Modular intelligent battery management system

Abstract

The invention relates to the technical field of battery management, in particular to a modular intelligent battery management system, which comprises a sensing module for realizing full-dimensional monitoring of a battery through a multi-mode sensor array, implanting an MEMS stress sensing network and constructing a holographic stress tensor model; the operation and maintenance and decision-making module is used for realizing cooperative control of the battery units through dynamic impedance matrix construction, plasma resonance frequency monitoring and unbalanced thermodynamic gradient analysis; and the execution module is used for receiving the instruction sent by the stress analysis and optimization unit, controlling the piezoelectric composite material to generate a self-adaptive support structure, adjusting the internal mechanical structure of the battery, forming a stress shielding area and reducing the influence of stress on the performance and the service life of the battery. Through a dynamic impedance matrix and a main control unit election mechanism, the single-point bottleneck of traditional centralized management is avoided, and even if part of units fail, the system can still maintain the function through matrix recombination, so that the anti-interference capability and reliability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and in particular to a modular intelligent battery management system. Background Art

[0002] With the rapid development of new energy technologies, the application scale of lithium-ion batteries in electric vehicles, energy storage power stations, and consumer electronics continues to expand. The technical complexity and performance requirements of the battery management system, which serves as the brain of the battery pack, are also constantly increasing. The traditional centralized BMS architecture has gradually exposed defects such as poor scalability, insufficient computing power, and high maintenance costs.

[0003] Traditional BMS usually uses a single controller to monitor key parameters such as voltage, current and temperature of all single cells in the entire battery pack. This architecture has many disadvantages. When the number of battery cells exceeds 200, the complexity of the data acquisition harness increases exponentially, which not only increases system cost and volume, but is also susceptible to electromagnetic interference, resulting in reduced measurement accuracy. From the perspective of functional implementation, the balancing strategy of traditional BMS is relatively extensive, and most can only achieve simple passive balancing, that is, dissipating excess power in high-capacity single cells through resistive discharge to achieve power balance within the group. However, this method consumes a lot of energy and cannot accurately control the balancing process, which can easily reduce the overall energy efficiency of the battery pack and shorten the battery cycle life. Summary of the Invention

[0004] The present invention addresses the technical problems existing in the prior art and provides a modular intelligent battery management system.

[0005] The present invention solves the above technical problems with the following technical solutions: A modular intelligent battery management system comprising:

[0006] Perception module: A multi-modal sensor array is used to monitor the entire battery, while a MEMS stress sensing network is implanted to build a holographic stress tensor model.

[0007] Operation and maintenance and decision-making module: Achieve coordinated control of battery cells through dynamic impedance matrix construction, plasma resonance frequency monitoring, and non-equilibrium thermodynamic gradient analysis;

[0008] Execution module: Receives instructions from the stress analysis and optimization unit, controls the piezoelectric composite material to generate an adaptive support structure, adjusts the internal mechanical structure of the battery, adjusts the electromagnetic field generating device, reconstructs the electromagnetic field distribution, forms a stress shielding area, and reduces the impact of stress on battery performance and life.

[0009] In a preferred embodiment, the sensing module arranges the three-axis AMR sensor on the side of the battery cell, forming a 2×2 sensor array on the side of the battery cell, which is connected to the microcontroller through a telecommunications interface to obtain characteristic magnetic vortex information during the charging and discharging process. MEMS sensor nodes are evenly deployed at the corners of the battery cell shell, the electrode connection, and the contact area between the diaphragm and the electrode sheet inside the battery to ensure full coverage of the area where the battery may generate stress. A star topology is used to build a MEMS sensor network with a central node as the data aggregation center. Each MEMS sensor node is connected to the central node through wireless communication technology. The MEMS sensor node preliminarily encodes and processes the collected stress data and transmits it to the central node through a wireless communication link. The central node verifies the received data. If data errors or loss are found, a retransmission request is promptly sent to the corresponding MEMS sensor node. The collected initial stress data is preprocessed, including filtering, denoising, and normalization. Based on the MEMS stress sensing network, a holographic stress tensor model is constructed. The model is constructed as follows:

[0010] According to the geometric structure and physical properties of the battery cell, the battery cell is divided into several tiny three-dimensional units. The stress state of each unit can be described by its six stress components, which include three normal stresses and three shear stresses.

[0011] Based on the data collected by each MEMS sensor node, combined with mechanical principles and sensor position information, the six stress components of each tiny unit are calculated to construct the stress tensor of each unit;

[0012] The stress tensors of all tiny units are combined to form a holographic stress tensor model of the entire battery unit, which fully reflects the distribution and changes of stress inside the battery.

[0013] The steps of operating the stress tensor of each unit cell are as follows:

[0014] The MEMS sensor acquires the three-dimensional strain data of each node (ε x ,ε y ,ε z ,γ xy ,γ yz ,γ zx ), establish a rectangular coordinate system (x, y, z axis) with the center of mass of the battery cell as the origin;

[0015] Input the elastic modulus E and Poisson's ratio v of the battery material to calculate the shear modulus G. The specific calculation formula for the shear modulus is as follows:

[0016]

[0017] Substitute it into Hooke's law formula to calculate the normal stress (σ x ,σ y ,σ z ) and shear stress (τ xy ,τ yz ,τ zx ), the specific calculation formula of normal stress is as follows:

[0018]

[0019] Normal stress is related to the strain in three directions and the Poisson's ratio of the material, reflecting the influence of volume deformation. The specific calculation formula of shear stress is as follows:

[0020]

[0021] According to the stress components of each point, the corresponding stress tensor matrix σ is generated. The specific calculation formula is as follows:

[0022]

[0023] Diagonal elements (σ x ,σ y ,σ z ) is the normal stress, and the off-diagonal elements (τ xy ,τ yz ,τ zx ) is the shear stress, and satisfies τ ij =τ ji (Principle of shear stress reciprocity).

[0024] In a preferred embodiment, the operation and decision-making module includes a dynamic impedance matrix construction unit, a matrix reorganization control unit, and a stress analysis and optimization unit;

[0025] The dynamic impedance matrix construction unit maps the battery cells into four-dimensional supermatrix elements, constructs the cross-impedance relationship between the cells based on the anomalous Hall effect, measures the coupling strength between the matrix elements in real time, and completes the construction of the dynamic impedance matrix. The battery pack is divided into a three-dimensional grid, each grid point corresponds to a battery cell with coordinates (e, s, w), introduces the time dimension t, records the state changes of the battery cells, and forms four-dimensional coordinates (e, s, w, t). The impedance value, state of charge, and health state of each battery cell are mapped to the element value in the four-dimensional supermatrix, and a vertical magnetic field B is applied to the electrode surface of each battery cell. z , measure the transverse voltage V H , the Hall resistance is calculated according to the anomalous Hall effect formula. The specific calculation formula is as follows:

[0026]

[0027] Among them, RH Represents the Hall resistance, R0 represents the normal Hall coefficient, R s Represents the anomalous Hall coefficient, M represents the material magnetization intensity, and for adjacent battery cells i and j, the Hall resistance change rate is measured. Establish the cross impedance relationship, the specific calculation formula is as follows:

[0028]

[0029] Among them, Z ij represents cross impedance, k represents material constant, f pl represents the plasma resonance frequency, The rate of change of the magnetic induction intensity B corresponding to the rate of change of the Hall resistance is expressed by repeating the process of establishing the cross-impedance relationship to construct the cross-impedance matrix Z between all battery cells. n×n , microwave pulses are applied to the edge of the battery cell array to excite spin waves. The spin wave interference pattern is detected using a magnetic tunnel junction sensor. The coupling strength is calculated by measuring the phase difference Δφ and amplitude A of the interference pattern. The specific calculation formula is as follows:

[0030]

[0031] Among them, C ij represents the coupling strength, A0 represents the reference amplitude, A ij represents the interference pattern amplitude detected by the magnetic tunnel junction sensor, Δφ ij The phase difference of the interference pattern is caused by the difference in the propagation path of the spin wave, which is the coupling strength C ij As the weight factor, the dynamic impedance matrix is ​​updated. The specific calculation formula is as follows:

[0032]

[0033] Among them, M'(e i ,s i ,w i ,t) represents the updated dynamic impedance matrix, and the impedance fluctuation coefficient of each battery cell is calculated. The specific calculation formula is as follows:

[0034]

[0035] Where α represents the impedance fluctuation coefficient, σZ represents the impedance standard deviation, Represents the average impedance, sets a first preset threshold α0, selects units with α<α0 as master candidates, and among the candidate units, selects the unit with the impedance value closest to the global average as the master unit and issues control authority, and re-evaluates the matrix stability every T seconds;

[0036] The matrix reorganization control unit continuously calculates the impedance fluctuation coefficient α of the main control unit and compares it with the second preset threshold α1. When α>α1 and the duration exceeds T1, it is determined that the main control unit is invalid and re-election is triggered. The invalid main control unit will change its own coordinates (s u ,s v ,s w ), failure timestamp t0, current impedance value Z fail The signal is encoded into a characteristic wave signal, and the signal is modulated using spread spectrum technology to generate a pulse sequence with specific frequency characteristics. The characteristic wave is transmitted in all directions through the phased array antenna with a transmission power of P tx Ensure that all battery cells within the radius R are covered, and the phased array antennas of the surrounding battery cells receive the characteristic wave at the same time, and each antenna channel records the receiving time t rx The signal strength RSSI is used to calculate the location of the failed master controller using the TDOA algorithm for at least three adjacent battery cells. The specific calculation formula is as follows:

[0037]

[0038] Among them, s ui , s vi , s wi represents the coordinates of the receiving unit, c represents the propagation speed of the electromagnetic wave, demodulates the characteristic wave, and extracts the state parameters of the failed unit including Z fail The failure severity is evaluated by using the value and α value. Each battery cell broadcasts its own status information, establishes a temporary neighbor table, and constructs an undirected graph G = (D, Q) based on the neighbor table, where the node V represents the battery cell and the weight of the edge Q is the cross impedance Z between the battery cells. ij , each battery cell calculates the initial virtual impedance matrix according to the received neighbor status The specific calculation formula is as follows:

[0039]

[0040] Among them, d ij Represents the Euclidean distance between cells, and λ represents the attenuation coefficient, which is usually taken as twice the average spacing between battery cells. Battery cells that meet the following conditions are selected to enter the candidate set. When α<α0 and the state of charge is within the specified range, avoid overcharged or over-discharged cells from becoming the master. The comprehensive score S is calculated for the candidate battery cells. The specific calculation formula is as follows:

[0041]

[0042] Among them, ω1, ω2, ω3 represent weight coefficients, d failIndicates the distance from the failed master. The Byzantine fault-tolerant algorithm is used for voting. The unit with the highest number of votes is elected as the new master. The elected unit broadcasts its own ID, coordinates, and initial control parameters. Each battery unit updates the routing table based on the new master's position. The new master unit collects the latest status of each battery unit and rebuilds the dynamic impedance matrix.

[0043] The stress analysis and optimization unit performs a tensor product operation on the holographic stress tensor model and the dynamic impedance matrix. When a stress singularity point is detected, it triggers the generation of an adaptive support structure and electromagnetic field reconstruction to optimize the mechanical stress distribution and ensure the safety of the battery structure. Specifically, the following steps are included:

[0044] S1. Preparation for tensor product operation: Map the three-dimensional spatial coordinates (x, y, z) of the holographic stress tensor model with the four-dimensional coordinates (e, s, w, t) of the dynamic impedance matrix to establish the coordinate transformation relationship T z :(x,y,z)→(e,s,w), ensure that the parameters at the same physical location can be calculated accordingly, perform time slicing on the dynamic impedance matrix, and select the matrix D corresponding to the stress tensor acquisition time t0 y (e, s, w, t0), expand the stress tensor σ into a four-dimensional tensor σ', and repeat the value in the time dimension to make it consistent with the dimension of the dynamic impedance matrix;

[0045] S2. Tensor product operation: Perform a tensor product operation on the adapted stress tensor σ' and the dynamic impedance matrix. The specific calculation formula is as follows:

[0046]

[0047] Among them, i, j, k, and l correspond to the dimension indexes of the stress tensor and the impedance matrix respectively, generating a four-dimensional coupling tensor C(x, y, z, e, s, w), and calculating the local eigenvalue λ of the coupling tensor C C1 ,λ C2 , ..., λ C16 , evaluate its distribution in space and define sensitivity index Quantify the coupling strength of each tiny unit cell;

[0048] S3. Stress singular point detection: Setting the singular point determination threshold when When the corresponding small unit body is marked as a potential singular point, it is further verified by combining the second invariant I2 and the third invariant I3 of the stress tensor. If |I2|>∈ I2 And |I3|>∈ I3 , then the point is confirmed to be a stress singular point. For the confirmed singular point (x0, y0, z0), the sensitivity gradient of the adjacent unit is used to Calculate its impact range R, the specific calculation formula is as follows:

[0049]

[0050] Where d represents the distance, ∈ G represents the gradient threshold;

[0051] S4. Adaptive support structure generation: Combined with the influence area of ​​the singular point, the polarization direction of the piezoelectric composite material is determined according to the main direction of the stress tensor. For example, if the maximum principal stress σ max The direction is (n x ,n y ,n z ), the piezoelectric material is polarized along this direction, and the required compensation stress is calculated as: Δσ=-diag(σ max ,σ mid ,σ min ), where σ mid represents the intermediate principal stress, σ min Represents the minimum principal stress, according to the piezoelectric effect formula Δε=Y d ·d v , where Y d represents the piezoelectric constant matrix, d v Represents the voltage vector, and the voltage distribution d is solved inversely v * =Y d -1 ·R j -1 Δσ, applied to the piezoelectric material by the MEMS microcontroller v * , generating the support stress field;

[0052] S5. Electromagnetic field reconstruction: Design a circular electromagnetic coil outside the singular point influence area and calculate the coil current I and the number of turns. Among them, r represents the coil radius, μ0 represents the vacuum permeability, and the electromagnetic field distribution is iteratively optimized using finite element simulation software to adjust the coil position, current size and direction so that the stress mean square error MSE in the frequency-closed region is σ Minimize, the specific calculation formula is as follows:

[0053]

[0054] Among them, σ i Represents each stress point in the region, represents the mean stress;

[0055] S6: After reconstruction, the MEMS sensor re-collects strain data and calculates the updated stress vector σ new , compare the stress index of the singular point area, if the drop exceeds the preset threshold ∈ R, the optimization is determined to be effective. If the optimization does not meet the standards, the piezoelectric material voltage and electromagnetic field parameters are automatically adjusted, and steps S4-S5 are repeated until the stress distribution meets the safety standards. A full-process detection is performed every T time period to achieve dynamic stress management.

[0056] In a preferred embodiment, the execution module receives the control instructions sent by the stress analysis and optimization unit, including the coordinates of the stress singular point, the compensation stress tensor, the support structure parameters, and the electromagnetic field configuration parameters, verifies the integrity and timeliness of the instructions, analyzes the stress tensor data, extracts the principal stress direction and amplitude, determines the area and direction requiring key support, decodes the electromagnetic field configuration parameters, and generates a coil control signal to map the stress tensor components to voltage values. The specific calculation formula is as follows:

[0057] Y V (x,y,z)=f(σ xx ,σ yy ,σ zz ,τ xy ,τ yz ,τ zx )

[0058] Where f represents the mapping function. A multi-channel high-voltage drive circuit is used to apply precise voltage to the piezoelectric composite array. This pulse voltage is applied to the piezoelectric material in a specific area to adjust the direction of its internal electric dipoles, activate the electromagnetic coil array outside the singular point, and calculate the current in each coil. The specific calculation formula is as follows:

[0059] I i =g(B target ,r i ,θ i ,φ i )

[0060] Where g represents the magnetic field-current mapping function, r i ,θ i ,φ i The spatial coordinates of the coil are represented, and the phase of the current of each coil is adjusted through phased array technology so that the synthetic magnetic field forms a uniform shielding field in the target area. According to the stress fluctuation frequency, the electromagnetic field frequency is adjusted to form a resonance suppression effect and maximize the shielding effect. The strain data after regulation is collected through the MEMS sensor network and compared with the target stress distribution to evaluate the regulation effect. If the improvement effect does not meet the expectations, the control parameters are automatically adjusted and the regulation process is re-executed to form a fast-response closed-loop system.

[0061] The beneficial effects of the present invention are: the present invention avoids the single-point bottleneck of traditional centralized management through the dynamic impedance matrix and the main control unit election mechanism. Even if some units fail, the system can still maintain its function through matrix reorganization, improve anti-interference ability and reliability, and dynamically optimize the energy transmission path based on the impedance coupling strength. It can adapt to dynamic working conditions such as battery aging and temperature changes, maintain energy management efficiency, and realize monitoring-analysis-execution closed loop through coupling analysis of stress tensor model and impedance matrix. It suppresses stress concentration from the dual dimensions of mechanics and electromagnetics, reduces electrode / diaphragm damage, and improves cycle life. Different from traditional passive heat dissipation / support structures, it adapts to extreme working scenarios of high power density batteries through active regulation of piezoelectric materials and electromagnetic fields. Each module can be upgraded or replaced independently to adapt to different types of batteries, reducing system modification costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 is a flow chart of the present invention;

[0063] Figure 2 This is the modular collaborative logic diagram of the present invention. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0065] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the described features. In the description of this application, "plurality" means two or more, unless otherwise specifically specified.

[0066] In the description of this application, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art will recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in this application.

[0067] like Figure 1 This embodiment provides: a modular intelligent battery management system, including:

[0068] Perception module: A multi-modal sensor array is used to monitor the entire battery, while a MEMS stress sensing network is implanted to build a holographic stress tensor model.

[0069] In this embodiment, the sensing module needs to be specifically explained. The sensing module arranges the three-axis AMR sensor on the side of the battery cell to form a 2×2 sensor array on the side of the battery cell, which is connected to the microcontroller through a telecommunications interface to obtain characteristic magnetic vortex information during the charging and discharging process. MEMS sensor nodes are evenly deployed at the corners of the battery cell shell, the electrode connection, and the contact area between the diaphragm and the electrode sheet inside the battery to ensure full coverage of the area where the battery may generate stress. A star topology is used to build a MEMS sensor network with a central node as the data aggregation center. Each MEMS sensor node is connected to the central node through wireless communication technology. The MEMS sensor node preliminarily encodes and processes the collected stress data and transmits it to the central node through a wireless communication link. The central node verifies the received data. If data errors or loss are found, a retransmission request is promptly sent to the corresponding MEMS sensor node. The collected initial stress data is preprocessed, including filtering, denoising, and normalization. Based on the MEMS stress sensing network, a holographic stress tensor model is constructed. The model is constructed as follows:

[0070] According to the geometric structure and physical properties of the battery cell, the battery cell is divided into several tiny three-dimensional units. The stress state of each unit can be described by its six stress components, which include three normal stresses and three shear stresses.

[0071] Based on the data collected by each MEMS sensor node, combined with mechanical principles and sensor position information, the six stress components of each tiny unit are calculated to construct the stress tensor of each unit;

[0072] The stress tensors of all tiny units are combined to form a holographic stress tensor model of the entire battery unit, which fully reflects the distribution and changes of stress inside the battery.

[0073] The steps of operating the stress tensor of each unit cell are as follows:

[0074] The MEMS sensor acquires the three-dimensional strain data of each node (ε x ,ε y ,τ z ,γ xy ,γ yz ,γ zx ), establish a rectangular coordinate system (x, y, z axis) with the center of mass of the battery cell as the origin;

[0075] Input the elastic modulus E and Poisson's ratio v of the battery material to calculate the shear modulus G. The specific calculation formula for the shear modulus is as follows:

[0076]

[0077] Substitute it into Hooke's law formula to calculate the normal stress (σ x ,σ y ,σ z ) and shear stress (τ xy ,τ yz ,τ zx ), the specific calculation formula of normal stress is as follows:

[0078]

[0079] Normal stress is related to the strain in three directions and the Poisson's ratio of the material, reflecting the influence of volume deformation. The specific calculation formula of shear stress is as follows:

[0080]

[0081] According to the stress components of each point, the corresponding stress tensor matrix σ is generated. The specific calculation formula is as follows:

[0082]

[0083] Diagonal elements (σ x ,σ y ,σ z ) is the normal stress, and the off-diagonal elements (τ xy ,τ yz ,τ zx ) is the shear stress, and satisfies τij =τ ji (Principle of shear stress reciprocity);

[0084] Operation and maintenance and decision-making module: Achieve coordinated control of battery cells through dynamic impedance matrix construction, plasma resonance frequency monitoring, and non-equilibrium thermodynamic gradient analysis;

[0085] In this embodiment, the operation and maintenance and decision-making module needs to be specifically explained, and the operation and maintenance and decision-making module includes a dynamic impedance matrix construction unit, a matrix reorganization control unit, and a stress analysis and optimization unit;

[0086] The dynamic impedance matrix construction unit maps the battery cells into four-dimensional supermatrix elements, constructs the cross-impedance relationship between the cells based on the anomalous Hall effect, measures the coupling strength between the matrix elements in real time, and completes the construction of the dynamic impedance matrix. The battery pack is divided into a three-dimensional grid, each grid point corresponds to a battery cell, and the coordinates are (e, s, w). The time dimension t is introduced to record the state change of the battery cell (charging and discharging process) to form a four-dimensional coordinate (e, s, w, t). The impedance value, state of charge, and health state of each battery cell are mapped to the element value in the four-dimensional supermatrix, and a vertical magnetic field B is applied to the electrode surface of each battery cell. z , measure the transverse voltage V H , the Hall resistance is calculated according to the anomalous Hall effect formula. The specific calculation formula is as follows:

[0087]

[0088] Among them, R H Represents the Hall resistance, R0 represents the normal Hall coefficient, R s Represents the anomalous Hall coefficient, M represents the material magnetization intensity, and for adjacent battery cells i and j, the Hall resistance change rate is measured. Establish the cross impedance relationship, the specific calculation formula is as follows:

[0089]

[0090] Among them, Z ij represents cross impedance, k represents material constant, f pl represents the plasma resonance frequency, The rate of change of the magnetic induction intensity B corresponding to the rate of change of the Hall resistance is expressed by repeating the process of establishing the cross-impedance relationship to construct the cross-impedance matrix Z between all battery cells. n×n , microwave pulses are applied to the edge of the battery cell array to excite spin waves. The spin wave interference pattern is detected using a magnetic tunnel junction sensor. The coupling strength is calculated by measuring the phase difference Δφ and amplitude A of the interference pattern. The specific calculation formula is as follows:

[0091]

[0092] Among them, C ij represents the coupling strength, A0 represents the reference amplitude, A ij represents the interference pattern amplitude detected by the magnetic tunnel junction sensor, Δφ ij The phase difference of the interference pattern is caused by the difference in the propagation path of the spin wave, which is the coupling strength C ij As the weight factor, the dynamic impedance matrix is ​​updated. The specific calculation formula is as follows:

[0093]

[0094] Among them, M'(e i ,s i ,w i ,t) represents the updated dynamic impedance matrix, and the impedance fluctuation coefficient of each battery cell is calculated. The specific calculation formula is as follows:

[0095]

[0096] Where α represents the impedance fluctuation coefficient, σZ represents the impedance standard deviation, Represents the average impedance, sets a first preset threshold α0, selects units with α<α0 as master candidates, and among the candidate units, selects the unit with the impedance value closest to the global average as the master unit and issues control authority, and re-evaluates the matrix stability every T seconds (e.g., T=60);

[0097] The matrix reorganization control unit continuously calculates the impedance fluctuation coefficient α of the main control unit and compares it with the second preset threshold α1. When α>α1 and the duration exceeds T1, it is determined that the main control unit is invalid and re-election is triggered. The invalid main control unit will change its own coordinates (s u ,s v ,s w ), failure timestamp t0, current impedance value Z fail The signal is encoded into a characteristic wave signal, and the signal is modulated using spread spectrum technology to generate a pulse sequence with specific frequency characteristics. The characteristic wave is transmitted in all directions through the phased array antenna with a transmission power of P tx Ensure that all battery cells within a radius R are covered (R is usually 1.2 times the diagonal length of the battery pack). The phased array antennas of the surrounding battery cells receive the characteristic wave at the same time, and each antenna channel records the receiving time t rx The signal strength RSSI is used to calculate the location of the failed master controller using the TDOA algorithm for at least three adjacent battery cells. The specific calculation formula is as follows:

[0098]

[0099] Among them, sui , s vi , s wi represents the coordinates of the receiving unit, c represents the propagation speed of the electromagnetic wave, demodulates the characteristic wave, and extracts the state parameters of the failed unit including Z fail The failure severity is evaluated by using the value and α value. Each battery cell broadcasts its own status information (including coordinates, current impedance value, and state of charge), establishes a temporary neighbor table, and constructs an undirected graph G = (D, Q) based on the neighbor table, where the node V represents the battery cell and the weight of the edge Q is the cross impedance Z between the battery cells. ij , each battery cell calculates the initial virtual impedance matrix according to the received neighbor status The specific calculation formula is as follows:

[0100]

[0101] Among them, d ij Represents the Euclidean distance between cells, and λ represents the attenuation coefficient, which is usually taken as twice the average spacing between battery cells. Battery cells that meet the following conditions are selected to enter the candidate set. When α<α0 and the state of charge is within a specified range (e.g., 30%-70%), avoid overcharged or over-discharged cells from becoming the master. The comprehensive score S is calculated for the candidate battery cells. The specific calculation formula is as follows:

[0102]

[0103] Among them, ω1, ω2, ω3 represent weight coefficients, d fail Indicates the distance from the failed master. The Byzantine fault-tolerant algorithm is used for voting. The unit with the highest number of votes is elected as the new master. The elected unit broadcasts its own ID, coordinates, and initial control parameters. Each battery unit updates the routing table based on the new master's position. The new master unit collects the latest status of each battery unit and rebuilds the dynamic impedance matrix.

[0104] It should be noted that the Byzantine fault tolerance algorithm is an existing technology and will not be described in detail here.

[0105] The stress analysis and optimization unit performs a tensor product operation on the holographic stress tensor model and the dynamic impedance matrix. When a stress singularity point is detected, it triggers the generation of an adaptive support structure and electromagnetic field reconstruction to optimize the mechanical stress distribution and ensure the safety of the battery structure. Specifically, the following steps are included:

[0106] S1. Preparation for tensor product operation: Map the three-dimensional spatial coordinates (x, y, z) of the holographic stress tensor model with the four-dimensional coordinates (e, s, w, t) of the dynamic impedance matrix to establish the coordinate transformation relationship T z :(x,y,z)→(e,s,w), ensure that the parameters at the same physical location can be calculated accordingly, perform time slicing on the dynamic impedance matrix, and select the matrix D corresponding to the stress tensor acquisition time t0y (e, s, w, t0), expand the stress tensor σ into a four-dimensional tensor σ', and repeat the value in the time dimension, that is, σ'(x, y, z, t) = σ(x, y, z) for all t, making it consistent with the dimension of the dynamic impedance matrix;

[0107] S2. Tensor product operation: Perform a tensor product operation on the adapted stress tensor σ' and the dynamic impedance matrix. The specific calculation formula is as follows:

[0108]

[0109] Among them, i, j, k, and l correspond to the dimension indexes of the stress tensor and the impedance matrix respectively, generating a four-dimensional coupling tensor C(x, y, z, e, s, w), and calculating the local eigenvalue λ of the coupling tensor C C1 ,λ C2 , ..., λ C16 , evaluate its distribution in space and define sensitivity index Quantify the coupling strength of each tiny unit cell;

[0110] S3. Stress singular point detection: Setting the singular point determination threshold when When the corresponding small unit body is marked as a potential singular point, it is further verified by combining the second invariant I2 and the third invariant I3 of the stress tensor. If |I2|>∈ I2 And |I3|>∈ I3 , then the point is confirmed to be a stress singular point. For the confirmed singular point (x0, y0, z0), the sensitivity gradient of the adjacent unit is used to Calculate its impact range R, the specific calculation formula is as follows:

[0111]

[0112] Where d represents the distance, ∈ G represents the gradient threshold;

[0113] S4. Adaptive support structure generation: Combined with the influence area of ​​the singular point, the polarization direction of the piezoelectric composite material is determined according to the main direction of the stress tensor. For example, if the maximum principal stress σ max The direction is (n x ,n y ,n z ), the piezoelectric material is polarized along this direction, and the required compensation stress is calculated as: Δσ=-diag(σ max ,σ mid ,σ min ), where σ mid represents the intermediate principal stress, σ min Indicates the minimum principal stress, according to the piezoelectric effect formula Δε=Yd ·d v , where Y d represents the piezoelectric constant matrix, d v Represents the voltage vector, and the voltage distribution d is solved inversely v * =Y d -1 ·R j -1 Δσ, applied to the piezoelectric material by the MEMS microcontroller v * , generating the support stress field;

[0114] S5. Electromagnetic field reconstruction: Design a circular electromagnetic coil outside the singular point influence area and calculate the coil current I and the number of turns. Among them, r represents the coil radius, μ0 represents the vacuum permeability, and the electromagnetic field distribution is iteratively optimized using finite element simulation software to adjust the coil position, current size and direction so that the stress mean square error MSE in the frequency-closed region is σ Minimize, the specific calculation formula is as follows:

[0115]

[0116] Among them, σ i Represents each stress point in the region, represents the mean stress;

[0117] S6: After reconstruction, the MEMS sensor re-collects strain data and calculates the updated stress vector σ new , compare the stress index of the singular point area, if the drop exceeds the preset threshold ∈ R , the optimization is determined to be effective. If the optimization does not meet the standards, the piezoelectric material voltage and electromagnetic field parameters are automatically adjusted, and steps S4-S5 are repeated until the stress distribution meets the safety standards. A full-process detection is performed every T time period to achieve dynamic stress management.

[0118] Execution module: The instructions issued by the stress analysis and optimization unit control the piezoelectric composite material to generate an adaptive support structure, adjust the internal mechanical structure of the battery, adjust the electromagnetic field generating device, reconstruct the electromagnetic field distribution, form a stress shielding area, and reduce the impact of stress on battery performance and life.

[0119] In this embodiment, the execution module needs to be specifically explained. The execution module receives the control instructions sent by the stress analysis and optimization unit, including the coordinates of the stress singular point, the compensation stress tensor, the support structure parameters, and the electromagnetic field configuration parameters (such as the coil current and position), verifies the integrity and timeliness of the instructions, analyzes the stress tensor data, extracts the principal stress direction and amplitude, determines the area and direction requiring key support, decodes the electromagnetic field configuration parameters, generates the coil control signal (current magnitude, phase, direction), and maps the stress tensor components to voltage values. The specific calculation formula is as follows:

[0120] Y V (x,y,z)=f(σ xx ,σ yy ,σ zz ,τ xy ,τ yz ,τ zx )

[0121] Where f represents the mapping function. A multi-channel high-voltage drive circuit is used to apply precise voltage to the piezoelectric composite array. This pulse voltage is applied to the piezoelectric material in a specific area to adjust the direction of its internal electric dipoles, activate the electromagnetic coil array outside the singular point, and calculate the current in each coil. The specific calculation formula is as follows:

[0122] I i =g(B target ,r i ,θ i ,φ i )

[0123] Where g represents the magnetic field-current mapping function, r i ,θ i ,φ i The spatial coordinates of the coil are represented, and the phase of the current of each coil is adjusted through phased array technology so that the synthetic magnetic field forms a uniform shielding field in the target area. According to the stress fluctuation frequency, the electromagnetic field frequency is adjusted to form a resonance suppression effect and maximize the shielding effect. The strain data after regulation is collected through the MEMS sensor network and compared with the target stress distribution to evaluate the regulation effect. If the improvement effect does not meet the expectations, the control parameters are automatically adjusted and the regulation process is re-executed to form a fast-response closed-loop system.

[0124] It should be noted that the target stress distribution is determined based on the battery type and application scenario, the basic stress safety threshold and long-term life optimization target are determined, and the finite element software is used to build a three-dimensional battery model to simulate the stress distribution under different working conditions. The stress distribution under different working conditions is:

[0125] Mechanical load: volume expansion / contraction during charging and discharging, vibration and shock, etc.

[0126] Thermal load: thermal stress caused by heat generation (such as the difference in thermal expansion coefficient between the tab and the electrode interface);

[0127] Electrochemical reaction load: lattice stress caused by ion insertion / deintercalation (such as lithium expansion of graphite anode).

[0128] Combining the perception module, operation and maintenance and decision module, and execution module, by embedding the battery structure, the stress tensor components of each battery cell are collected, the original analog signal is converted into digital information that the system can recognize, and key features are extracted. An electrical connection model between battery cells is established to identify the main control unit. Each battery cell is abstracted as an element in a super matrix. The element value represents the AC impedance of the cell. The current-voltage coupling strength between cells is measured, and the mutual impedance matrix between battery cells is constructed. The larger the mutual impedance value, the stronger the electrical correlation between battery cells. The impedance values ​​of each battery cell are compared with the first preset threshold, and the cell with the lowest impedance value is used as the main control unit. The main control unit undertakes data aggregation and coordination functions. Low-impedance cells are usually the nodes with the best performance and are suitable as network hubs. The impedance value of the main control unit is monitored in real time. If it exceeds the second preset threshold, it is determined to be a main control failure. The failed battery cell sends a characteristic wave containing the location code and failure type. The surrounding battery cells locate the failed node through the phased array antenna, start the election algorithm, elect a new main control unit, and recalculate the mutual impedance between each battery cell. A new dynamic impedance matching network is constructed. When a local failure occurs, a new main control unit is elected through self-repair to avoid single-point failure leading to system crash. Through stress-impedance coupling analysis, risk areas are identified and control is triggered. The holographic stress tensor model is constructed using MEMS sensor data to solve the stress tensor eigenvalues ​​(maximum / minimum principal stress, shear stress extreme value) of each battery cell, construct a three-dimensional stress distribution hologram, mark stress singularities (such as areas with stress concentration coefficient >3), perform a tensor product operation on the stress tensor and the dynamic impedance matrix, identify stress-impedance coupling areas, and when a stress singularity is detected and the coupling impedance is abnormal, send instructions to the execution unit: generate an adaptive support structure (driven by piezoelectric composite materials), reconstruct the electromagnetic field (shield stress-induced microcrack propagation), and suppress stress damage in real time through mechanical support and electromagnetic field control. The support direction and strength parameters of the stress analysis and optimization unit are received, and voltage is applied to the piezoelectric array. The piezoelectric material deforms through the inverse piezoelectric effect to form a dynamic support structure. According to the location of the stress singularity, the electromagnetic coil array is controlled to generate a gradient field. The magnetic field acts on charged particles through the Lorentz force, suppressing stress-induced ion migration anomalies and forming a stress shielding area. After regulation, the sensing module re-collects stress data. If the improvement is insufficient (such as stress reduction <30%), the voltage / current parameters are automatically adjusted until the standard is met.

[0129] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0130] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0131] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0132] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0133] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0134] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0135] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. Modular intelligent battery management system, characterized by: include: Perception module: A multi-modal sensor array is used to monitor the entire battery, while a MEMS stress sensing network is implanted to build a holographic stress tensor model. Operation and maintenance and decision-making module: Achieve coordinated control of battery cells through dynamic impedance matrix construction, plasma resonance frequency monitoring, and non-equilibrium thermodynamic gradient analysis; Execution module: Receives instructions from the stress analysis and optimization unit, controls the piezoelectric composite material to generate an adaptive support structure, adjusts the internal mechanical structure of the battery, adjusts the electromagnetic field generating device, reconstructs the electromagnetic field distribution, forms a stress shielding area, and reduces the impact of stress on battery performance and life.

2. The modular intelligent battery management system according to claim 1, characterized in that: The sensing module arranges three-axis AMR sensors on the side of the battery cell, forming a 2×2 sensor array on the side of the battery cell, which is connected to a microcontroller via a telecommunications interface to obtain characteristic magnetic vortex information during the charging and discharging process. MEMS sensor nodes are evenly deployed at the corners of the battery cell shell, the electrode connection, and the contact area between the diaphragm and the electrode sheet inside the battery. A star topology is used to build a MEMS sensor network with a central node as the data aggregation center. Each MEMS sensor node is connected to the central node via wireless communication technology. The MEMS sensor node preliminarily encodes and processes the collected stress data and transmits it to the central node via a wireless communication link. The central node verifies the received data and preprocesses the collected initial stress data, including filtering, denoising, and normalization. Based on the MEMS stress sensing network, a holographic stress tensor model is constructed.

3. The modular intelligent battery management system according to claim 2, characterized in that: The holographic stress tensor model is constructed as follows: According to the geometric structure and physical properties of the battery cell, the battery cell is divided into several tiny three-dimensional units. The stress state of each unit can be described by its six stress components, which include three normal stresses and three shear stresses. Based on the data collected by each MEMS sensor node, combined with mechanical principles and sensor position information, the six stress components of each tiny unit are calculated to construct the stress tensor of each unit; The stress tensors of all tiny units are combined to form a holographic stress tensor model of the entire battery unit, which fully reflects the distribution and changes of stress inside the battery.

4. The modular intelligent battery management system according to claim 3, characterized in that: The steps of operating the stress tensor of each unit cell are as follows: The MEMS sensor acquires the three-dimensional strain data of each node (ε x ,ε y ,ε z ,γ xy ,γ yz ,γ zx ), establish a rectangular coordinate system (x, y, z axis) with the center of mass of the battery cell as the origin; Input the elastic modulus E and Poisson's ratio v of the battery material, calculate the shear modulus G, substitute it into the Hooke's law formula, and calculate the normal stress (σ x ,σ y ,σ z ) and shear stress (τ xy ,τ yz ,τ zx ), and generate the corresponding stress tensor matrix σ according to the stress components of each point.

5. The modular intelligent battery management system according to claim 1, characterized in that: The operation and maintenance and decision-making module includes a dynamic impedance matrix construction unit, a matrix reorganization control unit, and a stress analysis and optimization unit.

6. The modular intelligent battery management system according to claim 5, characterized in that: The dynamic impedance matrix construction unit maps the battery cells into four-dimensional supermatrix elements, constructs the cross-impedance relationship between the cells based on the anomalous Hall effect, measures the coupling strength between the matrix elements in real time, and completes the construction of the dynamic impedance matrix. The battery pack is divided into a three-dimensional grid, each grid point corresponds to a battery cell with coordinates (e, s, w), introduces the time dimension t, records the state changes of the battery cells, and forms four-dimensional coordinates (e, s, w, t). The impedance value, state of charge, and health state of each battery cell are mapped to the element value in the four-dimensional supermatrix, and a vertical magnetic field B is applied to the electrode surface of each battery cell. z , measure the transverse voltage V H , the Hall resistance is calculated according to the anomalous Hall effect formula. For adjacent battery cells i and j, the Hall resistance change rate is measured. Establish the cross impedance relationship, the specific calculation formula is as follows: Among them, Z ij represents cross impedance, k represents material constant, f pl represents the plasma resonance frequency, The rate of change of the magnetic induction intensity B corresponding to the rate of change of the Hall resistance is expressed by repeating the process of establishing the cross-impedance relationship to construct the cross-impedance matrix Z between all battery cells. n×n , microwave pulses are applied to the edge of the battery cell array to excite spin waves, and the spin wave interference pattern is detected using a magnetic tunnel junction sensor. By measuring the phase difference Δφ and amplitude A of the interference pattern, the coupling strength is calculated. The coupling strength C is caused by the difference in the propagation path of the spin wave. ij As a weight factor, the dynamic impedance matrix is ​​updated and the impedance fluctuation coefficient of each battery cell is calculated. The specific calculation formula is as follows: Where α represents the impedance fluctuation coefficient, σZ represents the impedance standard deviation, Represents the average impedance, sets the first preset threshold α0, selects the unit with α<α0 as the master candidate, and among the candidate units, selects the unit with the impedance value closest to the global average as the master unit and issues the control authority, and re-evaluates the matrix stability every T seconds.

7. The modular intelligent battery management system according to claim 5, characterized in that: The matrix reorganization control unit continuously calculates the impedance fluctuation coefficient α of the main control unit and compares it with the second preset threshold α1. When α>α1 and the duration exceeds T1, it is determined that the main control unit is invalid and re-election is triggered. The invalid main control unit will change its own coordinates (s u ,s v ,s w ), failure timestamp t0, current impedance value Z fail The signal is encoded into a characteristic wave signal, and the signal is modulated using spread spectrum technology to generate a pulse sequence with specific frequency characteristics. The characteristic wave is transmitted in all directions through the phased array antenna with a transmission power of P tx Ensure that all battery cells within the radius R are covered, and the phased array antennas of the surrounding battery cells receive the characteristic wave at the same time, and each antenna channel records the receiving time t rx and signal strength RSSI, calculate the failed master location through TDOA algorithm for at least three adjacent battery cells, demodulate the characteristic wave, and extract the failed cell state parameters including Z fail The failure severity is evaluated by using the value and α value. Each battery cell broadcasts its own status information, establishes a temporary neighbor table, and constructs an undirected graph G = (D, Q) based on the neighbor table, where the node V represents the battery cell and the weight of the edge Q is the cross impedance Z between the battery cells. ij , each battery cell calculates the initial virtual impedance matrix according to the received neighbor status The specific calculation formula is as follows: Among them, d ij Represents the Euclidean distance between cells, and λ represents the attenuation coefficient, which is usually taken as twice the average spacing between battery cells. Battery cells that meet the following conditions are selected to enter the candidate set. When α<α0 and the state of charge is within the specified range, avoid overcharged or over-discharged cells from becoming the master. The comprehensive score S is calculated for the candidate battery cells. The specific calculation formula is as follows: Among them, ω1, ω2, ω3 represent weight coefficients, d fail Indicates the distance from the failed master. The Byzantine fault-tolerant algorithm is used for voting. The unit with the highest number of votes is elected as the new master. The elected unit broadcasts its own ID, coordinates, and initial control parameters. Each battery unit updates the routing table based on the new master's position. The new master unit collects the latest status of each battery unit and rebuilds the dynamic impedance matrix.

8. The modular intelligent battery management system according to claim 5, characterized in that: The stress analysis and optimization unit performs a tensor product operation on the holographic stress tensor model and the dynamic impedance matrix. When a stress singularity point is detected, it triggers the generation of an adaptive support structure and electromagnetic field reconstruction to optimize the mechanical stress distribution and ensure the safety of the battery structure. Specifically, the following steps are included: S1. Preparation for tensor product operation: Map the three-dimensional spatial coordinates (x, y, z) of the holographic stress tensor model with the four-dimensional coordinates (e, s, w, t) of the dynamic impedance matrix to establish the coordinate transformation relationship T z :(x,y,z)→(e,s,w), ensure that the parameters at the same physical location can be calculated accordingly, perform time slicing on the dynamic impedance matrix, and select the matrix D corresponding to the stress tensor acquisition time t0 y (e, s, w, t0), expand the stress tensor σ into a four-dimensional tensor σ', and repeat the value in the time dimension to make it consistent with the dimension of the dynamic impedance matrix; S2, tensor product operation: perform a tensor product operation on the adapted stress tensor σ' and the dynamic impedance matrix; S3. Stress singular point detection: Setting the singular point determination threshold when When the corresponding small unit body is marked as a potential singular point, it is further verified by combining the second invariant I2 and the third invariant I3 of the stress tensor. If |I2|>∈ I2 And |I3|>∈ I3 , then the point is confirmed to be a stress singular point. For the confirmed singular point (x0, y0, z0), the sensitivity gradient of the adjacent unit is used to Calculate its impact range R; S4. Adaptive support structure generation: Combined with the influence area of ​​the singular point, the polarization direction of the piezoelectric composite material is determined according to the main direction of the stress tensor, and the required compensation stress is calculated: Δσ=-diag(σ max ,σ mid ,σ min ), where σ mid represents the intermediate principal stress, σ min Represents the minimum principal stress, according to the piezoelectric effect formula Δε=Y d ·d v , where Y d represents the piezoelectric constant matrix, d v Represents the voltage vector, and the voltage distribution d is solved inversely v * =Y d -1 ·R j -1 Δσ, applied to the piezoelectric material by the MEMS microcontroller v * , generating the support stress field; S5. Electromagnetic field reconstruction: Design a circular electromagnetic coil outside the singular point influence area and calculate the coil current I and the number of turns N: Among them, r represents the coil radius, μ0 represents the vacuum permeability, and the electromagnetic field distribution is iteratively optimized using finite element simulation software to adjust the coil position, current size and direction so that the stress mean square error MSE in the frequency-closed region is σ minimize; S6: After reconstruction, the MEMS sensor re-collects strain data and calculates the updated stress vector σ new , compare the stress index of the singular point area, if the drop exceeds the preset threshold ∈ R , the optimization is determined to be effective. If the optimization does not meet the standards, the piezoelectric material voltage and electromagnetic field parameters are automatically adjusted, and steps S4-S5 are repeated until the stress distribution meets the safety standards. A full-process detection is performed every T time period to achieve dynamic stress management.

9. The modular intelligent battery management system according to claim 1, characterized in that: The execution module receives the control instructions sent by the stress analysis and optimization unit, including the coordinates of the stress singular point, the compensation stress tensor, the support structure parameters, and the electromagnetic field configuration parameters, verifies the integrity and timeliness of the instructions, analyzes the stress tensor data, extracts the principal stress direction and amplitude, determines the area and direction requiring key support, decodes the electromagnetic field configuration parameters, and generates a coil control signal to map the stress tensor components to voltage values. The specific calculation formula is as follows: Y V (x,y,z)=f(σ xx ,s yy ,s zz ,t xy ,t yz ,t zx ) Where f represents the mapping function. A multi-channel high-voltage drive circuit is used to apply precise voltage to the piezoelectric composite array. This pulse voltage is applied to the piezoelectric material in a specific area to adjust the direction of its internal electric dipoles, activate the electromagnetic coil array outside the singular point, and calculate the current in each coil. The specific calculation formula is as follows: I i =g(B target ,r i ,i i ,f i ) Where g represents the magnetic field-current mapping function, r i ,θ i ,φ i The spatial coordinates of the coil are represented, and the phase of the current of each coil is adjusted through phased array technology so that the synthetic magnetic field forms a uniform shielding field in the target area. According to the stress fluctuation frequency, the electromagnetic field frequency is adjusted to form a resonance suppression effect and maximize the shielding effect. The strain data after regulation is collected through the MEMS sensor network and compared with the target stress distribution to evaluate the regulation effect. If the improvement effect does not meet the expectations, the control parameters are automatically adjusted and the regulation process is re-executed to form a fast-response closed-loop system.

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