Battery health status prediction method for new energy vehicle energy storage system

Abstract

The present invention discloses a method for predicting the health status of a battery in an energy storage system of a new energy vehicle, and relates to the technical field of battery monitoring for new energy vehicles. The main scheme is: by collecting real-time operation and historical fault data of the battery in real time, calculating the capacity decay factor, etc., and then calculating the real-time health index and the historical fault impact index, while acquiring environmental data, calculating the temperature and other impact coefficients and the real-time environmental index, calculating and predicting a comprehensive health value based on the above indexes, and setting a threshold set, and judging whether to trigger an alarm by comparison. The method comprehensively considers battery performance, fault history and environmental factors, can accurately predict the health status, timely warn of risks, prevent faults, extend life, ensure safe operation of the vehicle, and improve performance and user experience. It is an effective and innovative solution for battery management of new energy vehicles, and has important application value and market prospects.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicle battery monitoring, and in particular to a method for predicting the health status of batteries in energy storage systems of new energy vehicles. Background Art

[0002] With the rapid development of the new energy vehicle industry, the health status of energy storage system batteries, as core components of new energy vehicles, is directly related to the performance, mileage, safety and service life of the vehicle. However, batteries will gradually deteriorate due to the influence of various factors during use, and it is crucial to accurately predict their health status. Due to the complex performance degradation mechanism of batteries, which are affected by the interaction of multiple factors such as internal chemical reactions, external use environment and historical failures, how to accurately evaluate the health status of batteries has become one of the key issues that need to be urgently solved in the field of new energy vehicles.

[0003] The existing technology mainly evaluates the health status of the battery by monitoring and analyzing some key parameters of the battery, and monitors the real-time operating data such as the battery's voltage, current, temperature, etc., and uses these data to infer the current status of the battery. However, this processing method only focuses on real-time operating data or historical fault data, but does not fully and comprehensively consider the synergistic effect of all relevant factors. When analyzing the battery health status, the real-time operating data is not deeply integrated with the historical fault data and environmental data for analysis, resulting in an incomplete and inaccurate assessment of the battery health status. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the shortcomings of the prior art, the present invention provides a method for predicting the health status of batteries in energy storage systems of new energy vehicles. By comprehensively collecting real-time operation data, historical fault data and environmental data, the method overcomes the defect of the prior art that only part of the data is focused on, and realizes the fusion and utilization of multi-source data, so as to more comprehensively evaluate the health status of the battery; calculates the capacity decay factor and the comprehensive performance decay factor, and comprehensively considers the influence of historical faults and real-time environment, which more accurately reflects the complex process of battery performance decay compared with simple empirical models and fixed threshold judgments, improves the accuracy of prediction, and can more timely and accurately discover the changing trend of battery health status and potential faults; collects and processes various types of data in real time, dynamically calculates relevant indicators and predicts comprehensive health values ​​according to the real-time status of the battery, effectively responds to the real-time changes in the health status of the battery and the dynamic influence of environmental factors, solves the shortcomings of the prior art that lack real-time and dynamic adaptability, can issue health risk alarms in time, and provide more timely and effective support for battery management and maintenance.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for predicting the health status of a battery in an energy storage system of a new energy vehicle, comprising:

[0008] Collect real-time operation data and historical fault data of the battery in real time;

[0009] Calculate the capacity decay factor based on real-time operating data ; Obtain historical operation data, based on capacity decay factor , real-time operation data and historical operation data, calculate the comprehensive performance degradation factor , according to the comprehensive performance degradation factor Calculate real-time health index ; Calculate the historical failure impact index based on historical failure data ;

[0010] Obtain historical environmental data and real-time environmental data during battery operation, and calculate the temperature impact coefficient based on the historical environmental data , Humidity influence coefficient And the pressure effect coefficient , according to the temperature influence coefficient , Humidity influence coefficient , Air pressure influence coefficient And real-time environmental data, calculate real-time environmental index ;

[0011] According to the real-time health index , Historical Failure Impact Index and real-time environment Index, calculates the predicted comprehensive health value of battery operation ;

[0012] Set the comprehensive health value threshold set , based on the predicted comprehensive health value and comprehensive health value threshold set The comparison result is used to determine whether a battery health risk alarm is triggered.

[0013] In the preferred embodiment of the above-mentioned method for predicting the health status of the battery in the new energy vehicle energy storage system: calculating the capacity decay factor The specific steps are:

[0014] Real-time operating data including real-time capacity ;

[0015] Setting the battery base capacity ;

[0016] Based on battery base capacity and real-time capacity , calculate the capacity decay factor , the specific formula is as follows:

[0017]

[0018] Among them, the battery base capacity The value of >0.

[0019] In the preferred solution of the above-mentioned new energy vehicle energy storage system battery health status prediction method: calculate the real-time health index The specific steps are:

[0020] Real-time operating data also includes real-time voltage , real-time current , Real-time temperature , Real-time internal resistance And real-time charge and discharge cycle times ;

[0021] Historical operation data includes the maximum number of historical charge and discharge cycles ;

[0022] According to real-time operation data, capacity decay factor And the maximum number of historical charge and discharge cycles , calculate the comprehensive performance degradation factor , the specific formula is as follows:

[0023]

[0024] in, Is the real-time internal resistance The adjustment parameter range is >0; Real-time temperature The adjustment parameter is >0; Real-time charge and discharge cycle times The adjustment parameter range is >0; The real-time voltage The adjustment parameter is >0; The real-time current The adjustment parameter is >0;

[0025] According to the comprehensive performance degradation factor Calculate real-time health index , the specific formula is as follows:

[0026] .

[0027] In the preferred scheme of the above-mentioned new energy vehicle energy storage system battery health status prediction method: Calculate the historical fault impact index The specific steps are:

[0028] Historical fault data including fault occurrence frequency , Fault Severity Index , Fault Attenuation Factor ;

[0029] Calculate the historical failure impact index based on historical failure data The specific formula is as follows:

[0030]

[0031] in, No. The frequency of the failure, It is The severity index of the fault, It is The time decay factor of the fault, The value range is 1, 2, 3... , The total number of historical fault types.

[0032] In the preferred embodiment of the above-mentioned method for predicting the health status of the battery in the new energy vehicle energy storage system: Calculate the temperature influence coefficient The specific steps are:

[0033] Historical environmental data includes historical temperature data, historical humidity data, and historical air pressure data;

[0034] Historical temperature data including historical critical high temperature values , historical critical low temperature value , high temperature value , high temperature value frequency , low temperature value and low temperature value frequency ;

[0035] Setting the normal operating temperature threshold ;

[0036] According to the normal operating temperature threshold And historical temperature data, calculate the high temperature impact coefficient The specific formula is as follows:

[0037]

[0038] in, Represents the historical temperature data High temperature value, Represents the historical temperature data The frequency of high temperature values, The value range is 1, 2, 3... , The total number of historical high temperature data, Represents the high temperature weight coefficient, with a value of 0< <1;

[0039] According to the historical critical low temperature value And the normal operating temperature threshold , low temperature value and low temperature value frequency , calculate the low temperature influence coefficient The specific formula is as follows:

[0040]

[0041] in, Represents the historical temperature data Low temperature value, Represents the historical temperature data The frequency of low temperature values, The value range is 1, 2, 3... , The total number of historical low temperature data, Represents the low temperature weight coefficient, with a value of 0< <1;

[0042] According to the high temperature influence coefficient And low temperature effect coefficient , calculate the temperature influence coefficient The specific formula is as follows:

[0043] .

[0044] In the preferred embodiment of the above-mentioned method for predicting the health status of the battery in the energy storage system of a new energy vehicle: Calculate the humidity influence coefficient The specific steps are:

[0045] Set the normal operating humidity threshold ;

[0046] Historical humidity data including historical maximum humidity values , Historical abnormal humidity values and the frequency of historical abnormal humidity values ;

[0047] According to the normal operating humidity threshold And historical humidity data, calculate the humidity impact coefficient The specific formula is as follows:

[0048]

[0049] Among them, among them, Represents the historical humidity data Abnormal humidity value, Represents the historical humidity data The frequency of abnormal humidity values, The value range is 1, 2, 3... , The total number of historical humidity data, Represents the humidity weight coefficient, with a value of 0< <1.

[0050] In the preferred embodiment of the above-mentioned method for predicting the health status of the battery in the energy storage system of a new energy vehicle: Calculating the air pressure influence coefficient The specific steps are:

[0051] Set the normal operating pressure threshold ;

[0052] Historical air pressure data including historical maximum air pressure values 、Historical abnormal air pressure values And the historical abnormal air pressure frequency ;

[0053] According to the normal operating pressure threshold Historical air pressure data, calculate air pressure influence coefficient The specific formula is as follows:

[0054]

[0055] Among them, among them, Represents the historical air pressure data Abnormal air pressure value, Represents the historical air pressure data The frequency of abnormal air pressure values, The value range is 1, 2, 3... , The total number of historical air pressure data, Represents the air pressure weight coefficient, with a value of 0< <1.

[0056] In the preferred solution of the above-mentioned new energy vehicle energy storage system battery health status prediction method: calculate the real-time environmental index The specific steps are:

[0057] Real-time environmental data including real-time temperature , Real-time humidity And real-time air pressure ;

[0058] According to real-time environmental data, temperature influence coefficient , Humidity influence coefficient And the pressure effect coefficient , calculate the real-time environment index , the specific formula is as follows:

[0059]

[0060] in, Real-time temperature The adjustment parameter is 0.1< <3; Real-time humidity The adjustment parameter is 0.1< <3; Real-time air pressure The adjustment parameter is 0.1< <3.

[0061] In the preferred embodiment of the above-mentioned method for predicting the health status of the battery of the new energy vehicle energy storage system: calculating and predicting the comprehensive health value , the specific formula is as follows:

[0062]

[0063] in, The value is a positive number. The value of is a positive number.

[0064] In the preferred embodiment of the method for predicting the health status of a battery in an energy storage system of a new energy vehicle, the specific steps for determining whether a battery health risk alarm is triggered are:

[0065] Comprehensive health value threshold set Including health thresholds , primary health risk threshold and intermediate health risk threshold ,in, < < ;

[0066] When the task runs index ≤ No health risk alert is triggered when

[0067] when <Task running index ≤ When the primary health risk alert is triggered;

[0068] when <Task running index ≤ When the virus is detected, a medium-level health risk alert is triggered;

[0069] When the task runs index > When a high-level health risk alert is triggered.

[0070] (III) Beneficial effects

[0071] The present invention provides a method for predicting the health status of batteries in a new energy vehicle energy storage system, which has the following beneficial effects:

[0072] (1) Real-time collection of real-time operating data and historical fault data of each new energy vehicle battery can effectively ensure the comprehensiveness and timeliness of the data, provide a solid foundation for subsequent analysis and processing, and improve the accuracy and efficiency of battery health management.

[0073] (2) By calculating the capacity decay factor and the comprehensive performance decay factor, the current performance status of the battery can be accurately evaluated. The real-time health index reflects the immediate health status of the battery, while the historical fault impact index takes into account the past failure history of the battery. The combination of the two provides an important basis for the comprehensive health assessment of the battery.

[0074] (3) By analyzing historical and real-time environmental data, the calculated environmental impact coefficient can quantify the impact of environmental factors on battery performance. The real-time environmental index directly reflects the adaptability of the battery under current environmental conditions, which helps to detect potential threats to battery health in advance.

[0075] (4) The predicted comprehensive health value combines the battery's health status, historical fault records, and current environmental factors to provide a comprehensive evaluation index for battery health management, which helps to more accurately predict future performance changes of the battery.

[0076] (5) By setting a set of comprehensive health value thresholds and automatically comparing the predicted comprehensive health value with its relationship, the battery health risk can be discovered in time, triggering an alarm, so that preventive measures can be taken to avoid safety problems caused by battery failure, thereby improving the safety and reliability of new energy vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1It is a schematic diagram of the flow chart of the method for predicting the health status of batteries in the energy storage system of a new energy vehicle according to the present invention; DETAILED DESCRIPTION

[0078] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0079] See also Figure 1 The present invention provides a method for predicting the health status of a battery in a new energy vehicle energy storage system, comprising: collecting real-time operating data and historical fault data of the battery in real time.

[0080] Real-time collection of real-time operating data and historical fault data of the battery is the basis of the entire prediction method. Real-time operating data includes the parameters of the battery at every moment, reflecting its current working status. Through continuous monitoring, it provides first-hand information to ensure the safe operation of the battery. Historical fault data helps to analyze the root cause and development trend of the fault. Repeated overcharging failures may indicate problems with the charging system, thereby providing a reference for preventing similar failures in the future and laying the foundation for subsequent accurate assessment of the battery health status.

[0081] Calculate the capacity decay factor based on real-time operating data ; Obtain historical operation data, based on capacity decay factor , real-time operation data and historical operation data, calculate the comprehensive performance degradation factor , according to the comprehensive performance degradation factor Calculate real-time health index ; Calculate the historical failure impact index based on historical failure data .

[0082] Calculating the capacity decay factor based on real-time operating data is a key step in measuring battery health. The capacity decay factor directly reflects changes in the battery's ability to store energy. As the battery capacity increases over time, it will gradually decrease. By calculating this factor, we can quantitatively understand the degree of battery capacity decay.

[0083] Furthermore, after obtaining the historical operating data, the comprehensive performance degradation factor is calculated by combining the capacity degradation factor and the real-time operating data. The comprehensive performance degradation factor not only considers the capacity, but also incorporates factors such as changes in internal resistance and changes in charge and discharge efficiency. These factors combined can more comprehensively reflect the overall performance degradation of the battery.

[0084] Furthermore, a real-time health index is calculated based on the comprehensive performance degradation factor, which intuitively presents the current comprehensive health status of the battery in numerical form, providing an important basis for subsequent evaluation and decision-making. The historical fault impact index is calculated based on the historical fault data, which quantifies the potential impact of historical faults on the current health of the battery. Faults of different types and frequencies have different degrees of damage to the battery. By analyzing the historical fault data and assigning corresponding weights to each fault, the calculated historical fault impact index can accurately reflect the long-term impact of fault accumulation on battery health.

[0085] Obtain historical environmental data and real-time environmental data during battery operation, and calculate the temperature impact coefficient based on the historical environmental data , Humidity influence coefficient And the pressure effect coefficient , according to the temperature influence coefficient , Humidity influence coefficient , Air pressure influence coefficient And real-time environmental data, calculate real-time environmental index .

[0086] Obtaining historical environmental data and real-time environmental data during battery operation is crucial to accurately assessing battery health. Environmental factors such as temperature, humidity and air pressure have a significant impact on battery performance. Too high or too low temperature can affect the rate of chemical reactions inside the battery, humidity can cause corrosion of battery components, and changes in air pressure can also affect the sealing performance of the battery.

[0087] Furthermore, the temperature influence coefficient, humidity influence coefficient and air pressure influence coefficient are calculated based on historical environmental data. These coefficients are the key to quantifying the impact of environmental factors on battery health. The real-time environmental index is calculated based on the temperature influence coefficient, humidity influence coefficient, air pressure influence coefficient and real-time environmental data. This index combines the real-time impact of multiple environmental factors and provides us with an indicator for comprehensively evaluating the impact of the current environment on battery health. When the real-time environmental index increases, it means that the environmental conditions are not good for battery health and corresponding measures need to be taken.

[0088] According to the real-time health index , Historical Failure Impact Index and real-time environment Index, calculates the predicted comprehensive health value of battery operation .

[0089] Set the comprehensive health value threshold set , based on the predicted comprehensive health value and comprehensive health value threshold set The comparison result is used to determine whether a battery health risk alarm is triggered.

[0090] The predicted comprehensive health value of battery operation is calculated based on the real-time health index, historical fault impact index and real-time environmental index. This is a comprehensive and integrated assessment of the battery's health status. The predicted comprehensive health value integrates information such as the battery's own performance, historical fault conditions and current environmental impacts, and combines these factors through a reasonable algorithm. The real-time health index reflects the current performance status of the battery, the historical fault impact index reflects the cumulative effect of past faults, and the real-time environmental index takes into account the real-time effect of the current environment on the battery. The predicted comprehensive health value obtained by combining these indices can more accurately reflect the overall health status of the battery in actual operation and provide a scientific basis for battery management and maintenance.

[0091] Furthermore, a comprehensive health value threshold set is set, and whether to trigger a battery health risk alarm is determined based on the comparison result between the predicted comprehensive health value and the comprehensive health value threshold set. The threshold set sets different levels of health standards. When the predicted comprehensive health value falls into a lower threshold range, it indicates that there may be risks to battery health. The system will promptly issue an alarm to notify the owner to conduct battery inspection or maintenance in a timely manner, or the vehicle control system will automatically adjust the operating mode to reduce the battery burden, thereby effectively preventing the occurrence of battery failure and ensuring the safe and reliable operation of new energy vehicles.

[0092] In the preferred embodiment of the above-mentioned method for predicting the health status of the battery in the new energy vehicle energy storage system: calculating the capacity decay factor The specific steps are:

[0093] Real-time operating data including real-time capacity .

[0094] Set the battery base capacity based on the test results of the battery base capacity during the factory test. .

[0095] Based on battery base capacity and real-time capacity , calculate the capacity decay factor The specific formula is as follows:

[0096]

[0097] Among them, the battery base capacity The value of >0.

[0098] It should be noted that the real-time capacity in the real-time operation data Reflects the actual energy storage capacity of the battery at the current moment, while the battery base capacity It is a set reference value, which usually represents the capacity of the battery under ideal or initial conditions.

[0099] When the battery base capacity and real-time capacity When comparing, The ratio of the current battery capacity to the benchmark capacity is used to measure the state of the battery's current capacity relative to the benchmark capacity. If the ratio is close to 1, it means that the current battery capacity is close to the benchmark capacity and the battery is in good health. As the battery is used and aged, the real-time capacity will gradually decrease, and the ratio will also decrease accordingly. If the battery base capacity The current real-time capacity is 100Ah. For example, if the battery capacity is 80Ah, then 80 / 100=0.8, indicating that the current capacity of the battery has declined to 80% of the benchmark capacity, reflecting that the battery has a certain degree of performance degradation. Through this formula, we can intuitively understand the degree of battery capacity degradation and provide important quantitative indicators for subsequent evaluation of the battery's health status. >0, ensuring that the denominator is not zero, making the calculation meaningful and feasible.

[0100] This formula provides an intuitive and quantitative indicator. By comparing the ratio of real-time capacity to baseline capacity, it can clearly reflect the current capacity status of the battery, allowing users and maintenance personnel to quickly understand the health of the battery and help to find problems in a timely manner. When the value gradually decreases, the decline of battery capacity can be detected in time so that corresponding maintenance measures can be taken, such as adjusting the charging strategy and conducting in-depth inspections, to avoid further deterioration of battery performance and extend battery life. This provides a decision-making basis for the battery management system and improves the management level of the entire new energy vehicle energy storage system.

[0101] In the preferred solution of the above-mentioned new energy vehicle energy storage system battery health status prediction method: calculate the real-time health index The specific steps are:

[0102] Real-time operating data also includes real-time voltage , real-time current , Real-time temperature , Real-time internal resistance And real-time charge and discharge cycle times .

[0103] Get the maximum value of historical charge and discharge cycles based on historical operation data .

[0104] According to real-time operation data, capacity decay factor And the maximum number of historical charge and discharge cycles , calculate the comprehensive performance degradation factor The specific formula is as follows:

[0105]

[0106] in, Is the real-time internal resistance The adjustment parameter range is >0; Real-time temperature The adjustment parameter is >0; Real-time charge and discharge cycle times The adjustment parameter range is >0; The real-time voltage The adjustment parameter is >0; The real-time current The adjustment parameter is >0.

[0107] It should be noted that when the real-time internal resistance When increasing, The value of will also change accordingly. >0, which means that the increase of internal resistance will have a positive correlation with the overall performance degradation, because the increase of internal resistance usually leads to adverse consequences such as battery heating and increased energy loss; real-time temperature The changes through To reflect its impact on the overall performance, too high temperature may accelerate the chemical reaction inside the battery, resulting in performance degradation. The value of will increase, reflecting the negative effect of temperature on the degradation of comprehensive performance.

[0108] This reflects the effect of the number of charge and discharge cycles on the comprehensive performance degradation. As the number of charge and discharge cycles increases, the value of this item will gradually decrease, indicating that the increase in the number of charge and discharge cycles will lead to a gradual decline in battery performance; This reflects the influence of voltage on comprehensive performance. When the voltage increases, >0, the value of this item will decrease, indicating that excessively high voltage may have an adverse effect on battery performance. It represents the effect of current on the degradation of overall performance. Changes in current will be reflected through this index. Larger currents may lead to increased internal losses in the battery, thus affecting overall performance.

[0109] According to the comprehensive performance degradation factor Calculate real-time health index The specific formula is as follows:

[0110] .

[0111] It should be noted that this formula takes into account the performance degradation factor Transformed into real-time health index ,when The larger the value, the more serious the overall performance degradation of the battery. The smaller the value, the real-time health index The lower the battery is, the worse the health of the battery is. When the value of is small, the real-time health index A higher value indicates that the battery is in a better health state.

[0112] This formula comprehensively considers the impact of multiple key factors on battery performance. The capacity decay factor reflects the change in the battery's ability to store energy; the internal resistance adjustment parameter takes into account the energy loss and heat generation caused by the change in the battery's internal resistance; the temperature adjustment parameter can capture the impact of temperature changes on battery performance, which helps to accurately evaluate the battery status at different ambient temperatures; the charge and discharge cycle adjustment parameter can measure the impact of battery usage on performance; and the voltage and current adjustment parameters focus on the key electrical parameters during operation. By integrating these factors, the comprehensive performance decay of the battery can be more accurately evaluated.

[0113] The real-time health index formula concisely and intuitively converts comprehensive performance decline into a percentage of health status, making it easy for users and managers to quickly understand the battery health status and providing a clear basis for taking timely maintenance measures and adjusting vehicle operation strategies.

[0114] In the preferred scheme of the above-mentioned new energy vehicle energy storage system battery health status prediction method: Calculate the historical fault impact index The specific steps are:

[0115] Obtain fault frequency based on historical fault data , Fault Severity Index , Fault Attenuation Factor .

[0116] Calculate the historical failure impact index based on historical failure data , the specific formula is as follows:

[0117]

[0118] in, No. The frequency of the failure, It is The severity index of the fault, It is The time decay factor of the fault, The value range is 1, 2, 3... , The total number of historical fault types.

[0119] It should be noted that Represents the The frequency of the occurrence of various faults, including poor electrical contact, short circuit, mechanical wear, breakage, program error, and virus attack. If a certain fault occurs frequently, it may have a greater impact on battery health. If the battery frequently overcharges, then the corresponding The value will be higher, reflecting the importance of this fault in historical faults; Measured the The severity of the fault can be divided into:

[0120] Minor. This type of fault has little or no effect on the normal operation of the system and can usually be resolved through simple maintenance measures. For example, poor contact may only temporarily affect the performance of the equipment but will not cause the equipment to stop working.

[0121] Moderate. This type of failure affects some of the system's functionality. This may result in reduced performance or unavailability of certain features, but will not immediately cause the device to stop functioning. For example, wear and tear may mean that the device no longer performs at its optimal level, but it may continue to operate.

[0122] Severe failures of this type can lead to the loss of important functions of the system and require immediate repair measures, otherwise the equipment will not be able to continue to work normally. For example, a short circuit will not only cause the equipment to stop working, but may also cause safety hazards such as fire.

[0123] Catastrophic, which is the most serious level, the failure will cause the system to fail completely and may be accompanied by the risk of casualties or significant property damage. For example, the breakage of a key component may cause the collapse of the entire system and there is a risk of endangering life safety.

[0124] Different faults cause different degrees of damage to the battery, and severe faults will have a greater negative impact on the battery's health; Taking into account the attenuation effect of the interval between the fault occurrence time and the current time on the fault impact, over time, the impact of older faults on the current battery health status may gradually decrease. By multiplying these three parameters and summing up all historical fault types, we can comprehensively consider the frequency, severity and time decay factors of various faults to obtain an index that can fully reflect the impact of historical faults on the battery health status. This index can help us better evaluate the overall health status of the battery and provide an important basis for predicting the comprehensive health value and determining whether to trigger a battery health risk alarm.

[0125] This formula comprehensively considers multiple key aspects of failures. The frequency of failures can accurately reflect which failures are more common, providing a basis for focusing on and preventing these high-frequency failures. The inclusion of the severity of the failure ensures that serious failures can be given a greater weight, highlighting their significant impact on battery health. The time decay factor reasonably considers the time factor, so that the impact of long-term failures gradually decreases, which is more in line with the actual situation, avoids over-emphasizing old failures and ignoring the current status, and provides an important dimension for battery health assessment. The comprehensive impact of historical faults on current battery health can be quantified to help users and maintenance personnel understand the historical health status of the battery so as to make more targeted maintenance plans and decisions.

[0126] In the preferred embodiment of the above-mentioned method for predicting the health status of the battery in the new energy vehicle energy storage system: Calculate the temperature influence coefficient The specific steps are:

[0127] Obtain historical temperature data, historical humidity data, and historical air pressure data based on historical environmental data.

[0128] Historical temperature data including historical critical high temperature values , historical critical low temperature value , high temperature value And high temperature frequency , low temperature value and low temperature value frequency .

[0129] Set the normal operating temperature threshold based on historical temperature experiment data .

[0130] When the temperature value > normal operating temperature threshold When the high temperature value When the temperature value is less than the normal operating temperature threshold When .

[0131] According to the historical temperature data, if the temperature exceeds or falls below the threshold value for multiple times in a certain period of time, the temperature during this period of time is the historical critical high temperature value or the historical critical low temperature value.

[0132] According to the normal operating temperature threshold And historical temperature data, calculate the high temperature impact coefficient The specific formula is as follows:

[0133]

[0134] in, Represents the historical temperature data High temperature value, Represents the historical temperature data The frequency of high temperature values, The value range is 1, 2, 3... , The total number of historical high temperature data, Represents the high temperature weight coefficient, with a value of 0< <1.

[0135] It should be noted that Indicates the historical temperature data The difference between the high temperature value and the normal operating temperature value reflects the degree of deviation of the high temperature value from the normal temperature. The larger the difference, the further away the high temperature value is from the normal operating temperature, which may have a greater impact on battery health. It is the difference between the historical critical high temperature value and the normal operating temperature value. It is used as a benchmark to measure the relative degree of deviation of different high temperature values ​​from the normal temperature. By dividing the numerator by the denominator, a proportional coefficient is obtained, which is then multiplied by the frequency of occurrence of the corresponding high temperature value. , and sums up all historical high temperature data. This calculation method comprehensively considers the deviation degree and frequency of occurrence of each high temperature value, so that the high temperature impact coefficient can reflect the comprehensive impact of historical high temperature data on battery health. If a high temperature value appears frequently and deviates greatly from the normal temperature, then its contribution to the high temperature impact coefficient will be greater.

[0136] According to the historical critical low temperature value And the normal operating temperature threshold , low temperature value and low temperature value frequency , calculate the low temperature influence coefficient The specific formula is as follows:

[0137]

[0138] in, Represents the historical temperature data Low temperature value, Represents the historical temperature data The frequency of low temperature values, The value range is 1, 2, 3... , The total number of historical low temperature data, Represents the low temperature weight coefficient, with a value of 0< <1.

[0139] It should be noted that Indicates the normal operating temperature value and the historical temperature data. The difference between the low temperature values ​​reflects the degree of deviation of the low temperature value from the normal temperature. The larger the difference, the farther the low temperature value is from the normal operating temperature, which has a greater impact on battery health. It is the difference between the normal operating temperature value and the historical critical low temperature value. It is used as a benchmark to measure the relative degree of deviation of different low temperature values ​​from the normal temperature. Divide the numerator by the denominator to get the proportional coefficient, and then multiply it by the frequency of occurrence of the corresponding low temperature value. , and sum up all historical low-temperature data. This way, the deviation degree and frequency of each low-temperature value can be comprehensively considered to derive the low-temperature influence coefficient, which reflects the comprehensive impact of historical low-temperature data on battery health. If a low-temperature value occurs frequently and deviates greatly from the normal temperature, then its contribution to the low-temperature influence coefficient will be greater.

[0140] According to the high temperature influence coefficient And low temperature effect coefficient , calculate the temperature influence coefficient , the specific formula is as follows:

[0141]

[0142] It should be noted that the temperature influence coefficient is obtained by adding the low temperature influence coefficient and the high temperature influence coefficient. This calculation method comprehensively considers the impact of temperature on battery health under two extreme conditions. If the historical environmental data of a region has both frequent low temperatures and frequent high temperatures, then The value of will be determined based on the combined impact of these two situations. In this way, the overall impact of temperature factors on battery health can be comprehensively evaluated, providing an important reference for the subsequent calculation of the real-time environmental index and the prediction of the comprehensive health value.

[0143] This formula calculates the low-temperature and high-temperature influence coefficients separately, which can more carefully consider the impact of different temperature extremes on the battery. The low-temperature influence coefficient can accurately reflect the possible damage to the battery performance in a low-temperature environment, while the high-temperature influence coefficient focuses on the adverse effects of high temperature on the battery. By analyzing the two situations separately, different protective measures can be taken in a targeted manner; adding the two together gives the comprehensive temperature influence coefficient. , providing quantitative indicators for comprehensively evaluating the health status of batteries in complex temperature environments, which helps to formulate more accurate battery management strategies. For example, in areas with large temperature changes, Adjust charging strategies and optimize heat dissipation or insulation measures to reduce the negative impact of temperature factors on battery life and performance, improve battery reliability and safety, and extend its service life.

[0144] In the preferred embodiment of the above-mentioned method for predicting the health status of the battery in the energy storage system of a new energy vehicle: Calculate the humidity influence coefficient The specific steps are:

[0145] According to the humidity value of the historical humidity operation test under normal operating conditions, set the normal operating humidity threshold .

[0146] Based on historical humidity data, including obtaining the highest historical humidity value , Historical abnormal humidity values and the frequency of historical abnormal humidity values .

[0147] When the historical humidity value > normal operating humidity threshold , is the historical abnormal humidity value .

[0148] According to the normal operating humidity threshold And historical humidity data Humidity influence coefficient The specific formula is as follows:

[0149]

[0150] Among them, among them, Represents the historical humidity data Abnormal humidity value, Represents the historical humidity data The frequency of abnormal humidity values, The value range is 1, 2, 3... , The total number of historical humidity data, Represents the humidity weight coefficient, with a value of 0< <1.

[0151] It should be noted that Indicates the historical humidity data The abnormal humidity value and the lower limit of the normal operating humidity threshold The difference reflects the degree of deviation of the humidity value from the lower limit of the normal humidity range. The larger the difference, the further away the humidity value is from the normal humidity range, which may have a greater impact on battery health. It is the difference between the highest historical humidity value and the lower limit of the normal operating humidity threshold. It is used as a benchmark to measure the relative degree of deviation of different humidity values ​​from the normal humidity range. First, divide the numerator by the denominator to get a proportional coefficient. This proportional coefficient reflects the deviation ratio of each historical humidity value relative to the normal humidity range, and then multiply it by the frequency of occurrence of the corresponding humidity value. , which takes into account the contribution of the frequency of occurrence of different humidity values ​​to the overall impact, and finally multiplies it by the humidity weight coefficient , sum up all historical humidity data. This calculation method comprehensively considers the deviation degree, occurrence frequency and weight coefficient of each humidity value, so that the humidity influence coefficient can reflect the comprehensive impact of historical humidity data on battery health. If a humidity value appears frequently and deviates greatly from the normal humidity range, then its contribution to the humidity influence coefficient will be greater.

[0152] This formula can accurately quantify the impact of humidity on battery health by considering the relationship between historical humidity values ​​and the normal operating humidity threshold. When the humidity deviates from the normal range, it will lead to chemical reactions inside the battery, electrode corrosion and other problems. This formula can clearly define the magnitude of this impact. Combining the frequency of occurrence of humidity values ​​and the weight coefficient, the calculation results are more comprehensive and reliable, and the cumulative impact of high-frequency humidity values ​​on battery health is reflected. The weight coefficient can be adjusted according to actual conditions to meet the needs of different environments and battery types. This helps to formulate targeted battery management strategies, strengthen moisture-proof measures in high-humidity areas, optimize the sealing performance of batteries, etc., thereby reducing the negative impact of humidity on battery life and performance and improving battery reliability and stability.

[0153] In the preferred embodiment of the above-mentioned method for predicting the health status of the battery in the energy storage system of a new energy vehicle: Calculating the air pressure influence coefficient The specific steps are:

[0154] According to the air pressure parameters of the historical air pressure test under normal operating conditions, the normal operating air pressure threshold under normal operating conditions can be set .

[0155] Get the highest historical air pressure value based on historical air pressure data 、Historical abnormal air pressure values And the historical abnormal air pressure frequency .

[0156] When the historical air pressure value > normal operating air pressure threshold , which is the historical abnormal air pressure value .

[0157] Calculate the air pressure impact coefficient based on the normal operating air pressure threshold and historical air pressure data The specific formula is as follows:

[0158]

[0159] Among them, among them, Represents the historical air pressure data Abnormal air pressure value, Represents the historical air pressure data The frequency of abnormal air pressure values, The value range is 1, 2, 3... , The total number of historical air pressure data, Represents the air pressure weight coefficient, with a value of 0< <1.

[0160] It should be noted that Indicates the historical air pressure data. The normal operating pressure threshold The difference reflects the deviation of the air pressure value from the normal air pressure range. The larger the difference, the further away the air pressure value is from the normal air pressure range, which may have a greater impact on battery health. It is the difference between the highest historical air pressure value and the normal operating air pressure threshold. It is used as a benchmark to measure the relative degree of deviation of different air pressure values ​​from the normal air pressure range. First, divide the numerator by the denominator to get a proportional coefficient. This proportional coefficient reflects the deviation ratio of each historical air pressure value relative to the normal air pressure range, and then multiply it by the air pressure weight coefficient. , all historical air pressure data are summed up. This calculation method comprehensively considers the deviation degree and weight coefficient of each air pressure value, so that the air pressure influence coefficient can reflect the comprehensive impact of historical air pressure data on battery health.

[0161] This formula can accurately quantify the impact of air pressure on battery health. Different air pressure conditions may affect the chemical reactions and sealing performance inside the battery. This formula can clarify the magnitude of this impact, and combine the deviation ratio and weight coefficient of the air pressure value to make the calculation result more comprehensive and reliable. The weight coefficient can be adjusted according to actual conditions to adapt to different usage environments and battery types. This will help to formulate more targeted battery management strategies, such as strengthening battery protection measures and optimizing battery design in areas with large changes in air pressure, thereby reducing the negative impact of air pressure on battery life and performance, improving battery reliability and stability, and providing better protection for the safe operation of new energy vehicles.

[0162] In the preferred solution of the above-mentioned new energy vehicle energy storage system battery health status prediction method: calculate the real-time environmental index The specific steps are:

[0163] Get real-time environment data based on weather forecast, including real-time temperature , Real-time humidity And real-time air pressure .

[0164] According to real-time environmental data, temperature influence coefficient , Humidity influence coefficient And the pressure effect coefficient , calculate the real-time environment index , the specific formula is as follows:

[0165]

[0166] in, Real-time temperature The adjustment parameter is 0.1< <3; Real-time humidity The adjustment parameter is 0.1< <3; Real-time air pressure The adjustment parameter is 0.1< <3.

[0167] It should be noted that Reflects the comprehensive impact of historical temperature data on battery health. is the real-time temperature. The multiplication of the two represents the comprehensive value of the current real-time temperature taking into account the influence of the historical temperature. Then this value is exponentially calculated. ,if A smaller value indicates that the real-time temperature has a relatively small impact on the environmental index; if A larger value indicates that the real-time temperature has a greater weight in the environmental index and has a more significant impact on battery health; is the humidity impact coefficient, which reflects the comprehensive impact of historical humidity data on battery health. is the real-time humidity, It indicates the comprehensive value of the current real-time humidity taking into account the influence of historical humidity. , It is the adjustment parameter of real-time humidity, which determines the importance of real-time humidity in the environmental index; is the air pressure influence coefficient, which reflects the comprehensive impact of historical air pressure data on battery health. is the real-time air pressure. It indicates the comprehensive value of the current real-time air pressure taking into account the influence of historical air pressure. In Real-time air pressure The adjustment parameters are used to adjust the weight of real-time air pressure in the environmental index; by combining the influence of temperature, humidity and air pressure, and adjusting them according to their respective adjustment parameters, a real-time environmental index can be obtained that can fully reflect the impact of the current environment on battery health. This index can help us timely understand the potential threat of environmental changes to batteries and provide a basis for taking corresponding protective measures.

[0168] This formula comprehensively considers the impact of three key environmental factors, temperature, humidity and air pressure, on battery health. By introducing the temperature influence coefficient, humidity influence coefficient and air pressure influence coefficient respectively, and combining real-time temperature, humidity and air pressure data, the comprehensive effect of the current environmental conditions on the battery can be comprehensively evaluated; adjusting the parameter settings makes the formula flexible and adaptable. Different application scenarios and battery types may have different sensitivities to temperature, humidity and air pressure. By adjusting the parameters, the weight of each factor in the environmental index can be adjusted according to actual conditions, thereby more accurately evaluating the health status of the battery under a specific environment. This helps to formulate targeted battery management strategies, improve battery reliability and service life, and ensure the stable operation of new energy vehicles.

[0169] In the preferred embodiment of the above-mentioned method for predicting the health status of the battery of the new energy vehicle energy storage system: calculating and predicting the comprehensive health value The specific formula is as follows:

[0170]

[0171] in, The value is a positive number. The value of is a positive number.

[0172] It should be noted that It is a real-time health index that reflects the current comprehensive performance status of the battery. is the historical failure impact index, which represents the potential impact of historical failures on the current health of the battery. Ensure that the denominator is a positive number. When increasing, The value of will decrease, which means that the more historical faults there are and the greater the impact is, the greater the negative contribution to the comprehensive health value is, which reflects the drag effect of historical faults on the current battery health status; For real-time health index, It is a real-time environmental index that reflects the impact of the current environment on battery health. Ensure that the denominator is positive, when the real-time environment index When increasing, The value of will decrease, which means that the more unfavorable the current environment is, the greater the negative impact on the comprehensive health value is, which reflects the restrictive effect of the current environment on the battery health status. By adding these two parts, the predicted comprehensive health value is obtained by comprehensively considering the impact of three important aspects on the battery health status, namely, the real-time performance, historical faults and the current environment. It can more comprehensively and accurately reflect the overall health status of the battery in actual operation, provide a scientific basis for battery management and maintenance, and help users and managers take corresponding measures in a timely manner to ensure the safe and reliable operation of new energy vehicles.

[0173] This formula comprehensively integrates multiple key factors. The real-time health index reflects the current performance status of the battery, the historical fault impact index reflects the cumulative effect of past faults, and the real-time environmental index takes into account the real-time effect of the current environment on the battery. By combining the three through this formula, the health status of the battery can be comprehensively evaluated from multiple angles, avoiding the one-sidedness of a single factor evaluation; it accurately reflects the actual situation. Different factors have different degrees of impact on battery health. Through a reasonable calculation method, this formula allows historical faults and adverse environments to make corresponding negative contributions to the comprehensive health value; it provides an accurate basis for battery management, helps to formulate targeted maintenance plans, optimize vehicle operation modes, improve battery reliability and service life, and ensure the safe and stable operation of new energy vehicles.

[0174] In the preferred embodiment of the method for predicting the health status of a battery in an energy storage system of a new energy vehicle, the specific steps for determining whether a battery health risk alarm is triggered are:

[0175] Comprehensive health value threshold set Including health thresholds , primary health risk threshold and intermediate health risk threshold ,in, < < .

[0176] When the task runs index ≤ No health risk alert is triggered.

[0177] when <Task operation index ≤ When the primary health risk alert is triggered.

[0178] when <Task operation index ≤ A medium health risk alert is triggered.

[0179] When the task runs index > When a high-level health risk alert is triggered.

[0180] Based on various battery data tested by the manufacturer, a mathematical model is established. By simulating the performance of the battery under different conditions and analyzing the parameter range representing a good health state in the model, a theoretical health threshold is determined.

[0181] The health threshold indicates that the battery is in a relatively ideal health state. ≤ When the battery is in good health, there is no need to trigger a health risk alarm. At this time, the battery performance is stable and can normally meet the use needs of new energy vehicles.

[0182] Conduct accelerated aging tests on the batteries to simulate the aging process of the batteries under different usage conditions. By monitoring the performance changes of the batteries during the aging process, determine the state node where the battery performance begins to decline significantly but is still within an acceptable range. Set the comprehensive health value corresponding to this state as the primary health risk threshold.

[0183] Primary health risk threshold, when <Task operation index ≤ When the battery is at a low level, a primary health risk alarm is triggered, which means that the health of the battery begins to decline to a certain extent, but is still within a relatively controllable range. There may be some minor performance degradation or potential problems that require attention and preliminary inspection and maintenance.

[0184] In the laboratory, we simulated different degrees of battery failure and observed the performance changes of the battery during the failure development process. By controlling the severity of the failure, we found the state point where the battery health status was at a medium risk level, and set the comprehensive health value corresponding to this point as the medium health risk threshold.

[0185] Intermediate health risk threshold, when <Task operation index ≤ When the battery is at a medium health risk level, an intermediate health risk alert is triggered. At this time, the battery health problem is more obvious, and there may be more serious performance degradation, potential faults or environmental impacts. More in-depth testing and analysis are required, and corresponding measures should be taken to prevent further deterioration of the battery failure.

[0186] When the running task index exceeds the set intermediate health risk threshold, it is judged as a high-level health risk alarm.

[0187] Advanced health risk alerts, when task run index > When the battery is in a critical condition, an advanced health risk alert is triggered, indicating that the battery's health is very dangerous, a serious fault has occurred or it is about to fail, and emergency measures need to be taken immediately, such as stopping the vehicle, performing emergency repairs or replacing the battery to ensure the safety of the vehicle and personnel.

[0188] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware or in combination with computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0189] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0190] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for predicting the health status of a battery in a new energy vehicle energy storage system, characterized in that: include: Collect real-time operation data and historical fault data of the battery in real time; Calculate the capacity decay factor based on real-time operating data ; Obtain historical operating data and calculate capacity decay factor , real-time operation data and historical operation data, calculate the comprehensive performance degradation factor , according to the comprehensive performance degradation factor Calculate real-time health index ; Calculate the historical failure impact index based on historical failure data ; Obtain historical environmental data and real-time environmental data during battery operation, and calculate the temperature impact coefficient based on the historical environmental data , Humidity influence coefficient And the pressure effect coefficient , according to the temperature influence coefficient , Humidity influence coefficient , Air pressure influence coefficient And real-time environmental data, calculate real-time environmental index ; According to the real-time health index , Historical Failure Impact Index and real-time environment Index, calculates the predicted comprehensive health value of battery operation ; Set the comprehensive health value threshold set , based on the predicted comprehensive health value and comprehensive health value threshold set The comparison result determines whether to trigger a battery health risk alarm; Calculating the capacity decay factor The specific steps are: Real-time operating data including real-time capacity ; Setting the battery base capacity ; Based on battery base capacity and real-time capacity , calculate the capacity decay factor , the specific formula is as follows: Among them, the battery base capacity The value of >0; Calculate real-time health index The specific steps are: Real-time operating data also includes real-time voltage , real-time current , Real-time temperature , Real-time internal resistance And real-time charge and discharge cycle times ; Historical operation data includes the maximum number of historical charge and discharge cycles ; According to real-time operation data, capacity decay factor And the maximum number of historical charge and discharge cycles , calculate the comprehensive performance degradation factor The specific formula is as follows: in, Is the real-time internal resistance The adjustment parameter range is >0; Real-time temperature The adjustment parameter is >0; Real-time charge and discharge cycle times The adjustment parameter range is >0; The real-time voltage The adjustment parameter is >0; The real-time current The adjustment parameter is >0; According to the comprehensive performance degradation factor Calculate real-time health index The specific formula is as follows: ； Calculate the historical failure impact index The specific steps are: Historical fault data including fault occurrence frequency , Fault Severity Index , Fault Attenuation Factor Calculate the historical failure impact index based on historical failure data , the specific formula is as follows: in, No. The frequency of the failure, It is The severity index of the fault, It is The time decay factor of the fault, The value range is 1, 2, 3... , The total number of historical fault types; Calculating the Temperature Effect Factor The specific steps are: Historical environmental data includes historical temperature data, historical humidity data, and historical air pressure data; Historical temperature data including historical critical high temperature values , historical critical low temperature value , high temperature value And high temperature frequency , low temperature value and low temperature value frequency ; Setting the normal operating temperature threshold ; According to the normal operating temperature threshold And historical temperature data, calculate the high temperature impact coefficient The specific formula is as follows: in, Represents the historical temperature data High temperature value, Represents the historical temperature data The frequency of high temperature values, The value range is 1, 2, 3... , The total number of historical high temperature data, Represents the high temperature weight coefficient, with a value of 0< <1; According to the historical critical low temperature value And the normal operating temperature threshold , low temperature value and low temperature value frequency , calculate the low temperature influence coefficient , the specific formula is as follows: in, Represents the historical temperature data Low temperature value, Represents the historical temperature data The frequency of low temperature values, The value range is 1, 2, 3... , The total number of historical low temperature data, Represents the low temperature weight coefficient, with a value of 0< <1; According to the high temperature influence coefficient And low temperature effect coefficient , calculate the temperature influence coefficient The specific formula is as follows: ； Calculating humidity influence coefficient The specific steps are: Set the normal operating humidity threshold ; Historical humidity data including historical maximum humidity values , Historical abnormal humidity values and the frequency of historical abnormal humidity values ; According to the normal operating humidity threshold And historical humidity data Humidity influence coefficient The specific formula is as follows: Among them, among them, Represents the historical humidity data Abnormal humidity value, Represents the historical humidity data The frequency of abnormal humidity values, The value range is 1, 2, 3... , The total number of historical humidity data, Represents the humidity weight coefficient, with a value of 0< <1; Calculating the Air Pressure Effect Coefficient The specific steps are: Set the normal operating pressure threshold ; Historical air pressure data including historical maximum air pressure values 、Historical abnormal air pressure values And the historical abnormal air pressure frequency ; Calculate the air pressure impact coefficient based on the normal operating air pressure threshold and historical air pressure data The specific formula is as follows: Among them, among them, Represents the historical air pressure data Abnormal air pressure value, Represents the historical air pressure data The frequency of abnormal air pressure values, The value range is 1, 2, 3... , The total number of historical air pressure data, Represents the air pressure weight coefficient, with a value of 0< <1; Calculate real-time environmental index The specific steps are: Real-time environmental data including real-time temperature , Real-time humidity And real-time air pressure ; According to real-time environmental data, temperature influence coefficient , Humidity influence coefficient And the pressure effect coefficient , calculate the real-time environment index , the specific formula is as follows: in, Real-time temperature The adjustment parameter is 0.1< <3; Real-time humidity The adjustment parameter is 0.1< <3; Real-time air pressure The adjustment parameter is 0.1< <3; Calculate and predict comprehensive health value The specific formula is as follows: in, The value is a positive number. The value of is a positive number; The specific steps to determine whether a battery health risk alert is triggered are: Comprehensive health value threshold set Including health thresholds , primary health risk threshold and intermediate health risk threshold ,in, < < ; When the task runs index ≤ No health risk alert is triggered when when <Task operation index ≤ When the primary health risk alert is triggered; when <Task operation index ≤ When the virus is detected, a medium-level health risk alert is triggered; When the task runs index > When a high-level health risk alert is triggered.

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