Battery temperature abnormity real-time monitoring system, method and terminal based on multi-dimensional analysis

Abstract

The invention provides a battery temperature anomaly real-time monitoring system and method based on multi-dimensional analysis and a terminal, and the method comprises the steps: receiving temperature data collected by a plurality of temperature sensors on a target battery in real time, and caching the temperature data according to a time sequence; then, multi-dimensional detection is carried out based on the data, the absolute high temperature, the temperature rise rate, the overall temperature difference and local hot spot indexes are covered, and an anomaly detection result is accurately obtained. And a temperature anomaly sensor list is output according to a result, and powerful assistance is provided for fault positioning and maintenance decision making. According to the method, multiple indexes such as the absolute high temperature, the temperature rise rate, the overall temperature difference, the local temperature difference and historical frequency statistics are comprehensively considered, comprehensive evaluation and early warning are conducted on the temperature state of the battery pack, excellent real-time performance and robustness are achieved, and the intelligent degree and the safety guarantee capacity of a BMS are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of battery management system technology, and in particular to a real-time monitoring system, method and terminal for abnormal battery temperature based on multi-dimensional analysis. Background Technology

[0002] With the widespread application of lithium-ion batteries in electric vehicles, energy storage systems, and other fields, their safety hazards have become a major concern. During the dynamic process of battery charging and discharging, the battery is highly susceptible to localized overheating and abnormal temperature rise rates due to the combined effects of multiple factors, including uneven internal chemical reactions, complex external environmental interference, and manufacturing defects. If these abnormalities worsen, they can trigger battery thermal runaway, ultimately leading to serious and irreversible safety accidents.

[0003] Currently, mainstream battery temperature monitoring methods mainly rely on a single threshold judgment mechanism. When the temperature detected by a sensor exceeds a pre-set threshold, the system issues an alarm. However, this seemingly simple and direct monitoring method has several significant drawbacks: First, the false alarm rate is high, as judging based solely on the instantaneous high temperature value at a single point in time is easily affected by external instantaneous disturbances, leading to incorrect judgments. Second, the lag is extremely significant; because this mechanism does not fully consider temperature change trends, it is difficult to issue warnings before potential risks develop into actual hazards. Third, spatial perception capabilities are severely lacking, completely ignoring the spatial distribution relationship between sensors and failing to accurately identify potential local hotspots within the battery. Fourth, there is a lack of an effective statistical feedback mechanism, preventing comprehensive recording and analysis of historical high-temperature events, which undoubtedly poses a significant obstacle to fault location and subsequent maintenance and optimization work. Summary of the Invention

[0004] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide a real-time monitoring system, method and terminal for abnormal battery temperature based on multi-dimensional analysis, which solves the technical problems of existing battery temperature monitoring relying on a single threshold judgment, resulting in high false alarm rate, strong lag, lack of spatial perception and lack of statistical feedback mechanism.

[0005] To achieve the above and other related objectives, this invention provides a real-time monitoring system for abnormal battery temperature based on multi-dimensional analysis. The system includes: a data acquisition and caching module, used to receive real-time temperature data collected by various temperature sensors installed on the target battery and cache it in chronological order; an anomaly detection module, connected to the data acquisition and caching module, used to perform absolute high temperature, temperature rise rate, overall temperature difference, and local hot spot detection based on the temperature data from each temperature sensor to obtain anomaly detection results; and a result output module, connected to the anomaly detection module, used to output a list of temperature anomaly sensors based on the anomaly detection results to assist in fault location and maintenance decisions.

[0006] In one embodiment of the present invention, the data acquisition and caching module includes: a data acquisition unit, used to receive temperature data synchronously acquired by each temperature sensor installed on the target battery; wherein each temperature data acquired by each temperature sensor includes the acquired temperature value and the corresponding acquisition timestamp; and a data caching unit, used to cache the temperature values ​​of each temperature sensor in a sliding window with a maximum cache length in chronological order.

[0007] In one embodiment of the present invention, the anomaly detection module includes: an absolute high temperature detection unit, used to determine whether an absolute high temperature event has occurred at the corresponding temperature sensor based on the temperature value currently collected by each temperature sensor, and to obtain an absolute high temperature event detection result; a temperature rise rate detection unit, used to determine whether a temperature rise rate event has occurred at the corresponding temperature sensor based on the temperature value currently cached in the sliding window of each temperature sensor, and to obtain a temperature rise rate detection result; an overall temperature difference detection unit, used to calculate the difference between the highest and lowest temperatures among the temperature values ​​currently collected by all temperature sensors, and to determine whether an overall temperature difference event has occurred at the current target battery, and to obtain an overall temperature difference event; and a local hot spot detection unit, used to determine whether a local hot spot event has occurred at the corresponding temperature sensor based on the temperature sensor layout information of the target battery and the temperature value currently collected by each temperature sensor, and to obtain a local hot spot detection result.

[0008] In one embodiment of the present invention, the step of determining whether an absolute high temperature event has occurred at the corresponding temperature sensor based on the temperature value currently collected by each temperature sensor, and obtaining the absolute high temperature event detection result, includes: comparing the temperature value currently collected by each temperature sensor with a set absolute temperature threshold, and recording the temperature value greater than the absolute temperature threshold as an absolute high temperature event of the corresponding temperature sensor, updating the absolute high temperature event count value of the temperature sensor, and obtaining the absolute high temperature event detection result; wherein, the absolute high temperature event detection result includes: the time and temperature value of the absolute high temperature event occurring at each temperature sensor, and the current absolute high temperature event count value.

[0009] In one embodiment of the present invention, determining whether a temperature rise event has occurred for the corresponding temperature sensor based on the temperature value of each temperature sensor currently cached in the sliding window, and obtaining a temperature rise detection result, includes: extracting the temperature values ​​of each temperature sensor within the most recently cached preset window size in the sliding window, calculating the temperature rise rate of each temperature sensor within the preset window; comparing the temperature rise rate corresponding to each temperature sensor within the preset window with a temperature rise rate threshold, and recording a temperature rise event for the corresponding temperature sensor when the temperature rise rate is greater than the temperature rise rate threshold, thereby obtaining a temperature rise detection result; wherein, the temperature rise detection result includes: the time when each temperature sensor experiences a temperature rise event and the corresponding temperature rise rate.

[0010] In one embodiment of the present invention, the step of calculating the difference between the highest and lowest temperatures among the temperature values ​​currently collected by all temperature sensors and determining whether the current target battery has experienced an event of excessive overall temperature difference to obtain an overall temperature difference detection result includes: obtaining the highest and lowest temperatures among the temperature values ​​currently collected by all temperature sensors and calculating the difference between the highest and lowest temperatures; comparing the difference between the highest and lowest temperatures with an overall temperature difference threshold; and recording an event of excessive overall temperature difference when the difference between the highest and lowest temperatures is greater than the overall temperature difference threshold to obtain an overall temperature difference detection result.

[0011] In one embodiment of the present invention, the step of determining whether a local hotspot event has occurred at a corresponding temperature sensor based on the temperature sensor layout information of the target battery and the temperature value currently collected by each temperature sensor, and obtaining the local hotspot detection result, includes:

[0012] Based on the index of all temperature sensors in the target battery and their physical layout, the adjacent sensors of each temperature sensor are identified; the current temperature value collected by each temperature sensor and its adjacent sensors is compared, and if the current temperature value of the current temperature sensor is significantly higher than the temperature value of its adjacent sensors, it is recorded as a local hot spot event of that temperature sensor, and the local hot spot detection result is obtained; wherein, the local hot spot detection result includes: the time of occurrence of the local hot spot event of each temperature sensor and the corresponding temperature value and the temperature value of the adjacent sensors.

[0013] In one embodiment of the present invention, the buffer length of the sliding window is set to twice the preset window size.

[0014] To achieve the above and other related objectives, this invention provides a real-time monitoring method for abnormal battery temperature based on multi-dimensional analysis, comprising: receiving temperature data collected in real time by multiple temperature sensors installed on the target battery and caching them in chronological order; performing absolute high temperature, temperature rise rate, overall temperature difference, and local hot spot detection based on the temperature data of each temperature sensor to obtain anomaly detection results; and outputting a list of temperature anomaly sensors based on the anomaly detection results to assist in fault location and maintenance decisions.

[0015] To achieve the above and other related objectives, the present invention provides an electronic terminal, comprising: one or more memories and one or more processors; the one or more memories are used to store a computer program; the one or more processors are connected to the memories and are used to run the computer program to execute the real-time monitoring system for abnormal battery temperature based on multi-dimensional analysis.

[0016] As described above, this invention is a real-time monitoring system, method, and terminal for abnormal battery temperature based on multi-dimensional analysis, offering the following advantages: This invention receives real-time temperature data collected by multiple temperature sensors on the target battery and caches it in chronological order. Subsequently, multi-dimensional detection is performed based on this data, covering absolute high temperature, temperature rise rate, overall temperature difference, and local hotspot indicators, accurately obtaining anomaly detection results. A list of abnormal temperature sensors is output based on the results, providing strong support for fault location and maintenance decisions. This invention comprehensively considers multiple indicators such as absolute high temperature, temperature rise rate, overall temperature difference, local temperature difference, and historical frequency statistics to provide a comprehensive assessment and early warning of the battery pack's temperature status, exhibiting excellent real-time performance and robustness, effectively improving the intelligence and safety assurance capabilities of the BMS system. Attached Figure Description

[0017] Figure 1 The diagram shown is a structural schematic of a real-time battery temperature anomaly monitoring system based on multi-dimensional analysis according to an embodiment of the present invention.

[0018] Figure 2 The diagram shown is a structural schematic of a real-time battery temperature anomaly monitoring system based on multi-dimensional analysis according to an embodiment of the present invention.

[0019] Figure 3 The diagram shown is a flowchart illustrating a real-time monitoring method for abnormal battery temperature based on multi-dimensional analysis, according to an embodiment of the present invention.

[0020] Figure 4 The diagram shown is a structural schematic of an electronic terminal according to an embodiment of the present invention. Detailed Implementation

[0021] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0022] It should be noted that in the following description, reference is made to the accompanying drawings, which illustrate several embodiments of the invention. It should be understood that other embodiments may also be used, and changes in mechanical composition, structure, electrical system, and operation may be made without departing from the spirit and scope of the invention. The following detailed description should not be considered limiting, and the scope of the embodiments of the invention is defined only by the claims of the published patents. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. Spatially related terms, such as “upper,” “lower,” “left,” “right,” “below,” “below,” “lower part,” “above,” “upper part,” etc., may be used herein to illustrate the relationship between one element or feature shown in the figures and another element or feature.

[0023] Throughout this specification, when it is said that a part is "connected" to another part, this includes not only "direct connection" but also "indirect connection" by placing other elements in between. Furthermore, when it is said that a part "includes" a certain constituent element, unless otherwise stated otherwise, this does not exclude other constituent elements, but rather means that other constituent elements may also be included.

[0024] The terms "first," "second," and "third," etc., used herein are for the purpose of describing various parts, components, regions, layers, and / or segments, but are not limiting. These terms are used only to distinguish one part, component, region, layer, or segment from others. Therefore, the "first part," "component," "region," "layer," or "segment" described below may refer to a "second part," "component," "region," "layer," or "segment" without departing from the scope of this invention.

[0025] Furthermore, as used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context indicates otherwise. It should be further understood that the terms “comprising,” “including,” indicate the presence of the stated feature, operation, element, component, item, kind, and / or group, but do not preclude the presence, occurrence, or addition of one or more other features, operations, elements, components, items, kinds, and / or groups. The terms “or” and “and / or” as used herein are interpreted as inclusive, or mean any one or any combination thereof. Thus, “A, B, or C” or “A, B, and / or C” means “any one of: A; B; C; A and B; A and C; B and C; A, B, and C.” Exceptions to this definition arise only when combinations of elements, functions, or operations are inherently mutually exclusive in some manner.

[0026] This invention provides a real-time battery temperature anomaly monitoring system based on multi-dimensional analysis. It receives real-time temperature data collected by multiple temperature sensors on the target battery and caches it chronologically. Subsequently, based on this data, multi-dimensional detection is performed, covering absolute high temperature, temperature rise rate, overall temperature difference, and local hotspot indicators, accurately obtaining anomaly detection results. A list of temperature anomaly sensors is output based on the results, providing strong support for fault location and maintenance decisions. This invention comprehensively considers multiple indicators such as absolute high temperature, temperature rise rate, overall temperature difference, local temperature difference, and historical frequency statistics to provide a comprehensive assessment and early warning of the battery pack's temperature status. It possesses excellent real-time performance and robustness, effectively improving the intelligence and safety assurance capabilities of the BMS system.

[0027] The present invention will now be described in detail with reference to the accompanying drawings, so that those skilled in the art can readily implement it. The present invention can be embodied in many different forms and is not limited to the embodiments described herein.

[0028] like Figure 1 This diagram illustrates the structure of a real-time battery temperature anomaly monitoring system based on multi-dimensional analysis, as described in an embodiment of the present invention.

[0029] The system includes:

[0030] Data acquisition and caching module 1 connects to all temperature sensors installed on the target battery, receiving temperature data collected by these sensors in real time. Each temperature sensor monitors the temperature at a specific location on the battery, and multiple sensors working together provide comprehensive temperature information from different parts of the battery. The received temperature data is cached in chronological order. Caching can be achieved by setting up a specific storage area within the module, storing data sequentially according to the time of acquisition. This preserves the temporal characteristics of the temperature data, providing a complete and ordered data foundation for subsequent anomaly detection and facilitating analysis of temperature changes at different points in time by subsequent modules.

[0031] Anomaly detection module 2, connected to the data acquisition and caching module 1, is used to detect absolute high temperature, temperature rise rate, overall temperature difference, and local hot spots based on temperature data from various temperature sensors, obtaining anomaly detection results. The purpose of absolute high temperature detection is to directly identify the critical state where the power battery temperature exceeds the safe operating threshold, avoiding serious safety accidents such as cell thermal runaway and electrolyte decomposition caused by excessive temperature, and ensuring the basic operational safety of the battery pack. The purpose of temperature rise rate detection is to capture abnormal temperature change trends in the power battery and provide early warning of impending thermal runaway. The purpose of overall temperature difference detection is to assess the uniformity of temperature distribution within the power battery pack, determining whether the heat dissipation system has failed or whether cell consistency has deteriorated. Uneven temperature distribution can cause some cells to bear higher cyclic stress, accelerating capacity decay. Detecting the overall temperature difference can guide the optimization of heat dissipation strategies and cell balance management. The purpose of local hot spot detection is to accurately locate abnormal temperature areas within the battery pack and quickly pinpoint the location of faulty cells or sensors. Local hot spots are a major cause of battery thermal diffusion. By identifying temperature change points in local areas, early fault location and accurate maintenance can be achieved, reducing subsequent repair costs.

[0032] The result output module 3, connected to the anomaly detection module 2, is used to output a list of temperature anomaly sensors based on the anomaly detection results. This list clearly indicates which temperature sensors are monitoring locations where temperature anomalies have occurred, including the type of anomaly (such as absolute high temperature, excessively rapid temperature rise rate, excessive overall temperature difference, localized hot spots, etc.). By outputting this list, relevant personnel can be provided with intuitive information to assist them in fault location, quickly identify potentially problematic parts of the battery, and make reasonable maintenance decisions, such as arranging for repair personnel to inspect the abnormal areas or replace faulty components.

[0033] In one embodiment, such as Figure 2 The data acquisition and caching module 1 includes:

[0034] The data acquisition unit receives temperature data synchronously collected by various temperature sensors installed on the target battery. Each sensor's data collection contains two core elements: a precise timestamp (recording the specific moment of data collection) and the corresponding temperature value (reflecting the real-time temperature at the sensor's location at that moment). For example, if the battery has N temperature sensors deployed, a single data collection will contain N temperature fields (such as temp_1, temp_2...temp_N) and their corresponding timestamps, forming a complete "time and temperature" data pair. This unit ensures synchronous access to multi-sensor data through a standardized interface, avoiding analytical biases caused by time misalignment.

[0035] The data caching unit stores temperature values ​​from various temperature sensors in a sliding window with a maximum cache length, arranged chronologically. Specifically, temperature data from all sensors is stored in a structured DataFrame, ordered by timestamp, forming a time-series data table. When new data writes cause the cache length to reach its maximum limit, the earliest historical data is automatically removed (from oldest to newest timestamp), ensuring that only the latest time-series data, not exceeding the maximum length, is retained in the cache, creating a sliding window effect. To balance computational efficiency and data integrity, the cache unit has a maximum length limit (typically twice the analysis window size). This design ensures that, with limited memory, it can both track the latest temperature dynamics and backtrack to a sufficiently long historical data set to support anomaly detection algorithms. The DataFrame's tabular structure facilitates quick retrieval of data for specific time periods by time index, while the length limit prevents memory overflow, ensuring the long-term stability of the system.

[0036] In one embodiment, such as Figure 2 The anomaly detection module 2 includes:

[0037] The absolute high temperature detection unit is used to determine whether an absolute high temperature event has occurred at the corresponding temperature sensor based on the temperature value currently collected by each temperature sensor, and to obtain the absolute high temperature event detection result.

[0038] The temperature rise rate detection unit is used to determine whether the corresponding temperature sensor has experienced an excessively rapid temperature rise event based on the temperature value of each temperature sensor currently cached in the sliding window, and to obtain the excessively rapid temperature detection result.

[0039] The overall temperature difference detection unit is used to calculate the difference between the highest and lowest temperatures among all the temperature values ​​currently collected by all temperature sensors, and to determine whether the current target battery has an overall temperature difference too large event, and obtain the overall temperature difference too large detection result;

[0040] The local hotspot detection unit is used to determine whether a local hotspot event has occurred at the corresponding temperature sensor based on the temperature sensor layout information of the target battery and the currently collected temperature values ​​of each temperature sensor, thereby obtaining the local hotspot detection result.

[0041] In one embodiment, determining whether an absolute high temperature event has occurred at the corresponding temperature sensor based on the temperature values ​​currently collected by each temperature sensor, and obtaining the absolute high temperature event detection result, includes:

[0042] For each temperature sensor, the system directly compares the current temperature value collected in real time with a preset absolute temperature threshold. If the temperature value of a certain temperature sensor exceeds the threshold, the system immediately determines that the sensor has triggered an "absolute high temperature event" and records the specific time (based on a timestamp) and the corresponding temperature value of the event.

[0043] For sensors that trigger absolute high-temperature events, their dedicated high-temperature event counters are updated synchronously: the counter value increases by 1 each time an event is triggered, quantifying the frequency of high-temperature anomalies occurring within the monitoring period. The final absolute high-temperature event detection results include: the specific time each sensor triggers an absolute high-temperature event (e.g., "2025-12-08 10:30:00"), supporting subsequent tracing of the time node where the anomaly occurred; the actual temperature value at the time of the triggering event (e.g., "68°C"), intuitively reflecting the severity of the anomaly; and the current cumulative number of absolute high-temperature events for the sensor, assisting in assessing the frequency of fault occurrence and the potential risk level.

[0044] In one embodiment, determining whether a temperature sensor has experienced an excessively rapid temperature rise event based on the temperature value of each temperature sensor currently cached in the sliding window, and obtaining the excessively rapid temperature detection result, includes:

[0045] Based on a sliding window caching structure, the temperature value sequence of the most recently cached window of a preset window size (e.g., 5 sampling points) for each temperature sensor is extracted. For example, if the preset window is 5 time points, the temperature data of the latest 5 consecutive time points are extracted from the end of the sliding window to form the local temperature time series of the sensor.

[0046] For each sensor's local temperature sequence, the temperature rise rate within a preset window is calculated. Specifically, the instantaneous temperature rise rate is obtained by dividing the temperature difference between adjacent time points in the sequence by the corresponding time interval. Then, the average or maximum value of the rates of all adjacent points within the window is taken to finally obtain the comprehensive temperature rise rate value of the sensor within the preset window (unit: ℃ / min).

[0047] The calculated temperature rise rate is compared with a preset temperature rise rate threshold (e.g., 3℃ / min). If the rate value of a sensor exceeds the threshold, the sensor is determined to have triggered a "temperature rise too fast event", and the specific time of the event (based on the timestamp of the latest time point within the window) and the corresponding temperature rise rate value are recorded.

[0048] The final results of the rapid temperature rise detection include: specifying the exact time when each sensor triggers the rapid temperature rise event (e.g., "2025-12-08 10:35:00"), supporting subsequent tracing of the time node where the anomaly occurred; and recording the actual temperature rise rate value at the time of the triggering event, intuitively reflecting the severity of the abnormal rise.

[0049] This detection process employs a dynamic caching mechanism using a sliding window to ensure that the latest temperature data is always used for calculations. Simultaneously, by limiting the preset window size, it balances computational efficiency with detection sensitivity, avoiding misjudgments due to noise interference caused by an excessively small window, while preventing detection delays caused by an excessively large window. The final output is a structured list of detection results, providing accurate information on rate anomalies for subsequent fault location and maintenance decisions.

[0050] In a preferred embodiment, the buffer length of the sliding window is designed to be twice the size of a preset window (e.g., 10 points are buffered when the preset window has 5 sampling points). This mechanism dynamically maintains a first-in-first-out queue, ensuring that the most recent complete preset window data (e.g., the last 5 points) is extracted each time the temperature rise rate is calculated, while also retaining additional historical data as redundant backups to cope with scenarios where data is missing or backtracking analysis is required. When new data arrives, the new value is added to the tail of the queue and the oldest data is automatically removed, always keeping the buffer length unchanged. This balances computational efficiency and detection reliability with limited memory, avoiding misjudgments due to an excessively small window or delays due to an excessively large window. At the same time, it provides continuous time-temperature sequence support for the accurate location of abnormal events (e.g., sudden temperature rise).

[0051] In one embodiment, calculating the difference between the highest and lowest temperatures among all currently collected temperature values ​​from all temperature sensors, and determining whether the target battery is experiencing an event of excessive overall temperature difference, to obtain an excessive overall temperature difference detection result includes:

[0052] First, the current temperature values ​​of all temperature sensors on the target battery are collected in real time, forming a set containing multiple temperature data. Then, the highest and lowest temperature values ​​are selected from this set. These two values ​​represent the extreme cases of the current temperature distribution of the battery and directly reflect the non-uniformity of the internal temperature of the battery.

[0053] The difference between the highest and lowest extracted temperatures is calculated to obtain the overall temperature difference of the battery. This value quantifies the dispersion of the internal temperature distribution of the battery: the larger the temperature difference, the more significant the temperature difference between different areas of the battery, which may indicate local overheating or insufficient cooling.

[0054] Next, the calculated temperature difference value is compared with the preset overall temperature difference threshold. The threshold setting needs to be combined with the battery type, usage scenario and safety standards. For example, the temperature difference of a power battery under normal operating conditions should usually be controlled within 15°C. If it exceeds 20°C, it may cause performance degradation or safety hazards.

[0055] If the current temperature difference exceeds the threshold, it is immediately determined that the target battery has experienced an "overall excessive temperature difference event," and the following key information is recorded as the detection result: a timestamp based on the temperature data, accurate to the second or millisecond (e.g., "2025-12-08 14:30:45"), to support subsequent tracing of the specific time when the anomaly occurred; the actual temperature difference value at the time the event was triggered, to intuitively reflect the severity of the anomaly; and the highest and lowest temperatures are recorded simultaneously to help locate the source of the temperature difference, such as abnormally high or low sensor temperatures in a certain area.

[0056] In one embodiment, the step of determining whether a local hotspot event has occurred at a corresponding temperature sensor based on the temperature sensor layout information of the target battery and the currently collected temperature values ​​of each temperature sensor to obtain the local hotspot detection result includes:

[0057] Based on the temperature sensor indexes and physical layout of the target battery, the adjacent sensors of each temperature sensor are identified. Specifically, a mapping relationship between sensor indexes and physical locations is first established based on the temperature sensor layout diagram or topology of the target battery (such as matrix, ring, or custom arrangement). This mapping determines the set of adjacent sensors for each sensor. For example, in a matrix layout, the adjacent sensors of the central sensor may include neighbors in the four directions of top, bottom, left, and right; in a ring layout, the adjacent sensors are the sensors on the left and right sides. The adjacency relationships of each sensor are stored in a list, such as the adjacent sensors of sensor 5 being [3,4,6,7].

[0058] The temperature difference between the current sensor and the temperature values ​​of each adjacent sensor is calculated. If the temperature difference between the current sensor and most of the adjacent sensors exceeds a preset threshold, it is determined to be a local hotspot event. The local hotspot detection results include: the event occurrence time, the current sensor temperature value, the temperature values ​​of adjacent sensors, and the temperature difference between the current sensor and the adjacent sensors.

[0059] This mechanism identifies abnormally high temperatures in localized areas within the battery, precisely pinpointing hotspots caused by localized faults (such as internal short circuits or poor contact), uneven heat dissipation, or sensor calibration deviations. In power batteries, localized hotspots may indicate cell aging or internal defects; timely detection can prevent the risk of thermal runaway. In energy storage power stations, it can guide maintenance personnel to prioritize inspecting battery modules in hotspot areas, improving maintenance efficiency.

[0060] In one embodiment, the result output module 3 has multi-dimensional statistical and standardized output capabilities: statistically, it accumulates the number of times each sensor triggers a high-temperature event at a preset period (e.g., hour, day), taking a single sensor as a unit, and simultaneously associates key information such as event occurrence time and duration; the output format includes a structured list of high-frequency high-temperature sensors (arranged in descending order of trigger count, labeled with sensor number, cumulative count, and the time of the most recent anomaly), and integrates the detection results of absolute high-temperature events, rapid temperature increase detection results, overall large temperature difference detection results, and local hotspot detection results into JSON format data, with fields covering anomaly type, sensor identifier, detection time, threshold comparison results, etc. The results output by this module can directly assist maintenance personnel in quickly locating high-frequency faulty sensors and corresponding battery areas. The JSON data is compatible with upper-layer BMS system calls or log archiving analysis, taking into account both manual decision-making and automated system integration needs, and greatly improving the practicality and cross-scenario adaptability of anomaly data.

[0061] Similar to the principles of the above embodiments, the present invention provides a method for real-time monitoring of abnormal battery temperature based on multi-dimensional analysis.

[0062] The following specific embodiments are provided in conjunction with the accompanying drawings:

[0063] like Figure 3 This document illustrates a flowchart of a real-time battery temperature anomaly monitoring method based on multi-dimensional analysis, as described in an embodiment of the present invention. The method includes:

[0064] Step S1: Receive temperature data collected in real time by multiple temperature sensors installed on the target battery, and cache it in chronological order.

[0065] Step S2: Based on the temperature data from each temperature sensor, perform absolute high temperature, temperature rise rate, overall temperature difference, and local hot spot detection to obtain anomaly detection results.

[0066] Step S3: Output a list of temperature anomaly sensors based on the anomaly detection results to assist in fault location and maintenance decisions.

[0067] Since the implementation principle of the real-time monitoring method for abnormal battery temperature based on multi-dimensional analysis has been described in the foregoing embodiments, it will not be repeated here.

[0068] The real-time battery temperature anomaly monitoring system based on multi-dimensional analysis provided in this invention can be implemented on the terminal side or the server side. For details regarding the hardware structure of the electronic terminal, please refer to [link to relevant documentation]. Figure 4 This is a schematic diagram of an optional hardware structure of an electronic terminal 1000 provided in an embodiment of the present invention. The terminal 1000 can be a mobile phone, computer device, tablet device, personal digital processing device, factory back-end processing device, etc. The terminal 1000 includes: at least one processor 1001, a memory 1002, at least one network interface 10010, and a user interface 1009. The various components in the device are coupled together through a bus system 1005. It is understood that the bus system 1005 is used to realize the connection and communication between these components. In addition to a data bus, the bus system 1005 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 4 The general will label all buses as bus systems.

[0069] The user interface 1009 may include a monitor, keyboard, mouse, trackball, clicker, button, touchpad, or touch screen.

[0070] It is understood that memory 1002 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM) or programmable read-only memory (PROM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM) and synchronous static random access memory (SSRAM). The memories described in the embodiments of this invention are intended to include, but are not limited to, these and any other suitable categories of memory.

[0071] In this embodiment of the invention, the memory 1002 is used to store various types of data to support the operation of the terminal 1000. Examples of this data include: any executable program for operation on the terminal 1000, such as the operating system 10021 and application program 10022; the operating system 10021 contains various system programs, such as the framework layer, core library layer, driver layer, etc., for implementing various basic services and handling hardware-based tasks. The application program 10022 may contain various applications, such as a media player, browser, etc., for implementing various application services. The real-time battery temperature anomaly monitoring system based on multi-dimensional analysis provided in this embodiment of the invention can be included in the application program 10022.

[0072] The methods disclosed in the above embodiments of the present invention can be applied to or implemented by the processor 1001. The processor 1001 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor 1001 or by instructions in the form of software. The processor 1001 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 1001 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor 1001 may be a microprocessor or any conventional processor, etc. The steps of the accessory optimization method provided in the embodiments of the present invention can be directly reflected as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium, which is located in a memory. The processor reads the information in the memory and combines it with its hardware to complete the steps of the aforementioned method.

[0073] In an exemplary embodiment, the terminal 1000 may be used to execute the aforementioned method by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), or complex programmable logic devices (CPLDs).

[0074] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented using computer program-related hardware. The aforementioned computer program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0075] In the embodiments provided in this application, the computer-readable and writable storage medium may include read-only memory, random access memory, EEPROM, CD-ROM or other optical disc storage devices, disk storage devices or other magnetic storage devices, flash memory, USB flash drive, portable hard drive, or any other medium capable of storing desired program code in the form of instructions or data structures and accessible by a computer. Additionally, any connection may be appropriately referred to as a computer-readable medium. For example, if instructions are transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. However, it should be understood that computer-readable and writable storage media and data storage media do not include connections, carrier waves, signals, or other transient media, but are intended for non-transient, tangible storage media. The disks and optical discs used in the application include compact discs (CDs), laser discs, optical discs, digital multifunction discs (DVDs), floppy disks, and Blu-ray discs, where disks typically copy data magnetically, while optical discs use lasers to copy data optically.

[0076] The present invention has the following advantages over the prior art:

[0077] 1. Multi-dimensional anomaly detection mechanism: Constructing a comprehensive anomaly identification system

[0078] This invention innovatively integrates four core indicators—absolute high temperature, temperature rise rate, overall temperature difference, and local temperature difference—to construct a multi-dimensional, complementary anomaly detection mechanism. This mechanism comprehensively monitors and analyzes battery status from multiple dimensions, with each indicator corroborating and complementing the others, effectively avoiding the limitations of single-indicator detection. This comprehensive anomaly identification method significantly reduces false alarm and false negative rates, greatly improving detection accuracy. Simultaneously, the parallel processing of multiple indicators accelerates the detection process, improves system response speed, and ensures timely alerts when battery anomalies occur.

[0079] 2. Dynamic sliding window mechanism: Optimizes data storage and real-time computing.

[0080] This invention introduces a dynamic sliding window mechanism to efficiently store historical data. This mechanism can dynamically adjust the window size according to actual needs, precisely controlling the amount of data stored, effectively avoiding storing excessive redundant information and saving system resources. Furthermore, based on the historical data within the sliding window, the system can quickly calculate the temperature rise rate and perform trend analysis. This real-time computing capability enables the system to promptly capture subtle changes in battery temperature, providing more accurate and timely data support for anomaly detection, thereby enhancing the system's real-time performance and reliability.

[0081] 3. Spatial correlation analysis mechanism: Enhancing the ability to detect local anomalies

[0082] This invention fully considers the physical layout information of sensors and incorporates it into anomaly detection, constructing a local temperature comparison model. Through this model, the system can perform spatial correlation analysis of the temperature in different areas of the battery, accurately identifying "local hotspots" that are difficult to detect using traditional methods. This precise perception of local anomalies helps to detect potential safety hazards in the battery in advance, such as local short circuits and poor heat dissipation, providing stronger protection for the safe operation of the battery and significantly improving the system's spatial perception capability.

[0083] 4. Historical statistical feedback mechanism: assisting in fault diagnosis and health assessment

[0084] This invention features a comprehensive historical statistical feedback mechanism, enabling long-term, systematic statistics on high-temperature events. This statistical data provides maintenance personnel with abundant diagnostic information, helping them gain a deeper understanding of battery operating conditions and fault occurrence patterns, thereby developing more scientific and reasonable preventative maintenance strategies. Simultaneously, the system can generate a list of high-frequency high-temperature sensors, visually displaying which sensor locations are prone to high-temperature anomalies. This provides crucial reference for system health assessment, helping to identify potential system faults early and ensuring stable system operation.

[0085] 5. Parameter Configurability: Enhanced versatility and scalability

[0086] This invention features highly configurable parameters, allowing all detection thresholds to be flexibly adjusted based on different battery types and usage scenarios. Whether it's different types of power batteries or energy storage battery packs used in different environments, the system can be adapted to specific detection needs through reasonable parameter configuration. This flexible parameter configuration method gives the invention excellent versatility and scalability, enabling its widespread application to battery packs of various structures and sizes, providing a reliable solution for battery safety monitoring in different fields.

[0087] In summary, the battery temperature anomaly real-time monitoring system, method, and terminal based on multi-dimensional analysis of the present invention receive real-time temperature data collected by multiple temperature sensors on the target battery and cache it in chronological order. Subsequently, multi-dimensional detection is performed based on this data, covering absolute high temperature, temperature rise rate, overall temperature difference, and local hotspot indicators, accurately obtaining anomaly detection results. A list of temperature anomaly sensors is output based on the results, providing strong support for fault location and maintenance decisions. This invention comprehensively considers multiple indicators such as absolute high temperature, temperature rise rate, overall temperature difference, local temperature difference, and historical frequency statistics to provide a comprehensive assessment and early warning of the battery pack's temperature status, exhibiting excellent real-time performance and robustness, effectively improving the intelligence and safety assurance capabilities of the BMS system. Therefore, this invention effectively overcomes the various shortcomings of existing technologies and has high industrial application value.

[0088] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A real-time monitoring system for abnormal battery temperature based on multi-dimensional analysis, characterized in that, The system includes: The data acquisition and caching module is used to receive real-time temperature data collected by various temperature sensors installed on the target battery and cache it in chronological order. An anomaly detection module, connected to the data acquisition and caching module, is used to detect absolute high temperature, temperature rise rate, overall temperature difference and local hot spots based on the temperature data of each temperature sensor, and obtain anomaly detection results. The result output module is connected to the anomaly detection module and is used to output a list of temperature anomaly sensors based on the anomaly detection results to assist in fault location and maintenance decisions.

2. The real-time battery temperature anomaly monitoring system based on multi-dimensional analysis as described in claim 1, characterized in that, The data acquisition and caching module includes: The data acquisition unit is used to receive temperature data synchronously collected by each temperature sensor installed on the target battery; wherein each temperature data collected by each temperature sensor includes the collected temperature value and the corresponding collection timestamp. The data caching unit is used to cache the temperature values ​​of each temperature sensor in chronological order within a sliding window that limits the maximum cache length.

3. The real-time battery temperature anomaly monitoring system based on multi-dimensional analysis as described in claim 2, characterized in that, The anomaly detection module includes: The absolute high temperature detection unit is used to determine whether an absolute high temperature event has occurred at the corresponding temperature sensor based on the temperature value currently collected by each temperature sensor, and to obtain the absolute high temperature event detection result. The temperature rise rate detection unit is used to determine whether the corresponding temperature sensor has experienced an excessively rapid temperature rise event based on the temperature value of each temperature sensor currently cached in the sliding window, and to obtain the excessively rapid temperature detection result. The overall temperature difference detection unit is used to calculate the difference between the highest and lowest temperatures among all the temperature values ​​currently collected by all temperature sensors, and to determine whether the current target battery has an overall temperature difference too large event, and obtain the overall temperature difference too large detection result; The local hotspot detection unit is used to determine whether a local hotspot event has occurred at the corresponding temperature sensor based on the temperature sensor layout information of the target battery and the temperature value currently collected by each temperature sensor, and to obtain the local hotspot detection result.

4. The real-time battery temperature anomaly monitoring system based on multi-dimensional analysis as described in claim 3, characterized in that, The step of determining whether an absolute high temperature event has occurred at the corresponding temperature sensor based on the temperature value currently collected by each temperature sensor, and obtaining the absolute high temperature event detection result includes: The temperature value currently collected by each temperature sensor is compared with the set absolute temperature threshold. When the temperature value is greater than the absolute temperature threshold, it is recorded as an absolute high temperature event for the corresponding temperature sensor. The absolute high temperature event count value of the temperature sensor is updated to obtain the absolute high temperature event detection result. The absolute high temperature event detection result includes: the time and temperature value of the absolute high temperature event of each temperature sensor, and the current absolute high temperature event count value.

5. The real-time battery temperature anomaly monitoring system based on multi-dimensional analysis as described in claim 3, characterized in that, The step of determining whether a temperature rise too fast event has occurred in the corresponding temperature sensor based on the temperature value currently cached in the sliding window, and obtaining the temperature rise too fast detection result, includes: Extract the temperature values ​​of each temperature sensor that are most recently cached in the sliding window and are within a preset window size, and calculate the temperature rise rate of each temperature sensor within the preset window. The temperature rise rate of each temperature sensor within a preset window is compared with a temperature rise rate threshold. When the temperature rise rate is greater than the temperature rise rate threshold, it is recorded as an excessively rapid temperature rise event for the corresponding temperature sensor, thus obtaining the excessively rapid temperature detection result. The excessively rapid temperature detection result includes the time when the excessively rapid temperature rise event occurs for each temperature sensor and the corresponding temperature rise rate.

6. The real-time battery temperature anomaly monitoring system based on multi-dimensional analysis as described in claim 3, characterized in that, The calculation of the difference between the highest and lowest temperatures among all currently collected temperature values ​​from all temperature sensors, and the determination of whether the target battery is experiencing an excessive overall temperature difference event, to obtain the excessive overall temperature difference detection result includes: Obtain the highest and lowest temperatures among all currently collected temperature values ​​from all temperature sensors, and calculate the difference between the highest and lowest temperatures; The difference between the highest and lowest temperatures is compared with the overall temperature difference threshold. When the difference between the highest and lowest temperatures is greater than the overall temperature difference threshold, it is recorded as an event of excessive overall temperature difference, thus obtaining the detection result of excessive overall temperature difference.

7. The real-time battery temperature anomaly monitoring system based on multi-dimensional analysis as described in claim 3, characterized in that, The temperature sensor layout information based on the target battery is used to determine whether a local hotspot event has occurred at the corresponding temperature sensor based on the currently collected temperature value of each temperature sensor, and the local hotspot detection result is obtained as follows: Identify the adjacent sensors for each temperature sensor based on the index of all temperature sensor numbers and their physical layout in the target battery. The temperature values ​​collected by each temperature sensor are compared with those of its neighboring sensors. If the temperature value of the current temperature sensor is significantly higher than that of its neighboring sensors, it is recorded as a local hotspot event of that temperature sensor, and the local hotspot detection result is obtained. The local hotspot detection result includes: the time of occurrence of the local hotspot event of each temperature sensor and the corresponding temperature value, as well as the temperature value of the neighboring sensor.

8. The real-time battery temperature anomaly monitoring system based on multi-dimensional analysis as described in claim 5, characterized in that, The buffer length of the sliding window is set to twice the preset window size.

9. A method for real-time monitoring of abnormal battery temperature based on multi-dimensional analysis, characterized in that, The method includes: It receives temperature data collected in real time from multiple temperature sensors installed on the target battery and caches it in chronological order. Based on the temperature data from each temperature sensor, absolute high temperature, temperature rise rate, overall temperature difference, and local hot spots are detected to obtain anomaly detection results. The system outputs a list of temperature anomaly sensors based on the anomaly detection results to assist in fault location and maintenance decisions.

10. An electronic terminal, characterized in that, include: One or more memories and one or more processors; The one or more memories are used to store computer programs; The one or more processors are connected to the memory and are used to run the computer program to perform the method as described in any one of claims 1 to 8.

Images