Control method and system for low-voltage miniature energy storage

Abstract

The invention discloses a control method and system for low-voltage miniature energy storage, and relates to the technical field of energy storage control, and the method comprises the steps: defining physical components of the low-voltage miniature energy storage as nodes, defining an energy transmission path between the nodes as edges, constructing an energy flow network, traversing the energy flow network through a breadth-first search algorithm, and generating an adjacent matrix. Calculating a manifold field curvature based on the power flow adjustment factor; and performing uniform interval division on the curvature of the flow field to obtain discrete states, generating a control parameter for each discrete state, calculating the estimated power of the control parameter by using a power amplitude limiting method, performing adjustment by using the power amplitude limiting method, and generating a final current control parameter. According to the invention, through multi-source data acquisition and energy flow network construction, the energy transfer efficiency and the system stability are improved, and through heat diffusion and impedance cooperative manifold field control, the energy transfer efficiency of the low-voltage micro energy storage system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage control, and in particular to a control method and system for low-voltage micro energy storage. Background Art

[0002] With the continuous evolution of distributed energy technology and the widespread deployment of microgrid architecture, low-voltage micro energy storage systems, as key equipment to support local energy autonomy, peak shaving and valley filling, and enhance energy resilience, have been widely used in smart buildings, edge computing nodes, and IoT power supply platforms. Traditional low-voltage micro energy storage control methods are mostly based on static topology structures and preset power scheduling strategies, relying on fixed-parameter PID control and the linear mapping relationship between voltage, current, and state of charge. They are difficult to dynamically reflect the operating status under local thermal disturbances, electromagnetic wave interference, and complex energy interaction conditions.

[0003] Existing technologies still have significant shortcomings in the control of low-voltage micro energy storage systems. Traditional control methods usually rely on only a single or a small number of parameters and lack comprehensive analysis of multi-source data, resulting in the inability to fully capture the dynamic characteristics of energy flow and heat diffusion, especially when the load changes suddenly or the ambient temperature fluctuates, the control accuracy decreases. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a control method and system for low-voltage micro energy storage, which solves the problem that traditional control methods generally rely only on a single or a small number of parameters and lack comprehensive analysis of multi-source data, resulting in the inability to fully capture the dynamic characteristics of energy flow and heat diffusion, especially when the load suddenly changes or the ambient temperature fluctuates, resulting in a decrease in control accuracy.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a method for controlling low-voltage micro energy storage, which comprises: Collect and preprocess multi-source data, define the physical components of low-voltage micro-energy storage as nodes, define the energy transfer paths between nodes as edges, construct an energy flow network, use a breadth-first search algorithm to traverse the energy flow network, generate an adjacency matrix, calculate the energy transfer efficiency of the energy flow network, use the gradient descent method to adjust the interaction strength, recalculate the edge weights based on the adjusted interaction strength, and generate the final adjacency matrix based on the new edge weights; Calculate the local thermal diffusion rate, set the reference thermal diffusion rate using the experimental calibration method, calculate the final power upper limit, use the sliding average to calculate the smooth current change rate, calculate the power flow adjustment factor based on the final adjacency matrix, and calculate the manifold field curvature based on the power flow adjustment factor; The curvature of the manifold field is divided into uniform intervals to obtain discrete states. Control parameters are generated for each discrete state. The estimated power of the control parameters is calculated using the power limiting method. The power limiting method is used to adjust the control parameters to generate the final current control parameters. The pulse width modulation method is used to convert the control parameters into PWM signals for transmission and execution.

[0007] As a preferred solution of the control method of the low-voltage micro energy storage of the present invention, the step of calculating the energy transfer efficiency of the energy flow network and generating the final adjacency matrix includes: Use discrete wavelet transform to extract the low-frequency component of the electromagnetic signal, calculate the energy spectrum density of the low-frequency component, calculate the thermal convection velocity based on the three-dimensional temperature, calculate the average thermal convection velocity, and calculate the interaction intensity based on the energy spectrum density and the average thermal convection velocity; The physical components of low-voltage micro energy storage are defined as nodes, and the energy transfer paths between nodes are defined as edges. Based on the interaction strength, the edge weights are calculated to construct an energy flow network. The energy flow network is traversed using a breadth-first search algorithm to generate an adjacency matrix. Calculate the energy transfer efficiency of the energy flow network, use statistical thresholds to set monitoring thresholds, compare the energy transfer efficiency with the monitoring threshold, and trigger adjustments if the energy transfer efficiency is less than the monitoring threshold. Use the gradient descent method to adjust the interaction strength. The edge weights are recalculated based on the adjusted interaction strengths, and the final adjacency matrix is ​​generated according to the new edge weights.

[0008] As a preferred solution of the control method of the low-voltage micro energy storage of the present invention, wherein: the use of the experimental calibration method to set the reference thermal diffusion rate and calculate the final power upper limit includes: Based on the two-dimensional temperature, calculate the local heat diffusion rate and the average heat diffusion rate; Use the experimental calibration method to set the reference thermal diffusion rate. Compare the average thermal diffusion rate with the reference thermal diffusion rate. If the average thermal diffusion rate is greater than the reference thermal diffusion rate, use the linear adjustment method to calculate the power upper limit for the thermal diffusion rate and ambient temperature at the next moment. Use the minimum value selection method to select the minimum value as the final power upper limit. Otherwise, continue operation.

[0009] As a preferred solution of the control method of the low-voltage micro energy storage of the present invention, the calculation of the manifold field curvature based on the final adjacency matrix includes: Calculate the equivalent resistance using Ohm's law, smooth the current rate of change using a sliding average, and calculate the power flow adjustment factor based on the final adjacency matrix; Based on the power flow adjustment factor, the manifold field curvature is calculated.

[0010] As a preferred solution of the control method of the low-voltage micro energy storage of the present invention, wherein: the estimated power of the control parameter is calculated using the power limiting method to generate the final current control parameter, including: The curvature of the manifold field is divided into uniform intervals to obtain discrete states, and control parameters are generated for each discrete state. The estimated power of the control parameters is calculated using the power limiting method. The estimated power is compared with the final power upper limit. If the estimated power is greater than the final power upper limit, the power limiting method is used to make adjustments to generate the final current control parameters. Otherwise, the operation continues.

[0011] As a preferred solution of the control method of the low-voltage micro energy storage of the present invention, wherein: the use of the pulse width modulation method to convert the control parameters into a PWM signal and transmit and execute it includes: The final current control parameters are normalized, the switching frequency control parameters are mapped to PWM frequency using the direct mapping method, the normalized final current control parameters and PWM frequency are converted into PWM signals using the pulse width modulation method, and the PWM signals are transmitted using the I2C protocol for transmission and execution.

[0012] As a preferred solution of the control method of low-voltage micro energy storage according to the present invention, the collecting of multi-source data and pre-processing thereof include: Based on smart sensors, a downsampling method is used to collect multi-source data of low-voltage micro energy storage at a unified frequency, and the data is synchronized, denoised, and normalized. The smart sensors include infrared thermal imaging, fiber optic temperature, thermocouple, voltage, current and Hall effect sensors; The multi-source data includes two-dimensional temperature, three-dimensional temperature, ambient temperature, voltage, current and electromagnetic signal data.

[0013] In a second aspect, the present invention provides a low-voltage micro energy storage control system, comprising: The collection network module is used to collect and preprocess multi-source data, define the physical components of low-voltage micro energy storage as nodes, define the energy transfer paths between nodes as edges, construct an energy flow network, use a breadth-first search algorithm to traverse the energy flow network, generate an adjacency matrix, calculate the energy transfer efficiency of the energy flow network, use the gradient descent method to adjust the interaction strength, recalculate the edge weights based on the adjusted interaction strength, and generate the final adjacency matrix based on the new edge weights; The power curvature module is used to calculate the local thermal diffusion rate, set the reference thermal diffusion rate using the experimental calibration method, calculate the final power upper limit, calculate the smooth current change rate using the sliding average, calculate the power flow adjustment factor based on the final adjacency matrix, and calculate the manifold field curvature based on the power flow adjustment factor; The control execution module is used to divide the manifold field curvature into uniform intervals to obtain discrete states, generate control parameters for each discrete state, calculate the estimated power of the control parameters using the power limiting method, adjust them using the power limiting method, generate the final current control parameters, and use the pulse width modulation method to convert the control parameters into PWM signals for transmission and execution.

[0014] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the low-voltage micro energy storage control method as described in the first aspect of the present invention is implemented.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the control method for low-voltage micro energy storage as described in the first aspect of the present invention is implemented.

[0016] The beneficial effects of the present invention are as follows: the present invention improves energy transfer efficiency and system stability through multi-source data acquisition and energy flow network construction, and improves the energy transmission efficiency of low-voltage micro energy storage systems through manifold field control that coordinates thermal diffusion and impedance. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 This is a flow chart of the control method for low-voltage micro energy storage in Example 1.

[0019] Figure 2 Schematic diagram of the control system of low-voltage micro energy storage in Example 1. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0021] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0023] Example 1, with reference to Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a control method for low-voltage micro energy storage, comprising the following steps: S1. Collect multi-source data and pre-process them. Define the physical components of low-voltage micro energy storage as nodes and the energy transfer paths between nodes as edges. Construct an energy flow network. Use a breadth-first search algorithm to traverse the energy flow network, generate an adjacency matrix, calculate the energy transfer efficiency of the energy flow network, use the gradient descent method to adjust the interaction strength, recalculate the edge weights based on the adjusted interaction strength, and generate the final adjacency matrix based on the new edge weights. Specifically, multi-source data is collected and preprocessed, including: Based on smart sensors, a downsampling method is used to collect multi-source data of low-voltage micro energy storage at a unified frequency, and the data is synchronized, denoised, and normalized. The smart sensors include infrared thermal imaging, fiber optic temperature, thermocouple, voltage, current and Hall effect sensors; The multi-source data includes two-dimensional temperature, three-dimensional temperature, ambient temperature, voltage, current and electromagnetic signal data.

[0024] Through the intelligent sensor network, comprehensive multi-dimensional perception of the energy storage unit is carried out, covering the data collection of physical quantities such as heat, electricity, and magnetism. The downsampling method unifies the sampling frequency, which not only avoids the information redundancy caused by the imbalance of data dimensions, but also reduces the system load, ensures the temporal and spatial consistency of the data, and effectively avoids the timing drift caused by inconsistent sampling frequencies between sensors.

[0025] Furthermore, the energy transfer efficiency of the energy flow network is calculated to generate the final adjacency matrix, including: Use discrete wavelet transform to extract the low-frequency component of the electromagnetic signal and calculate the energy spectrum density of the low-frequency component. The formula is: ; in is the energy density of the electromagnetic signal, N is the number of sampling points, is the rth approximation coefficient, representing the low-frequency component; Based on the three-dimensional temperature, the heat convection velocity is calculated using the formula: ; ; in is the heat convection velocity at the spatial point (x, y, z) at time t, 、 as well as They are electrolyte density, electrolyte specific heat capacity and electrolyte thermal conductivity, provided by the equipment manufacturer. is the three-dimensional temperature-time derivative, calculated using the backward difference method, is the three-dimensional temperature at the spatial point (x, y, z) at time t, is the sampling interval, which is set using the fixed time interval method; Calculate the average heat convection velocity using the formula: ; in is the average heat convection velocity at time t, and X, Y, and Z are the three-dimensional temperature grid sizes, which are set using the uniform meshing method; Based on the energy spectrum density and the average heat convection velocity, the interaction intensity is calculated as follows: ; in is the interaction strength, and are the reference electromagnetic signal energy density and the reference thermal convection velocity, respectively, which are set using the experimental calibration method; The physical components of low-voltage micro energy storage are defined as nodes. Physical components include battery units and load units. The energy transfer paths between nodes are defined as edges. Based on physical connections, including electrical connections (such as wires from batteries to processors) and thermal conduction paths (such as heat flow from batteries to cooling pads), the edge weights are calculated based on the interaction strength to construct an energy flow network. The formula is: ; in is the edge weight between nodes i and j at time t, negative values ​​indicate energy dissipation, and positive values ​​indicate energy transfer. and is the power at node i and node j, calculated based on the product of voltage and current; Use breadth-first search algorithm to traverse the energy flow network and generate the adjacency matrix; Calculate the energy transfer efficiency of the energy flow network using the formula: ; in is the energy transfer efficiency at time t, and The number of nodes that consume and provide power is the number of nodes that consume and provide power. The current of the battery node is greater than 0, and the current of the load node is less than 0. Use statistical thresholds to set the monitoring threshold, compare the energy transfer efficiency with the monitoring threshold, and trigger adjustment if the energy transfer efficiency is less than the monitoring threshold. Use the gradient descent method to adjust the interaction intensity. The formula is: ; in is the corrected interaction strength at time t, is the learning rate, set using a network search method, The target efficiency is set using the fixed calibration method. is the partial derivative of the energy transfer efficiency with respect to the interaction strength, calculated using numerical differences; The edge weights are recalculated based on the adjusted interaction strengths, and the final adjacency matrix is ​​generated based on the new edge weights. The formula is: ; in is the final adjacency matrix at time t, is the new edge weight at time t, is the initial adjacency matrix element, 1 indicates the edge exists, 0 indicates it does not exist.

[0026] Temperature, electromagnetic, and flow field signals have different response delays and signal time domain distributions. After fusion processing, they can bridge the control lag problem caused by physical information asymmetry. Regularizing the data structure helps to form a sparse graph structure, improve the processing efficiency of the adjacency matrix, and reduce computational complexity. The low-frequency component of the electromagnetic disturbance can better reflect the steady-state power disturbance of the system. After being distinguished from the high-frequency interference, the physical significance of the control signal can be improved. Daubechies wavelet is superior to Fourier transform in processing signal mutations and boundary preservation, and is suitable for time-varying analysis of perturbation systems. The thermal convection velocity is the natural diffusion rate of heat loss inside the energy storage unit, which can reveal the internal local fault trend. The electromagnetic fluctuation energy density and the thermal convection velocity are normalized and compounded to reflect the electromagnetic The dual effects of disturbance and local heat accumulation are avoided to avoid misjudgment caused by isolated analysis. The normalization standard is established through experimental calibration, so that various physical parameters have a unified control weight meaning. The power term can reflect the instantaneous supply and demand matching situation, and enhance the system's response ability to sudden load changes. The adjacency matrix formed after traversal using breadth-first search (BFS) has real-time and dynamic topological expression capabilities. Different from traditional static topology modeling, the interaction intensity is adaptively adjusted through gradient descent, avoiding manual parameter adjustment and improving the intelligence level of the system. The dynamic changes of the adjacency matrix can trigger the addition or deletion of edges, simulating the physical response effects of "virtual disconnection" and "priority channel". After reconstruction, the network can automatically avoid faulty edges, improving anti-interference and system robustness.

[0027] S2. Calculate the local heat diffusion rate, set the reference heat diffusion rate using the experimental calibration method, calculate the final power upper limit, calculate the smoothed current change rate using a sliding average, calculate the power flow adjustment factor based on the final adjacency matrix, and calculate the manifold field curvature based on the power flow adjustment factor; Specifically, an experimental calibration method is used to set a reference thermal diffusion rate and calculate the final power upper limit, including: Based on the two-dimensional temperature, the local heat diffusion rate is calculated as follows: ; in is the thermal diffusion rate at time t and position (x, y), calculated using the backward difference method, is the two-dimensional temperature at position (x,y) at time t; Calculate the average thermal diffusion rate using the formula: ; in is the average thermal diffusion rate at time t, m is the time window index, R and N are the grid sizes of the two-dimensional temperature field, which are set using the uniform grid division method, and M is the size of the time window, which is set using the sliding window method; Use the experimental calibration method to set the reference thermal diffusion rate, compare the average thermal diffusion rate with the reference thermal diffusion rate, and use the linear adjustment method to calculate the power upper limit of the thermal diffusion rate and ambient temperature at the next moment if the average thermal diffusion rate is greater than the reference thermal diffusion rate. Use the minimum value selection method to select the minimum value as the final power upper limit. Otherwise, continue to run. The formula is: ; ; in and are the power upper limits of the heat diffusion rate and ambient temperature at the next moment, is the reference thermal diffusion rate, To adjust the coefficient, the heat conduction model method is used to set it. is the basic reduction factor, set using the thermal stability method, is the temperature deviation coefficient, set using the linear thermal expansion method, is the ambient temperature at time t, is the ambient temperature threshold, which is set using the experimental calibration method.

[0028] Based on the backward difference method, local temperature gradient changes are extracted in real time to predict the rapid temperature rise area, so as to intervene in the power distribution path in advance and reduce the risk of thermal stress concentration. The use of two-dimensional temperature distribution provides a more comprehensive heat diffusion map than point monitoring, which can be used to identify design defects of the heat dissipation structure and improve the efficiency of hardware tuning. The time series of heat diffusion rate is calculated by sliding average, and the stable and unstable heat diffusion curves can be fitted, providing a mathematical basis for subsequent thermal runaway prediction. The coupling heat-power model improves the control accuracy. After adding heat diffusion constraints, a cross-domain energy control strategy can be implemented, so as to give full play to the electrical performance of the system while ensuring thermal safety. By introducing parameters such as temperature deviation coefficient and basic reduction factor, the power limit boundary is linearly smoothed, effectively reducing mechanical fatigue and electrochemical instability caused by severe power fluctuations.

[0029] Furthermore, based on the final adjacency matrix, the manifold field curvature is calculated, including: Use Ohm's law to calculate the equivalent resistance and use sliding average to calculate the smoothed current change rate. The formula is: ; in is the smoothed current change rate at time t, is the equivalent resistance at time t, is the current at time t; Based on the final adjacency matrix, the power flow adjustment factor is calculated as follows: ; in is the power flow adjustment factor at time t, is a very small constant; Based on the power flow adjustment factor, the manifold field curvature is calculated as follows: ; in is the manifold field curvature at time t, is the reference current change rate, set using the experimental calibration method, is the battery state of charge at time t, calculated using the Coulomb counting method, The optimal battery state of charge is set using the energy efficiency optimization method.

[0030] The originally discretized energy nodes are mapped into continuous manifolds through the adjacency matrix, so that the distribution, aggregation and transfer of power can be expressed by geometric curvature, providing a new description paradigm for energy optimization control. It is composed of the current current change rate and the equivalent resistance, which can significantly amplify the weight of the high-resistance-high-change area in the curvature, so that energy management prioritizes key transmission paths to avoid bottleneck effects. By introducing the relative change deviation between the state of charge (SOC) and the current change rate, a curvature model is established, and thermal, electrical and state information are embedded in the geometric structure to form a dynamic description framework for multi-field collaboration. Through characterization parameters such as heat diffusion rate and manifold curvature, the transition from local point state recognition to global domain energy flow dynamic modeling is completed, and a real-time feedback loop between power control and environmental variables is constructed to enhance the robustness and energy efficiency of the system in a dynamic environment.

[0031] S3. Divide the curvature of the manifold field into uniform intervals to obtain discrete states, generate control parameters for each discrete state, calculate the estimated power of the control parameters using a power limiting method, adjust them using the power limiting method to generate final current control parameters, convert the control parameters into PWM signals using a pulse width modulation method, and transmit and execute them; Specifically, the power limiting method is used to calculate the estimated power of the control parameter to generate the final current control parameter, including: The curvature of the manifold field is divided into uniform intervals to obtain discrete states, and the control parameters are generated for each discrete state. The formula is: ; ; in is the current control parameter of the kth discrete state, k is the discrete state index, K is the number of partitions, is the target power at time t, set using the fixed calibration method, is the nominal frequency, set using the efficiency optimization method, is the switching frequency control parameter of the kth discrete state; The estimated power of the control parameter is calculated using the power limiting method, and the formula is: ; in is the estimated power at time t, is the nominal voltage, set using the efficiency optimization method; Compare the estimated power with the final power limit. If the estimated power is greater than the final power limit, adjust it using the power limit method to generate the final current control parameters. Otherwise, continue to run. The formula is: ; in is the final current control parameter of the kth discrete state, is the final power upper limit at the next moment.

[0032] Although continuous space modeling is accurate, it has high computational overhead and is not conducive to real-time control. Each state corresponds to a set of preset control parameters, which can significantly reduce the decision-making time of the controller. After the discrete state is divided, the control strategy can be reused or expanded in different application scenarios (such as different battery types and power levels), thereby improving the portability of the control framework. In the case of strong disturbances or large changes, the boundary value warning mechanism of the discrete interval can trigger protection measures more quickly to prevent the system from entering extreme states such as thermal runaway or voltage drop. Once the estimated power exceeds the "ultimate power upper limit" obtained by the previous heat diffusion and ambient temperature constraints, the limiting mechanism is triggered, which effectively avoids heat accumulation and capacitor damage caused by overpower. Compared with hard switching The cutting mechanism and the limiting method maintain output smoothness through slope adjustment and scaling transformation, which helps to avoid system oscillation and improve the response coordination of the drive module. The present invention sets independent frequency control parameters to make the current control parameters and the switching frequency coupled rather than overlapping, thereby improving control flexibility and reducing modulation errors. The control parameters are calculated based on the target power, discrete state control factors and frequency characteristics, which not only ensures power controllability but also guarantees optimal switching efficiency, and achieves coordination in the three-dimensional space of energy-heat-frequency. By configuring a set of control parameters for each discrete state, the software optimization results can be coupled and deployed with the hardware control circuit, reducing the real-time calculation burden and improving the response frequency of the power controller.

[0033] Furthermore, a pulse width modulation method is used to convert the control parameters into a PWM signal and transmit and execute the signal, including: The final current control parameters are normalized, and the switching frequency control parameters are mapped to PWM frequency using the direct mapping method. The normalized final current control parameters and PWM frequency are converted into PWM signals using the pulse width modulation method. The PWM signals are transmitted using the I2C protocol and transmitted to the power control module for execution.

[0034] The PWM method precisely controls the output current by changing the duty cycle, making it suitable for micro-power control scenarios with extremely high requirements for regulation accuracy. The I2C protocol supports a multi-master-multi-slave structure, low-power communication, and anti-interference capabilities, making it suitable for deployment in embedded systems and multi-node energy networks. The introduction of normalization processing and mapping mechanisms effectively avoids the expansion of quantization errors, improves signal analysis accuracy, and ensures that control signals remain synchronized under high-frequency switching.

[0035] This embodiment also provides a low-voltage micro energy storage control system, including: The collection network module is used to collect and preprocess multi-source data, define the physical components of low-voltage micro energy storage as nodes, define the energy transfer paths between nodes as edges, construct an energy flow network, use a breadth-first search algorithm to traverse the energy flow network, generate an adjacency matrix, calculate the energy transfer efficiency of the energy flow network, use the gradient descent method to adjust the interaction strength, recalculate the edge weights based on the adjusted interaction strength, and generate the final adjacency matrix based on the new edge weights; The power curvature module is used to calculate the local thermal diffusion rate, set the reference thermal diffusion rate using the experimental calibration method, calculate the final power upper limit, calculate the smooth current change rate using the sliding average, calculate the power flow adjustment factor based on the final adjacency matrix, and calculate the manifold field curvature based on the power flow adjustment factor; The control execution module is used to divide the manifold field curvature into uniform intervals to obtain discrete states, generate control parameters for each discrete state, calculate the estimated power of the control parameters using the power limiting method, adjust them using the power limiting method, generate the final current control parameters, and use the pulse width modulation method to convert the control parameters into PWM signals for transmission and execution.

[0036] This embodiment also provides a computer device suitable for the control method of low-voltage micro energy storage, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the control method of low-voltage micro energy storage proposed in the above embodiment.

[0037] The computer device may be a terminal, comprising a processor, memory, a communication interface, a display, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage media. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication. Wireless communication may be achieved via Wi-Fi, a carrier network, NFC (near-field communication), or other technologies. The display of the computer device may be a liquid crystal display or an electronic ink display. The input device may be a touchscreen overlay on the display, buttons, a trackball, or a touchpad on the computer device housing, or an external keyboard, touchpad, or mouse.

[0038] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the control method for realizing low-voltage micro energy storage as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0039] In summary, the present invention improves energy transfer efficiency and system stability through multi-source data acquisition and energy flow network construction, and enhances the energy transmission efficiency of low-voltage micro energy storage systems through manifold field control that coordinates thermal diffusion and impedance.

[0040] 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 the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A control method for low-voltage micro energy storage, characterized by: include, Collect and preprocess multi-source data, define the physical components of low-voltage micro-energy storage as nodes, define the energy transfer paths between nodes as edges, construct an energy flow network, use a breadth-first search algorithm to traverse the energy flow network, generate an adjacency matrix, calculate the energy transfer efficiency of the energy flow network, use the gradient descent method to adjust the interaction strength, recalculate the edge weights based on the adjusted interaction strength, and generate the final adjacency matrix based on the new edge weights; Calculate the local thermal diffusion rate, set the reference thermal diffusion rate using the experimental calibration method, calculate the final power upper limit, use the sliding average to calculate the smooth current change rate, calculate the power flow adjustment factor based on the final adjacency matrix, and calculate the manifold field curvature based on the power flow adjustment factor; The curvature of the manifold field is divided into uniform intervals to obtain discrete states. Control parameters are generated for each discrete state. The estimated power of the control parameters is calculated using the power limiting method. The power limiting method is used to adjust the control parameters to generate the final current control parameters. The pulse width modulation method is used to convert the control parameters into PWM signals for transmission and execution.

2. The control method for low-voltage micro energy storage according to claim 1, characterized in that: The step of calculating the energy transfer efficiency of the energy flow network and generating a final adjacency matrix includes: Use discrete wavelet transform to extract the low-frequency component of the electromagnetic signal, calculate the energy spectrum density of the low-frequency component, calculate the thermal convection velocity based on the three-dimensional temperature, calculate the average thermal convection velocity, and calculate the interaction intensity based on the energy spectrum density and the average thermal convection velocity; The physical components of low-voltage micro energy storage are defined as nodes, and the energy transfer paths between nodes are defined as edges. Based on the interaction strength, the edge weights are calculated to construct an energy flow network. The energy flow network is traversed using a breadth-first search algorithm to generate an adjacency matrix. Calculate the energy transfer efficiency of the energy flow network, use statistical thresholds to set monitoring thresholds, compare the energy transfer efficiency with the monitoring threshold, and trigger adjustments if the energy transfer efficiency is less than the monitoring threshold. Use the gradient descent method to adjust the interaction strength. The edge weights are recalculated based on the adjusted interaction strengths, and the final adjacency matrix is ​​generated according to the new edge weights.

3. The control method for low-voltage micro energy storage according to claim 2, characterized in that: The experimental calibration method is used to set the reference thermal diffusion rate and calculate the final power upper limit, including: Based on the two-dimensional temperature, calculate the local heat diffusion rate and the average heat diffusion rate; Use the experimental calibration method to set the reference thermal diffusion rate. Compare the average thermal diffusion rate with the reference thermal diffusion rate. If the average thermal diffusion rate is greater than the reference thermal diffusion rate, use the linear adjustment method to calculate the power upper limit for the thermal diffusion rate and ambient temperature at the next moment. Use the minimum value selection method to select the minimum value as the final power upper limit. Otherwise, continue operation.

4. The control method for low-voltage micro energy storage according to claim 3, characterized in that: The calculation of the manifold field curvature based on the final adjacency matrix includes: Calculate the equivalent resistance using Ohm's law, smooth the current rate of change using a sliding average, and calculate the power flow adjustment factor based on the final adjacency matrix; Based on the power flow adjustment factor, the manifold field curvature is calculated.

5. The control method for low-voltage micro energy storage according to claim 4, characterized in that: The method of calculating the estimated power of the control parameter using the power limiting method to generate the final current control parameter includes: The curvature of the manifold field is divided into uniform intervals to obtain discrete states, and control parameters are generated for each discrete state. The estimated power of the control parameters is calculated using the power limiting method. The estimated power is compared with the final power upper limit. If the estimated power is greater than the final power upper limit, the power limiting method is used to make adjustments to generate the final current control parameters. Otherwise, the operation continues.

6. The control method for low-voltage micro energy storage according to claim 5, characterized in that: The method of converting the control parameter into a PWM signal using a pulse width modulation method and transmitting and executing the PWM signal includes: The final current control parameters are normalized, the switching frequency control parameters are mapped to PWM frequency using the direct mapping method, the normalized final current control parameters and PWM frequency are converted into PWM signals using the pulse width modulation method, and the PWM signals are transmitted using the I2C protocol for transmission and execution.

7. The control method for low-voltage micro energy storage according to claim 1, characterized in that: The multi-source data collection and pre-processing includes: Based on smart sensors, a downsampling method is used to collect multi-source data of low-voltage micro energy storage at a unified frequency, and the data is synchronized, denoised, and normalized. The smart sensors include infrared thermal imaging, fiber optic temperature, thermocouple, voltage, current and Hall effect sensors; The multi-source data includes two-dimensional temperature, three-dimensional temperature, ambient temperature, voltage, current and electromagnetic signal data.

8. A control system for low-voltage micro energy storage, based on the control method for low-voltage micro energy storage according to any one of claims 1 to 7, characterized in that: include, The collection network module is used to collect and preprocess multi-source data, define the physical components of low-voltage micro energy storage as nodes, define the energy transfer paths between nodes as edges, construct an energy flow network, use a breadth-first search algorithm to traverse the energy flow network, generate an adjacency matrix, calculate the energy transfer efficiency of the energy flow network, use the gradient descent method to adjust the interaction strength, recalculate the edge weights based on the adjusted interaction strength, and generate the final adjacency matrix based on the new edge weights; The power curvature module is used to calculate the local thermal diffusion rate, set the reference thermal diffusion rate using the experimental calibration method, calculate the final power upper limit, calculate the smooth current change rate using the sliding average, calculate the power flow adjustment factor based on the final adjacency matrix, and calculate the manifold field curvature based on the power flow adjustment factor; The control execution module is used to divide the manifold field curvature into uniform intervals to obtain discrete states, generate control parameters for each discrete state, calculate the estimated power of the control parameters using the power limiting method, adjust them using the power limiting method, generate the final current control parameters, and use the pulse width modulation method to convert the control parameters into PWM signals for transmission and execution.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the control method of low-voltage micro energy storage according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the control method of low-voltage micro energy storage according to any one of claims 1 to 7 are implemented.

Images