temperature, and voltage data of the battery cluster in real time; Calculate the battery health status indicator based on the transmitted data : ； Among them, , , respectively represent the average values of the internal resistance, temperature, and voltage of the main battery cluster, , , respectively represent the maximum allowable values of the internal resistance, temperature, and voltage of the main battery cluster; If it is detected that the internal resistance of a single battery suddenly increases by more than 10% within 10ms, immediately trigger the converter to limit the current to 50% of the rated value and switch to the standby battery cluster within 100ms; The selection of the backup battery cluster is based on the SOC matching degree and satisfies: ； Among them, represents the state of charge of the main battery cluster, represents the state of charge of the backup battery cluster.

8. An energy storage device, characterized in that, It includes an energy storage converter as described in any one of claims 1-7, and a battery pack connected to the converter; the battery pack is divided into at least two independent clusters, each cluster is connected in parallel through a solid-state relay, and the SOC of the backup battery cluster is always maintained within the range of ±5% of the main battery cluster.

9. An energy storage device according to claim 8, characterized in that, The thermal management pipeline of the battery pack is coupled with the converter cooling system. When the temperature of the converter exceeds 50°C, the flow rate of the battery pack coolant pump is increased to 1.5 times, and the rotational speed of the converter fan is synchronously increased by 20%.

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