Vehicle maintenance identification method and system based on big data

Abstract

The invention relates to the technical field of vehicle maintenance, in particular to a vehicle maintenance identification method and system based on big data. According to the method, the running states of the vehicle-mounted power supply system under different working conditions can be comprehensively known by acquiring multiple pieces of characteristic information such as energy management, vehicle states and behavior characteristics of the vehicle, and the behavior of the battery under transient change can be more accurately identified by comprehensively analyzing the current-power unbalance degree, so that the reliability of the vehicle-mounted power supply system is improved. Through the voltage characteristic information of a plurality of single batteries, the voltage change of the battery under the transient load condition can be captured from the perspective of different single batteries, the potential performance degradation or fault problem of the power supply can be identified as soon as possible by calculating the transient performance change degree of the power supply in real time, and health diagnosis is performed by combining the indexes. According to the invention, information of more dimensions can be provided for a maintenance identification system, thereby improving the reliability of a power supply, reducing the maintenance cost, prolonging the service life of the power supply, and improving the intelligent level of a vehicle-mounted power supply management system.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle maintenance, and particularly relates to a method and system for vehicle maintenance identification based on big data. Background Art

[0002] When a vehicle breaks down, most traditional vehicle maintenance methods are that users often judge the cause of the vehicle failure based on maintenance experience. With the development of technology, the functions of vehicles are constantly increasing, and it is difficult for traditional maintenance methods to accurately predict the specific life of each component. Usually, it is decided when to repair or replace based on the average service life or mileage.

[0003] Especially for the health status of in-vehicle power supplies, in order to facilitate maintenance and monitoring, currently, a voltage monitoring device is generally connected to the in-vehicle battery terminal to monitor the battery voltage of the in-vehicle power supply in real time. However, the above method can only monitor the real-time in-vehicle power supply voltage and cannot diagnose the impact of real-time transient voltage changes on the health status of the in-vehicle power supply. Summary of the Invention

[0004] The main object of the present invention is to provide a method for vehicle maintenance identification based on big data, aiming to solve the technical problems in the prior art.

[0005] The present invention proposes a method for vehicle maintenance identification based on big data, which is applied to a maintenance identification device and includes: Obtain the energy management characteristics, vehicle state characteristics, and vehicle behavior characteristics of the target vehicle; Obtain the current-power imbalance degree according to the energy management characteristics, vehicle state characteristics, and vehicle behavior characteristics; Judge whether the current-power imbalance degree is greater than a preset threshold; If the current-power imbalance degree is greater than the preset threshold and it is determined that the in-vehicle power supply system of the target vehicle operates abnormally, then obtain the multiple single-cell voltage characteristic information, discharge current characteristic information, and internal resistance characteristic information of the target vehicle; Calculate the transient voltage change value according to the multiple single-cell voltage characteristic information; Obtain the transient discharge efficiency according to the multiple single-cell voltage characteristic information, multiple discharge current characteristic information, and multiple internal resistance characteristic information; Calculate the degree of change in the transient performance of the power supply according to the transient voltage change value and the transient discharge efficiency, and send the degree of change in the transient performance of the power supply to the maintenance identification system; The maintenance identification system obtains the in-vehicle power supply diagnosis result according to the degree of change in the transient performance of the power supply, and feeds back the in-vehicle power supply health diagnosis result to the target vehicle.

[0006] Preferably, the step of obtaining the current-power imbalance degree according to the energy management characteristics, vehicle state characteristics, and vehicle behavior characteristics includes: Obtaining power system state data and charging system state data according to the vehicle state characteristics; Obtaining the battery capacity according to the energy management characteristics; Obtaining on-vehicle charging index data according to the battery capacity and charging system state data; Obtaining the start-stop frequency according to the vehicle behavior characteristics; Obtaining on-vehicle power index data according to the start-stop frequency, battery capacity, and power system state data; Obtaining the current-power imbalance degree according to the on-vehicle charging index data and on-vehicle power index data.

[0007] Preferably, the step of obtaining on-vehicle charging index data according to the battery capacity and charging system state data includes: Obtaining the charging current, battery internal resistance, nominal voltage, and charging temperature coefficient according to the charging system state data; Obtaining the battery change rate according to the charging current and battery capacity; Obtaining the charging thermal effect according to the charging current and charging temperature coefficient; Obtaining the battery voltage according to the charging current and battery internal resistance; Obtaining the battery capacity, and obtaining on-vehicle charging index data according to the battery capacity, charging thermal effect, charging current, battery voltage, and nominal voltage.

[0008] Preferably, the step of obtaining on-vehicle power index data according to the start-stop frequency, battery capacity, and power system state data includes: Obtaining the power system load factor, battery current, start duration, and stop duration according to the power system state data; Obtaining the load power according to the start-stop frequency and power system load factor, and obtaining the battery voltage change rate according to the load power and start-stop frequency; Obtaining the battery capacity, and obtaining the state of charge change rate of the battery according to the battery capacity, battery current, start duration, and stop duration; Obtaining the battery charge and discharge frequency according to the start-stop frequency and battery capacity, and obtaining on-vehicle power index data according to the state of charge change rate of the battery, battery voltage change rate, and battery charge and discharge frequency.

[0009] Preferably, the step of calculating the transient voltage change value according to multiple pieces of the single-cell voltage characteristic information includes: Obtaining the corresponding no-load single-cell battery voltage and loaded single-cell battery voltage according to each piece of the single-cell voltage characteristic information; Obtain the no-load average cell voltage based on the multiple no-load cell voltages, and obtain the standard deviation of the no-load cell voltages based on the no-load average cell voltage and the multiple no-load cell voltages; Obtain the loaded average cell voltage based on the multiple loaded cell voltages, and obtain the standard deviation of the loaded cell voltages based on the loaded average cell voltage and the multiple loaded cell voltages; Obtain the transient voltage change value based on the standard deviation of the no-load cell voltages and the standard deviation of the loaded cell voltages.

[0010] Preferably, the step of calculating the degree of change in the power supply transient performance according to the transient voltage change value and the transient discharge efficiency includes: Obtain the transient voltage waveform diagram according to the transient voltage change value and the transient discharge efficiency; Obtain the steady-state voltage, the maximum voltage value, and the minimum voltage value according to the transient voltage waveform diagram; Obtain the voltage fluctuation amplitude according to the maximum voltage value and the minimum voltage value; Obtain the oscillation frequency and the time characteristics according to the transient voltage waveform diagram, where the time characteristics include the response duration and the transition duration; Obtain the transient response gain according to the steady-state voltage, the voltage fluctuation amplitude, the oscillation frequency, the response duration, and the transition duration; Obtain the degree of change in the power supply transient performance according to the transient response gain.

[0011] This application also provides a big data-based vehicle maintenance identification system, which is applied to maintenance identification equipment and includes: An acquisition module, configured to acquire the energy management characteristics, vehicle state characteristics, and vehicle behavior characteristics of the target vehicle, and obtain the current-power imbalance degree according to the energy management characteristics, vehicle state characteristics, and vehicle behavior characteristics; A judgment module, configured to judge whether the current-power imbalance degree is greater than a preset threshold; If the current-power imbalance degree is greater than the preset threshold, and if it is determined that the in-vehicle power supply system of the target vehicle is operating abnormally, then acquire the multiple cell voltage characteristic information, discharge current characteristic information, and internal resistance characteristic information of the target vehicle; A first calculation module, configured to calculate the transient voltage change value according to the multiple cell voltage characteristic information, and obtain the transient discharge efficiency according to the multiple cell voltage characteristic information, the multiple discharge current characteristic information, and the multiple internal resistance characteristic information; A second calculation module, configured to calculate the degree of change in the power supply transient performance according to the transient voltage change value and the transient discharge efficiency, and send the degree of change in the power supply transient performance to the maintenance identification system; A feedback module is used for the maintenance identification system to obtain the on-vehicle power diagnosis result according to the degree of change in the power transient performance, and feedback the on-vehicle power health diagnosis result to the target vehicle.

[0012] Preferably, the acquisition module includes: A first acquisition unit is used for acquiring the power system state data and the charging system state data according to the vehicle state characteristics; A second acquisition unit is used for acquiring the battery capacity according to the energy management characteristics, and acquiring the on-vehicle charging index data according to the battery capacity and the charging system state data; A third acquisition unit is used for acquiring the start-stop frequency according to the vehicle behavior characteristics, and acquiring the on-vehicle power index data according to the start-stop frequency, the battery capacity and the power system state data; A fourth acquisition unit is used for acquiring the current-power imbalance degree according to the on-vehicle charging index data and the on-vehicle power index data.

[0013] The present invention also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned big data-based vehicle maintenance identification method are implemented.

[0014] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned big data-based vehicle maintenance identification method are implemented.

[0015] The beneficial effects of the present invention are as follows: By acquiring multiple characteristic information such as the energy management, vehicle state and behavior characteristics of the vehicle, the present invention can comprehensively understand the operating state of the on-vehicle power supply system under different working conditions, enabling the system to more accurately identify the working conditions of the battery, power management system, drive system, etc. By comprehensively analyzing the current-power imbalance degree, the behavior of the battery under transient changes can be more accurately identified. By judging whether the current-power imbalance degree is greater than a preset threshold, the unbalanced state of the system can be identified in advance. Through the voltage characteristic information of multiple single cells, the voltage changes of the battery under transient load conditions can be captured from the perspective of different battery single cells. By combining the voltage, discharge current and internal resistance characteristic information, the transient discharge efficiency can be accurately calculated, and the response ability and health status of the battery under load transient can be evaluated. By calculating the degree of change in the power transient performance in real time, potential performance degradation or fault problems of the power supply can be identified early. By combining these indicators for health diagnosis, more dimensional information can be provided for the maintenance identification system, making the diagnosis result more comprehensive and accurate, thereby improving the reliability of the power supply, reducing the maintenance cost, extending the service life of the power supply, and enhancing the intelligent level of the on-vehicle power management system. Description of the Drawings

[0016] Figure 1 Schematic diagram of the method flow according to an embodiment of the present invention.

[0017] Figure 2 Schematic diagram of the system structure according to an embodiment of the present invention.

[0018] Figure 3 Schematic diagram of the internal structure of a computer device according to an embodiment of the present application.

[0019] The realization, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Specific embodiments

[0020] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0021] As Figures 1-3 shown, the present application provides a big data-based vehicle maintenance identification method, which is applied to a maintenance identification device and includes: S1. Obtain the energy management characteristics, vehicle state characteristics and vehicle behavior characteristics of the target vehicle; S2. Obtain the current-power imbalance degree according to the energy management characteristics, vehicle state characteristics and vehicle behavior characteristics; S3. Judge whether the current-power imbalance degree is greater than a preset threshold; If the current-power imbalance degree is greater than the preset threshold, and if it is determined that the on-vehicle power supply system of the target vehicle is operating abnormally, then obtain multiple single-cell voltage characteristic information, discharge current characteristic information and internal resistance characteristic information of the target vehicle; S4. Calculate the transient voltage change value according to multiple pieces of the single-cell voltage characteristic information; S5. Obtain the transient discharge efficiency according to multiple pieces of the single-cell voltage characteristic information, multiple pieces of the discharge current characteristic information and multiple pieces of the internal resistance characteristic information; S6. Calculate the degree of change in the transient performance of the power supply according to the transient voltage change value and the transient discharge efficiency, and send the degree of change in the transient performance of the power supply to the maintenance identification system; S7. The maintenance identification system obtains the on-vehicle power supply diagnosis result according to the degree of change in the transient performance of the power supply, and feeds back the on-vehicle power supply health diagnosis result to the target vehicle.

[0022] As described in the above steps S1 - S7, when a vehicle breaks down, in most traditional vehicle repair methods, users often judge the cause of the vehicle's failure based on repair experience. With the development of technology, the functions of vehicles are constantly increasing, and it is difficult for traditional repair methods to accurately predict the specific lifespan of each component. Usually, it is determined when to repair or replace based on the average service life or mileage. Especially for the health status of in - vehicle power supplies, for the convenience of repair and monitoring, currently, a voltage monitoring device is generally connected to the in - vehicle battery terminal to monitor the battery voltage of the in - vehicle power supply in real - time. However, the above - mentioned method can only monitor the real - time in - vehicle power supply voltage and cannot diagnose the impact of real - time transient voltage changes on the health status of the in - vehicle power supply. The present invention obtains the energy management characteristics, vehicle state characteristics, and vehicle behavior characteristics of the in - vehicle system characteristics of the target vehicle, and obtains the current - power imbalance degree through the energy management characteristics, vehicle state characteristics, and vehicle behavior characteristics. Then, by judging whether the current - power imbalance degree is greater than a preset threshold, if it is greater, it is determined that the in - vehicle power supply system of the target vehicle is operating abnormally. By obtaining the single - cell voltage characteristics, discharge current characteristics, and internal resistance characteristics of multiple in - vehicle power supply characteristics information of the target vehicle, through obtaining multiple characteristic information such as the vehicle's energy management, vehicle state, and behavior characteristics, the operating state of the in - vehicle power supply system under different working conditions can be comprehensively understood, rather than being limited to voltage monitoring. This method considers the mutual relationship between current and power, enabling the system to more accurately identify the working conditions of the battery, power management system, drive system, etc. Voltage changes are often the consequences of system problems, while the current - power imbalance degree can reveal potential abnormalities in the battery management system or power system earlier. For example, during the discharge process of the battery, the current may change violently due to load fluctuations or system failures. These changes may not immediately affect the battery voltage but can affect the health status of the power supply. Transient voltage changes have a great impact on the health status of the in - vehicle power supply. Traditional voltage monitoring can only capture the instantaneous changes in battery voltage and cannot effectively distinguish voltage fluctuations caused by system load changes or battery aging, etc. By comprehensively analyzing the current - power imbalance degree, the behavior of the battery under transient changes can be more accurately identified, such as the voltage fluctuation pattern when the current fluctuates violently, thereby helping to diagnose whether there is an abnormality in the battery or whether the coordination between the battery and the in - vehicle power supply management system is good. By judging whether the current - power imbalance degree is greater than a preset threshold, the unbalanced state of the system can be identified in advance, especially in scenarios with large load changes. Through the analysis of the current - power imbalance degree, the health status of the battery under different working conditions can be more accurately evaluated, and then measures can be taken in advance (such as optimizing the charging strategy, performing maintenance, or replacing the battery, etc.). The present invention calculates the transient voltage change value through multiple single - cell voltage characteristic information and obtains the transient discharge efficiency through multiple single - cell voltage characteristic information, multiple discharge current characteristic information, and multiple internal resistance characteristic information. Traditional voltage monitoring devices mainly monitor the terminal voltage of the battery,Although it can reflect the change of battery power, it is difficult to capture the behavior of the battery under transient loads. For example, during vehicle acceleration, braking, or other rapid load changes, the battery voltage may experience short-term fluctuations. The impact of these transient changes on battery health usually cannot be captured by single voltage monitoring. Through the voltage characteristic information of multiple individual cells, the voltage changes of the battery under transient loads (such as rapid charging and discharging) can be captured from the perspective of different battery cells. The transient voltage changes reflect the accumulation and release of charge in the battery within a short period, thus revealing the rapid reactions and energy transfer efficiency inside the battery. The internal resistance is an important indicator of battery health, which increases with the increase of battery usage time. The change in internal resistance will directly affect the discharge efficiency of the battery, especially during load transients. If the internal resistance is too large, the battery will not be able to effectively respond to load changes, resulting in severe fluctuations in battery voltage, and even overheating or safety hazards. By combining the voltage, discharge current, and internal resistance characteristic information, the transient discharge efficiency can be accurately calculated, and the response ability and health status of the battery under load transients can be evaluated. Through the analysis of transient voltage changes and discharge current, the transient discharge efficiency can be calculated, that is, the actual efficiency and ability of the battery during discharge. This efficiency value can reflect the performance state of the battery, especially in the case of rapid discharge. The relationship between the battery internal resistance and transient voltage changes can reveal the internal health status of the battery (for example, the efficiency decline caused by the increase in internal resistance). During the charging and discharging process of the battery, the transient voltage changes are usually closely related to the health status of the battery, especially when the load changes sharply (such as during the acceleration and braking of electric vehicles). The transient voltage fluctuations will intensify. If there are aging or defective individual cells inside the battery, the transient voltage changes will be more obvious and unstable. By combining the characteristic information of multiple individual voltages, discharge currents, and internal resistances, these changes can be diagnosed earlier, and potential faults or abnormalities in the battery system, such as battery aging, individual cell failure, and battery management system failure, can be detected in advance. Calculate the degree of change in power supply transient performance through the transient voltage change value and transient discharge efficiency, and send the degree of change in power supply transient performance to the maintenance identification system. The maintenance identification system obtains the on-vehicle power supply diagnosis result based on the degree of change in power supply transient performance, and feeds back the on-vehicle power supply health diagnosis result to the target vehicle. The monitoring of transient voltage changes and transient discharge efficiency can reveal the working state of the power supply under high load conditions. By calculating the degree of change in power supply transient performance in real time, potential performance degradation or fault problems of the power supply can be identified early, avoiding faults or vehicle outages caused by failure to detect problems in time. The changes in transient voltage changes and transient discharge efficiency not only reflect the voltage state of the battery, but also reflect the comprehensive performance of the power supply system under dynamic working conditions. By combining these indicators for health diagnosis, more dimensional information can be provided for the maintenance identification system, making the diagnosis result more comprehensive and accurate. This multi-dimensional monitoring method, compared with traditional voltage monitoring,It can better capture the changing trend of power supply health. Through the diagnosis of transient performance, potential problems in the power supply system can be identified in a timely manner, and maintenance or replacement can be carried out in advance, thus avoiding greater losses caused by power supply failures. Compared with the traditional method of only monitoring the battery voltage, this method can identify performance degradation at the system level, reduce the risk of outages, accidents or equipment damage caused by system failures, thereby reducing the overall maintenance cost and improving the reliability of the vehicle. By feeding back the degree of change in the transient performance of the power supply to the maintenance identification system and further to the target vehicle, intelligent management of the on-vehicle power supply can be achieved. This feedback mechanism can help the maintenance system achieve real-time monitoring and dynamic adjustment, improve the intelligent level of the entire power management system, better reflect the dynamic response ability of the power supply in actual use, discover potential problems in advance, thereby improving the reliability of the power supply, reducing the maintenance cost, extending the service life of the power supply, and enhancing the intelligent level of the on-vehicle power supply management system.

[0023] In one embodiment, step S2 of obtaining the current-power imbalance degree according to the energy management feature, vehicle state feature and vehicle behavior feature includes: S21. Obtain the power system state data and the charging system state data according to the vehicle state feature; S22. Obtain the battery capacity according to the energy management feature; S23. Obtain the on-vehicle charging index data according to the battery capacity and the charging system state data; S24. Obtain the start-stop frequency obtained according to the vehicle behavior feature; S25. Obtain the on-vehicle power index data according to the start-stop frequency, the battery capacity and the power system state data; S26. Calculate the current-power imbalance degree according to the on-vehicle charging index data and the on-vehicle power index data, where the calculation formula is: ; Wherein, represents the current-power imbalance degree, represents the on-vehicle charging index data, represents the on-vehicle power index data.

[0024] As described in the above steps S21 - S26, the calculation formulas of the current - power imbalance degree perform normalization processing on parameters such as in - vehicle charging index data and in - vehicle power index data in advance to eliminate the dimensional differences between different variables. The purpose is to ensure that all variables are on the same order of magnitude, thereby making the calculation more stable and effective. The present invention obtains power system state data and charging system state data through vehicle state characteristics, obtains the battery capacity through energy management characteristics, and then obtains in - vehicle charging index data through the battery capacity and charging system state data. By comprehensively considering characteristics such as the power system state, charging system state, and battery capacity, the battery health status can be analyzed more comprehensively, rather than relying solely on voltage data. This helps to detect potential problems that may occur in the battery at an early stage, such as battery degradation, faults, or other potential risks, thereby extending the service life of the battery and avoiding the occurrence of faults. By analyzing the charging system state and battery capacity in detail, the charging strategy can be optimized, the charging efficiency can be improved, and the heat accumulation that may occur during the charging process can be reduced. The transient voltage change of the battery is often closely related to factors such as the charging rate and charging temperature. Traditional voltage monitoring methods are difficult to identify the specific impact of these transient changes on the battery. By using a more comprehensive state - characteristic analysis, the charging strategy can be adjusted in a timely manner to avoid damage to the battery caused by over - charging or improper charging, improving the safety and efficiency of the battery charging process. The transient voltage change may stem from multiple factors, including load changes, dynamic adjustments during the charging process, and internal chemical reactions of the battery. By comprehensively considering the power system, charging system, and battery capacity data of the vehicle, a more accurate model can be established to predict and analyze the impact of transient voltage. This can effectively diagnose the transient behavior of the battery and discover potential problems of the battery under specific working conditions. The capacity and charging state of the battery are key factors in energy management. Accurate battery capacity and charging state data can help the system make more precise energy distribution and scheduling decisions. By dynamically analyzing vehicle state, charging system state, battery capacity, and transient voltage data, the battery management strategy can be adjusted according to different environments and working conditions, providing more intelligent energy scheduling and management. The present invention obtains the start - stop frequency through vehicle behavior characteristics, obtains in - vehicle power index data through the start - stop frequency, battery capacity, and power system state data, and then calculates the current - power imbalance degree through in - vehicle charging index data and in - vehicle power index data. By combining data such as the start - stop frequency, battery capacity, and power system state, not only can the voltage of the battery be monitored in real - time, but also the health status of the battery can be evaluated more comprehensively through the start - stop frequency of vehicle behavior characteristics and the working state of the power system. For example, a vehicle with frequent start - stops may put greater pressure on the battery, which cannot be reflected solely by voltage monitoring. However, by combining the operation data of the power system and the calculation of the current - power imbalance degree, the health status of the battery can be identified more accurately. By calculating the current - power imbalance degree, the subtle changes in the energy conversion process between the power system and the battery can be captured.This can help the system determine in real time whether the battery is under abnormal operating conditions. The comprehensive analysis of data such as start-stop frequency, battery capacity, current-power imbalance, etc. can help the system predict the health trend of the battery, evaluate the remaining service life of the battery, and identify potential fault hazards in advance. This is a function that cannot be achieved by traditional voltage monitoring because voltage monitoring only reflects the immediate state of the battery and cannot warn of potential health problems over a long period. The comprehensive utilization of on-vehicle power indicators and charging indicator data can help optimize the energy management system better and achieve the best charge-discharge strategy for the battery. By calculating the current-power imbalance, not only can the real-time state of the battery be understood, but the battery management strategy can also be dynamically adjusted according to the actual operating conditions of the system.

[0025] In one embodiment, the step S23 of obtaining on-vehicle charging indicator data according to the battery capacity and charging system status data includes: S231. Obtain the charging current, battery internal resistance, nominal voltage, and charging temperature coefficient according to the charging system status data; S232. Obtain the battery change rate according to the charging current and battery capacity; S233. Calculate the charging thermal effect according to the charging current and charging temperature coefficient, where the calculation formula is: ; Where represents the charging thermal effect, represents the charging temperature coefficient, represents the charging current; S234. Obtain the battery voltage according to the charging current and battery internal resistance; S235. Obtain the battery capacity, and calculate the on-vehicle charging indicator data according to the battery capacity, charging thermal effect, charging current, battery voltage, and nominal voltage, where the calculation formula is: ; Where represents the on-vehicle charging indicator data, represents the charging current, represents the battery capacity, represents the battery voltage, represents the nominal voltage, represents the charging thermal effect.

[0026] As described in the above steps S231 - S235, the calculation formulas of the charging thermal effect and the in - vehicle charging index data both perform normalization processing on their calculation parameters in advance to eliminate the dimensional differences between different variables. The purpose is to ensure that all variables are on the same order of magnitude, so as to make the calculation more stable and effective. The present invention obtains the charging current, battery internal resistance, nominal voltage, and charging temperature coefficient through the charging system status data, obtains the battery change rate through the charging current and battery capacity, and then calculates the charging thermal effect through the charging current and charging temperature coefficient. Traditional voltage monitoring can only provide real - time data of the battery voltage. Although the change in battery voltage can reflect some battery states, it cannot provide in - depth diagnosis for transient voltage changes (such as voltage surges or drops during charging). By combining data such as charging current, battery internal resistance, nominal voltage, and charging temperature coefficient, it can more accurately reflect the dynamic changes of various indicators of the battery during charging, and more comprehensively capture the health status of the battery, especially the response during transient changes. This method helps to diagnose whether there are health problems such as increased internal resistance, over - charging, and abnormal temperature in the battery, which are usually difficult to detect in traditional voltage monitoring. By monitoring the charging current and battery internal resistance, the charging thermal effect of the battery can be calculated in real - time, that is, the heat generated by the battery due to internal resistance and current during charging. Excessive temperature will accelerate battery aging and reduce its service life, which is crucial for the battery health management of the vehicle. It is difficult to capture the details of these changes through voltage monitoring because the voltage itself may only be a surface phenomenon and it is impossible to deeply understand its causes and its long - term impact on battery health. By calculating the battery change rate and charging thermal effect, it can be diagnosed whether the battery has experienced potential problems such as over - charge and discharge, overheating, or internal resistance change, thus helping to prevent possible failures in advance. By combining the charging current, battery capacity, and charging temperature coefficient, the charging strategy can be adjusted more accurately to avoid over - charging or overheating of the battery during charging, thereby reducing the risk of battery failure. The present invention obtains the battery voltage through the charging current and battery internal resistance, and then calculates the in - vehicle charging index data through the battery capacity, charging thermal effect, charging current, battery voltage, and nominal voltage. Traditional voltage monitoring methods can only provide the instantaneous voltage of the battery in real - time, but during the charging or discharging process of the battery, the voltage may have transient fluctuations or mutations, which are usually caused by factors such as current load, internal resistance change, and charging rate. Simply relying on voltage monitoring is difficult to deeply understand the long - term impact of these transient changes on battery health. By combining the charging current and battery internal resistance to calculate the battery voltage and further analyzing multi - dimensional data such as battery capacity and charging thermal effect, it can more accurately judge the impact of these transient voltage changes on the battery and timely discover potential health problems. By combining multiple indicators such as charging current, battery internal resistance, and charging thermal effect, the real - time health status of the battery can be more accurately estimated.For example, changes in battery internal resistance and increases in battery temperature may both lead to abnormal voltage fluctuations, but these issues may not be immediately reflected in voltage changes. Through this comprehensive analysis method, potential risks during the battery charging process (such as overheating, increased internal resistance, etc.) can be diagnosed in real time, avoiding the limitations of single voltage monitoring. By analyzing data such as charging current, battery internal resistance, and charging thermal effects, a battery health prediction model can be established to accurately evaluate the remaining life and failure risks of the battery. For example, an increase in battery internal resistance will result in higher charging thermal effects and power losses, and this change may not necessarily be immediately reflected in voltage monitoring. Combining data such as battery capacity and charging thermal effects helps to early warn of the battery degradation trend, thereby improving the intelligence and reliability of the in-vehicle battery management system. By analyzing charging current, internal resistance, and thermal effects in real time, the charging strategy can be optimized to reduce overcharging or overheating phenomena that may occur during battery charging. For example, if the battery internal resistance is large or the battery temperature is too high, this will lead to higher thermal effects and may affect the charging efficiency of the battery. Through this data fusion method, the charging rate can be dynamically adjusted to reduce losses during battery charging and ensure that the battery operates in the best state. Through multi-parameter data analysis, the battery management system can identify abnormal battery states earlier, issue early warnings in a timely manner, and take corresponding protective measures to reduce failure risks.

[0027] In one embodiment, the step S25 of obtaining in-vehicle power index data according to the start-stop frequency, battery capacity, and power system status data includes: S251. Obtain the battery charge-discharge frequency according to the product of the start-stop frequency and the battery capacity. S252. Obtain the power system load factor, battery current, start duration, and stop duration according to the power system status data. S253. Obtain the load power according to the product of the start-stop frequency and the power system load factor, and obtain the battery voltage change rate according to the product of the load power and the start-stop frequency. S254. Obtain the battery capacity, and calculate the battery state of charge change rate according to the battery capacity, battery current, start duration, and stop duration, where the calculation formula is: ; Wherein, represents the battery state of charge change rate, represents the battery capacity, represents the battery current, represents the stop duration, represents the start duration; S255. Calculate the in-vehicle power index data according to the battery state of charge change rate, battery voltage change rate, and battery charge-discharge frequency, where the calculation formula is: ; Among them, represents the vehicle power index data, represents the rate of change of the state of charge of the battery, represents the weight of the rate of change of the state of charge of the battery, represents the rate of change of the battery voltage, represents the weight of the rate of change of the battery voltage, represents the charge and discharge frequency of the battery, represents the weight of the charge and discharge frequency of the battery.

[0028] As described in the above steps S251 - S255, the calculation formulas of the battery state of charge change rate and the vehicle power index data both perform normalization processing on their calculation parameters in advance to eliminate the dimensional differences between different variables. The purpose is to ensure that all variables are on the same order of magnitude, so as to make the calculation more stable and effective. The present invention obtains the battery charge and discharge frequency by multiplying the start - stop frequency and the battery capacity, obtains the power system load factor, battery current, start duration, and stop duration through the power system state data, obtains the load power by multiplying the start - stop frequency and the power system load factor, and obtains the battery voltage change rate by multiplying the load power and the start - stop frequency. Furthermore, the battery state of charge change rate is calculated through the battery capacity, battery current, start duration, and stop duration. Traditional voltage monitoring devices can monitor the voltage change of the vehicle - mounted battery in real time, but they can only provide single - voltage data and have limited ability to capture the transient voltage changes of the battery, and cannot directly reflect the health status of the battery under complex load conditions. By calculating more comprehensive dynamic parameters such as the start - stop frequency, load factor, and battery current to calculate the battery voltage change rate and the state of charge change rate, these parameters can more accurately reflect the performance of the battery under different working conditions, thus providing more valuable information. Especially in the case of high load or frequent start / stop, the battery may experience large transient voltage changes. Traditional voltage monitoring can only monitor the real - time voltage. If the voltage has transient fluctuations or large fluctuation amplitudes, it may not be able to reveal the internal health problems of the battery in time. By calculating the product of the start - stop frequency and the load factor to obtain the load power, and then deriving the change rate of the battery voltage based on this, more accurate battery health status assessment can be achieved. The impact of transient voltage changes on battery health can be diagnosed through a more comprehensive model, rather than relying solely on static voltage monitoring. By combining multiple data such as the start - stop frequency, load factor, battery current, start duration, and stop duration, the performance of the battery under complex usage conditions can be more accurately simulated. For example, in the case of frequent start - stop, the battery load fluctuates greatly, and the battery may be subjected to strong transient current impacts. This state can be captured through data such as the start - stop frequency and the load factor, so as to diagnose possible problems in advance. By calculating indicators such as the battery voltage change rate and the battery state of charge change rate, more diagnostic information can be provided for the battery management system. Therefore, through the comprehensive analysis of multiple data such as the start - stop frequency, power system load factor, battery current, start duration, and stop duration, the health status of the battery can be diagnosed more comprehensively, dynamically, and accurately. This method can not only effectively capture the transient voltage changes of the battery under different working conditions, but also predict in advance the possible failures of the battery, and then take more scientific battery management measures to maximize the battery life and improve the stability and safety of the battery system.

[0029] In one embodiment, step S4 of calculating the transient voltage change value according to the multiple single - cell voltage characteristic information includes: S41. Obtain the corresponding no-load single-cell voltage and load single-cell voltage according to each of the single-cell voltage characteristic information; S42. Obtain the no-load single-cell average voltage according to the multiple no-load single-cell voltages, and calculate the no-load single-cell voltage standard deviation according to the no-load single-cell average voltage and the multiple no-load single-cell voltages, where the calculation formula is: ; where, represents the no-load single-cell voltage standard deviation, represents the th no-load single-cell voltage, represents the no-load single-cell average voltage, represents the number of no-load single-cell voltages, represents the serial number of the no-load single-cell voltage; S43. Obtain the load single-cell average voltage according to the multiple load single-cell voltages, and obtain the load single-cell voltage standard deviation according to the load single-cell average voltage and the multiple load single-cell voltages; S44. Calculate the transient voltage change value according to the no-load single-cell voltage standard deviation and the load single-cell voltage standard deviation, where the calculation formula is: ; where, represents the transient voltage change value, represents the no-load single-cell voltage standard deviation, represents the load single-cell voltage standard deviation.

[0030] As described in the above steps S41 - S44, the calculation formulas of the standard deviation of the single - cell voltage and the transient voltage change value both perform normalization processing on their calculation parameters in advance to eliminate the dimensional differences between different variables. The purpose is to ensure that all variables are on the same order of magnitude, so as to make the calculation more stable and effective. The present invention obtains the corresponding no - load single - cell voltage and load single - cell voltage through each single - cell voltage characteristic information, obtains the no - load single - cell average voltage through multiple no - load single - cell voltages, and calculates the standard deviation of the no - load single - cell voltage through the no - load single - cell average voltage and multiple no - load single - cell voltages. By monitoring the voltage changes of each single - cell under no - load and load conditions, the response of the battery under load transient changes can be identified, and further analyze whether there are problems such as battery capacity decline and internal impedance increase. The no - load single - cell voltage usually reflects the basic health status of the battery itself and is not affected by load fluctuations. Calculating the average value of multiple no - load single - cell voltages can provide the overall health condition of the entire battery pack without external load, and further understand the differences between batteries. If the standard deviation is large, it means that the voltage differences between batteries are large, which may indicate that the health status of some single - cells is poor, and there is a risk of capacity decline or internal failure. By comprehensively combining the voltage data under no - load and load states, the health status of the battery pack can be evaluated more accurately. Only through real - time voltage monitoring cannot reflect the performance of the battery under different working conditions. By combining the comparison of transient voltage changes, no - load voltage and load voltage, and the analysis of the standard deviation, it is possible to more comprehensively diagnose whether the battery is abnormal or aging. The present invention obtains the load single - cell average voltage through multiple load single - cell voltages, and obtains the standard deviation of the load single - cell voltage through the load single - cell average voltage and multiple load single - cell voltages, and then calculates the transient voltage change value through the standard deviation of the no - load single - cell voltage and the standard deviation of the load single - cell voltage. By monitoring these transient fluctuations, the response characteristics of the battery under load fluctuations or current changes can be discovered in advance. Traditional voltage monitoring devices can only provide static voltage data and cannot reflect the performance of the battery under dynamic loads. By calculating the transient voltage change, the performance degradation, aging signs or other problems of the battery during use can be monitored in real - time, so as to obtain a more comprehensive health status assessment. By monitoring the transient voltage change, it can help identify these potential problems and perform maintenance or replacement in advance, thus avoiding battery failures in actual use and reducing the risk of accidents. By real - time monitoring the transient voltage change, it can help the battery management system optimize the charging and discharging strategies. By monitoring the transient voltage change of the load single - cell, the aging condition of the battery can be diagnosed more accurately. By combining the voltage standard deviation and the transient voltage change, it can respond more sensitively to load changes and adjust the battery management strategy in real - time to ensure that the battery system can quickly adapt to transient load changes.

[0031] In one embodiment, step S6 of calculating the degree of change in the power supply transient performance according to the transient voltage change value and the transient discharge efficiency includes: S61. Obtain a transient voltage waveform diagram according to the transient voltage change value and the transient discharge efficiency; S62. Obtain the steady-state voltage, the maximum voltage value, and the minimum voltage value according to the transient voltage waveform diagram; S63. Obtain the voltage fluctuation amplitude according to the maximum voltage value and the minimum voltage value; S64. Obtain the oscillation frequency and the time characteristics according to the transient voltage waveform diagram, where the time characteristics include the response duration and the transition duration; S65. Calculate the transient response gain according to the steady-state voltage, the voltage fluctuation amplitude, the oscillation frequency, the response duration, and the transition duration, where the calculation formula is: ; where represents the transient response gain, represents the voltage fluctuation amplitude, represents the steady-state voltage, represents the response duration, represents the oscillation frequency, represents the transition duration; S66. Obtain the degree of change in the power supply transient performance according to the transient response gain.

[0032] As described in the above steps S61 - S66, the calculation formulas of the transient response gain all perform normalization processing on their calculation parameters in advance to eliminate the dimensional differences between different variables. The purpose is to ensure that all variables are on the same order of magnitude, so as to make the calculation more stable and effective. The present invention obtains the transient voltage waveform diagram through the transient voltage change value and the transient discharge efficiency, obtains the steady - state voltage, the maximum voltage value and the minimum voltage value through the transient voltage waveform diagram, and obtains the voltage fluctuation amplitude through the maximum voltage value and the minimum voltage value. Traditional in - vehicle battery voltage monitoring devices usually can only provide real - time voltage information and cannot deeply analyze the transient response of the battery under different working conditions. The transient voltage waveform diagram can reflect the dynamic performance of the battery under conditions such as load change, charge and discharge, etc., helping to detect whether there are abnormalities in the battery under instantaneous fluctuations and discover potential faults or aging problems in advance. Through the transient voltage waveform diagram, it is possible to observe the transient overvoltage or undervoltage conditions generated by the battery under peak load, which is very important for evaluating key indicators such as the load response ability, over - voltage protection mechanism, and charging efficiency of the battery. The maximum voltage value and the minimum voltage value of the battery will change over time. Monitoring these changes can effectively capture the early signs of battery aging. Through the analysis of the transient voltage waveform, it is possible to discover the downward trends in the discharge efficiency, response ability, and health status of the battery. The present invention obtains the response duration and transition duration of the oscillation frequency and time characteristics through the transient voltage waveform diagram, and then calculates the transient response gain through the steady - state voltage, voltage fluctuation amplitude, oscillation frequency, response duration, and transition duration. The transient voltage waveform diagram can help identify the instantaneous response of the battery during load changes, analyze its characteristics such as oscillation frequency, transition duration, and response duration, and then be able to more accurately evaluate the performance of the battery under dynamic loads. By monitoring parameters such as oscillation frequency and transition duration, it is possible to determine whether the battery can withstand rapid load changes and avoid abnormalities or failures in the in - vehicle power supply system caused by unstable battery performance. The analysis of the transient response gain and transition duration can also help design more appropriate charging and discharging strategies to avoid damage to the battery due to overloading or rapid discharging. Through the analysis of the transient voltage waveform, especially the calculation of parameters such as oscillation frequency, response duration, and transition duration, it is possible to provide more comprehensive and accurate monitoring of the health status of the in - vehicle power supply battery. This method pays more attention to the dynamic characteristics of the battery compared with traditional voltage monitoring, can identify potential health problems in advance, optimize the service life of the battery, and improve the fault diagnosis ability of the battery management system, thus effectively improving the reliability and safety of the in - vehicle power supply system.

[0033] The present application also provides a big - data - based vehicle maintenance identification system applied to a maintenance identification device, including: An acquisition module, configured to acquire the energy management characteristics, vehicle state characteristics, and vehicle behavior characteristics of a target vehicle, and acquire a current-power imbalance degree according to the energy management characteristics, vehicle state characteristics, and vehicle behavior characteristics; A judgment module, configured to judge whether the current-power imbalance degree is greater than a preset threshold; If the current-power imbalance degree is greater than the preset threshold, and if it is determined that the on-vehicle power supply system of the target vehicle is operating abnormally, then acquire multiple single-cell voltage characteristic information, discharge current characteristic information, and internal resistance characteristic information of the target vehicle; A first calculation module, configured to calculate a transient voltage change value according to multiple pieces of the single-cell voltage characteristic information, and acquire a transient discharge efficiency according to multiple pieces of the single-cell voltage characteristic information, multiple pieces of discharge current characteristic information, and multiple pieces of internal resistance characteristic information; A second calculation module, configured to calculate a degree of change in the power supply transient performance according to the transient voltage change value and the transient discharge efficiency, and send the degree of change in the power supply transient performance to a maintenance identification system; A feedback module, configured to enable the maintenance identification system to acquire an on-vehicle power supply diagnosis result according to the degree of change in the power supply transient performance, and feedback the on-vehicle power supply health diagnosis result to the target vehicle.

[0034] In one embodiment, the acquisition module includes: A first acquisition unit, configured to acquire power system state data and charging system state data according to the vehicle state characteristics; A second acquisition unit, configured to acquire a battery capacity according to the energy management characteristics, and acquire on-vehicle charging index data according to the battery capacity and the charging system state data; A third acquisition unit, configured to acquire a start-stop frequency according to the vehicle behavior characteristics, and acquire on-vehicle power index data according to the start-stop frequency, the battery capacity, and the power system state data; A fourth acquisition unit, configured to acquire a current-power imbalance degree according to the on-vehicle charging index data and the on-vehicle power index data.

[0035] It should be noted that each module and unit in the big data-based vehicle maintenance identification system corresponds one by one to the steps in the big data-based vehicle maintenance identification method.

[0036] As Figure 3 shown, the present application further provides a computer device, which may be a server, and its internal structure may be as Figure 3As shown. The computer device includes a processor, a memory, a network interface, and a database connected via a system bus. Among them, the processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store all the data required for the process of the big data vehicle maintenance identification method. The network interface of the computer device is used to communicate with an external terminal via a network connection. The computer program, when executed by the processor, implements the big data vehicle maintenance identification method.

[0037] Those skilled in the art can understand that Figure 3 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied.

[0038] An embodiment of this application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements any one of the above-mentioned big data vehicle maintenance identification methods.

[0039] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to the memory, storage, database, or other media provided in this application and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or an external cache. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0040] It should be noted that, in this text, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, apparatus, article or method comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, apparatus, article or method. Without further limitation, an element defined by the phrase "comprising an..." does not exclude the presence of additional identical elements in the process, apparatus, article or method comprising such element.

[0041] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall equally be included in the patent protection scope of the present invention.

Claims

1. A vehicle maintenance recognition method based on big data, characterized in that Applied to a maintenance identification device, including: Obtain the energy management characteristics, vehicle state characteristics, and vehicle behavior characteristics of the target vehicle, and obtain the current-power imbalance degree according to the energy management characteristics, vehicle state characteristics, and vehicle behavior characteristics; Judge whether the current-power imbalance degree is greater than a preset threshold; If the current-power imbalance degree is greater than the preset threshold, and if it is determined that the on-vehicle power supply system of the target vehicle operates abnormally, then obtain multiple single-cell voltage characteristic information, discharge current characteristic information, and internal resistance characteristic information of the target vehicle; Calculate the transient voltage change value according to the multiple single-cell voltage characteristic information, and obtain the transient discharge efficiency according to the multiple single-cell voltage characteristic information, multiple discharge current characteristic information, and multiple internal resistance characteristic information; Calculate the degree of change in the power supply transient performance according to the transient voltage change value and the transient discharge efficiency, and send the degree of change in the power supply transient performance to the maintenance identification system; The maintenance identification system obtains the on-vehicle power supply diagnosis result according to the degree of change in the power supply transient performance, and feeds back the on-vehicle power supply health diagnosis result to the target vehicle.

2. The method for identifying vehicle maintenance based on big data according to claim 1, wherein The step of obtaining the current-power imbalance degree according to the energy management characteristics, vehicle state characteristics, and vehicle behavior characteristics includes: Obtain the power system state data and the charging system state data according to the vehicle state characteristics; Obtain the battery capacity according to the energy management characteristics, and obtain the on-vehicle charging index data according to the battery capacity and the charging system state data; Obtain the start-stop frequency according to the vehicle behavior characteristics, and obtain the on-vehicle power index data according to the start-stop frequency, battery capacity, and power system state data; Obtain the current-power imbalance degree according to the on-vehicle charging index data and the on-vehicle power index data.

3. The method for identifying vehicle maintenance based on big data according to claim 2, wherein, The step of obtaining the on-vehicle charging index data according to the battery capacity and the charging system state data includes: Obtain the charging current, battery internal resistance, nominal voltage, and charging temperature coefficient according to the charging system state data, and obtain the battery change rate according to the charging current and the battery capacity; Obtain the charging thermal effect according to the charging current and the charging temperature coefficient, and obtain the battery voltage according to the charging current and the battery internal resistance; Obtain the battery capacity, and obtain the on-vehicle charging index data according to the battery capacity, charging thermal effect, charging current, battery voltage, and nominal voltage.

4. The method for identifying vehicle maintenance based on big data according to claim 2, wherein The step of obtaining the on-vehicle power index data according to the start-stop frequency, battery capacity, and power system state data includes: Obtain the power system load factor, battery current, start duration, and stop duration according to the power system state data, and obtain the battery state of charge change rate according to the battery capacity, battery current, start duration, and stop duration; Obtain the load power according to the start-stop frequency and the power system load factor, and obtain the battery voltage change rate according to the load power and the start-stop frequency; Obtain the battery charge and discharge frequency according to the start-stop frequency and the battery capacity, and obtain the on-vehicle power index data according to the battery state of charge change rate, battery voltage change rate, and battery charge and discharge frequency.

5. The method for identifying vehicle maintenance based on big data according to claim 1, wherein, The step of calculating the transient voltage change value according to the multiple single-cell voltage characteristic information includes: Obtain the corresponding no-load single-cell voltage and load single-cell voltage according to each of the single-cell voltage characteristic information; Obtain the no-load single-cell average voltage according to the multiple no-load single-cell voltages, and obtain the no-load single-cell voltage standard deviation according to the no-load single-cell average voltage and the multiple no-load single-cell voltages; Obtain the load single-cell average voltage according to the multiple load single-cell voltages, and obtain the load single-cell voltage standard deviation according to the load single-cell average voltage and the multiple load single-cell voltages; Obtain the transient voltage change value according to the no-load single-cell voltage standard deviation and the load single-cell voltage standard deviation.

6. The method for identifying vehicle maintenance based on big data according to claim 1, wherein The step of calculating the degree of change in the power supply transient performance according to the transient voltage change value and the transient discharge efficiency includes: Obtain the transient voltage waveform diagram according to the transient voltage change value and the transient discharge efficiency; Obtain the steady-state voltage, the maximum voltage value and the minimum voltage value according to the transient voltage waveform diagram, and obtain the voltage fluctuation amplitude according to the maximum voltage value and the minimum voltage value; Obtain the oscillation frequency and the time characteristics according to the transient voltage waveform diagram, wherein the time characteristics include the response duration and the transition duration; Obtain the transient response gain according to the steady-state voltage, the voltage fluctuation amplitude, the oscillation frequency, the response duration and the transition duration, and obtain the degree of change in the power supply transient performance according to the transient response gain.

7. A vehicle maintenance identification system based on big data, characterized in that, Applied to a maintenance identification device, including: An acquisition module, configured to acquire the energy management characteristics, vehicle state characteristics and vehicle behavior characteristics of a target vehicle, and obtain the current-power imbalance degree according to the energy management characteristics, vehicle state characteristics and vehicle behavior characteristics; A judgment module, configured to judge whether the current-power imbalance degree is greater than a preset threshold; If the current-power imbalance degree is greater than a preset threshold, and if it is determined that the in-vehicle power supply system of the target vehicle is operating abnormally, then acquire the multiple single-cell voltage characteristic information, discharge current characteristic information and internal resistance characteristic information of the target vehicle; A first calculation module, configured to calculate the transient voltage change value according to the multiple single-cell voltage characteristic information, and obtain the transient discharge efficiency according to the multiple single-cell voltage characteristic information, the multiple discharge current characteristic information and the multiple internal resistance characteristic information; A second calculation module, configured to calculate the degree of change in the power supply transient performance according to the transient voltage change value and the transient discharge efficiency, and send the degree of change in the power supply transient performance to the maintenance identification system; A feedback module, configured to enable the maintenance identification system to obtain the in-vehicle power supply diagnosis result according to the degree of change in the power supply transient performance, and feedback the in-vehicle power supply health diagnosis result to the target vehicle.

8. The vehicle maintenance identification system based on big data according to claim 7, wherein, The acquisition module includes: A first acquisition unit, configured to acquire the power system state data and the charging system state data according to the vehicle state characteristics; A second acquisition unit, configured to acquire the battery capacity according to the energy management characteristics, and acquire the in-vehicle charging index data according to the battery capacity and the charging system state data; A third acquisition unit, configured to acquire the start-stop frequency according to the vehicle behavior characteristics, and acquire the in-vehicle power index data according to the start-stop frequency, the battery capacity and the power system state data; A fourth obtaining unit, configured to obtain a current-power imbalance degree according to the in-vehicle charging index data and the in-vehicle power index data.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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