Battery fault detection method and system

Abstract

The invention discloses a battery fault detection method and system, and the method comprises the following steps: S1, collecting multi-source data in the operation process of a battery, and carrying out the preprocessing of the multi-source data; s2, integrating the preprocessed multi-source data through a multi-modal data fusion algorithm to form a unified data feature space; s3, performing feature extraction on the fused data by using a deep learning model, extracting feature vectors related to faults, and constructing a fault detection model of deep learning; s4, training the fault detection model by using historical fault data; and S5, inputting multi-modal data acquired in real time into the trained fault detection model, outputting a fault detection result and generating an early warning signal. Through the multi-modal data fusion and deep learning technology, the accuracy and robustness of battery fault detection are remarkably improved, early faults can be quickly recognized, early warning can be provided, and the method is suitable for various application scenes such as electric automobiles and energy storage systems and has wide application prospects.

Description

Technical Field

[0001] The present invention relates to the field of battery fault detection, and in particular to a battery fault detection method and system. Background Art

[0002] With the development of electric vehicles and energy storage systems, battery safety and reliability have become critical issues. Traditional battery fault detection methods rely on single data points (such as voltage, current, or temperature), making it difficult to accurately identify early-stage faults under complex operating conditions. In recent years, deep learning and multimodal data fusion technologies have shown great potential in the field of fault diagnosis. However, existing technologies lack mature solutions that can efficiently integrate multi-source data and utilize deep learning models for accurate battery fault detection. Summary of the Invention

[0003] In order to solve the existing problems, the present invention provides a battery fault detection method and system, the specific scheme is as follows:

[0004] A battery fault detection method comprises the following steps:

[0005] S1, collects multi-source data during battery operation and performs preprocessing;

[0006] S2, integrates the pre-processed multi-source data through a multimodal data fusion algorithm to form a unified data feature space;

[0007] S3, uses a deep learning model to extract features from the fused data, extracts feature vectors related to the fault, and builds a deep learning fault detection model;

[0008] S4, using historical fault data to train the fault detection model and optimize the model parameters;

[0009] S5, inputs the multimodal data collected in real time into the trained fault detection model, outputs the fault detection results and generates a warning signal.

[0010] Preferably, the multi-source data collected in step S1 include voltage, current, temperature, internal resistance, charge and discharge cycle data, and log data of the battery management system BMS; preprocessing of the multi-source data includes filtering and normalization to remove noise and outliers.

[0011] Preferably, the multimodal data fusion algorithm in step S2 adopts a multi-view discriminant analysis (MvDA) algorithm to associate and fuse different sensor data into a common feature space.

[0012] Preferably, the deep learning model in step S3 includes an encoder-decoder architecture and a classifier, the encoder uses a convolutional neural network (CNN) to extract spatial features, the decoder uses a long short-term memory (LSTM) network to generate residuals to detect anomalies, and the classifier is a multi-layer perceptron (MLP) for classifying fault types.

[0013] Preferably, the warning signal generated in step S4 is displayed through a visual interface to indicate the type, location and severity of the fault.

[0014] Preferably, the system based on any of the above methods includes:

[0015] Data acquisition module, used to collect multi-source data during battery operation;

[0016] Data fusion module, used to pre-process and fuse the collected multi-source data;

[0017] Deep learning model module, used for feature extraction and fault detection of fused data;

[0018] A fault diagnosis module is used to generate a fault warning signal according to the fault detection result;

[0019] User interface, used to display fault diagnosis results and warning information.

[0020] The present invention also discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is run, any of the above methods is executed.

[0021] The present invention also discloses a computer system, including a processor and a storage medium, wherein a computer program is stored on the storage medium, and the processor reads and runs the computer program from the storage medium to execute any of the methods described above.

[0022] The beneficial effects of the present invention are:

[0023] Through multimodal data fusion and deep learning technology, the present invention significantly improves the accuracy and robustness of battery fault detection, can quickly identify early faults and provide early warnings, and is suitable for various application scenarios such as electric vehicles and energy storage systems, and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0025] Figure 1 This is a system architecture diagram of the present invention.

[0026] Figure 2 This is the flow chart of multimodal data fusion.

[0027] Figure 3 This is a deep learning model architecture diagram.

[0028] Figure 4 This is a schematic diagram of the fault warning interface. DETAILED DESCRIPTION

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0030] To address the existing problems of low battery fault detection accuracy, reliance on a single data source, and difficulty adapting to complex operating conditions, a battery fault detection method and system based on deep learning and multimodal data fusion is proposed. Specifically, a battery fault detection method includes the following steps:

[0031] S1 collects and preprocesses multi-source data during battery operation. This data includes voltage, current, temperature, internal resistance, charge and discharge cycle data, and battery management system (BMS) log data. Preprocessing of this data includes filtering and normalization to remove noise and outliers.

[0032] S2, the pre-processed multi-source data is integrated through a multimodal data fusion algorithm to form a unified data feature space. The multimodal data fusion algorithm uses a multi-view discriminant analysis (MvDA) algorithm to associate and fuse different sensor data into a common feature space. Figure 2 As shown in Figure 2, multimodal data fusion algorithms improve the accuracy and robustness of fault detection by integrating data from multiple sensors.

[0033] S3, use a deep learning model to extract features from the fused data, extract feature vectors related to the fault, and build a deep learning fault detection model. The deep learning model in step S3 includes an encoder-decoder architecture and a classifier. The encoder uses a convolutional neural network (CNN) to extract spatial features, the decoder uses a long short-term memory (LSTM) network to generate residuals to detect anomalies, and the classifier is a multi-layer perceptron (MLP) for classifying fault types. Figure 3As shown in Figure 2, an encoder-decoder architecture and a classifier are used to achieve early detection and classification of faults.

[0034] S4, use historical fault data to train the fault detection model and optimize the model parameters.

[0035] S5, inputs the multimodal data collected in real time into the trained fault detection model, outputs the fault detection results and generates an early warning signal. The generated early warning signal displays the fault type, location and severity through a visual interface. Figure 4 Real-time data processing and fault warning can quickly respond to faults and provide diagnostic results.

[0036] like Figure 1 As shown, the present invention also provides a complete battery fault detection system that supports multiple application scenarios. Specifically, the system based on any of the above methods includes:

[0037] Data acquisition module, used to collect multi-source data during battery operation;

[0038] Data fusion module, used to pre-process and fuse the collected multi-source data;

[0039] Deep learning model module, used for feature extraction and fault detection of fused data;

[0040] A fault diagnosis module is used to generate a fault warning signal according to the fault detection result;

[0041] User interface, used to display fault diagnosis results and warning information.

[0042] In summary, the present invention significantly improves the accuracy and robustness of battery fault detection through multimodal data fusion and deep learning technology, can quickly identify early faults and provide early warnings, and is suitable for various application scenarios such as electric vehicles and energy storage systems, and has broad application prospects.

[0043] The present invention also discloses a computer-readable storage medium and a computer system. A computer-readable storage medium stores a computer program, which, when executed, performs any of the methods described above. A computer system includes a processor and a storage medium, wherein the storage medium stores the computer program, and the processor reads and executes the computer program from the storage medium to perform any of the methods described above.

[0044] Those skilled in the art will further appreciate that the various illustrative logic blocks, modules, circuits, and algorithmic steps described in conjunction with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination of the two. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps are generally described above in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. A skilled person may implement the described functionality in different ways for each specific application, but such implementation decisions should not be interpreted as resulting in a departure from the scope of the present invention.

[0045] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read and write information from / to the storage medium. In an alternative, the storage medium may be integrated into the processor. The processor and storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and storage medium may reside in a user terminal as discrete components.

[0046] The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0047] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A battery fault detection method, characterized in that: The following steps are involved: S1, collects multi-source data during battery operation and performs preprocessing; S2, integrates the pre-processed multi-source data through a multimodal data fusion algorithm to form a unified data feature space; S3, uses a deep learning model to extract features from the fused data, extracts feature vectors related to the fault, and builds a deep learning fault detection model; S4, using historical fault data to train the fault detection model and optimize the model parameters; S5, inputs the multimodal data collected in real time into the trained fault detection model, outputs the fault detection results and generates a warning signal.

2. The method according to claim 1, wherein: The multi-source data collected in step S1 include voltage, current, temperature, internal resistance, charge and discharge cycle data, and log data of the battery management system BMS; the preprocessing of the multi-source data includes filtering and normalization to remove noise and outliers.

3. The method according to claim 1, wherein: The multimodal data fusion algorithm in step S2 adopts the multi-view discriminant analysis (MvDA) algorithm to associate and fuse different sensor data into a common feature space.

4. The method according to claim 1, wherein: The deep learning model in step S3 includes an encoder-decoder architecture and a classifier. The encoder uses a convolutional neural network (CNN) to extract spatial features, the decoder uses a long short-term memory (LSTM) network to generate residuals to detect anomalies, and the classifier is a multi-layer perceptron (MLP) for classifying fault types.

5. The method according to claim 1, wherein: The warning signal generated in step S4 is displayed through a visual interface to indicate the fault type, location and severity.

6. A system based on the method according to any one of claims 1 to 5, characterized in that: include: Data acquisition module, used to collect multi-source data during battery operation; Data fusion module, used to pre-process and fuse the collected multi-source data; Deep learning model module, used for feature extraction and fault detection of fused data; A fault diagnosis module is used to generate a fault warning signal according to the fault detection result; User interface, used to display fault diagnosis results and warning information.

7. A computer-readable storage medium, characterized in that: The medium stores a computer program, and after the computer program is run, the method according to any one of claims 1 to 5 is executed.

8. A computer system, characterized in that: The method comprises a processor and a storage medium, wherein the storage medium stores a computer program, and the processor reads and runs the computer program from the storage medium to execute the method according to any one of claims 1 to 5.

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