New energy automobile energy storage equipment intelligent detection method based on data analysis

Abstract

The invention discloses a new energy vehicle energy storage equipment intelligent detection method based on data analysis, and relates to the technical field of data analysis and intelligent detection, and the main scheme is that basic operation data, energy storage real-time operation data, vehicle real-time operation data and energy storage historical operation data are collected, and a basic operation index is calculated by using the basic operation data; calculating voltage, current and temperature influence coefficients according to historical data, calculating an operation parameter influence index, obtaining a vehicle real-time operation index in combination with the basic operation index and vehicle real-time operation data, calculating an operation performance influence index according to the vehicle real-time operation index and the operation parameter influence index, and setting an operation parameter and an operation performance influence index threshold set; whether a fault risk alarm is triggered or not is judged, an energy storage estimated life value is calculated according to the operation parameters and the performance influence index, a threshold set is set, an optimization scheme is generated, and comprehensive intelligent detection and optimization of the energy storage equipment are achieved through the system.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis and intelligent detection, and specifically to an intelligent detection method for energy storage equipment of new energy vehicles based on data analysis. Background Art

[0002] With the rapid development of new energy vehicles, energy storage equipment is of vital importance as its core component. However, energy storage equipment faces many uncertainties during operation, such as fluctuations in voltage, current, and temperature. These problems may cause energy storage equipment failures and affect the reliability and safety of new energy vehicles. At the same time, accurately assessing the life of energy storage equipment and promptly discovering potential failures are crucial to reducing maintenance costs and improving vehicle operating efficiency.

[0003] In existing technologies, the operating status of energy storage devices is usually determined by monitoring a single parameter of the device, such as voltage, current or temperature. Some methods also use historical data for simple trend analysis to predict the life of the device. Some technologies also combine some operating data of the vehicle, such as battery power usage, to evaluate the impact of energy storage devices on vehicle performance.

[0004] The existing technology for monitoring a single parameter cannot fully reflect the overall operating status of the energy storage equipment, and is prone to misjudgment. Simple data trend analysis is not accurate and it is difficult to accurately predict the life of the equipment. The existing technology does not make full use of vehicle operation data, fails to deeply combine the real-time vehicle operation data with the energy storage equipment data for comprehensive analysis, lacks a systematic intelligent detection method and a complete threshold system to accurately judge the risk of failure and generate optimization solutions, resulting in difficulty in effectively managing and maintaining energy storage equipment in actual applications. Summary of the invention

[0005] 1. Technical issues to be resolved In view of the deficiencies in the prior art, the present invention provides an intelligent detection method for energy storage equipment of new energy vehicles based on data analysis. By comprehensively considering the energy storage equipment operating parameter impact index and the operating performance impact index, etc., the operating status of the energy storage equipment is comprehensively and accurately evaluated, overcoming the limitation of the prior art that only a single parameter is monitored; the energy storage estimated life value is calculated by the operating parameter impact index and the operating performance impact index, and a corresponding threshold set is set to determine whether to trigger a parameter fault alarm and a performance risk alarm, thereby improving the accuracy of life prediction and fault risk judgment; through the comparison result of the energy storage estimated life value and the energy storage estimated life threshold set, a corresponding optimization plan is generated, making up for the deficiency of the prior art lacking a systematic optimization plan; through comprehensive analysis of the vehicle real-time operation data and the energy storage equipment data, the management level of the energy storage equipment is improved.

[0006] (II) Technical solution To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent detection method for energy storage equipment of new energy vehicles based on data analysis, comprising: Real-time collection of basic operation data of energy storage equipment, real-time operation data of energy storage, real-time operation data of vehicles, and historical operation data of energy storage; Calculate the basic operation index based on basic operation data , calculate the voltage impact coefficient based on the historical operation data of energy storage , Current influence coefficient And the temperature effect coefficient ; According to the voltage influence coefficient , Current influence coefficient , Temperature influence coefficient And real-time operation data of energy storage, calculate the impact index of operation parameters ; According to the basic operation index And the real-time operation data of the vehicle, calculate the real-time operation index of the vehicle ; According to the operating parameters affecting the index and vehicle real-time operation index , calculate the performance impact index ; Set the threshold value set of the impact index of the running parameters , according to the operating parameters affecting the index The threshold value set of the impact index of the operating parameters Compare the results of the test to determine whether to trigger a parameter failure risk alarm; set the threshold set of the operating performance impact index , according to the operating performance impact index and the threshold set for the performance impact index The comparison result is used to determine whether to trigger an energy storage equipment operation failure risk alarm; According to the operating parameters, the index and the performance impact index , calculate the estimated life value of energy storage ; Set the estimated lifespan threshold of energy storage , based on the estimated life value of energy storage and energy storage estimated life threshold set The comparison results are used to generate the corresponding optimization plan.

[0007] In the preferred scheme of the above-mentioned intelligent detection method for new energy vehicle energy storage equipment based on data analysis: Calculate the basic operation index The specific steps are: Basic operating data including voltage parameters , Current parameters And temperature parameters ; Setting voltage parameter thresholds , Current parameter threshold And temperature parameter threshold ; According to basic operating data, voltage parameter threshold , Current parameter threshold And temperature parameter threshold , calculate the basic operation index , the specific formula is as follows: Among them, the voltage parameter threshold The value of >0; Current parameter threshold The value of >0; temperature parameter threshold The value of >0.

[0008] In the preferred embodiment of the intelligent detection method for energy storage equipment of new energy vehicles based on data analysis: Calculate the voltage influence coefficient The specific steps are: The energy storage historical operation data includes historical voltage parameters; Historical voltage parameters include historical stable voltage values ​​within a unit time period , historical low pressure value And historical high pressure values ; Set the standard voltage value ; According to historical voltage parameters and standard voltage values , calculate the voltage influence coefficient , the specific formula is as follows: in, Represents the low-pressure basic adjustment coefficient, and its value is ; Represents the low pressure index adjustment parameter, and its value is >0; The low pressure index adjustment coefficient is >0 or <0; Represents the high pressure basic adjustment coefficient, and its value is ; Represents the high pressure index adjustment parameter, and its value is >0; The representative high pressure index adjustment coefficient is >0 or <0.

[0009] In the preferred embodiment of the intelligent detection method for energy storage equipment of new energy vehicles based on data analysis: calculating the current influence coefficient The specific steps are: The energy storage historical operation data also includes historical current data; Set the standard current value ; Historical current data includes historical stable current values ​​within a unit time period , the lowest current value in history And the highest current value in history ; According to historical current parameters and standard current values , calculate the current influence coefficient , the specific formula is as follows: in, is the low current basic adjustment coefficient, and its value is ; Adjust the parameters for low current, the value is >0; is the high current basic adjustment coefficient, and its value is ; Adjust the parameters for high current, the value is >0.

[0010] In the preferred embodiment of the intelligent detection method for energy storage equipment of new energy vehicles based on data analysis: Calculate the temperature influence coefficient The specific steps are: The energy storage historical operation data also includes historical temperature data; Historical temperature data includes historical constant temperature values ​​within a unit time period , historical low temperature value And historical high temperature values ; Set standard temperature value ; Based on historical temperature data and standard temperature values , calculate the temperature influence coefficient , the specific formula is as follows: in, is the low temperature linear coefficient, and its value is ; is the low temperature logarithmic adjustment coefficient, which is ; is the high temperature linear coefficient, and its value is ; is the high temperature logarithmic adjustment coefficient, which is .

[0011] In the preferred embodiment of the intelligent detection method for energy storage equipment of new energy vehicles based on data analysis: calculating the operating parameter influence index The specific steps are: Energy storage real-time operation data including real-time voltage , real-time current And real-time temperature ; According to the voltage influence coefficient , Current influence coefficient , Temperature influence coefficient And real-time operation data of energy storage, calculate the impact index of operation parameters The specific formula is as follows: .

[0012] In the preferred scheme of the above-mentioned intelligent detection method for new energy vehicle energy storage equipment based on data analysis: calculating the real-time operation index of the vehicle The specific steps are: Real-time vehicle operation data including voltage deviation value , Current deviation value and temperature deviation ; Calculate voltage deviation value , Current deviation value and temperature deviation The formula is as follows: According to the basic operation index And the real-time operation data of the vehicle, calculate the real-time operation index of the vehicle The specific formula is as follows: in, Indicates voltage deviation value ; Indicates the flow deviation value The weight coefficient of Indicates temperature deviation value The weight coefficient of .

[0013] In the preferred scheme of the above-mentioned intelligent detection method for new energy vehicle energy storage equipment based on data analysis: calculating the operating performance impact index The specific steps are: Set the standard value of the vehicle real-time operation index ; According to the operating parameters, the index , Vehicle real-time operation index standard value and vehicle real-time operation index , calculate the performance impact index , the specific formula is as follows: in, Represents the weight coefficient of the operating parameter impact index, with a value of 0.3< <0.5, The weight coefficient representing the vehicle real-time operation index deviation is set to 0.5< <0.7, and + =1.

[0014] In the preferred embodiment of the intelligent detection method for energy storage equipment of new energy vehicles based on data analysis: the threshold value of the operating parameter impact index Including health thresholds , Primary parameter fault threshold and intermediate parameter fault threshold ,in, < < ; When operating parameters affect the index ≤ , the parameter fault alarm is not triggered; when <Operation parameter impact index ≤ When , the primary parameter fault alarm is triggered; when <Operation parameter impact index ≤ When , the intermediate parameter fault alarm is triggered; When operating parameters affect the index > When the advanced parameter fault alarm is triggered; Runtime performance impact index threshold set Including health thresholds , Primary risk threshold and medium risk threshold ,in, < < ; When running performance impact index ≤ When , no performance risk alert is triggered; when <Operational Performance Impact Index ≤ When the primary performance risk alarm is triggered; when <Operational Performance Impact Index ≤ When the medium performance risk alarm is triggered; When running performance impact index > When the advanced performance risk alert is triggered.

[0015] In the preferred scheme of the above-mentioned intelligent detection method for new energy vehicle energy storage equipment based on data analysis: Calculate the estimated life value of energy storage The specific steps are: According to the operating parameters, the index and the performance impact index , calculate the estimated life value of energy storage , the specific formula is as follows: in, Represents the operating parameter impact index The adjustment coefficient is >0; Represents the operational performance impact index The adjustment coefficient is >0.

[0016] Energy storage estimated life threshold set Including first-level optimization threshold and the secondary optimization threshold ,in, < ; When the estimated life of energy storage ≤ When , a first-level optimization plan is generated; when <Estimated life of energy storage ≤ When , a secondary optimization plan is generated; when <Estimated life of energy storage , no optimization is done.

[0017] (III) Beneficial effects The present invention provides an intelligent detection method for energy storage equipment of new energy vehicles based on data analysis, which has the following beneficial effects: (1) By collecting the basic operating data of energy storage equipment, real-time operating data of energy storage, real-time operating data of vehicles, and historical operating data of energy storage in real time, this method can comprehensively and accurately grasp the operating status and performance changes of energy storage equipment, providing a reliable data basis for subsequent analysis and judgment.

[0018] (2) By calculating the basic operation index based on the basic operation data, the baseline operation status of the energy storage equipment can be evaluated; by calculating the voltage influence coefficient, current influence coefficient and temperature influence coefficient based on the historical operation data of the energy storage, the influence of different factors on the performance of the energy storage equipment can be quantified, providing a scientific basis for the calculation of the subsequent operation parameter influence index and operation performance influence index.

[0019] (3) By setting a set of operating parameter impact index thresholds and an operating performance impact index threshold set and comparing them with the actually calculated indexes, this method can timely detect and warn of parameter failure risks and operating failure risks of energy storage equipment, so as to take preventive measures to avoid the occurrence or expansion of failures.

[0020] (4) The estimated life value of energy storage is calculated based on the operating parameter impact index and the operating performance impact index, which can predict the remaining service life of the energy storage equipment, provide a scientific basis for the maintenance, replacement and updating of the equipment, extend the service life of the equipment and reduce operating costs.

[0021] (5) Based on the comparison results between the estimated energy storage life value and the estimated energy storage life threshold set, a corresponding optimization plan is generated, such as maintenance suggestions, replacement suggestions or vehicle use suggestions, etc., which can guide users or maintenance personnel to reasonably arrange the maintenance and use of energy storage equipment, improve the reliability and safety of the equipment, and reduce operating costs and environmental impacts. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flow chart of the intelligent detection method of energy storage equipment for new energy vehicles based on data analysis of the present invention. DETAILED DESCRIPTION

[0023] 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.

[0024] See also Figure 1 The present invention provides an intelligent detection method for energy storage equipment of new energy vehicles based on data analysis, comprising: Real-time collection of basic operating data of energy storage equipment, real-time operating data of energy storage, real-time operating data of vehicles, and historical operating data of energy storage.

[0025] Calculate the basic operation index based on basic operation data , calculate the voltage impact coefficient based on the historical operation data of energy storage , Current influence coefficient And the temperature effect coefficient ; According to the voltage influence coefficient , Current influence coefficient , Temperature influence coefficient And real-time operation data of energy storage, calculate the impact index of operation parameters .

[0026] Specifically, the basic operation index is calculated It can be used as a basic reference value for subsequent calculation of other indexes to help determine the degree of deviation of the equipment from its initial state.

[0027] Furthermore, for the calculation of the voltage influence coefficient, the deviation between the voltage value in the historical operation data of the energy storage and the standard voltage value is analyzed; for the calculation of the current influence coefficient, the historical current data is compared with the standard current value; for the temperature influence coefficient, it is mainly calculated based on the relationship between the historical temperature data and the standard operating temperature range. Too high or too low temperature will have an adverse effect on the performance and life of the energy storage equipment. High temperature accelerates the internal chemical reaction of the battery, resulting in a decrease in capacity; low temperature reduces the activity of the battery and affects the charging and discharging efficiency. By analyzing the historical temperature data, the degree of temperature impact on the equipment can be determined and converted into a temperature influence coefficient.

[0028] Furthermore, the operating parameter impact index is calculated by comprehensively considering the voltage impact coefficient, current impact coefficient, temperature impact coefficient and real-time operation data of energy storage. This index can be used as an important indicator to judge the health status of the equipment. When the index value exceeds a certain range, it indicates that the equipment may have a failure risk.

[0029] According to the basic operation index And the real-time operation data of the vehicle, calculate the real-time operation index of the vehicle ; According to the operating parameters affecting the index and vehicle real-time operation index , calculate the performance impact index .

[0030] Specifically, the vehicle real-time operation index is calculated by combining the basic operation index and the vehicle real-time operation data. This index can reflect the performance of the energy storage equipment during the actual operation of the vehicle and provide a basis for optimizing vehicle performance and energy storage equipment management.

[0031] Furthermore, the operation performance impact index is calculated based on the operation parameter impact index and the vehicle real-time operation index, which can take into account the real-time operation status of the equipment and the use of the vehicle, and more comprehensively reflect the performance of the equipment. When the operation performance impact index decreases, it indicates that there may be problems with the performance of the equipment, and further inspection and maintenance are required.

[0032] Set the threshold value set of the impact index of the running parameters , according to the operating parameters affecting the index The threshold value set of the impact index of the operating parameters Compare the results of the test to determine whether to trigger a parameter failure risk alarm; set the threshold set of the operating performance impact index , according to the operating performance impact index and the threshold set for the performance impact index The comparison result is used to determine whether to trigger the energy storage equipment operation failure risk alarm.

[0033] Specifically, a threshold set of operating parameter impact index is set, and based on the comparison result between the operating parameter impact index and the threshold set, it is determined whether to trigger a parameter failure risk alarm. When the operating parameter impact index exceeds the threshold range, the system can issue an alarm to remind users and maintenance personnel to pay attention to abnormal parameters of the equipment.

[0034] Furthermore, a threshold set of the operating performance impact index is set. Based on the comparison result between the operating performance impact index and the threshold set, it is determined whether to trigger the energy storage equipment operating failure risk alarm. When the operating performance impact index exceeds the threshold range, it indicates that there are major problems with the overall operating performance of the equipment and there is a serious risk of failure, and immediate measures need to be taken for repair or replacement.

[0035] According to the operating parameters, the index and the performance impact index , calculate the estimated life value of energy storage .

[0036] Specifically, the estimated life value of energy storage is calculated based on the operating parameter impact index and the operating performance impact index. This value can provide users and maintenance personnel with a reference for the remaining life of the equipment, helping them to reasonably arrange equipment maintenance and replacement plans.

[0037] Set the estimated lifespan threshold of energy storage , based on the estimated life value of energy storage and energy storage estimated life threshold set The comparison results are used to generate the corresponding optimization plan.

[0038] Specifically, a set of threshold values ​​for the estimated energy storage life is set, and corresponding optimization plans are generated based on the comparison results between the estimated energy storage life value and the threshold value set. These optimization plans can help users and maintenance personnel better manage energy storage equipment and improve the performance and reliability of the equipment.

[0039] In the preferred scheme of the above-mentioned intelligent detection method for new energy vehicle energy storage equipment based on data analysis: Calculate the basic operation index The specific steps are: Basic operating data including voltage parameters , Current parameters And temperature parameters .

[0040] Collect voltage data of energy storage equipment in normal operation and fault state, identify the normal fluctuation range of voltage through analysis of historical data, and set it as voltage parameter threshold ; Carry out charge and discharge tests on energy storage equipment, record current data, determine the normal fluctuation range of current, and set it as the current parameter threshold ; Install temperature sensors in the environment where the energy storage equipment is running, measure the real-time equipment temperature, establish normal values ​​for temperature changes, and determine temperature parameter thresholds .

[0041] According to basic operating data, voltage parameter threshold , Current parameter threshold And temperature parameter threshold , calculate the basic operation index , the specific formula is as follows: Among them, the voltage parameter threshold The value of >0; Current parameter threshold The value of >0; temperature parameter threshold The value of >0.

[0042] It should be noted that this formula is used to calculate the basic operation index , whose purpose is to evaluate the basic operating status of energy storage equipment by comprehensively considering voltage, current and temperature parameters.

[0043] The numerator part of the formula , which indicates the comprehensive influence of voltage, current and temperature parameters under the current operating conditions. Among them, the square of voltage parameter, the square of current parameter and the square of temperature parameter respectively reflect the influence of these three parameters on the operation of the equipment. They are combined by taking the square root of the sum of squares to reflect the overall influence.

[0044] Denominator It represents the comprehensive influence of voltage, current and temperature parameters under standard or reference conditions, among which, , and They are reference voltage, reference current and reference temperature, and these three reference parameters are combined by summing the squares and taking the square root.

[0045] The entire formula obtains a relative basic operation index by dividing the comprehensive impact degree in the current state by the comprehensive impact degree in the reference state. If the current state is close to the reference state, then this index will be close to 1; if the current state is significantly different from the reference state, then this index will deviate from 1, thereby reflecting the change in the basic operating state of the equipment relative to the reference state.

[0046] The voltage parameter threshold, current parameter threshold, and temperature parameter threshold are set to ensure that the values ​​of each parameter are within a reasonable range during the calculation process. If a parameter exceeds the corresponding threshold, it may mean that the device has an abnormality in this parameter and requires further inspection and processing. If the voltage parameter reading is greater than 0, it means that the normal voltage should be positive to avoid negative voltage. The temperature parameter threshold conforms to the physical property that the temperature is usually not negative in actual conditions.

[0047] By comprehensively considering the voltage, current and temperature parameters, this formula can fully reflect the basic operating status of the energy storage equipment, avoiding the problem of inaccurate evaluation caused by focusing only on a single parameter, and providing a more reliable basis for subsequent further analysis and decision-making; setting parameter thresholds helps to detect abnormal situations in a timely manner. The voltage parameter reading greater than 0 can prevent the adverse effects of negative voltage on the equipment. The temperature parameter threshold is non-negative and conforms to the actual physical characteristics. When the parameter exceeds the threshold, it can quickly remind inspection and processing, thereby improving the safety and stability of the equipment; the formula uses the square root method, which is more sensitive to parameter changes and can capture small abnormal fluctuations. By comparing the current state with the reference state, the degree of deviation of the equipment from the standard state can be intuitively understood, facilitating targeted maintenance and optimization measures.

[0048] In the preferred embodiment of the intelligent detection method for energy storage equipment of new energy vehicles based on data analysis: Calculate the voltage influence coefficient The specific steps are: The energy storage historical operation data includes historical voltage parameters.

[0049] Historical voltage parameters include historical stable voltage values ​​within a unit time period , historical low pressure value And historical high pressure values .

[0050] It should be noted that the conventional voltage parameter to ensure normal operation within a unit time period is the historical stable voltage value. , which is lower than the historical stable voltage, is the historical low voltage value , higher than the historical stable voltage is the historical high voltage value .

[0051] Set standard voltage values ​​according to industry specifications for new energy vehicle energy storage equipment .

[0052] According to historical voltage parameters and standard voltage values , calculate the voltage influence coefficient The specific formula is as follows: in, Represents the low-pressure basic adjustment coefficient, and its value is ; Represents the low pressure index adjustment parameter, and its value is >0; The low pressure index adjustment coefficient is >0 or <0; Represents the high pressure basic adjustment coefficient, and its value is ; Represents the high pressure index adjustment parameter, and its value is >0; The representative high pressure index adjustment coefficient is >0 or <0.

[0053] It should be noted that this formula is used to calculate the voltage influence coefficient The purpose is to evaluate the impact of voltage on new energy vehicle energy storage equipment based on historical voltage parameters and standard voltage values. By conducting actual tests on new energy vehicle energy storage equipment under different voltage conditions, observing changes in equipment performance, and setting low-voltage basic adjustment coefficients based on new energy vehicle energy storage equipment reference books and industry regulations And the high pressure basic adjustment factor ; Verify and adjust the index adjustment coefficient based on actual data so that the parameters can accurately reflect the exponential impact of voltage on equipment performance and determine the low voltage index adjustment parameters And high pressure index adjustment parameters ; Combining industry experience and expert opinions, and Make a preliminary estimate. According to different application scenarios and equipment types, experienced engineers can give a rough range of values ​​based on previous practical experience. Then, through actual testing and data analysis, continuously adjust the value of the coefficient to better reflect the performance changes of the equipment under low and high pressure conditions, and determine the low pressure index adjustment coefficient. and the high pressure index adjustment factor .

[0054] When the historical voltage is lower than the standard voltage value ( ):In this part, Indicates the degree of deviation of the historical low voltage from the standard voltage; As a low voltage index adjustment parameter, it is used to adjust the exponential impact of the low voltage deviation degree. As the deviation degree changes, this impact is amplified or reduced through exponential operation; As the low voltage index adjustment coefficient, the influence of the low voltage deviation degree on the voltage influence coefficient is further adjusted; As the low voltage basic adjustment coefficient, it is used to preliminarily adjust the weight of the low voltage impact coefficient on the voltage.

[0055] When the historical voltage is higher than the standard voltage value ( )hour: Indicates the degree of deviation of the historical high voltage from the standard voltage; , and They play similar roles as the index adjustment parameter, index adjustment coefficient and high-voltage basic adjustment coefficient under high voltage conditions respectively. Through such calculation method, it can more sensitively reflect the impact of different voltage conditions on energy storage equipment and provide a more accurate voltage impact coefficient for intelligent detection.

[0056] This formula can more accurately reflect the impact of different voltage conditions on energy storage equipment by distinguishing between low voltage and high voltage conditions and performing calculations separately. For low voltage, the degree of deviation from the standard voltage, the index adjustment parameters and coefficients, and the basic adjustment coefficient are taken into account, and the potential harm of low voltage to equipment performance and life can be accurately evaluated, and high voltage conditions can also be analyzed in a targeted manner; the introduction of index adjustment parameters and coefficients makes the calculation results more sensitive to the degree of voltage deviation. When the voltage deviation is small, potential risks can be captured and early warnings can be given, which helps to take timely maintenance measures to avoid equipment damage due to voltage problems; the basic adjustment coefficient provides the possibility for preliminary adjustment of the weight, which can be set according to actual conditions and experience, improving the applicability and flexibility of the formula and better adapting to different types of energy storage equipment and usage scenarios.

[0057] In the preferred embodiment of the intelligent detection method for energy storage equipment of new energy vehicles based on data analysis: calculating the current influence coefficient The specific steps are: The energy storage historical operation data also includes historical current data.

[0058] Set standard current values ​​according to industry specifications for new energy vehicle energy storage equipment .

[0059] Historical current data includes historical stable current values ​​within a unit time period , the lowest current value in history And the highest current value in history .

[0060] It should be noted that the conventional current parameter that ensures normal operation within a unit time period is the historical stable current value. A current value lower than the historical stable current is a historical low current value, and a current value higher than the historical stable current is a historical high current value.

[0061] According to historical current parameters and standard current values , calculate the current influence coefficient The specific formula is as follows: Among them, according to the new energy vehicle energy storage equipment reference book settings is the low current basic adjustment coefficient, and its value is ; Set according to the new energy vehicle energy storage equipment reference book and industry regulations Adjust the parameters for low current, the value is >0; set according to the new energy vehicle energy storage equipment reference book is the high current basic adjustment coefficient, and its value is ; Set according to the new energy vehicle energy storage equipment reference book and industry regulations Adjust the parameters for high current, the value is >0.

[0062] It should be noted that this formula is used to calculate the current influence coefficient , in order to evaluate the impact of current on the energy storage equipment of new energy vehicles.

[0063] When the historical current is lower than the standard current value ( )hour: In this part, Indicates the degree of deviation of the historical low current from the standard current; As a low current adjustment parameter, it is used to adjust the impact of low current deviation. When the deviation changes, Works together with the deviation value to change the value of the entire formula; As the low current basic adjustment coefficient, it is used to preliminarily adjust the weight of the low current on the current influence coefficient. Its value range is [0,1] and can be set according to actual conditions and experience.

[0064] When the historical current is higher than the standard current value ( )hour: middle, Indicates the degree of deviation of the historical high current from the standard current. and low current Similarly, the influence used to adjust the degree of high current deviation jointly determines the current influence coefficient under high current conditions; through such a calculation method, the influence of different current states on the energy storage device can be accurately reflected based on historical current parameters and standard current values.

[0065] This formula can more accurately evaluate the impact of different current states on energy storage equipment by distinguishing between low current and high current situations for calculation. The setting of low current basic adjustment coefficient and high current adjustment parameter enables flexible adjustment of weights in different current scenarios to adapt to various actual situations. For low current situations, the structure in the formula can reasonably reflect the impact of low current on equipment performance according to the degree of deviation from the standard current and the adjustment parameters. When the low current deviates from the standard by a small amount, it can also sensitively capture the changes by adjusting the parameters, and warn of potential risks in advance, providing a basis for timely maintenance measures. Similarly, in high current situations, the degree of harm of high current to equipment can be accurately measured. Overall, this formula helps to improve the accuracy and reliability of intelligent detection of energy storage equipment for new energy vehicles, and better ensure the safe operation and stable performance of the equipment.

[0066] In the preferred embodiment of the intelligent detection method for energy storage equipment of new energy vehicles based on data analysis: Calculate the temperature influence coefficient The specific steps are: Energy storage historical operation data also includes historical temperature data.

[0067] Historical temperature data includes historical constant temperature values ​​within a unit time period , historical low temperature value And historical high temperature values .

[0068] It should be noted that the normal temperature parameter that ensures normal operation within a unit time period is the historical stable temperature value. The temperature below the historical stable temperature is the historical low temperature value, and the temperature above the historical stable temperature is the historical high temperature value.

[0069] Set the standard temperature value according to the expected temperature value achieved during operation .

[0070] Based on historical temperature data and standard temperature values , calculate the temperature influence coefficient , the specific formula is as follows: in, is the low temperature linear coefficient, and its value is ; is the low temperature logarithmic adjustment coefficient, which is ; is the high temperature linear coefficient, and its value is ; is the high temperature logarithmic adjustment coefficient, which is .

[0071] It should be noted that this formula is used to calculate the temperature influence coefficient , to evaluate the impact of temperature on new energy vehicle energy storage equipment; set the low temperature linear coefficient according to the new energy vehicle energy storage equipment reference book and industry regulations and high temperature linear coefficient , so that it can better reflect the linear influence of temperature change on equipment performance; according to temperature testing and data analysis, the value of the coefficient is continuously adjusted to make it better fit the actual data, improve the accuracy of temperature influence coefficient calculation, and determine the low temperature logarithmic adjustment coefficient And the high temperature logarithmic adjustment factor .

[0072] When the historical temperature is lower than the standard temperature value ( )hour: In this part, Indicates the degree of deviation of the historical low temperature from the standard temperature. As the low temperature linear coefficient, it is used to preliminarily adjust the linear weight of the low temperature influence coefficient on the temperature. Its value is in [0,1], and the emphasis on the low temperature influence can be adjusted according to the actual situation; Further adjustments were made to account for the effects of low temperatures. Among them, as the low temperature logarithmic adjustment coefficient, combined with logarithmic operation, it can more accurately reflect the effect of low temperature deviation on the temperature influence coefficient. Especially when the degree of low temperature deviation changes greatly, logarithmic operation can make the adjustment more sensitive.

[0073] When the historical temperature is higher than the standard temperature value ( )hour: In As the high temperature linear coefficient, the linear weight of the high temperature on the temperature influence coefficient is preliminarily adjusted; In As a high-temperature logarithmic adjustment coefficient, the temperature influence coefficient is adjusted more finely in combination with logarithmic operations under high temperature conditions; through such calculation method, the impact of different temperature conditions on energy storage equipment can be accurately reflected based on historical temperature data and standard temperature values.

[0074] This formula can more accurately evaluate the impact of different temperature conditions on energy storage equipment by distinguishing between low temperature and high temperature conditions and calculating them separately. The low temperature linear coefficient and the high temperature linear coefficient can preliminarily adjust the linear weight of the temperature impact, so that the calculation results are more in line with the actual situation. The introduction of the logarithmic adjustment coefficient makes it more sensitive to changes in temperature deviations from the standard value, and can also capture potential risks when the temperature deviation is small, providing a basis for taking maintenance measures in advance. Regardless of low or high temperature, logarithmic operations combined with linear coefficients can more accurately reflect the impact of temperature on equipment performance and life. The coefficient value is within a specific range, such as [0,1], which increases the flexibility and applicability of the formula. It can be adjusted according to different energy storage equipment characteristics and usage scenarios, improving the accuracy and reliability of intelligent detection of new energy vehicle energy storage equipment.

[0075] In the preferred embodiment of the intelligent detection method for energy storage equipment of new energy vehicles based on data analysis: calculating the operating parameter influence index The specific steps are: Energy storage real-time operation data including real-time voltage , real-time current And real-time temperature ; According to the voltage influence coefficient , Current influence coefficient , Temperature influence coefficient And real-time operation data of energy storage, calculate the impact index of operation parameters The specific formula is as follows: .

[0076] It should be noted that this formula is used to calculate the operating parameter impact index , the purpose of which is to comprehensively consider the voltage influence coefficient , Current influence coefficient , Temperature influence coefficient And real-time voltage , real-time current , Real-time temperature Evaluate the impact of operating parameters of energy storage devices on their overall performance.

[0077] In the formula, The form of square root of the sum of squares is adopted. For each item, it is multiplied by the square of the corresponding influence coefficient. For example, the real-time voltage Reflects the actual voltage status of the current equipment, and the voltage influence coefficient The square of reflects the effect of the magnified degree of voltage influence on equipment performance in historical data. In this way, the multiplication of the two represents the comprehensive influence of the current real-time voltage state after considering the historical voltage influence. and They respectively represent the comprehensive impact of real-time current and real-time temperature after considering the impact of historical current and temperature. By adding these three items and taking the square root, a comprehensive operating parameter impact index is obtained, which can comprehensively reflect the impact of the current operating status of the energy storage device under the joint action of multiple key parameters such as voltage, current, and temperature on the overall performance of the equipment.

[0078] This formula comprehensively considers the real-time voltage, current, temperature and the corresponding voltage, current and temperature influence coefficients, and can fully reflect the status of the energy storage equipment in actual operation. By combining real-time parameters with historical influence coefficients, it avoids the problem of inaccurate evaluation caused by considering only a single factor, and provides a more reliable basis for accurately judging the operating status of the equipment; the square root form is adopted to make the influence of each parameter more balanced in the final result, and the influence coefficient is squared to enhance the influence weight of historical data on the current operating parameters, which can more sensitively reflect the impact of small changes on equipment performance; this formula provides a quantitative indicator for the intelligent detection of energy storage equipment, which helps to timely discover potential fault risks, take maintenance measures in advance, extend equipment life, and improve the reliability and stability of new energy vehicle energy storage systems.

[0079] In the preferred scheme of the above-mentioned intelligent detection method for new energy vehicle energy storage equipment based on data analysis: calculating the real-time operation index of the vehicle The specific steps are: Real-time vehicle operation data including voltage deviation value , Current deviation value and temperature deviation .

[0080] According to the real-time operation data of energy storage and standard voltage value , Standard current value , Standard temperature value , calculate the voltage deviation value , Current deviation value and temperature deviation , the specific formula is as follows: According to the basic operation index And the real-time operation data of the vehicle, calculate the real-time operation index of the vehicle , the specific formula is as follows: in, Indicates voltage deviation value ; Indicates the flow deviation value The weight coefficient of Indicates temperature deviation value The weight coefficient of .

[0081] It should be noted that this formula is used to calculate the vehicle real-time operation index , the purpose is to combine the basic operation index The deviation value in the real-time operation data of the vehicle is used to evaluate the impact of the vehicle's current operation on the energy storage device.

[0082] The basic operation index reflects the basic operation status of the energy storage device itself and is added to the calculation of the vehicle real-time operation index as an important reference value. For this part, , and Indicates the deviation between each real-time operating parameter of the vehicle and the corresponding reference value. , and It can be understood as and Corresponding weight coefficients, different deviation values ​​may have different impacts on vehicle operation and energy storage equipment, which can be achieved through , and By taking a weighted sum of the deviation values ​​and adding them to the basic operation index, the vehicle real-time operation index is obtained. This index comprehensively considers the basic state of the energy storage device itself and the deviation of the vehicle's real-time operation parameters. It can more comprehensively reflect the impact of the vehicle on the energy storage device in its current state, and provide a basis for further evaluating the overall operation performance of the energy storage device.

[0083] This formula combines the basic operation index and the deviation value in the vehicle's real-time operation data, and can comprehensively consider the energy storage device's own status and the impact of vehicle operation on it. The basic operation index reflects the basic situation of the equipment, while the deviation value reflects the difference between the vehicle's real-time operation parameters and the benchmark value. The combination of the two makes the evaluation more accurate. By assigning weight coefficients to different deviation values, the degree of influence of each parameter on the vehicle's real-time operation index can be adjusted according to actual conditions. For those deviation values ​​that have a greater impact on the energy storage device, a higher weight can be given, so that more targeted evaluation and early warning can be carried out. This formula provides an effective indicator for the intelligent detection of new energy vehicle energy storage equipment, which helps to promptly discover possible problems in vehicle operation, take corresponding measures to optimize vehicle performance and protect energy storage equipment, and improve the reliability and stability of the entire system.

[0084] In the preferred scheme of the above-mentioned intelligent detection method for new energy vehicle energy storage equipment based on data analysis: calculating the operating performance impact index The specific steps are: The performance of the energy storage device under ideal working conditions, and its corresponding vehicle real-time operation index is set as the vehicle real-time operation index standard value .

[0085] According to the operating parameters, the index , Vehicle real-time operation index standard value and vehicle real-time operation index , calculate the performance impact index , the specific formula is as follows: in, Represents the weight coefficient of the operating parameter impact index, with a value of 0.3< <0.5, The weight coefficient representing the vehicle real-time operation index deviation is set to 0.5< <0.7, and + =1.

[0086] It should be noted that this formula is used to calculate the operating performance impact index , the purpose of which is to comprehensively consider the impact index of operating parameters and vehicle real-time operation index The overall operating performance of the energy storage equipment is evaluated by the deviation from the standard value.

[0087] in, This part, As a weight coefficient, it is used to adjust the impact of the operating parameter impact index on the overall operating performance. It integrates real-time operating parameters such as voltage, current, temperature and the corresponding influence coefficients, reflecting the operating status of the equipment under the influence of these key parameters.

[0088] and This part, is the vehicle real-time operation index, is the standard value of the vehicle's real-time operation index. The difference between the two indicates the deviation between the current vehicle's real-time operation status and the standard status. The contribution of this deviation to the operating performance impact index is adjusted as a weight coefficient. Through such calculation method, the deviation of the equipment's operating parameter status and the vehicle's operating status from the standard value is combined, which can more comprehensively evaluate the operating performance of the energy storage equipment and provide a basis for judging whether the equipment has a failure risk and formulating optimization plans.

[0089] This formula comprehensively considers the deviation between the operating parameter impact index and the vehicle's real-time operating index and the standard value, and can comprehensively evaluate the operating performance of the energy storage equipment. It not only pays attention to the operating status of the key parameters of the equipment itself, but also combines the actual impact of vehicle operation on the equipment, avoiding the limitations of single-factor evaluation. By setting the weight coefficients and, the degree of influence of different factors on the operating performance can be flexibly adjusted according to actual conditions. This formula provides a quantitative indicator for the intelligent detection of new energy vehicle energy storage equipment, which helps to timely discover potential performance problems, take targeted optimization measures, improve the reliability and stability of the equipment, and extend the service life of the equipment.

[0090] In the preferred embodiment of the intelligent detection method for energy storage equipment of new energy vehicles based on data analysis: the threshold value of the operating parameter impact index Including health thresholds , Primary parameter fault threshold and intermediate parameter fault threshold ,in, < < .

[0091] When operating parameters affect the index ≤ , the parameter fault alarm is not triggered.

[0092] when <Operation parameter impact index ≤ When the primary parameter fault alarm is triggered.

[0093] when <Operation parameter impact index ≤ When the intermediate parameter fault alarm is triggered.

[0094] When operating parameters affect the index > , an advanced parameter failure alarm is triggered.

[0095] Runtime performance impact index threshold set Including health thresholds , Primary risk threshold and medium risk threshold ,in, < < .

[0096] When running performance impact index ≤ , no performance risk alert is triggered.

[0097] when <Operational Performance Impact Index ≤ When the primary performance risk alert is triggered.

[0098] when <Operational Performance Impact Index ≤ A medium performance risk alert is triggered.

[0099] When running performance impact index > When the advanced performance risk alert is triggered.

[0100] It should be noted that the threshold of the operating parameter impact index is obtained by analyzing and counting the historical operation data of a large number of energy storage devices. , determine the health value based on the normal operating status, different degrees of fault conditions and equipment performance , Primary parameter fault threshold and intermediate parameter fault threshold ,in < < .

[0101] When operating parameters affect the index ≤ , it indicates that the equipment is operating in a healthy state, all parameters are within a relatively ideal range, and there is no obvious risk of failure.

[0102] when <Operation parameter impact index ≤ When the primary parameter fault alarm is triggered, the equipment may have some minor abnormal conditions, but it has not reached the level of serious failure, triggering the primary parameter fault alarm to remind users and maintenance personnel to pay attention and conduct preliminary inspections.

[0103] when <Operation parameter impact index ≤ When the device is at a high risk of parameter failure, it triggers an intermediate parameter failure alarm and requires immediate, more in-depth inspection and repair measures.

[0104] When operating parameters affect the index > , it indicates that the equipment has a very serious risk of parameter failure, triggering an advanced parameter failure alarm and requiring immediate shutdown.

[0105] By analyzing and summarizing the performance data of a large number of new energy vehicle energy storage devices under normal operating conditions and other abnormal operating conditions, a set of operating performance impact index thresholds is obtained. . Three thresholds are determined , and ,in < < .

[0106] When running performance impact index ≤ , the device performance is in good condition and no performance risk alarm is triggered.

[0107] when <Operational Performance Impact Index ≤ When the device performance drops to a certain extent, the primary performance risk alarm is triggered, indicating that there are some potential problems that need attention.

[0108] when <Operational Performance Impact Index ≤ When the device performance drops significantly, a medium-level performance risk alert is triggered, indicating that there is a problem that must be resolved.

[0109] When running performance impact index > When the device performance is seriously damaged, the high-level performance risk alarm is triggered and the device is immediately stopped. A comprehensive overhaul and optimization is required as soon as possible.

[0110] By setting multiple thresholds, the operating status of energy storage equipment can be evaluated more finely. Different levels of thresholds correspond to different degrees of failure risks and performance degradation, so that users and maintenance personnel can understand the specific status of the equipment in a timely manner and take corresponding measures; thresholds are set for the operating parameter impact index and the operating performance impact index respectively, taking into account the parameter status and overall performance of the equipment, so that it can be more accurately judged whether the equipment has a failure risk and avoid the limitations of single indicator evaluation; triggering different levels of alarms within different threshold ranges helps to improve the timeliness and accuracy of fault detection. Low-level alarms can remind you to conduct preliminary inspections, and high-level alarms require immediate action, thereby effectively reducing the losses caused by equipment failures, improving the reliability and safety of new energy vehicle energy storage equipment, and extending the service life of the equipment.

[0111] In the preferred scheme of the above-mentioned intelligent detection method for new energy vehicle energy storage equipment based on data analysis: Calculate the estimated life value of energy storage The specific steps are: According to the operating parameters, the index and the performance impact index , calculate the estimated life value of energy storage The specific formula is as follows: in, Represents the operating parameter impact index The adjustment coefficient is >0; Represents the operational performance impact index The adjustment coefficient is >0.

[0112] Energy storage estimated life threshold set Including first-level optimization threshold and the secondary optimization threshold ,in, < .

[0113] When the estimated life of energy storage ≤ When , a first-level optimization plan is generated.

[0114] when <Estimated life of energy storage ≤ , a secondary optimization solution is generated.

[0115] when <Estimated life of energy storage , no optimization is done.

[0116] It should be noted that this formula is used to calculate the estimated life value of energy storage , which comprehensively considers the impact index of operating parameters and Operational Performance Impact Index .

[0117] first, It is the initial life value of the energy storage device under ideal conditions.

[0118] Index part ,in It is the operating parameter impact index, which reflects the impact of the operating status of the equipment under key parameters such as voltage, current, and temperature on its life. It is the operating performance impact index, which reflects the impact of the overall operating performance of the equipment on its life. and are the adjustment coefficients, Used to adjust the impact of operating parameter impact index on life. Used to adjust the impact of the operating performance index on the life span. >0, indicating that the operating performance impact index has a positive impact on life, that is, the worse the operating performance, the greater the negative impact on life.

[0119] when and When it increases, the value of the exponential part increases, resulting in a decrease in the value of the entire exponential function, thereby reducing the estimated life of the energy storage. On the contrary, when and When it is smaller, the estimated life value of the energy storage is higher. Through this calculation method, the life of the energy storage equipment can be dynamically estimated according to the current operating status and performance of the equipment.

[0120] This formula comprehensively considers the operating parameter impact index and the operating performance impact index, and can more comprehensively reflect the impact of the actual operating conditions of the energy storage equipment on its life. It not only pays attention to key parameters such as voltage, current, and temperature, but also combines the overall operating performance, avoiding the limitations of evaluating life by a single factor. By adjusting the coefficients and, it can be flexibly adjusted according to different types of energy storage equipment and usage scenarios, thereby improving the applicability of the formula. This formula provides a quantitative method for predicting the life of new energy vehicle energy storage equipment, which helps users and maintenance personnel plan equipment maintenance and replacement plans in advance, reduce the risk of equipment failure, and improve the reliability and safety of new energy vehicles. At the same time, it also provides strong support for the intelligent management of energy storage equipment.

[0121] It should be noted that the estimated life threshold set of energy storage is obtained by analyzing and counting the historical life data of a large number of similar energy storage devices. Study the distribution of equipment life under different operating conditions, combine the equipment's design parameters, performance indicators and empirical data from actual use, and determine the first-level optimization threshold and the secondary optimization threshold ,in < .

[0122] When the estimated life of energy storage ≤ When , it indicates that the life of the energy storage equipment is approaching a state where more urgent optimization processing is required. At this time, a first-level optimization plan is generated, including more frequent inspection and maintenance of the equipment, adjustment of the charging and discharging strategy to reduce the loss of the equipment, and optimization of vehicle use to reduce the burden on the energy storage equipment.

[0123] when <Estimated life of energy storage ≤ When the equipment life is in a state that requires attention and a certain degree of optimization, a secondary optimization plan is generated, which may be relatively milder than the primary optimization plan, such as regular equipment inspections and appropriate adjustments to operating parameters.

[0124] when <Estimated life of energy storage , indicating that the equipment is currently in good service life and does not require optimization for the time being. It can continue to be used in normal operation and maintenance methods.

[0125] By setting multi-level optimization thresholds, the life status of energy storage equipment can be divided and evaluated in more detail. Different thresholds correspond to different degrees of optimization needs, so that maintenance personnel can take targeted measures to improve maintenance efficiency. When the estimated life value of energy storage is close to different thresholds, a corresponding optimization plan is generated, which helps to prevent equipment failures in advance and extend the service life of the equipment. The first-level optimization plan can take effective measures when the life status is more urgent, and the second-level optimization plan can make appropriate adjustments when attention is needed, avoiding excessive maintenance or untimely maintenance. When the equipment life status is good, no optimization is performed, avoiding unnecessary waste of resources and interference with normal operation. Overall, this solution provides a scientific and effective method for the management of new energy vehicle energy storage equipment, improves the reliability and safety of the equipment, reduces operating costs, and also provides users with a better user experience.

[0126] 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.

[0127] 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.

[0128] 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. An intelligent detection method for energy storage equipment of new energy vehicles based on data analysis, characterized in that: include: Real-time collection of basic operation data of energy storage equipment, real-time operation data of energy storage, real-time operation data of vehicles, and historical operation data of energy storage; Calculate the basic operation index based on basic operation data , calculate the voltage impact coefficient based on the historical operation data of energy storage , Current influence coefficient And the temperature effect coefficient ; According to the voltage influence coefficient , Current influence coefficient , Temperature influence coefficient And real-time operation data of energy storage, calculate the impact index of operation parameters ; According to the basic operation index And the real-time operation data of the vehicle, calculate the real-time operation index of the vehicle ; According to the operating parameters affecting the index and vehicle real-time operation index , calculate the performance impact index ; Set the threshold value set of the impact index of the running parameters , according to the operating parameters affecting the index The threshold value set of the impact index of the operating parameters The comparison result determines whether to trigger a parameter failure risk alarm; Set the threshold value set for the performance impact index , according to the operating performance impact index and the threshold set for the performance impact index The comparison result is used to determine whether to trigger an energy storage equipment operation failure risk alarm; According to the operating parameters, the index and the performance impact index , calculate the estimated life value of energy storage ; Set the estimated lifespan threshold of energy storage , based on the estimated life value of energy storage and energy storage estimated life threshold set The comparison results are used to generate the corresponding optimization plan.

2. The intelligent detection method for new energy vehicle energy storage equipment based on data analysis according to claim 1 is characterized in that: Calculate the basic operation index The specific steps are: Basic operating data including voltage parameters , Current parameters And temperature parameters ; Setting voltage parameter thresholds , Current parameter threshold And temperature parameter threshold ; According to basic operating data, voltage parameter threshold , Current parameter threshold And temperature parameter threshold , calculate the basic operation index The specific formula is as follows: Among them, the voltage parameter threshold The value of >0; Current parameter threshold The value of >0; temperature parameter threshold The value of >

0.

3. The intelligent detection method for new energy vehicle energy storage equipment based on data analysis according to claim 2 is characterized in that: Calculating the voltage influence factor The specific steps are: The energy storage historical operation data includes historical voltage parameters; Historical voltage parameters include historical stable voltage values ​​within a unit time period , historical low pressure value And historical high pressure values ; Set the standard voltage value ; According to historical voltage parameters and standard voltage values , calculate the voltage influence coefficient The specific formula is as follows: in, Represents the low-pressure basic adjustment coefficient, and its value is ; Represents the low pressure index adjustment parameter, and its value is >0; The low pressure index adjustment coefficient is >0 or <0; Represents the high pressure basic adjustment coefficient, and its value is ; Represents the high pressure index adjustment parameter, and its value is >0; The representative high pressure index adjustment coefficient is >0 or <0.

4. The intelligent detection method for new energy vehicle energy storage equipment based on data analysis according to claim 3 is characterized in that: Calculate the current influence coefficient The specific steps are: The energy storage historical operation data also includes historical current data; Set the standard current value ; Historical current data includes historical stable current values ​​within a unit time period , the lowest current value in history And the highest current value in history ; According to historical current parameters and standard current values , calculate the current influence coefficient The specific formula is as follows: in, is the low current basic adjustment coefficient, and its value is ; Adjust the parameters for low current, the value is >0; is the high current basic adjustment coefficient, and its value is ; Adjust the parameters for high current, the value is >

0.

5. The intelligent detection method for energy storage equipment of new energy vehicles based on data analysis according to claim 4 is characterized in that: Calculating the Temperature Effect Factor The specific steps are: The energy storage historical operation data also includes historical temperature data; Historical temperature data includes historical constant temperature values ​​within a unit time period , historical low temperature value And historical high temperature values ; Set standard temperature value ; Based on historical temperature data and standard temperature values , calculate the temperature influence coefficient The specific formula is as follows: in, is the low temperature linear coefficient, and its value is ; is the low temperature logarithmic adjustment coefficient, which is ; is the high temperature linear coefficient, and its value is ; is the high temperature logarithmic adjustment coefficient, which is .

6. The intelligent detection method for new energy vehicle energy storage equipment based on data analysis according to claim 5 is characterized in that: Calculate the operating parameter impact index The specific steps are: Energy storage real-time operation data including real-time voltage , real-time current And real-time temperature ; According to the voltage influence coefficient , Current influence coefficient , Temperature influence coefficient And real-time operation data of energy storage, calculate the impact index of operation parameters The specific formula is as follows: 。 7. The intelligent detection method for new energy vehicle energy storage equipment based on data analysis according to claim 6 is characterized in that: Calculate vehicle real-time operation index The specific steps are: Real-time vehicle operation data including voltage deviation value , Current deviation value and temperature deviation ; Calculate voltage deviation value , Current deviation value and temperature deviation The formula is as follows: According to the basic operation index And the real-time operation data of the vehicle, calculate the real-time operation index of the vehicle The specific formula is as follows: in, Indicates voltage deviation value The weight coefficient of Indicates the flow deviation value The weight coefficient of Indicates temperature deviation value The weight coefficient of .

8. The intelligent detection method for new energy vehicle energy storage equipment based on data analysis according to claim 7 is characterized in that: Calculate the performance impact index The specific steps are: Set the standard value of the vehicle real-time operation index ; According to the operating parameters, the index , Vehicle real-time operation index standard value and vehicle real-time operation index , calculate the performance impact index The specific formula is as follows: in, Represents the weight coefficient of the operating parameter impact index, with a value of 0.3< <0.5, The weight coefficient representing the vehicle real-time operation index deviation is set to 0.5< <0.7, and + =1.

9. The intelligent detection method for energy storage equipment of new energy vehicles based on data analysis according to claim 8 is characterized in that: Operation parameter impact index threshold Including health thresholds , Primary parameter fault threshold and intermediate parameter fault threshold ,in, < < ; When operating parameters affect the index ≤ , the parameter fault alarm is not triggered; when <Operation parameter impact index ≤ When , the primary parameter fault alarm is triggered; when <Operation parameter impact index ≤ When , the intermediate parameter fault alarm is triggered; When operating parameters affect the index > When the advanced parameter fault alarm is triggered; Runtime performance impact index threshold set Including health thresholds , Primary risk threshold and medium risk threshold ,in, < < ; When running performance impact index ≤ When , no performance risk alert is triggered; when <Operational Performance Impact Index ≤ When the primary performance risk alarm is triggered; when <Operational Performance Impact Index ≤ When the medium performance risk alarm is triggered; When running performance impact index > When the advanced performance risk alert is triggered.

10. The intelligent detection method for energy storage equipment of new energy vehicles based on data analysis according to claim 9 is characterized in that: Calculate the estimated lifespan of energy storage The specific steps are: According to the operating parameters, the index and the performance impact index , calculate the estimated life value of energy storage The specific formula is as follows: in, Represents the operating parameter impact index The adjustment coefficient is >0; Represents the operational performance impact index The adjustment coefficient is >

0. Energy storage estimated life threshold set Including first-level optimization threshold and the secondary optimization threshold ,in, < ; When the estimated life of energy storage ≤ When , a first-level optimization plan is generated; when <Estimated life of energy storage ≤ When , a secondary optimization plan is generated; when <Estimated life of energy storage , no optimization is done.

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