Vehicle storage battery detection method and vehicle

Abstract

The invention discloses a vehicle storage battery detection method and a vehicle, and belongs to the technical field of vehicle storage battery detection, and the method comprises the steps: obtaining the state data of a vehicle storage battery, the vehicle use behavior data and the weather temperature; establishing a vehicle behavior prediction model according to the vehicle use behavior data and the weather temperature, and generating a use interval of the vehicle at the next moment; according to the state data of the vehicle storage battery and the use interval of the vehicle at the next moment, the vehicle storage battery load maintaining capacity is generated; generating a vehicle storage battery detection report according to the vehicle storage battery load maintenance capability; according to the method, the vehicle storage battery state data, the use behavior data and the weather temperature are obtained, the dynamically predicted vehicle use interval is combined, the storage battery load maintaining capacity is comprehensively calculated, the detection report is generated, and the problem that the traditional detection method cannot dynamically predict the storage battery performance is solved; the detection accuracy of the vehicle storage battery is improved, and the performance of the storage battery is dynamically predicted to prevent the vehicle from being unable to start.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle battery detection, and in particular relates to a vehicle battery detection method and a vehicle. Background Art

[0002] With the rapid development of automotive electronics, modern vehicles are increasingly reliant on batteries. As a core component for starting a vehicle and powering electronic devices, the battery's performance is directly related to the vehicle's proper operation. However, battery performance gradually degrades due to a variety of factors, such as frequent short-distance driving, extreme temperatures, prolonged parking, and discharge from electronic devices. Failure to promptly detect battery performance degradation can result in a vehicle failure to start, or even pose a safety hazard.

[0003] Currently, traditional battery testing methods primarily rely on static voltage measurement or internal resistance testing. While simple and easy to implement, these methods have significant limitations. Static voltage measurement only reflects the battery's instantaneous state and cannot predict its long-term performance. While internal resistance testing can indirectly assess battery health, it is significantly affected by ambient temperature and test conditions, making accuracy difficult to guarantee. Furthermore, these methods often overlook the combined impact of vehicle usage behavior and the external environment (such as temperature) on battery performance, resulting in test results that are out of sync with actual needs. More specifically, existing technologies lack the ability to collaboratively analyze vehicle usage data (such as ignition off and on timestamps) with weather and temperature data, making it impossible to dynamically predict the vehicle's next usage interval, making it difficult to accurately assess the battery's load-sustaining capacity in real-world usage scenarios. Summary of the Invention

[0004] In view of the deficiencies in the prior art, the present invention provides a vehicle battery detection method and a vehicle, which solve the above problems.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A vehicle battery detection method includes the following steps:

[0006] Obtain vehicle battery status data, vehicle usage behavior data, and weather temperature;

[0007] A vehicle behavior prediction model is established based on vehicle usage behavior data and weather temperature to generate the vehicle's next usage interval; wherein the vehicle usage behavior data includes the shutdown timestamp and the ignition timestamp;

[0008] Generate the vehicle battery load maintenance capacity based on the vehicle battery status data and the vehicle's next usage interval;

[0009] Generate a vehicle battery test report based on the vehicle battery's load maintenance capability.

[0010] On the basis of the above technical solutions, the present invention also provides the following optional technical solutions:

[0011] Further technical solution: The method for generating the vehicle's next usage interval specifically includes:

[0012] Generate vehicle usage interval based on the shutdown timestamp and ignition timestamp;

[0013] Generate smoothing factors based on vehicle usage intervals and weather temperature;

[0014] A vehicle behavior prediction model is established based on the smoothing factor and the vehicle usage interval to generate the vehicle usage interval at the next moment.

[0015] Further technical solution: The vehicle usage interval is generated in the following manner:

[0016] By formula:

[0017]

[0018] Generate vehicle usage intervals ;

[0019] In the formula, It represents the timestamp of the vehicle being turned off when it is used for the nth time. It represents the ignition timestamp when the vehicle was used for the n+1th time.

[0020] Further technical solution: The smoothing factor is generated in the following manner:

[0021] By formula:

[0022]

[0023] Generate smoothing factor ;

[0024] In the formula, It represents the upper limit of the smoothing factor. It represents the lower limit of the smoothing factor. It represents the preset basic value of the smoothing factor. It represents the fluctuation correction coefficient of vehicle usage interval, It represents the weather temperature correction factor. It represents the timeliness correction factor.

[0025] Further technical solution: The method for obtaining the volatility correction coefficient of the vehicle usage interval is specifically as follows:

[0026] By formula:

[0027]

[0028] Generates a volatility correction factor for vehicle usage intervals ;

[0029] In the formula, It represents the fluctuation sensitivity coefficient. It represents the standard deviation of the historical vehicle usage interval. It represents the average value of historical vehicle usage intervals.

[0030] Further technical solution: The method for obtaining the weather temperature correction coefficient specifically includes:

[0031] By formula:

[0032]

[0033] Generate weather temperature correction factor ;

[0034] In the formula, It represents the temperature sensitivity coefficient. It represents the reference temperature. Indicates the weather temperature.

[0035] Further technical solution: The generation method of the timeliness correction coefficient specifically includes:

[0036] By formula:

[0037]

[0038] Generate timeliness correction factor ;

[0039] In the formula, It represents the vehicle usage interval weight coefficient, represents the i-th historical vehicle usage interval, It represents the average value of historical vehicle usage intervals. It represents the number of historical data of vehicle usage intervals.

[0040] Further technical solution: The expression of the vehicle behavior prediction model is specifically:

[0041]

[0042] In the formula, It indicates the next usage interval of the vehicle. represents the smoothing factor, It represents the average value of historical vehicle usage intervals. It represents the minimum value of the historical vehicle usage interval.

[0043] Further technical solution: The generation method of the vehicle battery load maintenance capability specifically includes:

[0044] By formula:

[0045]

[0046] Generate vehicle battery load maintenance capability ;

[0047] In the formula, Indicates the remaining capacity of the vehicle battery. It indicates the next usage interval of the vehicle. It indicates the necessary consumption rate of battery capacity when the vehicle is parked. It indicates the minimum reserve of battery capacity. It represents the static discharge current of the vehicle. It represents the temperature compensation coefficient. It represents the natural discharge current of the vehicle battery;

[0048] The method for obtaining the static discharge current of the vehicle specifically includes:

[0049] By formula:

[0050]

[0051] Generates vehicle static discharge current ;

[0052] In the formula, It represents the current of the jth power-consuming device, and m represents the number of active power-consuming devices.

[0053] A vehicle adopts the above-mentioned vehicle battery detection method.

[0054] The present invention provides a vehicle battery detection method and a vehicle, which have the following beneficial effects compared with the prior art:

[0055] The present invention obtains vehicle battery status data, usage behavior data and weather temperature, combines them with dynamically predicted vehicle usage intervals, comprehensively calculates the battery load maintenance capacity and generates a test report. This solves the problem that traditional detection methods cannot dynamically predict battery performance, improves vehicle battery detection accuracy, and dynamically predicts battery performance to avoid vehicle starting failure. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A schematic flow chart of a vehicle battery detection method provided by the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] The specific implementation of the present invention is described in detail below with reference to specific embodiments.

[0059] See also Figure 1 , provided in one embodiment of the present invention, is a vehicle battery detection method, comprising the following steps:

[0060] Step S10: Acquire vehicle battery status data, vehicle usage behavior data, and weather temperature;

[0061] Step S20: Establishing a vehicle behavior prediction model based on the vehicle usage behavior data and weather temperature to generate the vehicle's next usage interval; wherein the vehicle usage behavior data includes an ignition off timestamp and an ignition on timestamp;

[0062] Step S30: generating the vehicle battery load maintenance capacity according to the vehicle battery status data and the vehicle's next usage interval;

[0063] Step S40: generating a vehicle battery test report based on the vehicle battery load maintenance capability;

[0064] The vehicle battery status data refers to a set of physical parameters that reflect the current performance of the battery. Specifically, it can be implemented using indicators such as voltage, internal resistance, and remaining capacity, and is used to quantify the immediate health status of the battery. In addition, this status data is the status data of the vehicle battery after the vehicle is turned off.

[0065] Vehicle usage behavior data refers to the time series information that records the vehicle's operating status. Specifically, it can be achieved by using the engine shutdown timestamp and ignition timestamp recorded by the vehicle controller to analyze the regularity of vehicle usage intervals.

[0066] Weather temperature refers to the temperature parameter of the vehicle's environment. It can be achieved by using an on-board temperature sensor or networked meteorological data to correct the impact of temperature on the battery self-discharge rate.

[0067] The vehicle behavior prediction model is an algorithmic model that predicts future usage intervals based on historical data. It can be implemented by combining time series analysis with a temperature compensation algorithm to dynamically adjust the time window for battery capacity evaluation.

[0068] The vehicle battery load maintenance capability refers to the battery's ability to maintain the normal operation of on-board equipment within a predicted time interval. This can be achieved by calculating the difference between the remaining capacity and the predicted power consumption, and is used to determine whether the battery can meet subsequent usage requirements.

[0069] Specifically, the method first collects state parameters such as battery voltage and internal resistance, while also acquiring the vehicle's ignition and shutdown time series data and ambient temperature. By analyzing the ignition and shutdown timestamps in the historical data, the average vehicle usage interval is calculated and a time series model is established. The model is dynamically modified using temperature sensor data to predict the time interval for the vehicle's next start-up. The predicted time interval is input into the battery capacity assessment model, and the capacity decay under the influence of static discharge current and temperature during this time period is calculated. Finally, based on the capacity decay value within the predicted interval and the battery's current remaining capacity, it is determined whether the battery can support the next vehicle start-up, and a test report containing maintenance recommendations is generated.

[0070] Compared with existing technologies, traditional methods only perform static evaluation based on the current battery status, while this solution establishes a dynamic evaluation model by predicting vehicle usage intervals; existing technologies do not consider the usage patterns formed by user driving behavior, while this solution establishes a personalized prediction model by analyzing historical ignition and shutdown timestamps; existing technologies ignore the impact of temperature on battery self-discharge, while this solution introduces a temperature parameter to correct the capacity attenuation calculation model; existing technologies cannot predict battery performance changes during vehicle parking, while this solution achieves forward-looking capacity evaluation by predicting usage intervals.

[0071] Through the above technical solution, the present application can accurately predict the capacity attenuation trend of the battery during vehicle parking, avoiding misjudgment caused by ignoring the usage interval regularity; by integrating temperature parameters to correct the self-discharge rate calculation, the detection accuracy under different climatic conditions can be improved; based on the test report generated by dynamic prediction results, personalized battery maintenance recommendations can be provided to users, such as charging in advance when the predicted usage interval is long, or shortening the detection cycle in extreme temperature environments.

[0072] Preferably, the present invention further proposes that the method for generating the vehicle's next usage interval specifically includes:

[0073] Step S21: generating a vehicle usage interval based on the ignition off timestamp and the ignition on timestamp;

[0074] Step S22: generating a smoothing factor according to the vehicle usage interval and the weather temperature;

[0075] Step S23: establishing a vehicle behavior prediction model based on the smoothing factor and the vehicle usage interval to generate the vehicle usage interval at the next moment;

[0076] The vehicle usage interval refers to the time difference between two consecutive vehicle usage behaviors. It can be calculated by the absolute value of the ignition shutdown timestamp and the next ignition on timestamp to quantify the periodic characteristics of the user's vehicle usage behavior.

[0077] The smoothing factor refers to the parameter that dynamically adjusts the weight of the forecast model. It can be calculated by combining the temperature correction coefficient with the historical data volatility coefficient to balance the impact of historical data patterns and ambient temperature changes on the forecast results.

[0078] The vehicle behavior prediction model refers to a prediction function that integrates historical usage intervals and temperature factors. Specifically, it can be constructed using a weighted average algorithm combined with extreme value correction to generate vehicle usage interval prediction values ​​that adapt to dynamic environmental changes.

[0079] Specifically, the time interval between two adjacent vehicle uses is calculated by continuously collecting the timestamps of engine shutdown and ignition on, forming time series data that reflects the characteristics of user behavior; the volatility correction coefficient is calculated based on the standard deviation and average value of the time series, and the temperature correction coefficient is generated by combining the difference between the current weather temperature and the reference temperature. The smoothing factor is generated by combining the exponential function and the linear function; the smoothing factor is used as the weight coefficient, and the historical average usage interval and the minimum usage interval are weighted and fused to establish a prediction model to output the usage interval at the next moment; the model adjusts the prediction weight in real time through the temperature parameter, increases the reference ratio of recent data in high and low temperature environments, and enhances the stability of the historical average value in normal temperature environments.

[0080] Compared with existing technologies, traditional methods usually only use historical averages within a fixed time window to predict vehicle usage intervals, without considering the impact of temperature changes on user vehicle usage frequency. For example, in high and low temperature environments, users may more frequently shorten vehicle parking time (which may increase usage frequency), but existing prediction models cannot capture such dynamic behavior characteristics. This solution introduces temperature correction coefficients and volatility correction coefficients to enable the prediction model to adapt to ambient temperature changes and user behavior fluctuations, maintaining the reliability of prediction results in scenarios with sudden temperature changes.

[0081] Through the above technical solution, this application solves the problem of prediction bias caused by ignoring the correlation between temperature and usage behavior. By dynamically adjusting the prediction weight coefficient, it improves the spatiotemporal adaptability of vehicle usage interval prediction. For example, under low temperature conditions in winter or high temperature conditions in summer, the model automatically increases the reference weight of recent short-term parking data to accurately predict the trend that users may shorten their parking time. Under normal temperature conditions, the model strengthens the stability of historical averages to avoid interference from abnormal short-term parking data on the prediction results. This improvement in prediction accuracy directly enhances the reliability of the battery load maintenance capacity assessment and provides an accurate time reference for the subsequent generation of test reports.

[0082] Preferably, the present invention further proposes that the vehicle usage interval is generated in the following manner:

[0083] By formula:

[0084]

[0085] Generate vehicle usage intervals ;

[0086] In the formula, It represents the timestamp of the vehicle being turned off when it is used for the nth time. It represents the ignition timestamp when the vehicle was used for the n+1th time;

[0087] The engine shutdown timestamp refers to the time when the vehicle's engine is shut down at the end of its nth use cycle. It can be implemented using the precise time data recorded by the vehicle's electronic control unit to mark the moment when the vehicle stopped running.

[0088] The ignition timestamp refers to the time when the engine is started at the beginning of the vehicle's (n+1) usage cycle. It can be achieved by collecting time information in real time from on-board sensors and is used to mark the end time of the vehicle's resumption of operation.

[0089] Absolute value operation is used to eliminate the negative value error that may be caused by the time sequence of two adjacent operations. Specifically, this can be achieved by using the mathematical operation module to perform non-negative processing on the timestamp difference to ensure the physical correctness of the interval duration.

[0090] Specifically, the duration between two consecutive uses is calculated by precisely capturing the key moments between vehicle shutdowns and restarts. When the vehicle is shut down for the nth time, the system records that moment as the endpoint of the time series. When the ignition is restarted for the n+1th time, the system immediately acquires a new timestamp. By calculating the absolute value of the time difference between these two key events, the actual parking duration of the vehicle can be accurately reflected.

[0091] Preferably, the present invention further proposes that the smoothing factor is generated in the following manner:

[0092] By formula:

[0093]

[0094] Generate smoothing factor ;

[0095] In the formula, It represents the upper limit of the smoothing factor. It represents the lower limit of the smoothing factor. It represents the preset basic value of the smoothing factor. It represents the fluctuation correction coefficient of vehicle usage interval, It represents the weather temperature correction factor. It represents the timeliness correction factor;

[0096] in, It refers to the maximum value allowed by the smoothing factor, which can be implemented as 0.9 to prevent the model from being overly dependent on the current data and causing unstable predictions;

[0097] It refers to the minimum value allowed by the smoothing factor, which can be implemented as 0.1 to prevent the model from relying entirely on historical data and losing its dynamic adjustment ability;

[0098] It refers to the initial benchmark value of the smoothing factor, which can be implemented as 0.5 as the starting point for multi-factor correction;

[0099] It refers to the adjustment coefficient based on the degree of fluctuation in historical vehicle usage intervals. It can be calculated by the ratio of standard deviation to mean value, and is used to suppress the impact of abnormal fluctuations on the forecast;

[0100] It refers to the adjustment coefficient that reflects the deviation between the ambient temperature and the reference temperature. It can be realized by calculating the ratio of the temperature difference and is used to compensate for the influence of temperature on the vehicle usage interval.

[0101] Refers to a weighting factor based on the timeliness of historical data, used to strengthen the forecast contribution of recent data;

[0102] The generation method of the timeliness correction coefficient specifically includes:

[0103] By formula:

[0104]

[0105] Generate timeliness correction factor ;

[0106] In the formula, It represents the vehicle usage interval weight coefficient, represents the i-th historical vehicle usage interval, It represents the average value of historical vehicle usage intervals. It represents the number of historical data of vehicle usage intervals;

[0107] Specifically, the smoothing factor Optimize the prediction model parameters through multi-dimensional dynamic adjustment: First, based on As the initial value, combined Correct the volatility of historical usage intervals, for example, when the standard deviation increases, reduce the correction coefficient to suppress noise interference; secondly, Introducing temperature effects, for example, when the actual temperature is lower than the reference temperature, increasing the correction coefficient to reflect the tendency of prolonged parking time in low temperature environments; further, Weighting the timeliness of historical data, for example, giving higher weight to the recent usage interval to improve the prediction sensitivity; finally, and The calculation results are limited. For example, when the calculation result exceeds 0.9, it is forced to be 0.9 to avoid model distortion caused by extreme parameters. This multi-factor joint calculation method enables the smoothing factor to adapt to different environmental conditions and data characteristics, thereby improving the robustness of vehicle usage interval prediction.

[0108] Compared with existing technologies, traditional methods usually use fixed smoothing factors or single-factor adjustment methods, which cannot simultaneously deal with data fluctuations, temperature changes and time decay effects; for example, some schemes only calculate the predicted value based on the historical average value, ignoring the impact of high and low temperature environments on vehicle usage intervals; other schemes introduce time decay weights but do not establish a correction model for multi-factor coupling; this scheme realizes dynamic optimization of prediction parameters by jointly combining volatility, temperature and timeliness correction coefficients, and solves the problem of insufficient adaptability of single-factor models in complex scenarios.

[0109] Through the above technical solution, the present application can dynamically balance the impact of historical data patterns and real-time environmental interference on the prediction model, effectively suppress the prediction deviation caused by abnormal fluctuations, and accurately reflect the impact of temperature changes on vehicle usage behavior; further strengthen the weight of recent data through timeliness correction, so that the prediction results are more in line with the user's actual car usage habits, thereby providing a reliable time interval prediction basis for the calculation of battery load maintenance capacity.

[0110] Preferably, the present invention further proposes that the method for obtaining the volatility correction coefficient of the vehicle usage interval is specifically as follows:

[0111] By formula:

[0112]

[0113] Generates a volatility correction factor for vehicle usage intervals ;

[0114] In the formula, It represents the fluctuation sensitivity coefficient. It represents the standard deviation of the historical vehicle usage interval. It represents the average value of historical vehicle usage intervals;

[0115] Among them, the fluctuation sensitivity coefficient This parameter is used to adjust the model's sensitivity to historical data volatility. This can be achieved using a preset value range, such as 0.1 to 0.5. Increasing this value increases the volatility suppression effect.

[0116] Standard deviation It refers to a quantitative indicator of the degree of dispersion of historical vehicle usage intervals, which can be calculated through statistical methods and is used to reflect the stability of vehicle usage behavior;

[0117] Average value of historical vehicle usage intervals It refers to the arithmetic mean of the intervals between multiple uses of a vehicle. It can be obtained by accumulating historical interval data and dividing it by the number of times. It is used as the calculation basis for standardizing the fluctuation range.

[0118] Specifically, by using the ratio of the standard deviation to the mean as a quantitative indicator of volatility, combined with the dynamic mapping of the exponential function, when the historical data fluctuates greatly, the ratio increases, resulting in Decrease, thereby reducing the smoothing factor's dependence on recent data. For example, when the standard deviation of vehicle usage intervals reaches 50% of the mean, It can be attenuated to less than 60% of the original value, so that the model automatically reduces its sensitivity to abnormal fluctuation data; fluctuation sensitivity coefficient As an adjustable parameter, it can be set to different thresholds according to actual needs.

[0119] Compared to existing technologies, traditional methods typically use fixed weights or linear corrections to handle data fluctuations. For example, directly using the absolute value of the standard deviation as the correction basis can lead to increased prediction bias in extreme fluctuation scenarios. This solution leverages the nonlinear characteristics of the exponential function to convert the relative fluctuation amplitude into a dynamic attenuation coefficient of the smoothing factor, effectively suppressing the impact of abnormal fluctuations while preserving the data distribution characteristics. For example, when vehicle usage intervals are occasionally extremely short or long, existing technologies may not be able to fully attenuate the impact of outliers due to linear correction. However, this solution automatically reduces the weight of such data on the prediction results through an exponential attenuation mechanism.

[0120] Through the above technical solution, the present application can dynamically adjust the smoothing factor based on the fluctuation characteristics of historical vehicle usage intervals, allowing the vehicle behavior prediction model to maintain prediction accuracy even in the face of unstable usage behavior. For example, when users frequently change their vehicle usage habits, resulting in significant differences in interval time, the modified smoothing factor can adaptively reduce reliance on recent abnormal interval data, thereby avoiding overly optimistic or pessimistic misjudgments in the battery load maintenance capacity calculation and improving the reliability of the detection results.

[0121] Preferably, the present invention further proposes that the method for obtaining the weather temperature correction coefficient specifically includes:

[0122] By formula:

[0123]

[0124] Generate weather temperature correction factor ;

[0125] In the formula, It represents the temperature sensitivity coefficient. It represents the reference temperature. It indicates the weather temperature;

[0126] The temperature sensitivity coefficient is a parameter used to quantify the impact of temperature changes on vehicle usage interval prediction. It can be implemented using empirical values ​​or experimental calibration values. Its function is to dynamically adjust the correction amplitude according to temperature differences.

[0127] The reference temperature is a benchmark temperature used to measure actual temperature changes. It can be achieved by using the average temperature of the vehicle's operating environment or the standard operating temperature. Its function is to provide a reference point for temperature difference calculations.

[0128] Weather temperature refers to the real-time temperature data of the vehicle's environment, which can be collected by temperature sensors or obtained through a meteorological data interface. Its function is to reflect the actual impact of the current environment on battery performance and vehicle usage behavior.

[0129] Specifically, the weather temperature correction coefficient normalizes the absolute difference between the real-time temperature and the reference temperature, and generates a proportional adjustment factor based on the temperature sensitivity coefficient. When the real-time temperature is lower or higher than the reference temperature (i.e., the temperature difference is large), the correction coefficient increases, indicating that high and low temperature environments may shorten the vehicle usage interval. When the real-time temperature is close to the reference temperature, the correction coefficient decreases, indicating that normal temperature environments may extend the vehicle usage interval. After this coefficient is introduced into the vehicle behavior prediction model, it can dynamically adjust the prediction results according to temperature changes, so that the predicted value of the vehicle's usage interval at the next moment is closer to the actual working conditions. For example, in low temperatures in winter or high temperatures in summer, the correction coefficient automatically increases, and the prediction model will shorten the estimated vehicle parking time.

[0130] Compared with existing technologies, traditional methods only rely on historical behavior data when predicting vehicle usage intervals, and do not consider the impact of temperature changes on users' vehicle usage habits; for example, users may shorten the vehicle parking time in low or high temperature environments; this application establishes a quantitative correlation between temperature and behavior prediction by introducing a temperature correction coefficient, so that the prediction model can automatically adjust the calculation logic according to the real-time temperature, solving the problem of prediction deviation caused by ignoring the temperature factor.

[0131] Through the above technical solution, the present application effectively improves the accuracy of vehicle usage interval prediction, especially in extreme temperature environments, and can more accurately evaluate the battery's load maintenance capacity during the estimated parking time; thereby avoiding misjudgment caused by the temperature effect not being included in the calculation, thereby preventing the vehicle from having difficulty starting later.

[0132] Preferably, the present invention further proposes that the expression of the vehicle behavior prediction model is specifically:

[0133]

[0134] In the formula, It indicates the next usage interval of the vehicle. represents the smoothing factor, It represents the average value of historical vehicle usage intervals. It represents the minimum value of the historical vehicle usage interval;

[0135] The smoothing factor is a parameter used to adjust the weight of the historical average usage interval and the shortest usage interval in the prediction results. Specifically, it can be achieved by using a coefficient that is dynamically adjusted based on the volatility of vehicle usage intervals, weather temperature, and data timeliness. Its value range can be constrained by preset upper and lower limits.

[0136] The average value of historical vehicle usage intervals refers to the arithmetic mean of the difference between the vehicle's multiple shutdown timestamps and ignition timestamps. It can be calculated by statistically analyzing historical data and is used to reflect the regular usage patterns of the vehicle.

[0137] The minimum value of the historical vehicle usage interval refers to the shortest time interval in the vehicle's historical usage intervals. It can be determined by filtering the minimum value in the historical data and is used to capture extreme or sudden usage scenarios.

[0138] Specifically, the predicted value of the vehicle's usage interval at the next moment is generated by weighted calculation of the historical average usage interval and the shortest usage interval; the smoothing factor is dynamically adjusted according to the stability of vehicle usage behavior, ambient temperature changes and data timeliness. When the vehicle usage interval fluctuates slightly and the temperature conditions are stable, the smoothing factor approaches the upper limit value, and the prediction result relies more on the historical average interval; when the usage interval fluctuates significantly or high or low temperature weather occurs, the smoothing factor is reduced, and the prediction result shifts towards the shortest historical interval; by introducing the shortest interval as a benchmark, when the user suddenly changes his usage habits or encounters an emergency scenario, the prediction model can respond quickly, avoiding prediction lags or deviations caused by relying solely on the average value.

[0139] Compared with existing technologies, traditional methods usually only use fixed weights or a single historical average to predict vehicle usage intervals, which cannot effectively cope with sudden short-interval usage scenarios. This scheme combines the shortest historical interval and introduces a dynamic smoothing factor. It not only retains the characteristics of long-term usage patterns, but also enhances adaptability to abnormal scenarios, significantly improving the robustness of the prediction results.

[0140] Through the above technical solution, this application solves the problem of prediction deviation caused by not considering the dynamic changes in vehicle usage intervals and extreme scenarios. By dynamically balancing historical rules and sudden behaviors, it generates prediction intervals that are closer to actual usage patterns, providing reliable input for battery remaining capacity calculation and fault warning, and avoiding the risk of misjudgment of battery power due to prediction errors.

[0141] Preferably, the present invention further proposes that the generation method of the vehicle battery load maintenance capability specifically includes:

[0142] By formula:

[0143]

[0144] Generate vehicle battery load maintenance capability ;

[0145] In the formula, Indicates the remaining capacity of the vehicle battery. It indicates the next usage interval of the vehicle. It indicates the necessary consumption rate of battery capacity when the vehicle is parked. It indicates the minimum reserve of battery capacity. It represents the static discharge current of the vehicle. It represents the temperature compensation coefficient. It represents the natural discharge current of the vehicle battery;

[0146] The method for obtaining the static discharge current of the vehicle specifically includes:

[0147] By formula:

[0148]

[0149] Generates vehicle static discharge current ;

[0150] In the formula, It represents the current of the jth power-consuming device, and m represents the number of active power-consuming devices;

[0151] The remaining capacity refers to the current available power of the battery. Specifically, the remaining capacity can be estimated using the voltage-capacity curve combined with a temperature compensation algorithm to reflect the real-time status of the battery.

[0152] The future usage interval refers to the length of time the vehicle will be parked before the next start. This prediction can be dynamically adjusted using a vehicle behavior prediction model to correlate battery consumption with parking time. The required consumption rate refers to the minimum rate of power consumption required to maintain basic functionality while the vehicle is parked. Different thresholds can be preset based on vehicle model to ensure the battery retains the necessary starting charge.

[0153] The minimum reserve capacity refers to the safe capacity threshold of the battery that cannot be discharged. Specifically, a fixed value can be set based on the battery type to prevent excessive discharge and damage to the battery.

[0154] The vehicle's static discharge current refers to the sum of the currents of all electrical devices when the vehicle is stationary. This can be achieved by detecting the working status of each device and accumulating its nominal current to quantify the actual discharge load.

[0155] The temperature compensation coefficient refers to the correction factor for the effect of temperature on the discharge rate. Specifically, the temperature-discharge curve fitting coefficient can be used to compensate for the decrease in discharge efficiency caused by low temperature.

[0156] The natural discharge current refers to the self-discharge of the battery and the tiny current of inactive devices. It can be determined by long-term monitoring of the average static current and used to improve the calculation of the total discharge amount.

[0157] Specifically, when calculating the load maintenance capability, the actual available capacity of the battery is first obtained by subtracting the product of the future usage interval and the necessary consumption rate from the remaining capacity, and then deducting the minimum reserve amount; then, the total discharge current is multiplied by the temperature compensation coefficient and added to the natural discharge current to obtain the comprehensive discharge rate; finally, the available capacity is divided by the comprehensive discharge rate to obtain the sustainable power supply time of the battery under load; this process dynamically associates the battery status, future usage requirements, ambient temperature and equipment power consumption characteristics, and incorporates multi-dimensional influencing factors into a unified calculation model, thereby achieving accurate prediction of load maintenance capability.

[0158] Compared with existing technologies, traditional methods rely solely on static voltage or internal resistance tests to estimate battery performance. They do not consider the impact of vehicle parking time on necessary consumption, nor do they integrate temperature changes to correct the discharge rate. They also lack accurate measurement of the parallel discharge of multiple devices. This solution dynamically adjusts the necessary consumption calculation by introducing future usage interval prediction values, corrects the discharge rate based on the temperature compensation coefficient, and obtains the total discharge load based on the accumulated current of active devices, so that the calculation results simultaneously cover key variables in the time dimension, environmental dimension, and load dimension.

[0159] Through the above technical solution, the present application can accurately predict the duration that the battery will maintain the load in real usage scenarios, avoiding evaluation deviations caused by ignoring the vehicle parking time, temperature changes and the superposition of power consumption of multiple devices; by quantifying the dynamic relationship between available capacity and comprehensive discharge rate, it can provide early warning of the risk of insufficient battery performance, provide a basis for optimizing the management strategy of power-consuming equipment, and ensure that the battery retains the minimum safe power to support the vehicle starting function.

[0160] The present invention further provides a vehicle, which adopts the above-mentioned vehicle battery detection method.

[0161] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A vehicle battery detection method, characterized in that: The following steps are involved: Obtain vehicle battery status data, vehicle usage behavior data, and weather temperature; A vehicle behavior prediction model is established based on vehicle usage behavior data and weather temperature to generate the vehicle's next usage interval; wherein the vehicle usage behavior data includes the shutdown timestamp and the ignition timestamp; Generate the vehicle battery load maintenance capacity based on the vehicle battery status data and the vehicle's next usage interval; Generate a vehicle battery test report based on the vehicle battery load maintenance capability; The method for generating the vehicle's next usage interval specifically includes: Generate vehicle usage interval based on the shutdown timestamp and ignition timestamp; Generate smoothing factors based on vehicle usage intervals and weather temperature; A vehicle behavior prediction model is established based on the smoothing factor and the vehicle usage interval to generate the vehicle usage interval at the next moment.

2. A vehicle battery detection method according to claim 1, characterized in that: The vehicle usage interval is generated in the following manner: By formula: ； Generate vehicle usage intervals ; In the formula, It represents the timestamp of the vehicle being turned off when it is used for the nth time. It represents the ignition timestamp when the vehicle was used for the n+1th time.

3. A vehicle battery detection method according to claim 2, characterized in that: The smoothing factor is generated in the following manner: By formula: ； Generate smoothing factor ; In the formula, It represents the upper limit of the smoothing factor. It represents the lower limit of the smoothing factor. It represents the preset basic value of the smoothing factor. It represents the fluctuation correction coefficient of vehicle usage interval, It represents the weather temperature correction factor. It represents the timeliness correction factor.

4. A vehicle battery detection method according to claim 3, characterized in that: The method for obtaining the fluctuation correction coefficient of the vehicle usage interval is specifically as follows: By formula: ； Generates a volatility correction factor for vehicle usage intervals ; In the formula, It represents the fluctuation sensitivity coefficient. It represents the standard deviation of the historical vehicle usage interval. It represents the average value of historical vehicle usage intervals.

5. A vehicle battery detection method according to claim 3, characterized in that: The method for obtaining the weather temperature correction coefficient specifically includes: By formula: ； Generate weather temperature correction factor ; In the formula, It represents the temperature sensitivity coefficient. It represents the reference temperature. Indicates the weather temperature.

6. A vehicle battery detection method according to claim 3, characterized in that: The generation method of the timeliness correction coefficient specifically includes: By formula: ； Generate timeliness correction factor ; In the formula, It represents the vehicle usage interval weight coefficient, represents the i-th historical vehicle usage interval, It represents the average value of historical vehicle usage intervals. It represents the number of historical data of vehicle usage intervals.

7. A vehicle battery detection method according to claim 3, characterized in that: The expression of the vehicle behavior prediction model is specifically: ； In the formula, It indicates the next usage interval of the vehicle. represents the smoothing factor, It represents the average value of historical vehicle usage intervals. It represents the minimum value of the historical vehicle usage interval.

8. A vehicle battery detection method according to claim 1, characterized in that: The generation method of the vehicle battery load maintenance capability specifically includes: By formula: ； Generate vehicle battery load maintenance capability ; In the formula, Indicates the remaining capacity of the vehicle battery. It indicates the next usage interval of the vehicle. It indicates the necessary consumption rate of battery capacity when the vehicle is parked. It indicates the minimum reserve of battery capacity. It represents the static discharge current of the vehicle. It represents the temperature compensation coefficient. It represents the natural discharge current of the vehicle battery; The method for obtaining the static discharge current of the vehicle specifically includes: By formula: ； Generates vehicle static discharge current ; In the formula, It represents the current of the jth power-consuming device, and m represents the number of active power-consuming devices.

9. A vehicle, characterized in that: A vehicle battery detection method according to any one of claims 1 to 8 is adopted.

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