Temperature rise abnormity detection method and device of electric vehicle and nonvolatile storage medium

Abstract

The invention discloses a temperature rise anomaly detection method and device of an electric vehicle and a nonvolatile storage medium. The method is applied to a cloud server and comprises the following steps: acquiring the temperature of a battery in the electric vehicle; calculating a temperature difference value between the battery temperature collected at the current moment and the battery temperature collected at the previous time; determining a target temperature rise risk coefficient based on the temperature difference value and the battery temperature collected at the current moment; and determining a temperature rise detection result of the electric vehicle based on the target temperature rise risk coefficient. The technical problem that the risk cannot be warned in advance due to the fact that off-line calculation is mostly adopted in temperature rise monitoring of the electric vehicle at present and real-time monitoring cannot be achieved is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric vehicle power battery, in particular to a temperature rise abnormality detection method and device of electric vehicle and a nonvolatile storage medium. BACKGROUND

[0002] With the popularity of electric vehicles, the safety of the batteries of electric vehicles has attracted more and more attention. A large amount of heat is generated in the charging and discharging process of the power battery, causing the local temperature of the battery pack to rise. The high-temperature environment reduces the charging and discharging efficiency of the battery, affecting the endurance and performance of the electric vehicle. In addition, uneven temperature distribution also causes inconsistent battery performance, affecting the overall vehicle performance. Excessive temperature or uneven distribution may cause battery thermal runaway, which is a chain reaction of heat release of battery monomers, leading to uncontrollable temperature rise of the battery. Thermal runaway not only damages the battery, but also may cause fire or explosion, seriously threatening the safety of the vehicle and passengers.

[0003] Therefore, as a core indicator of the thermal imbalance in the battery, the abnormal evolution rule identification of the temperature rise in the time domain is crucial for accident prevention. However, current research on temperature rise algorithms mostly uses offline calculation, which cannot achieve real-time detection and identification, and cannot provide early warning of risks.

[0004] At present, there is no effective solution to the above problems. SUMMARY

[0005] The embodiments of the present application provide a temperature rise abnormality detection method and device of an electric vehicle and a nonvolatile storage medium to at least solve the technical problem that most of the current temperature rise monitoring of electric vehicles uses offline calculation, which cannot achieve real-time monitoring, resulting in the inability to provide early warning of risks.

[0006] According to an aspect of an embodiment of the present application, a temperature rise abnormality detection method of an electric vehicle is provided, including: collecting a battery temperature in the electric vehicle; calculating a temperature difference value of the battery temperature collected at a current time and the battery temperature collected at a previous time; determining a target temperature rise risk coefficient based on the temperature difference value and the battery temperature collected at the current time; and determining a temperature rise detection result of the electric vehicle based on the target temperature rise risk coefficient.

[0007] Optionally, calculating the temperature difference value of the battery temperature collected at the current time and the battery temperature collected at the previous time includes: determining a collection time corresponding to the battery temperature collected at the previous time; determining an actual time interval based on the collection time and the current time; judging whether the actual time interval exceeds a preset time interval; and calculating the temperature difference value of the battery temperature collected at the current time and the battery temperature collected at the previous time in a case where the actual time interval does not exceed the preset time interval.

[0008] Optionally, in the case that the battery temperature at the current time includes multiple temperature values and the temperature difference values include multiple temperature difference values, based on the temperature difference values and the battery temperature collected at the current time, the target temperature rise risk coefficient is determined, including: obtaining an initial temperature rise risk coefficient, wherein the initial temperature rise risk coefficient is determined based on the battery temperature collected last time; determining a temperature difference value average based on the multiple temperature difference values; calculating the difference between the multiple temperature difference values and the temperature difference value average to obtain the deviation corresponding to each of the multiple temperature difference values; adjusting the initial temperature rise risk coefficient based on the deviation corresponding to each of the multiple temperature difference values and a preset deviation threshold to obtain the target temperature rise risk coefficient.

[0009] Optionally, based on the target temperature rise risk coefficient, the temperature rise detection result of the electric vehicle is determined, including: determining whether the target temperature rise risk coefficient exceeds a preset coefficient threshold; in the case that the target temperature rise risk coefficient exceeds the coefficient threshold, determining that the temperature rise detection result is abnormal.

[0010] Optionally, based on the target temperature rise risk coefficient, the temperature rise detection result of the electric vehicle is determined, wherein, in the case that the battery temperature at the current time includes multiple temperature values, including: arranging the multiple battery temperature values in order of size to obtain a temperature sequence; determining a temperature median based on the temperature sequence; calculating the difference between each of the multiple battery temperature values and the temperature median to obtain multiple temperature difference values; determining the number of temperature difference values that exceed a preset difference value as an abnormal temperature number; and determining the temperature rise detection result of the electric vehicle based on the target temperature rise risk coefficient and the abnormal temperature number.

[0011] Optionally, the battery temperature corresponding to the temperature difference value that exceeds the preset difference value is determined as an abnormal battery temperature; based on the temperature probe corresponding to the abnormal battery temperature, a fault position in the battery is determined; and based on the fault position, a warning prompt is generated.

[0012] According to another aspect of the embodiment of the present application, a temperature rise anomaly detection device for an electric vehicle is also provided, including: an acquisition module configured to acquire a battery temperature in an electric vehicle; a receiving module configured to calculate a temperature difference value between the battery temperature collected at a current time and the battery temperature collected last time; a first determination module configured to determine a target temperature rise risk coefficient based on the temperature difference value and the battery temperature collected at the current time; and a second determination module configured to determine a temperature rise detection result of the electric vehicle based on the target temperature rise risk coefficient.

[0013] According to still another aspect of the embodiment of the present application, a non-volatile storage medium is also provided, including a stored program, wherein when the program is running, the device in which the non-volatile storage medium is located is controlled to perform any one of the above-mentioned temperature rise anomaly detection methods for an electric vehicle.

[0014] According to a further aspect of the embodiments of the present application, a computer device is also provided, which comprises a processor configured to execute a program, wherein the program is configured to implement any one of the above-mentioned temperature rise abnormality detection methods for an electric vehicle.

[0015] According to a further aspect of the embodiments of the present application, a computer program product is also provided, which comprises a computer program configured to implement any one of the above-mentioned temperature rise abnormality detection methods for an electric vehicle.

[0016] In the embodiments of the present application, the temperature rise abnormality detection method for an electric vehicle is adopted, the battery temperature in the electric vehicle is collected, the temperature difference between the battery temperature collected at the current time and the battery temperature collected at the previous time is calculated, the target temperature rise risk coefficient is determined based on the temperature difference and the battery temperature collected at the current time, and the temperature rise detection result of the electric vehicle is determined based on the target temperature rise risk coefficient, so as to achieve the purpose of monitoring the battery temperature rise in the electric vehicle in real time, thereby realizing the technical effect of reducing the safety risk caused by the abnormal battery in the electric vehicle, and further solving the technical problem that the temperature rise monitoring of the electric vehicle is mostly offline calculation at present, which cannot be monitored in real time, and thus cannot early warn the risk. BRIEF DESCRIPTION OF DRAWINGS

[0017] The accompanying drawings, which are included to provide a further understanding of the present application and constitute a part of this application, illustrate embodiments of the present application and together with the description serve to explain the present application. In the drawings:

[0018] Figure 1 Fig. 1 shows a hardware structure block diagram of a computer terminal for implementing the temperature rise abnormality detection method for an electric vehicle;

[0019] Figure 2 Fig. 2 is a flow diagram of the temperature rise abnormality detection method for an electric vehicle according to an embodiment of the present application;

[0020] Figure 3 Fig. 3 is a step diagram of the temperature rise abnormality detection method for an electric vehicle according to an optional embodiment of the present application;

[0021] Figure 4 Fig. 4 is a structure block diagram of the temperature rise abnormality detection device for an electric vehicle according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to better understand the technical scheme of the present application, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts should fall within the scope of the present application.

[0023] It should be noted that the terms "first", "second" and the like in the description and claims of the present application and the above-described accompanying drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0024] According to the embodiments of the present application, a method embodiment of a temperature rise abnormality detection method of an electric vehicle is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown herein.

[0025] The method embodiment provided by the first embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 A hardware structure block diagram of a computer terminal for implementing a temperature rise abnormality detection method of an electric vehicle is shown. As shown in Figure 1 The computer terminal 10 can include one or more processors (the processor can include but is not limited to a microprocessor MCU or a programmable logic device FPGA processing device) (shown in 102a, 102b, …, 102n in the figure), a memory 104 for storing data. In addition, it can also include a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which can be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. Those skilled in the art can understand that Figure 1 The structure shown is only schematic, which does not limit the structure of the above-mentioned electronic device. For example, the computer terminal 10 can also include more or fewer components than those shown in Figure 1 the above-mentioned electronic device. For example, the computer terminal 10 can also include more or fewer components than those shown inFigure 1 different configurations.

[0026] It should be noted that the one or more processors and / or other data processing circuitry described above can be referred to herein generally as "data processing circuitry". The data processing circuitry can be embodied in whole or in part as software, hardware, firmware, or any combination thereof. In addition, the data processing circuitry can be a single standalone processing module, or incorporated in whole or in part within any one of the other elements of the computer terminal 10. As referred to in the embodiments of the present application, the data processing circuitry serves as a processor to control, for example, the selection of the variable resistance terminal path connected to the interface.

[0027] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the temperature rise anomaly detection method of the electric vehicle in the embodiments of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, i.e. implements the temperature rise anomaly detection method of the electric vehicle of the application program described above. The memory 104 can include a high-speed random access memory, and can also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some examples, the memory 104 can further include a memory disposed remotely with respect to the processor, and these remote memories can be connected to the computer terminal 10 through a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0028] The display can be, for example, a touch screen type liquid crystal display (LCD) that can enable a user to interact with the user interface of the computer terminal 10.

[0029] Figure 2 is a flowchart of the temperature rise anomaly detection method of the electric vehicle according to the embodiments of the present application, as shown in Figure 2 The method is applied to a cloud server, and includes the following steps:

[0030] In step S202, the temperature of the battery in the electric vehicle is collected.

[0031] In this step, temperature sensors (such as NTC thermistors, thermocouples, etc.) are installed in key locations within the battery pack of the electric vehicle, such as between battery modules, battery pack shell, battery monomers, etc., to obtain comprehensive temperature information. These sensors can continuously monitor the temperature of the battery and convert temperature changes into electrical signals. The signal output by each temperature sensor is an analog signal. These signals need to be properly amplified and filtered by a signal conditioning circuit, and then converted to digital signals by an Analog-to-Digital Converter (ADC). Digital signals are convenient for computer system processing and storage.

[0032] Step S204, calculate the temperature difference between the current battery temperature and the previous battery temperature.

[0033] In this step, the latest temperature sampling data is obtained, which is the current battery temperature and the previous temperature sampling data. These data can include temperature readings of each temperature sensor in the battery. Ensure that the temperature sensor data collected at the current time and the previous time are one-to-one corresponding, that is, each probe has two consecutive readings at the same position. For each temperature sensor in the battery pack, calculate the difference between its current temperature value and the previous temperature value.

[0034] Through this real-time temperature difference calculation, the battery management system can monitor the instantaneous temperature change of the battery, which is crucial for early identification of possible thermal runaway situations.

[0035] Step S206, determine the target temperature rise risk coefficient based on the temperature difference and the current battery temperature.

[0036] In this step, the temperature difference and the preset abnormal threshold can be used to identify which sensors have a temperature rise or drop that exceeds the normal range. Calculate the average temperature and median temperature of all sensor readings at the current time. Compare the difference between each sensor temperature at the current time and the average temperature and median temperature to evaluate the uniformity and abnormality of the temperature distribution. According to the temperature difference, the average temperature difference and the median temperature difference, and the overall temperature state of the battery, combined with the working condition (whether the vehicle is continuously used), calculate the temperature rise risk factor of each temperature sensor. Weighted average all sensor temperature rise risk factors, or calculate a comprehensive risk coefficient according to different risk factor values.

[0037] Through the above algorithm steps, the temperature rise risk coefficient can be dynamically calculated based on the temperature difference and the current battery temperature, effectively identifying and warning abnormal temperature rise of the battery.

[0038] Step S208, determine the temperature rise detection result of the electric vehicle based on the target temperature rise risk coefficient.

[0039] In this step, a series of risk coefficient thresholds can be pre-set, which represent different levels of temperature rise risks. For example, set the first-level alarm threshold to 5, and the second-level alarm threshold to 8 or higher, which can be adjusted according to actual application requirements and safety standards. According to the comparison of the calculated target temperature rise risk coefficient with the pre-set threshold, the temperature rise risk is divided into several levels. Specifically, it can be:

[0040] No risk: If the temperature rise risk coefficient is below a certain lower threshold (such as 1), it indicates that the temperature change is within a safe range and no abnormality is detected.

[0041] Minor risk: The coefficient is between the lower threshold and the first-level alarm threshold (such as 1 to 5), which indicates that there may be a slight temperature rise anomaly and needs to be closely monitored.

[0042] First-level risk: When the coefficient exceeds the first-level alarm threshold (such as greater than 5) but does not reach the second-level alarm threshold, it means that the temperature rise anomaly is relatively obvious and preliminary measures should be taken, such as adjusting the battery charging and discharging strategy.

[0043] Second-level risk: If the coefficient reaches or exceeds the second-level alarm threshold (such as greater than or equal to 8), it indicates that the temperature rise anomaly is serious and may soon lead to thermal runaway, so emergency measures should be taken immediately, such as shutting down the battery system, starting the cooling system, and notifying the driver or maintenance personnel.

[0044] Once the temperature rise risk is detected, relevant information can be recorded, including but not limited to: vehicle identification, temperature rise risk level, fault location, fault timestamp, etc.

[0045] Through the above process, the battery management system of the electric vehicle can detect temperature rise anomalies in a timely and accurate manner based on the target temperature rise risk coefficient, so as to take appropriate measures to avoid potential thermal runaway risks and ensure the safety of the vehicle and passengers.

[0046] Through the above steps, the purpose of real-time monitoring of battery temperature rise in electric vehicles is achieved, thereby achieving the technical effect of reducing the safety risks caused by battery abnormalities in electric vehicles, and further solving the technical problem that current temperature rise monitoring of electric vehicles mostly uses offline calculation, which cannot be monitored in real time, resulting in the inability to early warn risks.

[0047] The cloud server as the execution subject can efficiently process a large amount of real-time data, and can dynamically allocate resources according to needs. This means that when the electric vehicle fleet or user base grows, the cloud server can seamlessly increase computing and storage resources to accommodate larger-scale data processing needs without the need for physical upgrades to hardware or expansion of data centers. Moreover, algorithms and software can be easily updated on the cloud server without the need for individual upgrades to each electric vehicle, greatly simplifying maintenance processes and reducing update costs. This ensures that the monitoring system always uses the latest algorithm version, enabling more accurate identification of temperature rise abnormalities.

[0048] In summary, the cloud server as the execution subject of the electric vehicle battery temperature rise monitoring system can provide stable, efficient and safe services.

[0049] As an optional embodiment, the temperature difference between the current collected battery temperature and the previous collected battery temperature is calculated, including: determining the collection time corresponding to the previous collected battery temperature; determining the actual time interval based on the collection time and the current time; determining whether the actual time interval exceeds the preset time interval; in the case where the actual time interval does not exceed the preset time interval, calculating the temperature difference between the current collected battery temperature and the previous collected battery temperature.

[0050] Optionally, the collection timestamp of the previous temperature data can be obtained. Then the current system time or data collection time is obtained. This can usually be obtained through the system clock, ensuring the same precision as the previous collection time, usually on the order of milliseconds or seconds. The actual time interval is calculated and it is determined whether the time interval exceeds the preset time interval, wherein the preset time interval is usually set according to the requirements of the monitoring system. For example, in continuous use conditions, the preset time interval may be 30 seconds (data transmission period), while in the case of long-term non-use and then power-on, the preset time interval may be 3 minutes. If it is less than the preset time interval, the data is processed; if it exceeds the preset time interval, it may be necessary to recalibrate the data or skip the current data frame to avoid misjudgment. After determining that the time interval does not exceed the preset value, the temperature difference can be safely calculated.

[0051] Through the above steps, it can be ensured that in battery temperature monitoring, only data collected within a reasonable time interval will be used for temperature rise anomaly detection, avoiding misjudgment due to long time data interval and improving the accuracy and reliability of the monitoring system. This provides basic data for subsequent temperature rise risk coefficient calculation, which is a key link to realize cloud real-time monitoring of temperature rise abnormalities.

[0052] The optional embodiment ensures the accuracy of the temperature difference calculation by matching the temperature data before and after the accurate timestamp, effectively filters the temperature difference anomalies caused by long-time non-data collection, avoids false positives, reduces unnecessary alarms, and improves the stability and reliability of the system. In other embodiments, the data collection frequency or the time interval threshold can be optimized to adapt to the temperature rise monitoring needs of different vehicle models and use scenarios.

[0053] As an optional embodiment, in the case where the battery temperature at the current time includes multiple temperature differences, the target temperature rise risk coefficient is determined based on the temperature difference and the battery temperature collected at the current time, including: obtaining an initial temperature rise risk coefficient, wherein the initial temperature rise risk coefficient is determined based on the battery temperature collected last time; determining a temperature difference average value based on the multiple temperature differences; calculating the difference between the multiple temperature differences and the temperature difference average value to obtain the respective deviations of the multiple temperature differences; adjusting the initial temperature rise risk coefficient based on the respective deviations of the multiple temperature differences and a preset deviation threshold to obtain the target temperature rise risk coefficient.

[0054] Optionally, in the case where the battery temperature at the current time includes multiple sensor readings and the temperature difference also includes multiple temperature differences, the process of determining the target temperature rise risk coefficient needs to consider the average trend and individual deviation of temperature change. The initial temperature rise risk coefficient is based on the battery temperature data collected last time and the risk coefficient calculated in the last monitoring cycle. This is usually set when the system is initialized or powered on, or it can be the final temperature rise risk coefficient value of the last monitoring period. Since each sensor may have a temperature change between the current time and the last collection time, the average value of all these temperature differences needs to be calculated first. This will give the average rate of temperature rise of the entire battery pack. Next, the deviation between the temperature difference of each sensor and the temperature difference average value is calculated. This will help identify which sensors have a temperature rise that deviates significantly from the average trend. According to the deviation of each sensor and the preset deviation threshold, the initial temperature rise risk coefficient is adjusted. The deviation threshold can be flexibly set according to the battery type, battery state and vehicle usage to determine whether the individual temperature difference is abnormal.

[0055] If it exceeds the positive deviation threshold, it indicates that the temperature of the sensor is rising too fast, and the risk coefficient should be increased; if it is below the negative deviation threshold, it indicates that the temperature is abnormally decreasing, which may also increase the risk coefficient. If a certain sensor exceeds the deviation threshold for several times in a row, it may indicate that there is a continuous abnormal temperature rise in that area, and the risk coefficient should be further increased.

[0056] The initial temperature rise risk coefficients adjusted based on the deviations are aggregated to obtain a target temperature rise risk coefficient reflecting the overall temperature rise risk of the battery. This process can involve a weighted average of the risk coefficients of all sensors, with weights based on the impact of sensor location on battery safety or based on the degree of deviation from the average trend.

[0057] Through the above steps, the system can dynamically adjust the temperature rise risk coefficient based on real-time readings from multiple temperature sensors by calculating the average of temperature differences and individual deviations, thereby more accurately identifying and warning of abnormal temperature rise conditions in the battery. This method not only considers local temperature rise abnormalities, but also focuses on the persistence of temperature rise and deviation from the global trend, effectively improving the accuracy and reliability of battery safety monitoring.

[0058] This optional embodiment can comprehensively evaluate the overall temperature rise condition of the battery pack by comparing the temperature differences of each temperature probe horizontally. By calculating the temperature difference deviation, the probe location of the temperature rise anomaly can be identified, further refining the judgment criteria for temperature rise anomalies. The technology in this embodiment can accurately locate the area of temperature rise anomaly in the battery pack, facilitating quick troubleshooting and repair, and improving the efficiency of battery maintenance. In other embodiments, temperature distribution maps or thermal imaging technology can also be introduced to visually display the temperature distribution of the battery pack, assisting in determining the specific location of temperature rise anomalies.

[0059] As an optional embodiment, based on the target temperature rise risk coefficient, determining the temperature rise detection result of the electric vehicle includes: judging whether the target temperature rise risk coefficient exceeds a preset coefficient threshold; in the case where the target temperature rise risk coefficient exceeds the coefficient threshold, determining that the temperature rise detection result is abnormal.

[0060] Optionally, one or more temperature rise risk coefficient thresholds can be determined according to factors such as battery type, battery capacity, vehicle operating conditions, etc. These thresholds represent different levels of abnormal temperature rise risk, for example, set the first threshold to 5, the second threshold to 8, and the third threshold to 10. The setting of the threshold needs to be based on historical data and experimental results to ensure that potential risks can be captured without generating too many false positives. After calculating the target temperature rise risk coefficient at the current time, it is compared with the preset coefficient threshold. If the target temperature rise risk coefficient is less than or equal to the first threshold, it is considered that the battery temperature rise is within the normal controllable range, and the temperature rise detection result is normal. If the target temperature rise risk coefficient exceeds the first threshold but does not exceed the second threshold, the temperature rise detection result is marked as a first abnormality, indicating that there is a certain degree of temperature rise risk and monitoring needs to be strengthened. If the target temperature rise risk coefficient exceeds the second threshold but does not exceed the third threshold, the temperature rise detection result is upgraded to a second abnormality, indicating that the temperature rise risk is significant and more stringent battery management measures may need to be taken. If the target temperature rise risk coefficient reaches the third threshold or above, it is considered as a high-risk state, and the temperature rise detection result is a third abnormality or higher, at which point emergency measures must be taken immediately, such as activating the cooling system, reducing the charge and discharge rate, or even disconnecting the battery connection, to prevent thermal runaway.

[0061] This optional embodiment can issue an early warning at the early stage of battery temperature rise abnormality by setting reasonable temperature rise risk coefficient thresholds. Through dynamic adjustment of the temperature rise risk coefficient, it can adapt to the temperature rise abnormality detection needs under different working conditions, improving the flexibility of the warning. The technology in this embodiment can effectively prevent battery thermal runaway, ensuring the safety of the vehicle and passengers. In other embodiments, a historical database of temperature rise risk coefficients can also be established to analyze the patterns of temperature rise abnormalities, further optimize the threshold setting, and improve the accuracy of the warning.

[0062] As an optional embodiment, based on the target temperature rise risk coefficient, a temperature rise detection result of an electric vehicle is determined, wherein, in the case of multiple battery temperatures at the current time, the method comprises: arranging the multiple battery temperatures in order of size to obtain a temperature sequence; determining a temperature median based on the temperature sequence; calculating the difference between each of the multiple battery temperatures and the temperature median to obtain multiple temperature differences; determining the number of temperature differences that exceed a preset difference as an abnormal temperature number; and determining the temperature rise detection result of the electric vehicle based on the target temperature rise risk coefficient and the abnormal temperature number.

[0063] Optionally, the system obtains the temperature readings of all temperature sensors at the current time, arranges these temperature values in ascending order to form a temperature sequence, and calculates the median of the temperature sequence. This step helps to understand the central tendency of the battery pack temperature and provides a reference point for the subsequent steps. If the number of temperature sensors is odd, the median is the middle value in the sorted order; if even, the median is the average of the two middle values. For each battery temperature reading, the system calculates the difference between the temperature reading and the temperature median. This reveals the degree of deviation between each sensor reading and the overall temperature trend of the battery pack. The system sets a preset difference threshold to determine whether the temperature difference of each sensor exceeds the threshold. If it does, the temperature detected by the sensor is considered abnormal. The system counts the number of sensors whose temperature difference exceeds the preset difference threshold, i.e., the number of abnormal temperatures. The target temperature rise risk coefficient is combined with the number of abnormal temperatures to determine the temperature rise detection result. If the target temperature rise risk coefficient is already high and the number of abnormal temperatures is also increasing, it may indicate that the temperature distribution inside the battery is severely uneven, suggesting a potential risk of thermal runaway. If the target temperature rise risk coefficient is high and the number of abnormal temperatures reaches or exceeds the preset number, the temperature rise detection result is determined to be abnormal, which may trigger an alarm mechanism. If the target temperature rise risk coefficient is low, even if there are a small number of abnormal temperatures, it may be determined to be a non-abnormal state, but continuous monitoring is required. If the target temperature rise risk coefficient is moderate and the number of abnormal temperatures starts to increase, it may need to be adjusted to a warning state, prompting the driver to pay attention and possibly adjusting the working mode of the battery management system.

[0064] Through the above steps, the system can comprehensively and dynamically evaluate the temperature rise condition of the battery, not only considering the temperature rise rate but also focusing on the uniformity of temperature distribution, thereby more accurately and timely identifying temperature rise abnormalities and ensuring the safe operation of electric vehicles.

[0065] This optional embodiment can effectively identify local temperature rise abnormalities in the battery pack by calculating the temperature median and its deviation. By counting the number of abnormal temperatures, the degree of temperature rise abnormality can be quantified, which helps to grade the warning. The technology in this embodiment can timely feedback the health status of the battery pack, facilitating the after-sales department to take timely measures. In other embodiments, the concept of temperature gradient can also be introduced to analyze the trend of temperature change in the battery pack, further improving the sensitivity of temperature rise abnormality detection.

[0066] As an optional embodiment, the temperature difference exceeding the preset difference corresponds to an abnormal battery temperature, and based on the temperature probe corresponding to the abnormal battery temperature, the fault location in the battery is determined, and a warning prompt is generated based on the fault location.

[0067] Optionally, the temperature difference that exceeds the preset difference value is determined as an abnormal battery temperature, and the fault location is locked and a warning prompt is generated. After calculating the difference between each temperature probe and the temperature median, a preset difference threshold is set. If the temperature difference of a certain probe exceeds this threshold, it is marked as an abnormal battery temperature. The design of the preset difference threshold should take into account the temperature range of the normal operation of the battery and the sensitivity of the system to abnormal temperatures, and is usually determined based on the type of battery and working conditions through experimental data. For each abnormal battery temperature, identify the corresponding temperature probe. Each temperature probe has its unique identification, which can be a probe number or a location coordinate. Based on the location information of the abnormal probe, determine the possible fault location in the battery pack. In the battery pack, temperature probes are usually distributed in a certain spatial layout, so the distribution pattern of abnormal temperature probes may point to a specific battery module or battery cell. Once the fault location in the battery is confirmed, the system should immediately generate a warning prompt. The content of the warning prompt should include but is not limited to:

[0068] Fault type: abnormal temperature rise.

[0069] Fault level: determined according to the severity of the abnormal battery temperature and the size of the target temperature rise risk coefficient.

[0070] Fault location: specific to the battery module or cell, facilitating quick positioning of the problem by maintenance personnel.

[0071] Suggested measures: according to the fault level, propose appropriate measures such as reducing battery charging and discharging power, increasing cooling system operation, immediate inspection, etc.

[0072] Push warning: send the warning prompt to the driver and the background monitoring center through the vehicle information system, mobile APP or any available communication channel to ensure timely information transmission.

[0073] Through the above steps, the battery module or cell with abnormal temperature rise can be identified and located in time, and a warning prompt is generated, thereby effectively preventing safety risks such as thermal runaway and ensuring the safety of the vehicle and passengers. This real-time monitoring and warning mechanism is an important part of electric vehicle battery management technology, and also has positive significance for improving battery operation efficiency and prolonging service life.

[0074] This optional embodiment can quickly lock the fault location by locating the temperature probe corresponding to the abnormal battery temperature. In principle, by associating the fault location with the warning prompt, the relevance and practicality of the warning information can be improved. In terms of effect, the technology in this embodiment can provide specific fault location information, facilitating the rapid response of the after-sales department and reducing the time cost of fault troubleshooting. In other embodiments, a mapping table of fault location and repair scheme can also be established to automatically recommend repair schemes, further simplifying the fault handling process.

[0075] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the order of the described actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily necessary for the present application.

[0076] Through the description of the above embodiments, those skilled in the art can clearly understand that the temperature rise abnormality detection method of the electric vehicle according to the above embodiments can be realized by means of software and the necessary general hardware platform, of course, it can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes a plurality of instructions for causing a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present application.

[0077] The following is a specific embodiment, Figure 3 is a step schematic diagram of the temperature rise abnormality detection method of the electric vehicle provided according to an optional embodiment of the present application, as Figure 3 shown, which includes:

[0078] Step 1: Obtain the battery data of vehicle A on a certain day, including the following key parameters: time (accurate to seconds), temperature sensing array (℃), and probe number.

[0079] Step 2: Data preprocessing: judge whether the data in this time period appears abnormal dirty data due to the falling of the collection line or the transmission of the Tbox, if not, no processing is needed, if yes, replace the abnormal dirty data.

[0080] Step 3: Calculate the temperature rise risk coefficient according to the temperature difference of each temperature sensor of the adjacent two frames, the time interval and other factors.

[0081] Step 4: Compare the temperature rise risk coefficient with the comparison coefficient threshold value to judge whether the temperature rise is abnormal.

[0082] Step 5: If the temperature rise abnormality is detected, record the frame number, fault type, fault level, and fault position information, and trigger platform alarm.

[0083] Among them, there are usually two cases for electric vehicles, case one is that the vehicle is not used for a short period (the battery system is continuously used), and case two is that the vehicle is not used for a long time and then re-powered for use again.

[0084] For case one, take the data sending period of 30s as an example:

[0085] A. Determine whether the time interval of the collected temperature before and after the frame is less than 3 minutes, if it is, proceed to the next operation;

[0086] B. Take one of the temperature probes in the temperature array as an example to explain the temperature rise risk coefficient calculation process, wherein the temperature array includes the respective temperatures of multiple temperature probes:

[0087] When the current temperature difference ΔT>0 and the temperature is less than 254℃, calculate the average value AvgT of the temperature difference of the probes excluding the self-probe:

[0088] Condition 1: AvgT<0, temperature rise risk coefficient plus 1;

[0089] Condition 2: ΔT-AvgT>1 and ΔT-AvgT≤5, temperature rise risk coefficient plus 1;

[0090] Condition 3: ΔT-AvgT>5, temperature rise risk coefficient plus 2;

[0091] Condition 4: AvgT>0.5 and ΔT / AvgT>2, temperature rise risk coefficient plus 1;

[0092] Condition 5: ΔT>9 and ΔT≤19, temperature rise risk coefficient plus 1;

[0093] Condition 6: ΔT>19, temperature rise risk coefficient plus 2;

[0094] Condition 7: ΔT<0 and the temperature rise risk coefficient of the last time is greater than or equal to 2, temperature rise risk coefficient minus 2, otherwise temperature rise risk coefficient is 0;

[0095] Condition 8: Compare ΔT with the average value lastAvgT of all probe temperatures of the last frame, if ΔT-lastAvgT<-8 and the temperature rise risk coefficient of the last time is greater than or equal to 3, temperature rise risk coefficient minus 3, otherwise temperature rise risk coefficient is 0;

[0096] Condition 9: The current probe temperature and the average temperature of the last frame remain unchanged, and the current temperature is within 45℃, and the time interval is greater than 35s, and the temperature rise risk coefficient of the last frame is greater than or equal to 1, temperature rise risk coefficient minus 1, otherwise temperature rise risk coefficient is 0;

[0097] Condition 10: The current probe temperature decreases by more than 10°C at the last time and increases by more than 10°C at the current time, and the temperature rise risk coefficient of the last frame is greater than or equal to 3, the temperature rise risk coefficient is reduced by 3, otherwise the temperature rise risk coefficient is set to 0.

[0098] Condition 11: First comparison: the probe temperature at the current time is the same as the last time, and the temperature rise risk coefficient of the last frame is greater than or equal to 1, the temperature rise risk coefficient is reduced by 1, otherwise the temperature rise risk coefficient is set to 0. Non-first comparison: the probe temperature at the next time is the same as the last time and the time is continuous, and the temperature rise risk coefficient of the last frame is greater than or equal to 1, the temperature rise risk coefficient is reduced by 1, otherwise the temperature rise risk coefficient is set to 0.

[0099] C. Calculate the lateral temperature difference abnormal index array: Since the pre-stage of thermal runaway is the local temperature rise of the battery pack, sort the temperatures in the current time temperature array from small to large, take the median, and respectively subtract the current temperature, if the temperature difference is greater than 5, put the index of the position into the array, each temperature sensor is operated in this way, finally get the index set, and calculate the intersection with the set of the last time, if the intersection is not empty, the temperature difference abnormality count is added by 1.

[0100] For case two, judge whether the time of the previous and next frames is greater than 3 minutes, if it meets, the following operation is performed:

[0101] a. Calculate the temperature difference between the upper and lower frames of all probes;

[0102] b. Determine whether there is a temperature sensor within ±2 and above 5 in the temperature difference array, if there is, record the temperature difference above 5 probe position, and the temperature difference abnormality count is added by 1;

[0103] c. Continuously track whether the temperature of the probe at the position continuously rises, if it meets the continuous rise or the high temperature counter is added by 1 after several frames of rise, otherwise the temperature difference abnormality count is reduced by 1;

[0104] B. The temperature rise risk coefficient is calculated in the same way as case one;

[0105] In the judgment of temperature rise abnormality, the above two cases are judged respectively:

[0106] Case one: if the temperature rise risk coefficient is greater than 5 and the temperature difference abnormality count is greater than 5 and the current temperature is greater than 45°C, trigger level one alarm, if the temperature reaches 60°C or above, trigger level two alarm;

[0107] Case two: if the temperature abnormality count is greater than 5 and the current maximum temperature is greater than 45°C or the temperature rise risk coefficient is greater than 5 and the temperature difference abnormality count is greater than 5, trigger level one alarm, if the temperature reaches 60°C or above, trigger level two alarm.

[0108] If the temperature rise anomaly is detected, the chassis number, fault type, fault level, fault location and other information are recorded, and the platform alarm is triggered, when the temperature rise anomaly is identified, the alarm is triggered, and the alarm result is displayed in the big data platform, and the displayed content includes the temperature rise anomaly level, the fault probe serial number, the fault occurrence time and the fault end time.

[0109] According to the embodiment of the present application, a temperature rise anomaly detection device of an electric vehicle for implementing the temperature rise anomaly detection method of the electric vehicle is also provided, Figure 4 is a structural diagram of the temperature rise anomaly detection device of the electric vehicle provided by the embodiment of the present application, as Figure 4 shown, the temperature rise anomaly detection device of the electric vehicle includes an acquisition module 42, a receiving module 44, a first determination module 46 and a second determination module 48, and the temperature rise anomaly detection device of the electric vehicle will be described below.

[0110] The acquisition module 42 is configured to acquire the battery temperature in the electric vehicle.

[0111] The receiving module 44 is connected with the acquisition module 42 and is configured to calculate the temperature difference between the battery temperature acquired at the current time and the battery temperature acquired at the previous time.

[0112] The first determination module 46 is connected with the receiving module 44 and is configured to determine a target temperature rise risk coefficient based on the temperature difference and the battery temperature acquired at the current time.

[0113] The second determination module 48 is connected with the first determination module 46 and is configured to determine the temperature rise detection result of the electric vehicle based on the target temperature rise risk coefficient.

[0114] It should be noted that the acquisition module 42, the receiving module 44, the first determination module 46 and the second determination module 48 correspond to steps S202 to S208 in the embodiment, and the multiple modules have the same instances and application scenarios as the corresponding steps, but are not limited to the contents disclosed in the above embodiment. It should be noted that the above modules can run in the computer terminal 10 provided in the embodiment as a part of the device.

[0115] The embodiment of the present application can provide a computer device, which can be located in at least one network device of multiple network devices in a computer network in the embodiment. The computer device includes a memory and a processor.

[0116] The memory can be configured to store software programs and modules, such as program instructions / modules corresponding to the temperature rise abnormality detection method and device of the electric vehicle in the embodiments of the present application. The processor executes the software programs and modules stored in the memory to perform various functional applications and data processing, i.e., to implement the temperature rise abnormality detection method of the electric vehicle described above. The memory can include a high-speed random access memory, and can further include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some examples, the memory can further include a memory remotely disposed relative to the processor, which can be connected to a computer terminal through a network. Examples of the network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0117] The processor can call information and applications stored in the memory through the transmission device to perform the following steps: collecting a battery temperature in the electric vehicle; calculating a temperature difference between the battery temperature collected at the current time and the battery temperature collected at the previous time; determining a target temperature rise risk coefficient based on the temperature difference and the battery temperature collected at the current time; and determining a temperature rise detection result of the electric vehicle based on the target temperature rise risk coefficient.

[0118] Optionally, the processor can further execute program codes of the following steps: calculating a temperature difference between the battery temperature collected at the current time and the battery temperature collected at the previous time, including: determining a collection time corresponding to the battery temperature collected at the previous time; determining an actual time interval based on the collection time and the current time; judging whether the actual time interval exceeds a preset time interval; and calculating the temperature difference between the battery temperature collected at the current time and the battery temperature collected at the previous time in a case where the actual time interval does not exceed the preset time interval.

[0119] Optionally, the processor can further execute program codes of the following steps: in a case where the battery temperature at the current time includes a plurality of battery temperatures and the temperature difference includes a plurality of temperature differences, determining a target temperature rise risk coefficient based on the temperature difference and the battery temperature collected at the current time, including: obtaining an initial temperature rise risk coefficient, wherein the initial temperature rise risk coefficient is determined based on the battery temperature collected at the previous time; determining a temperature difference average value based on the plurality of temperature differences; calculating a difference between each of the plurality of temperature differences and the temperature difference average value to obtain a plurality of deviations corresponding to the plurality of temperature differences, respectively; and adjusting the initial temperature rise risk coefficient based on the plurality of deviations corresponding to the plurality of temperature differences, respectively, and a preset deviation threshold to obtain the target temperature rise risk coefficient.

[0120] Optionally, the processor can further execute program codes of the following steps: determining the temperature rise detection result of the electric vehicle based on the target temperature rise risk coefficient, including: judging whether the target temperature rise risk coefficient exceeds a preset coefficient threshold; in the case that the target temperature rise risk coefficient exceeds the coefficient threshold, determining that the temperature rise detection result is abnormal.

[0121] Optionally, the processor can further execute program codes of the following steps: determining the temperature rise detection result of the electric vehicle based on the target temperature rise risk coefficient, wherein in the case that the battery temperature at the current time includes multiple cases, including: arranging the multiple battery temperatures in size order to obtain a temperature sequence; determining a temperature median based on the temperature sequence; calculating the difference between each of the multiple battery temperatures and the temperature median to obtain multiple temperature differences; determining the number of temperature differences that exceed a preset difference value as an abnormal temperature number; determining the temperature rise detection result of the electric vehicle based on the target temperature rise risk coefficient and the abnormal temperature number.

[0122] Optionally, the processor can further execute program codes of the following steps: determining the battery temperature corresponding to the temperature difference that exceeds the preset difference value as an abnormal battery temperature; determining a fault position in the battery based on the temperature probe corresponding to the abnormal battery temperature; generating a pre-warning prompt based on the fault position.

[0123] By adopting the embodiment of the present application, a kind of temperature rise abnormality detection method of electric vehicle is provided, the battery temperature in electric vehicle is collected;The temperature difference between the battery temperature collected at the current time and the battery temperature collected last time is calculated;The target temperature rise risk coefficient is determined based on the temperature difference and the battery temperature collected at the current time;The temperature rise detection result of the electric vehicle is determined based on the target temperature rise risk coefficient, to achieve the purpose of monitoring the battery temperature rise in electric vehicle in real time, to realize the technical effect of reducing the safety risk caused by battery abnormality in electric vehicle, and to solve the technical problems that most of the temperature rise monitoring of electric vehicle is offline calculation at present, cannot be monitored in real time, leading to risk cannot be early warning.

[0124] Those skilled in the art can understand that all or part of the steps in the above-mentioned embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a non-volatile storage medium, which can include a flash disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0125] The embodiment of the present application also provides a non-volatile storage medium. Optionally, in the present embodiment, the non-volatile storage medium can be used to save the program codes executed by the temperature rise abnormality detection method of electric vehicle provided by the above-mentioned embodiments.

[0126] Optionally, in the embodiment, the non-volatile storage medium can be located in any one of the computer terminals in the computer terminal group in the computer network, or in any one of the mobile terminals in the mobile terminal group.

[0127] Optionally, in the embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: collecting a battery temperature in the electric vehicle; calculating a temperature difference between the battery temperature collected at the current time and the battery temperature collected at the previous time; determining a target temperature rise risk coefficient based on the temperature difference and the battery temperature collected at the current time; determining a temperature rise detection result of the electric vehicle based on the target temperature rise risk coefficient.

[0128] Optionally, in the embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: calculating a temperature difference between the battery temperature collected at the current time and the battery temperature collected at the previous time, including: determining a collection time corresponding to the battery temperature collected at the previous time; determining an actual time interval based on the collection time and the current time; determining whether the actual time interval exceeds a preset time interval; in the case where the actual time interval does not exceed the preset time interval, calculating the temperature difference between the battery temperature collected at the current time and the battery temperature collected at the previous time.

[0129] Optionally, in the embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: in the case where the battery temperature at the current time includes multiple and the temperature difference includes multiple, determining a target temperature rise risk coefficient based on the temperature difference and the battery temperature collected at the current time, including: obtaining an initial temperature rise risk coefficient, wherein the initial temperature rise risk coefficient is determined based on the battery temperature collected at the previous time; determining a temperature difference average value based on the multiple temperature differences; calculating a difference between the multiple temperature differences and the temperature difference average value, to obtain a deviation corresponding to each of the multiple temperature differences; adjusting the initial temperature rise risk coefficient based on the deviation corresponding to each of the multiple temperature differences and a preset deviation threshold, to obtain the target temperature rise risk coefficient.

[0130] Optionally, in the embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining a temperature rise detection result of the electric vehicle based on the target temperature rise risk coefficient, including: determining whether the target temperature rise risk coefficient exceeds a preset coefficient threshold; in the case where the target temperature rise risk coefficient exceeds the coefficient threshold, determining that the temperature rise detection result is abnormal.

[0131] Optionally, in the embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining the temperature rise detection result of the electric vehicle based on the target temperature rise risk coefficient, wherein, when the battery temperature at the current time comprises multiple cases, the steps comprise: arranging the multiple battery temperatures in order of size to obtain a temperature sequence; determining a temperature median based on the temperature sequence; calculating the difference between each of the multiple battery temperatures and the temperature median to obtain multiple temperature differences; determining the number of temperature differences that exceed the preset difference as the number of abnormal temperatures; and determining the temperature rise detection result of the electric vehicle based on the target temperature rise risk coefficient and the number of abnormal temperatures.

[0132] Optionally, in the embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining the temperature difference that exceeds the preset difference as the abnormal battery temperature; determining the fault location in the battery based on the temperature probe corresponding to the abnormal battery temperature; and generating a warning prompt based on the fault location.

[0133] The embodiment of the present application also provides a computer program product comprising a computer program, and optionally, when the computer program is executed by a processor, the computer program can realize: collecting the battery temperature in the electric vehicle; calculating the temperature difference between the battery temperature collected at the current time and the battery temperature collected at the previous time; determining the target temperature rise risk coefficient based on the temperature difference and the battery temperature collected at the current time; and determining the temperature rise detection result of the electric vehicle based on the target temperature rise risk coefficient.

[0134] The above-mentioned serial numbers of the embodiments of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0135] In the above-mentioned embodiments of the present application, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0136] In several embodiments provided in the present application, it should be understood that the disclosed technical contents can be realized by other ways. Among them, the above-mentioned device embodiments are only schematic, for example, the division of the units can be a logical function division, and actual implementation can have another division way, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed each other can be indirect coupling or communication connection through some interfaces, units or modules, which can be electrical or other forms.

[0137] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0138] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0139] The integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a non-volatile storage medium. Based on this understanding, the technical solutions of the present application, essentially or the part that contributes to the prior art, or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to make a computer device (which can be a personal computer, a server or a network device, etc.) execute all or part of the steps of the method described in each embodiment of the present application. The foregoing storage medium includes: a U disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk, and various program code storage media.

[0140] The above is only the preferred embodiment of the present application, it should be noted that for those skilled in the art, without departing from the principles of the present application, can make a number of improvements and refinements, these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A method for detecting abnormal temperature rise of an electric vehicle, characterized in that: Applied to cloud servers, including: Collect battery temperature in electric vehicles; Calculate the temperature difference between the current battery temperature and the last battery temperature. determining a target temperature rise risk coefficient based on the temperature difference and the battery temperature collected at the current moment; Based on the target temperature rise risk coefficient, a temperature rise detection result of the electric vehicle is determined.

2. The method according to claim 1, characterized in that The calculating the temperature difference between the battery temperature currently collected and the battery temperature previously collected includes: Determine the collection time corresponding to the last collected battery temperature; Determining an actual time interval based on the acquisition time and the current time; Determining whether the actual time interval exceeds a preset time interval; When the actual time interval does not exceed the preset time interval, a temperature difference between the battery temperature collected at the current moment and the battery temperature collected at the previous moment is calculated.

3. The method according to claim 1, characterized in that When the battery temperature at the current moment includes multiple values ​​and the temperature difference includes multiple values, determining the target temperature rise risk coefficient based on the temperature difference and the battery temperature collected at the current moment includes: Obtaining an initial temperature rise risk coefficient, wherein the initial temperature rise risk coefficient is determined based on the battery temperature collected last time; determining an average of the temperature differences based on the plurality of temperature differences; Calculating differences between the plurality of temperature differences and the average value of the temperature differences to obtain deviations corresponding to the plurality of temperature differences; Based on the deviations corresponding to the multiple temperature differences and a preset deviation threshold, the initial temperature rise risk coefficient is adjusted to obtain the target temperature rise risk coefficient.

4. The method according to claim 1, wherein The determining, based on the target temperature rise risk coefficient, a temperature rise detection result of the electric vehicle includes: Determining whether the target temperature rise risk coefficient exceeds a preset coefficient threshold; When the target temperature rise risk coefficient exceeds the coefficient threshold, the temperature rise detection result is determined to be abnormal.

5. The method according to any one of claims 1 to 4, characterized in that The determining of the temperature rise detection result of the electric vehicle based on the target temperature rise risk coefficient, wherein the battery temperature at the current moment includes multiple values, includes: Arrange multiple battery temperatures in order of size to obtain a temperature sequence; Based on the temperature sequence, determining a median temperature; Calculating a difference between each of the plurality of battery temperatures and the temperature median to obtain a plurality of temperature difference values; determining a number of the plurality of temperature differences exceeding a preset difference as an abnormal temperature number; A temperature rise detection result of the electric vehicle is determined based on the target temperature rise risk coefficient and the number of abnormal temperatures.

6. The method according to claim 5, characterized in that Also includes: determining that a battery temperature corresponding to a temperature difference exceeding the preset difference is an abnormal battery temperature; determining a fault location in the battery based on a temperature probe corresponding to the abnormal battery temperature; Based on the fault location, an early warning prompt is generated.

7. A device for detecting abnormal temperature rise of an electric vehicle, characterized in that: include: A collection module for collecting battery temperature in an electric vehicle; The receiving module is used to calculate the temperature difference between the battery temperature collected at the current moment and the battery temperature collected at the previous moment; a first determining module, configured to determine a target temperature rise risk coefficient based on the temperature difference and the battery temperature collected at the current moment; The second determination module is configured to determine a temperature rise detection result of the electric vehicle based on the target temperature rise risk coefficient.

8. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute the temperature rise abnormality detection method for an electric vehicle according to any one of claims 1 to 6.

9. A computer device, characterized in that: include: memory and processor, The memory stores a computer program; The processor is configured to execute a computer program stored in the memory, and when the computer program is run, the processor executes the method for detecting abnormal temperature rise of an electric vehicle according to any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for detecting abnormal temperature rise of an electric vehicle according to any one of claims 1 to 6 is implemented.

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