Efficient micro energy storage module based on intelligent energy management system and control method

Abstract

The invention provides a high-efficiency micro energy storage module based on an intelligent energy management system and a control method, the high-efficiency micro energy storage module comprises an energy storage unit, a power conversion unit, a dynamic equalization unit, an intelligent control unit and a communication interface unit, the energy storage unit is composed of multiple groups of battery clusters, each group of battery clusters comprises battery modules connected in parallel, bidirectional DC / DC converters are configured between the modules so as to realize inter-cluster energy dynamic balance; the power conversion unit comprises a bidirectional DC / DC converter and a multi-level inverter, and is used for realizing electric energy bidirectional conversion between a DC bus and an AC power grid / load. According to the invention, through the dynamic equalization design of the multiple groups of battery clusters and the bidirectional DC / DC converter, efficient energy distribution between the battery packs is realized, and capacity loss caused by inconsistency between the clusters is reduced; a multi-level inverter and a bidirectional DC / DC converter are integrated, so that the DC / AC electric energy conversion efficiency and the waveform quality are improved; the non-dissipative equalization technology based on SOC / SOH dual monitoring reduces energy loss and prolongs the service life of the battery.

Description

Technical Field

[0001] The present invention relates to the field of energy storage technology, and in particular to a high-efficiency micro energy storage module and a control method based on an intelligent energy management system. Background Art

[0002] Traditional micro energy storage systems have the following technical bottlenecks:

[0003] 1. Low battery balancing efficiency: Passive balancing or single SOC monitoring results in large energy loss, exacerbated inconsistencies between battery clusters, and shortened overall lifespan;

[0004] 2. Insufficient dynamic response: Fixed threshold control strategies are difficult to adapt to complex operating conditions (such as electricity price fluctuations and sudden load changes), and charging and discharging strategies have poor flexibility;

[0005] 3. Limited system reliability: Local failures are prone to spread in a centralized architecture, and there is a lack of fault-tolerant mechanisms for multi-source data collaboration.

[0006] 4. Unstable power quality: Line impedance changes and inverter control lags lead to voltage waveform distortion, affecting the power supply to sensitive loads;

[0007] 5. Difficulty in coordinating multiple objectives: Economic efficiency, environmental protection and equipment life optimization goals conflict with each other, and existing technologies are difficult to achieve global optimization.

[0008] To this end, an efficient micro energy storage module and control method based on intelligent energy management system are proposed. Summary of the Invention

[0009] The present invention aims to solve the problems raised in the background technology and provides a high-efficiency micro energy storage module and a control method based on an intelligent energy management system.

[0010] The specific technical solutions are as follows:

[0011] A high-efficiency micro energy storage module based on an intelligent energy management system, comprising:

[0012] The energy storage unit consists of multiple battery clusters. Each battery cluster contains parallel battery modules, and bidirectional DC / DC converters are configured between the modules to achieve dynamic energy balance between clusters.

[0013] The power conversion unit includes a bidirectional DC / DC converter and a multi-level inverter, which is used to realize bidirectional conversion of electric energy between the DC bus and the AC grid / load;

[0014] The dynamic balancing unit is integrated into the battery cluster and uses non-dissipative active balancing technology to achieve energy transfer within the cluster based on dual monitoring of SOC and SOH.

[0015] An intelligent control unit, including a multi-source data fusion module and an optimization algorithm module, collects grid electricity prices, weather forecasts, and load demand data in real time, and generates charging and discharging strategies using a mixed integer linear programming (MILP) algorithm.

[0016] The communication interface unit supports CAN bus and RS485 protocols, and is interconnected with external EMS energy management systems and distributed energy equipment.

[0017] The above-mentioned high-efficiency micro energy storage module based on the intelligent energy management system, wherein the power conversion unit includes:

[0018] The line impedance detection module detects the DC bus impedance in real time through a high-frequency ripple injection method and eliminates bus voltage deviations caused by impedance mismatch based on a voltage compensation algorithm.

[0019] The fault isolation device automatically disconnects the faulty module and maintains system operation through redundant paths when it detects overtemperature or overcurrent in the battery module.

[0020] In the above-mentioned high-efficiency micro energy storage module based on the intelligent energy management system, the working modes of the dynamic balancing unit include:

[0021] Charge equalization stage: Prioritize energy transfer to cells with low SOC and high SOH to extend battery life;

[0022] Discharge balancing stage: Dynamically adjust the balancing current according to load demand, supporting a maximum 10A balancing capability.

[0023] The above-mentioned high-efficiency micro energy storage module based on the intelligent energy management system, wherein the intelligent control unit includes:

[0024] The multi-objective optimization algorithm module generates a 24-hour rolling dispatch plan with the optimization goals of minimizing electricity costs, maximizing renewable energy absorption rates, and extending energy storage life;

[0025] The adaptive adjustment module dynamically switches between on-grid and off-grid modes based on real-time electricity price fluctuations and disaster warning information (such as typhoons and power grid failures).

[0026] The above-mentioned high-efficiency micro energy storage module based on the intelligent energy management system, wherein the energy storage unit adopts a modular design, supports hot-swappable replacement of battery modules, and each module has a built-in temperature-vibration composite sensor for monitoring mechanical stability and thermal runaway risks.

[0027] The present invention also provides a control method for a high-efficiency micro energy storage module, comprising the following steps:

[0028] Step 1: Real-time collection of battery cluster SOC, SOH, temperature and DC bus voltage data;

[0029] Step 2: Calculate the optimal energy transfer path through a dynamic balancing algorithm and adjust the duty cycle of the bidirectional DC / DC converter;

[0030] Step 3: Integrate grid dispatch instructions, weather forecast and load forecast data to generate charging and discharging power curves;

[0031] Step 4: Compensate for bus voltage deviation and optimize inverter output waveform based on line impedance detection results;

[0032] Step 5: In off-grid mode, prioritize cutting off non-critical loads and starting the backup energy storage unit to supply power.

[0033] In the control method of the above-mentioned high-efficiency micro energy storage module, the dynamic balancing algorithm in step 2 includes:

[0034] Based on the fuzzy logic controller, the balancing current threshold is dynamically adjusted in combination with the cell aging rate and temperature change rate;

[0035] A virtual impedance model is introduced between battery clusters to suppress the circulation phenomenon.

[0036] In the control method of the above-mentioned high-efficiency micro energy storage module, the method for generating the charge and discharge power curve in step 3 is:

[0037] Establish an optimization model with time-of-use electricity prices, photovoltaic output forecast errors, and battery degradation costs as constraints;

[0038] A deep reinforcement learning algorithm is used to update the strategy online to adapt to uncertain environments.

[0039] In the above-mentioned control method for a high-efficiency micro energy storage module, the voltage compensation algorithm in step 4 includes:

[0040] Substitute the detected line impedance value into the droop control equation to correct the inverter output voltage reference value;

[0041] Transient voltage fluctuations are eliminated through feed-forward control.

[0042] In the above-mentioned control method for the high-efficiency micro energy storage module, the method for identifying the non-critical load in step 5 is:

[0043] Based on the historical power consumption data of the load management module, cluster analysis is used to divide the load priorities;

[0044] During the off-grid period, a hierarchical power-off instruction is sent to the smart socket via ZigBee communication.

[0045] The present invention has the following beneficial effects:

[0046] 1. Improved battery life and efficiency:

[0047] Non-dissipative active balancing technology reduces energy loss, and differentiated charge and discharge balancing strategies delay cell aging;

[0048] The modular hot-swappable design reduces maintenance costs, and the composite sensor provides early warning of mechanical and thermal runaway risks.

[0049] 2. Enhanced system operation stability:

[0050] Dynamic line impedance compensation is combined with droop control equations to suppress voltage deviation and waveform distortion;

[0051] The fault isolation device enables rapid removal of local faults, and redundant paths ensure continuous system operation.

[0052] 3. Multi-objective collaborative optimization:

[0053] A hybrid optimization algorithm (MILP + deep reinforcement learning) balances economic efficiency, renewable energy consumption rate, and equipment lifespan;

[0054] The adaptive regulation module responds to sudden changes in the external environment and improves the robustness of micro-energy storage in complex scenarios.

[0055] 4. Intelligent energy management:

[0056] Multi-source data fusion drives dynamic scheduling strategies, enabling efficient interaction with the power grid and distributed energy resources;

[0057] Hierarchical load management and ZigBee communication optimize the efficiency of energy storage resource allocation in off-grid mode.

[0058] 5. Scalability and compatibility:

[0059] Multi-protocol communication interface supports interconnection with external EMS and smart devices to meet the needs of integrated energy systems;

[0060] The modular architecture facilitates capacity expansion and function upgrades, reducing the difficulty of deployment and transformation. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a block diagram of the composition of a high-efficiency micro energy storage module based on an intelligent energy management system provided by an embodiment of the present invention.

[0062] Figure 2 This is a flow chart of a control method for a high-efficiency micro energy storage module based on an intelligent energy management system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The technical solution of the present invention will be further described below with reference to the accompanying drawings and through specific implementation methods.

[0064] Among them, the drawings are only used for illustrative purposes and represent only schematic diagrams rather than actual pictures, and should not be understood as limiting this patent; in order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0065] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "inside", "outside" and the like indicate an orientation or position relationship based on the orientation or position relationship shown in the drawings, it is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting this patent. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0066] In the description of the present invention, unless otherwise expressly specified or limited, when the term "connection" or the like appears to indicate a connection relationship between components, such term should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be internal communication between two components or an interaction between two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood in specific circumstances.

[0067] Example

[0068] This embodiment provides a high-efficiency micro energy storage module based on an intelligent energy management system, such as Figure 1 As shown, it includes: an energy storage unit, a power conversion unit, a dynamic balancing unit, an intelligent control unit and a communication interface unit, wherein:

[0069] The energy storage unit consists of multiple battery clusters. Each battery cluster contains parallel battery modules, and bidirectional DC / DC converters are configured between the modules to achieve dynamic energy balance between clusters.

[0070] The power conversion unit includes a bidirectional DC / DC converter and a multi-level inverter to achieve bidirectional conversion of power between the DC bus and the AC grid / load;

[0071] The dynamic balancing unit is integrated into the battery cluster and uses non-dissipative active balancing technology to achieve energy transfer within the cluster based on dual monitoring of SOC and SOH.

[0072] The intelligent control unit includes a multi-source data fusion module and an optimization algorithm module, which are used to collect grid electricity prices, weather forecasts, and load demand data in real time, and generate charging and discharging strategies using a mixed integer linear programming (MILP) algorithm.

[0073] The communication interface unit supports CAN bus and RS485 protocols, and is interconnected with external EMS energy management systems and distributed energy equipment.

[0074] The high-efficiency micro energy storage module based on the intelligent energy management system adopts the above technical solution. Through the dynamic balancing design of multiple battery clusters and bidirectional DC / DC converters, it realizes efficient energy distribution among battery groups and reduces capacity loss caused by inconsistency between clusters. The integrated multi-level inverter and bidirectional DC / DC converter improves the DC / AC power conversion efficiency and waveform quality. The non-dissipative balancing technology based on SOC / SOH dual monitoring reduces energy loss and extends battery life. The intelligent control unit realizes dynamic adaptation of charging and discharging strategies through multi-source data fusion and optimization algorithm, improving system economy and grid interaction flexibility. The multi-protocol communication interface supports seamless collaboration with external systems, enhancing the scalability of micro energy storage in integrated energy systems.

[0075] The power conversion unit includes: a line impedance detection module and a fault isolation device, wherein:

[0076] The line impedance detection module detects the DC bus impedance in real time through the high-frequency ripple injection method and eliminates the bus voltage deviation caused by impedance mismatch based on the voltage compensation algorithm;

[0077] When the fault isolation device detects overtemperature or overcurrent in the battery module, it automatically disconnects the faulty module and maintains system operation through redundant paths.

[0078] In the power conversion unit using the above-mentioned technical solution, the line impedance detection module uses a high-frequency ripple injection method to sense impedance changes in real time. Combined with a voltage compensation algorithm, it suppresses voltage fluctuations caused by line aging or temperature changes, thereby improving system operation stability. The fault isolation device implements rapid module-level protection, preventing local faults from spreading to the entire energy storage system, significantly improving the system's fault tolerance and safety.

[0079] The working modes of the dynamic balancing unit include:

[0080] Charge equalization stage: Prioritize energy transfer to cells with low SOC and high SOH to extend battery life;

[0081] Discharge balancing stage: Dynamically adjust the balancing current according to load demand, supporting a maximum 10A balancing capability.

[0082] Using the above technical solution, low SOC and high SOH cells are prioritized for balancing during the charging phase to avoid the risk of overcharging and slow down the aging rate of high-health cells; the balancing current is dynamically adjusted during the discharge phase to adapt to different load power requirements, ensuring energy output continuity while reducing balancing losses; the two-stage differentiated balancing strategy takes into account both battery life and system efficiency.

[0083] The intelligent control unit includes a multi-objective optimization algorithm module and an adaptive adjustment module, wherein:

[0084] The multi-objective optimization algorithm module generates a 24-hour rolling scheduling plan with the optimization goals of minimizing electricity costs, maximizing renewable energy absorption rates, and extending energy storage life;

[0085] The adaptive regulation module dynamically switches between on-grid and off-grid modes based on real-time electricity price fluctuations and disaster warning information (such as typhoons and power grid failures).

[0086] Using the above technical solution, the multi-objective optimization algorithm generates a global optimal scheduling plan through multi-dimensional trade-offs among economy, environmental protection, and equipment life, breaking through the limitations of traditional single-objective control; the adaptive adjustment module quickly switches the operating mode according to sudden changes in the external environment (such as extreme weather or power grid failures), improving the robustness of micro-storage in complex scenarios.

[0087] Among them, the energy storage unit adopts a modular design, supports hot-swappable replacement of battery modules, and each module has a built-in temperature-vibration composite sensor to monitor mechanical stability and thermal runaway risks.

[0088] Using the above technical solution, the modular hot-swappable design simplifies the battery maintenance process, reducing system downtime and operation and maintenance costs; the temperature-vibration composite sensor monitors the mechanical and thermodynamic conditions in real time, providing early warning of potential faults (such as thermal runaway or structural loosening), thereby improving system safety and reliability.

[0089] like Figure 2 As shown, this embodiment also provides a control method for a high-efficiency micro energy storage module, comprising the following steps:

[0090] Step 1: Real-time collection of battery cluster SOC, SOH, temperature and DC bus voltage data;

[0091] Step 2: Calculate the optimal energy transfer path through a dynamic balancing algorithm and adjust the duty cycle of the bidirectional DC / DC converter;

[0092] Step 3: Integrate grid dispatch instructions, weather forecast and load forecast data to generate charging and discharging power curves;

[0093] Step 4: Compensate for bus voltage deviation and optimize inverter output waveform based on line impedance detection results;

[0094] Step 5: In off-grid mode, prioritize cutting off non-critical loads and starting the backup energy storage unit to supply power.

[0095] The control method of the efficient micro-energy storage module adopts the above technical solution, combining real-time multi-parameter acquisition with a dynamic balancing algorithm to achieve precise management of battery status and energy path optimization; the integrated control of grid dispatch instructions and weather / load data improves the coordinated absorption capacity of micro-energy storage and renewable energy; and hierarchical load management in off-grid mode ensures the continuity of power supply to key equipment and enhances the system's emergency response capabilities.

[0096] The dynamic balancing algorithm in step 2 includes:

[0097] Based on the fuzzy logic controller, the balancing current threshold is dynamically adjusted in combination with the cell aging rate and temperature change rate;

[0098] A virtual impedance model is introduced between battery clusters to suppress the circulation phenomenon.

[0099] Using the above technical solution, the fuzzy logic controller comprehensively considers the dynamic characteristics of battery cell aging and temperature to achieve adaptive adjustment of the balancing threshold, avoiding over-balancing or under-balancing problems caused by fixed thresholds; the virtual impedance model suppresses circulation between battery clusters, reduces invalid energy loss, and improves the overall efficiency of the system.

[0100] The method for generating the charge and discharge power curve in step 3 is:

[0101] Establish an optimization model with time-of-use electricity prices, photovoltaic output forecast errors, and battery degradation costs as constraints;

[0102] A deep reinforcement learning algorithm is used to update the strategy online to adapt to uncertain environments.

[0103] Using the above technical solution, the optimization model generates a charging and discharging strategy that takes into account both economy and equipment protection through multiple constraints such as time-of-use electricity prices, prediction errors, and battery degradation. The deep reinforcement learning algorithm updates the strategy online to adapt to the uncertainty of renewable energy output and electricity price fluctuations, thereby improving the dynamic adaptability of the control strategy.

[0104] The voltage compensation algorithm in step 4 includes:

[0105] Substitute the detected line impedance value into the droop control equation to correct the inverter output voltage reference value;

[0106] Transient voltage fluctuations are eliminated through feed-forward control.

[0107] With the above technical solution, the droop control equation corrects the voltage reference value, compensates for the voltage deviation caused by line impedance, and ensures the quality of the inverter output power; the feedforward control quickly suppresses transient voltage fluctuations, reduces the impact on sensitive loads, and improves the system power supply stability.

[0108] The identification method of non-critical loads in step 5 is:

[0109] Based on the historical power consumption data of the load management module, cluster analysis is used to divide the load priorities;

[0110] During the off-grid period, a hierarchical power-off instruction is sent to the smart socket via ZigBee communication.

[0111] The above technical solution accurately divides load priorities based on cluster analysis of historical data to avoid power waste of unnecessary loads when off-grid; ZigBee communication hierarchical power-off instructions enable rapid load response and optimize the allocation efficiency of limited energy storage resources.

[0112] Workflow principle:

[0113] This embodiment achieves efficient energy management through the collaboration of the following technologies:

[0114] 1. Hierarchical control architecture:

[0115] Hardware layer: Modular battery clusters and bidirectional DC / DC converters enable dynamic energy distribution, and multi-level inverters improve power conversion quality;

[0116] Balancing layer: Based on the non-dissipative balancing technology of SOC / SOH, the energy transfer path between cells is optimized, and the balancing threshold is dynamically adjusted in combination with the fuzzy logic controller;

[0117] Strategy layer: Multi-source data fusion (electricity price, weather, load) drives the MILP and deep reinforcement learning hybrid algorithm to generate adaptive charging and discharging strategies;

[0118] Protection layer: Fault isolation devices and impedance compensation algorithms work together to ensure system safety, and off-grid hierarchical load management improves emergency response capabilities.

[0119] 2. Dynamic closed-loop regulation:

[0120] Collect battery status, grid instructions and environmental data in real time, and generate control instructions through optimization algorithms;

[0121] Voltage compensation and virtual impedance model suppress power quality fluctuations and ensure stable inverter output;

[0122] Adaptively switch between on-grid and off-grid modes, combining backup energy storage with a graded power-off mechanism to maintain power supply continuity.

[0123] The above are only preferred embodiments of the present invention and do not limit the implementation mode and protection scope of the present invention. For those skilled in the art, it should be aware that all solutions obtained by equivalent substitutions and obvious changes made using the description and illustrations of the present invention should be included in the protection scope of the present invention.

Claims

1. A high-efficiency micro energy storage module based on an intelligent energy management system, characterized in that: include: The energy storage unit consists of multiple battery clusters. Each battery cluster contains parallel battery modules, and bidirectional DC / DC converters are configured between the modules to achieve dynamic energy balance between clusters. The power conversion unit includes a bidirectional DC / DC converter and a multi-level inverter, which is used to realize bidirectional conversion of electric energy between the DC bus and the AC grid / load; The dynamic balancing unit is integrated into the battery cluster and uses non-dissipative active balancing technology to achieve energy transfer within the cluster based on dual monitoring of SOC and SOH. An intelligent control unit, including a multi-source data fusion module and an optimization algorithm module, collects grid electricity prices, weather forecasts, and load demand data in real time, and generates charging and discharging strategies using a mixed integer linear programming (MILP) algorithm. The communication interface unit supports CAN bus and RS485 protocols, and is interconnected with external EMS energy management systems and distributed energy equipment.

2. The high-efficiency micro energy storage module based on the intelligent energy management system according to claim 1 is characterized in that: The power conversion unit includes: The line impedance detection module detects the DC bus impedance in real time through a high-frequency ripple injection method and eliminates bus voltage deviations caused by impedance mismatch based on a voltage compensation algorithm. The fault isolation device automatically disconnects the faulty module and maintains system operation through redundant paths when it detects overtemperature or overcurrent in the battery module.

3. The high-efficiency micro energy storage module based on the intelligent energy management system according to claim 1 is characterized in that: The working modes of the dynamic balancing unit include: Charge equalization stage: Prioritize energy transfer to cells with low SOC and high SOH to extend battery life; Discharge balancing stage: Dynamically adjust the balancing current according to load demand, supporting a maximum 10A balancing capability.

4. The high-efficiency micro energy storage module based on the intelligent energy management system according to claim 1 is characterized in that: The intelligent control unit comprises: The multi-objective optimization algorithm module generates a 24-hour rolling dispatch plan with the optimization goals of minimizing electricity costs, maximizing renewable energy absorption rates, and extending energy storage life; The adaptive adjustment module dynamically switches between on-grid and off-grid modes based on real-time electricity price fluctuations and disaster warning information.

5. The high-efficiency micro energy storage module based on the intelligent energy management system according to claim 1 is characterized in that: The energy storage unit adopts a modular design, supports hot-swappable replacement of battery modules, and each module has a built-in temperature-vibration composite sensor for monitoring mechanical stability and thermal runaway risks.

6. A control method based on the high-efficiency micro energy storage module according to any one of claims 1 to 5, characterized in that: The following steps are involved: Step 1: Real-time collection of battery cluster SOC, SOH, temperature and DC bus voltage data; Step 2: Calculate the optimal energy transfer path through a dynamic balancing algorithm and adjust the duty cycle of the bidirectional DC / DC converter; Step 3: Integrate grid dispatch instructions, weather forecast and load forecast data to generate charging and discharging power curves; Step 4: Compensate for bus voltage deviation and optimize inverter output waveform based on line impedance detection results; Step 5: In off-grid mode, prioritize cutting off non-critical loads and starting the backup energy storage unit to supply power.

7. The control method of the high-efficiency micro energy storage module according to claim 6, characterized in that: The dynamic balancing algorithm described in step 2 includes: Based on the fuzzy logic controller, the balancing current threshold is dynamically adjusted in combination with the cell aging rate and temperature change rate; A virtual impedance model is introduced between battery clusters to suppress the circulation phenomenon.

8. The control method of the high-efficiency micro energy storage module according to claim 6, characterized in that: The method for generating the charge and discharge power curve in step 3 is: Establish an optimization model with time-of-use electricity prices, photovoltaic output forecast errors, and battery degradation costs as constraints; A deep reinforcement learning algorithm is used to update the strategy online to adapt to uncertain environments.

9. The control method of the high-efficiency micro energy storage module according to claim 6, characterized in that: The voltage compensation algorithm described in step 4 includes: Substitute the detected line impedance value into the droop control equation to correct the inverter output voltage reference value; Transient voltage fluctuations are eliminated through feed-forward control.

10. The control method of the high-efficiency micro energy storage module according to claim 6, characterized in that: The method for identifying non-critical loads in step 5 is: Based on the historical power consumption data of the load management module, cluster analysis is used to divide the load priorities; During the off-grid period, a hierarchical power-off instruction is sent to the smart socket via ZigBee communication.

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