Intelligent management system of energy storage system

Abstract

The invention relates to the technical field of power supply management, and discloses an intelligent control and safety guarantee system for emergency power supply of a transformer substation, which collects full-link operation data through a sensor module, makes up for the defect of incomplete data collection dimension in the prior art, provides complete data support for management decision, and improves the control accuracy. The controller module comprehensively plans data analysis and module cooperation and cooperates with the communication module to guarantee reliable data interaction of all units, and the problem that a traditional system is single in communication and prone to interruption is solved. The management module realizes remote monitoring visualization and multi-terminal adaptation, breaks through the bottleneck of insufficient remote management and control capability in the prior art, and achieves global real-time management and control; the automatic diagnosis module realizes intelligent fault identification through a built-in algorithm, so that the diagnosis efficiency and accuracy of hidden and complex faults are improved, and fault expansion caused by dependence on artificial experience is avoided; the charging and discharging optimization module dynamically generates an optimal strategy in combination with multi-dimensional data, overcomes the defects of fixed charging and discharging parameters, reduces the energy cost and prolongs the service life of the battery.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power supply management, and specifically relates to an intelligent management system of an energy storage system. BACKGROUND

[0002] The global energy structure is transforming towards clean and low-carbon, and energy storage systems, as the core equipment for balancing the volatility of new energy power generation and ensuring the stability of power grids, are widely used in multiple scenarios. The key indicators such as the operation safety of the energy storage systems directly affect the new energy consumption efficiency and put forward strict requirements on the management technology.

[0003] The safety, reliability and economy of the operation of the energy storage system directly determine the energy utilization efficiency, and the existing technology cannot meet the related management and control requirements, so the management scheme needs to be optimized.

[0004] The current technical scheme for the management of the energy storage system mainly adopts the traditional management mode, covering core links such as data acquisition, remote management and control, fault diagnosis, charge and discharge control, equipment maintenance and communication transmission. The basic management is realized by monitoring the electrical parameters of the energy storage unit, some systems have preliminary remote monitoring function, fixed charge and discharge parameter control strategy is adopted, fault troubleshooting is carried out relying on manual experience, data is transmitted through single or conventional communication mode, and fixed logic algorithm is used for fault identification and parameter adjustment.

[0005] The current technical scheme has systematic shortcomings, and the overall adaptability, intelligent level and reliability are insufficient. Specifically, the data acquisition dimension is not comprehensive, the remote management and control ability is weak, the fault diagnosis efficiency and accuracy are low, the charge and discharge strategy lacks dynamic optimization, the equipment maintenance needs to interrupt power supply, the communication reliability is poor, the algorithm adaptation and iteration ability is weak. These defects jointly lead to high safety hidden danger of the operation of the energy storage system, high operation and maintenance cost, low energy utilization efficiency and short battery life, which seriously restricts the large-scale promotion and application of the energy storage technology. SUMMARY

[0006] The application provides an intelligent management system of an energy storage system, which solves the problems of high safety hidden danger of the operation of the energy storage system and high operation and maintenance cost.

[0007] To achieve the above-mentioned purpose, the application provides the following technical scheme: An intelligent management system of an energy storage system, comprising a sensor module, a controller module, a communication module and a management module. The sensor module collects system full-link operation data, including energy storage unit operation parameters, power conversion unit status data, load power consumption data and environmental data. After collection, the data is synchronously transmitted to the controller module. The controller module receives the data transmitted by the sensor module and performs parsing and processing. Based on the processing results, it issues execution instructions to the three core management modules and external units, while coordinating the action collaboration between the modules. The communication module realizes data interaction between the modules. The management module includes a remote monitoring module, an automatic diagnosis module, and a charge / discharge optimization module. The management module receives sensor data transmitted via the communication module, processes it for visualization, and pushes it to the user terminal. It also receives remote control commands issued by the user and feeds them back to the controller module. The automatic diagnosis module is responsible for fault identification and diagnosis result feedback. It receives component operation data collected by the sensor module and status data transmitted by related systems, performs anomaly analysis and identifies fault types through built-in algorithms, and feeds back the diagnosis results to the controller module. The charge / discharge optimization module receives battery characteristics, load requirements, and external charging resource data collected by the sensor module, calculates the optimal charge / discharge strategy, generates parameter adjustment commands, and transmits them to the controller module.

[0008] Preferably, the remote monitoring module is configured to support multi-terminal access, and users can obtain and visualize the key operating parameters of the energy storage unit, the operating status of the power conversion unit, and the load power consumption in real time through at least one of the computer management platform and mobile APP terminal, so as to realize remote global control without on-site supervision.

[0009] Preferably, the key operating parameters of the energy storage unit include at least two: remaining power, voltage and current, temperature, and battery health status.

[0010] Preferably, the automatic diagnostic module has a built-in intelligent algorithm configured to monitor the operating data of each component of the system in real time, identify at least one fault type among battery overcharging, battery over-discharging, poor interface contact, power module abnormality, and temperature exceeding the standard, and trigger the corresponding response mechanism.

[0011] Preferably, the response mechanism is executed by the controller module in coordination with the communication module, the audible and visual alarm unit, and the power conversion unit. Specifically, the controller module drives the audible and visual alarm unit to automatically issue audible and visual alarm signals, and simultaneously pushes fault reminder information containing the fault type, fault location, and fault time to the remote terminal through the communication module, while simultaneously recording the fault data to the fault log. The fault log is stored in the local storage unit of the controller module and can be uploaded to the remote management platform through the communication module. When a serious fault is detected, the controller module drives the power conversion unit to cut off the corresponding output circuit to prevent the fault from escalating. The fault log provides maintenance personnel with accurate fault location information and shortens the fault troubleshooting time.

[0012] Preferably, the charge / discharge optimization module is configured to automatically adjust at least one parameter among the charge / discharge current and charging time based on the battery characteristics of the energy storage unit, the power demand of the load, and the availability of external charging resources, so as to optimize energy costs and extend battery life.

[0013] Preferably, the charging and discharging optimization module has an electricity price linkage optimization function, configured to automatically control the energy storage unit to charge and store energy during periods of low grid electricity price, and to automatically control the energy storage unit to discharge and supply power during periods of high electricity price or periods of high load demand.

[0014] Preferably, the uninterrupted power replacement linkage function specifically includes: when a certain energy storage module is detected to need maintenance or replacement, the controller module automatically switches the power supply path to continuously supply power to the load through other energy storage modules or external backup power, while allowing safe disassembly of the old energy storage module and installation of the new energy storage module, and the load power supply is not interrupted throughout the entire process.

[0015] Preferably, the communication module supports at least one of wireless and wired communication, wherein the wireless communication includes at least one of 4G, 5G, WiFi, and LoRa.

[0016] Preferably, the intelligent algorithm includes a machine learning-based fault identification algorithm and a PID control algorithm. The fault identification algorithm establishes a data association with the local storage unit of the controller module, enabling it to call historical fault data in the storage unit for model training and optimization, thereby improving the accuracy of identifying complex fault types and shortening the fault diagnosis response time. The PID control algorithm outputs a PWM signal to the DC / DC converter of the power conversion unit to dynamically adjust the charging current. When the battery capacity is detected to be ≥95%, it automatically switches to float charging mode. When the automatic diagnosis module identifies a new type of fault, it records the fault data to the local storage unit and simultaneously uploads it to the remote management platform, providing data support for the continuous optimization of the algorithm.

[0017] Compared with existing technologies, this invention has the following advantages: This invention provides an intelligent management system for energy storage systems. By collecting end-to-end operational data through sensor modules, it overcomes the shortcomings of incomplete data collection dimensions in existing technologies, providing comprehensive data support for management decisions and improving control accuracy. The controller module coordinates data parsing and module collaboration, working with the communication module to ensure reliable data interaction between units, solving the problems of single communication and easy interruption in traditional systems. The management module enables remote monitoring visualization and multi-terminal adaptation, overcoming the bottleneck of insufficient remote control capabilities and achieving global real-time control. The automatic diagnosis module achieves intelligent fault identification through built-in algorithms, improving the efficiency and accuracy of diagnosing hidden and complex faults, and avoiding the expansion of faults due to reliance on human experience. The charge / discharge optimization module dynamically generates optimal strategies based on multi-dimensional data, overcoming the drawbacks of fixed charge / discharge parameters, reducing energy costs, and extending battery life. The overall solution improves the operational safety, reliability, and economy of energy storage systems, reduces operation and maintenance costs, meets the needs of scenarios with high power supply continuity, and facilitates the large-scale promotion and application of energy storage technology. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This invention relates to an intelligent management system for an energy storage system. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0021] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0022] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0023] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. In the description of this invention, it should be noted that unless otherwise explicitly specified and limited, the terms "installed," "connected," "linked," and "set up" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components.

[0024] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0025] like Figure 1 As shown, this embodiment of the invention provides an intelligent management system for an energy storage system, including a sensor module, a controller module, a communication module, and a management module; The sensor module collects system full-link operation data, including energy storage unit operation parameters, power conversion unit status data, load power consumption data and environmental data. After collection, the data is synchronously transmitted to the controller module. The controller module receives the data transmitted by the sensor module and performs parsing and processing. Based on the processing results, it issues execution instructions to the three core management modules and external units, while coordinating the action collaboration between the modules. The communication module realizes data interaction between the modules. The management module includes a remote monitoring module, an automatic diagnosis module, and a charge / discharge optimization module. The management module receives sensor data transmitted via the communication module, processes it for visualization, and pushes it to the user terminal. It also receives remote control commands issued by the user and feeds them back to the controller module. The automatic diagnosis module is responsible for fault identification and diagnosis result feedback. It receives component operation data collected by the sensor module and status data transmitted by related systems, performs anomaly analysis and identifies fault types through built-in algorithms, and feeds back the diagnosis results to the controller module. The charge / discharge optimization module receives battery characteristics, load requirements, and external charging resource data collected by the sensor module, calculates the optimal charge / discharge strategy, generates parameter adjustment commands, and transmits them to the controller module.

[0026] By collecting end-to-end operational data through sensor modules, the system overcomes the shortcomings of incomplete data collection dimensions in existing technologies, providing comprehensive data support for management decisions and improving control accuracy. The controller module coordinates data parsing and module collaboration, working with the communication module to ensure reliable data exchange between units, resolving the issues of single-channel and easily interrupted communication in traditional systems. The management module enables remote monitoring visualization and multi-terminal adaptation, overcoming the bottleneck of insufficient remote control capabilities and achieving global real-time control. The automatic diagnosis module uses built-in algorithms to intelligently identify faults, improving the efficiency and accuracy of diagnosing hidden and complex faults, and avoiding the escalation of faults due to reliance on human experience. The charge / discharge optimization module dynamically generates optimal strategies based on multi-dimensional data, overcoming the drawbacks of fixed charge / discharge parameters, reducing energy costs, and extending battery life. The overall solution improves the operational safety, reliability, and economy of the energy storage system, reduces maintenance costs, meets the needs of scenarios requiring high power supply continuity, and facilitates the large-scale promotion and application of energy storage technology.

[0027] In one possible implementation: For example, the remote monitoring module is configured to support multi-terminal access. Users can obtain and visualize the key operating parameters of the energy storage unit, the operating status of the power conversion unit, and the load power consumption in real time through at least one of the computer management platform and mobile APP terminal, so as to realize remote global control without on-site supervision.

[0028] Explained, the remote monitoring module is configured to support multi-terminal access, employing cross-platform adaptation protocols (such as HTTP / HTTPS, MQTT) to achieve compatible access between the computer management platform and the mobile APP terminal. The data formatting and processing module converts the collected raw operating data into visual charts (such as line graphs and dashboards), and establishes a cloud data transmission channel to ensure real-time data synchronization. Users can perform batch device management and historical data queries through the computer management platform, and receive real-time push notifications through the mobile APP terminal. Ultimately, this achieves remote global control without on-site supervision, solving the problems of poor multi-terminal compatibility, low data visualization, and reliance on on-site supervision in traditional energy storage systems. It allows users to conveniently obtain global operating data through multiple terminals, enabling real-time remote control, improving control flexibility and efficiency, and reducing on-site manpower costs.

[0029] In one possible implementation: For example, the key operating parameters of the energy storage unit include at least two of the following: remaining power, voltage and current, temperature, and battery health status. Interpretive data collection involves deploying dedicated sensors (such as voltage and current sensors, temperature sensors, and battery internal resistance testers) inside and around the energy storage unit to collect relevant parameters. The signal conditioning module filters and amplifies the collected analog signals, converting them into digital signals that are transmitted to the controller. The controller then calibrates and integrates the data to form a standardized dataset of key operating parameters. Beneficial effects: This addresses the shortcomings of traditional management solutions that only focus on certain electrical parameters, making data collection more targeted and focused. It provides crucial data support for subsequent management decisions such as fault diagnosis and charge / discharge optimization, improving the accuracy of control.

[0030] In one possible implementation: For example, the automatic diagnostic module has a built-in intelligent algorithm configured to monitor the operating data of various system components in real time, identify at least one fault type among battery overcharging, battery over-discharging, poor interface contact, power module malfunction, and excessive temperature, and trigger the corresponding response mechanism. The interpretable, automatic diagnostic module incorporates intelligent algorithms that integrate machine learning-based fault identification models (such as decision trees and neural network models). Model training is completed by pre-importing various fault sample data. After real-time collection of operational data from various system components, the data is compared with preset normal parameter thresholds and a fault feature library. Combined with the trained algorithm model, anomaly analysis is performed to accurately identify at least one fault type among battery overcharging, battery over-discharging, poor interface contact, power module malfunction, and excessive temperature, triggering a preset corresponding response mechanism. This solves the problems of traditional fault diagnosis relying on human experience, delayed response, and low accuracy in identifying hidden and complex faults. It achieves real-time intelligent fault identification, improving fault identification coverage and accuracy, and saving time for subsequent fault handling.

[0031] In one possible implementation: For example, the response mechanism is executed by the controller module in coordination with the communication module, the audible and visual alarm unit, and the power conversion unit. Specifically, the controller module drives the audible and visual alarm unit to automatically issue audible and visual alarm signals, and simultaneously pushes fault reminder information containing the fault type, fault location, and fault time to a remote terminal through the communication module, while simultaneously recording the fault data to the fault log. The fault log is stored in the controller module's local storage unit and can be uploaded to a remote management platform through the communication module. When a serious fault is detected, the controller module drives the power conversion unit to cut off the corresponding output circuit to prevent the fault from escalating. The fault log provides maintenance personnel with accurate fault location information, shortening the fault investigation time. The explanatory response mechanism is executed by the controller module in conjunction with the communication module, the audible and visual alarm unit, and the power conversion unit. The controller module has a built-in response logic processing unit that, upon receiving a fault identification signal, drives the audible and visual alarm unit (such as LED warning lights and buzzers) via the I / O interface. Simultaneously, it encapsulates information including fault type, location, and time via the communication module (wireless / wired) and pushes it to a remote terminal. A fault log database is established through the local storage unit to record all fault information and supports synchronous uploading to the cloud management platform. When a serious fault is detected (such as battery overcharging exceeding the threshold), the controller outputs a control signal to the switching element of the power conversion unit, cutting off the corresponding output circuit. This forms a complete fault handling closed loop, which not only promptly alerts users and maintenance personnel but also provides accurate fault location data through the fault log, shortening troubleshooting time and effectively preventing the escalation of serious faults, reducing equipment damage and downtime losses.

[0032] In one possible implementation: For example, the charge / discharge optimization module is configured to automatically adjust at least one parameter among the charge / discharge current and charging time based on the battery characteristics of the energy storage unit, the power demand of the load, and the availability of external charging resources, so as to optimize energy costs and extend battery life.

[0033] Explanatoryly, the charge / discharge optimization module can adjust parameters based on the battery characteristics of the energy storage unit, the load's power demand, and the availability of external charging resources. By building a multi-dimensional data acquisition and analysis model, it imports characteristic parameters such as battery SOC (State of Charge) and cycle life, and collects real-time power demand data such as load current and voltage, as well as resource data such as external charging pile power and grid power supply status. Through optimization algorithms (such as genetic algorithms and particle swarm optimization), it calculates the optimal charge / discharge parameters, generates control commands, and adjusts at least one parameter—charge / discharge current or charging time—through the PWM drive module. This overcomes the drawbacks of existing solutions that use fixed charge / discharge parameters, achieving dynamic matching between the charge / discharge strategy and actual operating conditions. This reduces energy costs, avoids damage to the battery from improper charge / discharge, and extends battery life.

[0034] In one possible implementation: For example, the charging and discharging optimization module has an electricity price linkage optimization function, configured to automatically control the energy storage unit to charge and store energy during periods of low grid electricity price, and to automatically control the energy storage unit to discharge and supply power during periods of high electricity price or periods of high load demand.

[0035] Interpretive, the system connects to the power grid dispatching platform or electricity price information release interface via a communication module to acquire time-of-use electricity price data in real time and store it in a local database. It employs preset electricity price threshold judgment logic: when electricity prices are detected to be in a low-price period, the controller outputs a charging command to control the energy storage unit to store energy using external charging resources; when electricity prices are detected to be in a high-price period or when load demand is high (judged by load current monitoring), it outputs a discharging command to control the energy storage unit to supply power to the load. The entire process ensures the timeliness of the strategy by periodically refreshing electricity price data. This further improves the economic efficiency of energy utilization, achieving optimal control of energy costs through peak-shaving charging and discharging, while also helping to balance grid load and improve grid operational stability.

[0036] In one possible implementation: For example, the uninterrupted power replacement linkage function specifically includes: when a certain energy storage module is detected to need maintenance or replacement, the controller module automatically switches the power supply path to continuously supply power to the load through other energy storage modules or external backup power, while allowing safe disassembly of the old energy storage module and installation of the new energy storage module, and the load power supply is not interrupted throughout the entire process.

[0037] Explanatoryly, a redundant power supply path design is adopted, setting up a backup power supply circuit in the energy storage system (connected to other energy storage modules or an external backup power source). Through the power supply path switching logic built into the controller module, when a fault is detected in an energy storage module (determined by module status data collected by sensors), a switching command is automatically sent to a switching switch (such as a contactor or solid-state relay) to switch the load power supply path to the backup circuit. Simultaneously, a lockout signal is output to ensure the safety of the faulty module during power outage, allowing maintenance personnel to remove the old module and install the new one. During the switching process, a voltage stabilization module ensures that voltage fluctuations at the load end remain within acceptable limits. This solves the problem of interrupting load power supply when replacing energy storage modules in traditional systems, meeting the needs of scenarios with extremely high power supply continuity requirements, such as medical facilities and data centers, and improving the system's applicability and reliability.

[0038] In one possible implementation: For example, the communication module supports at least one of wireless communication and wired communication, wherein the wireless communication includes at least one of 4G, 5G, WiFi, and LoRa.

[0039] Explained, wired communication utilizes Ethernet, RS485 bus, and other methods for short-range, highly stable data transmission (such as communication between the controller and local storage units). Wireless communication integrates at least one of the following: 4G / 5G module, WiFi module, and LoRa module. 4G / 5G is used for long-distance, wide-coverage remote data transmission; WiFi is used for short-range local terminal access; and LoRa is used for low-power, long-distance communication between field devices. The modules have built-in communication switching logic, automatically switching to a backup mode when one communication method is interrupted, ensuring communication continuity. This overcomes the shortcomings of traditional systems with single communication methods that are prone to interruption in complex environments. By using multiple communication methods, it ensures stable and reliable data interaction between modules, guarantees timely transmission of management commands and prevents data loss, and improves the overall operational stability of the system.

[0040] In one possible implementation: For example, the intelligent algorithm includes a machine learning-based fault identification algorithm and a PID control algorithm. The fault identification algorithm establishes a data association with the local storage unit of the controller module, and can call historical fault data in the storage unit for model training and optimization, thereby improving the accuracy of identifying complex fault types and shortening the fault diagnosis response time. The PID control algorithm is used to output a PWM signal to the DC / DC converter of the power conversion unit to dynamically adjust the charging current. When the battery capacity is detected to be ≥95%, it automatically switches to float charging mode. When the automatic diagnosis module identifies a new type of fault, it records the fault data to the local storage unit and uploads it to the remote management platform simultaneously, providing data support for the continuous optimization of the algorithm.

[0041] Explanatory The fault identification algorithm establishes a data association interface with the controller's local storage unit, and retrieves historical fault data through the data reading module, continuously optimizing model parameters using incremental training. The PID control algorithm is integrated into the controller's signal output unit, calculating the adjustment amount by collecting real-time battery voltage and current data, and outputting a PWM signal to the DC / DC converter in the power conversion unit. A preset float charging switching threshold of ≥95% battery capacity is established, and upon triggering, the output signal is automatically adjusted to achieve mode switching. A new fault identification module is added; when an unmatched fault type is detected, the data encapsulation module records all fault data, synchronously stores it in the local storage unit, and uploads it to the remote management platform via the communication module, providing data samples for algorithm iteration. This solves the problems of insufficient adaptability and iteration capability, low parameter adjustment accuracy, and weak ability to identify new faults in traditional algorithms. By optimizing the model with historical data, the accuracy and response speed of complex fault identification are improved; precise adjustment of charging and discharging parameters ensures battery safety; and the accumulation of new fault data enables continuous algorithm iteration, adapting to complex operating conditions.

[0042] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the scope of the invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0043] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. An intelligent management system for an energy storage system, characterized by, The system comprises a sensor module, a controller module, a communication module and a management module. The sensor module collects system full-link operation data, including energy storage unit operation parameters, power conversion unit state data, load power consumption data and environmental data, and synchronously transmits the data to the controller module after collection. The management module comprises a remote monitoring module, an automatic diagnosis module and a charge-discharge optimization module.

2. The intelligent management system of an energy storage system according to claim 1, wherein, The remote monitoring module is configured to support multi-terminal access, and the user can obtain and visually display the key operation parameters of the energy storage unit, the operation state of the power conversion unit and the load power consumption through at least one of a computer terminal management platform and a mobile phone APP terminal, thereby realizing remote global control without on-site attendance.

3. The intelligent management system of an energy storage system according to claim 1, wherein, The key operation parameters of the energy storage unit include at least two of the remaining power, voltage and current, temperature and battery health status.

4. The intelligent management system of an energy storage system according to claim 1, wherein, The automatic diagnosis module is configured to monitor the operation data of each component of the system in real time, identify at least one of battery overcharging, battery overdischarging, poor interface contact, power module abnormality and temperature exceeding the standard, and trigger the corresponding response mechanism.

5. The intelligent management system of an energy storage system according to claim 4, wherein, The response mechanism is executed by the controller module in cooperation with the communication module, the sound and light alarm unit and the power conversion unit, specifically including: the controller module drives the sound and light alarm unit to automatically send sound and light alarm signals, simultaneously pushes the fault reminder information containing the fault type, fault location and fault time to the remote terminal through the communication module, and synchronously records the fault data to the fault log; the fault log is stored in the local storage unit of the controller module and can be uploaded to the remote management platform through the communication module; when a serious fault is detected, the controller module drives the power conversion unit to cut off the corresponding output loop to avoid the expansion of the fault; the fault log provides accurate fault positioning basis for maintenance personnel and shortens the fault troubleshooting time.

6. The intelligent management system of an energy storage system according to claim 1, wherein, The charge-discharge optimization module is configured to automatically adjust at least one of the charge-discharge current and the charging time according to the battery characteristics of the energy storage unit, the load power consumption demand and the external charging resource condition, thereby realizing energy cost optimization and battery service life extension.

7. The intelligent management system of an energy storage system of claim 1, wherein, The charge-discharge optimization module has a power price linkage optimization function, which is configured to automatically control the energy storage unit to charge energy storage during the low valley period of the power grid price, and to automatically control the energy storage unit to discharge power supply during the peak period of the power price or the period of large load demand.

8. The intelligent management system of an energy storage system according to claim 1, wherein, The uninterrupted power replacement linkage function specifically includes: when it is detected that a certain energy storage module needs to be maintained or replaced, the controller module automatically switches the power supply path, continuously supplies power to the load through the remaining energy storage modules or external backup power supply, and allows safe disassembly of the old energy storage module and installation of the new energy storage module, and the load is not interrupted during the entire process.

9. The intelligent management system of an energy storage system of claim 1, wherein, The communication module supports at least one of wireless communication and wired communication, and the wireless communication includes at least one of 4G, 5G, WiFi, and LoRa.

10. The intelligent management system of an energy storage system of claim 1, wherein, The intelligent algorithm includes a fault identification algorithm based on machine learning and a PID adjustment algorithm, wherein the fault identification algorithm is associated with the local storage unit of the controller module to call historical fault data in the storage unit for model training and optimization, improve the identification accuracy of complex fault types, and shorten the fault diagnosis response time; the PID adjustment algorithm is used to output a PWM signal to the DC / DC converter of the power conversion unit to dynamically adjust the charging current, and automatically switch to the floating mode when the battery capacity is greater than or equal to 95%; when the automatic diagnosis module identifies a new type of fault, the fault data is recorded to the local storage unit and synchronously uploaded to the remote management platform, providing data support for continuous optimization of the algorithm.

Images