Solid-state battery bidirectional DCDC multi-objective optimization control system integrated with BMS

Abstract

The invention discloses a solid-state battery bidirectional DCDC multi-target optimization control system integrated with a BMS, and belongs to the technical field of battery management systems, and the system comprises an acoustic library building module which is used for applying current pulses of different amplitudes and rates, and collecting a high-frequency broadband sound wave response spectrum; analyzing the propagation speed, attenuation characteristics and frequency spectrum change of sound waves in the multilayer structure, and establishing an acoustic characteristic base library; the risk identification module is used for applying a micro-current detection pulse in a predetermined mode and capturing a transient acoustic response; accurate sensing of the microscopic physical state of the solid-state battery under the dynamic load working condition can be achieved, high-power charging and discharging and working condition abrupt change scenes can be rapidly adapted, the power response timeliness and the control precision are improved, meanwhile, global optimal balance of efficiency, service life and power stability is achieved, uneven deposition of lithium ions is restrained, and the service life of the solid-state battery is prolonged. Interface impedance and failure risks are reduced, and safe and reliable operation of the system is guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery management system, more particularly, to a solid-state battery bidirectional DCDC multi-objective optimization control system integrated with BMS. BACKGROUND

[0002] Solid-state batteries have become the energy storage choice for high-end scenarios such as new energy vehicles and energy storage power stations due to their high energy density and excellent safety. The control performance of the bidirectional DCDC system, as the core of energy conversion, directly determines the operation quality of the solid-state battery system. In the current engineering promotion of solid-state batteries, multi-objective collaborative optimization control under dynamic load conditions has become a prominent technical shortcoming. Although the existing control strategies optimize the running efficiency, battery life, and power stability, they generally have insufficient response timeliness and low control accuracy in complex scenarios such as high-power charging and discharging and sudden changes in working conditions. They cannot quickly adapt to dynamic changes in loads, nor can they achieve global optimal balance of multiple objectives, often resulting in unbalanced regulation and control, which seriously restricts the large-scale application of solid-state batteries in high-end energy storage fields.

[0003] The above problem lies in the serious disconnection between the battery management system (BMS) bottom model and the internal microphysical state of the solid-state battery. The macro equivalent circuit model or empirical parameter fitting model used by traditional BMS can only deduce the internal state of the battery through external measurable signals such as voltage and current, and cannot accurately perceive the physical phenomena such as the deposition and deintercalation dynamics of lithium ions at the solid-solid interface and the micro-stress evolution of the solid-state battery. This perceptual limitation leads to inherent deviations in the estimation results of the battery state of charge (SOC) and state of health (SOH), making the control strategy designed based on this model lose the basis for accurate regulation and control, and making it difficult to adapt to the complex internal working mechanism of the solid-state battery.

[0004] The technical contradiction is further exacerbated under dynamic high-stress working conditions such as high-power fast charging and emergency feedback charging. Such scenarios have strict requirements for power response speed and energy conversion efficiency. The existing control strategies mostly use aggressive current control schemes to meet performance requirements, but because the bottom model cannot predict the micro-dynamic changes in the battery, large currents can easily cause mismatch in the transport of lithium ions at the solid-solid interface, leading to local enrichment and uneven deposition of lithium ions. This phenomenon not only increases the interface impedance and reduces the energy conversion efficiency, but also damages the structural integrity of the electrode and electrolyte, significantly increasing the risk of internal short circuit of the battery, making the battery failure probability rise sharply, and posing a fatal threat to the operation safety of the solid-state battery system. SUMMARY

[0005] In view of the problems in the prior art, the purpose of the present application is to provide a solid-state battery bidirectional DCDC multi-objective optimization control system integrated with a BMS, which can accurately perceive the micro-physical state of the solid-state battery under dynamic load working conditions, quickly adapt to high-power charging and discharging and working condition mutation scenes, achieve global optimal balance of efficiency, service life and power stability while improving power response timeliness and control accuracy, inhibit uneven deposition of lithium ions, reduce interface impedance and failure risk, and ensure safe and reliable operation of the system.

[0006] To solve the above problems, the application adopts the following technical scheme:

[0007] A solid-state battery bidirectional DCDC multi-objective optimization control system integrated with a BMS, comprising:

[0008] An acoustic library building module for applying current pulses of different amplitudes and rates and collecting high-frequency wide-band acoustic response spectra; analyzing the propagation speed, attenuation characteristics and spectrum changes of sound waves in the multi-layer structure, and building an acoustic feature base library;

[0009] A risk identification module for applying predetermined mode micro-current detection pulses and capturing transient acoustic responses; matching and differentially analyzing the transient acoustic responses with the acoustic feature base library, identifying high-stress risk states, and generating a stress margin risk map;

[0010] A waveform adjustment module for adjusting the time-domain waveform of the power pulse according to the distribution characteristics of the stress margin risk map, and generating a non-standard power waveform instruction;

[0011] A phase fine-tuning module for monitoring high-frequency current ripple and fine-tuning the phase of the waveform corresponding to the non-standard power waveform instruction;

[0012] A self-healing regulation module for inserting a self-healing bias window and generating a small bias voltage according to the cumulative stress contribution degree determined by the stress margin risk map;

[0013] A cycle optimization module based on the collaborative work of the risk identification module, the waveform adjustment module, the phase fine-tuning module and the self-healing regulation module to form an intervention and repair cycle; the deterioration rate of the stress margin risk map is used as a trade-off parameter for multi-objective optimization decision-making.

[0014] Further, the acoustic library building module is used for:

[0015] Applying a composite electrical excitation composed of a series of continuously changing microsecond-level sub-pulses, wherein the current amplitude is continuously scanned according to a predetermined nonlinear function, and each amplitude maintains a short sinusoidal oscillation at a predetermined frequency;

[0016] Collecting acoustic wave signals while applying the composite electric excitation, jointly solving the acoustic wave signals, separating the individual contributions of the longitudinal wave, the transverse wave and the conversion wave thereof, and calculating the acoustic polarization state of each wave type.

[0017] Further, the acoustic library building module is further configured to:

[0018] Correlating the propagation velocity and the attenuation coefficient of the longitudinal wave, the transverse wave and the conversion wave thereof with the transient response of the battery terminal voltage, calculating the coupling coefficient of the sound velocity change rate and the battery differential conductance change rate of a specific acoustic mode in each excitation time period, and forming an acoustoelectric coupling coefficient matrix;

[0019] Based on the acoustoelectric coupling coefficient matrix, generating a corresponding dynamic stress transfer function for each of the longitudinal wave, the transverse wave and the conversion wave thereof, and constituting an acoustic feature basis library.

[0020] Further, the risk identification module is configured to:

[0021] Based on the acoustic feature basis library, generating a micro-current detection pulse sequence, wherein the time length, phase distribution and carrier frequency of the sub-pulse of the pulse sequence are set according to the acoustic feature basis library;

[0022] While applying the micro-current detection pulse sequence, capturing multiple transient acoustic responses; performing time reversal convolution operation on the transient acoustic responses, and using the pre-stored acoustic wave propagation operator in the acoustic feature basis library as the convolution kernel to separate the original acoustic wave components.

[0023] Further, the risk identification module is further configured to:

[0024] For the original acoustic wave components, analyzing the spatio-temporal evolution of the acoustic polarization state, calculating the variation coefficient of the polarization ellipse principal axis orientation in time and the polarization coherence coefficient decay rate between different spatial position components, and obtaining the polarization coherence loss value;

[0025] Matching the polarization coherence loss value with the theoretical stress acoustic coupling model stored in the acoustic feature basis library, calculating the residual error between the measured value and the theoretical value, synthesizing a scalar potential field according to the spatial gradient distribution of the residual error, and generating a stress margin risk map.

[0026] Further, the waveform adjustment module is configured to:

[0027] Analyzing the spatial gradient distribution characteristics of the stress margin risk map, quantifying it into a theoretical stress gradient tensor, and converting it into a spatial mode of expected ion flux distribution non-uniformity;

[0028] According to the spatial mode of the ion flux distribution unevenness, a compensation current profile composed of multiple non-uniform amplitude sub-pulses is constructed, wherein the time position, duration and amplitude envelope of each sub-pulse are configured according to the spatial mode;

[0029] The compensation current profile is decomposed into a set of waveform units, the current time integral and the current square time integral are calculated for each wave unit, and the equalization processing is performed;

[0030] The equalized wave unit sequence is reorganized according to the time position, the phase smoothing transition zone between adjacent wave units is introduced, and the non-standard power waveform instruction is synthesized.

[0031] Further, the phase fine-tuning module is used for:

[0032] During the execution of the non-standard power waveform instruction, the current signal is captured, the high-frequency current ripple component is separated out, and the envelope form, fundamental frequency and harmonic distribution of the high-frequency current ripple are extracted;

[0033] The fundamental frequency and harmonic distribution of the high-frequency current ripple are matched with the data in the acoustic feature base library, the key harmonic component is identified and its phase polarity characteristic is determined;

[0034] Based on the key harmonic component and its phase polarity characteristic, a phase compensation vector is generated;

[0035] The phase compensation vector is applied to the non-standard power waveform instruction, and a continuous phase change is introduced near the waveform zero-crossing point.

[0036] Further, the self-healing regulation module is used for:

[0037] The stress margin risk map is analyzed in space-time combination, the numerical change of each spatial point along the time axis is weighted and integrated to obtain a stress accumulation distribution map;

[0038] Based on the stress accumulation distribution map, a key area is identified, and repair pulse parameters are generated for each key area;

[0039] The repair pulse parameter set is converted into a scanning bias field in three-dimensional space;

[0040] During the self-healing bias window, the three-dimensional gradient scanning bias field is applied, and the spatial orientation of the bias field is dynamically adjusted.

[0041] Further, the cycle optimization module is used for:

[0042] The deterioration rate vector of the stress margin risk map is calculated, and a dynamic regulation urgency index is generated;

[0043] According to the dynamic regulation urgency index, a mode combination is selected from the regulation mode library to configure execution parameters and activation timing for the risk identification module, the waveform adjustment module, the phase fine-tuning module and the self-healing regulation module, and a customized intervention and repair scheme is formed.

[0044] Further, the cycle optimization module is also used for:

[0045] During the implementation of the customized intervention and repair scheme, the change rate of the deterioration rate is monitored, and the intervention intensity coefficients of the risk identification module, the waveform adjustment module, the phase fine-tuning module and the self-healing regulation module are dynamically adjusted.

[0046] The execution efficiency indicators of the risk identification module, the waveform adjustment module, the phase fine-tuning module and the self-healing regulation module and the reduction amount of the deterioration rate of the stress margin risk map are analyzed, and the strategy parameters in the regulation mode library are updated.

[0047] Compared with the prior art, the present application has the following advantages:

[0048] (1) The present scheme combines the real-time monitoring capability of the BMS to pre-warn potential faults such as ion deposition and structural damage, avoids safety problems such as short circuit and thermal runaway caused by local stress overload of the battery during the operation of the bidirectional DCDC system, and builds a strong safety line for stable operation of the system.

[0049] (2) The present scheme uses the synergistic effect of the waveform adjustment and phase fine-tuning modules to customize non-standard power waveforms according to the uneven ion flux distribution in space, separates and suppresses high-frequency current ripple, adapts to the bidirectional power conversion demand of the bidirectional DCDC system, reduces switching loss and circuit oscillation, improves energy conversion efficiency, solves the problem of poor adaptability of traditional fixed waveforms, maintains stable energy transmission efficiency in a wide load range, and meets the requirements of solid-state batteries for current smoothness.

[0050] (3) The present scheme carries the self-healing regulation module, accurately repairs the key areas of accumulated stress of the battery by three-dimensional gradient scanning bias field, dynamically adjusts the spatial orientation of the bias field to adapt to stress changes, effectively alleviates structural damage of the solid-state battery during long-term operation, reduces the battery degradation rate, prolongs the service life, reduces the cost of battery replacement and maintenance, avoids energy waste caused by excessive repair, balances the repair effect of the battery and the system energy consumption, and improves the whole life cycle cost performance.

[0051] (4) This scheme constructs a closed-loop intervention repair cycle with a cycle optimization module, adapts the optimal control mode according to the dynamic control urgency index, adjusts the module intervention intensity coefficient in real time, and continuously updates the control mode library, realizes the efficient cooperation of risk identification, waveform adjustment, phase fine-tuning and self-healing control, adapts to different life cycles and complex working condition changes of solid-state batteries, improves the response speed of the system to dynamic load, and balances safety, efficiency and life multi-objective optimization, and widens the adaptation range of the bidirectional DCDC system in new energy scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0053] Fig. 1 The flow chart between each module in the solid-state battery bidirectional DCDC multi-objective optimization control system integrated with BMS of the present application;

[0054] Fig. 2 The intervention strategy decision flow chart of the deterioration rate in the solid-state battery bidirectional DCDC multi-objective optimization control system integrated with BMS of the present application. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present application will be described clearly and completely in the embodiments of the present application combined with the drawings; obviously, the described embodiments are only some embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0056] Please refer to Figs. 1-2 A solid-state battery bidirectional DCDC multi-objective optimization control system integrated with BMS, comprising:

[0057] An acoustic library building module is used to apply current pulses of different amplitudes and rates, and collect high-frequency broadband acoustic response spectrum; analyze the propagation speed, attenuation characteristics and spectrum changes of sound waves in the multi-layer structure, and build an acoustic feature base library. The specific operation steps are as follows:

[0058] Step 11, a composite electric excitation composed of a series of continuous change microsecond sub-pulses is applied, in which the current amplitude is continuously scanned according to a predetermined nonlinear function, and each amplitude maintains a short sinusoidal oscillation of a predetermined frequency. The specific operation is as follows:

[0059] The acoustic database building module is composed of a series of continuously changing microsecond sub-pulses by the applied composite electric excitation. The microsecond time scale can accurately capture the rapid microprocesses such as lithium ion deintercalation and interface stress change inside the battery, avoid signal superposition and information loss caused by too long excitation pulse time, and continuously scan the current amplitude using a predetermined nonlinear function. This design fully adapts to the nonlinear characteristics of the internal electrochemical reaction of the solid-state battery, and can more comprehensively cover the battery state under different current intensities compared to linear scanning. Especially in the high current amplitude region and the low current amplitude region, which correspond to the high power working state and the static state of the battery respectively, a short sinusoidal oscillation of a predetermined frequency is superimposed at each amplitude maintenance point. The sinusoidal oscillation can enhance the recognition of the electric excitation signal, and make the different structural layers inside the battery produce differential acoustic responses.

[0060] Step 12, collect the acoustic wave signal while applying the composite electric excitation, jointly solve the acoustic wave signal, separate the individual contributions of the longitudinal wave, transverse wave and their conversion waves, and calculate the acoustic polarization state of each wave type. The specific operation is as follows:

[0061] Collecting the acoustic wave signal while applying the composite electric excitation can ensure that the captured acoustic wave response is completely matched with the electric excitation signal in the time dimension, accurately reflects the acoustic changes inside the battery under a specific electric excitation, and avoids feature distortion caused by time sequence misalignment. The collected acoustic wave signal contains multiple wave types such as longitudinal waves, transverse waves and their conversion waves. These wave types carry different structural layer physical state information when propagating in the multi-layer and multi-porous structure of the solid-state battery, for example, the longitudinal wave mainly reflects the density change of the electrode material, and the transverse wave is more sensitive to the interface stress. By separating the individual contributions of each type of wave through joint solving technology, the signal interference between different wave types can be eliminated, and the characteristic parameters of each type of wave can be accurately extracted. The calculation of the acoustic polarization state is based on the basic principle of acoustoelasticity. The polarization characteristics of the acoustic wave are directly related to the stress distribution inside the battery. By analyzing the acoustic polarization state of each type of wave, the stress direction and distribution uniformity inside the battery can be indirectly obtained.

[0062] Step 13, correlate the propagation speed and attenuation coefficient of the longitudinal wave, transverse wave and their conversion waves with the transient response of the battery terminal voltage, calculate the coupling coefficient of the sound speed change rate and the battery differential conductance change rate of a specific acoustic mode in each excitation time period, and form an acoustoelectric coupling coefficient matrix. The specific operation is as follows:

[0063] First, the propagation velocity and attenuation coefficient of the longitudinal wave, transverse wave and their converted waves obtained by step 12 are deeply associated with the transient response of the battery terminal voltage. The propagation velocity and attenuation coefficient reflect the mechanical properties and structural integrity of the battery internal material, while the transient response of the battery terminal voltage directly reflects the dynamic process of the electrochemical reaction. The association of the two realizes the cross-dimensional fusion of the battery micro-mechanical state and macro-electrical performance. In each excitation time period, the coupling coefficient of the sound velocity change rate and the battery differential conductivity change rate is calculated for a specific acoustic mode, where the sound velocity change rate represents the dynamic mechanical change of the battery internal structure, and the differential conductivity change rate reflects the instantaneous fluctuation of the lithium ion transport efficiency. The coupling coefficient of the two can quantify the degree of mutual influence between the mechanical state and the electrochemical state. The calculation of the coupling coefficient is realized by a formula as follows:

[0064]

[0065] The formula is based on the dynamic correlation characteristics of the acoustic and electrical parameters, and introduces a correction coefficient to compensate for the influence of environmental factors such as temperature and humidity. The quantitative relationship between the two is established through linear fitting, and the coupling coefficient is finally obtained. In the formula, represents the coupling coefficient of the i-th acoustic mode and the j-th voltage response interval; is the sound velocity change of the i-th wave type; is the initial sound velocity of the i-th wave type; is the differential conductivity change of the j-th interval; is the initial differential conductivity of the j-th interval; and

[0066] Step 14, based on the acoustic-electric coupling coefficient matrix, a corresponding dynamic stress transfer function is generated for each of the longitudinal wave, transverse wave and their converted waves, forming an acoustic feature basis library. The specific operation is as follows:

[0067] Based on the acoustic-electric coupling coefficient matrix formed in step 13, the corresponding dynamic stress transfer function is generated for each wave type in the longitudinal wave, the transverse wave and the converted wave, and this process fully utilizes the cross-dimensional correlation information stored in the acoustic-electric coupling coefficient matrix, converts the abstract coupling coefficient into a function model that can directly reflect the stress change, and the dynamic stress transfer function is dynamic in nature, can adapt to the battery state change under different excitation conditions in real time, compared with the static function model, can accurately capture the dynamic evolution process of the stress of the battery in the charging and discharging cycle, and the function also integrates the correlation logic of multiple acoustic parameters such as sound velocity, attenuation coefficient and polarization state and stress, realizes the multi-dimensional characterization of the stress state in the battery, and the dynamic stress transfer function corresponding to each wave type jointly constitutes the acoustic feature base library, which covers the typical acoustic features of the solid-state battery under different current amplitudes and different excitation frequencies, forms a complete acoustic feature reference benchmark, which not only includes the acoustic features under normal working conditions, but also covers the feature data under different stress levels.

[0068] In a preferred embodiment of the present application, a risk identification module is further included for applying a predetermined mode of micro-current detection pulse and capturing a transient acoustic response; matching and differentially analyzing the transient acoustic response with the acoustic feature base library to identify a high stress risk state and generate a stress margin risk map, and the specific operation steps are as follows:

[0069] Step 21, based on the acoustic feature base library, a micro-current detection pulse sequence is generated, wherein the time length, phase distribution and carrier frequency of the sub-pulse of the pulse sequence are set according to the acoustic feature base library, and the specific operation is as follows:

[0070] The acoustic feature base library covers the dynamic stress transfer function, the acoustic-electric coupling coefficient matrix and other core information of the longitudinal wave, the transverse wave and the converted wave, and these data provide a direct basis for the setting of the pulse sequence parameters, the time length setting of the pulse sequence needs to match the propagation characteristics of the acoustic wave in the multi-layer structure of the battery, which not only ensures that a single pulse can fully excite the acoustic response of each layer structure in the battery, avoids incomplete acquisition of the response signal due to too short time, but also prevents adjacent pulse signals from being superimposed due to too long time, which affects the subsequent signal analysis, the phase distribution setting corresponds to the phase characteristics of different acoustic modes in the acoustic feature base library, and by reasonably planning the phase distribution, the response signals of different types of acoustic waves can form differentiated characteristics in the time domain, which provides convenience for subsequent signal separation, and the carrier frequency selection of the sub-pulse needs to cover the characteristic frequency range corresponding to each stress state recorded in the acoustic feature base library, so as to ensure that the detection pulse can fully excite the acoustic signals corresponding to the possible stress in the battery, and at the same time, the frequency setting is accurately set to avoid signal energy dispersion, improve the signal-to-noise ratio of the detection signal, and ensure the effectiveness of the subsequent response capture.

[0071] Step 22, capture the multi-channel transient acoustic response while applying the micro-current detection pulse sequence; perform time deconvolution operation on the transient acoustic response, use the pre-stored acoustic wave propagation operator in the acoustic characteristic base library as the deconvolution kernel to separate the original acoustic wave component, and the specific operation is as follows:

[0072] Starting multi-channel transient acoustic response capture while applying the micro-current detection pulse sequence can strictly ensure the accurate correspondence of acoustic signals and electrical excitation signals in the time dimension, avoid feature correlation distortion caused by timing misalignment, and the multi-channel acquisition mode can comprehensively cover the acoustic signals at different spatial positions of the solid-state battery, completely capture the spatial difference information of the stress distribution inside the battery, and make up for the limitations of single-point acquisition. Since the acoustic wave propagates in the multi-layer and multi-porous structure of the solid-state battery, it will be scattered, attenuated and mode-converted, and the captured transient acoustic response contains a large amount of interference components, so time deconvolution operation is needed to restore the intrinsic characteristics of the signal. The pre-stored acoustic wave propagation operator in the acoustic characteristic base library is a standard model established based on the acoustic propagation law under the normal state of the battery. Using it as the deconvolution kernel can accurately correspond to the propagation path and attenuation characteristics of the acoustic wave inside the battery. Through time deconvolution operation, the distorted signals and external environmental noise generated in the acoustic wave propagation process can be effectively stripped, and the original acoustic wave component reflecting only the microscopic physical state inside the battery is separated. This component truly carries key information such as stress and interface state inside the battery.

[0073] Step 23, analyze the space-time evolution of the acoustic polarization state of the original acoustic wave component, calculate the variation coefficient of the polarization ellipse principal axis orientation in time of each component, and the polarization coherence coefficient decay rate between components at different spatial positions, to obtain the polarization coherence loss value, and the specific operation is as follows:

[0074] The risk characteristic parameters are quantitatively extracted through space-time evolution analysis of the acoustic polarization state. The change of the acoustic polarization state has a direct correlation with the stress distribution and evolution inside the solid-state battery, and the space-time evolution analysis can simultaneously capture the time dynamic change and spatial distribution difference of the stress; in the time dimension, the variation coefficient of the polarization ellipse principal axis orientation in time of each original acoustic wave component is calculated. This coefficient can accurately reflect the stability of the polarization state at the same spatial position, while the unstable change of the polarization state directly corresponds to the violent fluctuation of the stress inside the battery. The calculation formula is realized, and the calculation formula is as follows:

[0075]

[0076] The formula is based on the principle of statistical variation analysis, combines the linear correlation characteristics of acoustic polarization state and stress, takes the ratio of the standard deviation and the mean of the polarization ellipse principal axis orientation as the calculation relationship, quantifies the change degree in the time dimension through the percentage form, eliminates the influence of the initial polarization state difference of different battery individuals, and CV represents the time variation coefficient of the polarization ellipse principal axis orientation; is the standard deviation of the polarization ellipse principal axis orientation angle in the time sequence; is the mean of the polarization ellipse principal axis orientation angle in the time sequence; in the spatial dimension, the polarization coherence coefficient decay rate between different spatial position components is calculated, which reflects the consistency of the polarization state between different regions of the battery, and the higher the decay rate, the more uneven the stress distribution in space, and the polarization coherence loss value comprehensively reflects the quantitative results of the time variation coefficient and the spatial decay rate, and comprehensively characterizes the acoustic polarization characteristic degradation degree caused by the stress change in the battery, and provides a quantitative index for risk level determination.

[0077] Step 24, matching the polarization coherence loss value with the theoretical stress acoustic coupling model stored in the acoustic characteristic base library, calculating the residual of the measured value and the theoretical value, synthesizing the scalar potential field according to the spatial gradient distribution of the residual, and generating a stress margin risk map, the specific operation is as follows:

[0078] The polarization coherence loss value is an acoustic characterization of the actual stress state in the battery, and the theoretical stress acoustic coupling model stored in the acoustic characteristic base library covers the standard acoustic loss characteristics corresponding to different stress levels. By matching the two, the deviation between the actual state and the theoretical normal state can be accurately located. By calculating the residual of the measured polarization coherence loss value and the theoretical value, the size of the residual directly corresponds to the severity of the risk. The larger the residual, the farther the stress state at this position deviates from the normal range, and the higher the high stress risk. By using the accurate algorithm of scalar field gradient calculation, the scalar potential field is synthesized according to the spatial gradient distribution of the residual. The spatial gradient can reflect the change trend of the residual at different positions, that is, the diffusion direction and rate of the risk. By synthesizing the scalar potential field, discrete residual data can be converted into a continuous field model with spatial distribution characteristics. The scalar potential field directly presents the stress margin condition of each region in the battery, and then generates a stress margin risk map. The map clearly marks the location, range and risk level of the high stress risk area.

[0079] In a preferred embodiment of the present application, a waveform adjustment module is further included, which is used to adjust the time-domain waveform of the power pulse according to the distribution characteristics of the stress margin risk map, and generate a non-standard power waveform instruction, and the specific operation steps are as follows:

[0080] Step 31, analyze the spatial gradient distribution characteristics of the stress margin risk map, quantify it as a theoretical stress gradient tensor, and convert it into a spatial pattern of expected ion flux distribution unevenness, which is implemented as follows:

[0081] The stress margin risk map visually presents the stress conditions in different regions inside the solid-state battery. Its spatial gradient distribution characteristics not only include the strength differences of the stress, but also contain the direction and rate information of the stress change. Analyzing these characteristics can accurately locate the high stress concentration areas and the key paths of stress diffusion. Quantifying these characteristics as a theoretical stress gradient tensor is a precise description of the complex stress distribution in three-dimensional space using tensor mathematical tools. This tensor can completely retain the component information of the stress in each direction, overcoming the limitations of traditional scalar parameters that cannot fully represent the spatial stress state. Based on the internal coupling relationship between stress and ion transport in solid-state batteries, the existence of stress gradient will directly change the transport path and rate of lithium ions, leading to uneven ion flux distribution. By establishing a mapping relationship between the stress gradient tensor and the ion flux, the mechanical stress distribution can be converted into a spatial pattern of expected ion flux distribution unevenness in the electrochemical layer. This pattern clearly defines the imbalance degree of ion transport in different regions.

[0082] Step 32, according to the spatial pattern of ion flux distribution unevenness, construct a compensation current profile composed of multiple non-uniform amplitude sub-pulses, where the time position, duration, and amplitude envelope of each sub-pulse are configured according to the spatial pattern, which is implemented as follows:

[0083] Based on the spatial pattern of ion flux distribution unevenness, a customized compensation current profile is constructed to accurately correct the imbalance of ion transport, solving the technical problem that traditional uniform current cannot adapt to local ion flux differences. The spatial pattern of ion flux distribution unevenness clearly identifies the weaknesses of ion transport in each region. For regions with low ion flux, the current excitation needs to be enhanced to promote lithium ion migration. For regions with high ion flux that may cause deposition, the current intensity needs to be appropriately reduced. This differentiated demand determines that the compensation current must use a non-uniform amplitude sub-pulse structure. The parameter configuration of each sub-pulse precisely corresponds to the spatial pattern. The time position is set to match the ion transport delay characteristics inside the battery, ensuring that the current excitation can play a role in the key period of ion transport. The duration is adjusted according to the severity of ion flux imbalance in the corresponding region. The more severe the imbalance, the more precise the duration needs to be calculated to ensure the compensation effect. The amplitude envelope is directly related to the size of the ion flux gap. By setting a gradient amplitude, the ion flux is smoothly compensated, avoiding new stress fluctuations caused by sudden amplitude changes. The overall compensation current profile can accurately regulate the ion flux distribution point by point.

[0084] Step 33, decompose the compensation current profile into a set of waveform units, calculate the current time integral and current square time integral for each wave unit, and perform balancing processing, the specific operation is as follows:

[0085] Decomposing the compensation current profile into a set of wave units is to convert the complex whole current signal into several independent analysis and adjustment basic units, which facilitates precise control of each local current signal and avoids the problem of local parameter imbalance caused by overall adjustment. The current time integral and current square time integral are calculated for each wave unit, wherein the current time integral reflects the amount of charge transferred by the unit, which is directly related to the total amount of ion compensation, and the current square time integral is closely related to the heat loss generated by the current passing through the battery. These two integral parameters correspond to the capacity demand and efficiency demand of the battery operation respectively. Balancing processing is to coordinate and adjust the two integral parameters of each wave unit under the framework of multi-objective optimization, balance the charge contribution and heat loss of different wave units, ensure the realization of the overall ion flux compensation target, and avoid the negative impact of local overheating or uneven charge distribution on battery life, so that each wave unit can reach the optimal working state under multi-objective constraints.

[0086] Step 34, reorganize the balanced wave unit sequence according to the time position, introduce a phase smooth transition zone between adjacent wave units, and synthesize a non-standard power waveform instruction, the specific operation is as follows:

[0087] Reorganizing the balanced wave unit sequence according to the original time position is to restore the overall control logic of the compensation current profile, ensuring that the compensation functions of each unit can be connected in order, and realizing complete correction of ion flux distribution. Direct connection between adjacent wave units may cause phase mutation, which in turn may cause high-frequency current ripple. This will exacerbate the stress impact on the battery. The introduction of a phase smooth transition zone can effectively solve this problem. The transition zone realizes smooth connection between adjacent units through gradual phase change, greatly reducing the negative impact of waveform mutation. The synthesized non-standard power waveform instruction not only integrates the compensation logic and balancing strategy of the previous steps, but also has good dynamic adaptability. Its non-standard feature lies in its complete adaptation to the current ion flux and stress state of solid-state batteries, which is different from the traditional fixed-mode standard waveform. It can flexibly respond to changes in the internal state of the battery under dynamic load conditions.

[0088] In a preferred embodiment of the present application, a phase fine-tuning module is further included for monitoring high-frequency current ripple and fine-tuning the phase of the waveform corresponding to the non-standard power waveform instruction. The specific operation steps are as follows:

[0089] Step 41, capture the current signal during the execution of the non-standard power waveform instruction, separate the high-frequency current ripple component, extract the envelope form, fundamental frequency and harmonic distribution of the high-frequency current ripple, the specific operation is as follows:

[0090] During the actual execution of the non-standard power waveform, influenced by factors such as circuit parasitic parameters and internal impedance fluctuations of the battery, high-frequency current ripples will inevitably be generated. These ripples will exacerbate the unevenness of ion transport within the battery, further inducing microscopic stress accumulation. Therefore, real-time monitoring and targeted processing are necessary. The current signal is captured simultaneously with the execution of the non-standard power waveform instruction, ensuring that the collected signal corresponds to the real-time working state of the waveform and avoiding feature distortion caused by time delay. By separating the high-frequency current ripple component through signal transformation technology, the fundamental component and low-frequency interference in the current signal can be effectively removed, focusing on the high-frequency fluctuation part that negatively affects battery performance. Extracting the envelope form, fundamental frequency and harmonic distribution of the high-frequency current ripple is because these three characteristics carry the intensity variation law, core frequency attribute and frequency component composition of the ripple. The envelope form reflects the dynamic change trend of the ripple amplitude, the fundamental frequency determines the basic fluctuation period of the ripple, and the harmonic distribution reflects the complexity of the ripple. Together, they form a complete feature system for evaluating the impact of the ripple.

[0091] Step 42, match the fundamental frequency and harmonic distribution of the high-frequency current ripple with the data in the acoustic feature base library, identify the key harmonic components and determine their phase polarity characteristics, the specific operation is as follows:

[0092] The acoustic feature base library stores data such as dynamic stress transfer functions and acoustic-electric coupling coefficient matrices of longitudinal waves, transverse waves and their conversion waves. These data systematically link the acoustic features of the battery with internal stress, ion transport and other microscopic states. The frequency characteristics of the high-frequency current ripple are inherently coupled with the acoustic modes within the battery. Specific harmonic components often correspond to specific microscopic stress changes or interface state fluctuations. By matching the fundamental frequency and harmonic distribution of the high-frequency current ripple with the acoustic feature base library, we essentially reverse-map the microscopic mechanics and electrochemical state of the battery through electrical characteristics. This allows us to filter out harmonic components that correspond to high stress risk in the acoustic feature library, i.e., key harmonic components. Additionally, the phase polarity characteristic directly determines the influence direction of the harmonic component on the internal state of the battery. In-phase harmonics will superimpose and exacerbate stress fluctuations, while anti-phase harmonics may cancel each other out. Therefore, determining the phase polarity characteristic of the key harmonic component is a prerequisite for developing an accurate phase compensation strategy.

[0093] Step 43, generate a phase compensation vector based on the key harmonic components and their phase polarity characteristics, the specific operation is as follows:

[0094] The amplitude of the key harmonic component determines the severity of its impact on the battery operation, and the phase polarity characteristic determines its direction of action. The generation of the phase compensation vector needs to consider both elements to achieve targeted counteraction and regulation. The process needs to construct a phase compensation vector through a formula, which can be:

[0095]

[0096] The formula is based on the influence weight of harmonic component and the phase cancellation principle. First, set the weight coefficient according to the contribution of each key harmonic component to the battery stress. Then, determine the compensation amplitude combined with the phase value of each key harmonic component. Finally, through the unit vector, locate the harmonic dimension of compensation, and form a comprehensive phase compensation vector through linear superposition. In the formula, represents the phase compensation vector; k is the serial number of the key harmonic component; n is the total number of key harmonic components; is the influence weight coefficient of the kth key harmonic component; is the phase compensation value of the kth key harmonic component, which is opposite in size to the original harmonic phase, and the polarity is determined by the original harmonic phase polarity; is the unit vector corresponding to the kth key harmonic component, used to locate the frequency dimension of compensation. The phase compensation vector generated by this formula can achieve precise and coordinated compensation of multiple key harmonic components.

[0097] Step 44, apply the phase compensation vector to the non-standard power waveform instruction, introduce continuous phase change near the waveform zero crossing point, the specific operation is as follows:

[0098] The phase compensation vector carries the precise compensation parameters of each key harmonic component. When it is applied to the non-standard power waveform instruction, it is essentially a directional correction of the waveform phase to offset the negative effects of key harmonic components. The continuous phase change near the waveform zero crossing point is chosen because the current amplitude is zero at the zero crossing point, so phase adjustment at this point will not cause sudden changes in the current signal, which can minimize the generation of new interference ripples during the adjustment process, ensuring smooth transition of the waveform. The introduction of continuous phase change instead of step change can further reduce the impact of phase adjustment on the integrity of the waveform, ensuring that the fundamental characteristics of the non-standard power waveform are not damaged, achieving both high-frequency ripple suppression and maintaining the original power transmission function of the waveform. After phase fine-tuning, the non-standard power waveform not only has the original ion flux compensation ability, but also significantly reduces the microscopic stress impact caused by high-frequency ripples, effectively improving the stability, efficiency and safety of the battery operation, providing a solid guarantee for the multi-objective optimization of the entire system.

[0099] In a preferred embodiment of the present application, a self-healing regulation module is further included, which is used to determine the cumulative stress contribution degree according to the stress margin risk map, insert a self-healing bias window, and generate a small bias voltage. The specific operation steps are as follows:

[0100] Step 51, the stress margin risk map is analyzed in space-time joint analysis, and the numerical change of each space point along the time axis is weighted and integrated to obtain a stress cumulative distribution map. The specific operation is as follows:

[0101] The stress margin risk map can only reflect the stress spatial distribution state inside the battery at a certain moment, while the structural damage of the solid-state battery is mainly caused by long-term stress accumulation. The spatial distribution at a single time point cannot reflect the superposition effect of stress evolution with time, so it is necessary to carry out space-time joint analysis, considering both the spatial position attribute and the time change characteristic of stress. The numerical change of each space point along the time axis is weighted and integrated, because the contribution of stress at different time periods to the battery damage is different. The influence of high stress generated in the near future on the current structure state is more significant, and the influence of early stress gradually decays over time. Weighted processing can accurately reflect this decay characteristic in the time dimension. This weighted integration process needs to be realized by a formula, and the formula is as follows:

[0102]

[0103] The formula is based on the physical nature of stress accumulation, combined with the time weight effect in damage mechanics, based on the instantaneous stress value of the space point, and introduces a time weight function to differentially weight the stress at different times. The total cumulative stress of the space point is obtained by definite integral operation, which completely quantifies the superposition effect of long-term stress action. In the formula, S(x, y, z) represents the cumulative stress value at coordinates (x, y, z); w(t) is the time weight function, which monotonically decreases with time; is the instantaneous stress value at coordinates (x, y, z) at time t; and are the starting time and ending time of the integral, respectively. The stress cumulative distribution map calculated by the formula clearly presents the cumulative damage degree of each spatial position inside the battery.

[0104] Step 52, based on the stress cumulative distribution map, identify the key areas, and generate repair pulse parameters for each key area. The specific operation is as follows:

[0105] Based on the stress accumulation distribution map to identify key areas, a scientific cumulative stress threshold needs to be set, and the area with cumulative stress value exceeding the threshold is designated as the key repair area. Meanwhile, combined with the stress gradient change characteristics, the area with large stress gradient and easy stress concentration and diffusion is included in the key range. Such areas are often potential risk points of battery structure failure, and preferential repair can effectively prevent damage expansion. For each key area, repair pulse parameters need to be generated, and a quantitative mapping relationship between cumulative stress value and repair parameters needs to be established. The design of repair pulse parameters needs to fully adapt to the damage state of the corresponding area. The pulse amplitude is directly related to the size of the repair energy. The higher the cumulative stress of the area, the larger the required repair pulse amplitude to ensure the repair effect. The pulse duration needs to be accurately set according to the damage range of the area. The larger the range, the longer the duration, so as to avoid secondary damage due to incomplete repair. The pulse frequency needs to match the inherent frequency of lithium ion migration in the area. Through frequency resonance, the efficiency of ion redistribution is improved, and then the stress is effectively released. The repair pulse parameters of each key area together constitute a targeted repair parameter system.

[0106] Step 53, convert the repair pulse parameter set into a scanning bias field in three-dimensional space, and the specific operation is as follows:

[0107] The repair pulse parameter set belongs to a discrete electrical signal parameter, which can only represent the repair requirements of each key area. However, the stress distribution inside the solid-state battery is a continuous three-dimensional structure. Discrete parameters cannot achieve uniform coverage and precise action of repair energy. Therefore, field conversion must be performed. The scanning bias field in three-dimensional space needs to rely on the three-dimensional structure model of the battery to establish a one-to-one mapping relationship between the repair pulse parameters of each key area and the corresponding three-dimensional space coordinates. The pulse amplitude corresponds to the strength parameter of the bias field, the pulse duration corresponds to the action time parameter of the bias field, and the pulse frequency corresponds to the oscillation frequency parameter of the bias field. This conversion process needs to refer to the relevant technical principles of three-dimensional magnetic field generation to realize the construction of space field through multi-dimensional coil cooperation, ensuring that the intensity distribution of the bias field in the x, y, and z space dimensions completely matches the spatial distribution of the repair pulse parameters, and the scanning bias field formed can completely cover all key areas, and the field strength distribution is accurately adapted to the repair requirements of each area.

[0108] Step 54, during the self-healing bias window, apply a three-dimensional gradient scanning bias field and dynamically adjust the spatial orientation of the bias field, and the specific operation is as follows:

[0109] The self-healing bias window is a battery repair period specially reserved, and the setting of the window avoids the normal charging and discharging working period of the battery, which can not only ensure that the repair process is not disturbed by the normal working current, but also avoid the influence of the repair operation on the normal power output of the battery, so as to ensure the stability of the overall work of the battery. The applied three-dimensional gradient scanning bias field has gradient field characteristics, and the field strength continuously changes along the spatial coordinates, which can adapt to the stress difference of different positions in the key area, realize the gradient supply of repair energy, avoid the problem of local repair shortage or energy surplus caused by the uniformization of energy supply, and dynamically adjust the spatial orientation of the bias field. Because in the repair process, the stress distribution state in the battery will continuously change with the rearrangement of ions, and the original bias field orientation may no longer adapt to the new stress distribution. By monitoring the dynamic change of the stress state in the repair process in real time, the orientation angle of the bias field in the three-dimensional space is adjusted, so that the action direction of the bias field can always be aligned with the un-repaired area with concentrated stress, thereby improving the accuracy and overall effect of self-healing repair, and effectively prolonging the service life of the battery.

[0110] In a preferred embodiment of the present application, a cycle optimization module is further included, which forms an intervention repair cycle based on the cooperative work of the risk identification module, the waveform adjustment module, the phase fine-tuning module and the self-healing control module; a multi-objective optimization decision is made with the deterioration rate of the stress margin risk map as a trade-off parameter; and the specific operation steps are as follows:

[0111] Step 61, the deterioration rate vector of the stress margin risk map is calculated to generate a dynamic control urgency index, and the specific operation is as follows:

[0112] The deterioration of the stress margin risk map is not a single-dimensional numerical change, but a differentiated evolution trend in three-dimensional space. The stress deterioration speed and diffusion trend of different areas differ significantly, and a single numerical value cannot fully reflect the true situation of the risk. Calculating the deterioration rate vector needs to integrate the risk map data at different time nodes to capture the change rate of the stress margin of each spatial coordinate point, form a vector matrix that can cover the entire space, and completely present the spatial distribution and intensity gradient of the risk deterioration. This process is realized by the following formula:

[0113]

[0114] Based on the physical nature of gradient operation and the time-space coupling characteristics of stress accumulation, the spatial three-dimensional coordinates and the change rate of the time dimension are integrated to construct a four-dimensional vector to fully describe the deterioration trend, overcoming the limitations of traditional single-dimensional rate calculation. In the formula, represents the stress margin deterioration rate vector; is the gradient operator of stress accumulation distribution; S(x,y,z,t) is the cumulative stress value of the coordinates (x,y,z) at time t; , , respectively as the stress deterioration partial derivative of three spatial dimensions; is the stress deterioration partial derivative of time dimension, the dynamic regulation urgency index generated based on the vector, the multi-dimensional vector is converted into quantifiable comparison decision parameters through normalization processing, and the intervention priority under different working conditions is clearly defined.

[0115] Step 62, according to the dynamic regulation urgency index, select the mode combination from the regulation mode library, configure the execution parameters and activation timing for the risk identification module, waveform adjustment module, phase fine-tuning module and self-healing regulation module, form a customized intervention repair scheme, the specific operation is as follows:

[0116] The regulation mode library is a strategy set accumulated by previous engineering practice and algorithm optimization, which stores module combination schemes for different urgency levels, covering detection frequency of risk identification, compensation intensity of waveform adjustment, response speed of phase fine-tuning and energy supply of self-healing regulation, etc. Multi-dimensional strategy templates, similar to the intelligent configuration scheme library in the wind-light hydrogen storage integrated system, according to the value of the dynamic regulation urgency index, the system will select the appropriate mode combination from the library, in high urgency scenarios, high-intensity and fast-response combination strategies will be selected first, the activation priority of the self-healing regulation module and the monitoring frequency of the risk identification module will be strengthened; In low urgency scenarios, mild regulation mode is adopted to balance the intervention effect and energy consumption cost. The execution parameters of the four modules need to establish a quantitative mapping relationship between urgency and parameters to ensure that each parameter accurately matches the risk deterioration degree. The planning of activation timing needs to avoid action conflicts between modules to form an orderly collaborative link of risk identification, waveform adjustment, phase fine-tuning and self-healing regulation, so that the intervention effect of each module forms a superposition effect, and the overall repair efficiency is improved.

[0117] Step 63, monitor the deterioration rate change rate during the implementation of the customized intervention repair scheme, dynamically adjust the intervention intensity coefficient of the risk identification module, waveform adjustment module, phase fine-tuning module and self-healing regulation module, the specific operation is as follows:

[0118] In the implementation process of the customized intervention repair scheme, the stress state inside the battery will dynamically change with the execution of the intervention action, and the initially configured parameters may gradually deviate from the optimal interval, therefore, the deterioration rate change rate needs to be continuously monitored, which can directly reflect the implementation effect of the scheme, if the change rate is negative, it means that the risk deterioration is curbed, if it is positive, the scheme needs to be adjusted in time. The intervention intensity coefficient is the core action throughout the four modules, for the risk identification module, the coefficient adjustment affects the amplitude and frequency of the detection signal, for the waveform adjustment module, the coefficient determines the maximum amplitude of the compensation current, for the phase fine-tuning module, the coefficient is related to the strength of the phase compensation vector, and for the self-healing regulation module, the coefficient controls the energy density of the bias field. This dynamic adjustment mechanism draws on the closed-loop management logic of the non-invasive sleep regulation system, continuously corrects the execution parameters through real-time feedback monitoring data, avoids both the risk of risk spreading caused by insufficient intervention and the energy waste and battery damage caused by excessive intervention, and realizes the dynamic balance of intervention effect and operation cost.

[0119] In step 64, the execution efficiency indicators of the risk identification module, the waveform adjustment module, the phase fine-tuning module and the self-healing regulation module and the deterioration rate reduction of the stress margin risk map are analyzed, and the strategy parameters in the regulation mode library are updated, and the specific operation is as follows:

[0120] The execution efficiency indicator is a comprehensive parameter system for evaluating the working state of the four modules, covering the accuracy of risk identification, the response delay of waveform adjustment, the ripple suppression rate of phase fine-tuning and the repair success rate of self-healing regulation and other key dimensions. These indicators and the deterioration rate reduction of the stress margin risk map together constitute the evaluation standard of the strategy. In the analysis process, the system compares the actual execution effect with the expected target, if the performance indicators of a certain strategy combination are excellent and the deterioration rate reduction is significant, the strategy is retained and its parameter details are optimized, if the effect is not as expected, the analysis module cooperates with the defect and parameter deviation to adjust the parameter configuration in the strategy combination. This updating mechanism is similar to the data iteration logic of the poverty prevention monitoring system, through the continuous accumulation of actual operation data, the adaptability and accuracy of the regulation mode library are continuously improved, so that the subsequent customized scheme can better cope with the complex risk state of the battery in different life cycles and different working conditions, and promote the intelligent evolution of the entire intervention repair cycle.

[0121] The above describes only the preferred specific embodiments of the present application; however, the protection scope of the present application is not limited thereto. Any person skilled in the art can make equivalent replacements or changes to the technical solutions and improvement concepts of the present application within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A solid-state battery bidirectional DC-DC multi-objective optimization control system with integrated BMS, characterized in that, include: The acoustic library module is used to apply current pulses of different amplitudes and rates and to acquire high-frequency broadband acoustic response spectra. Analyze the propagation speed, attenuation characteristics and spectral changes of sound waves in multilayer structures, and establish a base library of acoustic features. The risk identification module is used to apply microcurrent probe pulses of a predetermined pattern and capture transient acoustic responses; it matches and performs differential analysis on the transient acoustic responses with an acoustic feature base library to identify high stress risk states and generate a stress margin risk map. The waveform adjustment module is used to adjust the time-domain waveform of the power pulse according to the distribution characteristics of the stress margin risk map and generate non-standard power waveform instructions. The phase fine-tuning module is used to monitor high-frequency current ripple and fine-tune the phase of the waveform corresponding to non-standard power waveform commands. The self-healing control module is used to insert a self-healing bias window and generate a small bias voltage based on the cumulative stress contribution determined by the stress margin risk map. The cyclic optimization module, based on the collaborative work of the risk identification module, waveform adjustment module, phase fine-tuning module and self-healing regulation module, forms an intervention and repair cycle; The deterioration rate of the stress margin risk map is used as a trade-off parameter for multi-objective optimization decision-making.

2. The solid-state battery bidirectional DC-DC multi-objective optimization control system with integrated BMS according to claim 1, characterized in that, The acoustic library building module is used for: A composite electrical excitation consisting of a series of continuously varying microsecond-level sub-pulses is applied, wherein the current amplitude is continuously scanned according to a predetermined nonlinear function, and each amplitude maintenance point is superimposed with a brief sinusoidal oscillation of a predetermined frequency. Acoustic signals are acquired while the composite electrical excitation is applied, and the acoustic signals are jointly solved to separate the individual contributions of longitudinal waves, transverse waves and their converted waves, and to calculate the acoustic polarization state of each wave type.

3. The solid-state battery bidirectional DC-DC multi-objective optimization control system with integrated BMS according to claim 2, characterized in that, The acoustic library building module is also used for: The propagation speed and attenuation coefficient of longitudinal waves, transverse waves and their converted waves are correlated with the transient response of the battery terminal voltage. The coupling coefficient between the rate of change of sound velocity of a specific acoustic mode and the rate of change of battery differential conductivity during each excitation time period is calculated to form an acoustic-electric coupling coefficient matrix. Based on the acoustic-electric coupling coefficient matrix, a corresponding dynamic stress transfer function is generated for each of the longitudinal waves, transverse waves, and their converted waves, thus forming an acoustic feature base library.

4. The solid-state battery bidirectional DC-DC multi-objective optimization control system with integrated BMS according to claim 3, characterized in that, The risk identification module is used for: Based on the acoustic feature base library, a microcurrent detection pulse sequence is generated, wherein the pulse sequence time length, phase distribution and sub-pulse carrier frequency are set according to the acoustic feature base library; While applying a microcurrent detection pulse sequence, multiple transient acoustic responses are captured; time-deconvolution operation is performed on the transient acoustic responses, and the original acoustic wave components are separated by using the acoustic wave propagation operator pre-stored in the acoustic feature base library as the deconvolution kernel.

5. The solid-state battery bidirectional DC-DC multi-objective optimization control system with integrated BMS according to claim 4, characterized in that, The risk identification module is also used for: For the original acoustic wave components, analyze the spatiotemporal evolution of their acoustic polarization state, calculate the coefficient of variation of the polarization ellipse principal axis orientation of each component over time, and the attenuation rate of the polarization coherence coefficient between components at different spatial locations, and obtain the polarization coherence loss value. The polarization coherence loss value is matched with the theoretical stress acoustic coupling model stored in the acoustic feature base library. The residual between the measured value and the theoretical value is calculated. Based on the spatial gradient distribution of the residual, a scalar potential field is synthesized to generate a stress margin risk map.

6. The solid-state battery bidirectional DC-DC multi-objective optimization control system with integrated BMS according to claim 5, characterized in that, The waveform adjustment module is used for: The spatial gradient distribution characteristics of the stress margin risk map are analyzed, quantified into a theoretical stress gradient tensor, and converted into a spatial pattern of expected ion flux distribution inhomogeneity. Based on the spatial pattern of ion flux distribution non-uniformity, a compensation current profile consisting of multiple non-uniform amplitude sub-pulses is constructed, wherein the temporal position, duration and amplitude envelope of each sub-pulse are configured according to the spatial pattern. The compensation current profile is decomposed into a set of waveform elements. The current-time integral and the current-square-time integral are calculated for each waveform element, and then equalization processing is performed. The equalized waveform unit sequence is reorganized according to its time position, and a phase smooth transition band is introduced between adjacent waveform units to synthesize a non-standard power waveform command.

7. The solid-state battery bidirectional DC-DC multi-objective optimization control system with integrated BMS according to claim 6, characterized in that, The phase fine-tuning module is used for: During the execution of non-standard power waveform commands, current signals are captured, high-frequency current ripple components are separated, and the envelope shape, fundamental frequency, and harmonic distribution of the high-frequency current ripple are extracted. The fundamental frequency and harmonic distribution of high-frequency current ripple are matched with data in the acoustic feature base library to identify key harmonic components and determine their phase polarity characteristics. A phase compensation vector is generated based on the key harmonic components and their phase polarity characteristics. Apply the phase compensation vector to non-standard power waveform commands to introduce continuous phase changes near the zero-crossing point of the waveform.

8. The solid-state battery bidirectional DC-DC multi-objective optimization control system with integrated BMS according to claim 7, characterized in that, The self-healing control module is used for: A spatiotemporal joint analysis of the stress margin risk map was performed, and the numerical changes of each spatial point were weighted and integrated along the time axis to obtain the stress cumulative distribution map. Key areas are identified based on the stress accumulation distribution map, and repair pulse parameters are generated for each key area. The set of repair pulse parameters is converted into a scanning bias field in three-dimensional space; During the self-healing bias window, a three-dimensional gradient scan bias field is applied, and the spatial orientation of the bias field is dynamically adjusted.

9. The solid-state battery bidirectional DC-DC multi-objective optimization control system with integrated BMS according to claim 8, characterized in that, The loop optimization module is used for: Calculate the deterioration rate vector of the stress margin risk map and generate a dynamic control urgency index. Based on the dynamic regulation urgency index, a combination of modes is selected from the regulation mode library, and execution parameters and activation timing are configured for the risk identification module, waveform adjustment module, phase fine-tuning module and self-healing regulation module to form a customized intervention and repair plan.

10. The solid-state battery bidirectional DC-DC multi-objective optimization control system with integrated BMS according to claim 9, characterized in that, The loop optimization module is also used for: During the implementation of customized intervention and repair programs, the rate of change in deterioration is monitored, and the intervention intensity coefficients of the risk identification module, waveform adjustment module, phase fine-tuning module, and self-healing regulation module are dynamically adjusted. Analyze the performance indicators of the risk identification module, waveform adjustment module, phase fine-tuning module, and self-healing control module, as well as the rate of deterioration reduction of the stress margin risk map, and update the strategy parameters in the control mode library.

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