Temperature rise fusion risk perception battery low-temperature pulse heating control device and equipment

Abstract

The invention belongs to the field of lithium ion batteries, and provides a temperature rise fusion risk perception battery low-temperature pulse heating control device and equipment in order to solve the problem that an existing blood relationship data analysis process is complex. The device comprises a time sequence input matrix construction module; embedding a sequence vector forming module; a sequence feature extraction module; a time sequence converter model training module and a transient pulse current control module; and the transient pulse current control module is used for calculating a first-order difference item and a second-order difference item of an internal temperature predicted value sequence based on a battery internal temperature sequence value predicted by a trained time sequence converter model in real time, constructing a temperature rise trend regulation factor according to the first-order difference item and the second-order difference item, and calculating a temperature rise trend prediction value by combining a temperature rise momentum item and a risk perception index. Therefore, the multi-factor correction control of the transient pulse current is realized, and the multi-factor correction control of the transient pulse current is realized.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of lithium ion batteries, and particularly relates to a battery low-temperature pulse heating control device and equipment for temperature rise fusion risk perception. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute prior art.

[0003] Under low-temperature conditions, the temperature inside the battery rises slowly and is unevenly distributed. If the heating control is not accurate, it is easy to cause temperature overshoot, local overheating or structural stress concentration, and thus to trigger thermal damage or even safety accidents, affecting the battery life and system stability. Traditional low-temperature heating control relies on preset heating strategies or simple temperature feedback regulation, and it is difficult to accurately capture the internal temperature rise dynamics of the battery and the thermal shock effect caused by short-time pulse current. Some methods trigger heating based on static temperature thresholds, ignoring the time-varying characteristics of temperature rise trends and structural stress risks, resulting in delayed or excessive heating control response, and failing to achieve fine control.

[0004] In addition, the existing heating control scheme lacks a joint perception mechanism for the internal temperature variation trend and structural risk of the battery, and it is difficult to effectively identify the thermal shock risk and dynamically adjust the heating pulse parameters, limiting the intelligent level of battery thermal safety management in low-temperature environments. SUMMARY

[0005] To solve the above technical problems, the present application provides a battery low-temperature pulse heating control device and equipment for temperature rise fusion risk perception, which can combine limited external measurement point data, predict internal temperature dynamics in real time, perceive structural risks, intelligently adjust heating pulse output, realize accurate control and effective protection of the battery thermal state in low-temperature environments, and improve the safety and reliability of the battery system.

[0006] To achieve the above purpose, the present application adopts the following technical solutions: The first aspect of the present application provides a battery low-temperature pulse heating control device for temperature rise fusion risk perception.

[0007] In one or more embodiments, a battery low-temperature pulse heating control device for temperature rise fusion risk perception is provided, comprising: a time sequence input matrix construction module for constructing a time sequence input matrix of the battery operating state based on multi-dimensional battery sensing data; an embedded sequence vector forming module for performing high-dimensional linear mapping on the time sequence input matrix of the battery operating state and injecting time position information to form an embedded sequence vector; a sequence feature extraction module configured to introduce a temperature-stress coupling gating mechanism to gate modulate the embedding vector, to obtain a gated modulated embedding sequence vector, and to utilize a multi-layer stacked transformer encoder to perform deep temporal feature extraction and non-linear enhancement on the gated modulated embedding sequence vector, to obtain sequence features; a time series transformer model training module configured to aggregate the sequence features into a single feature vector, to map the aggregated feature vector to an internal temperature prediction scalar, and to construct a weighted loss function fused with smoothing and constraint terms, to train the time series transformer model; a transient pulse current control module configured to calculate a first-order difference term and a second-order difference term of the internal temperature prediction value sequence based on the internal temperature sequence value predicted by the trained time series transformer model in real time, to construct a temperature rise trend regulation factor, and to combine a temperature rise momentum term and a risk perception index, to implement multi-factor correction control on the transient pulse current.

[0008] As an embodiment, the expression of the multi-factor correction control on the transient pulse current is:

[0009] wherein, is the output executed pulse current output value; is the standard current reference value; and is the first-order difference term and the second-order difference term of the internal temperature prediction value sequence; is the internal temperature sequence value predicted by the time series transformer model in real time; is the suppression factor; is the risk adjustment factor; , is the absolute value construction adjustment factor.

[0010] As an embodiment, the expression of the risk adjustment factor is:

[0011]

[0012] wherein, is a suppression amplitude hyperparameter for controlling risk perception; m is the number of external temperature monitoring points, and n is the number of stress monitoring points; represents the temperature of the th external monitoring point; represents the rate of change of the temperature of the th external monitoring point with time, represents the rate of change of the stress of the th stress monitoring point with time, is the local structure stress state monitored; is the hyperbolic tangent function.

[0013] As an embodiment, the expression of the inhibition factor is:

[0014]

[0015] wherein, is a momentum smoothing factor, is a temperature amplitude sensitive hyperparameter; and represents a momentum term.

[0016] As an embodiment, the constructed weighted loss function of fusion smoothing and constraint term is:

[0017]

[0018]

[0019]

[0020] wherein, is a weight; is a weighted mean square error loss function with weight regulation; is a time smoothing constraint term; is a penalty term; , , is a loss weight; and is a temperature prediction value; is a temperature maximum value; is a maximum function; is the total number of monitoring points.

[0021] As an embodiment, the embedded sequence vector is set to : ; is a position encoding vector; is a high-dimensional linear mapping vector of the state vector of each time step of the input matrix; L is the total number of time steps.

[0022] As an embodiment, by introducing a stress-temperature coupling perception gating mechanism, a gating modulation vector consistent with the input transformer embedding dimension is generated; the gating modulation vector is applied to the embedded sequence vector to obtain a gated and modulated embedded sequence vector.

[0023] The second aspect of the present application provides a battery low-temperature pulse heating control method with temperature rise fusion risk perception.

[0024] In one or more embodiments, a battery low-temperature pulse heating control method with temperature rise fusion risk perception comprises: Based on multi-dimensional battery sensing data, a time sequence input matrix of battery operating state is constructed; The time sequence input matrix of battery operating state is subjected to high-dimensional linear mapping and injection of time position information to form an embedded sequence vector; A temperature-stress coupling gating mechanism is introduced to gate modulate the embedded vector to obtain a gated and modulated embedded sequence vector, and a multi-layer stacked transformer encoder is used to extract deep time sequence features and nonlinearly enhance the gated and modulated embedded sequence vector to obtain sequence features; The sequence features are aggregated into a single feature vector, and the aggregated feature vector is mapped to an internal temperature prediction scalar, and a weighted loss function with fusion smoothing and constraint terms is constructed based on this to train a time sequence transformer model; Based on the internal temperature sequence value predicted by the trained time sequence transformer model in real time, the first-order difference term and the second-order difference term of the internal temperature prediction value sequence are calculated, and a temperature rise trend regulation factor is constructed, combined with a temperature rise momentum term and a risk perception index, to realize multi-factor correction control of the transient pulse current.

[0025] The third aspect of the present application provides a computer storage medium.

[0026] A computer storage medium having a computer program stored thereon, the program being executed by a processor to implement the steps in the battery low-temperature pulse heating control method with temperature rise fusion risk perception as described above.

[0027] The fourth aspect of the present application provides an electronic device.

[0028] An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps in the battery low-temperature pulse heating control method with temperature rise fusion risk perception as described above when executing the program.

[0029] Compared with the prior art, the present application has the following advantages: (1) The application first proposes a battery low-temperature pulse heating control device based on temperature rise trend prediction and risk perception, which combines time series temperature rise change characteristics and multi-source structure response information, builds a multi-factor regulation mechanism, and can realize dynamic self-adaptive adjustment of pulse heating behavior under limited monitoring conditions. The method models the first and second order difference of the internal temperature prediction sequence, introduces the temperature rise momentum and structure risk index, effectively identifies the potential thermal shock risk, and corrects the control output accordingly, achieving the goal of safe and rapid heating of the battery in a low-temperature environment.

[0030] (2) Based on the dynamic characteristics of the internal temperature prediction sequence, the application builds multi-level temperature rise trend regulation factors to realize precise control of the pulse heating amplitude and frequency, effectively inhibiting the thermal shock problem caused by excessive transient temperature rise rate; the application combines external temperature and stress perception data to propose a structure response driven risk regulation mechanism, so that the heating behavior can adapt to the dynamic changes of the battery structure state, thereby enhancing the safety robustness of the system under complex working conditions.

[0031] (3) The application introduces temperature rise momentum modeling into pulse control strategy design, significantly improving the heating system's perception and response ability to short-term cumulative thermal effects, avoiding stress mutation risks during the heating process; the regulation method proposed by the application can be jointly deployed with the internal temperature estimation model, without the need for additional hardware resources, and has good embedded implementation capability, suitable for online operation and control in various battery application scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0032] The drawings accompanying the specification of this application form a part thereof, serve to further provide a further understanding of the application, and together with the description of the exemplary embodiments of the application and the explanation thereof, explain the application without imposing undue limitation on the application.

[0033] Figure 1 is a temperature rise fusion risk perception battery low-temperature pulse heating control device structure schematic diagram of the embodiment of the application; Figure 2 is a flowchart of a temperature rise fusion risk perception battery low-temperature pulse heating control method of the embodiment of the application; Figure 3 is a schematic diagram of an electronic device according to an embodiment of the application. DETAILED DESCRIPTION

[0034] The application will be further described below in conjunction with the drawings and embodiments.

[0035] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as generally understood by those skilled in the art to which the application belongs.

[0036] It is to be noted that the terms used herein are merely for the purpose of describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. It is further to be understood that the terms "comprise" and / or "comprising," when used in this specification, specify the presence of stated features, steps, operations, devices, components and / or combinations thereof, but do not preclude the presence or addition of one or more other features, steps, operations, devices, components and / or combinations thereof.

[0037] Figure 1 is a structure schematic diagram of a battery low-temperature pulse heating control device for temperature rise fusion risk perception in an embodiment of the present application, as shown in the embodiment, the battery low-temperature pulse heating control device 100 for temperature rise fusion risk perception can include the following: Figure 1 a time sequence input matrix construction module 101, configured to construct a time sequence input matrix of a battery operating state based on multi-dimensional battery sensing data; an embedded sequence vector forming module 102, configured to perform high-dimensional linear mapping on the time sequence input matrix of the battery operating state and inject time position information to form an embedded sequence vector; a sequence feature extraction module 103, configured to introduce a temperature-stress coupling gating mechanism to gate modulate the embedded vector to obtain a gated and modulated embedded sequence vector, and then utilize a multi-layer stacked transformer encoder to perform deep time sequence feature extraction and nonlinear enhancement on the gated and modulated embedded sequence vector to obtain sequence features; a time sequence transformer model training module 104, configured to aggregate the sequence features into a single feature vector, map the aggregated feature vector to an internal temperature prediction scalar, and construct a weighted loss function with fusion smoothing and constraint terms to train a time sequence transformer model; a transient pulse current control module 105, configured to calculate a first-order difference term and a second-order difference term of an internal temperature prediction value sequence based on a battery internal temperature sequence value predicted in real time by the trained time sequence transformer model, construct a temperature rise trend regulation factor in combination with a temperature rise momentum term and a risk perception index to realize multi-factor correction control of the transient pulse current.

[0038] Figure 2 is a structure schematic diagram of a battery low-temperature pulse heating control method for temperature rise fusion risk perception in an embodiment of the present application, as shown in the embodiment, the battery low-temperature pulse heating control method for temperature rise fusion risk perception includes steps S101-S105. Figure 2

[0039] It is to be noted that the various modules in the above Figure 1 Figure 2 ​​​The steps in the battery low-temperature pulse heating control method with temperature rise fusion risk perception correspond one-to-one, and their specific implementation processes are the same, so they will not be described in detail here.

[0040] The specific implementation process of steps S101 to S105 is as follows: Step S101: Construct a time-series input matrix of battery operating status based on multi-dimensional battery sensing data.

[0041] For example, sensor data such as temperature, stress, and electrical signals can be fused to construct a time-series input matrix for battery operating status. The fused data includes, but is not limited to, parameters such as current, voltage, stress, strain, battery surface temperature, battery internal temperature, and data acquisition time.

[0042] The specific steps of step S101 are as follows: Step S1011: Obtain the surface temperature distribution of the battery by using m temperature sensors and n stress sensors deployed on the surface of the battery casing. and stress distribution Time-series data. Simultaneously, an internal battery temperature sensor is deployed to acquire the battery's internal temperature data at time t. .

[0043]

[0044]

[0045] The sampling frequency of the sensor can be selected according to the control accuracy and response requirements, and its layout can be appropriately optimized according to the specific battery casing shape.

[0046] Step S1012: Synchronously record battery voltage Current Ambient temperature With data collection timestamp.

[0047] Step S1013: Align the collected data with timestamps to construct a d-dimensional vector (d=m+n+3) representing the current battery operating state. :

[0048] Step S1014: Select a sliding window length of L (L is set according to the sampling period), and construct an input matrix containing L time steps: .

[0049] Step S102: Perform high-dimensional linear mapping on the time-series input matrix of the battery operating state and inject time position information to form an embedded sequence vector.

[0050] In the specific implementation process, step S102 is as follows: Step S1021: Use a linear mapping function to transform the input matrix. The state vector at each time step Perform high-dimensional linear mapping:

[0051] in, The dimension is , and These are trainable parameters. At this point, the sequence input... Turn to :

[0052] Step S1022: To inject time series information and enable the model to distinguish the order of time steps, standard sine and cosine position coding is adopted, and a position coding vector is introduced. :

[0053]

[0054] in, .

[0055] S23: Construct an input encoding representation based on the time-series converter. This vector contains both sensor information and clearly defines the time step sequence position, providing sufficient contextual information for subsequent converter processing. .

[0056] Step S103: Introduce a temperature-stress coupling gating mechanism to perform gating modulation on the embedded vector to obtain a gated modulated embedded sequence vector. Then, use a multi-layer stacked converter encoder to perform deep temporal feature extraction and nonlinear enhancement on the gated modulated embedded sequence vector to obtain sequence features.

[0057] The specific process of step S103 is as follows: Step S1031: To enhance the response capability to transient thermal stress behavior characteristics, extract the temperature-stress sensing sub-vector from the battery operating state vector at the current time t:

[0058] By introducing a stress-temperature coupled sensing gating mechanism, a gated modulation vector with the same embedding dimension as the converter input is generated:

[0059] in, For at any time The extracted temperature-stress sensing sub-vector is derived from the battery surface temperature distribution. Stress distribution and ambient temperature Composition, used to characterize the instantaneous thermo-mechanical coupling state of the battery; It is a trainable gate weight matrix used to learn the contribution relationship of different sensor information in gate modulation; It is a bias vector used to correct the feature benchmark and improve adaptability under different working conditions; For nonlinear activation functions (such as Sigmoid), the modulation factor is compressed to the [0,1] interval, allowing it to be interpreted as gating strengths of different dimensions. The resulting... This is the temperature-stress sensing gated modulation vector.

[0060] The gated modulation vector Acting on the input encoded sequence The gated and modulated embedded sequence is obtained as follows:

[0061] in, This represents element-wise multiplication, where each dimension of the gated vector is multiplied by the corresponding dimension of the input embedding vector, achieving dimension-wise weighted modulation of the input features. Its physical meaning is that in the input embedding sequence, feature dimensions deemed important under the current temperature-stress state are amplified, while those with insignificant effects are weakened, thus achieving adaptive feature selection for environmental perception.

[0062] Step S1032: The gated modulated input sequence is processed using a multi-layer stacked converter encoder. Deep feature extraction is performed. For any layer transformer encoder structure, first, through a multi-head self-attention module (h heads), for the... Layer input Calculate query ,key ,value matrix:

[0063]

[0064]

[0065] in, , and This is the weight matrix.

[0066] Step S1033: Calculate the attention weight matrix, representing the attention weight of each time step in the sequence to all other time steps:

[0067]

[0068] Step S1034: Calculate any single-head output And concatenate the outputs of h heads:

[0069]

[0070] Step S1035: Project the output mapping matrix back into the model's hidden dimension space, after the first step... The layer outputs:

[0071] Step S1036: Concatenate the multi-head self-attention output with the input residual, and obtain the result after layer normalization. :

[0072] Step S1037: ... The input is a feedforward fully connected network, consisting of two fully connected layers, which further abstracts the features:

[0073] To facilitate gradient flow and feature fusion, a residual connection is made between the FFN output and input:

[0074] right After performing layer normalization, the output of the last encoder layer is obtained: .

[0075] Step S104: Aggregate the sequence features into a single feature vector, then map the aggregated feature vector to the internal temperature prediction scalar, and construct a weighted loss function that integrates smoothing and constraint terms to train the time series transformer model.

[0076] Step S104 outputs the internal temperature through feature aggregation and fully connected mapping, constructs a weighted loss function to fuse smoothing and constraint terms, and improves prediction accuracy and physical plausibility. The specific steps are as follows: S41: Employ the time-step feature convergence method to integrate the sequence features output by the encoder. Aggregate into a single vector:

[0077] in, Indicates the first Encoder output features at each time step, The time step length is used. The deep features from all time steps are averaged to obtain a comprehensive feature vector that can represent the current battery operating state as a whole. This avoids over-reliance on data from a single instant and improves the overall ability to grasp temporal behavior.

[0078] S42: A fully connected layer is used to map the aggregated feature vector to the internal temperature prediction scalar output.

[0079] in, This represents the battery's internal temperature predicted by the model. The weight matrix is ​​trainable and determines the contribution of different aggregation features to temperature prediction. This is the bias vector used to correct the prediction baseline. The physical significance of this step is to transform the time-series feature aggregation results into specific internal temperature prediction values, realizing the mapping from "multi-dimensional operating state features" to "single physical quantity output".

[0080] S43: Utilizing a label with actual internal temperature The training data is used for supervised learning. A weighted mean square error loss function with weighted adjustment is constructed, and weights are applied to different prediction error intervals to enhance the sensitivity to extreme temperature deviations.

[0081] Among them, weight , This is the adjustment factor. To prevent unreasonable time jumps in the prediction results, a first-order difference smoothing regularization is introduced to construct a time smoothing constraint term:

[0082] If the predicted temperature is outside the reasonable range Add a penalty item:

[0083] The total loss function is:

[0084] Introducing hyperparameters The three loss weights are defined as follows:

[0085]

[0086]

[0087] Among them, the first-order difference variance based on the stationarity of the predicted sequence is introduced as a confidence factor. Using the current predicted sequence The normalized first-order difference variance characterizes the smoothness of the prediction results:

[0088] in, The standard deviation of the training temperature is used for normalization. All weight matrices are optimized using the backpropagation algorithm, allowing the model to gradually learn the complex nonlinear mapping relationship between input features and the internal temperature of the battery, achieving high-precision online temperature prediction. .

[0089] Step S105: Based on the real-time predicted battery internal temperature sequence values ​​from the trained time-series converter model, calculate the first-order and second-order difference terms of the predicted internal temperature sequence. Construct a temperature rise trend control factor accordingly, combining it with the temperature rise momentum term and risk perception indicators to achieve multi-factor correction control of transient pulse current. The specific steps are as follows: S51: Based on the current-temperature mapping table calibrated at the battery's factory, determine the pulse current reference value set for the current battery temperature within a specific temperature range. .

[0090] S52: Real-time prediction of the battery's internal temperature sequence using the aforementioned time-series converter model. Calculate the first and second differences between the predicted internal temperature sequence:

[0091]

[0092] Construct adjustment factors based on their absolute values:

[0093]

[0094] in, , To balance the hyperparameter of temperature rise response sensitivity.

[0095] S53: To reflect the short-term cumulative effect of the temperature rise trend, a momentum term is introduced as a moving weighted average:

[0096] And based on this, an inhibitory factor is constructed:

[0097] in, The momentum smoothing factor, It is a hyperparameter sensitive to the temperature rise.

[0098] S54: Given m external temperature monitoring points and n stress monitoring points, calculate the risk perception index based on the predicted sequence:

[0099] in, Indicates the first The rate of temperature change over time at each external monitoring point Indicates the first The rate of change of stress over time at each stress monitoring point It is the monitored local structural stress state, and its rate of change can reflect the potential risk evolution of the battery casing or key connection parts under thermo-mechanical coupling. Through a comprehensive weighted average of temperature and stress, It can simultaneously capture transient risk levels under the influence of multiple electric, thermal, and mechanical fields.

[0100] Constructing risk adjustment factors:

[0101] in, It is the hyperparameter for controlling the suppression magnitude of risk perception.

[0102] S55: Based on the internal temperature change trend, the cumulative effect of heating momentum, and the structural response risk, at the standard current reference value... Based on the above, the output pulse current value is modified to produce the final output value. This enables proactive avoidance of transient thermal shock risks. .

[0103] The structure of the electronic device according to an embodiment of the present invention will be described in detail below. Figure 3 This is a schematic diagram of the composition structure of an electronic device provided in an embodiment of the present invention. It can be understood that... Figure 3 The diagram shows only an exemplary structure of the electronic device, not the entire structure. Some or all of the structures shown may be implemented as needed.

[0104] The electronic device provided in this embodiment of the invention includes: at least one processor 301, a memory 302, a user interface 303, and at least one network interface 304. The various components in the temperature rise fusion risk perception battery low-temperature pulse heating control device are coupled together via a bus system 305. It can be understood that the bus system 305 is used to realize the connection and communication between these components. In addition to a data bus, the bus system 305 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 3 The general designated all buses as Bus System 305.

[0105] The user interface 303 may include a monitor, keyboard, mouse, trackball, click wheel, buttons, touchpad, or touch screen.

[0106] It is understood that memory 302 can be volatile memory or non-volatile memory, or both. In this embodiment of the invention, memory 302 is capable of storing data to support the operation of the terminal. Examples of this data include any computer programs used to operate on the terminal, such as operating systems and applications. The operating system includes various system programs, such as framework layers, core library layers, driver layers, etc., used to implement various basic services and handle hardware-based tasks. Applications can include various applications.

[0107] In some embodiments, the battery low-temperature pulse heating control device 300 with temperature rise fusion risk perception provided in this invention can be implemented using a combination of hardware and software. For example, the battery low-temperature pulse heating control device 300 with temperature rise fusion risk perception provided in this invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the battery low-temperature pulse heating control method with temperature rise fusion risk perception provided in this invention. For example, the processor in the form of a hardware decoding processor can employ one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0108] As an example, processor 301 can be an integrated circuit chip with signal processing capabilities, such as a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., wherein the general-purpose processor can be a microprocessor or any conventional processor, etc.

[0109] As an example of the hardware implementation of the battery low-temperature pulse heating control device 300 for temperature rise fusion risk perception provided in this embodiment of the invention, the device provided in this embodiment of the invention can be directly executed by a processor 301 in the form of a hardware decoding processor. For example, it can be executed by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components to implement the battery low-temperature pulse heating control method for temperature rise fusion risk perception provided in this embodiment of the invention.

[0110] The memory 302 in this embodiment of the invention is used to store various types of data to support the operation of the battery low-temperature pulse heating control device for temperature rise fusion risk perception, or to store data for execution. Figure 1 The program code for the method shown. Examples of this data include: any executable instructions for operation on a battery cryogenic pulse heating control device for temperature rise fusion risk perception, such as executable instructions that can be included in the executable instructions to implement the battery cryogenic pulse heating control method for temperature rise fusion risk perception of embodiments of the present invention.

[0111] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including functions for executing... Figure 1 The program code for the method shown. In such an embodiment, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by the central processing unit, it performs the various functions defined in the apparatus of this application.

[0112] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0113] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A battery low-temperature pulse heating control device with temperature rise fusion risk perception, characterized in that, include: The timing input matrix construction module is used to construct a timing input matrix of the battery operating state based on multi-dimensional battery sensing data. The embedded sequence vector forming module is used to perform high-dimensional linear mapping on the time-series input matrix of the battery operating state and inject time position information to form an embedded sequence vector. The sequence feature extraction module is used to introduce a temperature-stress coupling gating mechanism to gating and modulate the embedded vector to obtain a gated and modulated embedded sequence vector. Then, a multi-layer stacked converter encoder is used to perform deep temporal feature extraction and nonlinear enhancement on the gated and modulated embedded sequence vector to obtain sequence features. The time series converter model training module is used to aggregate sequence features into a single feature vector, then map the aggregated feature vector to the internal temperature prediction scalar, and construct a weighted loss function that integrates smoothing and constraint terms to train the time series converter model. The transient pulse current control module is used to calculate the first and second difference terms of the predicted internal temperature sequence of the battery based on the real-time predicted internal temperature sequence value of the trained time-series converter model. Based on this, a temperature rise trend regulation factor is constructed, which is combined with the temperature rise momentum term and risk perception index to achieve multi-factor correction control of transient pulse current.

2. The battery low-temperature pulse heating control device with temperature rise fusion risk perception as described in claim 1, characterized in that, The expression for achieving multi-factor correction control of transient pulse current is as follows: in, The output value is the pulse current value executed by outputting the pulse current. This is the standard current reference value; and These are the first and second difference terms of the internal temperature prediction sequence; The real-time predicted internal temperature sequence of the battery by the time-series converter model; It is an inhibitory factor; As a risk adjustment factor; , Construct adjustment factors for absolute values.

3. The battery low-temperature pulse heating control device with temperature rise fusion risk perception as described in claim 2, characterized in that, The expression for the risk adjustment factor is: in, It is the suppression magnitude hyperparameter for controlling risk perception; m is the number of external temperature monitoring points, and n is the number of stress monitoring points; Indicates the first Temperature at one external monitoring point; Indicates the first The rate of temperature change over time at each external monitoring point Indicates the first The rate of change of stress over time at each stress monitoring point It is the local structural stress state obtained through monitoring; It is the hyperbolic tangent function.

4. The battery low-temperature pulse heating control device with temperature rise fusion risk perception as described in claim 2, characterized in that, The expression for the inhibitory factor is: in, The momentum smoothing factor, This is a hyperparameter sensitive to temperature rise. and This represents the momentum term.

5. The battery low-temperature pulse heating control device with temperature rise fusion risk perception as described in claim 1, characterized in that, Constructed weighted loss function of fusion smoothing and constraint terms for: in, As weight; This is the weighted mean square error loss function with weight adjustment; For time smoothing constraints; This is an additional penalty item; , , For loss weights; and This is a predicted temperature value; This represents the maximum temperature. It is a function for maximizing the value; This represents the total number of monitoring points.

6. The battery low-temperature pulse heating control device with temperature rise fusion risk perception as described in claim 1, characterized in that, Embedded sequence vectors are set as : ; This is a position-encoded vector; The vector is the result of a high-dimensional linear mapping of the state vector at each time step of the input matrix; L is the total number of time steps.

7. The battery low-temperature pulse heating control device with temperature rise fusion risk perception as described in claim 1, characterized in that, By introducing a stress-temperature coupled sensing gating mechanism, a gated modulation vector with the same embedding dimension as the converter input is generated; this gated modulation vector is then applied to the embedding sequence vector to obtain the gated modulated embedding sequence vector.

8. A battery low-temperature pulse heating control method integrating temperature rise risk perception, characterized in that, A battery low-temperature pulse heating control device based on temperature rise fusion risk perception as described in any one of claims 1-7, comprising: Based on multi-dimensional battery sensing data, a time-series input matrix of battery operating status is constructed; The time-series input matrix of the battery operating status is subjected to high-dimensional linear mapping and injected with time position information to form an embedded sequence vector; A temperature-stress coupling gating mechanism is introduced to perform gating modulation on the embedded vector to obtain a gated modulated embedded sequence vector. Then, a multi-layer stacked converter encoder is used to perform deep temporal feature extraction and nonlinear enhancement on the gated modulated embedded sequence vector to obtain sequence features. The sequence features are aggregated into a single feature vector, and then the aggregated feature vector is mapped to the internal temperature prediction scalar. Based on this, a weighted loss function that integrates smoothing and constraint terms is constructed to train the time series transformer model. Based on the real-time predicted battery internal temperature sequence value based on the trained time-series converter model, the first-order difference term and the second-order difference term of the predicted internal temperature value sequence are calculated, and a temperature rise trend regulation factor is constructed accordingly. Combined with the temperature rise momentum term and risk perception index, a multi-factor correction control for transient pulse current is achieved.

9. A computer storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the battery low-temperature pulse heating control method for temperature rise fusion risk perception as described in claim 8.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the battery low-temperature pulse heating control method for temperature rise fusion risk perception as described in claim 8.

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