A method for testing vehicle batteries and a vehicle

Abstract

This invention discloses a vehicle battery testing method and vehicle, belonging to the field of vehicle battery testing technology. The method includes acquiring vehicle battery status data, vehicle usage behavior data, and weather temperature; establishing a vehicle behavior prediction model based on the vehicle usage behavior data and weather temperature to generate the vehicle's next usage interval; generating the vehicle battery load maintenance capability based on the vehicle battery status data and the next usage interval; and generating a vehicle battery testing report based on the vehicle battery load maintenance capability. This invention solves the problem of traditional testing methods being unable to dynamically predict battery performance by acquiring vehicle battery status data, usage behavior data, and weather temperature, combined with dynamically predicted vehicle usage intervals, to comprehensively calculate the battery load maintenance capability and generate a testing report. This improves the accuracy of vehicle battery testing, dynamically predicts battery performance, and thus prevents vehicles from failing to start.

Description

Technical Field

[0001] This invention belongs to the field of vehicle battery testing technology, and particularly relates to a vehicle battery testing method and a vehicle. Background Technology

[0002] With the rapid development of automotive electronics technology, modern vehicles are increasingly reliant on batteries. As a core component for vehicle starting and powering electronic devices, the performance of the battery directly affects the normal operation of the vehicle. However, battery performance gradually degrades due to various factors, such as frequent short-distance driving, extreme temperature environments, prolonged parking, and discharge from electronic devices. Failure to detect battery performance degradation in time may result in the vehicle failing to start or even pose safety hazards.

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

[0004] To address the shortcomings of existing technologies, this invention provides a vehicle battery testing method and a vehicle, thus solving the aforementioned problems.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a vehicle battery testing method, comprising the following steps:

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

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

[0008] Based on the vehicle battery status data and the vehicle's next usage interval, generate the vehicle battery load maintenance capability.

[0009] A vehicle battery test report is generated based on the vehicle battery's load maintenance capability.

[0010] Based on 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 moment usage interval specifically includes:

[0012] The vehicle usage interval is generated based on the engine shutdown timestamp and ignition timestamp;

[0013] A smoothing factor is generated based on vehicle usage intervals and weather temperature.

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

[0015] Further technical solution: The method for generating the vehicle usage interval is as follows:

[0016] Through the formula:

[0017]

[0018] Generate vehicle usage interval ;

[0019] In the formula, This represents the timestamp when the vehicle is turned off during its nth use. This represents the ignition timestamp when the vehicle is used for the (n+1)th time.

[0020] Further technical solution: The method for generating the smoothing factor specifically includes:

[0021] Through the formula:

[0022]

[0023] Generate smoothing factor ;

[0024] In the formula, This represents the upper limit of the smoothing factor. This represents the lower limit of the smoothing factor. This represents the preset base value of the smoothing factor. This represents the correction factor for the volatility of vehicle usage intervals. This represents the weather temperature correction factor. This represents the timeliness correction factor.

[0025] A further technical solution: The method for obtaining the fluctuation correction coefficient of the vehicle usage interval is as follows:

[0026] Through the formula:

[0027]

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

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

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

[0031] Through the formula:

[0032]

[0033] Generate weather temperature correction coefficient ;

[0034] In the formula, This represents the temperature sensitivity coefficient. This indicates a reference temperature. It indicates the weather temperature.

[0035] Further technical solution: The specific method for generating the timeliness correction coefficient includes:

[0036] Through the formula:

[0037]

[0038] Generate timeliness correction coefficient ;

[0039] In the formula, This represents the vehicle usage interval weighting coefficient. This represents the interval between the i-th historical vehicle usage instances. This represents the average historical vehicle usage interval. This represents the number of historical data points related to vehicle usage intervals.

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

[0041]

[0042] In the formula, This indicates the interval at which the vehicle will be used in the next moment. This represents the smoothing factor. This represents the average historical vehicle usage interval. This represents the minimum historical interval between vehicle uses.

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

[0044] Through the formula:

[0045]

[0046] Generate vehicle battery load sustaining capability ;

[0047] In the formula, This indicates the remaining capacity of the vehicle's battery. This indicates the interval at which the vehicle will be used in the next moment. This indicates the necessary battery capacity consumption rate when the vehicle is parked. This indicates the minimum reserve capacity of the battery. This represents the vehicle's static discharge current. This represents the temperature compensation coefficient. This represents the natural discharge current of the vehicle's battery;

[0048] The specific methods for obtaining the vehicle's static discharge current include:

[0049] Through the formula:

[0050]

[0051] Generate vehicle static discharge current ;

[0052] In the formula, This represents the current of the j-th electrical device, and m represents the number of active electrical devices.

[0053] A vehicle that uses the aforementioned vehicle battery testing method.

[0054] This invention provides a method for testing vehicle batteries and a vehicle, which has the following advantages compared with the prior art:

[0055] This invention acquires vehicle battery status data, usage behavior data, and weather temperature, and combines this with dynamically predicted vehicle usage intervals to comprehensively calculate the battery load maintenance capacity and generate a test report. This solves the problem that traditional testing methods cannot dynamically predict battery performance, improves the accuracy of vehicle battery testing, and dynamically predicts battery performance, thereby preventing vehicles from failing to start. Attached Figure Description

[0056] Figure 1 This is a flowchart illustrating a vehicle battery testing method provided by the present invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this invention clearer, the 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 merely illustrative and not intended to limit the invention.

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

[0059] Please see Figure 1 The present invention provides a vehicle battery testing method according to one embodiment, comprising the following steps:

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

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

[0062] Step S30: Generate the vehicle battery load maintenance capability based on the vehicle battery status data and the vehicle's next usage interval;

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

[0064] Among them, the status data of the vehicle battery refers to the set of physical parameters that reflect the current performance of the battery. Specifically, it can be realized by indicators such as voltage, internal resistance, and remaining capacity, which are used to quantify the real-time 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, which is used to analyze the patterns of vehicle usage intervals.

[0066] Weather temperature refers to the air temperature parameter of the vehicle's environment. It can be obtained by using an on-board temperature sensor or networked meteorological data, and is used to correct the effect of temperature on the battery's self-discharge rate.

[0067] Vehicle behavior prediction model refers to an algorithm model that predicts future usage intervals based on historical data. Specifically, it can be implemented by combining time series analysis and temperature compensation algorithms, and is used to dynamically adjust the time window for battery capacity assessment.

[0068] Vehicle battery load maintenance capacity refers to the ability of a battery to maintain the normal operation of on-board equipment within a predicted time interval. Specifically, it can be calculated by the difference between the remaining capacity and the predicted power consumption, and is used to determine whether the battery meets the subsequent usage requirements.

[0069] Specifically, this method first collects state parameters such as battery voltage and internal resistance, while simultaneously acquiring ignition and shutdown time-series data of the vehicle and ambient temperature. By analyzing the ignition and shutdown timestamps in historical data, the average usage interval of the vehicle is calculated, and a time-series model is established. The model is dynamically corrected using temperature sensor data to predict the time interval for the next vehicle start-up. The predicted time interval is input into the battery capacity assessment model to calculate the capacity decay under the influence of static discharge current and temperature during that period. Finally, based on the capacity decay value within the predicted interval and the current remaining battery 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 state of the battery, while this solution establishes a dynamic evaluation model by predicting vehicle usage intervals; existing technologies do not consider 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 temperature parameters to correct the capacity decay 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 solutions, this application can accurately predict the capacity decay trend of the battery during vehicle parking, avoiding misjudgments caused by ignoring the usage interval pattern; by integrating temperature parameters to correct the self-discharge rate calculation, the detection accuracy under different climatic conditions is improved; the detection report generated based on the dynamic prediction results can provide users with personalized battery maintenance suggestions, such as charging in advance when the predicted usage interval is long, or suggesting shortening the detection cycle under extreme temperature conditions.

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

[0073] Step S21: Generate vehicle usage intervals based on the engine shutdown timestamp and ignition timestamp;

[0074] Step S22: Generate a smoothing factor based on vehicle usage intervals and weather temperature;

[0075] Step S23: Establish a vehicle behavior prediction model based on the smoothing factor and vehicle usage interval, and generate the vehicle usage interval for the next moment;

[0076] Among them, vehicle usage interval refers to the time difference between two adjacent vehicle usage behaviors, which can be calculated by the absolute value of the engine shutdown timestamp and the next ignition timestamp, and is used to quantify the periodic characteristics of user vehicle usage behavior.

[0077] The smoothing factor is a parameter that dynamically adjusts the weights of the prediction model. Specifically, it can be calculated by combining the temperature correction coefficient and the historical data volatility coefficient to balance the impact of historical data patterns and changes in ambient temperature on the prediction results.

[0078] Vehicle behavior prediction models refer to prediction functions that integrate historical usage intervals and temperature factors. Specifically, they 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, by continuously collecting the timestamps of engine shutdown and ignition, the time interval between two adjacent vehicle uses is calculated, forming time series data reflecting user behavior characteristics. Based on the standard deviation and average value of this time series, a volatility correction coefficient is calculated, and a temperature correction coefficient is generated by combining the difference between the current weather temperature and the reference temperature. A smoothing factor is generated by combining exponential and linear functions. The smoothing factor is used as a weighting coefficient to weight and fuse the historical average usage interval and the minimum usage interval to establish a prediction model that outputs the usage interval at the next moment. This model adjusts the prediction weights in real time through temperature parameters, increasing the reference proportion of recent data in high and low temperature environments, and strengthening the stability of historical average values ​​in normal temperature environments.

[0080] Compared with existing technologies, traditional methods typically 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 shorten vehicle parking time more frequently (and use the vehicle more often), but existing prediction models cannot capture such dynamic behavioral characteristics. This solution introduces temperature correction coefficients and volatility correction coefficients, enabling the prediction model to adapt to changes in ambient temperature and fluctuations in user behavior, maintaining the reliability of prediction results even in scenarios with sudden temperature changes.

[0081] Through the above technical solution, this application solves the prediction bias problem caused by ignoring the correlation between temperature and usage behavior. By dynamically adjusting the prediction weight coefficients, the spatiotemporal adaptability of vehicle usage interval prediction is improved. 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, accurately predicting the trend of users potentially shortening parking time; under normal temperature conditions, the model strengthens the stability of historical averages, avoiding interference from abnormal short-term parking data on the prediction results. This improvement in prediction accuracy directly enhances the reliability of battery load maintenance capacity assessment, providing an accurate time benchmark for subsequent test report generation.

[0082] Preferably, the present invention further proposes a specific method for generating the vehicle usage interval as follows:

[0083] Through the formula:

[0084]

[0085] Generate vehicle usage interval ;

[0086] In the formula, This represents the timestamp when the vehicle is turned off during its nth use. This represents the ignition timestamp when the vehicle is used for the (n+1)th time.

[0087] Among them, the engine shutdown timestamp refers to the time record point at the end of the nth use cycle of the vehicle when the engine is turned off. Specifically, it can be implemented using the precise time data recorded by the vehicle's electronic control unit, which is used to mark the start time of the vehicle stopping operation.

[0088] Ignition timestamp refers to the time record point when the engine is started at the beginning of the vehicle's n+1th usage cycle. Specifically, it can be achieved by real-time collection of time information by on-board sensors, and is used to mark the end time when the vehicle resumes operation.

[0089] Absolute value operations are used to eliminate negative value errors that may be caused by the time sequence of two adjacent operations. Specifically, this can be achieved by using a mathematical operation module to non-negatively process the timestamp difference, ensuring the correct physical meaning of the interval duration.

[0090] Specifically, the interval between two consecutive uses is calculated by accurately capturing the key moments of vehicle stopping and restarting. When the vehicle is turned off for the nth time, the system records that moment as the endpoint of the time series; when the (n+1)th ignition occurs, the system immediately acquires a new time stamp. By calculating the absolute value of the time difference between these two key events, the actual parking time of the vehicle can be accurately reflected.

[0091] Preferably, the present invention further proposes a method for generating the smoothing factor that specifically includes:

[0092] Through the formula:

[0093]

[0094] Generate smoothing factor ;

[0095] In the formula, This represents the upper limit of the smoothing factor. This represents the lower limit of the smoothing factor. This represents the preset base value of the smoothing factor. This represents the correction factor for the volatility of vehicle usage intervals. This represents the weather temperature correction factor. This represents the timeliness correction factor;

[0096] in, This refers to the maximum allowed value of the smoothing factor, which can be implemented using 0.9 to prevent the model from becoming overly reliant on the current data and causing prediction instability.

[0097] This refers to the minimum allowed value of the smoothing factor, which can be implemented using 0.1 to avoid the model relying entirely on historical data and losing its dynamic adjustment capability.

[0098] This refers to the initial baseline value of the smoothing factor, which can be implemented using 0.5 as the starting point for multi-factor correction.

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

[0100] It refers to the adjustment coefficient that reflects the deviation between the ambient temperature and the reference temperature. Specifically, it can be calculated by the ratio of the temperature difference and is used to compensate for the impact of temperature on the vehicle's service interval.

[0101] It refers to a weighted coefficient based on the timeliness of historical data, used to enhance the predictive contribution of recent data;

[0102] The specific methods for generating the timeliness correction coefficient include:

[0103] Through the formula:

[0104]

[0105] Generate timeliness correction coefficient ;

[0106] In the formula, This represents the vehicle usage interval weighting coefficient. This represents the interval between the i-th historical vehicle usage instances. This represents the average historical vehicle usage interval. This represents the amount of historical data on vehicle usage intervals;

[0107] Specifically, smoothing factor Optimization of prediction model parameters is achieved through multi-dimensional dynamic adjustment: First, based on As an initial value, combined Corrections are made for the volatility of historical usage intervals, for example, by reducing the correction factor to suppress noise interference when the standard deviation increases; secondly, through... Introducing the influence of temperature, for example, increasing the correction factor when the actual temperature is lower than the reference temperature to reflect the trend of longer vehicle parking time in low-temperature environments; furthermore, through... Weighting historical data by timeliness, such as assigning higher weight to recent usage intervals, can improve predictive sensitivity; finally, through... and Limiting the calculation results, for example, forcing the result to 0.9 when it exceeds 0.9, avoids extreme parameters causing model distortion; 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 typically employ fixed smoothing factors or single-factor adjustment methods, which cannot simultaneously address data fluctuations, temperature changes, and time decay effects. For example, some schemes calculate predicted values ​​based solely on historical averages, ignoring the impact of high and low temperature environments on vehicle usage intervals. Other schemes, while introducing time decay weights, fail to establish a multi-factor coupled correction model. This scheme achieves dynamic optimization of prediction parameters by simultaneously adjusting for volatility, temperature, and timeliness, thus solving the problem of insufficient adaptability of single-factor models in complex scenarios.

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

[0110] Preferably, the present invention further proposes a specific method for obtaining the fluctuation correction coefficient of the vehicle usage interval as follows:

[0111] Through the formula:

[0112]

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

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

[0115] Among them, the fluctuation sensitivity coefficient This refers to the parameter used to adjust the model's sensitivity to the volatility of historical data. Specifically, it can be achieved by using a preset value range, such as 0.1 to 0.5. As the value increases, the effect of suppressing volatility is enhanced.

[0116] Standard deviation It refers to a quantitative indicator of the 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 historical vehicle usage interval It refers to the arithmetic mean of the intervals between multiple uses of a vehicle. Specifically, it can be obtained by summing up historical interval data and dividing by the number of uses. It is used as a benchmark for standardizing the calculation of fluctuation range.

[0118] Specifically, by using the ratio of standard deviation to mean as a volatility quantification indicator, and combining it with the dynamic mapping of an exponential function, when historical data fluctuates significantly, an increase in this ratio leads to… This reduces 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 decay to below 60% of its original value, allowing the model to automatically reduce its sensitivity to abnormal fluctuations; 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, such as directly using the absolute value of the standard deviation as the basis for correction. This leads to increased prediction bias in extreme fluctuation scenarios. Our proposed solution utilizes the nonlinear characteristics of an exponential function to transform the relative fluctuation amplitude into a dynamic decay coefficient of the smoothing factor, effectively suppressing the impact of abnormal fluctuations while preserving the data distribution characteristics. For example, when vehicle usage intervals exhibit occasional extremely short or long intervals, existing technologies may fail to adequately decay the impact of outliers due to linear corrections. Our solution, however, automatically reduces the weight of such data in the prediction results through an exponential decay mechanism.

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

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

[0122] Through the formula:

[0123]

[0124] Generate weather temperature correction coefficient ;

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

[0126] Among them, the temperature sensitivity coefficient refers to the adjustment parameter used to quantify the impact of temperature changes on the prediction of vehicle service interval. Specifically, it can be achieved by using empirical values ​​or experimental calibration values. Its function is to dynamically adjust the correction range according to temperature differences.

[0127] Reference temperature refers to the baseline temperature value used to measure actual temperature changes. Specifically, 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 benchmark point for calculating temperature differences.

[0128] Weather temperature refers to the real-time temperature data of the environment in which the vehicle is located. It can be obtained by collecting temperature sensors or by acquiring meteorological data interfaces. Its purpose is to reflect the actual impact of the current environment on battery performance and vehicle usage behavior.

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

[0130] Compared with existing technologies, traditional methods rely solely on historical behavior data when predicting vehicle usage intervals, without considering the impact of temperature changes on user driving habits. For example, users may shorten vehicle parking time in low or high temperature environments. This application introduces a temperature correction coefficient to establish a quantitative correlation between temperature and behavior prediction, enabling the prediction model to automatically adjust its calculation logic based on real-time temperature, thus solving the prediction bias problem caused by ignoring temperature factors.

[0131] Through the above technical solution, this application effectively improves the accuracy of vehicle usage interval prediction, especially in extreme temperature environments, it can more accurately assess the battery's load maintenance capability during the estimated parking time; thereby avoiding misjudgments caused by the failure to include temperature effects in the calculation, thus preventing the vehicle from experiencing subsequent starting difficulties.

[0132] Preferably, the present invention further proposes the following expression for the vehicle behavior prediction model:

[0133]

[0134] In the formula, This indicates the interval at which the vehicle will be used in the next moment. This represents the smoothing factor. This represents the average historical vehicle usage interval. This represents the minimum historical vehicle usage interval;

[0135] Among them, 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 result. Specifically, it can be implemented by using a coefficient that is dynamically adjusted based on the volatility of vehicle usage interval, weather temperature and data timeliness. Its value range can be constrained by preset upper and lower limits.

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

[0137] The minimum 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 historical data and is used to capture extreme or sudden usage scenarios.

[0138] Specifically, the predicted interval of vehicle usage at the next moment is generated by weighted calculation of the historical average interval and the shortest interval. The smoothing factor is dynamically adjusted based on the stability of vehicle usage behavior, changes in ambient temperature, and the timeliness of data. When the vehicle usage interval fluctuates little and the temperature conditions are stable, the smoothing factor approaches the upper limit, and the prediction result relies more on the historical average interval. When the usage interval fluctuates significantly or there is high or low temperature weather, the smoothing factor decreases, and the prediction result shifts towards the historical shortest interval. By introducing the shortest interval as a benchmark, the prediction model can respond quickly when users suddenly change their usage habits or encounter unexpected scenarios, avoiding prediction lag or bias caused by relying solely on the average value.

[0139] Compared with existing technologies, traditional methods typically use only fixed weights or a single historical average to predict vehicle usage intervals, which cannot effectively cope with sudden short-interval usage scenarios. This solution combines the historical shortest interval and introduces a dynamic smoothing factor, which not only preserves the characteristics of long-term usage patterns but also enhances the 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 patterns 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 misjudging battery power due to prediction errors.

[0141] Preferably, the present invention further proposes a method for generating the vehicle battery load maintenance capability, specifically including:

[0142] Through the formula:

[0143]

[0144] Generate vehicle battery load sustaining capability ;

[0145] In the formula, This indicates the remaining capacity of the vehicle's battery. This indicates the interval at which the vehicle will be used in the next moment. This indicates the necessary battery capacity consumption rate when the vehicle is parked. This indicates the minimum reserve capacity of the battery. This represents the vehicle's static discharge current. This represents the temperature compensation coefficient. This represents the natural discharge current of the vehicle's battery;

[0146] The specific methods for obtaining the vehicle's static discharge current include:

[0147] Through the formula:

[0148]

[0149] Generate vehicle static discharge current ;

[0150] In the formula, This represents the current of the j-th electrical device, and m represents the number of active electrical devices.

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

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

[0153] The minimum reserve capacity refers to the safe capacity threshold of a battery before it can be discharged. It can be set as a fixed value based on the battery type to prevent over-discharge from damaging the battery.

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

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

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

[0157] Specifically, when calculating load sustainability, the actual usable capacity of the battery is first obtained by subtracting the product of future usage intervals and necessary consumption rate from the remaining capacity, and then subtracting the minimum reserve amount. Subsequently, 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 usable capacity is divided by the comprehensive discharge rate to obtain the time the battery can continuously supply power under load. This process incorporates multiple influencing factors into a unified calculation model by dynamically linking battery status, future usage needs, ambient temperature, and equipment power consumption characteristics, thereby achieving accurate prediction of load sustainability.

[0158] Compared with existing technologies, traditional methods rely solely on static voltage or internal resistance tests to estimate battery performance, without considering the impact of vehicle parking time on necessary consumption, nor integrating temperature changes to correct for discharge rate, and lacking accurate measurement of parallel discharge of multiple devices. This solution dynamically adjusts the calculation of necessary consumption by introducing predicted future usage intervals, corrects the discharge rate by combining temperature compensation coefficients, and obtains the total discharge load based on the accumulation of current from active devices, so that the calculation results simultaneously cover key variables in the time, environmental, and load dimensions.

[0159] Through the above technical solutions, this application can accurately predict the duration for which the battery can maintain the load in real-world usage scenarios, avoiding evaluation bias caused by ignoring vehicle parking time, temperature changes, and the combined power consumption of multiple devices; by quantifying the dynamic relationship between available capacity and overall discharge rate, it can provide early warning of the risk of insufficient battery performance, providing a basis for optimizing electrical equipment management strategies, while ensuring that the battery retains a minimum safe charge to support vehicle starting function.

[0160] The present invention further proposes a vehicle that employs the above-described vehicle battery testing method.

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

Claims

1. A method for testing vehicle batteries, characterized in that, Includes the following steps: Acquire vehicle battery status data, vehicle usage behavior data, and weather temperature; A vehicle behavior prediction model is built based on vehicle usage behavior data and weather temperature to generate the vehicle's next usage interval; the vehicle usage behavior data includes engine shutdown timestamp and ignition timestamp. Based on the vehicle battery status data and the vehicle's next usage interval, generate the vehicle battery load maintenance capability. Generate a vehicle battery test report based on the vehicle battery's load maintenance capability; The method for generating the vehicle's next usage interval specifically includes: The vehicle usage interval is generated based on the engine shutdown timestamp and ignition timestamp; A smoothing factor is generated based on vehicle usage intervals and weather temperature. A vehicle behavior prediction model is established based on the smoothing factor and vehicle usage interval to generate the vehicle's next usage interval. The specific method for generating the vehicle usage interval is as follows: Through the formula: ; Generate vehicle usage interval ; In the formula, This represents the timestamp when the vehicle is turned off during its nth use. This represents the ignition timestamp when the vehicle is used for the (n+1)th time. The specific methods for generating the smoothing factor include: Through the formula: ; Generate smoothing factor ; In the formula, This represents the upper limit of the smoothing factor. This represents the lower limit of the smoothing factor. This represents the preset base value of the smoothing factor. This represents the correction factor for the volatility of vehicle usage intervals. This represents the weather temperature correction factor. This represents the timeliness correction factor.

2. The vehicle battery testing method according to claim 1, characterized in that, The method for obtaining the fluctuation correction coefficient for vehicle usage intervals is as follows: Through the formula: ; Generate a volatility correction factor for vehicle usage intervals ; In the formula, This represents the fluctuation sensitivity coefficient. This represents the standard deviation of historical vehicle usage intervals. This represents the average historical vehicle usage interval.

3. The vehicle battery testing method according to claim 1, characterized in that, The specific methods for obtaining the weather temperature correction coefficient include: Through the formula: ; Generate weather temperature correction coefficient ; In the formula, This represents the temperature sensitivity coefficient. This indicates a reference temperature. It indicates the weather temperature.

4. The vehicle battery testing method according to claim 1, characterized in that, The specific methods for generating the timeliness correction coefficient include: Through the formula: ; Generate timeliness correction coefficient ; In the formula, This represents the vehicle usage interval weighting coefficient. This represents the interval between the i-th historical vehicle usage instances. This represents the average historical vehicle usage interval. This represents the number of historical data points related to vehicle usage intervals.

5. The vehicle battery testing method according to claim 1, characterized in that, The specific expression of the vehicle behavior prediction model is as follows: ; In the formula, This indicates the interval at which the vehicle will be used in the next moment. This represents the smoothing factor. This represents the average historical vehicle usage interval. This represents the minimum historical interval between vehicle uses.

6. The vehicle battery testing method according to claim 1, characterized in that, The specific methods for generating the vehicle battery load maintenance capability include: Through the formula: ; Generate vehicle battery load sustaining capability ; In the formula, This indicates the remaining capacity of the vehicle's battery. This indicates the interval at which the vehicle will be used in the next moment. This indicates the necessary battery capacity consumption rate when the vehicle is parked. This indicates the minimum reserve capacity of the battery. This represents the vehicle's static discharge current. This represents the temperature compensation coefficient. This represents the natural discharge current of the vehicle's battery; The specific methods for obtaining the vehicle's static discharge current include: Through the formula: ; Generate vehicle static discharge current ; In the formula, This represents the current of the j-th electrical device, and m represents the number of active electrical devices.

7. A vehicle, characterized in that, The vehicle battery testing method described in any one of claims 1-6 is adopted.

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