Abnormal state detection method of liquid-cooled energy storage system

Abstract

The invention relates to the technical field of energy storage system anomaly detection, and particularly discloses an abnormal state detection method for a liquid-cooled energy storage system, and the method comprises the steps: analyzing the environment where the liquid-cooled energy storage system is located, obtaining the feature value of the environment, analyzing the use state of a cooling system of the liquid-cooled energy storage system, and obtaining the feature value of the environment; judging whether the cooling system is abnormal or not and determining a cooling threshold value of the liquid-cooled energy storage system, analyzing an actual operation state of the liquid-cooled energy storage system, judging whether the liquid-cooled energy storage system is abnormal or not and performing abnormal detection on a battery unit of the liquid-cooled energy storage system, and determining an overheating detection abnormal level of the battery unit; the problem that traditional liquid cooling system anomaly detection depends on a fixed rule or a preset threshold value and lacks intelligent data processing and self-adaptive adjustment capability is solved, the intelligent level of system operation is improved, and the stability and safety of the system are further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of abnormality detection of energy storage systems, and in particular to an abnormal state detection method for a liquid-cooled energy storage system. Background Art

[0002] Modern society is increasingly dependent on renewable energy sources, such as solar and wind energy. However, due to the intermittent nature of these energy sources, it is difficult to achieve stable power supply. In this context, energy storage systems play a role in balancing loads, storing excess energy, and releasing energy during peak demand periods, becoming an important part of the renewable energy industry. Large-scale battery energy storage systems generate a lot of heat during the charging and discharging process. If the heat is not dissipated in time, the battery performance will decline, and may even lead to dangers such as thermal runaway. Liquid cooling technology has become one of the mainstream technologies for thermal management of energy storage systems due to its efficient heat dissipation capacity and uniform cooling effect, especially for high-power and high-density battery packs. Compared with traditional air cooling, liquid cooling technology can conduct heat more effectively. The coolant directly contacts the cooling components and can quickly take away the heat from the battery module or other heat-generating components. Especially in high-power intensive energy storage systems, liquid cooling technology can provide more accurate and efficient temperature control. The operation of the liquid cooling system involves multiple complex factors, such as the flow rate, pressure, and temperature control of the coolant. If the liquid cooling system fails and cannot effectively control the heat, it may cause overheating of the battery pack and even cause safety problems. Therefore, abnormal state detection of the liquid cooling system is crucial.

[0003] Nowadays, there are still some deficiencies in the research on abnormal state detection of liquid-cooled energy storage systems. Specifically, traditional liquid cooling system abnormality detection relies on fixed rules or preset thresholds, lacks intelligent data processing and adaptive adjustment capabilities, and cooling system performance degradation, battery aging and other problems have an impact on system operation. Fixed detection methods cannot adapt to these changes. Traditional detection systems cannot automatically adjust parameters according to real-time working conditions or environmental conditions, resulting in the inability to respond effectively in time. Most of the detection methods for liquid cooling systems do not fully consider the impact of changes in external environmental conditions on the system, lack dynamic adjustment capabilities, and cannot detect and handle system abnormalities in a timely manner. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method for detecting an abnormal state of a liquid-cooled energy storage system, which can effectively solve the problems involved in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for detecting an abnormal state of a liquid-cooled energy storage system, comprising the following steps: analyzing the environment in which the liquid-cooled energy storage system is located to obtain characteristic values ​​of the environment; analyzing the use status of the cooling system of the liquid-cooled energy storage system to determine whether the cooling system is abnormal: if the cooling system is abnormal, a cooling system abnormality alarm is issued; if the cooling system is not abnormal, the cooling threshold of the liquid-cooled energy storage system is determined in combination with the characteristic values ​​of the environment; analyzing the actual operating status of the liquid-cooled energy storage system to determine whether the liquid-cooled energy storage system has an operating abnormality and obtaining a detection data set of each battery unit, and determining the battery unit overheating detection abnormality level in combination with the cooling threshold of the liquid-cooled energy storage system.

[0006] As a further method, the environment in which the liquid-cooled energy storage system is located is analyzed to obtain characteristic values ​​of the environment. The specific analysis process is: obtaining a data set of the environment in which the liquid-cooled energy storage system is located, and the data set of the environment in which the liquid-cooled energy storage system is located specifically includes the ambient temperature, the ambient wind speed, and the cooling system outlet temperature; based on the obtained data set of the environment in which the liquid-cooled energy storage system is located, a comprehensive analysis is performed to obtain characteristic values ​​of the environment, and the characteristic values ​​of the environment are used as an analysis basis for determining the cooling threshold of the liquid-cooled energy storage system.

[0007] As a further method, it is determined whether there is an abnormality in the cooling system. The specific analysis process is: obtain a cooling system usage status data set, and based on the obtained cooling system usage status data set, comprehensively analyze to obtain a cooling system usage status evaluation value, and use the cooling system usage status evaluation value as an analysis basis for determining whether there is an abnormality in the cooling system; compare the cooling system usage status evaluation value with the cooling system usage status threshold stored in the database; if the cooling system usage status evaluation value is lower than the cooling system usage status threshold, then the cooling system corresponding to the cooling system usage status evaluation value is abnormal, and a cooling system abnormality alarm is issued; if the cooling system usage status evaluation value is not lower than the cooling system usage status threshold, then the cooling system corresponding to the cooling system usage status evaluation value is not abnormal.

[0008] As a further method, the cooling system uses a state data set, specifically including the absolute value of the difference between the coolant flow rate and the reference coolant flow rate, the absolute value of the difference between the coolant pressure and the reference coolant pressure, and the absolute value of the difference between the coolant level and the reference coolant level.

[0009] As a further method, the cooling system uses the state evaluation value, and the specific analysis process is as follows:

[0010]

[0011] Wherein, β is the evaluation value of the cooling system usage status, ls is the absolute value of the difference between the coolant flow rate and the reference coolant flow rate, yl is the absolute value of the difference between the coolant pressure and the reference coolant pressure, yw is the absolute value of the difference between the coolant level and the reference coolant level, τ1 is the compensation factor of the set ls, τ2 is the compensation factor of the set yl, τ3 is the compensation factor of the set yw, and e is a natural constant.

[0012] As a further method, the cooling threshold of the liquid-cooled energy storage system is determined. The specific analysis process is: obtaining the cooling system usage status evaluation value and the environmental characteristic value, storing the cooling system usage status evaluation value and the environmental characteristic value as a designated tag, and comparing the designated tag with the liquid-cooled energy storage system cooling threshold corresponding to each designated tag stored in the database to obtain the liquid-cooled energy storage system cooling threshold corresponding to the designated tag.

[0013] As a further method, it is determined whether the liquid-cooled energy storage system has any operational abnormality. The specific analysis process is as follows: obtaining a data set of actual operating status of the liquid-cooled energy storage system, and based on the obtained data set of actual operating status of the liquid-cooled energy storage system, comprehensively analyzing to obtain an actual operating status evaluation value, and using the actual operating status evaluation value as an analysis basis for determining whether the liquid-cooled energy storage system has any operational abnormality; comparing the actual operating status evaluation value with an actual operating status threshold stored in a database; if the actual operating status evaluation value is lower than the actual operating status threshold, then the liquid-cooled energy storage system corresponding to the actual operating status evaluation value has an operational abnormality, and an abnormal operating alarm for the liquid-cooled energy storage system is issued; if the actual operating status evaluation value is not lower than the actual operating status threshold, then the liquid-cooled energy storage system corresponding to the actual operating status evaluation value does not have an operational abnormality.

[0014] As a further method, the actual operating status data set of the liquid-cooled energy storage system specifically includes the absolute value of the difference between the system power output and the reference power output, the absolute value of the difference between the system load rate and the reference load rate, and the absolute value of the difference between the system voltage and the reference voltage.

[0015] As a further method, each battery cell detection data set specifically includes the average temperature of the battery cell, the maximum temperature difference inside the battery cell, and the difference between the outlet temperature and the inlet temperature of the battery cell coolant.

[0016] As a further method, combined with the cooling threshold of the liquid-cooled energy storage system, the battery cell overheating detection abnormality level is determined. The specific analysis process is: based on the acquired battery cell detection data sets, a comprehensive analysis is performed to obtain the detection evaluation value of each battery cell, and each battery cell detection evaluation value is used as the analysis basis for determining the battery cell overheating detection abnormality level; each battery cell detection evaluation value is compared with the liquid-cooled energy storage system cooling threshold stored in the database; the difference between each battery cell detection evaluation value and the liquid-cooled energy storage system cooling threshold is recorded as each battery cell detection deviation value; each battery cell detection deviation value is compared with the battery cell detection deviation first threshold stored in the database; if the battery cell detection deviation value is higher than the battery cell detection deviation first threshold If the number of values ​​is greater than the first threshold value of the battery cell detection deviation, the battery cell overheat detection abnormality level is marked as the fourth level; if the number of battery cell detection deviation values ​​higher than the first threshold value of the battery cell detection deviation is not greater than the first threshold value of the battery cell detection deviation, but greater than the second threshold value of the battery cell detection deviation, the battery cell overheat detection abnormality level is marked as the third level; if the number of battery cell detection deviation values ​​higher than the first threshold value of the battery cell detection deviation is not greater than the second threshold value of the battery cell detection deviation, but is not 0, the battery cell overheat detection abnormality level is marked as the second level; if the number of battery cell detection deviation values ​​higher than the first threshold value of the battery cell detection deviation is 0, the battery cell overheat detection abnormality level is marked as the first level.

[0017] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0018] (1) The present invention provides a method for detecting abnormal conditions of a liquid-cooled energy storage system, analyzes environmental characteristic values, and monitors the state of the cooling system, thereby maintaining stable operation of the energy storage system. Early warning of cooling system abnormalities helps prevent overheating and potential thermal runaway of the battery pack, thereby reducing downtime and damage risks. Through a multi-level abnormality detection mechanism, it is possible to timely determine whether the system operation and cooling status are abnormal, which helps to discover potential faults as early as possible. In particular, abnormality detection and overheating detection of battery cells can effectively prevent the occurrence of battery thermal runaway events. Through environmental analysis and real-time data monitoring, the system can automatically adjust the cooling threshold, thereby improving the intelligence level of the system operation. It can also reduce false alarms or missed alarms through precise monitoring methods, further improving the stability and safety of the system.

[0019] (2) The present invention determines the cooling threshold of the liquid-cooled energy storage system by combining the environmental characteristic values ​​when there is no abnormality in the cooling system. The environmental characteristic values ​​directly affect the heat dissipation effect of the cooling system and can dynamically adjust the cooling threshold. By dynamically adjusting the cooling threshold in combination with the environmental characteristic values, the energy storage system can achieve better stability in a complex environment. The static cooling threshold setting often cannot adapt to changes in environmental conditions and is prone to false alarms or misjudgments. Under high ambient temperature conditions, the static threshold of the cooling system may cause frequent temperature over-limit alarms, even though the system is actually still within the safe operating range. By dynamically setting the cooling threshold in combination with the environmental characteristic values, false alarms or misjudgments can be effectively reduced, the detection accuracy of the system can be improved, and the environmental characteristic values ​​can be incorporated into the dynamic adjustment process of the cooling threshold, so that the system has intelligent and adaptive capabilities.

[0020] (3) The present invention obtains the detection data set of each battery cell, combines it with the cooling threshold of the liquid-cooled energy storage system, determines the abnormal level of battery cell overheat detection, and combines the detection data of the battery cell and the threshold of the cooling system to accurately detect whether the battery is in an overheated state. Battery overheating is one of the main risk factors for thermal runaway in the energy storage system. Detailed detection and classification of the temperature of each battery cell combined with the cooling threshold can help identify early overheating trends and prevent the battery temperature from continuing to rise to an out-of-control state. The hierarchical management method enables the system to respond in time to avoid serious safety accidents such as battery explosion or fire. Combined with environmental characteristics and the threshold of the cooling system, it helps to reduce false alarms or misjudgments. The system can dynamically adjust the threshold of overheat detection to reduce false alarms caused by changes in external environmental temperature. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0022] Figure 1 It is a schematic diagram of the method flow of the present invention.

[0023] Figure 2 A flowchart of the steps for determining whether a liquid-cooled energy storage system has an operating abnormality. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0025] Reference Figure 1 As shown, the present invention provides a method for detecting an abnormal state of a liquid-cooled energy storage system, comprising: analyzing the environment in which the liquid-cooled energy storage system is located to obtain characteristic values ​​of the environment.

[0026] The specific analysis process is as follows: obtaining the environmental data set of the liquid-cooled energy storage system, which specifically includes the ambient temperature, ambient wind speed, and cooling system outlet temperature; based on the obtained environmental data set of the liquid-cooled energy storage system, a comprehensive analysis is performed to obtain the environmental characteristic values, which are used as the analysis basis for determining the cooling threshold of the liquid-cooled energy storage system.

[0027] In a specific embodiment, the ambient temperature refers to the air temperature of the external environment or indoor environment where the energy storage system is located. The temperature sensor (such as a thermistor, a thermocouple, an RTD, etc.) is used to measure the ambient temperature. The ambient wind speed refers to the speed of air flow around the energy storage system. The wind speed has a direct impact on the heat dissipation performance of the cooling system. The wind speed sensor (such as an ultrasonic anemometer, a thermal anemometer, a mechanical anemometer, etc.) is used to measure the wind speed in the environment. The cooling system outlet temperature refers to the temperature of the coolant when it is discharged from the system after absorbing heat inside the energy storage system. The temperature sensor (such as a thermocouple, PT100, an NTC thermistor) is used to monitor the outlet temperature of the coolant.

[0028] It needs to be explained that the above-mentioned ambient temperature directly affects the heat dissipation efficiency of the cooling system. The higher the ambient temperature, the weaker the heat dissipation capacity of the coolant in the radiator, because it is more difficult for heat to be transferred from the coolant to the external environment. The lower the ambient temperature, the better the heat dissipation effect, and the coolant can dissipate heat faster. The wind speed directly affects the heat dissipation capacity of the cooling system (such as the radiator). The higher the ambient wind speed, the faster the heat exchange between the coolant and the air when passing through the radiator, the heat can be quickly carried away by the wind, the cooling system outlet temperature is reduced, and the heat dissipation effect is improved. The ambient temperature and wind speed jointly affect the cooling effect. Under high ambient temperature and low wind speed conditions, the heat dissipation effect is the worst, the cooling system outlet temperature is the highest, the cooling load increases, and the battery may be at risk of overheating. Under low ambient temperature and high wind speed conditions, the heat dissipation effect is the best, the cooling system outlet temperature is the lowest, and the coolant can efficiently take away the heat generated by the battery.

[0029] It needs to be explained that the above-mentioned external factors such as ambient temperature and wind speed directly affect the working effect of the cooling system. The environmental characteristic values ​​obtained by comprehensive environmental data analysis can reflect the heat dissipation conditions of the current system and help to reasonably adjust the cooling threshold. When the threshold of the cooling system can dynamically adapt to the environment, unnecessary cooling operations can be avoided, thereby saving energy. When the ambient temperature is low, the cooling system does not need to work at a high intensity. A reasonable cooling threshold can reduce the flow rate of the coolant or reduce the operating frequency of the cooling equipment, thereby reducing energy consumption. A higher ambient wind speed also contributes to natural heat dissipation and reduces dependence on the liquid cooling system, which further reduces the system's energy consumption. Setting the cooling threshold based on the environmental characteristic value can ensure that the system is always in a stable operating environment. Different environmental conditions have different thermal management requirements for the battery. By dynamically adjusting the cooling threshold, the cooling system can better adapt to complex environmental changes. Analyzing environmental data and flexibly adjusting the cooling threshold can effectively avoid this situation and ensure that the cooling intensity always meets actual needs.

[0030] It should be explained that the specific analysis process of the above environmental characteristic values ​​is as follows:

[0031]

[0032] In the formula, α is the characteristic value of the environment, hw is the ambient temperature, hf is the ambient wind speed, ck is the outlet temperature of the cooling system, σ1 is the compensation factor of the set hw, σ2 is the compensation factor of the set hf, and σ3 is the compensation factor of the set ck.

[0033] It needs to be explained that the above-mentioned environmental characteristic values ​​are calculated through the ambient temperature, ambient wind speed, and cooling system outlet temperature, and hw, hf, and ck are normalized. The environmental characteristic values ​​combine external factors such as ambient temperature and wind speed with the outlet temperature of the cooling system, and can more accurately reflect the impact of the external environment on the performance of the cooling system. The higher the ambient temperature, the greater the cooling difficulty. A higher wind speed helps to improve the heat dissipation effect. The system can better understand the impact of external conditions on the cooling effect and make timely adjustments. The environmental characteristic values ​​provide the system with real-time feedback on the current environment, so that the abnormal judgment of the cooling system can be dynamically adjusted according to different environmental conditions. The environmental characteristic values ​​can dynamically reflect the pressure brought by the environment to the cooling system. If the ambient temperature is too high and the wind speed is low, the cooling effect may be weakened. The system can predict the overheating risk in advance based on the environmental characteristic values ​​and take measures to avoid overheating of the battery unit. This prevention mechanism helps to prevent safety hazards such as thermal runaway caused by insufficient heat dissipation and ensure the safe operation of the system.

[0034] It should be explained that the compensation factors of hw, hf, and ck set above are obtained from the database. According to historical data, a mapping set of the historically measured ambient temperature, ambient wind speed, cooling system outlet temperature and the compensation factors of hw, hf, and ck is established to obtain the compensation factors of hw, hf, and ck corresponding to the current hw, hf, and ck.

[0035] It should be noted that τ1, τ2, τ3, ε1, ε2, ε3, μ1, μ2, and μ3 mentioned below are also obtained through a mapping set of historical data and compensation factors established in a database, that is, the corresponding compensation factors are obtained based on the current data.

[0036] In a specific embodiment, the ambient temperature is 35 degrees Celsius, the ambient wind speed is 2 meters per second, the cooling system outlet temperature is 50 degrees Celsius, the compensation factor of hw is 1.2, the compensation factor of hf is 0.8, and the compensation factor of ck is 1.1. The calculated environmental characteristic value α is 19.82, indicating that the current ambient temperature is high and the wind speed is low, and the cooling effect is relatively poor.

[0037] The cooling system usage status of the liquid-cooled energy storage system is analyzed to determine whether there is any abnormality in the cooling system: if there is an abnormality in the cooling system, a cooling system abnormality alarm is issued; if there is no abnormality in the cooling system, the cooling threshold of the liquid-cooled energy storage system is determined in combination with the characteristic values ​​of the environment.

[0038] The specific analysis process is as follows: obtain a cooling system usage status data set, and based on the obtained cooling system usage status data set, perform a comprehensive analysis to obtain a cooling system usage status evaluation value, and use the cooling system usage status evaluation value as an analysis basis for determining whether there is an abnormality in the cooling system; compare the cooling system usage status evaluation value with the cooling system usage status threshold stored in the database; if the cooling system usage status evaluation value is lower than the cooling system usage status threshold, then the cooling system corresponding to the cooling system usage status evaluation value is abnormal, and a cooling system abnormality alarm is issued; if the cooling system usage status evaluation value is not lower than the cooling system usage status threshold, then the cooling system corresponding to the cooling system usage status evaluation value is not abnormal.

[0039] It should be explained that the above-mentioned acquisition of the actual usage status data of the cooling system and comprehensive analysis, the obtained cooling system usage status evaluation value can accurately reflect the operating status of the system. By comparing it with the threshold value stored in the database, it can be effectively determined whether the system has abnormalities. If the usage status evaluation value is lower than the threshold, the system can immediately identify the abnormality and issue an alarm to help take quick measures. This accurate abnormality identification reduces the possibility of misjudgment. Timely detection and alarm of abnormal conditions in the cooling system can avoid potential risks caused by failure or overwork of the cooling system. For example, failure of the cooling system may cause the battery pack of the energy storage system to overheat, which may eventually cause safety accidents such as battery thermal runaway and fire. Through automatic detection and alarm mechanisms, the system can issue warnings when problems first occur to prevent serious accidents and ensure the safety of the energy storage system. Through dynamic analysis and evaluation of usage status data, the system can identify abnormal operating conditions of the cooling system, such as insufficient coolant flow rate, high coolant pressure or low liquid level, and can achieve intelligent adjustment, so that the cooling system has higher adaptability.

[0040] Furthermore, the cooling system uses a status data set, specifically including the absolute value of the difference between the coolant flow rate and the reference coolant flow rate, the absolute value of the difference between the coolant pressure and the reference coolant pressure, and the absolute value of the difference between the coolant level and the reference coolant level.

[0041] In a specific embodiment, the absolute value of the difference between the coolant flow rate and the reference coolant flow rate is the absolute value of the difference between the actual coolant flow rate and the reference flow rate, reflecting the degree of deviation between the actual flow rate and the ideal flow rate. The larger the difference, the greater the degree of deviation of the coolant flow rate from the design value, which may mean that the pump efficiency is reduced, the pipeline is blocked, or the cooling system fails. The flow meter (such as an electromagnetic flow meter, an ultrasonic flow meter, a turbine flow meter) is used to measure the actual flow rate of the coolant. The absolute value of the difference between the coolant pressure and the reference coolant pressure reflects the pressure deviation of the cooling system. A large difference may indicate that the pump performance is poor. The pressure sensor or pressure transmitter is installed in the coolant pipeline to measure the actual pressure of the coolant and the absolute value of the difference between the actual coolant level and the reference coolant level. The larger the difference, the more likely it is that the coolant is leaking, evaporating, or insufficiently replenished. A low level may cause the cooling system to fail to operate normally, and an excessively high level may indicate that the system is over-filled or overflowing. A level sensor (such as a float level gauge, ultrasonic level gauge, capacitive level gauge) is installed in the coolant storage tank to monitor the liquid level in real time.

[0042] It needs to be explained that the above-mentioned abnormal coolant flow rate (for example, the flow rate is too low) may cause changes in the coolant pressure, because the resistance to liquid flow will affect the pressure. When the flow rate is too low, the coolant may be retained in the pipeline, causing a local pressure drop or increase. If the flow rate is too high, it may cause the coolant pressure to increase. The change in flow rate may also be related to the change in liquid level. If the liquid level is low, the flow rate may also be low. There is a direct relationship between pressure and flow rate. Based on Bernoulli's principle, flow rate and pressure are often inversely proportional. If the pressure is too low, the flow rate may decrease because the pump cannot generate enough driving force. If the pressure is too high, the flow rate may increase. Abnormal pressure may also be related to the liquid level. When the coolant level is too low, the pump may not be able to generate enough pressure, resulting in a decrease in pressure and flow rate of the entire system. When the liquid level is too low, the coolant circulation will be restricted, resulting in a decrease in flow rate and pressure. When the liquid level is too high, the circulation resistance of the liquid may increase, resulting in an increase in the pump workload, increased pressure, and may even affect the flow rate.

[0043] Specifically, the cooling system usage status evaluation value, the specific analysis process is:

[0044]

[0045] Wherein, β is the evaluation value of the cooling system usage status, ls is the absolute value of the difference between the coolant flow rate and the reference coolant flow rate, yl is the absolute value of the difference between the coolant pressure and the reference coolant pressure, yw is the absolute value of the difference between the coolant level and the reference coolant level, τ1 is the compensation factor of the set ls, τ2 is the compensation factor of the set yl, τ3 is the compensation factor of the set yw, and e is a natural constant.

[0046] It should be explained that the above cooling system usage status evaluation value is calculated by the absolute value of the difference between the coolant flow rate and the reference coolant flow rate, the absolute value of the difference between the coolant pressure and the reference coolant pressure, and the absolute value of the difference between the coolant level and the reference coolant level. After normalizing ls, yl, and yw, and comprehensively analyzing the deviations in the coolant flow rate, pressure, and level, the system can comprehensively evaluate the overall operating status of the cooling system, and can help the system more accurately determine whether the cooling system is in normal working condition, whether there are problems such as insufficient cooling, overcooling, or equipment failure. A single flow rate, pressure, or level parameter may not be able to accurately determine whether the cooling system is in normal working condition, whether there are problems such as insufficient cooling, overcooling, or equipment failure. Accurately judge the operating status of the cooling system, but by combining the differences of these parameters, the cooling system usage status evaluation value can more comprehensively and accurately detect the abnormalities of the cooling system, reduce false alarms and missed alarms, and ensure that the alarm is triggered only when there is a real problem with the system. The balance of coolant flow rate, pressure and liquid level is essential to maintaining the normal operation of the cooling system. Any abnormality in any parameter will affect the cooling efficiency. By monitoring these parameters in multiple dimensions and calculating the difference, the system can quickly identify and solve potential problems, ensure that the cooling system is always in the best operating state, and thus maintain the overall operating stability of the energy storage system.

[0047] In a specific embodiment, at this time, the absolute value of the difference between the coolant flow rate and the reference coolant flow rate is 0.5 meters per second, the absolute value of the difference between the coolant pressure and the reference coolant pressure is 0.2 bar, the absolute value of the difference between the coolant level and the reference coolant level is 0.1 meter, the compensation factor of ls is 0.5, the compensation factor of yl is 0.3, and the compensation factor of yw is 1.1. The cooling system usage status evaluation value is calculated to be 1.7009, and the cooling system usage status threshold is 1.5. 1.7009 is less than 1.5, indicating that the overall state of the cooling system is good and no abnormalities occur.

[0048] Furthermore, the cooling threshold of the liquid-cooled energy storage system is determined. The specific analysis process is as follows: obtaining the cooling system usage status evaluation value and the environmental characteristic value, storing the cooling system usage status evaluation value and the environmental characteristic value as a designated tag, and comparing the designated tag with the liquid-cooled energy storage system cooling threshold corresponding to each designated tag stored in the database to obtain the liquid-cooled energy storage system cooling threshold corresponding to the designated tag.

[0049] In a specific embodiment, when the cooling system usage status evaluation value is 1.7009 and the environmental characteristic value is 19.82, the designated label is recorded as (1.7009, 19.82), and the cooling threshold of the liquid-cooled energy storage system is matched to 6. The matching rule is: the cooling system usage status evaluation value is 1.7009, rounded to 2, the environmental characteristic value is 19.82, rounded to 20, the updated designated label is recorded as (2, 20), 2 is recorded as the value 1, and 20 is recorded as the value 2. The values ​​1 and 2 in the designated label are imported into the matching model. The matching model is: liquid-cooled energy storage system cooling threshold = {[(value 1 + value 2) / 2]+1} / 2, and the cooling threshold of the liquid-cooled energy storage system is matched to 6.

[0050] In a specific embodiment, different environmental characteristic values ​​and cooling system usage status evaluation values ​​reflect the needs of the system under different operating conditions. By storing these data as designated tags, the system can select the most suitable cooling threshold from historical data according to actual conditions, realize dynamic adjustment, and help determine whether the battery unit cooling meets the standards. This dynamic adjustment mechanism can enable the system to maintain the best cooling effect and operating efficiency under complex and changing working conditions. By comparing the designated tags of the cooling system usage status and environmental characteristic values ​​with the corresponding tags in the database, the cooling threshold that best suits the current state can be found, realizing intelligent management of the cooling system. The system can independently select the appropriate cooling threshold, reduce manual intervention, and improve the intelligence level of the system, so that it can automatically adapt to changes in the environment and usage status according to real-time data, enhance the system's adaptability, and set the threshold based on historical data and environmental characteristic value tags to reduce false alarms caused by fluctuations in a single parameter. When the ambient temperature rises suddenly, the system can automatically select the appropriate cooling threshold by comparing historical tags to avoid too frequent false alarms.

[0051] The actual operating status of the liquid-cooled energy storage system is analyzed to determine whether the liquid-cooled energy storage system has any operating abnormalities: if the liquid-cooled energy storage system does not have any operating abnormalities, the battery cells of the liquid-cooled energy storage system are detected for abnormalities; if the liquid-cooled energy storage system has any operating abnormalities, an abnormal operating alarm of the liquid-cooled energy storage system is issued and the battery cells of the liquid-cooled energy storage system are detected for abnormalities.

[0052] The specific analysis process is as follows: obtaining a data set of actual operating status of the liquid-cooled energy storage system, and based on the obtained data set of actual operating status of the liquid-cooled energy storage system, comprehensively analyzing to obtain an actual operating status evaluation value, and using the actual operating status evaluation value as an analysis basis for determining whether the liquid-cooled energy storage system has an operating abnormality; comparing the actual operating status evaluation value with the actual operating status threshold stored in the database; if the actual operating status evaluation value is lower than the actual operating status threshold, then the liquid-cooled energy storage system corresponding to the actual operating status evaluation value has an operating abnormality, and an abnormal operating alarm for the liquid-cooled energy storage system is issued; if the actual operating status evaluation value is not lower than the actual operating status threshold, then the liquid-cooled energy storage system corresponding to the actual operating status evaluation value does not have an operating abnormality.

[0053] It should be explained that the above-mentioned continuous acquisition of the actual operating status data of the liquid-cooled energy storage system enables the system to achieve real-time monitoring, so that the system can quickly capture any signs of abnormalities during operation, ensuring that problems can be discovered and handled at an early stage, thereby avoiding system failures or accidents. The actual operating status assessment value is based on comprehensive data analysis, reflecting the current health status of the system. By comparing it with the historical thresholds stored in the database, it can accurately determine whether there are abnormal conditions. It can be more comprehensive and accurate than simple single parameter analysis, reducing the risk of misjudgment or omission. Once the assessment value is lower than the actual operating status threshold, the system can issue an abnormal alarm in time. The instant feedback mechanism allows operators to take quick action to prevent small problems from evolving into serious failures, which is crucial to ensuring the stability and safety of the liquid-cooled energy storage system.

[0054] Furthermore, the actual operating status data set of the liquid-cooled energy storage system specifically includes the absolute value of the difference between the system power output and the reference power output, the absolute value of the difference between the system load rate and the reference load rate, and the absolute value of the difference between the system voltage and the reference voltage.

[0055] In a specific embodiment, the absolute value of the difference between the system power output and the reference power output is the absolute value of the difference between the actual power output of the system and the reference power output, which can reflect whether the system provides sufficient power as expected or operates under overload. A larger absolute value of the difference may indicate that the system output deviates from expectations, such as insufficient output power or overload operation. A power meter (such as a smart electric energy meter) is used to monitor the actual output power of the system in real time. The absolute value of the difference between the system load rate and the reference load rate refers to the absolute value of the difference between the actual load rate and the reference load rate. The load rate difference can help determine whether the system is within a reasonable operating range. A large difference indicates that the system load deviates from the expected state, which may be overloaded or underloaded, affecting system efficiency. The absolute value of the difference between the system voltage and the reference voltage is obtained based on a smart electric energy meter or a load monitoring device. It refers to the absolute value of the difference between the actual system voltage and the reference voltage. The difference can reflect whether the system voltage is stable or deviates from the normal range. A large difference may cause unstable system operation or equipment damage. A voltmeter or a smart electric energy meter is used to monitor the actual voltage of the system in real time.

[0056] It needs to be explained that the above-mentioned power output is the power provided by the system to the load or the power grid, which directly affects the load rate of the system. The load rate is the ratio of the current power output of the system to its maximum design power, usually expressed as a percentage. The higher the load rate, the closer the power output of the system is to its maximum design power. If the power output increases, the load rate will also increase accordingly. When the power output difference (the difference between the actual and reference power output) increases, the system may enter a high-load operation state. At this time, the load rate will also increase, and the absolute value of the difference will increase. The voltage is positively correlated with the power output. When the voltage increases, the power output increases, and when the voltage decreases, the power output decreases. The larger the absolute value of the voltage difference, the greater the possibility that the power output deviates from the normal range. There is an inverse correlation between the system load rate and the voltage. An increase in the load rate may cause the voltage to drop. When the load rate decreases, the voltage may be more stable. Fluctuations in the load rate will also affect the stability of the voltage, thereby affecting the power output of the system operation.

[0057] It should be explained that the above actual operation status evaluation value, the specific analysis process is as follows:

[0058]

[0059] Wherein, γ is the actual operating status evaluation value, gl is the absolute value of the difference between the system power output and the reference power output, fz is the absolute value of the difference between the system load factor and the reference load factor, dy is the absolute value of the difference between the system voltage and the reference voltage, ε1 is the compensation factor of the set gl, ε2 is the compensation factor of the set fz, and ε3 is the compensation factor of the set dy.

[0060] It should be explained that the above-mentioned actual operating status evaluation value is calculated by the absolute value of the difference between the system power output and the reference power output, the absolute value of the difference between the system load rate and the reference load rate, and the absolute value of the difference between the system voltage and the reference voltage. gl, fz, and dy are normalized. Combined with the power output difference, load rate difference, and voltage difference, the actual operating status evaluation value can comprehensively reflect the overall operating health status of the system. This multi-dimensional analysis provides a more complete perspective and can identify potential problems that cannot be revealed by a single parameter. Through the linkage analysis of power output, load rate, and voltage, the system can judge abnormalities more accurately. The multi-dimensional analysis method helps to detect potential faults earlier. Even if the power output seems normal, voltage fluctuations or abnormal load rates may be early signals of system instability. By combining these differences into an evaluation value, the system can issue an early warning before the problem worsens, thereby providing sufficient response time for timely repairs and preventive maintenance and reducing the possibility of major failures.

[0061] In a specific embodiment, at this time, the absolute value of the difference between the system power output and the reference power output is 10 kilowatts, the absolute value of the difference between the system load rate and the reference load rate is 0.15, the absolute value of the difference between the system voltage and the reference voltage is 5 volts, the compensation factor of gl is 0.8, the compensation factor of fz is 1.2, and the compensation factor of dy is 1.0. The actual operating status evaluation value is calculated to be 0.5011, which is greater than the actual operating status threshold of 0.5 stored in the database, and there is no operating abnormality in the liquid-cooled energy storage system.

[0062] Obtain the detection data set of each battery cell, and determine the abnormal level of battery cell overheat detection in combination with the cooling threshold of the liquid-cooled energy storage system.

[0063] Specifically, the battery cell detection data set includes the average temperature of the battery cell, the maximum temperature difference inside the battery cell, and the difference between the outlet temperature and the inlet temperature of the battery cell coolant.

[0064] It should be explained that the above-mentioned average temperature of the battery cell refers to the average value of the temperature sensor readings in each area of ​​the entire battery cell, which reflects the overall thermal state of the battery cell under the current working state. Multiple temperature sensors (such as NTC thermistors, thermocouples, RTD sensors) are installed at different positions inside the battery cell to measure the temperature of each area. The maximum temperature difference inside the battery cell refers to the difference between the highest temperature and the lowest temperature between different areas inside the battery cell, which is used to evaluate the uniformity of heat dissipation in each part of the battery cell. If the temperature difference is too large, it indicates that the heat dissipation in the battery cell is uneven, which may cause local overheating, thereby accelerating the aging of some batteries. It may even cause safety problems (such as thermal runaway). Multiple temperature sensors distributed in different positions of the battery cell are used to monitor local temperature. The system calculates the difference between the highest temperature and the lowest temperature based on the readings of these sensors to obtain the maximum temperature difference. The difference between the coolant outlet temperature and the inlet temperature of the battery cell refers to the difference between the temperature of the coolant before entering the battery cell and the temperature after flowing out of the battery cell, which reflects the heat taken away by the coolant in the battery cell. Two temperature sensors are installed at the inlet and outlet of the coolant to monitor the temperature of the coolant when it enters and flows out of the battery cell respectively. By collecting the temperature data of these two sensors, the temperature difference of the coolant can be calculated.

[0065] It needs to be explained that the above-mentioned average temperature of the battery cell reflects the thermal state of the battery cell as a whole, and the maximum temperature difference inside the battery cell reflects whether the temperature distribution in different areas inside the battery is uniform. Although the average temperature of the battery is within a safe range, if the maximum internal temperature difference is large, it means that there may be hot spots in local areas and uneven heat dissipation. The temperature difference between the coolant outlet and the inlet indicates the heat taken away by the coolant in the battery cell. The temperature difference between the coolant outlet and the inlet directly affects the temperature distribution inside the battery cell. If the coolant can effectively take away the heat, the temperature distribution inside the battery cell will be more uniform and the maximum internal temperature difference will be smaller.

[0066] Furthermore, in combination with the cooling threshold of the liquid-cooled energy storage system, the abnormal level of overheat detection of the battery cell is determined. The specific analysis process is as follows: based on the acquired detection data sets of each battery cell, a comprehensive analysis is performed to obtain the detection evaluation value of each battery cell, and each battery cell detection evaluation value is used as the analysis basis for determining the abnormal level of overheat detection of the battery cell; the detection evaluation value of each battery cell is compared with the cooling threshold of the liquid-cooled energy storage system stored in the database; the difference between the detection evaluation value of each battery cell and the cooling threshold of the liquid-cooled energy storage system is recorded as the detection deviation value of each battery cell; the detection deviation value of each battery cell is compared with the first threshold of the battery cell detection deviation stored in the database; if the battery cell detection deviation value is higher than the first threshold of the battery cell detection deviation, If the number is greater than the first threshold value of the battery cell detection deviation, the battery cell overheat detection abnormality level is marked as the fourth level; if the number of battery cell detection deviation values ​​higher than the first threshold value of the battery cell detection deviation is not greater than the first threshold value of the battery cell detection deviation, but greater than the second threshold value of the battery cell detection deviation, the battery cell overheat detection abnormality level is marked as the third level; if the number of battery cell detection deviation values ​​higher than the first threshold value of the battery cell detection deviation is not greater than the second threshold value of the battery cell detection deviation, but is not 0, the battery cell overheat detection abnormality level is marked as the second level; if the number of battery cell detection deviation values ​​higher than the first threshold value of the battery cell detection deviation is 0, the battery cell overheat detection abnormality level is marked as the first level.

[0067] In a specific embodiment, the battery cell detection evaluation value, the specific analysis process is:

[0068]

[0069] Wherein, ω is the battery cell detection evaluation value, jw is the average temperature of the battery cell, wc is the maximum temperature difference inside the battery cell, cr is the difference between the outlet temperature and the inlet temperature of the battery cell coolant, cr0 is the reference difference between the outlet temperature and the inlet temperature of the battery cell coolant, μ1 is the compensation factor of the set jw, μ2 is the compensation factor of the set wc, and μ3 is the compensation factor of the set cr.

[0070] It should be explained that the above battery cell detection evaluation value is calculated by the average temperature of the battery cell, the maximum temperature difference inside the battery cell, and the difference between the outlet temperature and the inlet temperature of the battery cell coolant. JW, WC, and CR are normalized. By combining the average temperature of the battery cell, the maximum internal temperature difference, and the coolant temperature difference, the system can comprehensively evaluate the thermal management of the battery cell. A single parameter may not be able to fully reflect the actual state of the battery cell, and a multi-dimensional evaluation can comprehensively consider the overall temperature of the battery, the heat dissipation effect, and whether the temperature distribution is uniform, thereby more accurately reflecting the health of the battery cell. The battery cell detection evaluation value calculated by multiple parameters helps to improve the accuracy of overheating detection. Local overheating cannot be identified by relying solely on the average temperature, while the combination of the maximum internal temperature difference can detect the problem of uneven heat dissipation inside the battery cell, preventing risks caused by excessive local temperatures in certain areas. The outlet and inlet temperature difference of the coolant reflects the cooling efficiency, which can help determine whether the cooling system is working effectively, thereby preventing overheating caused by insufficient cooling. The combination of multi-dimensional parameters helps to reduce system misjudgment, and by comprehensively calculating and evaluating multiple indicators, the system can more comprehensively determine the true state of the battery cell.

[0071] For example:

[0072]

[0073]

[0074] In a specific embodiment, the compensation factor of jw is 1.0, the compensation factor of wc is 1.0, the compensation factor of cr is 1.0, the reference difference between the outlet temperature and the inlet temperature of the battery cell coolant is 30, there are ten battery cells, and the calculated battery cell detection evaluation values ​​are 11.5356, 8.4772, 15.0000, 9.0000, 4.9494, 24.8745, 13.3741, 31.4316, 4.6457, and 4.0495, respectively. When the cooling system usage status evaluation value is 1.7009, the environmental characteristic value α is 19.8 2, the cooling threshold of the liquid-cooled energy storage system is matched to be 6, the first threshold of the battery cell detection deviation is 6, the first threshold amount of the battery cell detection deviation is 6, and the second threshold amount of the battery cell detection deviation is 3. At this time, there are 4 battery cells whose battery cell detection deviation values ​​are higher than the first threshold of the battery cell detection deviation. Based on the scheme for determining the abnormal level of battery cell overheat detection, the number of battery cell detection deviation values ​​higher than the first threshold of the battery cell detection deviation is not greater than the first threshold amount of the battery cell detection deviation, but not less than the second threshold amount of the battery cell detection deviation, and the battery cell overheat detection abnormal level is marked as the third level.

[0075] It should be explained that the above classification of battery cell overheating anomalies into four levels can make detailed graded judgments based on different detection deviation values. Through multiple thresholds and different detection conditions, the system can accurately identify the severity of battery cell overheating and avoid overly simple abnormal judgments. This helps to more accurately assess overheating risks, thereby distinguishing between minor and severe overheating conditions, taking appropriate measures in a targeted manner, and comprehensively analyzing the detection evaluation values ​​of the battery cells and comparing them with different thresholds. This method can reduce false alarms caused by minor temperature fluctuations. A high-level alarm will only be triggered when the detection deviation values ​​of multiple battery cells exceed the set threshold. This helps to reduce system misjudgments, improve the accuracy of alarms, and avoid unnecessary system interference. When the battery cells are at different abnormal levels, the system can adjust the workload of the energy storage system according to the situation. When the overheating detection abnormality level reaches level three or four, the system can reduce the workload of the battery cells to avoid further overheating due to continuous high load. The system can strengthen cooling measures to reduce the thermal load of the battery cells. The dynamic adjustment mechanism can improve the stability and reliability of the system. The analysis process is based on real-time data acquisition and intelligent threshold comparison, which can realize the automatic management of the system. The detection evaluation value of each battery cell is comprehensively analyzed and compared with the cooling threshold in the database. The system can automatically determine the abnormality level and issue an alarm. The automated management method reduces the need for manual monitoring and improves the overall intelligence level of the system.

[0076] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A method for detecting abnormal state of a liquid-cooled energy storage system, characterized in that: The following steps are involved: Analyze the environment in which the liquid-cooled energy storage system is located to obtain characteristic values ​​of the environment; Analyze the usage status of the cooling system of the liquid-cooled energy storage system to determine whether there is any abnormality in the cooling system: If there is an abnormality in the cooling system, a cooling system abnormality alarm will be issued; If there is no abnormality in the cooling system, the cooling threshold of the liquid-cooled energy storage system is determined in combination with the characteristic values ​​of the environment; The actual operating status of the liquid-cooled energy storage system is analyzed to determine whether there are any operating abnormalities in the liquid-cooled energy storage system and obtain the detection data set of each battery unit. Combined with the cooling threshold of the liquid-cooled energy storage system, the battery unit overheating detection abnormality level is determined.

2. The abnormal state detection method of the liquid-cooled energy storage system according to claim 1, characterized in that: The environment in which the liquid-cooled energy storage system is located is analyzed to obtain the characteristic value of the environment in which the system is located. The specific analysis process is as follows: Obtaining an environmental data set of the liquid-cooled energy storage system, wherein the environmental data set of the liquid-cooled energy storage system specifically includes the ambient temperature, the ambient wind speed, and the cooling system outlet temperature; Based on the acquired data set of the environment in which the liquid-cooled energy storage system is located, a comprehensive analysis is performed to obtain characteristic values ​​of the environment, which are used as an analysis basis for determining the cooling threshold of the liquid-cooled energy storage system.

3. The abnormal state detection method of the liquid-cooled energy storage system according to claim 1, characterized in that: The specific analysis process of judging whether there is an abnormality in the cooling system is as follows: Acquire a cooling system usage status data set, and obtain a cooling system usage status evaluation value based on the acquired cooling system usage status data set through comprehensive analysis, and use the cooling system usage status evaluation value as an analysis basis for determining whether the cooling system is abnormal; comparing the cooling system usage status evaluation value with the cooling system usage status threshold value stored in the database; If the cooling system usage status evaluation value is lower than the cooling system usage status threshold, then the cooling system corresponding to the cooling system usage status evaluation value is abnormal, and a cooling system abnormality alarm is issued; If the cooling system usage status evaluation value is not lower than the cooling system usage status threshold, there is no abnormality in the cooling system corresponding to the cooling system usage status evaluation value.

4. The abnormal state detection method of the liquid-cooled energy storage system according to claim 3 is characterized in that: The cooling system usage status data set specifically includes the absolute value of the difference between the coolant flow rate and the reference coolant flow rate, the absolute value of the difference between the coolant pressure and the reference coolant pressure, and the absolute value of the difference between the coolant level and the reference coolant level.

5. The abnormal state detection method of the liquid-cooled energy storage system according to claim 3, characterized in that: The cooling system uses the state evaluation value, and the specific analysis process is as follows: Wherein, β is the evaluation value of the cooling system usage status, ls is the absolute value of the difference between the coolant flow rate and the reference coolant flow rate, yl is the absolute value of the difference between the coolant pressure and the reference coolant pressure, yw is the absolute value of the difference between the coolant level and the reference coolant level, τ1 is the compensation factor of the set ls, τ2 is the compensation factor of the set yl, τ3 is the compensation factor of the set yw, and e is a natural constant.

6. The abnormal state detection method of the liquid-cooled energy storage system according to claim 1, characterized in that: The specific analysis process of determining the cooling threshold of the liquid-cooled energy storage system is as follows: Obtain a cooling system usage status evaluation value and an environment characteristic value, store the cooling system usage status evaluation value and the environment characteristic value as a designated tag, compare the designated tag with a liquid-cooled energy storage system cooling threshold corresponding to each designated tag stored in a database, and obtain the liquid-cooled energy storage system cooling threshold corresponding to the designated tag.

7. The abnormal state detection method of the liquid-cooled energy storage system according to claim 1, characterized in that: The specific analysis process of judging whether the liquid-cooled energy storage system has an operating abnormality is as follows: Acquire a data set of actual operation status of the liquid-cooled energy storage system, and obtain an actual operation status evaluation value based on the acquired data set of actual operation status of the liquid-cooled energy storage system. The actual operation status evaluation value is used as an analysis basis for judging whether the liquid-cooled energy storage system has an operation abnormality; comparing the actual operating status evaluation value with the actual operating status threshold value stored in the database; If the actual operating state evaluation value is lower than the actual operating state threshold, the liquid-cooled energy storage system corresponding to the actual operating state evaluation value has an operating abnormality, and an abnormal operating alarm of the liquid-cooled energy storage system is issued; If the actual operating status evaluation value is not lower than the actual operating status threshold, then there is no operating abnormality in the liquid-cooled energy storage system corresponding to the actual operating status evaluation value.

8. The abnormal state detection method of the liquid-cooled energy storage system according to claim 7, characterized in that: The actual operation status data set of the liquid-cooled energy storage system specifically includes the absolute value of the difference between the system power output and the reference power output, the absolute value of the difference between the system load rate and the reference load rate, and the absolute value of the difference between the system voltage and the reference voltage.

9. The abnormal state detection method of the liquid-cooled energy storage system according to claim 1, characterized in that: The battery cell detection data sets specifically include the average temperature of the battery cell, the maximum temperature difference inside the battery cell, and the difference between the outlet temperature and the inlet temperature of the coolant in the battery cell.

10. The abnormal state detection method of the liquid-cooled energy storage system according to claim 1, characterized in that: The above-mentioned liquid-cooled energy storage system cooling threshold is combined to determine the abnormal level of overheat detection of the battery unit. The specific analysis process is as follows: Based on the acquired detection data sets of each battery cell, a comprehensive analysis is performed to obtain a detection evaluation value of each battery cell, and the detection evaluation value of each battery cell is used as an analysis basis for determining the abnormal level of battery cell overheat detection; Compare the detection evaluation value of each battery unit with the cooling threshold of the liquid-cooled energy storage system stored in the database; The difference between the detection evaluation value of each battery unit and the cooling threshold of the liquid-cooled energy storage system is recorded as the detection deviation value of each battery unit; Compare each battery cell detection deviation value with a first battery cell detection deviation threshold stored in a database; If the battery cell detection deviation value is higher than the battery cell detection deviation first threshold by an amount greater than the battery cell detection deviation first threshold amount, marking the battery cell overheat detection abnormality level as level 4; If the amount by which the battery cell detection deviation value is higher than the first threshold value of the battery cell detection deviation is not greater than the first threshold value of the battery cell detection deviation, but greater than the second threshold value of the battery cell detection deviation, then marking the battery cell overheat detection abnormality level as the third level; If the amount by which the battery cell detection deviation value is higher than the first battery cell detection deviation threshold is not greater than the second battery cell detection deviation threshold, but is not 0, the battery cell overheat detection abnormality level is marked as the second level; If the number by which the battery cell detection deviation value is higher than the first battery cell detection deviation threshold is 0, the battery cell overheat detection abnormality level is marked as the first level.

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