Battery protection method, system, and vehicle

Abstract

The application discloses a battery protection method and system and a vehicle, and relates to the technical field of battery safety. The method comprises the following steps: obtaining target multi-source data, wherein the target multi-source data is used for representing a plurality of types of battery monitoring data; performing feature-level fusion on the target multi-source data to obtain a fusion feature vector; determining whether to trigger a hierarchical early warning prompt according to a warning threshold range and the fusion feature vector, wherein the warning threshold range is obtained by adjusting historical state data and current state data of the battery, and the hierarchical early warning prompt is used for prompting that the battery has different levels of risks; and protecting the battery based on a preset measure in response to the hierarchical early warning prompt. The application solves the technical problem that the battery safety monitoring technology has defects in the related art.

Description

Technical Field

[0001] This invention relates to the field of battery safety technology, and more specifically, to a battery protection method, system, and vehicle. Background Technology

[0002] Current battery safety monitoring for new energy vehicles primarily relies on the Battery Management System (BMS), which collects cell parameters through contact-type voltage and temperature sensors and uses a single threshold for risk warning. Therefore, this existing solution suffers from significant lag in battery safety monitoring. Furthermore, the sensors must be directly connected to the cell terminals, making them susceptible to electromagnetic interference, and the implanted design disrupts the sealing structure, affecting long-term reliability.

[0003] No effective solution has yet been proposed to address the above issues. Summary of the Invention

[0004] This invention provides a battery protection method, system, and vehicle to at least address the technical problems of deficiencies in battery safety monitoring technology in related technologies.

[0005] According to one embodiment of the present invention, a battery protection method is provided, comprising: acquiring target multi-source data, wherein the target multi-source data is used to represent multiple types of battery monitoring data; performing feature-level fusion on the target multi-source data to obtain a fused feature vector; determining whether to trigger a graded warning prompt based on a warning threshold range and the fused feature vector, wherein the warning threshold range is obtained by adjusting historical state data and current state data of the battery, and the graded warning prompt is used to indicate that the battery has different levels of risk; and protecting the battery based on preset measures in response to triggering the graded warning prompt.

[0006] Optionally, acquiring target multi-source data includes: acquiring initial multi-source data, wherein the initial multi-source data is collected by various types of sensors; aligning the timestamps of the initial multi-source data to obtain intermediate multi-source data; and performing spatial coordinate unification processing on the intermediate multi-source data to obtain target multi-source data.

[0007] Optionally, feature-level fusion is performed on the target multi-source data to obtain a fused feature vector, including: feature extraction of the target multi-source data to obtain multi-dimensional feature parameters of the battery, wherein the multi-dimensional feature parameters include: infrared feature parameters, voltage feature parameters, temperature feature parameters, air pressure feature parameters, and acoustic signature feature parameters; and feature fusion is performed on the multi-dimensional feature parameters to obtain a fused feature vector.

[0008] Optionally, the warning threshold range includes: a first threshold range, which is used to determine whether an early warning is triggered; and determining whether a graded warning prompt is triggered based on the warning threshold range and the fused feature vector, including: analyzing the fused feature vector based on the first threshold range to obtain a first analysis result; and triggering an early warning in response to the first analysis result indicating that any single parameter of the battery is within the first threshold range.

[0009] Optionally, the warning threshold range includes: a second threshold range, which is used to determine whether a moderate warning is triggered. The warning level of a moderate warning is higher than that of an early warning. The determination of whether to trigger a graded warning prompt is based on the warning threshold range and the fused feature vector includes: analyzing the fused feature vector based on the second threshold range to obtain a second analysis result; and triggering a moderate warning in response to the second analysis result indicating that multiple parameters of the battery are within the second threshold range.

[0010] Optionally, the warning threshold range includes a third threshold range, which is used to determine whether an emergency warning is triggered. The warning level of an emergency warning is higher than that of a moderate warning. The determination of whether to trigger a graded warning prompt is based on the warning threshold range and the fused feature vector includes: analyzing the fused feature vector based on the third threshold range to obtain a third analysis result; and triggering an emergency warning in response to the third analysis result indicating that multiple parameters of the battery are within the third threshold range.

[0011] Optionally, in response to triggering a graded early warning prompt, the battery is protected based on preset measures, including: in response to triggering an early warning, recording abnormal parameters and marking potential risks; in response to triggering a moderate warning, limiting charging power and / or discharging power and activating auxiliary heat dissipation devices; and in response to triggering an emergency warning, cutting off the battery's main circuit and reporting to the management platform.

[0012] According to one embodiment of the present invention, a battery protection system is also provided for performing the above-described battery protection method, comprising: an infrared sensor array module for acquiring infrared characteristic parameters of the battery; a multi-sensor fusion module for integrating multi-dimensional characteristic parameters of the battery, including: infrared characteristic parameters, voltage characteristic parameters, temperature characteristic parameters, air pressure characteristic parameters, and acoustic signature characteristic parameters; a safety status assessment module for determining the risk level of the battery based on the multi-dimensional characteristic parameters; and a graded early warning and control module for executing corresponding early warning prompts and control strategies according to the risk level.

[0013] Optionally, the infrared sensor array module includes: a wide-angle infrared sensor, a side infrared sensor, and a miniature infrared sensor. The wide-angle infrared sensor is arranged on the top of the battery, the side infrared sensor is arranged on the sidewall of the battery, and the miniature infrared sensor is arranged in the module gap of the battery.

[0014] According to one embodiment of the present invention, a battery protection device is also provided, comprising: an acquisition module for acquiring target multi-source data, wherein the target multi-source data represents various types of battery monitoring data; a fusion module for performing feature-level fusion on the target multi-source data to obtain a fused feature vector; a determination module for determining whether to trigger a graded warning prompt based on a warning threshold range and the fused feature vector, wherein the warning threshold range is obtained by adjusting historical state data and current state data of the battery, and the graded warning prompt is used to indicate that the battery has different levels of risk; and a protection module for protecting the battery based on preset measures in response to triggering the graded warning prompt.

[0015] Optionally, the acquisition module is also used to acquire initial multi-source data, which is obtained by collecting data from various types of sensors; to align the timestamps of the initial multi-source data to obtain intermediate multi-source data; and to perform spatial coordinate unification processing on the intermediate multi-source data to obtain target multi-source data.

[0016] Optionally, the fusion module is also used to extract features from the target multi-source data to obtain multi-dimensional feature parameters of the battery, including: infrared feature parameters, voltage feature parameters, temperature feature parameters, air pressure feature parameters, and acoustic signature feature parameters; by fusing the multi-dimensional feature parameters, a fused feature vector is obtained.

[0017] Optionally, the warning threshold range includes: a first threshold range, which is used to determine whether an early warning is triggered. The determining module is also used to analyze the fused feature vector based on the first threshold range to obtain a first analysis result; in response to the first analysis result indicating that any single parameter of the battery is within the first threshold range, an early warning is triggered.

[0018] Optionally, the warning threshold range includes a second threshold range, which is used to determine whether a moderate warning is triggered. The warning level of a moderate warning is higher than that of an early warning. The determining module is also used to analyze the fused feature vector based on the second threshold range to obtain a second analysis result. In response to the second analysis result indicating that multiple parameters of the battery are within the second threshold range, a moderate warning is triggered.

[0019] Optionally, the warning threshold range includes a third threshold range, which is used to determine whether an emergency warning is triggered. The warning level of an emergency warning is higher than that of a moderate warning. The determining module is also used to analyze the fused feature vector based on the third threshold range to obtain a third analysis result. In response to the third analysis result indicating that multiple parameters of the battery are within the third threshold range, an emergency warning is triggered.

[0020] Optionally, the protection module is also used to record abnormal parameters and mark potential risks in response to triggering an early warning; to limit charging power and / or discharging power and activate auxiliary heat dissipation devices in response to triggering a moderate warning; and to disconnect the main circuit of the battery and report to the management platform in response to triggering an emergency warning.

[0021] According to one embodiment of the present invention, a vehicle is also provided, comprising: a memory storing an executable program; and a processor for running the executable program, wherein the executable program, when run on the processor, performs the battery protection method described in any of the preceding claims.

[0022] According to one embodiment of the present invention, a computer-readable storage medium is also provided, wherein the storage medium stores a computer program, wherein the computer program is configured to execute the battery protection method described above when run on a computer or processor.

[0023] According to one embodiment of the present invention, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the battery protection method described in any of the preceding claims.

[0024] According to one embodiment of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the battery protection method described in any of the above claims.

[0025] In this embodiment of the invention, target multi-source data is first acquired, representing various types of battery monitoring data. Next, feature-level fusion is performed on the target multi-source data to obtain a fused feature vector. Then, based on the warning threshold range and the fused feature vector, it is determined whether to trigger a tiered warning alert. The warning threshold range is obtained by adjusting the battery's historical and current state data, and the tiered warning alert indicates different levels of risk to the battery. Finally, in response to the triggering of the tiered warning alert, the battery is protected based on preset measures, achieving real-time and accurate thermal runaway protection. This enhances the safety and reliability of the battery system, thereby solving the technical problem of deficiencies in battery safety monitoring technologies in related fields. Attached Figure Description

[0026] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0027] Figure 1 This is a flowchart of a battery protection method according to one embodiment of the present invention;

[0028] Figure 2 This is a flowchart of the thermal runaway judgment logic and early warning classification according to one embodiment of the present invention;

[0029] Figure 3 This is a schematic diagram of a battery protection system according to one embodiment of the present invention;

[0030] Figure 4 This is a schematic diagram of an infrared sensor arrangement according to one embodiment of the present invention;

[0031] Figure 5 This is a multi-sensor data time-series correlation analysis diagram according to one embodiment of the present invention;

[0032] Figure 6(a) is a schematic diagram of the internal structure of a battery pack according to one embodiment of the present invention;

[0033] Figure 6(b) is a schematic diagram of the internal structure of a battery pack according to another embodiment of the present invention;

[0034] Figure 6(c) is a schematic diagram of the internal structure of a battery pack according to yet another embodiment of the present invention;

[0035] Figure 6(d) is a schematic diagram of the internal structure of a battery pack according to yet another embodiment of the present invention;

[0036] Figure 7 This is a structural block diagram of a battery protection device according to one embodiment of the present invention. Detailed Implementation

[0037] For ease of understanding, some concepts related to embodiments of the present invention are illustrated below for reference.

[0038] Distributed miniature infrared sensor array: refers to a cluster of miniature infrared sensors deployed in multiple key locations inside the battery pack (such as the top, side walls, and module gaps) to non-contactly acquire images of the thermal distribution on the battery surface, enabling all-round, blind-spot-free temperature monitoring.

[0039] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0040] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. In the description of these embodiments, unless otherwise stated, "a plurality of" means two or more. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0041] According to one embodiment of the present invention, an embodiment of a battery protection method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0042] This method embodiment can be executed in an electronic device, similar control device, or system that includes a memory and a processor. Taking an electronic device as an example, the electronic device may include one or more processors and a memory for storing data. Optionally, the electronic device may also include a communication device for communication functions and a display device. Those skilled in the art will understand that the above structural description is merely illustrative and does not limit the structure of the electronic device. For example, the electronic device may include more or fewer components than described above, or have a different configuration than described above.

[0043] A processor may include one or more processing units. For example, a processor may include a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processing (DSP) chip, a microcontroller unit (MCU), a field-programmable gate array (FPGA), a neural network processing unit (NPU), a tensor processing unit (TPU), or an artificial intelligence (AI) processor. Different processing units may be independent components or integrated into one or more processors. In some instances, electronic devices may also include one or more processors.

[0044] The memory can be used to store computer programs, such as the computer program corresponding to the battery protection method in this embodiment of the invention. The processor implements the battery protection method by running the computer program stored in the memory. The memory may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0045] Communication devices are used to receive or send data via a network. Specific examples of such networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the communication device includes a network interface controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the communication device may be a radio frequency (RF) module used for wireless communication with the Internet.

[0046] Display devices can be, for example, touchscreen liquid crystal displays (LCDs) and touch displays (also referred to as "touchscreens" or "touch displays"). The LCD allows users to interact with the user interface of the mobile terminal. In some embodiments, the mobile terminal has a graphical user interface (GUI), which allows users to interact with the GUI through finger contact and / or gestures on a touch-sensitive surface. Optional human-computer interaction functions include: creating web pages, drawing, word processing, creating electronic documents, playing games, video conferencing, instant messaging, sending and receiving emails, call interfaces, playing digital video, playing digital music, and / or web browsing, etc. Executable instructions for performing the above human-computer interaction functions are configured / stored in one or more processor-executable computer program products or readable storage media.

[0047] This embodiment provides a battery protection method for electronic devices. Figure 1 This is a flowchart of a battery protection method according to one embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:

[0048] Step S10: Obtain target multi-source data, wherein the target multi-source data is used to represent multiple types of battery monitoring data;

[0049] In this embodiment of the invention, the target multi-source data includes, but is not limited to: infrared thermal distribution image data of the battery surface, voltage and temperature sampling data of each cell, air pressure change data inside the battery pack, and characteristic acoustic signal data collected by an acoustic fingerprint sensor. The target multi-source data respectively reflect the battery's thermal field distribution, electrochemical state, internal gas generation behavior, and mechanical vibration characteristics, and together constitute multi-dimensional monitoring information for comprehensively assessing the battery's safety status.

[0050] Acquiring target multi-source data can be understood as simultaneously collecting real-time monitoring signals from different types of sensors that reflect the battery's operating status, including infrared thermal imaging data, cell voltage and temperature data, internal air pressure change data, and acoustic signature signals, in order to comprehensively obtain the battery's integrated response information under charging, discharging, and abnormal operating conditions.

[0051] It can be seen that by simultaneously acquiring battery monitoring data such as infrared thermal images, voltage, temperature, air pressure, and acoustic signatures, comprehensive collection of multi-physics field information is achieved, providing a reliable data foundation for accurately identifying early characteristics of thermal runaway and significantly improving the comprehensiveness and reliability of early warning.

[0052] Step S12: Perform feature-level fusion on the target multi-source data to obtain a fused feature vector;

[0053] In this embodiment of the invention, the fusion feature vector is a high-dimensional numerical vector composed of key feature parameters from multiple sources of sensors, such as infrared thermal distribution features (e.g., maximum temperature, temperature gradient, hot spot area), cell voltage fluctuation rate, temperature rise rate, air pressure change rate, and acoustic signature energy in a specific frequency band. It is used to characterize the current comprehensive safety status of the battery and serve as the input basis for subsequent risk assessment and graded early warning.

[0054] The fusion of multi-source target data at the feature level to obtain the fused feature vector can be understood as extracting key discriminative feature parameters from different sensor data such as infrared thermal imaging, voltage, temperature, air pressure and acoustic fingerprints, and then merging these heterogeneous features into a feature vector that comprehensively represents the battery safety status, i.e., the fused feature vector.

[0055] It can be seen that by integrating key features from multiple sources of sensors to construct a high-dimensional safety feature vector, the accuracy and robustness of thermal runaway identification are significantly improved, effectively overcoming the problem of false alarms from single parameters and providing a reliable decision-making basis for graded early warning.

[0056] Step S14: Determine whether to trigger a graded warning prompt based on the warning threshold range and the fused feature vector. The warning threshold range is obtained by adjusting the historical state data and current state data of the battery. The graded warning prompt is used to indicate that the battery has different levels of risk.

[0057] In this embodiment of the invention, the warning threshold range is a multi-dimensional interval value that is dynamically adjusted through adaptive learning based on the battery's historical charge and discharge temperature rise curve, aging trend, and real-time operating status, and is used to match the safety boundary under different life cycles.

[0058] The tiered early warning system has three response levels: Level 1 (potential risk warning), Level 2 (power limitation and enhanced cooling), and Level 3 (circuit disconnection and fire suppression activation), each corresponding to a proactive intervention strategy for a different risk level.

[0059] Determining whether to trigger a graded early warning based on the early warning threshold range and the fused feature vector can be understood as comparing the fused multi-dimensional safety features with the dynamically adaptive threshold range, identifying the risk level of the current battery state through multi-parameter collaborative judgment logic, and triggering the corresponding level of early warning and response strategy accordingly.

[0060] It can be seen that by dynamically and adaptively adjusting the warning threshold, the parameter drift problem caused by battery aging is effectively overcome. Combined with fused feature vectors, multi-dimensional risk assessment is achieved, significantly improving the accuracy and reliability of the warning. The graded warning mechanism can realize graded responses from mild anomaly alerts to emergency power cut-off, avoiding false alarm disturbances and ensuring early detection and intervention of thermal runaway risks, greatly improving the safety protection capability of the battery system throughout its entire life cycle.

[0061] Figure 2 This is a flowchart illustrating the thermal runaway judgment logic and early warning classification according to one embodiment of the present invention, such as... Figure 2 As shown, a multi-threshold graded early warning mechanism is adopted, dividing early warnings into three levels according to the degree of risk. Level 1 Early Warning (Early Warning): A single parameter (such as local temperature rise or voltage fluctuation) exceeds the normal range but does not reach the danger threshold; the system records the data and marks the potential risk. Level 2 Early Warning (Moderate Warning): Multiple parameters are abnormal simultaneously and correlated (such as simultaneous temperature rise and voltage drop); the system limits charging / discharging power and activates auxiliary cooling. Level 3 Early Warning (Emergency Warning): Clear thermal runaway characteristics are detected (such as a rapid temperature rise or gas generation sound signature); the system cuts off the main circuit, activates fire suppression systems, and notifies users and the management platform.

[0062] Step S16: In response to the triggering of a graded warning prompt, the battery is protected based on preset measures.

[0063] In this embodiment of the invention, the preset measures include, but are not limited to: executing corresponding safety intervention actions according to the warning level; Level 1 triggers data recording and maintenance reminders; Level 2 limits charging and discharging power and enhances the cooling intensity of the thermal management system; Level 3 immediately cuts off the main circuit relay, activates the battery pack's built-in fire extinguishing device, unlocks the mechanical lock to assist in heat dissipation or pressure relief, and simultaneously sends emergency alarm information to the cloud platform and user terminal, thereby realizing a multi-level, automated closed-loop battery safety protection system.

[0064] In response to triggering graded early warning prompts, the battery protection measures based on preset measures can be understood as automatically executing preset safety control strategies according to different risk levels, from mild early warning prompts to actively cutting off power and activating fire-fighting devices, thereby effectively protecting the battery.

[0065] As can be seen, the above steps achieve a closed-loop response across the entire chain from monitoring to intervention, ensuring that the development of battery thermal runaway can be suppressed quickly, accurately, and reliably without relying on human decision-making.

[0066] For example, the system control program adopts a front-end / back-end architecture. The front-end interrupt service routine handles emergency warnings, while the back-end main loop executes routine monitoring and communication tasks. The front-end interrupt service routine includes: an infrared thermal runaway monitoring interrupt service routine: first, it reads infrared sensor data and calculates the area temperature rise rate. If the area temperature rise rate is >3°C / s and lasts for 3 seconds, a thermal runaway flag is set. Then, multi-sensor verification is performed: checking barometric pressure sensor data (pressure change >1.5kPa / s) and acoustic signature sensor data (sudden energy increase in a specific frequency band). If multiple sensors confirm, a level-three warning is triggered: the main relay is disconnected and the fire extinguishing device is activated, and an alarm message is sent to the cloud. The back-end main loop includes: system initialization, acquisition of multi-source sensor data, data preprocessing and feature extraction, safety status assessment, execution of control strategies according to risk level, data storage and communication, and system status self-check.

[0067] Through the above steps, firstly, target multi-source data is acquired, representing various types of battery monitoring data. Next, feature-level fusion is performed on the target multi-source data to obtain a fused feature vector. Then, based on the warning threshold range and the fused feature vector, it is determined whether to trigger a tiered warning alert. The warning threshold range is obtained by adjusting historical and current battery state data, and the tiered warning alert indicates different levels of risk to the battery. Finally, in response to the triggering of the tiered warning alert, the battery is protected based on preset measures, achieving real-time and accurate thermal runaway protection. This enhances the safety and reliability of the battery system, thus addressing the shortcomings of existing battery safety monitoring technologies.

[0068] Optionally, in step S10, acquiring the target multi-source data may include the following steps:

[0069] Step S101: Acquire initial multi-source data, wherein the initial multi-source data is obtained through various types of sensors;

[0070] Step S102: Align the timestamps of the initial multi-source data to obtain intermediate multi-source data;

[0071] Step S103: Perform spatial coordinate unification processing on the intermediate multi-source data to obtain the target multi-source data.

[0072] In this embodiment of the invention, the initial multi-source data refers to the raw signals synchronously acquired by different types of devices such as infrared sensors, voltage acquisition units, temperature sensors, barometric pressure sensors, and acoustic fingerprint sensors. Exemplarily, the initial multi-source data may have characteristics such as different sampling frequencies, inconsistent time bases, and unmapped spatial locations, but these are not limited here.

[0073] The intermediate multi-source data is a data set that has been timestamped and aligned, meaning that the data from each sensor are unified to the same time reference in the time dimension.

[0074] Acquiring initial multi-source data can be understood as collecting raw monitoring signals of the battery in different physical dimensions in real time through various sensors such as infrared, voltage, temperature, air pressure, and acoustic signature, forming a raw heterogeneous data set.

[0075] Aligning the timestamps of the initial multi-source data to obtain intermediate multi-source data can be understood as performing synchronous interpolation or time compensation on the initial multi-source data based on a unified time base, so that all data are aligned on the time axis, thereby eliminating timing deviations.

[0076] By performing spatial coordinate unification processing on intermediate multi-source data to obtain target multi-source data, it can be understood that the intermediate multi-source data aligned on the time axis are mapped to a unified three-dimensional spatial coordinate system according to their physical installation position in the battery pack, so as to realize the spatial correspondence and association of multi-source information such as temperature distribution, voltage anomaly, and sound source.

[0077] It can be seen that by acquiring raw data from multi-source heterogeneous sensors and performing spatiotemporal alignment and coordinate unification, accurate matching of multi-dimensional information such as infrared thermal distribution, voltage fluctuation, and air pressure change in time and space is achieved, effectively improving the reliability of data fusion and the accuracy of early warning, thereby eliminating misjudgments caused by asynchronous sampling or misaligned positions, and providing a consistent and highly reliable input basis for the early identification of battery thermal runaway.

[0078] Optionally, in step S12, feature-level fusion of the target multi-source data is performed to obtain a fused feature vector, which may include the following steps:

[0079] Step S121: Extract features from the target multi-source data to obtain multi-dimensional feature parameters of the battery. The multi-dimensional feature parameters include: infrared feature parameters, voltage feature parameters, temperature feature parameters, air pressure feature parameters, and acoustic signature feature parameters.

[0080] Step S122: By fusing the multidimensional feature parameters, a fused feature vector is obtained.

[0081] In this embodiment of the invention, multidimensional feature parameters refer to a set of multi-physics field quantitative indicators extracted from target multi-source data to characterize the battery safety status.

[0082] Infrared characteristic parameters include, but are not limited to, local maximum temperature, temperature gradient, hot spot area, and thermal distribution symmetry.

[0083] Voltage characteristic parameters include, but are not limited to, voltage fluctuation amplitude, DC internal resistance change rate, and individual cell consistency deviation.

[0084] The temperature characteristic parameters are the rate of temperature rise and the temperature difference between adjacent cells.

[0085] The pressure characteristic parameters are the rate of pressure change and the absolute pressure value.

[0086] Acoustic signature parameters are the energy surge values ​​and durations in a specific frequency band, used to identify the acoustic features of gas generation or rupture.

[0087] Feature extraction from multi-source target data to obtain multi-dimensional feature parameters of the battery can be understood as extracting multi-dimensional key quantitative indicators that can characterize the battery from spatiotemporally aligned multi-sensor data and constructing a multi-dimensional feature system that reflects the battery state.

[0088] By fusing features from multidimensional feature parameters, the resulting fused feature vector can be understood as combining and weighting the independent features of each physical field according to semantic correlation to form a high-dimensional fused feature vector that can comprehensively characterize battery safety risks.

[0089] It can be seen that by extracting multi-dimensional features from target multi-source data and fusing them into a unified feature vector, the limitations of single-parameter criteria are overcome, significantly improving the comprehensiveness and robustness of risk identification, providing an effective decision-making basis for subsequent accurate graded early warning, thereby reducing false alarm and false negative rates.

[0090] Optionally, the warning threshold range includes: a first threshold range, which is used to determine whether an early warning is triggered. In step S14, determining whether to trigger a graded warning prompt based on the warning threshold range and the fused feature vector may include the following execution steps:

[0091] Step S141: Analyze the fused feature vector according to the first threshold range to obtain the first analysis result;

[0092] Step S142: In response to the first analysis result indicating that any single parameter of the battery is within the first threshold range, an early warning is triggered.

[0093] In this embodiment of the invention, the first threshold range refers to a preset range in which a single sensor characteristic (such as local temperature rise, voltage fluctuation, etc.) exceeds the normal operating baseline but does not reach a dangerous level.

[0094] The first analysis result is a preliminary risk conclusion to determine whether a certain parameter has early anomalies.

[0095] Early warning is the lowest level of response, which only records abnormal data, marks potential risks and prompts maintenance, without interfering with battery operation. It is used to achieve early detection and trend tracking of thermal runaway.

[0096] The analysis of the fused feature vector based on the first threshold range yields the first analysis result. This can be understood as comparing each component of the fused feature vector independently with a preset single-item threshold range to identify whether any physical field parameter exhibits abnormal fluctuations, thus obtaining the first analysis result.

[0097] In response to the first analysis result that any single parameter of the battery is within the first threshold range, triggering an early warning can be understood as follows: when only a single parameter (such as local temperature rise or voltage drift) is abnormal while other parameters are normal, it is determined that the battery has a potential risk, and a low-level warning is initiated to record the trend and trigger self-check.

[0098] It can be seen that by setting a first threshold range to independently discriminate the fused feature vector with a single parameter, accurate detection of early-stage weak anomalies in the battery is achieved, avoiding misjudgments or missed detections caused by drift of a single sensor or transient interference. Furthermore, triggering early warning can proactively record trends and initiate self-checks before the risk spreads, thereby significantly improving the reliability of the warning.

[0099] Optionally, the warning threshold range includes a second threshold range, which is used to determine whether a moderate warning is triggered. The warning level of a moderate warning is higher than that of an early warning. In step S14, determining whether to trigger a graded warning prompt based on the warning threshold range and the fused feature vector may include the following execution steps:

[0100] Step S143: Analyze the fused feature vector according to the second threshold range to obtain the second analysis result;

[0101] Step S144: In response to the second analysis result indicating that multiple parameters of the battery are within the second threshold range, a moderate warning is triggered.

[0102] In this embodiment of the invention, the second threshold range refers to a composite threshold range in which multiple physical field parameters (such as infrared temperature rise + voltage drop + air pressure rise) simultaneously exceed the normal correlation range and exhibit synergistic abnormality.

[0103] The second analysis result is a comprehensive judgment on whether there are significant abnormal correlations in the data from multiple sensors in time or space.

[0104] A moderate warning is a medium-level response, indicating that the risk of thermal runaway is accumulating. The system will actively limit the charging and discharging power and enhance cooling to prevent the risk from escalating, thus achieving controllable intervention and safety buffering.

[0105] The second analysis result obtained by analyzing the fused feature vector based on the second threshold range can be understood as the simultaneous correlation comparison of features in multiple dimensions of the fused feature vector based on the second threshold range to determine whether there are two or more parameters in an abnormal range.

[0106] The response to the second analysis results indicating that multiple battery parameters are within the second threshold range, triggering a moderate warning can be understood as follows: when the system confirms that multiple source signals show a strong correlation anomaly, indicating that the battery risk has a local spread trend, power limiting and active cooling are initiated to achieve risk suppression and safety buffering.

[0107] As can be seen, by performing multi-parameter collaborative analysis on the fused feature vector within the second threshold range, abnormal states before thermal runaway can be accurately identified, avoiding misjudgment based on a single parameter. Triggering a moderate warning can promptly reduce power and activate enhanced cooling, implementing proactive intervention before thermal runaway occurs, significantly improving response timeliness and safety, and effectively balancing false alarm rate and false negative rate.

[0108] Optionally, the warning threshold range includes a third threshold range, which is used to determine whether an emergency warning is triggered. The warning level of an emergency warning is higher than that of a moderate warning. In step S14, determining whether to trigger a graded warning prompt based on the warning threshold range and the fused feature vector may include the following execution steps:

[0109] Step S145: Analyze the fused feature vector according to the third threshold range to obtain the third analysis result;

[0110] Step S146: In response to the third analysis result indicating that multiple parameters of the battery are within the third threshold range, an emergency warning is triggered.

[0111] In this embodiment of the invention, the third threshold range refers to a composite threshold range in which multiple sensor data exhibit strong coupling and drastic change characteristics in the time series, such as a sudden temperature rise > 3°C / s, a sudden voltage drop > 5%, a sudden increase in air pressure > 1.5kPa / s, or a surge in specific acoustic energy, etc., which are not limited here.

[0112] The third analysis result is a conclusion to determine whether the battery exhibits thermal runaway.

[0113] Emergency warning is the highest level of response, which means immediately cutting off the main circuit, activating fire extinguishing devices, and reporting to the cloud to ensure that the accident is contained within milliseconds and to maximize the safety of personnel and vehicles.

[0114] The analysis of the fused feature vector based on the third threshold range yields the third analysis result, which can be understood as a comprehensive judgment of whether multi-dimensional signals such as infrared temperature rise rate, voltage drop, air pressure change and voiceprint features are synchronously within the third threshold range in a very short time.

[0115] In response to the third analysis results indicating that multiple battery parameters are within the third threshold range, triggering an emergency warning can be understood as immediately executing mandatory safety commands such as cutting off the high-voltage circuit and activating the fire extinguishing device when it is confirmed that the battery's thermal runaway state is irreversible, thereby achieving safety protection for the battery.

[0116] As can be seen, dynamic analysis of multi-source fusion features through the third threshold range accurately identifies the irreversible critical state of thermal runaway, avoiding false triggering. Triggering an emergency warning enables millisecond-level high-voltage power cut-off and fire extinguishing measures, significantly improving the reliability and timeliness of safety response, effectively avoiding the risk of battery fires, and ensuring the safety of occupants and the vehicle.

[0117] Optionally, in step S16, in response to triggering a graded warning prompt, protecting the battery based on preset measures may include the following execution steps:

[0118] Step S161: In response to triggering an early warning, record abnormal parameters and mark potential risks;

[0119] Step S162: In response to triggering a moderate warning, limit the charging power and / or discharging power, and activate the auxiliary heat dissipation device;

[0120] In step S163, in response to triggering an emergency warning, the main circuit of the battery is disconnected and the information is reported to the management platform.

[0121] In this embodiment of the invention, recording abnormal parameters and marking potential risks in response to triggering an early warning can be understood as archiving and marking weak abnormal signals for risk when an early warning is triggered, without interfering with operation, but only providing a basis for subsequent maintenance and trend analysis.

[0122] In response to triggering a moderate warning, limiting charging and / or discharging power and activating auxiliary cooling devices can be understood as actively reducing load and enhancing cooling to suppress heat accumulation and diffusion, thereby delaying the escalation of risks.

[0123] In response to triggering an emergency warning, cutting off the battery's main circuit and reporting to the management platform can be understood as immediately cutting off the battery's main circuit when an emergency warning is triggered, interrupting the thermal runaway chain reaction, and simultaneously pushing accident information to the cloud and user terminals to achieve remote monitoring and emergency measures.

[0124] It can be seen that by recording data in the early warning stage to support trend analysis and avoid false intervention, the moderate warning stage actively reduces power and starts heat dissipation to delay the evolution of risks, and the emergency warning stage immediately cuts off the main circuit and reports to the platform, achieving millisecond-level accident blocking and remote linkage, thereby significantly improving the safety protection capability of the battery system throughout its entire life cycle.

[0125] Figure 3This is a schematic diagram of a battery protection system according to one embodiment of the present invention, such as... Figure 3 As shown, the battery protection system is used to perform the above-described battery protection method, including:

[0126] Infrared sensor array module, used to collect infrared characteristic parameters of the battery;

[0127] The multi-sensor fusion module is used to integrate the battery's multi-dimensional characteristic parameters, including: infrared characteristic parameters, voltage characteristic parameters, temperature characteristic parameters, air pressure characteristic parameters, and acoustic signature characteristic parameters.

[0128] The safety status assessment module is used to determine the risk level of the battery based on multi-dimensional characteristic parameters.

[0129] The graded early warning and control module is used to execute corresponding early warning prompts and control strategies based on the risk level.

[0130] In this embodiment of the invention, the infrared sensor array module is a hardware unit consisting of multiple miniature infrared focal plane sensors distributed inside the battery pack, which non-contactly collect infrared characteristic parameters such as the surface temperature distribution and thermal gradient of the battery.

[0131] The multi-sensor fusion module is an algorithm unit that integrates multi-source signals such as voltage, temperature, air pressure, and acoustic signature. Through spatiotemporal registration and feature extraction, it constructs a unified multi-dimensional risk feature vector.

[0132] The safety status assessment module is an intelligent analysis unit based on the Dempster-Shafer Theory of Evidence (DS) or Bayesian inference. It quantifies the risk of the fused features and outputs the thermal runaway level.

[0133] The graded early warning and control module is the control center for implementing the three-level early warning response strategy. Based on the assessment results, it triggers differentiated safety actions such as prompts, power limiting, circuit disconnection, or fire suppression.

[0134] For example, the infrared sensor array module continuously acquires images of the battery surface thermal distribution, and performs noise reduction, enhancement, and digitization processing through data preprocessing. Simultaneously, multi-dimensional sensor data such as voltage, temperature, air pressure, and acoustic signature are collected and fed into the multi-sensor fusion module. The safety status assessment module integrates multi-source information, calculates the current battery safety status using a risk assessment model, and generates control commands based on preset thresholds and adaptive algorithms. Finally, the tiered early warning and control module alerts the user to risks and proactively intervenes in the battery's operating status.

[0135] As can be seen, the battery protection system proposed in this invention consists of an infrared sensor array module, a multi-sensor fusion module, a safety status assessment module, and a hierarchical early warning and control module, forming a complete monitoring-decision-execution closed loop. The battery protection system adopts a distributed architecture, deploying multiple miniature infrared sensor nodes at key locations within the battery pack to achieve comprehensive monitoring without altering the battery pack structure.

[0136] Optionally, the infrared sensor array module includes: a wide-angle infrared sensor, a side infrared sensor, and a miniature infrared sensor. The wide-angle infrared sensor is arranged on the top of the battery, the side infrared sensor is arranged on the sidewall of the battery, and the miniature infrared sensor is arranged in the module gap of the battery.

[0137] In this embodiment of the invention, the wide-angle infrared sensor is an uncooled infrared focal plane array sensor with a field of view of up to 120°. It is installed on the top crossbeam of the battery pack and scans downwards to cover the entire upper surface of the battery cells, thereby achieving panoramic monitoring of a large-area thermal distribution.

[0138] The side infrared sensor is a compact infrared sensor with a field of view of 90°, embedded in the side wall of the battery pack, specifically designed to monitor the side temperature rise of the edge module and eliminate blind spots in corner monitoring.

[0139] The miniature infrared sensor is a high-sensitivity infrared detection unit with a size of only 15mm×15mm×5mm. It is flexibly installed in the gap between battery cell modules to capture local hot spots at close range and high resolution, enabling accurate identification of thermal anomalies at the individual battery cell level.

[0140] For example, the infrared sensor array module employs a miniaturized infrared focal plane array sensor, which achieves sensitive response to specific infrared bands through a combination of optical lenses and filters. The sensor array is distributed and embedded within the battery pack, located between the battery cells and in the upper space, forming a multi-view monitoring network. Each sensing node includes: an uncooled microbolometer with a response band of 8-14μm and a noise equivalent temperature difference of <50mK; a wide-angle optical lens with a 120° field of view and a focal length of 0.5-1m, suitable for short-distance monitoring within the battery pack; a dynamic range adjustment circuit adaptable to a temperature measurement range of -20°C to +150°C; and a digital signal output: a two-wire interface / serial peripheral digital interface for direct output of temperature matrix data. This invention, by employing a pure infrared array sensor, overcomes the disadvantage of visible light cameras performing poorly in dark environments, while significantly reducing system size and power consumption. Furthermore, the sensors are directly installed inside the battery pack and maintain a safe distance from the battery cells, achieving thermal isolation through a thermally conductive insulating film to avoid affecting the battery temperature field.

[0141] Furthermore, to achieve accurate early warning, a three-level data fusion architecture is adopted to comprehensively improve monitoring reliability. The first level is spatiotemporal registration, which aligns timestamps and unifies spatial coordinates for monitoring data from different sensors and sampling rates. Infrared image data and voltage / temperature monitoring points are correlated through coordinate mapping to ensure data consistency across spatiotemporal dimensions. The second level is feature-level fusion, which extracts feature parameters from data from each sensor to form a fused feature vector, including: infrared data: maximum temperature, temperature gradient, hotspot area, thermal distribution symmetry; voltage data: voltage fluctuation, DC internal resistance, consistency deviation; temperature data: temperature difference, temperature rise rate; air pressure data: pressure change rate, absolute pressure value; and acoustic signature data: specific frequency amplitude, acoustic signature signal duration. Finally, based on DS evidence theory or Bayesian inference, uncertainty reasoning is performed on the evidence at each level to comprehensively determine the battery status.

[0142] To better understand the technical solution of this invention, the following uses a typical vehicle battery pack as an example to describe the system configuration and workflow in detail. This battery pack is a ternary lithium-ion battery with a rated voltage of 400V and a capacity of 80kWh, consisting of 24 modules, each containing 12 cells. The infrared sensor arrangement is as follows: Top scanning sensors: Four infrared array sensors are evenly distributed on the top crossbeam of the battery pack, installed at a downward angle, covering the entire upper surface of the modules. Side monitoring sensors: Two sensors are located on the sidewalls of the battery pack, responsible for monitoring the side temperature of the edge modules. Gap-inserted sensors: Eight miniature infrared sensors, only 15mm × 15mm × 5mm in size, are inserted at key locations between modules and connected via flexible circuit boards. The sensors are installed using thermally conductive insulating adhesive to ensure good thermal contact between the sensors and the internal structural components of the battery pack while maintaining electrical isolation. The wiring follows the existing wiring harness of the battery pack, using shielded twisted-pair cables to prevent electromagnetic interference. The infrared sensor windows are made of high-temperature resistant germanium glass with an anti-reflective coating to ensure an infrared transmittance of over 90%.

[0143] After powering on, the system first executes a self-test program to confirm the working status of each sensor and the normality of the communication link. It then enters the routine monitoring state: Step 1: Data Acquisition Cycle: The infrared sensor acquires thermal distribution images at a frequency of 2Hz, the battery cell voltage / temperature at a frequency of 10Hz, the barometric pressure sensor acquires barometric pressure data at a frequency of 100Hz, and the acoustic fingerprint sensor continuously monitors, recording waveforms when triggered. Step 2: Thermal Feature Analysis: The system processes the infrared thermal image and extracts the following features: the highest temperature in the area and its coordinates, the temperature standard deviation (reflecting the uniformity of temperature distribution), the direction and intensity of the thermal gradient, and the rate of temperature change compared to the previous cycle. Step 3: Multi-Source Data Fusion Judgment: The system fuses and analyzes the infrared features with data from other sensors. For example, if a local temperature anomaly is detected, the voltage and internal resistance of the corresponding battery cell are checked; if a sharp temperature rise is detected, barometric pressure changes and acoustic fingerprint features are verified, and a comprehensive evaluation is performed based on the charging / discharging status and ambient temperature. Step 4: Early Warning and Control Decision: Based on the fusion analysis results, the system executes corresponding control strategies: Level 1 Early Warning: Record data and prompt for inspection during the next system maintenance; Level 2 Early Warning: Limit charging and discharging power to 70% and increase cooling system power; Level 3 Early Warning: Cut off the main circuit, activate the fire extinguishing device, and unlock the battery pack mechanical lock. Step 5: Data Upload and Remote Notification: The system uploads early warning information, sensor data, and system status to the cloud management platform via the vehicle communication terminal, and simultaneously notifies users of relevant information through the vehicle display unit and mobile application.

[0144] To ensure long-term monitoring accuracy, the system is designed with an online self-calibration mechanism. Once a week or during each charge, the system uses a standard temperature reference point (blackbody) evenly distributed within the battery pack to calibrate the infrared sensors online, eliminating sensor drift errors. Simultaneously, the system records the performance indicators of each sensor, and when the performance of a sensor degrades beyond the allowable range, it prompts for maintenance or activation of redundant sensors.

[0145] Figure 4 This is a schematic diagram of an infrared sensor arrangement according to one embodiment of the present invention, such as... Figure 4As shown, four wide-angle infrared sensors (120° field of view) are evenly arranged on the top crossbeam of the battery pack to achieve panoramic thermal imaging coverage of the entire surface of the cells. One lateral infrared sensor (90° field of view) is installed on each side wall to specifically monitor areas prone to heat accumulation in the edge modules, eliminating blind spots in traditional top monitoring. n (e.g., 8) miniature infrared sensors (15mm×15mm×5mm in size) are embedded in the gaps between several (e.g., 24) modules to directly capture localized temperature rises at the individual cell level with high spatial resolution (8×8 pixels). All sensors are connected to the control unit via flexible circuit boards and fixed with thermally conductive insulating adhesive, ensuring both sensitive thermal conduction response and electrical isolation and structural safety. This invention overcomes the limitations of traditional single-point or single-surface monitoring, constructing a surface thermal field sensing network with no blind spots, high density, and spatial correlation, providing accurate spatial data support for early identification of thermal runaway.

[0146] Figure 5 This is a multi-sensor data time-series correlation analysis diagram according to one embodiment of the present invention, such as... Figure 5 The diagram illustrates the changes in different sensor data over time and the system's response strategy during battery thermal runaway. At time T0, all battery parameters are normal. At time T1, only infrared and voltage data show slight fluctuations. At time T2, infrared data shows a significant increase in local temperature, and voltage begins to fluctuate; at this point, the system issues a Level 1 warning. At time T3, the infrared temperature rises sharply, the voltage drops significantly, the gas pressure rises rapidly, and gas generation acoustic signatures are detected; the system issues a Level 2 warning and limits power operation. At time T4, all sensor data show thermal runaway characteristics; the system issues a Level 3 warning and executes a safety circuit breaker.

[0147] Figure 6(a) is a schematic diagram of the inside of a battery pack according to one embodiment of the present invention, Figure 6(b) is a schematic diagram of the inside of a battery pack according to another embodiment of the present invention, Figure 6(c) is a schematic diagram of the inside of a battery pack according to yet another embodiment of the present invention, and Figure 6(d) is a schematic diagram of the inside of a battery pack according to yet another embodiment of the present invention. As shown in Figures 6(a) to 6(d), the battery pack includes: an upper battery housing, a lower battery housing, a high-voltage matching system for the battery, a battery management system, a battery cooling and heating system, battery cells, a signal acquisition system, a battery safety protection system, and a battery insulation system.

[0148] The upper battery casing, as the upper encapsulation structure of the battery pack, primarily serves for mechanical protection, sealing against dust and water, installation support, and structural strength. It typically forms a closed shell together with the lower battery casing, protecting the internal cells and electronic components from external impacts, vibrations, and environmental corrosion. The lower battery casing, as the bottom load-bearing and protective structure of the battery pack, bears the vehicle's installation load and possesses excellent impact and corrosion resistance. It often integrates liquid cooling channels or heat dissipation fins to assist in thermal management and works with the upper battery casing to protect the battery. The high-voltage matching system is responsible for the connection, distribution, and protection of the high-voltage circuits within the battery pack. This includes components such as high-voltage positive and negative busbars, high-voltage connectors, fuses, and pre-charge circuits, ensuring the safe and efficient transmission of electrical energy to the vehicle's high-voltage system and preventing risks such as overcurrent and short circuits. The battery management system (BMS) collects real-time data on cell voltage, temperature, and current, estimates state of charge and health, implements equalization control, charge and discharge management, fault diagnosis, and safety protection, and is the core control unit for battery safety and performance optimization. The battery cooling and heating system regulates the battery's operating temperature within the optimal range (typically 15°C–35°C) through a liquid cooling circuit, heating film, or positive temperature coefficient thermistor element. This prevents thermal runaway caused by high temperatures or performance degradation caused by low temperatures, improving charging and discharging efficiency and lifespan. The battery cells are responsible for the conversion and storage of chemical and electrical energy. Multiple cells are connected in series and parallel to form modules, constituting the total energy capacity and voltage platform of the battery pack. The signal acquisition system collects various electrical and physical signals during battery operation, including voltage, current, temperature, air pressure, acoustic signature, and infrared thermal distribution. This provides real-time, high-precision sensing data for the battery management and safety systems, forming the basis for intelligent early warning and closed-loop control. The battery safety protection system integrates overvoltage, overcurrent, overtemperature, insulation monitoring, thermal runaway early warning, and active intervention mechanisms (such as main relay disconnection, fire extinguishing device activation, and mechanical pressure relief valve opening). It responds quickly under abnormal operating conditions, minimizing safety risks. Battery insulation systems are used in low-temperature environments to reduce heat loss, maintain stable cell temperature, improve low-temperature start-up performance and charge / discharge efficiency, and extend battery availability in cold environments by using thermal insulation materials (such as aerogel and foam materials) or passive thermal management structures.

[0149] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0150] This embodiment also provides a battery protection device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware implementations, or a combination of software and hardware, are also possible and contemplated.

[0151] Figure 7 This is a structural block diagram of a battery protection device according to one embodiment of the present invention, such as... Figure 7 As shown, taking a battery protection device 700 as an example, the device includes: an acquisition module 701 for acquiring target multi-source data, wherein the target multi-source data represents various types of battery monitoring data; a fusion module 702 for performing feature-level fusion on the target multi-source data to obtain a fused feature vector; a determination module 703 for determining whether to trigger a graded warning prompt based on a warning threshold range and the fused feature vector, wherein the warning threshold range is obtained by adjusting the battery's historical state data and current state data, and the graded warning prompt is used to indicate that the battery has different levels of risk; and a protection module 704 for protecting the battery based on preset measures in response to triggering the graded warning prompt.

[0152] Optionally, the acquisition module 701 is also used to acquire initial multi-source data, wherein the initial multi-source data is collected by various types of sensors; the timestamps of the initial multi-source data are aligned to obtain intermediate multi-source data; and the spatial coordinates of the intermediate multi-source data are unified to obtain target multi-source data.

[0153] Optionally, the fusion module 702 is also used to extract features from the target multi-source data to obtain multi-dimensional feature parameters of the battery, including: infrared feature parameters, voltage feature parameters, temperature feature parameters, air pressure feature parameters, and acoustic signature feature parameters; and to obtain a fused feature vector by performing feature fusion on the multi-dimensional feature parameters.

[0154] Optionally, the warning threshold range includes: a first threshold range, which is used to determine whether an early warning is triggered. The determining module 703 is further used to analyze the fused feature vector based on the first threshold range to obtain a first analysis result; in response to the first analysis result indicating that any single parameter of the battery is within the first threshold range, an early warning is triggered.

[0155] Optionally, the warning threshold range includes a second threshold range, which is used to determine whether a moderate warning is triggered. The warning level of a moderate warning is higher than that of an early warning. The determining module 703 is also used to analyze the fused feature vector based on the second threshold range to obtain a second analysis result. In response to the second analysis result indicating that multiple parameters of the battery are within the second threshold range, a moderate warning is triggered.

[0156] Optionally, the warning threshold range includes a third threshold range, which is used to determine whether an emergency warning is triggered. The warning level of the emergency warning is higher than that of the moderate warning. The determining module 703 is also used to analyze the fused feature vector based on the third threshold range to obtain a third analysis result. In response to the third analysis result indicating that multiple parameters of the battery are within the third threshold range, an emergency warning is triggered.

[0157] Optionally, the protection module 704 is also used to record abnormal parameters and mark potential risks in response to triggering an early warning; to limit charging power and / or discharging power and activate auxiliary heat dissipation devices in response to triggering a moderate warning; and to disconnect the main circuit of the battery and report to the management platform in response to triggering an emergency warning.

[0158] It should be noted that the above modules can be implemented by software or hardware. For the latter, they can be implemented in the following ways, but are not limited to: all the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0159] According to one embodiment of the present invention, a vehicle is also provided, comprising: a memory storing an executable program; and a processor for running the executable program, wherein the executable program, when run on the processor, performs the battery protection method described in any of the preceding claims.

[0160] Embodiments of the present invention also provide a computer-readable storage medium storing a computer program, wherein the computer program is configured to perform the steps in any of the above method embodiments when run on a computer or processor.

[0161] Optionally, in this embodiment, the computer-readable storage medium may be configured to store a computer program for performing the following steps:

[0162] Step S10: Obtain target multi-source data, wherein the target multi-source data is used to represent multiple types of battery monitoring data;

[0163] Step S12: Perform feature-level fusion on the target multi-source data to obtain a fused feature vector;

[0164] Step S14: Determine whether to trigger a graded warning prompt based on the warning threshold range and the fused feature vector. The warning threshold range is obtained by adjusting the historical state data and current state data of the battery. The graded warning prompt is used to indicate that the battery has different levels of risk.

[0165] Step S16: In response to the triggering of a graded warning prompt, the battery is protected based on preset measures.

[0166] Optionally, in this embodiment, the computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0167] Embodiments of the present invention also provide an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0168] Optionally, in this embodiment, the processor in the above-described electronic device may be configured to run a computer program to perform the following steps:

[0169] Step S10: Obtain target multi-source data, wherein the target multi-source data is used to represent multiple types of battery monitoring data;

[0170] Step S12: Perform feature-level fusion on the target multi-source data to obtain a fused feature vector;

[0171] Step S14: Determine whether to trigger a graded warning prompt based on the warning threshold range and the fused feature vector. The warning threshold range is obtained by adjusting the historical state data and current state data of the battery. The graded warning prompt is used to indicate that the battery has different levels of risk.

[0172] Step S16: In response to the triggering of a graded warning prompt, the battery is protected based on preset measures.

[0173] Embodiments of the present invention also provide a computer program product, including a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0174] Optionally, in this embodiment, the computer program in the above-described computer program product can be configured to perform the following steps when executed by a processor:

[0175] Step S10: Obtain target multi-source data, wherein the target multi-source data is used to represent multiple types of battery monitoring data;

[0176] Step S12: Perform feature-level fusion on the target multi-source data to obtain a fused feature vector;

[0177] Step S14: Determine whether to trigger a graded warning prompt based on the warning threshold range and the fused feature vector. The warning threshold range is obtained by adjusting the historical state data and current state data of the battery. The graded warning prompt is used to indicate that the battery has different levels of risk.

[0178] Step S16: In response to the triggering of a graded warning prompt, the battery is protected based on preset measures.

[0179] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0180] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0181] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0182] In the several embodiments provided by this invention, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection can be through some interfaces; the indirect coupling or communication connection of units or modules can be electrical or other forms.

[0183] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0184] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0185] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0186] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A battery protection method, characterized in that, include: Acquire target multi-source data, wherein the target multi-source data is used to represent multiple types of battery monitoring data; The target multi-source data is fused at the feature level to obtain a fused feature vector; Whether to trigger a graded warning prompt is determined based on the warning threshold range and the fused feature vector. The warning threshold range is obtained by adjusting the historical and current state data of the battery. The graded warning prompt is used to indicate that the battery has different levels of risk. In response to the triggering of the graded warning prompt, the battery is protected based on preset measures.

2. The method according to claim 1, characterized in that, The acquisition of target multi-source data includes: Acquire initial multi-source data, wherein the initial multi-source data is collected by various types of sensors; The timestamps of the initial multi-source data are aligned to obtain intermediate multi-source data; The intermediate multi-source data is processed to unify spatial coordinates to obtain the target multi-source data.

3. The method according to claim 1, characterized in that, The step of performing feature-level fusion on the target multi-source data to obtain a fused feature vector includes: Feature extraction is performed on the target multi-source data to obtain the multi-dimensional feature parameters of the battery, wherein the multi-dimensional feature parameters include: infrared feature parameters, voltage feature parameters, temperature feature parameters, air pressure feature parameters, and acoustic signature feature parameters; The fused feature vector is obtained by fusing the multidimensional feature parameters.

4. The method according to claim 1, characterized in that, The warning threshold range includes: a first threshold range, which is used to determine whether an early warning is triggered; the step of determining whether to trigger a tiered warning based on the warning threshold range and the fused feature vector includes: The fused feature vector is analyzed based on the first threshold range to obtain a first analysis result; The early warning is triggered in response to the first analysis result indicating that any single parameter of the battery is within the first threshold range.

5. The method according to claim 1, characterized in that, The warning threshold range includes a second threshold range, which is used to determine whether a moderate warning is triggered. The warning level of the moderate warning is higher than that of the early warning. The step of determining whether to trigger a graded warning prompt based on the warning threshold range and the fused feature vector includes: The fused feature vector is analyzed based on the second threshold range to obtain a second analysis result; In response to the second analysis result indicating that multiple parameters of the battery are within the second threshold range, the moderate warning is triggered.

6. The method according to claim 1, characterized in that, The warning threshold range includes a third threshold range, which is used to determine whether an emergency warning is triggered. The warning level of the emergency warning is higher than that of the moderate warning. The step of determining whether to trigger a graded warning prompt based on the warning threshold range and the fused feature vector includes: The fused feature vector is analyzed based on the third threshold range to obtain a third analysis result; The emergency warning is triggered in response to the third analysis result indicating that multiple parameters of the battery are within the third threshold range.

7. The method according to claim 1, characterized in that, The response to triggering the graded early warning prompt, based on preset measures to protect the battery, includes: In response to triggering early warnings, abnormal parameters are recorded and potential risks are marked; In response to triggering a moderate warning, the charging power and / or discharging power are limited, and the auxiliary cooling device is activated; In response to the triggering of an emergency warning, the main circuit of the battery is cut off and the information is reported to the management platform.

8. A battery protection system for performing the battery protection method of claim 1, characterized in that, include: Infrared sensor array module, used to collect infrared characteristic parameters of the battery; The multi-sensor fusion module is used to integrate the multi-dimensional characteristic parameters of the battery, including: infrared characteristic parameters, voltage characteristic parameters, temperature characteristic parameters, air pressure characteristic parameters, and acoustic signature characteristic parameters; A safety status assessment module is used to determine the risk level of the battery based on the multi-dimensional feature parameters. The graded early warning and control module is used to execute corresponding early warning prompts and control strategies based on the risk level.

9. The system according to claim 8, characterized in that, The infrared sensor array module includes a wide-angle infrared sensor, a side infrared sensor, and a miniature infrared sensor. The wide-angle infrared sensor is arranged on the top of the battery, the side infrared sensor is arranged on the side wall of the battery, and the miniature infrared sensor is arranged in the module gap of the battery.

10. A vehicle, characterized in that, include: Memory, which stores executable programs; A processor for running the executable program, wherein the executable program, when run on the processor, performs the battery protection method as described in any one of claims 1 to 7.

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