Infrared sensor-based battery pack monitoring method, system, medium and program product

Abstract

A method, system, medium, and program product for monitoring battery packs based on infrared sensors. In this method, a real-time temperature distribution map is received; a determination is made as to whether any surface temperature exceeds a preset dangerous temperature; if not, a determination is made as to whether any surface temperature falls outside a preset temperature range; if so, a single battery to be determined is identified; current driving parameters are obtained, and a discharge single battery is determined; a determination is made as to whether the single battery to be determined is a discharge single battery; if not, a cooling device is controlled to perform a cooling operation and record relevant information about the single battery to be determined; if so, an infrared sensor is controlled to continuously monitor the single battery to be determined; if any surface temperature exceeds the preset dangerous temperature, a dangerous single battery is identified; and the dangerous single battery is controlled to stop supplying power. This method achieves stable monitoring of battery packs of new energy vehicles while driving. Through infrared monitoring, the impact of starting, braking, and bumps on the monitoring process is reduced, thereby improving monitoring accuracy.

Description

Technical Field

[0001] The present application belongs to the field of radiation pyrometry, and in particular relates to a battery pack monitoring method, system, medium and program product based on infrared sensors. Background Art

[0002] With growing global awareness of environmental protection, new energy vehicles (NEVs) are becoming a significant force in the automotive market. The safety and stability of battery packs, the power source of these vehicles, are directly related to the overall performance of the vehicle and the safety of passengers. Therefore, the importance of NEV battery pack monitoring technology is becoming increasingly prominent.

[0003] In related technologies, the overall status information of the battery pack and the status information of each single cell in the battery pack are obtained during a historical monitoring period. Based on the overall status information of the battery pack and the status information of each single cell, the battery pack status monitoring result is obtained. The status monitoring result obtained by this battery pack status monitoring method has high accuracy.

[0004] However, the above monitoring method uses a sensor device to detect the status information of the battery pack, which requires the battery pack to be in a relatively stable state. However, new energy vehicles also need to monitor the battery pack during driving. The starting, braking and bumps of new energy vehicles may have a certain impact on the sensor equipment, causing it to deviate from the monitoring point, thereby reducing the accuracy of monitoring. Summary of the Invention

[0005] The present application provides a battery pack monitoring method, system, medium and program product based on infrared sensors, which are used to stably monitor the battery pack of new energy vehicles while driving. Through infrared monitoring, the impact of starting, braking and bumps on the monitoring process is reduced, thereby improving the accuracy of monitoring.

[0006] In a first aspect, the present application provides a battery pack monitoring method based on an infrared sensor, which receives a real-time temperature distribution map of a new energy vehicle battery pack sent by an infrared sensor. The new energy vehicle battery pack is composed of a plurality of individual batteries, and the real-time temperature distribution map includes the surface temperatures of the individual batteries;

[0007] Determine whether the surface temperature is higher than the preset dangerous temperature;

[0008] If there is no surface temperature greater than the preset dangerous temperature, then determining whether there is a surface temperature that is not within the preset temperature range;

[0009] If so, determining the single battery to be determined whose surface temperature is not within the preset temperature range;

[0010] Obtaining current driving parameters of the new energy vehicle and determining a discharged single battery in a battery pack of the new energy vehicle based on previous driving parameters;

[0011] Determining whether the single battery to be determined is a discharged single battery;

[0012] If not, control the cooling device to cool the single battery to be determined, and record relevant information of the single battery to be determined, including the surface temperature of the single battery to be determined and the location information of the battery pack of the new energy vehicle;

[0013] If so, the infrared sensor is controlled to continuously monitor the single battery to be determined;

[0014] If there is a surface temperature greater than the preset dangerous temperature, determining a dangerous single battery whose surface temperature is greater than the preset dangerous temperature;

[0015] Control Hazard Single Battery Deactivation.

[0016] By implementing the above technical solution, first, the introduction of infrared sensing technology significantly improves the sensitivity and resolution of temperature acquisition, enabling the system to detect subtle temperature fluctuations and promptly identify safety hazards. Second, the preset hazardous temperature and temperature range settings quantify the vague concept of temperature into clear judgment criteria, facilitating automated system processing and reducing false positives. Third, by combining vehicle operating conditions such as driving parameters and discharge status, the system can accurately identify the cause of the temperature rise. If it is caused by normal discharge, frequent intervention is unnecessary, avoiding unnecessary energy consumption. If it is caused by other faults, the cooling device can be activated promptly to prevent the problem from worsening. Finally, detailed recording of key information such as the temperature and location of abnormal batteries provides data support for subsequent work such as fault diagnosis and cause analysis, improving system traceability. This achieves stable monitoring of the battery pack of new energy vehicles while driving. Through infrared monitoring, the impact of starting, braking, and bumps on the monitoring process is reduced, thereby improving monitoring accuracy.

[0017] In conjunction with some embodiments of the first aspect, in some embodiments, after controlling the infrared sensor to continuously monitor the single battery to be determined, the method further includes:

[0018] Determine the real-time temperature distribution gradient according to the real-time temperature distribution graph;

[0019] Determine whether there is an abnormal single battery with elevated surface temperature based on the real-time temperature distribution gradient;

[0020] If so, determine whether the abnormal single battery is discharged single battery;

[0021] If it is not a single battery being discharged, increase the monitoring frequency of the infrared sensor for abnormal single batteries;

[0022] If a single battery is being discharged, the infrared sensor is controlled to maintain a monitoring frequency for the abnormal single battery.

[0023] By employing this technical solution, the temperature gradient reflects the rate of change of spatial temperature differences and represents the flow of heat within the battery. A larger gradient indicates a faster local temperature rise and more concentrated heat, signaling a higher likelihood of a fault. Real-time temperature gradient calculation and monitoring can identify problematic batteries that have not yet exceeded the threshold but are showing abnormal trends, further improving the early warning mechanism. The system also considers the relationship between gradient anomalies and the battery's discharge state. If a battery experiencing a rapid temperature rise is actually discharging, the severity of the anomaly is relatively low, as discharge itself causes a certain temperature rise. In this case, maintaining a normal monitoring frequency is sufficient, avoiding frequent startup and shutdown of the cooling system and reducing energy loss. However, if a battery experiencing an abnormal temperature rise is not discharging, it may indicate a serious fault such as a short circuit or damage, requiring close attention, increased monitoring frequency, and, if necessary, consideration of disconnecting the power supply. This adaptive monitoring strategy dynamically adjusts monitoring intensity and control measures based on actual operating conditions, achieving an optimal balance between safety and cost.

[0024] In conjunction with some embodiments of the first aspect, in some embodiments, if not, controlling the cooling device to perform a cooling operation on the single battery to be determined, and recording relevant information of the single battery to be determined, specifically includes:

[0025] If not, the discharge rate of the single battery to be determined is controlled;

[0026] Determining whether the surface temperature of the single battery to be determined has dropped to within a preset temperature range;

[0027] If the temperature has not dropped to within the preset temperature range, the cooling device is controlled to perform a cooling operation on the single battery to be determined;

[0028] If the temperature drops to within the preset temperature range, the monitoring frequency of the infrared sensor on the individual battery to be determined is increased.

[0029] Using this technical solution, if a battery's temperature is determined to be outside the normal range, the system first attempts to suppress the temperature rise by reducing its discharge rate. By controlling the discharge intensity, the problem battery can be naturally cooled without affecting the vehicle's power output, providing more time for the abnormality to resolve. Reducing the discharge rate means that the load borne by the battery is shared by other healthy batteries, which helps balance the state of charge and service life of each battery. In addition to discharge control, the system continuously monitors battery temperature. If the temperature is effectively controlled and returns to the normal range, the monitoring frequency for that battery can be appropriately increased to promptly detect recurring abnormalities. If the temperature remains above the standard after deceleration discharge, an external cooling device is activated for forced heat dissipation. Compared to directly disconnecting the power supply, this strategy of reducing speed first and then cooling significantly reduces the probability of power outages, improving battery availability and energy efficiency. Even if the cooling effect is not ideal, it can buy valuable time for rescue work and prevent the vehicle from breaking down. Furthermore, phased control helps clarify the cause of the abnormality. If deceleration can restore normal operation, it is likely due to excessive load. If deceleration does not help, the battery itself may be the cause.

[0030] In conjunction with some embodiments of the first aspect, in some embodiments, after controlling the cooling device to perform a cooling operation on the single battery to be determined if the temperature has not dropped to within the preset temperature range, the method further includes:

[0031] When it is determined that the operating power of the cooling device is at the maximum gear and the surface temperature of the single battery to be determined is still not within the preset temperature range, controlling the single battery to be determined to stop supplying power;

[0032] Isolate the single battery to be determined;

[0033] A warning message is sent to the vehicle terminal of the new energy vehicle, indicating that the battery pack of the new energy vehicle is damaged.

[0034] By adopting the above technical solution, sufficient self-protection and early warning must be carried out before disconnection, including uploading the faulty battery information to the cloud, initiating battery health assessment, and prompting users to repair it as soon as possible through multiple channels such as on-board terminals and mobile phone apps, etc., striving to control the impact of the accident to the minimum range and lay the foundation for subsequent emergency response and cause investigation.

[0035] In conjunction with some embodiments of the first aspect, in some embodiments, after controlling the dangerous single battery to stop supplying power, the method further includes:

[0036] Upload relevant information to the preset cloud database;

[0037] Evaluate the battery health of new energy battery packs based on the preset cloud database to obtain the current battery pack health;

[0038] When it is determined that the current battery pack health is lower than the preset health value, an early warning message is sent to the mobile terminal bound to the vehicle terminal. The early warning message is information that prompts the user to pay attention to the battery health.

[0039] By adopting this technical solution, the battery's real-time status data, such as temperature, location, and SOC, is uploaded to a cloud database. This provides an objective basis for subsequent cause analysis and responsibility determination, ensuring data security and traceability. Furthermore, the cloud also takes on the important task of battery health assessment. By comparing the vehicle's historical battery data horizontally and vertically with the battery data of other vehicles of the same model, a comprehensive and objective battery health report is generated through big data analysis and algorithm modeling. If the current battery health level falls below the minimum threshold required for safe operation, the system will immediately send an alert message, reminding the driver through multiple terminal channels to promptly carry out maintenance and minimize the risk of an accident.

[0040] In conjunction with some embodiments of the first aspect, in some embodiments, evaluating the battery health of the new energy battery pack according to a preset cloud database to obtain the current battery pack health specifically includes:

[0041] Determine the charging cycle data and temperature difference data of the new energy battery pack based on the preset cloud database;

[0042] Determine the decay rate of the charge cycle based on the charge cycle data;

[0043] Determine the failure rate based on temperature difference data;

[0044] The performance parameter score of the new energy battery pack is calculated using a weighted average algorithm based on the attenuation rate and failure rate;

[0045] Match the performance parameter scores in the preset performance parameter score table to obtain the corresponding current battery pack health.

[0046] By adopting the above technical solution, the two characteristic parameters most relevant to battery aging and failure are first extracted from the cloud database: the number of cycles and temperature variability. Among them, the number of cycles reflects the cumulative usage and physical and chemical aging of the battery, and the temperature difference indirectly describes the consistency of the battery cell and the tendency of thermal runaway. After obtaining the original parameters, the system further processes them into quantitative indicators that characterize the performance degradation rate. The number of cycles determines the decay rate by fitting the DCIR curve, and the temperature difference determines the abnormal risk by mapping the failure probability model, thereby forming two normalized performance evaluation factors. Finally, the weighted average method is used to integrate the two factors, integrating the battery capacity and stability, and calculating the performance score that takes into account both static and dynamic performance. The intuitive health level can be obtained by table lookup mapping.

[0047] In conjunction with some embodiments of the first aspect, in some embodiments, if no, controlling the cooling device to perform a cooling operation on the single battery to be determined and recording relevant information of the single battery to be determined, the method further includes:

[0048] If it does not exist, the operating parameters of the new energy vehicle battery pack are adjusted according to the real-time temperature distribution map so that the surface temperatures of all the single batteries being discharged in the real-time temperature distribution map are the same.

[0049] By adopting this technical solution, the infrared sensor array is first used to map the spatial distribution of each cell's surface temperature in real time. Discharge current is monitored to distinguish between active and passive temperature rise, identifying high-temperature anomalies. Then, the target cells requiring regulation and their corresponding temperature corrections are extracted by combining the temperature gradient and discharge power. While ensuring overall output, individual cells with excessive temperatures are suppressed, allowing the temperature curve of the entire battery pack to converge. This effectively improves the pack's consistency, reduces the risk of local thermal runaway, and increases energy conversion efficiency. While ensuring safety, it also extends battery life and enhances the vehicle's overall range.

[0050] In the second aspect, an embodiment of the present application provides a battery pack monitoring system based on an infrared sensor, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and one or more processors call the computer instructions to enable the system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0051] In a third aspect, an embodiment of the present application provides a computer-readable storage medium comprising instructions, which, when executed on a system, enables the system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0052] In a fourth aspect, an embodiment of the present application provides a computer program product, characterized in that when the computer program product is run on a system, the system executes the method described in any possible implementation manner in the first aspect.

[0053] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0054] 1. This application provides a battery pack monitoring method based on infrared sensors. First, the introduction of infrared sensing technology greatly improves the sensitivity and resolution of temperature acquisition, enabling the system to capture subtle temperature differences and promptly identify safety hazards. Second, the setting of preset dangerous temperatures and temperature ranges quantifies the vague concept of temperature into a clear judgment standard, facilitating the system's automated processing and reducing the error rate. Third, by combining vehicle operating conditions such as driving parameters and discharge status, the system can accurately identify the cause of the temperature rise. If it is caused by normal discharge, there is no need for frequent intervention, avoiding unnecessary energy consumption; if it is caused by other faults, the cooling device can be activated in time to prevent the problem from worsening. Finally, the temperature, location, and other key information of the abnormal battery are recorded in detail, providing data support for subsequent work such as fault diagnosis and cause analysis, and improving the traceability of the system. Stable monitoring of the battery pack of new energy vehicles on the road is achieved. Through infrared monitoring, the impact of starting, braking, and bumps on the monitoring process is reduced, thereby improving the accuracy of monitoring.

[0055] 2. This application provides a battery pack monitoring method based on infrared sensors. The temperature gradient reflects the rate of change of spatial temperature differences and represents the flow of heat within the battery. A larger gradient indicates a faster local temperature rise and more concentrated heat, signaling a higher likelihood of a fault. By calculating and monitoring the temperature gradient in real time, problematic batteries that have not yet exceeded the threshold but are showing abnormal trends can be detected, further improving the early warning mechanism. The relationship between abnormal gradients and the battery's discharge state is also considered. If a battery with a rapid temperature rise is actually discharging, the severity of the abnormality is relatively low, as discharge itself causes a certain temperature rise. In this case, maintaining a normal monitoring frequency is sufficient, avoiding frequent startup and shutdown of the cooling device and reducing energy loss. However, if a battery with an abnormal temperature rise is not discharging, it may indicate a serious fault such as a short circuit or damage, requiring close attention, increased monitoring frequency, and, if necessary, consideration of disconnecting the power supply circuit. This adaptive monitoring strategy dynamically adjusts monitoring intensity and control measures based on actual operating conditions, achieving an optimal balance between safety and cost.

[0056] 3. This application provides a battery pack monitoring method based on infrared sensors. Upon determining that a battery's temperature exceeds the normal range, the system first attempts to suppress the temperature rise by reducing its discharge rate. By controlling the discharge intensity, the problem battery can be naturally cooled without affecting the vehicle's power output, providing more time for the anomaly to resolve. Reducing the discharge rate means that the load borne by the battery is shared by other healthy batteries, which helps balance the state of charge and service life of each battery. In addition to discharge control, the system also continuously monitors the battery temperature. If the temperature is effectively controlled and returns to the normal range, the monitoring frequency of the battery can be appropriately increased to promptly detect recurring anomalies. If the temperature still exceeds the standard after deceleration discharge, an external cooling device is activated for forced heat dissipation. Compared with directly disconnecting the power supply, the strategy of reducing speed first and then cooling significantly reduces the probability of power outages, improving battery availability and energy utilization. Even if the cooling effect is not ideal, it can buy valuable time for engineering rescue and prevent the vehicle from breaking down. In addition, staged control can also help clarify the cause of the abnormality. If deceleration can return to normal, it is most likely caused by high load; if deceleration does not help, then consider the possibility of battery failure itself. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a flow chart of a battery pack monitoring method based on an infrared sensor in an embodiment of the present application.

[0058] Figure 2 This is another flow chart of a battery pack monitoring method based on an infrared sensor in an embodiment of the present application.

[0059] Figure 3 This is a schematic diagram of the physical device structure of a battery pack monitoring system based on an infrared sensor provided in an embodiment of the present application. DETAILED DESCRIPTION

[0060] The terms used in the following examples of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and appended claims of this application, the singular expressions "a," "an," "said," "above," "the," and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in this application refers to any or all possible combinations comprising one or more of the listed items.

[0061] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0062] The following describes an application scenario of an embodiment of the present application:

[0063] With the intensifying global energy crisis and rising environmental awareness, new energy vehicles have become an inevitable trend in the automotive industry. Among all new energy vehicles, pure electric vehicles (BEVs) have gained widespread consumer favor due to their zero emissions, low noise, and high efficiency. However, the safety and reliability of the power battery pack, the heart of BEVs, has long been a key bottleneck restricting the industry's development.

[0064] One weekend, a family embarked on a long-awaited road trip. They were driving their newly purchased all-electric SUV, boasting a range of 500 kilometers, more than enough for a full day of travel. The journey was smooth and powerful, and the family enjoyed the comfort and convenience of a low-carbon journey. However, just as they were about to reach their destination, the vehicle suddenly lost power, all electrical equipment shut down, and the steering became unusually heavy, nearly resulting in a traffic accident. An inspection revealed the culprit to be the vehicle's power battery pack. The continuous high-speed driving had caused the battery cells to heat up rapidly, causing some cells to experience thermal runaway, triggering a chain reaction throughout the entire pack and pushing the entire battery system to the brink of collapse.

[0065] Similar failures are not uncommon in the new energy vehicle sector. As a highly complex electrochemical system, battery performance is affected by multiple factors, including temperature, current, and SOC. This poses constant safety risks, including overcharge, overdischarge, short circuits, and fires. Battery vibrations are particularly pronounced during bumpy driving conditions, which can easily lead to loose connectors and insulation damage, further increasing the likelihood of thermal runaway and safety accidents. Therefore, timely and accurate monitoring of battery pack status and early warning of abnormal risks have become crucial for ensuring the safe operation of new energy vehicles.

[0066] Traditional battery monitoring solutions mainly rely on sensors such as pressure and temperature to assess the health level and remaining life of the battery by collecting the physical state parameters of the battery. However, this hardware-based monitoring method also has obvious limitations: on the one hand, the vehicle environment is harsh, bumps and vibrations are inevitable, and the measurement accuracy of the sensor is difficult to guarantee; on the other hand, the sensor deployment cost is high and the reliability is poor. Once a failure occurs, it is inconvenient to repair and may misjudge the battery status, bringing greater safety hazards. In order to solve the above technical problems, the present application provides a battery pack monitoring method based on infrared sensors, which is used to stably monitor the battery pack of new energy vehicles on the road. By using infrared monitoring, the impact of starting, braking and bumps on the monitoring process is reduced, thereby improving the accuracy of monitoring.

[0067] The following combination Figure 1 , a battery pack monitoring method based on an infrared sensor in an embodiment of the present application is described:

[0068] See also Figure 1 , is a flow chart of a battery pack monitoring method based on an infrared sensor in an embodiment of the present application.

[0069] S101, receiving a real-time temperature distribution map of a new energy vehicle battery pack sent by an infrared sensor;

[0070] The system receives a real-time temperature distribution map of a new energy vehicle battery pack sent by an infrared sensor. The new energy vehicle battery pack is composed of several individual batteries, and the real-time temperature distribution map includes the surface temperatures of several individual batteries.

[0071] During this step, the system uses infrared sensors to collect real-time thermal radiation signals from the battery pack surface and converts them into a digital temperature distribution image. This image visually displays the temperature distribution of each area of the battery pack in a thermodynamic manner, using different colors or grayscale values to represent different temperatures. This non-contact temperature measurement method offers the advantages of fast response, high sensitivity, and excellent visualization, accurately capturing the dynamic process of battery temperature changes.

[0072] Consider a new energy vehicle equipped with a battery pack consisting of 100 cells connected in series. An infrared sensor collects data every five seconds, generating a temperature distribution map with a resolution of 320 x 240 pixels. Using image processing algorithms, the system determines the temperature value for each pixel and calculates the battery pack's average temperature to be 42.5°C, with a maximum of 46.2°C and a minimum of 39.8°C, resulting in a temperature differential of 6.4°C. These quantitative temperature parameters provide a reliable basis for subsequent abnormality diagnosis and thermal management control.

[0073] S102, determining whether there is a surface temperature greater than a preset dangerous temperature;

[0074] After obtaining the temperature distribution map, the system first needs to determine whether there are any abnormal temperature points, that is, whether the surface temperature of any battery cell exceeds the preset danger threshold. This threshold is usually determined based on a combination of factors such as the battery's material properties, structural design, and safety level, and represents the critical point of battery thermal runaway. Once a high temperature point exceeds the threshold, it means that severe heat accumulation has occurred locally. If not handled in time, it may trigger a chain reaction, resulting in serious consequences such as battery explosion or fire. Therefore, this judgment step is crucial to preventing battery thermal runaway accidents.

[0075] Taking lithium iron phosphate batteries as an example, their upper limit for safe operating temperatures is generally 60°C, exceeding which they face the risk of thermal runaway. Assuming the preset danger temperature is 65°C, when analyzing the temperature distribution map, the system discovered that the surface temperature of battery cell #56 was as high as 68.3°C, significantly exceeding the preset threshold. At this point, the system will immediately trigger a warning and initiate an emergency response plan, including shutting off the battery's charge and discharge circuits, increasing cooling power, and monitoring temperature changes in neighboring batteries to prevent the situation from further deteriorating. At the same time, the system will also report this hotspot information to the central control system and remote monitoring platform to provide data support for accident analysis and improved design.

[0076] S103, determining whether there is a surface temperature outside the preset temperature range;

[0077] If there is no surface temperature greater than the preset dangerous temperature, the system continues to determine whether there is a surface temperature that is not within the preset temperature range.

[0078] In addition to determining whether there are hot spots that exceed the danger threshold, the system also needs to determine whether the surface temperature of the battery cell is within a preset safe temperature range. This range is centered on the optimal operating temperature of the battery, with a certain temperature deviation as the upper and lower limits. For example, the optimal operating temperature of a lithium-ion battery is 25°C, and the safe temperature range can be set to 20°C~30°C. If the battery temperature exceeds this range, although it has not yet reached a dangerous level, it will also affect its charge and discharge performance and cycle life, and needs to be adjusted in time. Through this judgment step, the system can achieve refined control of temperature and always keep the battery in the best condition.

[0079] Assume the preset safe temperature range is 20°C to 30°C, and the current surface temperature of a battery is 35.6°C, significantly above the upper limit of the range. Although the risk of thermal runaway has not yet been reached, prolonged exposure to high temperatures will accelerate battery aging and shorten its service life. In this case, the system automatically activates the temperature regulation mechanism. On the one hand, it reduces heat generation by reducing the charge and discharge current, and on the other hand, it increases the coolant flow rate to accelerate heat dissipation, so that the battery temperature returns to the safe range as soon as possible. At the same time, the system also records the temperature anomaly event as an important basis for battery health assessment.

[0080] S104, determining a single battery to be determined whose surface temperature is not within a preset temperature range;

[0081] If there is a single battery whose surface temperature is not within the preset temperature range, the system determines the single battery to be determined whose surface temperature is not within the preset temperature range.

[0082] If the previous step finds that there are batteries with temperatures outside the safe range, the system needs to further determine which specific cells have abnormalities. Since battery packs are composed of hundreds or even thousands of cells connected in series and parallel, the production process, material properties, and usage environment of each cell are inevitably different, so their temperature change trends are not completely consistent. By comparing and analyzing the temperature distribution map, the system can find abnormal points where the temperature curve deviates significantly from the normal level and record their numbers and location information. These initially screened problem batteries will be the focus of monitoring, providing accurate data support for subsequent diagnostic analysis and control optimization.

[0083] When the system detects that the temperature of certain batteries exceeds the safe range of 20°C to 30°C, it automatically calls the temperature distribution map data, calculates the deviation of each battery cell from the average temperature, and sets an abnormality threshold, such as 5°C. After investigation, it was found that the deviation values of three batteries exceeded 5°C, with surface temperatures of 36.2°C, 37.5°C, and 35.9°C, respectively. They were numbered #134, #278, and #461, and were distributed in the peripheral area of the battery pack. The system marked these three batteries as key targets and recorded their key parameters, including temperature, voltage, current, and SOC, to prepare for further analysis of the cause of the abnormality.

[0084] S105, adjusting the operating parameters of the new energy vehicle battery pack according to the real-time temperature distribution map so that the surface temperatures of all the individual batteries being discharged in the real-time temperature distribution map are the same;

[0085] If the surface temperature does not fall within the preset temperature range, the system adjusts the operating parameters of the new energy vehicle battery pack according to the real-time temperature distribution map so that the surface temperatures of all individual batteries being discharged in the real-time temperature distribution map are the same.

[0086] After identifying a battery cell with an abnormal temperature, the system needs to take measures to regulate its temperature and restore it to a safe range as quickly as possible. Since battery temperature is primarily determined by internal heat generation and external heat dissipation, temperature regulation can be achieved by controlling the charge and discharge process and cooling conditions. Specifically, based on the real-time temperature distribution map, the system dynamically adjusts the battery pack's operating parameters, including charge and discharge rate, peak power, and cutoff voltage, to match the current load borne by each cell with its current temperature, thereby suppressing the generation of local hot spots. At the same time, the system also controls the cooling system to increase the heat dissipation intensity in local areas, accelerating the dissipation of heat from high-temperature cells. Through bidirectional regulation of electrical and thermal coupling, the temperature of all discharging batteries is ultimately brought to a consistent level.

[0087] For example, in electric vehicles, when driving conditions change, such as encountering a hill climb or high-speed operation, the battery pack's discharge load suddenly increases, causing local temperatures to rise rapidly. In response to this situation, the system instantly retrieves a temperature distribution map, identifies the battery cells with the highest temperatures, and reduces their discharge rate accordingly, for example, from 2C to 1C. Simultaneously, it increases the coolant flow rate in the area where these cells are located to improve heat dissipation. After a period of dynamic adjustment, the temperatures of each battery cell return to the same level, with the maximum temperature difference controlled within 2-3°C. This effectively prevents thermal runaway accidents, slows the rate of battery capacity decay, and improves the safety and durability of the battery pack.

[0088] S106, obtaining current driving parameters of the new energy vehicle, and determining a discharged single battery in the battery pack of the new energy vehicle based on the previous driving parameters;

[0089] After step S104 , the system obtains the current driving parameters of the new energy vehicle and determines the discharged single battery in the battery pack of the new energy vehicle according to the previous driving parameters.

[0090] In order to more accurately determine the cause of abnormal battery temperature, the system also needs to analyze it in combination with the actual driving conditions of new energy vehicles. By obtaining driving parameters such as vehicle speed, acceleration, and slope, the battery pack's current discharge power, rate, and other electrical parameters can be inferred, and the discharge status of each single cell can be determined. Generally speaking, batteries in a discharged state will exhibit obvious heating characteristics, and their temperature will be higher than that of static batteries. Under the same discharge conditions, batteries with higher temperatures often have certain performance degradation or increased internal resistance, which require special attention and diagnosis. Therefore, distinguishing between discharged and non-discharged batteries is an important prerequisite for temperature anomaly analysis.

[0091] For example, assume the system collects the following driving parameters via onboard sensors: a speed of 60 km / h, an acceleration of 0.5 m / s², and a 3% grade. Based on this, the battery pack's discharge power can be estimated to be approximately 30 kW. Based on real-time current data recorded by the battery management system (BMS), the discharge rate of each battery cell can be further calculated and a threshold, such as 0.5°C, can be set. Analysis of the temperature distribution reveals that 20 battery cells have significantly higher temperatures than the rest of the pack. Furthermore, the discharge rate of these 20 cells exceeds 0.5°C, indicating that they are in a continuous discharge state. Combined with the abnormally sized batteries identified in the previous step (#134, #278, and #461), it is determined that #134 and #461 are both in a discharge state, while #278 is in a non-discharge state. This result provides important clues for subsequent diagnostic analysis: For abnormally discharged batteries, performance indicators such as internal resistance and capacity should be examined. For abnormally non-discharged batteries, the temperature sensor and thermal management system should be checked for malfunctions.

[0092] S107, determining whether the single battery to be determined is a discharged single battery;

[0093] After distinguishing between discharged and non-discharged batteries, the system needs to further determine whether the battery with the abnormal temperature is in a discharged state. If the abnormal battery is a discharged battery, the temperature rise is likely due to internal heating, requiring attention to its electrochemical and thermal properties. Conversely, if the abnormal battery is a non-discharged battery, the abnormal temperature is more likely caused by external factors, such as poor heat dissipation or temperature measurement failures, requiring inspection of its cooling and measurement and control systems. By determining the discharge state, a clearer direction can be provided for abnormality diagnosis, avoiding blind investigations and improving diagnostic efficiency.

[0094] For example: For the three batteries with abnormal temperatures identified in the previous step (#134, #278, and #461), the system further determines whether they are discharging batteries. By querying the BMS data, it was found that the discharge rates of #134 and #461 were 0.8C and 0.6C, respectively, both exceeding the judgment threshold of 0.5C, so it can be confirmed that they are in a continuous discharge state; while the discharge rate of #278 was only 0.1C, far below the threshold, so it can be confirmed that it is in a non-discharging state. For the two batteries with abnormal discharge, #134 and #461, the system will focus on the changing trends of their voltage, internal resistance and other parameters over time and temperature to assess their performance degradation; and for the non-discharging abnormal battery #278, the system will focus on checking the measurement accuracy of its temperature sensor and the unobstructedness of the cooling channel to ensure the normal operation of the cooling system. This classification diagnosis helps to quickly find the cause of the abnormality and formulate targeted repair plans.

[0095] S108, controlling the cooling device to cool the single battery to be determined, and recording relevant information of the single battery to be determined;

[0096] If it is not a single battery being discharged, the system controls the cooling device to cool the single battery to be determined and records relevant information of the single battery to be determined, including the surface temperature of the single battery to be determined and its location in the battery pack of the new energy vehicle. Specifically: If not, the discharge rate of the single battery to be determined is controlled;

[0097] Determining whether the surface temperature of the single battery to be determined has dropped to within a preset temperature range;

[0098] If the temperature does not fall within the preset temperature range, the cooling device is controlled to cool the single battery to be determined. After that, when the operating power of the cooling device is determined to be at the maximum gear and the surface temperature of the single battery to be determined is still not within the preset temperature range, the single battery to be determined is controlled to stop supplying power.

[0099] Isolate the single battery to be determined;

[0100] Send a warning message to the vehicle terminal of the new energy vehicle, indicating that the battery pack of the new energy vehicle is damaged;

[0101] If the temperature drops to within the preset temperature range, the monitoring frequency of the infrared sensor on the individual battery to be determined is increased.

[0102] For abnormal batteries in a non-discharged state, the system will first activate the cooling equipment to actively dissipate heat to restore the temperature to a safe range as quickly as possible. Common battery cooling methods include air cooling, liquid cooling, and heat pipes. Liquid cooling is the most widely used in power battery thermal management due to its high heat exchange efficiency and high control precision. The system will adjust the flow rate, temperature and other parameters of the coolant in real time according to the temperature level and change rate of the abnormal battery, and monitor the battery temperature response until the abnormality is completely eliminated. During the cooling process, the system will also record key information of the abnormal battery, such as temperature, location, cooling effect, etc., to provide data support for subsequent in-depth analysis and improvement.

[0103] For example, for battery #278, an abnormal battery that was not discharging, the system controlled the liquid cooling system to increase its coolant flow rate, increasing cooling power from 1kW to 3kW, while simultaneously reducing the coolant temperature from 30°C to 25°C. After two minutes of active cooling, the battery surface temperature dropped from 37.5°C to 32.3°C, essentially returning to a safe level. However, compared to other batteries in the group, #278 remained elevated. The system recorded detailed parameters for this battery: battery number #278, location: layer 3, column 12, abnormality time: 12:34:28, maximum temperature: 37.5°C, temperature before cooling: 37.5°C, temperature after cooling: 32.3°C, average cooling rate: 0.087°C / s, and cooling effectiveness assessed as "fair." This information helps engineers later reproduce the abnormal scenario, identify the cause of the heat dissipation problem, and optimize the cooling system layout and control strategy.

[0104] If the abnormal battery happens to be in a state of continuous discharge, it may be difficult to completely eliminate its temperature abnormality by cooling measures alone. This is because the heat generation power of the battery is proportional to its discharge current. The greater the current, the more intense the heat generation. Therefore, for batteries with abnormal discharge, the system also needs to reduce heat generation by controlling the source of their discharge rate. Specifically, the system will dynamically adjust the maximum discharge rate and continuous discharge time of the abnormal battery so that it operates within a relatively mild range. At the same time, the system will also increase the discharge load of the remaining normal batteries accordingly to ensure that the overall power performance is not affected. Through thermal-electric bidirectional coordinated control, local high-temperature points can be effectively suppressed to prevent the occurrence of thermal runaway.

[0105] For example, for the two abnormally discharged batteries, #134 and #461, the system first reduced their maximum discharge rate from 2C to 1C, halving the instantaneous discharge current. Secondly, the system shortened their maximum single discharge time from 60 seconds to 30 seconds, adding a 30-second rest period after each discharge to allow sufficient heat dissipation. Finally, while reducing the abnormal battery's current, the system increased the discharge rate of the remaining normal batteries from 0.5C to 0.75C to compensate for current loss. After a period of dynamic control, the temperatures of #134 and #461 gradually decreased and stabilized, reaching 35.2°C and 34.7°C, respectively, approaching the average level of 34.5°C for other batteries in the group. The system recorded characteristic parameters such as voltage, current, and SOC of the two abnormal batteries in real time, calculated their power curves and energy efficiency, and evaluated the control effectiveness. Results showed that through thermal-electrical coordinated control, the abnormal battery's maximum temperature dropped by 4.5°C, reducing temperature fluctuations by 60%, while the overall discharge power decreased by only 8%, and SOC uniformity improved by 12%. These data provide a reliable basis for further optimization and iteration of control strategies.

[0106] If, after a series of cooling and control measures, the temperature of the abnormal battery successfully drops to a safe range and remains stable, the system will determine that the anomaly is temporary and can be corrected through control measures. However, to prevent the problem from recurring, the system will increase the monitoring frequency of the battery and detect potential risks early. Specifically, the infrared sensor will pay more attention to these batteries that have experienced anomalies and shorten the interval between their temperature sampling. For example, under normal circumstances, the sensor scans the entire battery pack every 30 seconds to obtain a global temperature distribution map; for abnormal batteries, the sensor scans the temperature map every 5 seconds and performs trend analysis. If signs of temperature rebound are detected, the system will activate the control logic in advance to eliminate the anomaly before it fully occurs, thus achieving predictive maintenance.

[0107] S109, controlling the infrared sensor to continuously monitor the single battery to be determined;

[0108] If a single battery is being discharged, the infrared sensor is controlled to continuously monitor the single battery to be determined.

[0109] To promptly detect and address battery temperature anomalies, the system requires continuous, focused monitoring of suspicious batteries. By controlling the infrared sensor to scan the target battery frequently and within a small area, real-time surface temperature changes can be obtained, providing first-hand information for subsequent anomaly diagnosis and early warning. Compared to conventional periodic sampling, continuous monitoring can capture sudden temperature changes with higher temporal resolution, enhancing the system's ability to detect potential hazards. Parameters such as the infrared sensor's scanning frequency, measurement accuracy, and spatial resolution can be adjusted online based on factors such as battery type and operating conditions to balance performance and cost.

[0110] S110, determining a dangerous single battery whose surface temperature is greater than a preset dangerous temperature;

[0111] If there is a surface temperature greater than the preset dangerous temperature, a dangerous single battery with a surface temperature greater than the preset dangerous temperature is identified. Through continuous monitoring by the infrared sensor, the system can obtain a real-time change curve of the surface temperature of the battery to be determined. When the curve shows a steep upward trend and eventually exceeds a preset dangerous temperature threshold, the system determines that the battery is a dangerous battery. This threshold is usually determined based on factors such as battery material characteristics and safety level requirements. For lithium iron phosphate batteries, it is generally 60°C; for ternary lithium batteries, it is more stringent and often takes 45°C. Once a dangerous battery is identified, the system will enter emergency response mode and take a series of measures to prevent the situation from deteriorating, such as cutting off power and increasing cooling. Unlike static over-temperature alarms, this method comprehensively considers the instantaneous value and rate of change of temperature, and can more comprehensively assess the safety status of the battery.

[0112] S111, control the dangerous single battery to stop supplying power;

[0113] When the system detects that a battery's temperature exceeds a dangerous threshold, its first priority is to disconnect its power supply, halting charging and discharging to eliminate the source of the abnormal heating. This is typically achieved by controlling high-voltage DC relays or solid-state switches in the battery pack. These respond to BMS trip commands within milliseconds, reliably isolating the faulty battery. Simultaneously, to prevent arcing from igniting the electrolyte, the system also controls auxiliary relays in the pre-charge circuit to discharge any residual charge. Furthermore, for parallel battery packs, disconnecting a single battery must also consider the impact on the remaining batteries. Multiple redundant designs, such as bypass and backup, are required to ensure uninterrupted power system functionality. Safe power outages are a complex system engineering effort, requiring close coordination among multiple domain controllers, including the BMS, PCS, and EDS.

[0114] S112, determining the real-time temperature distribution gradient according to the real-time temperature distribution graph;

[0115] After steps S101-S111 above, the system determines the real-time temperature distribution gradient based on the real-time temperature distribution map. A key characteristic of thermal runaway is the diffusion of localized high temperatures to the surrounding area, manifested as heat transfer from high-temperature areas to low-temperature areas. To promptly detect and warn of this behavior, the system must not only monitor the temperature changes of individual batteries, but also grasp the overall temperature distribution of the battery pack and its dynamic evolution from a macro perspective. By filtering, correcting, and splicing the raw temperature data collected by the infrared sensor, a real-time two-dimensional temperature distribution map covering all batteries can be generated. Furthermore, the system can perform gradient calculations on the temperature map to obtain a temperature gradient vector field that reflects the direction and intensity of heat flow. The direction of the gradient points to the area where the temperature rises fastest, while the magnitude of the gradient indicates the severity of the temperature change. By analyzing the spatiotemporal distribution characteristics of the temperature gradient in real time, the system can accurately infer the location of thermal runaway, its expansion trend, and the scope of impact, providing a basis for precise warning and intervention.

[0116] S113, judging whether there is an abnormal single battery with an elevated surface temperature based on the real-time temperature distribution gradient;

[0117] While temperature gradient analysis can qualitatively indicate thermal runaway risk, quantitative anomaly diagnosis and early warning require further data processing and feature extraction. One approach is to first determine whether each battery is exothermic or endothermic based on the direction of the temperature gradient. Then, based on the magnitude of the gradient, the system infers the temperature rise or fall rate for each battery. Finally, these rates are compared with a set of pre-calibrated thresholds to determine whether the battery is abnormal and the severity of the abnormality. The setting of these thresholds requires comprehensive consideration of factors such as battery material, structure, and operating conditions, and can vary significantly for different battery models. Furthermore, because temperature gradients are sensitive to measurement errors, robust filtering of the gradient data is required before analysis to remove possible singular values. Overall, real-time temperature gradients are a highly valuable set of monitoring data. Through in-depth analysis and intelligent processing, rich information about battery status can be extracted.

[0118] S114, determining whether the abnormal single battery is discharged;

[0119] If so, determine whether the abnormal single battery is discharged.

[0120] After identifying batteries with abnormal temperatures, the system must further determine the cause of the anomaly. A key distinction is whether the abnormal battery is discharging or charging. Discharging anomalies may indicate a short circuit, overload, or overheating, requiring investigation of the power circuit and cooling system. Charging anomalies may indicate overcharging, water infiltration, or a loose connection, requiring investigation of the charger and auxiliary systems. Only by correctly identifying the source of the anomaly can the system take targeted countermeasures, improving diagnostic efficiency and control accuracy. Generally, the system can directly read the current operating mode parameters of the BMS to determine the charge and discharge status of the abnormal battery. However, in certain special operating conditions, such as during cell balancing, accurate determination based solely on the operating mode is difficult. In these cases, comprehensive analysis of characteristic parameters such as battery voltage, current, and SOC is required. Through big data analysis and machine learning techniques, intelligent inference of the battery's actual operating status is achieved.

[0121] S115. Increase the frequency of infrared sensor monitoring of abnormal single batteries;

[0122] If the battery isn't being discharged individually, the system increases the infrared sensor's monitoring frequency for abnormal individual cells. Once it confirms that certain cells have temperature anomalies and that the anomalies are not caused by discharge, the system increases the monitoring frequency of these cells to collect more intensive data and analyze their temperature trends in greater detail. This is primarily achieved by adjusting the infrared sensor's scanning cycle and exposure time. For example, by changing the scanning cycle for an abnormal cell from 30 seconds to every 5 seconds and increasing the exposure time from 10ms to 50ms, the temperature sampling frequency can be increased by 6 times and the temperature resolution by 2.2 times. The massive amount of temperature data generated by high-frequency monitoring is processed in real time by edge computing devices, enabling the creation of detailed three-dimensional temperature distribution maps, calculation of higher-order temperature gradients, and extraction of more complex anomaly signatures. This information is ultimately fed back to the BMS to optimize thermal management strategies. As can be seen, increasing the monitoring frequency not only improves the system's temperature sensing capabilities but also deepens understanding of battery thermal behavior, representing a typical data-driven approach.

[0123] S116. Control the infrared sensor to maintain the monitoring frequency of the abnormal single battery.

[0124] If a single battery is discharging, the system controls the infrared sensors to maintain the monitoring frequency for the abnormally discharged battery. Compared to abnormally discharged batteries, batteries currently discharging typically require closer attention. On the one hand, discharging means the battery is continuously generating heat, and the heat output can be significant. This accelerates temperature rise and shortens the warning period for runaway. On the other hand, because the battery is in operation, completely shutting off its discharge circuit could affect vehicle performance, necessitating careful intervention and thorough evaluation of the effects. For these reasons, the system maintains a high monitoring frequency for batteries with abnormal discharge until the anomaly is resolved or the battery ceases operation. This means that the infrared sensors will monitor these batteries continuously and for extended periods, ready to detect suspicious signs and issue timely warnings. Simultaneously, the BMS dynamically adjusts the battery's maximum discharge rate and strengthens information sharing with other controllers to mitigate temperature runaway.

[0125] The above embodiment has the following beneficial effects:

[0126] First, the introduction of infrared sensing technology significantly improves the sensitivity and resolution of temperature acquisition, enabling the system to detect subtle temperature fluctuations and promptly identify safety hazards. Second, the preset hazardous temperature and temperature range settings quantify the vague concept of temperature into clear judgment criteria, facilitating automated system processing and reducing false positives. Third, by combining vehicle operating conditions such as driving parameters and discharge status, the system can accurately identify the cause of the temperature rise. If it is caused by normal discharge, frequent intervention is unnecessary, avoiding unnecessary energy consumption. If it is caused by other faults, the cooling device can be activated promptly to prevent the problem from worsening. Finally, detailed recording of critical information such as the temperature and location of abnormal batteries provides data support for subsequent work such as fault diagnosis and cause analysis, improving system traceability. This system achieves stable monitoring of the battery pack of new energy vehicles while driving. Through infrared monitoring, the impact of starting, braking, and bumps on the monitoring process is reduced, thereby improving monitoring accuracy.

[0127] The temperature gradient reflects the rate of change of spatial temperature differences and represents the flow of heat within the battery. A larger gradient indicates a faster local temperature rise and more concentrated heat, signaling a higher likelihood of a fault. Real-time temperature gradient calculation and monitoring can identify problematic batteries that have not yet exceeded the threshold but are showing abnormal trends, further improving the early warning mechanism. The system also considers the relationship between abnormal gradients and the battery's discharge state. If a battery experiencing a rapid temperature rise is actually discharging, the severity of the abnormality is relatively low, as discharge itself causes a certain temperature rise. In this case, maintaining a normal monitoring frequency is sufficient, avoiding frequent startup and shutdown of the cooling system and reducing energy loss. However, if a battery experiencing an abnormal temperature rise is not discharging, it may indicate a serious fault such as a short circuit or damage, requiring close attention, increased monitoring frequency, and, if necessary, consideration of disconnecting the power supply. This adaptive monitoring strategy dynamically adjusts monitoring intensity and control measures based on actual operating conditions, achieving an optimal balance between safety and cost.

[0128] If a battery's temperature exceeds the normal range, the system first attempts to suppress the temperature rise by reducing its discharge rate. By controlling the discharge intensity, the problem battery can be cooled naturally without affecting the vehicle's power output, providing more time for the abnormality to resolve. Reducing the discharge rate means that the load borne by the battery is shared by other healthy batteries, balancing the state of charge and service life of each battery. In addition to discharge control, the system continuously monitors battery temperature. If the temperature is effectively controlled and returns to the normal range, the monitoring frequency for that battery can be increased appropriately to promptly detect recurring abnormalities. If the temperature remains above the standard after deceleration discharge, an external cooling device is activated for forced heat dissipation. Compared to directly disconnecting the power supply, this strategy of reducing speed before cooling significantly reduces the probability of power outages and improves battery availability and energy efficiency. Even if the cooling effect is not ideal, it can buy valuable time for rescue engineers and prevent the vehicle from breaking down. Furthermore, phased control helps clarify the cause of the abnormality. If deceleration can restore normality, it is likely due to excessive load. If deceleration does not help, the battery itself should be considered as a potential problem.

[0129] Before disconnection, sufficient self-protection and early warning must be carried out, including uploading faulty battery information to the cloud, initiating battery health assessment, and prompting users to repair as soon as possible through multiple channels such as on-board terminals and mobile phone apps, etc., striving to control the impact of the accident to the minimum extent and lay the foundation for subsequent emergency response and cause investigation.

[0130] First, an infrared sensor array is used to map the spatial distribution of each cell's surface temperature in real time. Discharge current is monitored to distinguish between active and passive temperature rise, identifying high-temperature anomalies. Then, the target cells requiring regulation and their corresponding temperature corrections are identified by combining the temperature gradient and discharge power. While ensuring overall output, individual cells with excessive temperatures are suppressed, allowing the temperature curve of the entire battery pack to converge. This effectively improves the pack's consistency, reduces the risk of local thermal runaway, and increases energy conversion efficiency. This ensures safety while extending battery life and enhancing the vehicle's overall range.

[0131] In the above embodiment, when the system detects a dangerous single battery through infrared monitoring, it will control the dangerous single battery to stop supplying power. After that, it is necessary to evaluate the health of the battery pack of the new energy vehicle to determine whether the new energy vehicle can continue to drive normally. Figure 2 , another battery pack monitoring method based on an infrared sensor in an embodiment of the present application is described:

[0132] See also Figure 2 , is another flow chart of a battery pack monitoring method based on an infrared sensor in an embodiment of the present application.

[0133] S201, uploading relevant information to a preset cloud database;

[0134] The system extracts two types of information closely related to battery health assessment from the massive amounts of data collected by on-board infrared monitoring equipment: charging cycle data and temperature differential data. The former reflects battery usage intensity and aging, while the latter reflects battery consistency and failure risk. To facilitate subsequent big data analysis, the system uploads this structured information in real time to a pre-set cloud database via wireless network. This database utilizes a distributed architecture with high availability, high concurrency, and high fault tolerance, meeting the needs of massive fleet data storage and management. Furthermore, the database seamlessly integrates with edge computing platforms, enabling training of machine learning models and providing data visualization services for business personnel.

[0135] S202, determining charging cycle data and temperature difference data of the new energy battery pack according to a preset cloud database;

[0136] Cloud databases store a vast amount of long-term operating data on vehicles, providing a rich sample for assessing battery health. The system queries the raw data for a specific vehicle within a preset timeframe and, after cleaning and aggregation, generates a standardized set of charging cycle data and temperature difference data. The former typically includes indicators such as the total number of charges, the number of fast charges, the number of partial cycles, and the number of full cycles, reflecting the degree of battery wear. The latter typically includes indicators such as the temperature difference across the entire group, the temperature difference between modules, and the temperature difference between cells, reflecting the level of consistency. It is worth noting that due to differences in operating conditions, the data characteristics of different vehicles may vary significantly. Therefore, the system also needs to normalize the data based on factors such as vehicle model, environment, and mileage to facilitate horizontal comparison. Standardized and structured data description is a prerequisite for conducting quantitative health assessments.

[0137] S203, determining a decay rate of the charging cycle according to the charging cycle data;

[0138] Charging cycle data can quantitatively reflect battery capacity decay. Generally speaking, battery capacity gradually decreases with increasing usage. However, the rate of decay varies from vehicle to vehicle and depends on many factors, such as operating conditions and temperature. To objectively assess capacity changes, the system employs an incremental decay model, using the capacity drop (%) per 100 cycles as the decay rate. Specifically, a linear fit is performed on the measured capacity data for the last N charging cycles of a specific vehicle, with the slope representing the current 100-cycle decay rate. The value of N should take into account both sample size and timeliness, and is typically set between 200 and 500. It is important to note that capacity data often exhibit significant noise due to measurement errors and fluctuations in operating conditions. To ensure fitting accuracy, the system smoothes the raw data using methods such as moving average and factorization before modeling. Furthermore, to reflect long-term capacity decay trends, the system refits every M cycles and records the evolution of the decay rate. This rolling update mechanism allows for timely capture of nonlinear changes in battery health. As a key metric for quantifying battery aging, decay rate has important applications in health assessment and lifespan prediction.

[0139] S204, determining a failure rate based on the temperature difference data;

[0140] Temperature difference data can sensitively indicate the consistency level and potential risks of a battery pack. For a brand-new battery pack, its internal temperature distribution should be highly consistent, with temperature differences at each point not exceeding 2-3°C. However, over time, due to factors such as material migration and increased interfacial impedance, the heating characteristics of some cells may shift, causing the temperature difference within the pack to widen. When the temperature difference exceeds a preset threshold (e.g., 5°C), it indicates that the performance of some cells has severely degraded, and continued use may induce failures such as short circuits and fires. Therefore, temperature difference data has become an important basis for assessing system safety. To quantitatively characterize consistency, the industry often uses the temperature coefficient of dispersion (COP), which is the ratio of the temperature variance at each sampling point to the mean. Based on large-sample statistics, the system can fit a functional relationship between the COP and failure rate, thereby inferring failure risk in real time based on online temperature difference data. It is generally believed that for every 1 percentage point increase in the COP, the failure rate increases by 3-5 percentage points.

[0141] S205. Calculate a performance parameter score of the new energy battery pack using a weighted average algorithm based on the attenuation rate and the failure rate;

[0142] Degradation rate and failure rate are two core indicators for assessing battery health. The former reflects the battery's capacity retention and is directly related to range, while the latter reflects the battery's consistency, impacting safety and reliability. Therefore, a weighted average of the two can be used to generate a comprehensive performance parameter score. The weightings should be determined based on factors such as usage scenario and operational requirements. For example, for long-distance vehicles with high range requirements, a higher weight could be assigned to degradation rate, while for buses with frequent fast charging, a greater weight should be assigned to failure rate. In addition to weighting, the scoring function in the scoring formula should also be customized based on large-sample statistics and expert experience. For example, degradation rate can be considered a health baseline of 0.2% / 100, with 10 points deducted for every 0.2% increase. Failure rate can be converted linearly, with 2% equaling 60 points. Finally, the degradation rate and failure rate scores are summed with preset weights to obtain a battery performance score on a 100-point scale. This score quantitatively characterizes the battery's health and provides an intuitive basis for operational optimization decisions.

[0143] For example, a logistics fleet uses the same model of lithium iron phosphate batteries and implements refined battery health management. The system evaluates the battery performance of each vehicle monthly and adjusts dispatch plans accordingly. For vehicle #1, after two years of operation, its battery degradation rate per 100 cycles has reached 0.61%, significantly exceeding the health benchmark of 0.2%, translating to a score of 85. Its failure rate is 3.1%, corresponding to a score of 45 on the linear scale. Assuming a weight of 60% for degradation rate and 40% for failure rate, the weighted average performance parameter score is 0.6 × 85 + 0.4 × 45 = 69. Further analysis of fleet-wide data reveals that 30% of vehicles currently score above 80, 40% are between 60 and 80, and 30% are below 60. Based on this, the system classifies vehicle #1 as having medium health and recommends routine maintenance every two months. The system also recommends reducing its average daily mileage by 10% and avoiding long-distance missions. Through scoring and grading, the fleet fully matches the vehicle status and usage intensity, ensuring service levels while maximizing battery life.

[0144] S206, matching the performance parameter scores in a preset performance parameter score table to obtain the corresponding current battery pack health;

[0145] Performance parameter scores are continuous variables that quantitatively represent the battery's health status. To facilitate tiered management, they need to be discretized into several levels. This requires pre-establishing a performance parameter scoring table based on empirical data and industry standards, establishing a correlation between scores and health levels. Taking a five-level scale as an example, a score of 90 or above is considered very good health, 80-90 is good, 60-80 is fair, 40-60 is poor, and below 40 is extremely poor. It should be noted that due to the varying degradation patterns and failure modes of different battery types, the grading criteria of the scoring table should also be tailored to each vehicle. For example, for ternary lithium batteries and lithium iron phosphate batteries, since the former generally offers better consistency than the latter and typically has a lower 100-cycle degradation rate, the penalty for degradation rate can be relaxed. However, the consistency of ternary materials generally deteriorates more rapidly, so the failure rate grading threshold can be lowered accordingly. Furthermore, the grading criteria must consider the specific application. For example, for private cars with high range requirements, the penalty for degradation rate can be appropriately increased; while for commercial fleets with extremely high safety requirements, the tolerance for failure rates should be significantly reduced. In short, the scoring table needs to be dynamically adjusted based on factors such as vehicle model, operating conditions, and operational objectives to meet the management needs of different scenarios. Embedding the performance parameter scoring table into the health management process can automatically trigger plans that match the health level, such as adjusting charging and discharging parameters and scheduling repairs and maintenance, thereby achieving refined and differentiated full lifecycle management.

[0146] S207: When it is determined that the current health of the battery pack is lower than the preset health value, a warning message is sent to the mobile terminal bound to the vehicle terminal.

[0147] When it is determined that the current battery pack health is lower than the preset health value, the system sends an early warning message to the mobile terminal bound to the vehicle terminal. The early warning message is information that prompts the user to pay attention to the battery health.

[0148] The above embodiment has the following beneficial effects:

[0149] Uploading the battery's real-time status data, such as temperature, location, and SOC, to a cloud database provides an objective basis for subsequent cause analysis and responsibility determination, ensuring data security and traceability. Furthermore, the cloud also handles battery health assessments. By comparing the vehicle's historical battery data horizontally and with the battery data of similar vehicles, big data analysis and algorithmic modeling are used to generate a comprehensive and objective battery health report. If the current battery health level falls below the minimum threshold for safe operation, the system will immediately send an alert, prompting the driver through multiple terminals to promptly inspect and repair the battery, minimizing the risk of an accident.

[0150] First, the two characteristic parameters most relevant to battery aging and failure are extracted from the cloud database: the number of cycles and temperature variability. Among them, the number of cycles reflects the cumulative usage and physical and chemical aging of the battery, while the temperature difference indirectly describes the consistency of the battery cell and the tendency of thermal runaway. After obtaining the original parameters, the system further processes them into quantitative indicators that characterize the performance degradation rate. The number of cycles determines the decay rate by fitting the DCIR curve, and the temperature difference determines the abnormal risk by mapping the failure probability model, thereby forming two normalized performance evaluation factors. Finally, the weighted average method is used to integrate the two factors, integrating the battery capacity and stability, and calculating the performance score that takes into account both static and dynamic. The intuitive health level can be obtained by table lookup mapping.

[0151] The following describes the system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , is a schematic diagram of the physical device structure of a battery pack monitoring system based on an infrared sensor provided in an embodiment of the present application.

[0152] It should be noted that Figure 3 The structure of the system shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0153] like Figure 3 As shown, the system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes, such as the methods described in the above embodiments, based on programs stored in a read-only memory (ROM) 302 or programs loaded from a storage unit 308 into a random access memory (RAM) 303. RAM 303 also stores various programs and data required for system operation. CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to bus 304.

[0154] The following components are connected to the I / O interface 305: an input section 306 including a camera, infrared sensor, and the like; an output section 307 including a liquid crystal display (LCD) and speakers; a storage section 308 including a hard disk and the like; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. Removable media 311, such as a magnetic disk, optical disk, magneto-optical disk, or semiconductor memory, is installed in the drive 310 as needed, so that computer programs read from the media can be installed in the storage section 308 as needed.

[0155] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for executing the methods illustrated in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via the communication section 309 and / or installed from removable media 311. When executed by the central processing unit (CPU) 301, the computer program performs the various functions defined in the present invention.

[0156] It should be noted that the computer-readable medium described in the embodiments of the present invention may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium may include a data signal transmitted in baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal may take any of a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof.

[0157] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the boxes can also occur in an order different from that marked in the accompanying drawings. For example, two boxes shown in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0158] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the system described in the above embodiments, or may exist independently and not incorporated into the system. The storage medium carries one or more computer programs, and when executed by a processor of a system, the system implements the methods provided in the above embodiments.

[0159] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0160] As used in the above embodiments, the term “when” may be interpreted to mean “if” or “after” or “in response to determining that” or “in response to detecting that”, depending on the context. Similarly, the phrases “upon determining that” or “if (stated condition or event) is detected” may be interpreted to mean “if determining that” or “in response to determining that” or “upon detecting (stated condition or event)” or “in response to detecting (stated condition or event)”, depending on the context.

[0161] In the above embodiments, all or part of the embodiments can be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, hard disk, tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive).

[0162] Those skilled in the art will appreciate that all or part of the process steps in the above-described method embodiments can be implemented by a computer program instructing the relevant hardware. The program can be stored in a computer-readable storage medium, and when executed, the program can include the process steps in the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A battery pack monitoring method based on infrared sensor, characterized in that: include: receiving a real-time temperature distribution map of a new energy vehicle battery pack sent by an infrared sensor, wherein the new energy vehicle battery pack is composed of a plurality of individual batteries, and the real-time temperature distribution map includes the surface temperatures of the plurality of individual batteries; Determining whether the surface temperature is greater than a preset dangerous temperature; If there is no surface temperature greater than the preset dangerous temperature, determining whether there is a surface temperature that is not within the preset temperature range; If so, determining the single battery to be determined whose surface temperature is not within the preset temperature range; Acquiring current driving parameters of the new energy vehicle, and determining a discharged single battery in a battery pack of the new energy vehicle based on the previous driving parameters; Determining whether the single battery to be determined is the discharged single battery; If not, control the cooling device to cool the single battery to be determined, and record relevant information of the single battery to be determined, including the surface temperature of the single battery to be determined and the location information of the battery pack of the new energy vehicle, specifically including: If not, controlling the discharge rate of the single battery to be determined; Determining whether the surface temperature of the single battery to be determined drops to within the preset temperature range; If the temperature has not dropped to within the preset temperature range, controlling the cooling device to cool the single battery to be determined; If the temperature drops to within the preset temperature range, increasing the monitoring frequency of the infrared sensor on the single battery to be determined; When it is determined that the operating power of the cooling device is at the maximum gear and the surface temperature of the single battery to be determined is still not within the preset temperature range, controlling the single battery to be determined to stop supplying power; performing an isolation operation on the single battery to be determined; Sending a warning message to the vehicle terminal of the new energy vehicle, wherein the warning message indicates that the battery pack of the new energy vehicle is damaged; If so, controlling the infrared sensor to continuously monitor the single battery to be determined; If there is a dangerous single battery whose surface temperature is greater than the preset dangerous temperature, determining that the dangerous single battery has a surface temperature greater than the preset dangerous temperature; Control the dangerous single battery to stop supplying power.

2. The method according to claim 1, characterized in that After controlling the infrared sensor to continuously monitor the single battery to be determined if yes, the method further includes: determining a real-time temperature distribution gradient according to the real-time temperature distribution graph; Determining whether there is an abnormal single battery with an increased surface temperature based on the real-time temperature distribution gradient; If so, determining whether the abnormal single battery is the discharged single battery; If it is not the discharged single battery, increasing the monitoring frequency of the infrared sensor on the abnormal single battery; If it is the discharged single battery, the infrared sensor is controlled to maintain the monitoring frequency of monitoring the abnormal single battery.

3. The method according to claim 1, characterized in that After controlling the dangerous single battery to stop supplying power, the method further includes: Uploading the relevant information to a preset cloud database; Evaluate the battery health of the new energy vehicle battery pack according to the preset cloud database to obtain the current battery pack health; When it is determined that the current battery pack health is lower than a preset health value, a warning message is sent to a mobile terminal bound to the vehicle terminal, where the warning message is information prompting the user to pay attention to the battery health.

4. The method according to claim 3, characterized in that The evaluating the battery health of the new energy vehicle battery pack according to the preset cloud database to obtain the current battery pack health specifically includes: Determining charging cycle data and temperature difference data of the new energy vehicle battery pack according to the preset cloud database; determining a decay rate of the charging cycle based on the charging cycle data; determining a failure rate based on the temperature difference data; Calculating a performance parameter score of the new energy vehicle battery pack using a weighted average algorithm according to the attenuation rate and the failure rate; The performance parameter score is matched in a preset performance parameter score table to obtain the corresponding current battery pack health.

5. The method according to claim 1, wherein If not, controlling the cooling device to perform a cooling operation on the single battery to be determined and recording relevant information of the single battery to be determined, the method further includes: If not, the operating parameters of the new energy vehicle battery pack are adjusted according to the real-time temperature distribution map so that the surface temperatures of all the discharging individual batteries in the real-time temperature distribution map are the same.

6. A battery pack monitoring system based on infrared sensors, characterized in that: The system comprises: One or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the system to execute the method according to any one of claims 1 to 5.

7. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a system, the system is caused to perform the method according to any one of claims 1 to 5.

8. A computer program product, characterized in that When the computer program product is run on a system, the system is caused to perform the method according to any one of claims 1 to 5.

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