Battery detection method and device, and storage medium

Abstract

The application discloses a battery detection method, device and storage medium, and belongs to the technical field of vehicle control. The method comprises the following steps: obtaining a preliminary risk detection result of the vehicle based on the working state of a key vehicle-mounted electrical equipment, the voltage and the current of the battery; in response to the acquisition of the potential risk of the battery power loss of the vehicle, obtaining the detection result of the strong related factor of the power loss, the risk level of the power loss of the battery and the detection result of the power decline based on the working state and the power consumption of the key vehicle-mounted electrical equipment, the voltage and the current of the battery, the environment of the vehicle and the driving mileage in the past first time length; in response to at least one of the following conditions: the acquisition of the detection result of the strong related factor of the power loss of the battery, the risk level of the power loss of the battery being greater than or equal to the level threshold, or the acquisition of the detection result that the power of the battery will be greatly reduced within the second time length, the power loss of the battery is warned. The accuracy of the risk detection of the power loss of the battery is improved.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to a method, apparatus and storage medium for detecting a battery. Background Technology

[0002] With the rapid development of vehicle intelligence, the amount of data generated by vehicles is increasing exponentially. As a critical component for vehicle operation, a battery failure can severely impact safe driving and normal use. Therefore, leveraging the massive amounts of data generated by vehicles to improve battery detection and maintenance, enabling battery failure detection, prevention, and tracing of causes, is crucial for enhancing vehicle driving safety and ensuring normal driving for users. Summary of the Invention

[0003] This application provides a method, apparatus, and storage medium for detecting battery discharge, which can be used to detect, prevent, and trace the causes of battery discharge. The technical solution is as follows:

[0004] On one hand, embodiments of this application provide a method for detecting a storage battery, the method comprising:

[0005] The system acquires the operating status of key in-vehicle electrical equipment, as well as the voltage and current of the vehicle's battery. The key in-vehicle electrical equipment includes the air conditioning system, infotainment system, ventilation system, lighting system, and camera.

[0006] Based on the operating status of the key vehicle electrical equipment, the voltage and current of the battery, the preliminary risk detection results of the vehicle are obtained. The preliminary risk detection results are used to indicate whether there is a potential risk of battery depletion in the vehicle.

[0007] In response to the detection result that the vehicle has a potential risk of battery depletion, the vehicle's environmental conditions, mileage in the past first time period, and power consumption of the key vehicle electrical equipment are obtained.

[0008] Based on the operating status of the key vehicle electrical equipment, the voltage and current of the battery, the environmental conditions of the vehicle, the mileage driven in the past first time period, and the power consumption of the key vehicle electrical equipment, the detection results of factors strongly related to power loss, the power loss risk level of the battery, and the detection results of power reduction are obtained. The detection results of factors strongly related to power loss are used to indicate whether the vehicle has the factors strongly related to power loss, and the detection results of power reduction are used to indicate whether the vehicle will experience a significant power reduction in the second time period, which is shorter than the first time period.

[0009] In response to at least one of the following detection results: the vehicle has a strong correlation with the factors of battery depletion, the battery depletion risk level is greater than the level threshold, or the vehicle will experience a significant drop in battery power within the second time period, an early warning is issued for battery depletion.

[0010] On the other hand, a battery testing device is provided, the device comprising:

[0011] The first acquisition module is used to acquire the working status of key vehicle electrical equipment, the voltage and current of the vehicle's battery, and the key vehicle electrical equipment includes air conditioning system, infotainment system, ventilation system, lighting system and camera;

[0012] The second acquisition module is used to acquire preliminary risk detection results of the vehicle based on the working status of the key vehicle electrical equipment, the voltage and current of the battery, and the preliminary risk detection results are used to indicate whether the vehicle has a potential risk of battery depletion.

[0013] The third acquisition module is used to acquire, in response to the detection result that the vehicle has a potential risk of battery depletion, the environmental conditions of the vehicle, the mileage driven in the past first time period, and the power consumption of the key vehicle electrical equipment.

[0014] The fourth acquisition module is used to acquire, based on the working status of the key vehicle electrical equipment, the voltage and current of the battery, the environmental conditions of the vehicle, the mileage driven in the past first time period, and the power consumption of the key vehicle electrical equipment, the detection results of factors strongly related to power loss, the power loss risk level of the battery, and the detection results of power reduction. The detection results of factors strongly related to power loss are used to indicate whether the vehicle has the factors strongly related to power loss, and the detection results of power reduction are used to indicate whether the vehicle will experience a significant power reduction in the second time period, which is shorter than the first time period.

[0015] The early warning module is used to issue an early warning for battery depletion in response to at least one of the following: the detection result indicates that the vehicle has a strong correlation factor with the battery depletion; the battery depletion risk level is greater than the level threshold; or the detection result indicates that the vehicle will experience a significant drop in battery power within the second time period.

[0016] On the other hand, a non-transitory computer-readable storage medium is also provided, characterized in that the computer-readable storage medium stores a computer program, which is loaded and executed by a processor to implement any of the above-described battery detection methods.

[0017] On the other hand, a computer program product is also provided, the computer program product including computer instructions, which, when executed by a processor, implement the steps of any of the above-described battery detection methods.

[0018] The technical solution provided in this application brings at least the following beneficial effects:

[0019] This application initially assesses the potential risk of battery depletion by acquiring the operating status of key vehicle electrical equipment and the voltage and current of the vehicle's battery. If a potential risk of battery depletion is identified, it acquires information on the vehicle's environmental conditions, mileage over a past first period, and the power consumption of key vehicle electrical equipment. This information is then combined with the operating status of the key vehicle electrical equipment and the voltage and current of the vehicle's battery to further obtain detection results of factors strongly correlated with battery depletion, the battery depletion risk level, and the detection results of battery charge reduction, thus more accurately determining whether the vehicle is at risk of battery depletion. If at least one of the following is obtained: detection results of factors strongly correlated with battery depletion, a battery depletion risk level greater than a threshold level, or a detection result indicating a significant decrease in battery charge within a second period, a warning is issued regarding battery depletion. This improves the accuracy of battery depletion risk detection, better prevents battery depletion, thereby enhancing vehicle driving safety and ensuring normal driving for users. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of an implementation environment provided in an embodiment of this application;

[0022] Figure 2 This is a flowchart of a battery testing method provided in an embodiment of this application;

[0023] Figure 3 This is a schematic diagram of the structure of a battery testing device provided in an embodiment of this application. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0025] This application provides a method for testing a storage battery. Please refer to the following embodiments. Figure 1The diagram illustrates the implementation environment of the method provided in this application embodiment. This implementation environment may include: a vehicle controller 11, a micro data acquisition and power monitoring module 12, a voltage sensor 13, a current sensor 14, a user terminal display screen 15, a center console display screen 16, a vehicle audio system 17, a temperature sensor 18, a humidity sensor 19, a light intensity sensor 20, an atmospheric pressure sensor 21, an electromagnetic environment intensity sensor 22, and a vehicle navigation device 23.

[0026] Optionally, the micro data acquisition and power monitoring module 12 consists of a microcontroller, a power metering chip, a voltage sensor, a current sensor, and a power management unit. It is integrated on the control circuit board of the key vehicle electrical equipment that needs to be monitored. It is used to collect the operating status and power consumption of the key vehicle electrical equipment and send it to the vehicle controller 11. The voltage sensor 13 collects the battery voltage and sends it to the vehicle controller 11; the current sensor 14 collects the battery current and sends it to the vehicle controller 11.

[0027] In one possible implementation, temperature sensor 18 is waterproof and dustproof, installed below the front bumper of the vehicle, to collect the temperature of the vehicle's environment and send it to the vehicle controller 11; humidity sensor 19 is installed on the side of the roof rack on the vehicle's roof, to collect the humidity of the vehicle's environment and send it to the vehicle controller 11; light intensity sensor 20 is installed on the side of the roof rack on the vehicle's roof, to collect the light intensity of the vehicle's environment and send it to the vehicle controller 11; atmospheric pressure sensor is installed on the side of the vehicle near the tires, to collect the atmospheric pressure of the vehicle's environment and send it to the vehicle controller 11; electromagnetic environment intensity sensor is installed on the bottom of the vehicle away from metal parts, has high sensitivity, to collect the electromagnetic environment intensity of the vehicle's environment and send it to the vehicle controller 11; and the vehicle navigation device is installed on the antenna on the top of the vehicle, to obtain the mileage traveled by the vehicle in the past first time period and send it to the vehicle controller 11.

[0028] For example, the user terminal's display screen 15 and the center console's display screen 16 are used to display the battery low-power warning information, the cause of the battery low-power, a first curve, a second curve, and a third curve according to the instructions of the vehicle controller 11; the vehicle audio system 17 is used to broadcast the battery low-power warning information and the cause of the battery low-power according to the instructions of the vehicle controller 11. The vehicle controller 11, the micro data acquisition and power monitoring module 12, the voltage sensor 13, the current sensor 14, the user terminal's display screen 15, the center console's display screen 16, the vehicle audio system 17, the temperature sensor 18, the humidity sensor 19, the light intensity sensor 20, the atmospheric pressure sensor 21, the electromagnetic environment intensity sensor 22, and the vehicle navigation device 23 establish a communication connection via a wired or wireless network.

[0029] Based on the above Figure 1 The implementation environment shown in this application embodiment provides a battery detection method as follows: Figure 2 As shown, taking the application of this method to a vehicle controller as an example, the method includes steps 201-205.

[0030] In step 201, the vehicle controller acquires the operating status of key in-vehicle electrical equipment, the voltage and current of the vehicle's battery, and the key in-vehicle electrical equipment includes the air conditioning system, infotainment system, ventilation system, lighting system, and camera.

[0031] In one possible implementation, the vehicle controller acquires the operating status of key on-board electrical equipment and the voltage and current of the vehicle's battery in the following ways: the vehicle controller acquires the operating status of key on-board electrical equipment through a micro data acquisition and power monitoring module, acquires the battery voltage through a voltage sensor, and acquires the battery current through a current sensor. The key on-board electrical equipment includes the air conditioning system, infotainment system, ventilation system, lighting system, and cameras, and the operating status of the key on-board electrical equipment includes normal operating status and abnormal operating status.

[0032] Optionally, the micro data acquisition and power monitoring module consists of a microcontroller, a power metering chip, a voltage sensor, a current sensor, and a power management unit. It can be integrated onto the control circuit board of the critical vehicle electrical equipment that needs monitoring, for monitoring the operating status and power consumption of the critical vehicle electrical equipment. For example, to improve the accuracy and precision of the acquired battery voltage, measures include, but are not limited to: selecting a voltage sensor based on quantum tunneling composite materials with a measurement accuracy of ±0.001V; using gold-plated interfaces and special sealing processes between the voltage sensor and the positive and negative terminals and casing of the battery; adding filtering circuits and anti-interference components to the circuit where the battery is located; and equipping the voltage sensor with a backup power supply.

[0033] In step 202, the vehicle controller obtains preliminary risk detection results of the vehicle based on the operating status of key on-board electrical equipment, battery voltage and current. The preliminary risk detection results are used to indicate whether there is a potential risk of battery depletion in the vehicle.

[0034] Optionally, after obtaining the operating status of key on-board electrical equipment and the voltage and current of the battery, the vehicle controller obtains preliminary risk detection results of the vehicle based on the operating status of key on-board electrical equipment and the voltage and current of the battery. The preliminary risk detection results are used to indicate whether there is a potential risk of battery depletion in the vehicle.

[0035] For example, preliminary risk detection results of a vehicle are obtained based on the operating status of key on-board electrical equipment, the voltage and current of the battery, including: in response to at least one of the following: the battery voltage is less than a voltage threshold, the battery current is less than a current threshold, or at least one key on-board electrical equipment malfunctions, a detection result indicating that the vehicle has a potential risk of battery depletion is obtained.

[0036] In one possible implementation, after acquiring the operating status of key on-board electrical equipment, the battery voltage, and the current, the vehicle controller compares the battery voltage with a voltage threshold, and the battery current with a current threshold. Optionally, if the battery voltage is less than the voltage threshold, the battery current is less than the current threshold, or at least one key on-board electrical device exhibits an abnormal operating state, the vehicle controller obtains a detection result indicating a potential risk of battery depletion. If the battery voltage is greater than or equal to the voltage threshold, the battery current is greater than or equal to the current threshold, or all key on-board electrical equipment is operating normally, the vehicle controller obtains a detection result indicating no potential risk of battery depletion. The voltage and current thresholds can be set empirically.

[0037] In step 203, in response to the detection result that the vehicle has a potential risk of battery depletion, the vehicle controller acquires the vehicle's environmental conditions, the mileage driven in the past first time period, and the power consumption of key on-board electrical equipment.

[0038] For example, after obtaining the detection result that the vehicle has a potential risk of battery depletion, the vehicle controller obtains the environmental conditions of the vehicle, the mileage driven in the past first period, and the power consumption of key vehicle electrical equipment.

[0039] Optionally, the vehicle controller acquires information about the vehicle's environment by: acquiring the temperature of the environment via a temperature sensor installed below the front bumper of the vehicle, wherein the temperature sensor uses a waterproof and dustproof probe; acquiring the humidity of the environment via a humidity sensor installed on the side of the roof rack of the vehicle; acquiring the light intensity of the environment via a light intensity sensor installed on the side of the roof rack of the vehicle; acquiring the atmospheric pressure of the environment via an atmospheric pressure sensor installed on the side of the vehicle near the tires; and acquiring the electromagnetic environment intensity of the environment via a high-sensitivity electromagnetic environment intensity sensor installed on the bottom of the vehicle away from metal parts.

[0040] In one possible implementation, the vehicle controller obtains the vehicle's mileage over a past first time period by using an in-vehicle navigation device. The in-vehicle navigation device is installed near an antenna on the vehicle's roof. Optionally, the first time period can be set empirically. The vehicle controller also obtains the power consumption of key in-vehicle electrical equipment by using a micro data acquisition and power monitoring module.

[0041] Optionally, after obtaining the vehicle's environmental conditions, the mileage driven in the past first period, and the power consumption of key vehicle electrical equipment, the operating status of the key vehicle electrical equipment, the voltage and current of the battery, the vehicle's environmental conditions, the mileage driven in the past first period, and the power consumption of key vehicle electrical equipment are uploaded to the cloud platform for subsequent data analysis.

[0042] For example, after receiving data from the vehicle controller, including the operating status of key vehicle electrical devices, battery voltage and current, vehicle environment conditions, mileage traveled in the past period, and power consumption of key vehicle electrical devices, the cloud platform cleans and preprocesses the data uploaded by the vehicle controller, removing data that is too significantly different due to unforeseen circumstances to be used for subsequent battery drain analysis. The cloud platform also securely stores the cleaned and preprocessed data. Optionally, the cloud platform employs distributed storage technology, using redundant storage and data backup strategies to store important data on multiple different physical nodes, preventing data loss due to the failure of a single node.

[0043] In step 204, the vehicle controller obtains the detection results of factors strongly related to battery depletion, the battery depletion risk level, and the detection results of battery power reduction based on the operating status of key on-board electrical equipment, the voltage and current of the battery, the environmental conditions of the vehicle, the mileage driven in the first time period, and the power consumption of key on-board electrical equipment. The detection results of factors strongly related to battery depletion are used to indicate whether the vehicle has factors strongly related to battery depletion, and the detection results of battery power reduction are used to indicate whether the vehicle will experience a significant drop in battery power in the second time period, which is shorter than the first time period.

[0044] Optionally, after acquiring the operating status of key on-board electrical equipment, battery voltage and current, vehicle environment conditions, mileage traveled in the first time period, and power consumption of key on-board electrical equipment, the vehicle controller acquires detection results of factors strongly related to battery depletion, battery depletion risk level, and power consumption reduction based on these data. The detection results of factors strongly related to battery depletion indicate whether such factors exist, while the detection results of power consumption reduction indicate whether a significant power drop will occur in a second time period, which is shorter than the first time period. The second time period can be set empirically, but it must be shorter than the first time period.

[0045] In one possible implementation, the detection results of factors strongly related to battery depletion are obtained based on the operating status of key vehicle electrical equipment, battery voltage and current, vehicle environmental conditions, mileage within a first time period, and power consumption of key vehicle electrical equipment. This includes: performing correlation analysis on the operating status of key vehicle electrical equipment, battery voltage and current, vehicle environmental conditions, mileage within a first time period, and power consumption of key vehicle electrical equipment based on an association rule mining model, which is trained using historical association parameters that lead to battery depletion; and obtaining the detection results of factors strongly related to battery depletion in the vehicle based on the existence of parameters in the operating status of key vehicle electrical equipment, battery voltage and current, vehicle environmental conditions, mileage within a first time period, and power consumption of key vehicle electrical equipment that match the historical association parameters that lead to battery depletion.

[0046] For example, the vehicle controller obtains historical records of battery depletion from the cloud platform and trains an association rule mining model based on historical correlation parameters that led to battery depletion in the historical records. The input of the association rule mining model includes at least two parameters from the following: the operating status of key vehicle electrical equipment, battery voltage and current, vehicle environmental conditions, mileage within a first time period, and power consumption of key vehicle electrical equipment. The output is the detection results of factors strongly correlated with battery depletion.

[0047] Optionally, the association rule mining model is used to compare the input parameters with historical association parameters that led to battery depletion in the historical records to determine whether there are any strongly correlated factors for battery depletion among the input parameters. If there are parameters among the operating status of key vehicle electrical equipment, battery voltage and current, vehicle environmental conditions, mileage within the first time period, and power consumption of key vehicle electrical equipment that match the historical association parameters that led to battery depletion, a detection result is obtained indicating that the vehicle has a strongly correlated factor for battery depletion; if there are no parameters among the operating status of key vehicle electrical equipment, battery voltage and current, vehicle environmental conditions, mileage within the first time period, and power consumption of key vehicle electrical equipment that match the historical association parameters that led to battery depletion, a detection result is obtained indicating that the vehicle does not have a strongly correlated factor for battery depletion. For example, the criteria for judging whether the parameters match can be set based on experience.

[0048] In one possible implementation, the battery depletion risk level is obtained based on the operating status of key on-board electrical equipment, battery voltage and current, vehicle environment, mileage within a first duration, and power consumption of key on-board electrical equipment. This includes: using a multi-level classification prediction model to analyze the battery depletion risk level based on the operating status of key on-board electrical equipment, battery voltage and current, vehicle environment, mileage within a first duration, and power consumption of key on-board electrical equipment. The multi-level classification prediction model includes the depletion risk level under various parameter values.

[0049] For example, the inputs to the multi-level classification prediction model are the operating status of key vehicle electrical equipment, battery voltage and current, vehicle environmental conditions, mileage within a first time period, and power consumption of key vehicle electrical equipment. The output is the battery depletion risk level, used to calculate the depletion risk level under different parameter values ​​and their corresponding weighting coefficients. The multi-level classification prediction model can be pre-established, and the weighting coefficients for each parameter can be pre-set in experiments based on the actual impact of different parameters on battery depletion. Optionally, the risk level settings include: high risk, medium risk, and low risk.

[0050] In one possible implementation, the detection result of battery power reduction is obtained based on the operating status of key vehicle electrical equipment, battery voltage and current, vehicle environment, mileage within a first duration, and power consumption of key vehicle electrical equipment. This includes: predicting the battery's state of charge (SOC) curve using a reinforcement learning time series prediction model based on the operating status of key vehicle electrical equipment, battery voltage and current, vehicle environment, mileage within a first duration, and power consumption of key vehicle electrical equipment. The reinforcement learning time series prediction model is used to predict the future trend of parameter changes based on existing parameters. In response to the battery's SOC curve showing a downward trend and the battery's SOC falling below the SOC threshold within a second duration, a detection result is obtained indicating that the vehicle will experience a significant drop in battery power within the second duration.

[0051] For example, the reinforcement learning time series prediction model is pre-built and used to predict the future trend of parameter changes based on existing parameters. Therefore, the operating status of key vehicle electrical equipment, battery voltage and current, vehicle environment conditions, mileage in the first time period, and power consumption of key vehicle electrical equipment are input into the reinforcement learning time series prediction model to predict the SOC (State of Charge) curve of the battery in the second time period.

[0052] Optionally, the vehicle controller further analyzes the battery's SOC curve. If the battery's SOC curve shows a downward trend and the battery's SOC is below the SOC threshold within the second time period, the vehicle controller detects that the vehicle will experience a significant drop in battery power within the second time period. If the battery's SOC curve does not show a downward trend, or although it shows a downward trend, the battery's SOC is not below the SOC threshold within the second time period, the vehicle controller detects that the vehicle will not experience a significant drop in battery power within the second time period. For example, the SOC threshold can be set empirically.

[0053] In step 205, in response to at least one of the following detection results: the vehicle has a strong correlation with the battery depletion factor, the battery depletion risk level is greater than the level threshold, or the vehicle will experience a significant drop in battery power within a second time period, the vehicle controller issues a warning for battery depletion.

[0054] In one possible implementation, after obtaining the detection results of factors strongly related to battery depletion, the battery depletion risk level, and the detection results of battery charge reduction, the battery depletion risk level is compared with a level threshold. If at least one of the following is obtained: the vehicle has detection results of factors strongly related to battery depletion, the battery depletion risk level is greater than the level threshold, or the vehicle will experience a significant drop in battery charge within a second time period, it is determined that the vehicle's battery has depleted.

[0055] Optionally, after determining that the vehicle's battery is depleted, a first curve is generated based on the dynamic changes in the operating status and power consumption of key on-board electrical equipment; a second curve is generated based on the dynamic changes in the battery's voltage and current; and the cause of the battery depletion is analyzed based on the vehicle's environmental conditions, the mileage driven in the past first time period, and the first and second curves.

[0056] For example, a first curve is plotted based on the dynamic changes in the operating status and power consumption of key vehicle electrical equipment, wherein the first curve includes curves showing the dynamic changes in the operating status and power consumption of the vehicle electrical equipment; a second curve is plotted based on the dynamic changes in the voltage and current of the battery, wherein the second curve includes curves showing the dynamic changes in the voltage and current of the battery.

[0057] Optionally, the vehicle controller uses Bayesian network fault tree analysis to analyze the cause of battery depletion based on the vehicle's environmental conditions, mileage traveled within the past first time period, and first and second graphs. In one possible implementation, the vehicle controller can also query the cause of battery depletion based on a pre-established fault cause knowledge base, considering the vehicle's environmental conditions, mileage traveled within the past first time period, and first and second graphs, and can also analyze the cause of battery depletion through expert intervention. In another possible implementation, the risk level threshold can be set empirically, for example, set to medium risk.

[0058] For example, after obtaining at least one of the following detection results: the vehicle has factors strongly correlated with battery depletion, the battery depletion risk level is greater than a level threshold, or the vehicle will experience a significant drop in battery charge within a second time period, the vehicle controller issues a warning for battery depletion. For example, the vehicle controller's warning for battery depletion includes: generating a third curve based on the dynamic changes in the depletion risk level; displaying a battery depletion warning message, the cause of the battery depletion, a first curve, a second curve, and a third curve.

[0059] In one possible implementation, after plotting the third curve based on the dynamic changes in the battery depletion risk level, the vehicle controller displays the battery depletion warning information, the cause of battery depletion, the first curve, the second curve, and the third curve through the display screen of the user terminal connected to the vehicle and the display screen of the center console; and broadcasts the battery depletion warning information and the cause of battery depletion through the vehicle audio system.

[0060] Optionally, if the battery is depleted after the user leaves the vehicle, the vehicle controller, upon receiving a message from the user terminal requesting the remote shutdown of critical in-vehicle electrical equipment, will control the shutdown of non-essential critical in-vehicle electrical equipment, such as adjusting the air conditioning system to energy-saving mode and turning off the infotainment system and lighting system.

[0061] This application embodiment obtains the operating status of key vehicle electrical equipment and the voltage and current of the vehicle's battery to initially determine whether there is a potential risk of battery depletion. If a potential risk of battery depletion is determined, the application obtains the vehicle's environmental conditions, mileage traveled in the past first time period, and power consumption of key vehicle electrical equipment. This data is then combined with the operating status of the key vehicle electrical equipment and the voltage and current of the vehicle's battery to further obtain the detection results of factors strongly correlated with battery depletion, the battery depletion risk level, and the detection results of battery charge reduction, thus more accurately determining whether the vehicle is at risk of battery depletion. If at least one of the following is obtained: the detection results of factors strongly correlated with battery depletion are obtained, the battery depletion risk level is greater than a level threshold, or the detection results indicate that the vehicle will experience a significant drop in battery charge within a second time period, an early warning for battery depletion is issued. This improves the accuracy of battery depletion risk detection, better prevents battery depletion, thereby improving vehicle driving safety and ensuring normal driving for users.

[0062] See Figure 3 This application provides a battery testing device, which includes:

[0063] The first acquisition module 301 is used to acquire the working status of key vehicle electrical equipment, the voltage and current of the vehicle's battery, and the key vehicle electrical equipment includes the air conditioning system, infotainment system, ventilation system, lighting system and camera;

[0064] The second acquisition module 302 is used to acquire preliminary risk detection results of the vehicle based on the working status of key vehicle electrical equipment, the voltage and current of the battery. The preliminary risk detection results are used to indicate whether there is a potential risk of battery depletion in the vehicle.

[0065] The third acquisition module 303 is used to acquire the environmental conditions of the vehicle, the mileage driven in the past first time period, and the power consumption of key vehicle electrical equipment in response to the detection result that the vehicle has a potential risk of battery depletion.

[0066] The fourth acquisition module 304 is used to acquire the detection results of factors strongly related to power loss, the power loss risk level of the battery, and the power consumption of the key vehicle electrical equipment based on the working status of the key vehicle electrical equipment, the voltage and current of the battery, the environmental conditions of the vehicle, the mileage in the past first time period, and the power consumption of the key vehicle electrical equipment. The detection results of factors strongly related to power loss are used to indicate whether the vehicle has factors strongly related to power loss, and the detection results of power consumption are used to indicate whether the vehicle will experience a significant drop in power consumption in the second time period, which is shorter than the first time period.

[0067] The warning module 305 is used to issue a warning for battery depletion in response to at least one of the following: the detection results indicate that the vehicle has strong factors related to battery depletion, the battery depletion risk level is greater than the level threshold, or the detection results indicate that the vehicle will experience a significant drop in battery power within a second time period.

[0068] In one possible implementation, the fourth acquisition module 304 is used to perform correlation analysis on the working status of key vehicle electrical equipment, battery voltage and current, vehicle environment, mileage within a first time period, and power consumption of key vehicle electrical equipment based on an association rule mining model. The association rule mining model is trained by historical association parameters that lead to battery depletion. In response to the existence of parameters in the working status of key vehicle electrical equipment, battery voltage and current, vehicle environment, mileage within a first time period, and power consumption of key vehicle electrical equipment that match the historical association parameters that lead to battery depletion, the detection result of strong correlation factors for vehicle depletion is obtained.

[0069] In one possible implementation, the fourth acquisition module 304 is used to analyze the battery depletion risk level based on the working status of key vehicle electrical equipment, battery voltage and current, vehicle environment, mileage within the first time period, and power consumption of key vehicle electrical equipment through a multi-level classification prediction model. The multi-level classification prediction model includes the depletion risk level under various parameter values.

[0070] In one possible implementation, the fourth acquisition module 304 is used to predict the state of charge (SOC) curve of the battery based on the working status of key vehicle electrical equipment, the voltage and current of the battery, the environmental conditions of the vehicle, the mileage during the first time period, and the power consumption of the key vehicle electrical equipment, using a reinforcement learning time series prediction model. The reinforcement learning time series prediction model is used to predict the future trend of parameter changes based on existing parameters. In response to the battery SOC curve showing a downward trend and the battery SOC being lower than the SOC threshold during the second time period, a detection result is obtained that the vehicle will experience a significant drop in battery power during the second time period.

[0071] In one possible implementation, the device further includes: a first generation module, configured to generate a first curve based on the dynamic changes in the operating status and power consumption of key on-board electrical equipment in response to at least one of the following: obtaining detection results indicating that the vehicle has strong correlation factors with battery depletion, the battery depletion risk level is greater than a level threshold, or obtaining detection results indicating that the vehicle will experience a significant drop in battery power within a second time period; a second generation module, configured to generate a second curve based on the dynamic changes in battery voltage and current; and a risk module, configured to analyze the causes of battery depletion based on the vehicle's environmental conditions, the mileage traveled in the past first time period, and the first and second curves.

[0072] In one possible implementation, the early warning module 305 is used to generate a third curve based on the dynamic changes in the battery depletion risk level; and to display the battery depletion warning information, the cause of the battery depletion, the first curve, the second curve, and the third curve.

[0073] In one possible implementation, the second acquisition module 302 is used to acquire a detection result indicating that the vehicle has a potential risk of battery depletion in response to at least one of the following: the battery voltage is less than a voltage threshold, the battery current is less than a current threshold, or at least one key vehicle electrical device malfunctions.

[0074] This device initially assesses the potential risk of battery depletion by acquiring the operating status of key vehicle electrical equipment and the voltage and current of the vehicle's battery. If a potential risk of battery depletion is identified, it acquires information about the vehicle's environment, mileage over a past first period, and the power consumption of key vehicle electrical equipment. This information is then combined with the operating status of the key electrical equipment and the battery's voltage and current to further obtain detection results of factors strongly correlated with battery depletion, the battery depletion risk level, and the detection results of battery charge reduction, thus more accurately determining whether the vehicle is at risk of battery depletion. If at least one of the following is detected: the battery depletion risk level exceeds a certain threshold, or a significant drop in battery charge is expected within a second period, a warning is issued regarding battery depletion. This improves the accuracy of battery depletion risk detection, better prevents battery depletion, enhances vehicle driving safety, and ensures normal driving for the user.

[0075] It should be noted that the apparatus provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0076] In an exemplary embodiment, a computer-readable storage medium is also provided, which stores at least one computer program, which is loaded and executed by a processor of a computer device to enable the computer to implement any of the above-described battery detection methods.

[0077] In one possible implementation, the aforementioned computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device, etc.

[0078] In an exemplary embodiment, a computer program product or computer program is also provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform any of the aforementioned battery detection methods.

[0079] It should be noted that all information (including but not limited to user equipment information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in this application have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the operating status of key vehicle electrical equipment, battery voltage and current, vehicle environmental conditions, mileage in the past first hour, power consumption of key vehicle electrical equipment, detection results of factors strongly related to battery depletion, battery depletion risk level, and battery charge reduction detection results involved in this application were all obtained with full authorization.

[0080] It should be understood that "multiple" as used in this article refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0081] It should be noted that the terms "first," "second," etc. (if applicable) in the specification and claims of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0082] The above description is merely an exemplary embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method for testing a storage battery, characterized in that, The method includes: The system acquires the operating status of key in-vehicle electrical equipment, as well as the voltage and current of the vehicle's battery. The key in-vehicle electrical equipment includes the air conditioning system, infotainment system, ventilation system, lighting system, and camera. Based on the operating status of the key vehicle electrical equipment, the voltage and current of the battery, the preliminary risk detection results of the vehicle are obtained. The preliminary risk detection results are used to indicate whether there is a potential risk of battery depletion in the vehicle. In response to the detection result that the vehicle has a potential risk of battery depletion, the vehicle's environmental conditions, mileage in the past first time period, and power consumption of the key vehicle electrical equipment are obtained. Based on the operating status of the key vehicle electrical equipment, the voltage and current of the battery, the environmental conditions of the vehicle, the mileage driven in the past first time period, and the power consumption of the key vehicle electrical equipment, the detection results of factors strongly related to power loss, the power loss risk level of the battery, and the detection results of power reduction are obtained. The detection results of factors strongly related to power loss are used to indicate whether the vehicle has the factors strongly related to power loss, and the detection results of power reduction are used to indicate whether the vehicle will experience a significant power reduction in the second time period, which is shorter than the first time period. In response to at least one of the following detection results: the vehicle has a strong correlation with the factors of battery depletion, the battery depletion risk level is greater than the level threshold, or the vehicle will experience a significant drop in battery power within the second time period, an early warning is issued for battery depletion.

2. The method according to claim 1, characterized in that, The detection results of factors strongly correlated with battery depletion, obtained based on the operating status of the key vehicle electrical equipment, the voltage and current of the battery, the environmental conditions of the vehicle, the mileage traveled in the past first period, and the power consumption of the key vehicle electrical equipment, include: The association rule mining model is used to perform correlation analysis on the working status of the key vehicle electrical equipment, the voltage and current of the battery, the environmental conditions of the vehicle, the mileage within the first time period, and the power consumption of the key vehicle electrical equipment. The association rule mining model is trained by historical association parameters that lead to the battery depletion. In response to the presence of parameters that match the historical correlation parameters that cause the battery to deplete among the operating status of the key vehicle electrical equipment, the voltage and current of the battery, the environmental conditions of the vehicle, the mileage within the first time period, and the power consumption of the key vehicle electrical equipment, a detection result is obtained indicating that the vehicle has a strong correlation factor for battery depletion.

3. The method according to claim 1, characterized in that, The battery depletion risk level is determined based on the operating status of the key vehicle electrical equipment, the voltage and current of the battery, the environmental conditions of the vehicle, the mileage traveled within the first time period, and the power consumption of the key vehicle electrical equipment, including: Using a multi-level classification prediction model, the battery's power depletion risk level is analyzed based on the working status of the key vehicle electrical equipment, the voltage and current of the battery, the vehicle's environmental conditions, the mileage within the first time period, and the power consumption of the key vehicle electrical equipment. The multi-level classification prediction model includes the power depletion risk level under various parameter values.

4. The method according to claim 1, characterized in that, The detection results of power consumption reduction are obtained based on the operating status of the key vehicle electrical equipment, the voltage and current of the battery, the environmental conditions of the vehicle, the mileage traveled within the first time period, and the power consumption of the key vehicle electrical equipment, including: Based on the working status of the key vehicle electrical equipment, the voltage and current of the battery, the environmental conditions of the vehicle, the mileage within the first time period, and the power consumption of the key vehicle electrical equipment, the state of charge (SOC) curve of the battery is predicted by a reinforcement learning time series prediction model. The reinforcement learning time series prediction model is used to predict the future trend of parameter changes based on existing parameters. In response to the downward trend of the SOC curve of the battery and the fact that the SOC of the battery is lower than the SOC threshold within the second time period, a detection result is obtained that the vehicle will experience a significant drop in battery power within the second time period.

5. The method according to claim 1, characterized in that, The method further includes: In response to at least one of the following: the detection result indicates that the vehicle has the strong correlation factor of power loss, the power loss risk level of the battery is greater than the level threshold, or the detection result indicates that the battery power will drop significantly within the second time period, a first curve is generated based on the dynamic changes in the working status and power consumption of the key vehicle electrical equipment. A second curve is generated based on the dynamic changes in the voltage and current of the battery. The reasons for the battery depletion are analyzed based on the vehicle's environmental conditions, the mileage traveled in the past first period of time, and the first and second curves.

6. The method according to claim 5, characterized in that, The method of providing early warning for low battery power includes: A third curve is generated based on the dynamic changes in the power shortage risk level. The system displays a message indicating that the battery is low on power, the reason for the low battery status, the first graph, the second graph, and the third graph.

7. The method according to claim 1, characterized in that, The preliminary risk detection results of the vehicle based on the operating status of the key on-board electrical equipment and the voltage and current of the battery include: In response to at least one of the following: the battery voltage is less than a voltage threshold, the battery current is less than a current threshold, or at least one of the critical on-board electrical devices malfunctions, a detection result is obtained indicating that the vehicle has a potential risk of battery depletion.

8. A battery testing device, characterized in that, The device includes: The first acquisition module is used to acquire the working status of key vehicle electrical equipment, the voltage and current of the vehicle's battery, and the key vehicle electrical equipment includes air conditioning system, infotainment system, ventilation system, lighting system and camera; The second acquisition module is used to acquire preliminary risk detection results of the vehicle based on the working status of the key vehicle electrical equipment, the voltage and current of the battery, and the preliminary risk detection results are used to indicate whether the vehicle has a potential risk of battery depletion. The third acquisition module is used to acquire, in response to the detection result that the vehicle has a potential risk of battery depletion, the environmental conditions of the vehicle, the mileage driven in the past first time period, and the power consumption of the key vehicle electrical equipment. The fourth acquisition module is used to acquire, based on the working status of the key vehicle electrical equipment, the voltage and current of the battery, the environmental conditions of the vehicle, the mileage driven in the past first time period, and the power consumption of the key vehicle electrical equipment, the detection results of factors strongly related to power loss, the power loss risk level of the battery, and the detection results of power reduction. The detection results of factors strongly related to power loss are used to indicate whether the vehicle has the factors strongly related to power loss, and the detection results of power reduction are used to indicate whether the vehicle will experience a significant power reduction in the second time period, which is shorter than the first time period. The early warning module is used to issue an early warning for battery depletion in response to at least one of the following: the detection result indicates that the vehicle has a strong correlation factor with the battery depletion; the battery depletion risk level is greater than the level threshold; or the detection result indicates that the vehicle will experience a significant drop in battery power within the second time period.

9. A computer program product comprising computer instructions that, when executed by a processor, implement the steps of the battery detection method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which is loaded and executed by a processor to implement the battery detection method as described in any one of claims 1 to 7.

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