Residual capacity balance control method and system for aged lithium battery

Abstract

The invention discloses a residual capacity balance control method and system for an aged lithium battery, and relates to the technical field of new energy materials and devices, and the method comprises the steps: inputting quantum magnetic field response data and infrasonic wave signals into a single-chip microcomputer, carrying out the wavelet noise reduction and histogram equalization processing based on an STM32 single-chip microcomputer, and generating an acoustic-magnetic coupling feature matrix; inputting a dynamic pheromone equalization model based on the acoustic-magnetic coupling characteristic matrix, calculating a capacity difference coupling coefficient between the battery packs through an ant colony optimization algorithm, and generating a capacity redistribution path topological graph; according to the capacity redistribution path topological graph, the energy transfer direction is controlled through a bidirectional Buck-Boost circuit, and an equalizing current threshold value is dynamically adjusted based on the quantum spin resonance magnetic field gradient; according to the invention, the quantum magnetic field response data of the aged lithium battery pack and the infrasonic wave signal generated by electrolyte decomposition are collected, so that the physical and chemical states in the battery are accurately monitored.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy materials and devices, and in particular to a method and system for balancing the remaining capacity of aging lithium batteries. Background Art

[0002] With the growing global demand for clean energy, lithium batteries, as high-efficiency, high-energy-density energy storage devices, are widely used in electric vehicles, renewable energy storage, and portable electronic devices. Traditionally, to address this issue, researchers have proposed various balancing control methods, such as passive balancing strategies based on voltage, current, or time, and active balancing technologies that utilize energy transfer via components such as switched capacitors and transformers to mitigate imbalances within battery packs.

[0003] Existing balancing control methods often lack a deep understanding and monitoring of the battery's internal physicochemical state. For example, they fail to effectively combine quantum magnetic field response data with infrasonic signals to assess battery aging and internal changes. Furthermore, traditional balancing algorithms often rely on simple mathematical models or fixed parameter settings, making them incapable of adapting to complex and changing practical application environments. Especially when dealing with aging lithium-ion battery packs, these methods often cannot accurately predict and compensate for capacity loss caused by factors such as electrolyte decomposition, thereby limiting the overall performance and lifespan of the battery pack. Summary of the Invention

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

[0005] Therefore, the present invention provides a method for balancing the remaining capacity of aging lithium batteries to solve the problems of inaccurate monitoring of the internal state of the battery and poor adaptability of the balancing algorithm in the prior art.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a remaining capacity balancing control method for aging lithium batteries, which includes collecting quantum magnetic field response data of an aging lithium battery pack and an infrasound signal generated by electrolyte decomposition; inputting the quantum magnetic field response data and the infrasound signal into a single-chip microcomputer, performing wavelet noise reduction and histogram equalization processing based on the STM32 single-chip microcomputer, and generating an acoustic-magnetic coupling characteristic matrix; inputting a dynamic pheromone balancing model based on the acoustic-magnetic coupling characteristic matrix, calculating the capacity difference coupling coefficient between battery packs through an ant colony optimization algorithm, and generating a capacity redistribution path topology map; according to the capacity redistribution path topology map, controlling the energy transfer direction through a bidirectional Buck-Boost circuit, and dynamically adjusting the balancing current threshold based on the quantum spin resonance magnetic field gradient; monitoring the frequency domain energy change of the infrasound signal in real time during the energy transfer process, and triggering balancing compensation when the decomposition liquid decomposition characteristic parameter in the acoustic-magnetic coupling characteristic matrix exceeds the dynamic threshold of the coding.

[0008] As a preferred solution of the remaining capacity balancing control method for aging lithium batteries of the present invention, wherein: the quantum magnetic field response data includes impedance phase difference, SEI film thickness gradient data and Li+ migration path topology map;

[0009] The infrasound wave signal includes the main frequency energy amplitude, the third harmonic distortion rate and the sound wave propagation delay difference.

[0010] As a preferred embodiment of the method for controlling the remaining capacity of an aged lithium battery according to the present invention, the quantum magnetic field response data and the infrasound wave signal are input into a single-chip microcomputer, wavelet noise reduction and histogram equalization are performed based on an STM32 single-chip microcomputer, and an acoustic-magnetic coupling characteristic matrix is generated. The specific steps are as follows:

[0011] The electrode-electrolyte interface quantum magnetic field response data and infrasound wave signals are received through the hardware interface of the STM32 microcontroller and stored in different cache areas on the chip.

[0012] The infrasound signal in the buffer is subjected to wavelet decomposition to separate noise and valid signal. The noise reduction threshold is dynamically adjusted based on the quantum magnetic field data in the buffer, the denoised signal is reconstructed, and the main frequency energy parameters are extracted.

[0013] Histogram equalization is performed based on the quantum magnetic field response data in the buffer area to generate the interface impedance gradient, which is then multimodally fused with the main frequency energy parameters to generate the acoustic-magnetic coupling characteristic matrix.

[0014] As a preferred solution of the remaining capacity balancing control method for aging lithium batteries described in the present invention, the following specific steps are used to input a dynamic pheromone balancing model based on the acoustic-magnetic coupling characteristic matrix, calculate the capacity difference coupling coefficient between battery packs through the ant colony optimization algorithm, and generate a capacity redistribution path topology map.

[0015] The acoustic-magnetic coupling characteristic matrix is input into the dynamic pheromone equilibrium model, and the pheromone concentration distribution of the ant colony optimization algorithm is initialized based on the correlation between the interface impedance gradient and the main frequency energy parameter.

[0016] According to the pheromone concentration distribution, the capacity difference coupling coefficient between each cell in the battery pack is iteratively calculated through the ant colony optimization algorithm;

[0017] Based on the capacity difference coupling coefficient, the capacity redistribution path topology map is generated through gradient optimization.

[0018] As a preferred solution of the remaining capacity balancing control method for aging lithium batteries of the present invention, wherein: the energy transfer direction is controlled by a bidirectional Buck-Boost circuit according to the capacity redistribution path topology diagram, and the specific steps are as follows:

[0019] The energy transmission link of the bidirectional Buck-Boost circuit is activated based on the capacity redistribution path topology. When the energy source voltage in the capacity redistribution path topology is higher than the receiving node, the PWM duty cycle of the first switch is controlled to perform step-down transmission of the inductor in Buck mode.

[0020] When the energy source voltage in the capacity redistribution path topology is lower than the receiving node, the Boss mode is switched and the PWM duty cycle of the second switch is adjusted, and the inductor completes the reverse flow of energy through boost transmission.

[0021] As a preferred solution of the remaining capacity balancing control method for aging lithium batteries of the present invention, the dynamic adjustment of the balancing current threshold based on the quantum spin resonance magnetic field gradient is carried out in the following specific steps:

[0022] During the energy transfer process of Buck and Boss, based on the change of the magnetic field gradient caused by quantum spin resonance, the electric dipole spin resonance is used to suppress the deviation of the Li+ migration path and generate a magnetic field gradient correction coefficient.

[0023] The magnetic field gradient correction coefficient is input into the acousto-magnetic coupling characteristic matrix, and the first and second switch timings are dynamically adjusted through the volt-second balance criterion to synchronize the balancing current threshold with the mid-main frequency energy parameter of the acousto-magnetic coupling characteristic matrix.

[0024] As a preferred embodiment of the remaining capacity equalization control method for aging lithium batteries of the present invention, the method includes: monitoring the frequency domain energy change of the infrasonic signal in real time during the energy transfer process; when the decomposition characteristic parameter of the decomposition liquid in the acoustic-magnetic coupling characteristic matrix exceeds the dynamic threshold of the inkjet printer, triggering equalization compensation until the battery capacity difference meets the standard; the specific steps are as follows:

[0025] During the energy transfer process, the frequency domain energy changes of the infrasound signal are monitored in real time, and the frequency domain energy distribution parameters are generated through fast Fourier transform.

[0026] The frequency domain energy distribution parameters are input into the acoustic-magnetic coupling characteristic matrix to calculate the real-time gradient change rate of the decomposition characteristic parameters of the decomposition liquid;

[0027] When the real-time gradient change rate exceeds the dynamic threshold of inkjet printing, the adjusted balanced current threshold is activated as the compensation current amplitude input to the Buck-Boost circuit;

[0028] The compensation current amplitude is continuously adjusted until the capacity difference coupling coefficient reaches the preset equilibrium threshold, terminating the energy transfer process.

[0029] In a second aspect, the present invention provides a residual capacity balancing control system for aging lithium batteries, comprising a data acquisition module, an acousto-magnetic processing module, an acousto-magnetic optimization module, a quantum magnetic control module, and an acousto-magnetic balancing module; the data acquisition module is used to collect quantum magnetic field response data of an aging lithium battery pack and an infrasound signal generated by electrolyte decomposition; the acousto-magnetic processing module is used to input the quantum magnetic field response data and the infrasound signal into a single-chip microcomputer, perform wavelet noise reduction and histogram equalization processing based on an STM32 single-chip microcomputer, and generate an acousto-magnetic coupling characteristic matrix; the acousto-magnetic optimization module is used to input the quantum magnetic field response data and the infrasound signal into a single-chip microcomputer, perform wavelet noise reduction and histogram equalization processing based on a STM32 single-chip microcomputer, and generate an acousto-magnetic coupling characteristic matrix; the acousto-magnetic optimization module is used to input the quantum magnetic field response data and the infrasound signal into a single-chip microcomputer based on a A dynamic pheromone balancing model is introduced, and the coupling coefficient of the capacity difference between battery packs is calculated through the ant colony optimization algorithm, and a capacity redistribution path topology map is generated; the quantum magnetic control module is used to control the direction of energy transfer through a bidirectional Buck-Boost circuit according to the capacity redistribution path topology map, and dynamically adjust the balancing current threshold based on the quantum spin resonance magnetic field gradient; the acoustic magnetic balancing module is used to monitor the frequency domain energy changes of the infrasonic signal in real time during the energy transfer process. When the decomposition characteristic parameters of the decomposition liquid in the acoustic-magnetic coupling characteristic matrix exceed the dynamic threshold of the inkjet printer, the balancing compensation is triggered until the battery capacity difference meets the standard.

[0030] 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 remaining capacity balancing control method for aging lithium batteries as described in the first aspect of the present invention is implemented.

[0031] 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 remaining capacity balancing control method for aging lithium batteries as described in the first aspect of the present invention is implemented.

[0032] The present invention achieves the following beneficial effects: by collecting quantum magnetic field response data from aging lithium battery packs and infrasound signals generated by electrolyte decomposition, it enables precise monitoring of the internal physical and chemical state of the battery. Furthermore, the use of an ant colony optimization algorithm for calculation not only improves the adaptability and flexibility of the balancing control, but also enables rapid response and strategy adjustment in complex and changing practical application environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] 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.

[0034] Figure 1 The figure is a flow chart of a remaining capacity balancing control method for aging lithium batteries.

[0035] Figure 2 Schematic diagram of the remaining capacity balancing control system for aging lithium batteries.

[0036] Figure 3 Flowchart for generating the acoustic-magnetic coupling characteristic matrix for the remaining capacity equalization control method for aging lithium batteries.

[0037] Figure 4 The figure shows the flow chart of the energy transfer control of the bidirectional Buck-Boost circuit for the remaining capacity balancing control method for aging lithium batteries. DETAILED DESCRIPTION

[0038] 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.

[0039] 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.

[0040] 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.

[0041] Reference Figures 1 to 4 The coding machine of the present invention belongs to a computer and an auxiliary device, and provides a method for balancing the remaining capacity of an aging lithium battery, comprising the following steps:

[0042] S1: Collect quantum magnetic field response data of aging lithium battery packs and infrasound signals generated by electrolyte decomposition.

[0043] S1.1: Quantum magnetic field response data include impedance phase difference, SEI film thickness gradient data and Li+ migration path topology map.

[0044] It should be noted that the acquisition of quantum magnetic field response data is achieved through alternating magnetic field excitation and multi-physics field synchronous detection: an alternating magnetic field with a frequency of 20-200kHz is applied to the electrode-electrolyte interface of the aged lithium battery pack. Impedance phase difference data is obtained using a quantum spin resonance spectrometer. Combined with X-ray photoelectron spectroscopy, SEI film thickness gradient data is generated. Simultaneously, neutron diffraction imaging technology is used to reconstruct the Li+ migration path topology. These three types of data are synchronously received through the hardware interface of the STM32 microcontroller and stored in the on-chip buffer. The impedance phase difference data reflects the interfacial charge transfer resistance, the SEI film thickness gradient data represents the heterogeneity of the interfacial passivation layer, and the Li+ migration path topology shows the geometric distribution of lithium ion diffusion channels in the active material.

[0045] S1.2: Infrasound signals include the main frequency energy amplitude, third harmonic distortion rate, and sound wave propagation delay difference.

[0046] It should be noted that infrasound signal acquisition is achieved by installing a piezoelectric sensor array on the surface of an aging lithium-ion battery pack. An acoustic sensor with a bandwidth of 50-200Hz captures the mechanical vibration signals generated by electrolyte decomposition. After conversion to a digital signal using a 24-bit high-precision ADC, the main frequency energy amplitude is extracted using a fast Fourier transform (for example, the main frequency energy is concentrated in the 78.5Hz±2Hz frequency band). The third harmonic distortion rate is extracted using a Hilbert transform, and the acoustic wave propagation delay differences between different sensors are analyzed using a cross-correlation algorithm. The main frequency energy amplitude reflects the intensity of the electrolyte decomposition reaction, the third harmonic distortion rate characterizes the nonlinear vibration characteristics, and the acoustic wave propagation delay differences are used to locate the gas production location within the battery pack. The data acquisition process is synchronized with the timer interrupt of the STM32 microcontroller to ensure a timestamp accuracy of 1μs.

[0047] S2: Input the quantum magnetic field response data and infrasound signal into the microcontroller, perform wavelet noise reduction and histogram equalization processing based on the STM32 microcontroller, and generate the acoustic-magnetic coupling characteristic matrix.

[0048] S2.1: Receive the electrode-electrolyte interface quantum magnetic field response data and infrasound wave signal through the hardware interface of the STM32 microcontroller, and store them in different cache areas on the chip.

[0049] The specific process includes: the STM32 microcontroller is a 32-bit microcontroller based on the ARM Cortex-M core, which is used to collect and process the electrode-electrolyte interface quantum magnetic field response data and infrasound signals in real time. The quantum magnetic field response data of the electrode-electrolyte interface is transmitted to the STM32 microcontroller in the form of a digital signal via the SPI interface (for example, a clock frequency of 10 MHz and a data bit width of 16 bits). The infrasound signal is input via the I2S interface. The quantum magnetic field response data and the infrasound signal are written to physically isolated buffers in the on-chip SRAM via the DMA controller. The quantum magnetic field response data of the electrode-electrolyte interface is stored in the buffer starting at address 0x20000000, and the infrasound signal is stored in the buffer starting at address 0x20001000. The two buffers have independent write pointers and status registers. The STM32 microcontroller ensures data access isolation through a memory protection unit. The quantum magnetic field response data cache of the electrode-electrolyte interface stores impedance phase difference, SEI film thickness gradient data, and Li+ migration path topology. The infrasound signal cache stores main frequency energy amplitude, third harmonic distortion rate, and acoustic wave propagation delay difference. The STM32 microcontroller core accesses the two buffers via bus matrix polling and automatically switches the data source when performing wavelet noise reduction and histogram equalization.

[0050] S2.2: Perform wavelet decomposition on the infrasound signal in the buffer to separate noise and valid signal, dynamically adjust the noise reduction threshold based on the quantum magnetic field data in the buffer, reconstruct the denoised signal and extract the main frequency energy parameters.

[0051] The specific process involves wavelet decomposition of the infrasound signal using Daubechies wavelet basis functions to achieve multi-scale analysis, decomposing the time-domain signal into subband coefficients of different frequency bands. At each decomposition level, the noise-dominant frequency band and the effective signal frequency band are identified and labeled by comparing the subband coefficient energy with the baseline noise level. The noise reduction threshold is dynamically set based on the SEI film thickness gradient data and the Li+ migration path topology map contained in the quantum magnetic field data. The dynamic adjustment mechanism of the noise reduction threshold integrates the multi-dimensional information in the quantum magnetic field data. The electrode-electrolyte interface impedance gradient data provides the frequency domain characteristics of the noise distribution. The SEI film thickness gradient data and the Li+ migration path topology map jointly construct the noise reduction threshold adjustment function. The impedance phase difference data adjusts the low-frequency band noise reduction threshold using an inverse proportional function. The updated noise reduction threshold parameters are applied in real time to the wavelet reconstruction process via the STM32 microcontroller. The reconstructed signal undergoes a windowed FFT transform to extract the dominant frequency energy amplitude.

[0052] S2.3: Perform histogram equalization processing based on the quantum magnetic field response data in the buffer area to generate the interface impedance gradient, and perform multimodal fusion with the main frequency energy parameters to generate the acoustic-magnetic coupling characteristic matrix.

[0053] The specific process includes: the histogram equalization processing of the quantum magnetic field response data uses a non-parametric method to remap the grayscale of the impedance phase difference distribution. First, the cumulative probability distribution function of the impedance phase difference data in the buffer area is statistically analyzed, and the original distribution is mapped to a uniform distribution space through the histogram equalization transformation function to generate an interface impedance gradient image with enhanced contrast, where each pixel value represents the impedance change rate at a specific position on the electrode-electrolyte interface.

[0054] Furthermore, the multimodal fusion of the main frequency energy parameters and the interface impedance gradient is realized by the feature cascade method. The time-frequency matrix of the main frequency energy amplitude is spatially aligned with the interface impedance gradient image, and the acoustic-magnetic coupling feature matrix is constructed through feature cross operation. The row vectors of the final generated acoustic-magnetic coupling feature matrix correspond to the time series, and the column vectors correspond to the spatial distribution characteristics.

[0055] S3: Based on the acoustic-magnetic coupling characteristic matrix, the dynamic pheromone balance model is input, the capacity difference coupling coefficient between battery packs is calculated through the ant colony optimization algorithm, and the capacity redistribution path topology map is generated.

[0056] S3.1: Establish the initial pheromone distribution based on the acoustic-magnetic coupling characteristic matrix, determine the frequency domain impedance tensor configuration by combining the electro-chemical-thermal three-field parameters, and generate the initial pheromone matrix.

[0057] The specific process includes: first, establishing the initial pheromone concentration distribution based on the numerical distribution characteristics of the acoustic-magnetic coupling characteristic matrix, determining the initial parameter configuration of the frequency domain impedance coupling tensor through the spatiotemporal characteristics of the electro-chemical-thermal three-field coupling parameter vector, and after feature fusion of the acoustic-magnetic coupling characteristic matrix and the electro-chemical-thermal three-field coupling parameter vector, using normalization processing to generate the initial pheromone matrix that meets the requirements of the dynamic pheromone equilibrium model. The parameter configuration of the frequency domain impedance coupling tensor directly affects the distribution morphology and concentration gradient of the initial pheromone matrix.

[0058] S3.2: Take the initial matrix as input, update the pheromone concentration through the ant colony algorithm, use the dynamic weight mechanism to balance the impedance gradient and energy parameters, and constrain the energy dissipation threshold to optimize the convergence process.

[0059] The specific process includes: in the battery pack balancing scenario, the optimized pheromone concentration distribution parameters are dynamically matched with the electro-chemical-thermal three-field coupling parameter vector through the ant colony optimization algorithm, the capacity difference coupling coefficient change trend of each battery cell is monitored in real time, and the pheromone volatilization rate and concentration gradient are dynamically adjusted according to the main frequency energy parameter fed back by the frequency domain impedance coupling tensor. When the capacity difference coupling coefficient reaches the preset balancing threshold, the pheromone concentration distribution completes adaptive convergence.

[0060] S3.3: Apply the optimized pheromone concentration to the battery pack balancing scenario, monitor the capacity difference changes in real time, and finally generate a dynamic pheromone balancing model with adaptive capabilities.

[0061] The specific process includes matching the optimized pheromone concentration with the electro-chemical-thermal three-field coupling parameter vector through the ant colony optimization algorithm, real-time monitoring of the changing trend of the capacity difference coupling coefficient in the battery pack balancing scenario, and dynamically adjusting the pheromone volatilization rate and concentration gradient based on the main frequency energy parameter feedback based on the frequency domain impedance coupling tensor. When the capacity difference coupling coefficient reaches the equilibrium threshold, the adaptive convergence of the pheromone concentration distribution is completed, and finally a dynamic pheromone equilibrium model with adaptive capabilities is generated.

[0062] S3.4: Input the acoustic-magnetic coupling characteristic matrix into the dynamic pheromone equilibrium model, and initialize the pheromone concentration distribution of the ant colony optimization algorithm based on the correlation between the interface impedance gradient and the main frequency energy parameter.

[0063] The specific process includes transmitting the acoustic-magnetic coupling characteristic matrix to the dynamic pheromone equilibrium model through the DMA channel of the STM32 microcontroller. The transmission process uses memory mapping to directly access the physical storage address of the acoustic-magnetic coupling characteristic matrix. The row and column structure of the acoustic-magnetic coupling characteristic matrix corresponds to the spatiotemporal sampling points of the electrode-electrolyte interface (for example: a 256-row × 256-column matrix, the row index represents the time series, and the column index represents the spatial position). The dynamic pheromone equilibrium model analyzes the numerical distribution characteristics of the acoustic-magnetic coupling characteristic matrix to provide initial environmental parameters for the ant colony optimization algorithm.

[0064] Furthermore, the correlation between the interface impedance gradient and the main frequency energy parameter is realized through the cross-feature quantization of the acoustic-magnetic coupling characteristic matrix. The interface impedance gradient reflects the charge transfer efficiency of the electrode-electrolyte interface, and the main frequency energy parameter characterizes the intensity of the electrolyte decomposition reaction (for example: the energy amplitude of the 78.5Hz frequency band is 20-50dB). The dynamic pheromone equilibrium model derives the Pearson correlation coefficient between the two in the time and space dimensions, and maps the absolute value of the correlation coefficient to the initial weight of the pheromone concentration. The initialized pheromone concentration distribution is stored in the dedicated memory block of the STM32 microcontroller for iterative use by the ant colony optimization algorithm.

[0065] S3.5: Based on the pheromone concentration distribution, the capacity difference coupling coefficient between the cells in the battery pack is iteratively calculated using the ant colony optimization algorithm. The expression is:

[0066]

[0067] in, represents the capacity difference coupling coefficient between battery cells i and j at time t, t represents the time variable, i represents the target cell to be analyzed in the battery pack, j represents the reference cell in the battery pack, T i,j represents the electro-chemical-thermal field coupling parameter vector between the i-th cell and the j-th cell in the battery pack, represents the Kronecker product operator, Φ i,j represents the frequency domain impedance coupling tensor between the i-th cell and the j-th cell in the battery pack, F represents the Frobenius norm, ∈ represents the energy dissipation threshold, tanh represents the hyperbolic tangent function, ΔC i,j Indicates the real-time capacity attenuation difference between the i-th cell and the j-th cell in the battery pack. represents the impedance gradient change rate of the electrode-electrolyte interface, represents the rate of change of the dynamic impedance gradient of the electrode-electrolyte interface at time t, η(t) represents the dynamic adaptive weight coefficient at time t, exp represents the natural exponential function, t1 represents the starting time of the acoustic wave energy integration, t2 represents the ending time of the acoustic wave energy integration, P(t) represents the instantaneous infrasound power at time t, n represents the number index of a single individual battery in the battery pack, N represents the total number of single batteries in the battery pack, R n Indicates the equivalent internal resistance of the nth single cell in the battery pack, It represents the square value of the charge and discharge current of the nth single cell at time t.

[0068] The specific process includes the iterative calculation process of the ant colony optimization algorithm based on the pheromone concentration distribution as follows: the pheromone concentration distribution is stored in the dedicated memory block of the STM32 microcontroller, and the current pheromone concentration value is read in each iteration. The electro-chemical-thermal three-field coupling parameter vector between the battery cells is combined, and the path heuristic factor is calculated through the Kronecker product operation. The product of the pheromone concentration and the heuristic factor is used as the state transition probability. The individual ants choose the movement path according to the probability. After completing a single iteration, the pheromone concentration is updated according to the hyperbolic tangent function. At the same time, the dynamic adaptive weight coefficient is adjusted according to the impedance gradient change rate and the infrasound power, and the capacity difference coupling coefficient is finally output.

[0069] Furthermore, the calculation process of the capacity difference coupling coefficient integrates multi-dimensional parameters: the electro-chemical-thermal three-field coupling parameter vector reflects the operating status of the single cell, the frequency domain impedance coupling tensor characterizes the interaction strength between batteries, the real-time capacity attenuation difference reflects the aging difference, the impedance gradient change rate indicates the interface reaction activity, and the infrasound power monitors the degree of electrolyte decomposition. It establishes associations through mathematical operations such as the natural exponential function and the Frobenius norm. The dynamic adaptive weight coefficient ensures that the calculation results respond to changes in the battery state in real time. The energy dissipation threshold constrains the physical boundaries of the optimization process. The final generated capacity difference coupling coefficient matrix provides a quantitative basis for subsequent balancing control.

[0070] S3.6: Based on the capacity difference coupling coefficient, generate a capacity redistribution path topology map through gradient optimization.

[0071] Specifically, the capacity difference coupling coefficient matrix is first input into the STM32 microcontroller's arithmetic unit. The cell pair corresponding to the maximum capacity difference coupling coefficient is located in the matrix. Starting with this cell pair as the initial node, the optimal transmission path is searched along the descending gradient of the capacity difference coupling coefficient. During the search, the rate of change of the capacity difference coupling coefficient between adjacent cells along the path is analyzed in real time. When the rate of change falls below the path convergence threshold, the current path search is terminated and the node sequence and weight coefficient of the path are recorded. After searching all feasible paths, the node connections and weight coefficients of each path are integrated into a directed graph structure to generate a capacity redistribution path topology. In this graph, nodes represent cell pairs, directed edges represent energy transfer directions, and edge weights represent transmission priorities. The path convergence threshold is dynamically set based on the real-time operating conditions of the battery pack, primarily referring to two key parameters: the rate of change of the electrode-electrolyte interface impedance gradient and the amplitude of the infrasonic main frequency energy. The path convergence threshold requirement is lowered when the impedance gradient changes dramatically or the infrasonic energy is high, and raised under stable operating conditions to ensure that the path search process matches the actual battery state.

[0072] S4: Based on the capacity redistribution path topology, the energy transfer direction is controlled through a bidirectional Buck-Boost circuit, and the equilibrium current threshold is dynamically adjusted based on the quantum spin resonance magnetic field gradient.

[0073] S4.1: Activate the energy transmission link of the bidirectional Buck-Boost circuit based on the capacity redistribution path topology diagram. When the energy source voltage in the capacity redistribution path topology diagram is higher than the receiving node, control the PWM duty cycle of the first switch to perform step-down transmission of the inductor in Buck mode.

[0074] The specific process includes: the capacity redistribution path topology diagram determines the energy transmission priority by analyzing the node connection relationship and weight coefficient; the STM32 microcontroller reads the high-priority path marked in the topology diagram, sends an enable signal to the bidirectional Buck-Boost circuit to activate the corresponding energy transmission link, and configures the GPIO pin status to establish the drive circuit of the power switch tube, completing the mapping conversion from the topology diagram to the hardware circuit.

[0075] Furthermore, when the energy source voltage in the capacity redistribution path topology is higher than that of the receiving node, the STM32 microcontroller compares the voltage sampling values of the two nodes (for example: energy source voltage 3.7V, receiving node voltage 3.5V), analyzes the target duty cycle of the first switch based on the voltage difference, and generates a PWM waveform through the timer to control the conduction time of the first switch, so that the inductor current shows a linear upward trend in Buck mode, realizing the step-down transmission of energy from the high-voltage node to the low-voltage node. During the transmission process, the inductor current slope is monitored in real time to ensure that it operates in continuous conduction mode.

[0076] S4.2: When the energy source voltage in the capacity redistribution path topology is lower than the receiving node, switch to Boss mode and adjust the PWM duty cycle of the second switch, and the inductor completes the reverse flow of energy through boost transmission.

[0077] The specific process includes: when the energy source voltage in the capacity redistribution path topology is lower than the receiving node, the STM32 microcontroller detects the voltage difference is reversed (for example: energy source voltage 3.3V, receiving node voltage 3.6V), immediately turns off the first switch and sets the mode switching flag, and configures the bidirectional Buck-Boost circuit to Boost working mode; reconfigures the PWM output channel through the timer module, analyzes the target duty cycle of the second switch according to the voltage difference and inductor parameters, generates a phase-complementary PWM waveform to control the conduction timing of the second switch, so that the inductor releases the stored energy during the shutdown period, and realizes the boost transmission of energy from the low-voltage node to the high-voltage node. During the transmission process, the duty cycle is calibrated in real time through the current sampling resistor (for example: the duty cycle step is adjusted by 0.5% every 100μs) to ensure that the output voltage is stable at the target value.

[0078] S4.3: During the energy transfer process between Buck and Boss, based on the change of the quantum spin resonance magnetic field gradient, the electric dipole spin resonance is used to suppress the deviation of the Li+ migration path and generate a magnetic field gradient correction coefficient, which is expressed as:

[0079]

[0080] Where D represents the magnetic field gradient correction coefficient, G represents the dynamic weight matrix, represents the reduced Planck constant, σ represents the angular frequency of the alternating electromagnetic field, ΔE1 represents the spin exchange interaction energy, and m represents the vertical direction in the coordinate system. represents the differential term of the magnetic field component in the vertical direction, represents the partial differential operation on the x-axis, J represents the spin exchange coupling constant, g represents the position number of a specific spin particle, u represents the position number of another spin particle adjacent to g, S g represents the quantum spin operator of the g-th spin particle, S u represents the quantum spin operator of the u-th spin particle, ΔE2 represents the interaction energy between the electric dipole and the magnetic field, φ represents the Boltz constant, T represents the absolute temperature, α represents the experimental fitting parameter, B represents the effective magnetic field strength, and B0 represents the reference static magnetic field strength.

[0081] The specific process involves real-time monitoring of quantum spin resonance magnetic field gradient changes during Buck and Boost energy transfer via the electric dipole spin resonance effect. When an alternating electromagnetic field acts on the electrode-electrolyte interface, the shift in the Li+ migration path perturbs the local magnetic field gradient distribution. This perturbation is captured by the quantum spin resonance sensor as a frequency-domain response signal. Electric dipole spin resonance regulates the exchange interaction energy between spin particles, generating a compensating torque in the opposite direction of the Li+ migration path shift and suppressing Li+ path deviation during transmission.

[0082] Furthermore, the generation process of the magnetic field gradient correction coefficient integrates multi-physics field information: the quantum spin operator component of the spin particle in the vertical direction reflects the degree of magnetic field inhomogeneity, the interaction energy between the electric dipole and the magnetic field characterizes the strength of the compensation effect, the ambient temperature affects the spin state distribution through the Boltz constant, and a coupling relationship is established through the principles of quantum mechanics. The final output magnetic field gradient correction coefficient is used to dynamically adjust the operating parameters of the Buck and Boost circuits to ensure that the energy transfer process is synchronized with the Li+ migration state.

[0083] S4.4: Input the magnetic field gradient correction coefficient into the acousto-magnetic coupling characteristic matrix, and dynamically adjust the first and second switch timings through the volt-second balance criterion to synchronize the balancing current threshold with the mid-main frequency energy parameter of the acousto-magnetic coupling characteristic matrix.

[0084] The specific process involves writing the magnetic field gradient correction coefficient into a designated storage area of the acousto-magnetic coupling characteristic matrix via the STM32 microcontroller's data bus and performing a characteristic cascade operation with the main frequency energy parameter. The volt-second balance criterion dynamically calculates the on-time ratio of the first and second switches based on the product of the magnetic field gradient correction coefficient and the main frequency energy parameter (for example, when the product increases by 20%, the on-time of the first switch increases by 15%). By adjusting the PWM timer's compare register value, the switching timing is altered to ensure that the voltage-time integral conservation principle is met during the inductor energy storage and release phases.

[0085] Furthermore, the balancing current threshold is set based on the normalized amplitude of the main frequency energy parameter in the acousto-magnetic coupling characteristic matrix. The main frequency energy amplitude reflects the intensity of the electrolyte decomposition reaction. The STM32 microcontroller maps the main frequency energy amplitude to the balancing current threshold reference value through a table lookup method, and then superimposes the magnetic field gradient correction factor for fine-tuning (for example, every 0.1 increase in the correction factor increases the balancing current threshold by 3%). The synchronization process is achieved by comparing the balancing current sample value with the balancing current threshold in real time. When a deviation is detected that exceeds the allowable range, the PWM duty cycle is immediately updated to make the actual current track the balancing current threshold.

[0086] S5: During the energy transfer process, the frequency domain energy changes of the infrasonic signal are monitored in real time. When the decomposition characteristic parameters of the decomposition liquid in the acoustic-magnetic coupling characteristic matrix exceed the dynamic threshold of the inkjet printer, the equalization compensation is triggered until the battery capacity difference meets the standard.

[0087] S5.1: Monitor the frequency domain energy changes of the infrasound signal in real time during the energy transfer process, and generate frequency domain energy distribution parameters through fast Fourier transform.

[0088] The specific process includes: during the energy transfer process, the piezoelectric sensor array continuously collects infrasound signals, the STM32 microcontroller obtains time-domain waveform data through the ADC channel at a fixed sampling rate, the infrasound signal in the buffer area is divided into frames, and each frame of data is subjected to a Hanning window and a fast Fourier transform is performed to obtain the square of the modulus value of each frequency point to generate a power spectrum. The characteristic parameters of the 50-200Hz frequency band are extracted from the power spectrum as frequency-domain energy distribution parameters (for example: the amplitude of the 78.5Hz frequency point is 35dB, and the distortion rate of the third harmonic at 157Hz is 5%). These parameters are written into the designated storage area of the acousto-magnetic coupling characteristic matrix for subsequent analysis.

[0089] S5.2: Input the frequency domain energy distribution parameters into the acousto-magnetic coupling characteristic matrix and calculate the real-time gradient change rate of the decomposition characteristic parameters of the decomposition liquid. The expression is:

[0090]

[0091] in, represents the dynamic gradient change rate of the electrolyte decomposition characteristic parameter at time t, δ represents the real part extraction operator of the complex number, ω represents the angular frequency of the infrasound signal, represents the time-varying differential term of the acousto-magnetic coupling matrix at the angular frequency ω, represents the partial differential operation on the time variable t, ° represents the tensor product operator, γ represents the magnetic field energy parameter, ⊙ represents the Hadamard product, h represents the imaginary part in constructing the complex expression, Δτ represents the time delay difference of the acoustic wave signal propagating in the electrolyte, a represents the acoustic wave propagation speed in the electrolyte, and G(ω,t) represents the adaptive weight coefficient at the angular frequency ω and at the time t.

[0092] The specific process involves transferring the frequency-domain energy distribution parameters via the STM32 microcontroller's DMA controller to a designated storage area of the acousto-magnetic coupling characteristic matrix, where they are then fused with the existing quantum magnetic field response data in the matrix. Within the acousto-magnetic coupling characteristic matrix, the infrasound main frequency energy amplitude is associated with the electrode-electrolyte interface magnetic field coupling matrix via a Hadamard product operation. Simultaneously, the acoustic wave propagation delay difference parameter is used to correct for phase deviations at different spatial locations, forming a composite matrix structure containing time-frequency characteristics.

[0093] Furthermore, the time-varying differential term at the angular frequency is extracted from the acoustic-magnetic coupling characteristic matrix, and the partial derivative of the time variable is taken to obtain the instantaneous change trend. The differential term is then tensor-producted with the acoustic wave propagation velocity parameter in the electrolyte to construct a propagation response function in complex form (for example, the real part represents the amplitude change, and the imaginary part represents the phase change). Finally, the normalized real-time gradient change rate is obtained through the real part extraction operator. This parameter is used to quantify the dynamic characteristics of the electrolyte decomposition reaction.

[0094] S5.3: When the real-time gradient change rate exceeds the dynamic threshold of the inkjet printer, the adjusted equalization current threshold is activated as the compensation current amplitude input to the Buck-Boost circuit.

[0095] The specific process includes: when the real-time gradient change rate exceeds the dynamic threshold of the inkjet coding, the STM32 microcontroller immediately triggers the interrupt service program, reads the latest balanced current threshold from the acoustic-magnetic coupling characteristic matrix, converts the threshold into an analog voltage signal through the DAC module, and inputs it into the current regulation loop of the Buck-Boost circuit. At the same time, the duty cycle parameters of the PWM timer are configured so that the inductor current tracks the target compensation current amplitude at a preset slope to complete the rapid response of energy compensation. The preset slope is determined according to the inductor parameters of the Buck-Boost circuit and the maximum allowable current change rate of the battery pack, and is written into the configuration register of the STM32 microcontroller.

[0096] Furthermore, the dynamic threshold for inkjet printing is set based on the historical statistical data of the electrolyte decomposition characteristic parameters and the working status of the battery pack: by analyzing the variation range of the decomposition characteristic parameters of the decomposition liquid stored in the acousto-magnetic coupling characteristic matrix under standard working conditions, three times the standard deviation of the average value is taken as the benchmark threshold, and then the correction coefficients of the current battery temperature (for example: the threshold is increased by 5% for every 10°C increase in temperature) and the charge and discharge rate (for example: the threshold is decreased by 8% at a rate of 1C) are superimposed, and finally a dynamically adjusted dynamic threshold for inkjet printing is generated. The threshold is stored in the non-volatile memory of the STM32 microcontroller and is updated every 30 minutes.

[0097] S5.4: Continue to adjust the compensation current amplitude until the capacity difference coupling coefficient reaches a preset equilibrium threshold, terminating the energy transfer process.

[0098] Specifically, the process of continuously adjusting the compensation current amplitude is achieved through closed-loop control: the STM32 microcontroller reads the capacity difference coupling coefficient once every 100μs, compares it with the preset equilibrium threshold, and adjusts the compensation current amplitude using a proportional-integral algorithm (for example: proportional coefficient 0.5, integral time constant 0.1s). By changing the PWM duty cycle of the Buck-Boost circuit, the capacity difference coupling coefficient approaches the preset equilibrium threshold in an exponential manner. When it is detected that the absolute difference between the capacity difference coupling coefficient and the preset equilibrium threshold is less than 1%, the power switch tube is turned off to terminate energy transfer.

[0099] Furthermore, the preset balancing threshold is set according to the rated capacity and aging characteristics of the battery pack: by analyzing the capacity decay curve in the historical charge and discharge data, 60% of the upper limit of the capacity difference is taken as the benchmark threshold, and then the SEI film thickness gradient data in the acoustic-magnetic coupling characteristic matrix is superimposed for dynamic correction. The final generated preset balancing threshold is stored in the Flash memory of the STM32 microcontroller.

[0100] This embodiment also provides a remaining capacity balancing control system for aging lithium batteries, including: a data acquisition module, an acoustic magnetic processing module, an acoustic magnetic optimization module, a quantum magnetic control module and an acoustic magnetic balancing module; the data acquisition module is used to collect quantum magnetic field response data of the aging lithium battery pack and the infrasound signal generated by the decomposition of the electrolyte; the acoustic magnetic processing module is used to input the quantum magnetic field response data and the infrasound signal into the single-chip microcomputer, perform wavelet noise reduction and histogram equalization processing based on the STM32 single-chip microcomputer, and generate an acoustic-magnetic coupling characteristic matrix; the acoustic magnetic optimization module is used to input the dynamic magnetic field response data and the infrasound signal based on the acoustic-magnetic coupling characteristic matrix. The state pheromone balancing model calculates the coupling coefficient of the capacity difference between battery packs through the ant colony optimization algorithm and generates a capacity redistribution path topology map; the quantum magnetic control module is used to control the direction of energy transfer through the bidirectional Buck-Boost circuit according to the capacity redistribution path topology map, and dynamically adjust the balancing current threshold based on the quantum spin resonance magnetic field gradient; the acoustic magnetic balancing module is used to monitor the frequency domain energy changes of the infrasonic signal in real time during the energy transfer process. When the decomposition characteristic parameters of the decomposition liquid in the acoustic-magnetic coupling characteristic matrix exceed the dynamic threshold of the inkjet coding, the balancing compensation is triggered until the battery capacity difference meets the standard.

[0101] This embodiment further provides a computer device applicable to the method for balancing the remaining capacity of aged lithium batteries, comprising: 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 method for balancing the remaining capacity of aged lithium batteries proposed in the above embodiment.

[0102] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0103] This embodiment further provides a storage medium having a computer program stored thereon. When the program is executed by a processor, the processor implements the remaining capacity balancing control method for aging lithium batteries 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.

[0104] In summary, this invention achieves precise monitoring of the internal physical and chemical state of batteries by collecting quantum magnetic field response data from aging lithium battery packs and infrasound signals generated by electrolyte decomposition. Furthermore, the use of an ant colony optimization algorithm for calculation not only improves the adaptability and flexibility of balancing control but also enables rapid response and strategy adjustment in complex and changing practical application environments.

[0105] 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 method for controlling the remaining capacity of an aging lithium battery, characterized by: include, Collect quantum magnetic field response data of aging lithium battery packs and infrasound signals generated by electrolyte decomposition; The quantum magnetic field response data and infrasound signal are input into the single-chip microcomputer, and wavelet noise reduction and histogram equalization processing are performed based on the STM32 single-chip microcomputer to generate the acoustic-magnetic coupling characteristic matrix; Based on the acoustic-magnetic coupling characteristic matrix, the dynamic pheromone balance model is input, and the capacity difference coupling coefficient between battery packs is calculated through the ant colony optimization algorithm, and a capacity redistribution path topology map is generated; According to the capacity redistribution path topology, the energy transfer direction is controlled through a bidirectional Buck-Boost circuit, and the equilibrium current threshold is dynamically adjusted based on the quantum spin resonance magnetic field gradient; During the energy transfer process, the frequency domain energy changes of the infrasonic signal are monitored in real time. When the decomposition characteristic parameters of the decomposition liquid in the acoustic-magnetic coupling characteristic matrix exceed the dynamic threshold of the inkjet printing, the equalization compensation is triggered until the battery capacity difference meets the standard.

2. The method for controlling the remaining capacity of an aged lithium battery according to claim 1, wherein: The quantum magnetic field response data includes impedance phase difference, SEI film thickness gradient data and Li+ migration path topology; The infrasound wave signal includes the main frequency energy amplitude, the third harmonic distortion rate and the sound wave propagation delay difference.

3. The method for controlling the remaining capacity of an aged lithium battery according to claim 2, wherein: The quantum magnetic field response data and infrasound signal are input into the single chip microcomputer, wavelet noise reduction and histogram equalization processing are performed based on the STM32 single chip microcomputer, and the acoustic-magnetic coupling characteristic matrix is generated. The specific steps are as follows: The electrode-electrolyte interface quantum magnetic field response data and infrasound wave signals are received through the hardware interface of the STM32 microcontroller and stored in different cache areas on the chip. The infrasound signal in the buffer is subjected to wavelet decomposition to separate noise and valid signal. The noise reduction threshold is dynamically adjusted based on the quantum magnetic field data in the buffer, the denoised signal is reconstructed, and the main frequency energy parameters are extracted. Histogram equalization is performed based on the quantum magnetic field response data in the buffer area to generate the interface impedance gradient, which is then multimodally fused with the main frequency energy parameters to generate the acoustic-magnetic coupling characteristic matrix.

4. The method for controlling the remaining capacity of an aged lithium battery according to claim 3, wherein: The dynamic pheromone balance model based on the acoustic-magnetic coupling characteristic matrix is input, the capacity difference coupling coefficient between battery packs is calculated by the ant colony optimization algorithm, and the capacity redistribution path topology is generated. The specific steps are as follows: The acoustic-magnetic coupling characteristic matrix is input into the dynamic pheromone equilibrium model, and the pheromone concentration distribution of the ant colony optimization algorithm is initialized based on the correlation between the interface impedance gradient and the main frequency energy parameter. According to the pheromone concentration distribution, the capacity difference coupling coefficient between each cell in the battery pack is iteratively calculated through the ant colony optimization algorithm; Based on the capacity difference coupling coefficient, the capacity redistribution path topology is generated through gradient optimization.

5. The method for controlling the remaining capacity of an aged lithium battery according to claim 4, wherein: According to the capacity redistribution path topology, the energy transfer direction is controlled by the bidirectional Buck-Boost circuit. The specific steps are as follows: The energy transmission link of the bidirectional Buck-Boost circuit is activated based on the capacity redistribution path topology. When the energy source voltage in the capacity redistribution path topology is higher than the receiving node, the PWM duty cycle of the first switch is controlled to perform step-down transmission of the inductor in Buck mode. When the energy source voltage in the capacity redistribution path topology is lower than the receiving node, the Boss mode is switched and the PWM duty cycle of the second switch is adjusted, and the inductor completes the reverse flow of energy through boost transmission.

6. The method for controlling the remaining capacity of an aged lithium battery according to claim 5, wherein: The specific steps of dynamically adjusting the equilibrium current threshold based on the quantum spin resonance magnetic field gradient are as follows: During the energy transfer process of Buck and Boss, based on the change of the magnetic field gradient caused by quantum spin resonance, the electric dipole spin resonance is used to suppress the deviation of the Li+ migration path and generate a magnetic field gradient correction coefficient. The magnetic field gradient correction coefficient is input into the acousto-magnetic coupling characteristic matrix, and the first and second switch timings are dynamically adjusted through the volt-second balance criterion to synchronize the balancing current threshold with the mid-main frequency energy parameter of the acousto-magnetic coupling characteristic matrix.

7. The method for controlling the remaining capacity of an aged lithium battery according to claim 6, wherein: The energy change of the frequency domain of the infrasonic signal is monitored in real time during the energy transfer process. When the decomposition characteristic parameter of the decomposition liquid in the acoustic-magnetic coupling characteristic matrix exceeds the dynamic threshold of the inkjet printer, the equalization compensation is triggered until the battery capacity difference meets the standard. The specific steps are as follows: During the energy transfer process, the frequency domain energy changes of the infrasound signal are monitored in real time, and the frequency domain energy distribution parameters are generated through fast Fourier transform. The frequency domain energy distribution parameters are input into the acoustic-magnetic coupling characteristic matrix to calculate the real-time gradient change rate of the decomposition characteristic parameters of the decomposition liquid; When the real-time gradient change rate exceeds the dynamic threshold of inkjet printing, the adjusted balanced current threshold is activated as the compensation current amplitude input to the Buck-Boost circuit; The compensation current amplitude is continuously adjusted until the capacity difference coupling coefficient reaches the preset equilibrium threshold, terminating the energy transfer process.

8. A residual capacity equalization control system for an aging lithium battery, based on the residual capacity equalization control method for an aging lithium battery according to any one of claims 1 to 7, characterized in that: Including data acquisition module, acoustic magnetic processing module, acoustic magnetic optimization module, quantum magnetic control module and acoustic magnetic balancing module; A data acquisition module is used to collect quantum magnetic field response data of aging lithium battery packs and infrasound signals generated by electrolyte decomposition; The acousto-magnetic processing module is used to input the quantum magnetic field response data and infrasound wave signal into the single-chip microcomputer, perform wavelet noise reduction and histogram equalization processing based on the STM32 single-chip microcomputer, and generate the acousto-magnetic coupling characteristic matrix; The acoustic-magnetic optimization module is used to input the dynamic pheromone balance model based on the acoustic-magnetic coupling characteristic matrix, calculate the capacity difference coupling coefficient between battery packs through the ant colony optimization algorithm, and generate a capacity redistribution path topology map; The quantum magnetic control module is used to control the direction of energy transfer through a bidirectional Buck-Boost circuit according to the capacity redistribution path topology and dynamically adjust the equilibrium current threshold based on the quantum spin resonance magnetic field gradient; The acoustic-magnetic balancing module is used to monitor the frequency domain energy changes of the infrasonic signal in real time during the energy transfer process. When the decomposition characteristic parameters of the decomposition liquid in the acoustic-magnetic coupling characteristic matrix exceed the dynamic threshold of the inkjet printer, the balancing compensation is triggered until the battery capacity difference meets the standard.

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 remaining capacity balancing control method for aging lithium batteries 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 remaining capacity balancing control method for aging lithium batteries according to any one of claims 1 to 7 are implemented.

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