Solid-state battery self-healing control method and device

Abstract

The invention provides a solid-state battery self-healing control method and device which are applied to a battery management system connected with a solid-state battery, the solid-state battery comprises a battery electrode, a nanoscale biological inspiration composite film and a solid-state electrolyte layer, and the nanoscale biological inspiration composite film comprises a sensing layer, a healing agent reservoir layer and a stimulation response layer. The sensing layer is connected with the battery management system; the method comprises the following steps: acquiring a real-time resistance signal of a sensing layer; determining the damage type and damage intensity of the solid-state battery based on the real-time resistance signal; determining repair parameters based on the damage type, the damage intensity, the current battery temperature and the current battery electric quantity; a pulse current is applied to the stimulus-responsive layer based on the repair parameters to cause the stimulus-responsive layer to generate heat, and the healing agent reservoir releases a healing agent to repair the damage. According to the method, the damage of the solid-state battery interface can be monitored in real time, and the damage can be repaired in time, so that instantly generated lithium dendrites are effectively inhibited.

Description

Technical Field

[0001] This invention relates to the field of power battery technology for new energy vehicles, and in particular to a method and device for controlling the self-healing of solid-state batteries. Background Technology

[0002] Solid-state batteries (SSBs) are considered the preferred system for next-generation power batteries due to their high energy density and high safety. However, during long-term charge-discharge cycles, the volume expansion / contraction of electrode materials (especially silicon-based anodes) (reaching over 300%) and the growth of lithium dendrites can lead to microcracks, pores, and interface delamination between the solid-state electrolyte (SSE) and the electrodes. These micro-damages drastically increase interfacial impedance, causing capacity decay and potentially forming short-circuit channels through which lithium dendrites pierce the electrolyte, triggering thermal runaway. Existing technologies mainly address interface issues through the following methods: (1) Interface modification layer: such as introducing an artificial SEI film (such as LiF, Li3N, etc.) between the lithium metal anode and SSE. However, this method is complicated and costly, and the modification layer is a static structure that cannot be regenerated once consumed or broken.

[0003] (2) Flexible electrolyte design: such as developing polymer / inorganic composite electrolytes to provide better mechanical compliance. Its flexibility is improved, but its self-healing ability is limited, and it has no active repair function for existing cracks.

[0004] (3) External pressure application: A constant pressure of several megapascals (MPa) is applied to the cell to maintain interfacial contact. This is a passive mechanical method that cannot repair chemical / electrochemical degradation and increases system complexity and weight.

[0005] In summary, existing technologies cannot respond to dynamically generated micro-damage. Damage can only be detected after macroscopic performance (such as capacity drop or internal resistance surge) deteriorates. Moreover, existing repair processes are mostly slow physical diffusion or require external stimuli (such as heating or light), resulting in slow response speeds and an inability to effectively suppress instantaneously generated lithium dendrites. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to provide a solid-state battery self-healing control method and device, which can monitor the damage to the solid-state battery interface in real time and repair it in a timely manner when damage occurs, effectively suppressing the instantaneous generation of lithium dendrites.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: In a first aspect, the present application provides a solid-state battery self-healing control method applied to a battery management system connected to a solid-state battery, the solid-state battery comprising a battery electrode, a nanoscale bio-inspired composite film, and a solid-state electrolyte layer, the nanoscale bio-inspired composite film comprising a sensing layer, a healing agent reservoir, and a stimulus-responsive layer, the sensing layer being connected to the battery management system; the method comprising: obtaining a real-time resistance signal of the sensing layer; determining a damage type and a damage intensity of the solid-state battery based on the real-time resistance signal; determining a repair parameter based on the damage type, the damage intensity, a current battery temperature, and a current battery power; wherein the repair parameter at least includes a current amplitude, a current frequency, and a current duration; and applying a pulse current to the stimulus-responsive layer based on the repair parameter, so that the stimulus-responsive layer generates heat and the healing agent reservoir releases a healing agent to repair the damage.

[0008] Optionally, determining the damage type and the damage intensity of the solid-state battery based on the real-time resistance signal comprises: calculating an instantaneous resistance change rate of the sensing layer based on the real-time resistance signal; determining that the solid-state battery has damage if the instantaneous resistance change rate is greater than a preset warning threshold; inputting the real-time resistance signal into a pre-trained damage identification network to obtain a probability value of the damage type of the solid-state battery, and determining the damage type of the solid-state battery based on the probability value of the damage type; and calculating the damage intensity of the solid-state battery based on the real-time resistance signal and an initial reference value of the resistance of the sensing layer pre-calibrated.

[0009] Optionally, after determining the damage type and the damage intensity of the solid-state battery based on the real-time resistance signal, the method further comprises: calculating a resistance growth rate of the sensing layer based on the real-time resistance signal and the initial reference value of the resistance of the sensing layer pre-calibrated; determining whether to trigger damage repair based on the instantaneous resistance change rate, the resistance growth rate, and the probability value of the damage type output by the damage identification network; and determining to trigger damage repair if the instantaneous resistance change rate is greater than a preset triggering threshold, or the resistance growth rate is greater than a first preset value, or a maximum value of the probability value of the damage type output by the damage identification network is greater than a second preset value.

[0010] Optionally, determining the repair parameter based on the damage type, the damage intensity, the current battery temperature, and the current battery power comprises: querying a preset repair strategy matrix based on the damage type, the damage intensity, the current battery temperature, and the current battery power to obtain an initial repair parameter; and correcting the initial repair parameter based on the current battery temperature and the current battery power to obtain a target repair parameter.

[0011] Optionally, correcting the initial repair parameter based on the current battery temperature and the current battery power to obtain the target repair parameter comprises: determining a temperature compensation coefficient based on the current battery temperature, and determining a power compensation coefficient based on the current battery power; and correcting the initial current amplitude based on the temperature compensation coefficient and the power compensation coefficient to obtain the current amplitude.

[0012] Optionally, the applying the pulse current to the stimulus-responsive layer based on the repair parameter comprises: calculating the heat generated by the sensing layer based on a predetermined Joule heat model, a current amplitude and a current duration; calculating a temperature rise value of the sensing layer based on a predetermined temperature rise model and the heat generated by the sensing layer; if the temperature rise value is not within a preset temperature range, determining a target current amplitude and a target current duration based on a pre-calibrated current-temperature-time relationship database; and applying the pulse current to the stimulus-responsive layer based on the target current amplitude, a current frequency and the target current duration.

[0013] Optionally, the method further comprises: after the repair is completed, obtaining a resistance recovery value of the sensing layer; calculating a repair effect index based on the resistance recovery value and the resistance failure value, and determining whether to perform secondary repair based on the repair effect index; if the repair effect index is greater than a third preset value, starting the secondary repair and determining a secondary repair parameter based on the repair parameter.

[0014] In a second aspect, the present application provides a solid-state battery self-healing control device applied to a battery management system connected with a solid-state battery, the solid-state battery comprising: a battery electrode, a nanoscale bio-inspired composite film and a solid-state electrolyte layer, the nanoscale bio-inspired composite film comprising: a sensing layer, a healing agent reservoir and a stimulus-responsive layer, the sensing layer being connected with the battery management system; the device comprising: a data acquisition module for obtaining a real-time resistance signal of the sensing layer; a damage diagnosis module for determining a damage type and a damage intensity of the solid-state battery based on the real-time resistance signal; a repair parameter determination module for determining a repair parameter based on the damage type, the damage intensity, a current battery temperature and a current battery power; wherein the repair parameter at least comprises: a current amplitude, a current frequency and a current duration; and a damage repair module for applying a pulse current to the stimulus-responsive layer based on the repair parameter, so that the stimulus-responsive layer generates heat and the healing agent reservoir releases a healing agent to repair the damage.

[0015] In a third aspect, the present application provides an electronic device comprising a processor and a memory, the memory storing computer executable instructions capable of being executed by the processor, and the processor executes the computer executable instructions to implement the steps of the method of any one of the first aspect.

[0016] In a fourth aspect, the present application provides a computer readable storage medium, the computer readable storage medium storing a computer program, and the computer program is executed by a processor to perform the steps of the method of any one of the first aspect.

[0017] The present application has the following beneficial effects: The application provides a self-healing control method and device for a solid-state battery.

[0018] Additional features and advantages of the application will be set forth in the description that follows, and in part will be apparent from the description, or can be learned by practice of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the description and claims.

[0019] In order to make the above-mentioned objectives, characteristics and advantages of the present application more apparent and easy to understand, the following preferred embodiments are described in detail below, and the accompanying drawings are used as reference. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0021] Figure 1 A structural schematic diagram of a solid-state battery provided by an embodiment of the present application; Figure 2 A flow chart of a solid-state battery self-healing control method provided for an embodiment of the present application is shown in FIG. 4; Figure 3 A schematic diagram of a repair strategy matrix provided for an embodiment of the present application is shown in FIG. 5; Figure 4 A schematic diagram of a solid-state battery self-healing control method provided for an embodiment of the present application is shown in FIG. 6; Figure 5 A structural schematic diagram of a solid-state battery self-healing control device provided for an embodiment of the present application is shown in FIG. 7; Figure 6 A structural schematic diagram of an electronic device provided for an embodiment of the present application is shown in FIG. 8.

[0022] Icon: 101 - positive electrode; 102 - solid-state electrolyte layer; 103 - nano-scale bio-inspired composite film; 104 - negative electrode; 105 - current collector; 107 - micro-crack; 108 - healing agent; 103a - stimulus-responsive layer; 103b - healing agent reservoir; 103c - sensing layer. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme of the present application will be described clearly and completely below with reference to the drawings. Obviously, the described embodiments are some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0024] Currently, all existing interface layers are "static" or "consumables", and cannot respond to dynamically generated micro-damage. The performance degradation of the battery is irreversible. There is a lack of real-time, in-situ monitoring of the micro-interface state inside the battery, and problems can only be found after the macro performance (such as capacity drop and internal resistance surge) declines. The existing repair process is mostly slow physical diffusion or requires external stimulation (such as heating and light), and the response speed is slow, which cannot effectively inhibit the instantaneous generation of lithium dendrites. The self-healing process is independent of the charge and discharge management and thermal management of the battery, which may cause interference with normal charging and discharging during the repair process, or low energy efficiency.

[0025] Based on this, the solid-state battery self-healing control method and device provided in the embodiments of the present application can monitor the damage of the interface of the solid-state battery in real time, and repair it in time when the damage occurs, effectively inhibiting the instantaneous generation of lithium dendrites.

[0026] In order to facilitate the understanding of the present embodiment, first, a solid-state battery disclosed in the embodiments of the present application will be described in detail, as shown in FIG. 1. Figure 1As shown, the solid-state battery includes: battery electrodes (positive electrode 101 and negative electrode 104), nanoscale bio-inspired composite film 103, solid-state electrolyte layer 102, current collector 105; the nanoscale bio-inspired composite film 103 includes: a stimulus-responsive layer 103a, a healing agent reservoir 103b, and a sensing layer 103c connected with a battery management system, and the healing agent reservoir 103b stores a healing agent 108.

[0027] In specific implementation, the solid-state battery is a nanoscale bio-inspired composite film enabled automotive solid-state battery self-healing structure, which includes: battery electrodes (such as lithium metal negative electrode or silicon-carbon composite negative electrode); nanoscale bio-inspired composite film (NBICM): coated on the surface of the electrode as the core functional layer; solid-state electrolyte (SSE) layer: ceramic electrolyte such as LLZO, LATP, or sulfide electrolyte.

[0028] The NBICM is a sandwich-structured multilayer composite film with a total thickness of 50-500 nm, which includes: (1) Sensing layer 103c (50-100 nm): the innermost layer, in contact with the electrode. It is composed of graphene quantum dots (GQDs) and MXene (Ti3C2Tx) nanosheet interwoven network as a sensitive medium. Its resistivity will change sensitively with the change of interfacial contact stress (i.e. the decrease of pressure caused by micro-crack).

[0029] (2) Healing agent reservoir 103b (200-300 nm): middle layer. It is composed of a thermoplastic polyurethane (TPU) matrix rich in microcapsules. The microcapsule shell material is polyurea formaldehyde (PUF), and the core material is a low-viscosity, high-ionic-conductivity liquid electrolyte (such as 1M LiPF6 in EC / DEC) and lithium salt precursor (such as lithium bis-trifluoromethanesulfonimide, LiTFSI).

[0030] (3) Stimulus-responsive layer 103a (50-100 nm): the outermost layer, in contact with the SSE. It is composed of polyvinylidene fluoride-hexafluoropropylene (PVDF-HFP) doped with joule heat material (ultrafine carbon nanotubes CNTs) and shape memory polymer (SMP) micro-elements. This layer can generate rapid and local heat when subjected to a specific electrical stimulus.

[0031] The sensing layer 103c is connected to a multifunctional battery management system (MF-BMS) through a micro-wire. The MF-BMS integrates: (1) High-precision impedance analysis module: used to monitor the resistance value of the NBICM sensing layer in real time (R(t)) ).

[0032] (2) Damage diagnosis algorithm unit: built-in machine learning-based algorithm for analyzing the change rate of the resistance value of the NBICM sensing layer (dR(t) / dt) ) and the change pattern, distinguishing whether it is a slow creep or a crack rapid expansion, and estimating the damage intensity (D).

[0033] (3) Synergistic control decision maker: according to the diagnosis result, the current battery SOC, temperature (T) and load state, decide whether to trigger repair and repair parameters.

[0034] (4) Pulse current source: can output pulse current with controllable amplitude (I), , frequency (f) f and duration (t) .

[0035] Next, a solid-state battery self-healing control method disclosed in the embodiment of the application is introduced in detail. The method is applied to a battery management system connected with the solid-state battery. Referring to the flow chart of a solid-state battery self-healing control method shown in Figure 2 , it is shown that the method mainly includes the following steps S201 to S204: Step S201: Obtain the real-time resistance signal of the sensing layer.

[0036] In an embodiment, the multifunctional battery management system (MF-BMS) collects the resistance signal of the NBICM sensing layer in real time at a frequency of 100 Hz . The initial reference value of the sensing layer resistance After the battery is assembled, it is calibrated by first measurement under rated pressure and stored to the MF-BMS.

[0037] Step S202: Determine the damage type and damage intensity of the solid-state battery based on the real-time resistance signal.

[0038] In an embodiment, the MF-BMS calculates the instantaneous resistance change rate of the sensing layer according to the real-time resistance signal, and judges whether the damage occurs at the battery interface according to the resistance point change rate, and determines the damage type and damage intensity of the solid-state battery according to the real-time resistance signal and the pre-trained damage recognition network.

[0039] Step S203: Determine the repair parameters based on the damage type, damage intensity, current battery temperature and current battery power.

[0040] In an embodiment, the MF-BMS can detect whether to trigger damage repair in real time, and when the repair is triggered, the repair parameters can be determined according to the damage type, damage intensity, current battery temperature and current battery power. Among them, the repair parameters at least include: current amplitude, current frequency and current duration.

[0041] Step S204: Apply pulse current to the stimulus response layer based on the repair parameters, so that the stimulus response layer generates heat, and the healing agent reservoir releases healing agent to repair the damage.

[0042] In an embodiment, the pulse current source of the MF-BMS can apply a pulse current to the stimulus-responsive layer according to the repair parameters. Specifically, the MF-BMS outputs a customized pulse current, which flows through the outermost stimulus-responsive layer (PVDF-HFP+CNTs+SMP microelement) of the NBICM. Carbon nanotubes (CNTs) act as a Joule heat material and quickly convert electrical energy into heat energy under the action of the current. The system precisely controls the local temperature rise of the stimulus-responsive layer to a medium temperature (about 70 degrees) by controlling the amplitude, frequency and duration of the pulse current. This temperature does not damage other components of the battery. The local heat is transferred to the healing agent reservoir (TPU matrix+PUF microcapsule) in the middle layer. The thermoplastic polyurethane (TPU) matrix softens under heat, reducing the binding of the microcapsule and providing a channel for the flow of healing agent. The polyurea formaldehyde (PUF) microcapsule shell material ruptures under the combined action of temperature and interfacial stress, releasing the internal core material. The released core material quickly flows through the softened TPU matrix to precisely fill the damage (microcracks, pores or peeling gaps) between the electrode and the solid-state electrolyte. The liquid electrolyte quickly infiltrates the damaged area, re-establishes the ion transport path, and reduces the interfacial impedance. Lithium salt precursors quickly dissociate under the action of an electric field and temperature, supplementing the lithium source required for ion conduction and strengthening the interfacial ion conductivity. The shape memory polymer (SMP) microelement restores the preset shape after being heated, assisting in extruding the healing agent to fill the gap and promoting the electrode and solid-state electrolyte to re-closely adhere, thereby consolidating the repair effect.

[0043] The above-mentioned solid-state battery self-healing control method provided by the embodiments of the present application is applied to a battery management system connected with the solid-state battery. The solid-state battery includes a nanoscale bio-inspired composite film, which includes a sensing layer, a healing agent reservoir and a stimulus-responsive layer. The sensing layer is connected with the battery management system, so that the resistance value of the sensing layer can be monitored through the battery management system, and whether damage occurs at the interface of the solid-state battery is monitored according to the resistance value of the sensing layer. After the damage is monitored, the repair parameters can be determined according to the damage type, damage intensity, current battery temperature and current battery power, and a pulse current is applied to the stimulus-responsive layer according to the repair parameters, so that the stimulus-responsive layer generates heat to locally heat up, and the healing agent reservoir releases healing agent to repair the damage. The above-mentioned method can monitor the damage of the interface of the solid-state battery in real time, and repair the damage in time when the damage occurs, thereby effectively inhibiting the instantaneous lithium dendrite.

[0044] In an embodiment, for the foregoing step S202, when determining the damage type and damage intensity of the automobile solid-state battery based on the real-time resistance signal, the following methods can be used, but are not limited to: Firstly, based on the real-time resistance signal, the instantaneous resistance change rate of the sensing layer is calculated; if the instantaneous resistance change rate is greater than the preset warning threshold, it is determined that the solid-state battery is damaged.

[0045] In specific implementation, the MF-BMS calculates the instantaneous resistance change rate of the sensing layer according to the acquired real-time resistance signal: , wherein, = 0.01 s. The warning threshold is set in advance (based on a large amount of experimental data fitting), if the instantaneous resistance change rate , it indicates that a small damage begins to form. Wherein, may be 0.1 Ω / s.

[0046] Secondly, the real-time resistance signal is input into the pre-trained damage identification network to obtain the probability value of the damage type of the solid-state battery, and the damage type of the solid-state battery is determined based on the probability value of the damage type.

[0047] In specific implementation, the time sequence window data with length L=100 is input into the damage identification network. The output feature map of the first layer convolution operation (taking the first layer as an example) is: l , wherein, is the first feature value of the th feature value of the output feature map of the first layer convolution operation (taking the first layer as an example) is: , wherein, is the weight vector (size = 5) of the m th convolution kernel, is the corresponding bias. is the ReLU activation function: . After pooling and full connection layer, the output layer calculates the probability of each damage class by the Softmax function: , wherein, is the value of the th node of the output layer, and the final output vector of the damage identification network is: . Wherein, represents the probability of the current battery internal microcrack, represents the probability of the current battery internal lithium dendrite growth, represents the probability of the current battery internal interface peeling. In the implementation of the present application, the type with the highest probability value can be determined as the damage type of the solid-state battery.

[0048] Further, the embodiment of the present application also provides a training process of the damage identification network, in which specific damages are introduced on different types of batteries through mechanical indentation, overcharge and the like in the laboratory, and Rs time sequence data is synchronously collected to form a training data set (each data is an Rs sequence with a length of 100 (i.e. 1 second), and the damage type is labeled).

[0049] The network structure of the damage identification network is as follows: an input layer: Input (100, 1) / / Rs values at 100 time points, a convolution layer 1: Conv1D (filters = 32, kernel_size = 5, activation ='relu'), a pooling layer 1: MaxPooling1D (pool_size = 2), a convolution layer 2: Conv1D (filters = 64, kernel_size = 3, activation ='relu'), a pooling layer 2: MaxPooling1D (pool_size = 2), a flattening layer: Flatten (), a fully connected layer: Dense (units = 100, activation ='relu'), and an output layer: Dense (units = 3, activation ='softmax') / / output probabilities of three damage types.

[0050] TensorFlow framework, Adam optimizer, learning rate 0.001, and cross-entropy loss function are used to train, and then the trained model is quantized and deployed to a DSP.

[0051] Finally, based on the real-time resistance signal and the initial reference value of the resistance of the sensing layer labeled in advance, the damage intensity of the solid-state battery is calculated.

[0052] In the embodiment of the present application, the damage intensity can be quantified as follows: wherein k is a proportional coefficient, which can be set as 2.0 for a crack and 1.5 for a dendrite.

[0053] In one embodiment, after determining the damage type and damage intensity of the solid-state battery based on the real-time resistance signal, the method further comprises: first, calculating the resistance growth rate of the sensing layer based on the real-time resistance signal and the initial reference value of the resistance of the sensing layer labeled in advance; and then, judging whether to trigger damage repair based on the instantaneous resistance change rate, the resistance growth rate and the probability value of the damage type output by the damage identification network; if the instantaneous resistance change rate is greater than a preset triggering threshold, or the resistance growth rate is greater than a first preset value, or the maximum value of the probability value of the damage type output by the damage identification network is greater than a second preset value, it is determined that damage repair is triggered.

[0054] In practical implementation, the resistance growth rate of the sensing layer is calculated using the real-time resistance signal of the MF-BMS and the pre-calibrated initial reference value of the sensing layer's resistance. Then, by combining the instantaneous resistance change rate, the resistance growth rate, and the probability value of the damage type output by the damage recognition network, it is determined whether to trigger damage repair. Specifically, condition one: the instantaneous resistance change rate is greater than a preset trigger threshold. Or the resistance growth rate is greater than the first preset value. Condition 2: The maximum value of the probability value of the damage type output by the damage recognition network is greater than the second preset value. Damage repair is triggered immediately when either condition one or condition two is met.

[0055] In one implementation, after triggering damage repair, the MF-BMS can determine the repair parameters. Specifically, for the aforementioned step S203, i.e., when determining the repair parameters based on the damage type, damage intensity, current battery temperature, and current battery charge, the following methods can be used, including but not limited to: First, based on the damage type, damage intensity, current battery temperature, and current battery charge, a preset repair strategy matrix is ​​queried to obtain initial repair parameters; then, based on the current battery temperature and current battery charge, the initial repair parameters are corrected to obtain the target repair parameters.

[0056] In practical implementation, MF-BMS can query a preset repair strategy matrix based on damage type, damage intensity, current battery temperature, and current battery charge (see [link]). Figure 3 As shown (for illustrative purposes only), the initial repair parameters are obtained. The repair parameters in the repair strategy matrix are not fixed and can be modified according to actual needs. Based on this, in this embodiment of the invention, a temperature compensation coefficient can be determined based on the current battery temperature, and a power compensation coefficient can be determined based on the current battery power; then, based on the temperature compensation coefficient and the power compensation coefficient, the initial current amplitude is corrected to obtain the current amplitude.

[0057] Specifically, the initial current amplitude is obtained by querying the preset repair strategy matrix: (For example: dendrites: 50 mA / cm²; cracks: 20 mA / cm²); temperature compensation coefficient is: ,in,( α The attenuation coefficient is... The optimal temperature is 25°C. The maximum allowable temperature is 50°C; the power compensation factor is: ,in,( β The attenuation coefficient is... This is 10% of the minimum SOC threshold. Based on this, the final executed current amplitude is: ,frequency f and duration The determination is mainly based on the damage type, which can be directly looked up in a table.

[0058] In one implementation, the current amplitude is calculated solely from the model ( ) and duration ( However, this does not guarantee that the local temperature of the stimulus-response layer will exactly reach the preset temperature range of 60-80°C. Therefore, in actual implementation, the current amplitude and duration must be adjusted and calibrated. Based on this, for the aforementioned step S204, that is, when applying a pulsed current to the stimulus-response layer based on the repair parameters, the following methods can be used, including but not limited to: First, calculate the heat generated by the sensing layer based on a predetermined Joule heating model, current amplitude, and current duration; then, calculate the temperature rise of the sensing layer based on a predetermined temperature rise model and the heat generated by the sensing layer; if the temperature rise is not within the preset temperature range, determine the target current amplitude and target current duration based on a pre-calibrated current-temperature-time relationship database; finally, apply a pulsed current to the stimulus-response layer based on the target current amplitude, current frequency, and target current duration.

[0059] In practical implementation, the Joule heating model states that the heat generated when a pulsed current is applied to the stimulus-response layer... Q Determined by Joule's law: ,in, It is the equivalent resistance of the stimulus-response layer, which is related to the content and distribution of CNTs and can be measured and calibrated in advance through experiments.

[0060] Temperature rise model: The heat generated by the stimulus-response layer is used to raise the local material temperature, and the approximate relationship is as follows: ,in: It is the mass of heat received. It is the specific heat capacity of the material. It is the material density. It is the cross-sectional area of ​​the current path. It refers to the thickness of the stimulus-response layer.

[0061] If the calculated temperature rise is not within the preset temperature range, the target current amplitude and duration are determined using a pre-calibrated current-temperature-time relationship database, and then controlled... and Precisely control the temperature rise Within the target range (60-80°C).

[0062] In this invention, the actual temperature rise of the NBICM layer on the battery sample is monitored in real time when pulse currents with different parameters are applied using devices such as infrared thermal imagers. Then, through a large number of experiments, a database of the correspondence between current / time / temperature is established.

[0063] In an embodiment, in order to improve the repair effect, the above method further comprises: after the repair is completed, obtaining a resistance recovery value of the sensing layer; based on the resistance recovery value and the resistance failure value, calculating a repair effect index, and based on the repair effect index, determining whether to perform secondary repair; if the repair effect index is greater than a third preset value, starting the secondary repair, and determining a secondary repair parameter based on the repair parameter.

[0064] In specific implementation, after the repair is completed, the recovery value of the sensing layer is monitored , and a repair effect index is calculated. The repair effect index is defined as: , wherein, is the resistance value at the time of failure. Ideally, , that is, the damage is completely recovered. If the resistance value is within , , that is, , secondary repair is started, and , , and the incremental rule can avoid side effects caused by over-repair.

[0065] The above method provided by the embodiment of the present application realizes in-situ real-time monitoring of micron-level interface damage, can perform damage early warning, and the early warning time is more than 100 cycles earlier than macroscopic capacity decay; the response time from damage occurrence to repair triggering is less than 10 ms, the repair action completion time is in the order of milliseconds to seconds, which is much higher than passive diffusion repair (in the order of hours), and the interface impedance after repair can be recovered to within 105% of the initial value; under the condition of 1C rate and 100% DOD cycle, the capacity retention rate of the NCM811|Li solid-state battery using the structure of the present application is improved from 70% of the traditional structure to more than 90% after 2000 cycles; the inhibition efficiency of lithium dendrites is greater than 99%, and the battery passes abuse tests such as needle puncture and heavy object impact without thermal runaway; in cooperation with the BMS, the repair strategy is based on adaptive optimization of multiple state inputs, and the energy consumption of single repair is less than 0.1% of the energy of single cycle of the battery, and the energy efficiency ratio is high.

[0066] For ease of understanding, the embodiment of the present application further provides a preparation method of a solid-state battery, mainly comprising: (1) sensing layer preparation Graphene quantum dots (GQDs): prepared by an electrochemical method, with a size of 3-8 nm, and a surface rich in -COOH functional groups.

[0067] MXene (Ti3C2Tx): obtained by ultrasonic stripping after etching Ti3AlC2 MAX phase by HF, with a single-layer thickness of about 1.5 nm and a lateral size of 1-3 μm.

[0068] ​Composite slurry: GQDs and MXene were dispersed in N-methyl pyrrolidone (NMP) with a mass ratio of 1:3, concentration of 5 mg / mL, and ultrasonic treatment for 2 hours to form a uniform ink.

[0069] Coating: The prepared composite slurry was coated on the surface of the pretreated anode by ultrasonic spraying under argon protection. The area specific capacity was set to 0.05 mAh / cm², and the sample was vacuum dried at 80°C for 12 hours. The initial sheet resistance of the layer was about 50-100 Ω / s.

[0070] (2) Preparation of healing agent reservoir Microcapsule synthesis: In-situ polymerization method was used. The aqueous phase was a 1 wt% ethylene-maleic anhydride copolymer (EMA) solution; the oil phase was a mixture of urea-formaldehyde prepolymer (wall material precursor) and core material solution (liquid electrolyte: LiTFSI = 4:1 by weight). The oil phase was added dropwise to the water phase under stirring at 45°C and 500 rpm, the pH was adjusted to 3.5 with 20% citric acid, and the reaction was carried out for 4 hours. The average particle size of the obtained microcapsules was 2-5 μm, and the wall thickness was 0.5-1 μm. The core material content was >85%.

[0071] Slurry preparation: The above microcapsules and thermoplastic polyurethane (TPU, BASF 1185A) particles were dispersed in DMF solvent with a mass ratio of 6:4, the solid content was 20%, and the ball milling mixing time was 6 hours.

[0072] Coating: Slit extrusion coating was performed on the sensing layer, the wet film thickness was 150 μm, and then the sample was cured at 60°C for 24 hours to volatilize the solvent and form a tough film.

[0073] (3) Preparation of stimulus-responsive layer Slurry preparation: PVDF-HFP (Solvay 21216) was dissolved in acetone to prepare an 8 wt% solution. Multi-walled carbon nanotubes (CNTs, NC7000, purified by mixed acid and shortened to an average length of 5 μm) were added in an amount of 8% of the mass of PVDF-HFP. Shape memory polyurethane (SMP) micro powder (DiAPLEX MM4520, particle size ~ 10 μm) was added in an amount of 15% of the mass of PVDF-HFP. High-speed shearing emulsification (10000 rpm, 30 min) was used to disperse them uniformly.

[0074] Coating: The sample was coated on the reservoir by doctor blade coating, the wet film thickness was 100 μm, and the sample was dried and shaped at 50°C.

[0075] (4) Integration and battery assembly The negative electrode coated with intact NBICM, solid-state electrolyte sheet (e.g. LLZO with thickness of 40 pm), and positive electrode sheet were stacked in order, 10 MPa pressure was applied for 5 minutes, then put into an aluminum plastic packaging bag, a small amount of electrolyte was injected (only wet the interface, 0.1 g / Ah), vacuum packaging to form a quasi-solid-state battery.

[0076] (5) MF-BMS hardware configuration Master chip: TITM S320F28379D dual-core DSP, 200 MHz, used to run control algorithm.

[0077] Impedance analysis IC: ADI AD5934, used to excite and measure Rs, measurement frequency range 1 Hz-100 kHz, accuracy 1%.

[0078] Pulse current source: H-bridge circuit based on GaN FETs, supporting bidirectional, amplitude 0-100 mA / cm2 (adjustable), frequency DC-10 kHz, minimum pulse width 100 ps current output.

[0079] Communication: support CANFD communication with the whole vehicle.

[0080] Prepare the battery according to the above method and assemble the system. Referring to Figure 4 After power-on, the MF-BMS starts to cyclically execute the perception-diagnosis-decision-implementation-correction process: 201: When a micro-crack 107 is generated at the interface due to negative electrode swelling during battery cycling, the conductive network of the sensing layer 103c at this point is broken, causing the overall Rs to start rising.

[0081] 202: The high-precision impedance analysis module of the MF-BMS detects that Rs has risen from the initial to within 10 ms.

[0082] 203: The damage diagnosis algorithm unit calculates: ( much larger than ), ( greater than 0.2), and at the same time, the 1D-CNN model outputs the probability vector [0.85, 0.10, 0.05].

[0083] 204: Trigger condition judgment: Condition 1 (2000>1 OR 0.25>0.2) is True, immediately trigger repair. Parameter decision: the cooperative control decision maker determines that the crack (Type=crack), damage strength . Query the strategy matrix to get the basic parameters , , . Assuming current T=25°C, SOC=50%, compensation coefficients C T =1, C SOC =1, the final repair parameters are , , .

[0084] 205: The pulse current source outputs the above parameters pulse. Assuming that the measured , the generated heat . The heat acts on the local area (assuming mass m≈10 -6 kg, c≈1000J / kg·K), the estimated temperature rise . This estimate is low, indicating that in practice the heat will be more concentrated in a smaller local area (such as the crack tip), and considering heat loss, the actual model needs to be accurately calibrated by experiment to determine the current-temperature relationship curve, and on this basis to control. Among them, through the infrared thermal imager, it is calibrated that: to reach 70°C, the required needs to be sustained for about .

[0085] 206: The NBICM stimulation response layer receives the pulse to generate Joule heat.

[0086] 207: Closed-loop feedback: after repair, drops and stabilizes at . Calculate , since 0.08>0.05, the effect is not good. The decision maker starts the second repair, and the parameter is enhanced to , . After the second repair, stabilizes at 80.4Ω, , and the repair is successful.

[0087] The embodiment of the present application provides a scalable and cost-controllable battery preparation method, which gives the interface a rapid autonomous healing ability, can start repair in a very short time (milliseconds to seconds) after micro-damage occurs, effectively inhibits the growth of interface impedance and lithium dendrite expansion; An intelligent control strategy deeply cooperates with BMS, which can realize in-situ and real-time monitoring of solid-state battery interface micro-cracks and pores, and gives early warning before macroscopic performance degradation; According to the battery state (SOC, temperature, health state SOH), damage strength and type, the repair trigger time, repair strength and working mode are adaptively decided, and the balance between maximum repair efficiency and minimum energy consumption is realized.

[0088] For the solid-state battery self-healing control method provided by the foregoing embodiment, an embodiment of the present application further provides a solid-state battery self-healing control device, which is applied to a battery management system connected with a solid-state battery, and the solid-state battery comprises a battery electrode, a nanoscale bio-inspired composite film and a solid-state electrolyte layer, the nanoscale bio-inspired composite film comprises a sensing layer, a healing agent storage layer and a stimulus-responsive layer, and the sensing layer is connected with the battery management system; see Figure 5 As shown in a structural schematic diagram of a solid-state battery self-healing control device, the device mainly comprises the following parts: A data acquisition module 501 is configured to acquire a real-time resistance signal of the sensing layer.

[0089] A damage diagnosis module 502 is configured to determine a damage type and a damage intensity of the solid-state battery based on the real-time resistance signal.

[0090] A repair parameter determination module 503 is configured to determine a repair parameter based on the damage type, the damage intensity, a current battery temperature and a current battery power, wherein the repair parameter at least comprises a current amplitude, a current frequency and a current duration.

[0091] A damage repair module 504 is configured to apply a pulse current to the stimulus-responsive layer based on the repair parameter, so that the stimulus-responsive layer generates heat and the healing agent storage layer releases a healing agent to repair the damage.

[0092] The above-mentioned solid-state battery self-healing control device provided by the embodiment of the present application is applied to a battery management system connected with a solid-state battery, and the solid-state battery comprises a nanoscale bio-inspired composite film, the nanoscale bio-inspired composite film comprises a sensing layer, a healing agent storage layer and a stimulus-responsive layer, and the sensing layer is connected with the battery management system, so that the resistance value of the sensing layer can be monitored through the battery management system, and whether damage occurs at the interface of the solid-state battery can be monitored according to the resistance value of the sensing layer, after the damage is monitored, the repair parameter can be determined according to the damage type, the damage intensity, the current battery temperature and the current battery power, and the pulse current is applied to the stimulus-responsive layer according to the repair parameter, so that the stimulus-responsive layer generates heat to locally heat up, and the healing agent storage layer releases a healing agent to repair the damage. The above-mentioned method can monitor the damage of the interface of the solid-state battery in real time, and repair the damage in time when the damage occurs, thereby effectively inhibiting the lithium dendrite generated instantaneously.

[0093] It should be noted that the device provided by the embodiment of the present application has the same implementation principle and technical effects as the foregoing method embodiment, and for brevity, the part not mentioned in the device embodiment can be referred to the corresponding content in the foregoing method embodiment. The specific numerical values provided in the embodiment of the present application are only exemplary and are not limited herein.

[0094] The embodiment of the present application further provides an electronic device, and specifically, the electronic device comprises a processor and a storage device; the storage device stores a computer program, and the computer program performs the method according to any one of the above embodiments when the computer program is run by the processor.

[0095] Figure 6 A structural schematic diagram of an electronic device provided by the embodiment of the present application is shown in the figure, and the electronic device 100 comprises a processor 60, a memory 61, a bus 62 and a communication interface 63, the processor 60, the communication interface 63 and the memory 61 are connected through the bus 62; the processor 60 is used for executing an executable module stored in the memory 61, for example, a computer program.

[0096] The memory 61 can contain a high-speed random access memory (RAM), and can also contain a non-volatile memory, for example, at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 63 (which can be wired or wireless), and the Internet, a wide area network, a local area network, a metropolitan area network and the like can be used.

[0097] The bus 62 can be an ISA bus, a PCI bus or an EISA bus and the like. The bus can be divided into an address bus, a data bus, a control bus and the like. For the convenience of representation, Figure 6 Only one bidirectional arrow is used in the figure, but it does not mean that there is only one bus or only one type of bus.

[0098] The memory 61 is used for storing a program, and the processor 60 executes the program after receiving an execution instruction; the method executed by the device defined by the flow process disclosed in any one of the above embodiments can be applied to the processor 60 or realized by the processor 60.

[0099] The processor 60 can be an integrated circuit chip with signal processing capability. In implementation, each step of the above method can be completed by integrated logic circuit of hardware in the processor 60 or by instructions in the form of software. The processor 60 described above can be a general processor, including a central processing unit (CPU), a network processor (NP), etc.; can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component. Each method, step and logic block diagram disclosed in the embodiments of the present application can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as a hardware code processor for execution, or a combination of hardware and software modules in the code processor for execution. The software module can be located in a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium in the art. The storage medium is located in the memory 61, and the processor 60 reads the information in the memory 61, and combines the hardware to complete the steps of the above method.

[0100] The computer program product of the readable storage medium provided by the embodiments of the present application comprises a computer readable storage medium storing program codes, and the program codes comprise instructions for executing the method described in the foregoing method embodiments. For specific implementation, reference can be made to the foregoing method embodiments, which will not be described here.

[0101] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the present application that essentially contribute to the prior art or the parts of the technical solutions can be embodied in the form of software products. The computer software product is stored in a storage medium and includes instructions for making a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in the embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0102] Finally, it should be noted that: the above-described embodiments are only specific embodiments of the present application, which are used to illustrate the technical solutions of the present application, but not to limit them. The protection scope of the present application is not limited to this. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily think of changes to the technical solutions recorded in the foregoing embodiments within the technical range disclosed by the present application, or make equivalent replacements to some technical features. The modifications, changes or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for controlling the self-healing of solid-state batteries, characterized in that, A battery management system connected to a solid-state battery, the solid-state battery comprising: battery electrodes, a nanoscale bio-inspired composite membrane, and a solid electrolyte layer, the nanoscale bio-inspired composite membrane comprising: a sensing layer, a healing agent reservoir, and a stimulus-responsive layer, the sensing layer being connected to the battery management system; the method comprising: Obtain the real-time resistance signal of the sensing layer; The damage type and damage intensity of the solid-state battery are determined based on the real-time resistance signal. Based on the damage type, the damage intensity, the current battery temperature, and the current battery charge, repair parameters are determined; wherein, the repair parameters include at least: current amplitude, current frequency, and current duration; A pulsed current is applied to the stimulus-response layer based on the repair parameters to generate heat in the stimulus-response layer, and the healing agent reservoir releases healing agent to repair the damage.

2. The method according to claim 1, characterized in that, Determining the damage type and damage intensity of the solid-state battery based on the real-time resistance signal includes: Based on the real-time resistance signal, the instantaneous resistance change rate of the sensing layer is calculated; If the instantaneous resistance change rate is greater than a preset warning threshold, then the solid-state battery is determined to be damaged. The real-time resistance signal is input into a pre-trained damage identification network to obtain the probability value of the damage type of the solid-state battery, and the damage type of the solid-state battery is determined based on the probability value of the damage type. The damage intensity of the solid-state battery is calculated based on the real-time resistance signal and the initial reference value of the resistance of the pre-calibrated sensing layer.

3. The method according to claim 2, characterized in that, After determining the damage type and damage intensity of the solid-state battery based on the real-time resistance signal, the method further includes: Based on the real-time resistance signal and the pre-calibrated initial reference value of the resistance of the sensing layer, the resistance growth rate of the sensing layer is calculated. Based on the instantaneous resistance change rate, the resistance growth rate, and the probability value of the damage type output by the damage identification network, it is determined whether damage repair should be triggered. If the instantaneous resistance change rate is greater than a preset trigger threshold, or the resistance growth rate is greater than a first preset value, or the maximum value of the probability value of the damage type output by the damage identification network is greater than a second preset value, then damage repair is determined to be triggered.

4. The method according to claim 1, characterized in that, Based on the damage type, damage intensity, current battery temperature, and current battery charge, repair parameters are determined, including: Based on the damage type, damage intensity, current battery temperature, and current battery charge, a preset repair strategy matrix is ​​queried to obtain initial repair parameters; Based on the current battery temperature and the current battery charge, the initial repair parameters are corrected to obtain the target repair parameters.

5. The method according to claim 4, characterized in that, Based on the current battery temperature and the current battery charge, the initial repair parameters are corrected to obtain the target repair parameters, including: A temperature compensation coefficient is determined based on the current battery temperature, and a power compensation coefficient is determined based on the current battery power level. Based on the temperature compensation coefficient and the power compensation coefficient, the initial current amplitude is corrected to obtain the current amplitude.

6. The method according to claim 1, characterized in that, Applying a pulsed current to the stimulus-response layer based on the repair parameters includes: The heat generated by the sensing layer is calculated based on a predetermined Joule thermal model, the current amplitude, and the current duration. Based on a predetermined temperature rise model and the heat generated by the sensing layer, the temperature rise of the sensing layer is calculated. If the temperature rise is not within the preset temperature range, the target current amplitude and target current duration are determined based on the pre-calibrated current-temperature-time relationship database. A pulsed current is applied to the stimulus response layer based on the target current amplitude, the current frequency, and the target current duration.

7. The method according to claim 1, characterized in that, Also includes: After the repair is completed, the resistance recovery value of the sensing layer is obtained; Based on the resistance recovery value and the resistance fault value, a repair effect index is calculated, and a second repair is performed based on the repair effect index. If the repair effect index is greater than the third preset value, then a second repair is initiated, and the second repair parameters are determined based on the repair parameters.

8. A solid-state battery self-healing control device, characterized in that, A battery management system connected to a solid-state battery, the solid-state battery comprising: battery electrodes, a nanoscale bio-inspired composite membrane, and a solid electrolyte layer, the nanoscale bio-inspired composite membrane comprising: a sensing layer, a healing agent reservoir, and a stimulus-responsive layer, the sensing layer being connected to the battery management system; the device comprising: The data acquisition module is used to acquire the real-time resistance signal of the sensing layer; The damage diagnosis module is used to determine the damage type and damage intensity of the solid-state battery based on the real-time resistance signal. The repair parameter determination module is used to determine repair parameters based on the damage type, the damage intensity, the current battery temperature, and the current battery charge; wherein the repair parameters include at least: current amplitude, current frequency, and current duration; The damage repair module is used to apply a pulsed current to the stimulus response layer based on the repair parameters, so that the stimulus response layer generates heat and the healing agent reservoir releases healing agent to repair the damage.

9. An electronic device, characterized in that, The method includes a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program thereon, characterized in that, The computer program is executed by the processor to perform the steps of the method described in any one of claims 1 to 7.

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