Automotive battery management system sleep control method, computer device and storage medium

Abstract

The present invention discloses a sleep control method for an automotive battery management system, a computer device, and a storage medium. The method includes the following steps: testing the automotive battery, obtaining battery status data, performing an assessment based on the battery status data, determining risk level information and occurrence probability information, and controlling the sleep mode of the battery management system based on the risk level information and the occurrence probability information. The present invention can control the sleep mode of the battery management system based on the battery status data of the automotive battery, and can control the battery management system to enter a delayed sleep mode when the battery status data of the automotive battery meets certain conditions. The battery management system in delayed sleep mode has a longer non-sleep time than a sleep mode according to preset rules, and can more fully monitor the automotive battery, thereby facilitating safe use and performance of the automotive battery. The present invention has broad application in the automotive technology field.

Description

Technical Field

[0001] The present invention relates to the field of automobile technology, and in particular to a dormancy control method, a computer device, and a storage medium for an automobile battery management system. Background Art

[0002] A battery management system (BMS), commonly known as a battery nanny or battery steward, intelligently manages and maintains each battery cell, monitoring battery status to prevent overcharging and over-discharging, thereby extending battery life. The power batteries used in electric vehicles (including pure electric vehicles and hybrid electric vehicles) feature large capacity, high discharge current, harsh operating environments, and high safety requirements. Therefore, they are typically equipped with a comprehensive battery management system for operational monitoring and control. For example, by monitoring parameters such as battery voltage, current, and temperature, a battery management system can determine whether a battery anomaly is occurring, enabling timely remedial measures to prevent accidents or timely reporting of incidents to minimize losses.

[0003] Since the battery management system itself is implemented through the operation of hardware and software systems, it consumes energy and generates heat. To achieve energy conservation and reduce heat generation, the battery management system can be set to a dormant state after the power battery is powered off, thereby reducing energy consumption. However, due to the high activity of the substances within the power battery, even when the power battery is powered off, it still faces the risk of abnormal events such as short circuits and fires. If the battery management system is put into a dormant state too early, the power battery will lose the protection of the battery management system, and the risk of abnormal events will remain at a high level. Summary of the Invention

[0004] In view of the technical problems in current automotive technology, such as the battery management system being prone to prematurely entering a dormant state, thereby causing the power battery to lose protection, the purpose of the present invention is to provide a dormancy control method, a computer device, and a storage medium for an automotive battery management system.

[0005] In one aspect, an embodiment of the present invention includes a sleep control method for an automobile battery management system, the sleep control method for an automobile battery management system comprising the following steps:

[0006] Test the car battery and obtain battery status data;

[0007] Evaluate the battery status data to determine danger level information and occurrence probability information; the danger level information indicates the danger level of the abnormal event faced by the vehicle battery, and the occurrence probability information indicates the probability of occurrence of the abnormal event faced by the vehicle battery;

[0008] The sleep mode of the battery management system is controlled according to the danger level information and the occurrence probability information.

[0009] Furthermore, the detecting of the vehicle battery to obtain battery status data includes:

[0010] Detecting the state of the vehicle battery before the first power-off event to obtain historical battery state data;

[0011] Detect the state of the car battery when the first power-off event occurs and obtain real-time battery status data;

[0012] The battery historical status data and the battery real-time status data are used as the battery status data.

[0013] Furthermore, the evaluating the battery status data to determine the danger level information and the occurrence probability information includes:

[0014] Evaluate based on the battery historical status data to obtain first evaluation information of the degree of danger, and evaluate based on the battery real-time status data to obtain first evaluation information of the probability of occurrence;

[0015] The first evaluation information of the risk level is used as the risk level information, and the first evaluation information of the occurrence probability is used as the occurrence probability information.

[0016] Furthermore, the evaluating the battery status data to determine the danger level information and the occurrence probability information includes:

[0017] Evaluate based on the real-time battery status data to obtain second evaluation information on the degree of danger, and evaluate based on the historical battery status data to obtain second evaluation information on the probability of occurrence;

[0018] The second evaluation information of the risk level is used as the risk level information, and the second evaluation information of the occurrence probability is used as the occurrence probability information.

[0019] Furthermore, the evaluating the battery status data to determine the danger level information and the occurrence probability information includes:

[0020] Evaluate based on the battery historical status data to obtain first evaluation information of the degree of danger, and evaluate based on the battery real-time status data to obtain first evaluation information of the probability of occurrence;

[0021] Evaluate based on the real-time battery status data to obtain second evaluation information on the degree of danger, and evaluate based on the historical battery status data to obtain second evaluation information on the probability of occurrence;

[0022] The danger level information and the occurrence probability information are determined based on the first danger level assessment information, the first occurrence probability assessment information, the second danger level assessment information, and the second occurrence probability assessment information.

[0023] Furthermore, the determining the danger level information and the occurrence probability information based on the first danger level assessment information, the first occurrence probability assessment information, the second danger level assessment information, and the second occurrence probability assessment information includes:

[0024] determining a first product according to the first assessment information of the degree of danger and the first assessment information of the probability of occurrence;

[0025] determining a second product according to the second evaluation information of the degree of danger and the second evaluation information of the probability of occurrence;

[0026] When the deviation between the first product and the second product is less than a preset threshold, the first risk level assessment information or the second risk level assessment information is used as the risk level information, and the first occurrence probability assessment information or the second occurrence probability assessment information is used as the occurrence probability information;

[0027] When the deviation between the first product and the second product is greater than or equal to a preset threshold, the larger of the first assessment information on the degree of danger and the second assessment information on the degree of danger is used as the degree of danger information, and the sum of the first assessment information on the probability of occurrence and the second assessment information on the probability of occurrence is used as the probability of occurrence information.

[0028] Furthermore, controlling the sleep mode of the battery management system according to the danger level information and the occurrence probability information includes:

[0029] Determining a delay period based on the risk level information and the occurrence probability information;

[0030] According to the delay time, the battery management system is controlled to perform delayed sleep.

[0031] Furthermore, determining the delay period according to the danger level information and the occurrence probability information includes:

[0032] Setting thresholds for the degree of danger and probability of occurrence;

[0033] When the danger level information is greater than the danger level threshold and the occurrence probability information is greater than the occurrence probability threshold, determining a third product based on the danger level information and the occurrence probability information, determining a fourth product based on the danger level threshold and the occurrence probability threshold, and determining the delay duration in a positive correlation based on a deviation between the third product and the fourth product;

[0034] When the danger level information is greater than the danger level threshold and the occurrence probability information is less than or equal to the occurrence probability threshold, the delay time is positively correlated with the deviation between the danger level information and the danger level threshold;

[0035] When the danger level information is less than or equal to the danger level threshold and the occurrence probability information is greater than the occurrence probability threshold, the delay duration is positively correlated with the deviation between the occurrence probability information and the occurrence probability threshold;

[0036] When the danger level information is less than or equal to the danger level threshold and the occurrence probability information is less than or equal to the occurrence probability threshold, the delay time is set to zero.

[0037] On the other hand, an embodiment of the present invention further includes a computer device including a memory and a processor, wherein the memory is used to store at least one program, and the processor is used to load at least one program to execute the automotive battery management system sleep control method of the embodiment.

[0038] On the other hand, an embodiment of the present invention further includes a computer-readable storage medium storing a program executable by a processor. When the program is executed by the processor, it is used to execute the automotive battery management system sleep control method of the embodiment.

[0039] The beneficial effects of the present invention are as follows: through the automotive battery management system sleep control method in the embodiment, the sleep mode of the battery management system can be controlled according to the battery status data of the automotive battery, and the battery management system can be controlled to perform delayed sleep when the battery status data of the automotive battery meets certain conditions; the battery management system that performs delayed sleep has a longer non-sleep time than the sleep according to preset rules, and can more fully monitor the automotive battery, which is beneficial to ensuring the safety of use and performance of the automotive battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 Schematic diagram of the steps of the automotive battery management system sleep control method in an embodiment;

[0041] Figure 2 This is a schematic diagram of the principle of step S201A in the embodiment;

[0042] Figure 3 This is a schematic diagram of the principle of step S201B in the embodiment;

[0043] Figure 4 Schematic diagram of the principle of S201C-S202C in the embodiment;

[0044] Figure 5 Schematic diagram of the principle of the automotive battery management system sleep control method in the embodiment. DETAILED DESCRIPTION

[0045] In this embodiment, refer to Figure 1 , the automotive battery management system sleep control method includes the following steps:

[0046] S1. Test the car battery and obtain battery status data;

[0047] S2. Evaluate the battery status data to determine the degree of danger and probability of occurrence;

[0048] S3. Control the sleep mode of the battery management system according to the danger level information and the occurrence probability information.

[0049] Each step in the automotive battery management system sleep control method can be executed by the battery management system installed in the vehicle, or by a control module independent of the battery management system. Specifically, the steps in the automotive battery management system sleep control method are described using the battery management system as an example.

[0050] In this embodiment, the vehicle battery mentioned in each step of the vehicle battery management system sleep control method can refer to a power battery installed in an electric vehicle, such as a pure electric vehicle or a hybrid electric vehicle, or a non-power battery, such as a storage battery that powers the vehicle computer system. In this embodiment, the power battery is used as an example of a vehicle battery.

[0051] In step S1, the battery management system (BMS) detects the vehicle battery and obtains battery status data, which represents the operating parameters of the vehicle battery at present or in the past.

[0052] In step S2, the battery management system BMS runs an evaluation algorithm locally, or calls a cloud server to run an evaluation algorithm, to evaluate the battery status data obtained in step S1, so as to determine the probability of occurrence of abnormal events such as overheating, overcharging, over-discharging, short circuit, fire, etc. in the vehicle battery under the state represented by the battery status data, that is, the occurrence probability information; and if it is assumed that an abnormal event occurs, then the danger level of such abnormal event, that is, the quantitative value of the loss of life and property that such an abnormal event will cause, that is, the danger level information.

[0053] In this embodiment, the formats of the danger level information and the occurrence probability information are shown in Table 1.

[0054] Table 1

[0055]

[0056] In Table 1, the danger level information is a quantified result based on the damage (maximum level or average level) that may be caused when an abnormal event occurs. For example, a larger number in the danger level information indicates more severe damage when an abnormal event occurs.

[0057] In step S3, the battery management system BMS determines its own sleep mode according to the danger level information and occurrence probability information obtained in step S2, or the control module controls the sleep mode of the battery management system BMS according to the danger level information and occurrence probability information obtained in step S2.

[0058] Specifically, the sleep mode of the battery management system (BMS) in step S3 refers to whether the BMS needs to delay sleep, and if so, the specific delay duration. Delayed sleep is the opposite of immediate sleep, in which the BMS immediately enters sleep at a certain point in time (e.g., when the power battery is powered down, i.e., when the vehicle's motor and other power components are disconnected from the power battery and the power battery no longer provides power). Delayed sleep is when the BMS enters sleep after a certain delay (i.e., the delay duration) relative to the start of immediate sleep. Within the delay duration, the BMS remains in a non-sleeping state, performing tasks such as battery management.

[0059] In this embodiment, by executing steps S1-S3, the sleep mode of the battery management system can be controlled according to the battery status data of the vehicle battery, and the battery management system can be controlled to perform delayed sleep when the battery status data of the vehicle battery meets certain conditions; the battery management system that performs delayed sleep has a longer non-sleep time than the sleep time according to the preset rules, and can more fully monitor the vehicle battery, which is beneficial to ensuring the safety of use and performance of the vehicle battery.

[0060] In this embodiment, when executing step S1, that is, testing the vehicle battery and obtaining battery status data, the following steps may be performed:

[0061] S101. Detect the state of the car battery before the first power-off event to obtain historical battery status data;

[0062] S102. Detect the state of the car battery when the first power-off event occurs to obtain real-time battery status data;

[0063] S103. Use the battery historical status data and the battery real-time status data as the battery status data.

[0064] Steps S101-S103 are steps for detecting battery status data. In this embodiment, it is assumed that the vehicle battery triggers the battery management system BMS to enter sleep mode after a power-off event, and this power-off event is the first power-off event.

[0065] Reference Figure 5 In step S101, the battery management system BMS detects the state of the vehicle battery before the first power-off event. Specifically, the battery management system BMS can detect the state of the vehicle battery. Battery performance data (such as cycle count, health, etc.), Recent discharge status (such as discharge depth, voltage and current, etc.) and The data such as the most recent charging status (such as charging method, charging time, charging amount, etc.) are recorded in the form of a time series to obtain the battery historical status data.

[0066] Reference Figure 5 In step S102, the battery management system BMS detects the state of the vehicle battery when the first power-off event (e.g., the vehicle's power system is turned off, commonly known as "pulling the car key") occurs. Specifically, the battery management system BMS can detect the state of the vehicle battery. Remaining power, Battery voltage (e.g. overall voltage, single cell maximum voltage, single cell minimum voltage, etc.), Cell temperature (e.g. overall temperature, maximum cell temperature, minimum cell temperature, etc.) and Other parameters (such as insulation resistance, current fluctuation, pressure change, etc.) and other data are collected to obtain real-time battery status data.

[0067] In step S103, the battery status data to be detected in step S1 is composed of the battery historical status data obtained in step S101 and the battery real-time status data obtained in step S102.

[0068] In this embodiment, when executing step S2, that is, evaluating the battery status data to determine the danger level information and the occurrence probability information, the following steps may be specifically performed:

[0069] S201A is evaluated based on the historical battery status data to obtain the first assessment information on the degree of danger, based on the real-time battery status data to obtain the first assessment information on the probability of occurrence;

[0070] S202A. Use the first evaluation information of the degree of danger as the degree of danger information, and use the first evaluation information of the probability of occurrence as the probability of occurrence information.

[0071] Steps S201A-S202A are the first execution mode of step S2.

[0072] Reference Figure 2 In step S201A, the battery management system BMS can run the first artificial intelligence model and the second artificial intelligence model locally or call the first artificial intelligence model running in the cloud; wherein, the first artificial intelligence model has been trained to identify data of the battery historical status data type and output data of the danger level information type; the second artificial intelligence model has also been trained to identify data of the battery real-time status data type and output data of the occurrence probability information type.

[0073] Reference Figure 2 In step S201A, the battery management system (BMS) inputs the historical battery status data into a first artificial intelligence model. The first artificial intelligence model extracts and identifies vital signs from the historical battery status data and outputs first risk assessment information. The first risk assessment information predicts the severity of damage that would occur if an abnormal condition were to occur in the vehicle battery, given the historical battery status data over a period of time.

[0074] Reference Figure 2 In step S201A, the battery management system (BMS) inputs the real-time battery status data into a second artificial intelligence model. The second artificial intelligence model extracts and identifies vital signs from the real-time battery status data and outputs first probability of occurrence assessment information. The first probability of occurrence assessment information predicts the probability of an abnormal condition occurring in the vehicle battery, given that the state of the vehicle battery at the time of the first power-off event is the real-time battery status data.

[0075] In step S202A, the first evaluation information of the degree of danger obtained in step S201A can be directly used as the degree of danger information obtained in step S2, and the first evaluation information of the probability of occurrence obtained in step S201A can be directly used as the probability of occurrence information obtained in step S2.

[0076] In this embodiment, when executing step S2, that is, evaluating the battery status data to determine the danger level information and the occurrence probability information, the following steps may be specifically performed:

[0077] S201B is evaluated based on real-time battery status data to obtain a second assessment of the degree of danger, based on historical battery status data to obtain a second assessment of the probability of occurrence;

[0078] S202B. Use the second evaluation information on the degree of danger as the degree of danger information, and use the second evaluation information on the probability of occurrence as the probability of occurrence information.

[0079] Steps S201B-S202B are a second execution method of step S2.

[0080] Reference Figure 3 In step S201B, the battery management system BMS can run locally or call the third artificial intelligence model and the fourth artificial intelligence model running in the cloud; wherein, the third artificial intelligence model has been trained to identify data of the battery real-time status data type and output data of the danger level information type; the fourth artificial intelligence model has also been trained to identify data of the battery historical status data type and output data of the occurrence probability information type.

[0081] Reference Figure 3 In step S201B, the battery management system (BMS) inputs the real-time battery status data into a third artificial intelligence model. The third artificial intelligence model extracts and identifies vital signs from the real-time battery status data and outputs a second assessment of the degree of danger. The second assessment of the degree of danger indicates the severity of damage that would occur if an abnormality were to occur in the vehicle battery, given the real-time battery status data over a period of time.

[0082] Reference Figure 3 In step S201B, the battery management system (BMS) inputs the historical battery status data into a fourth artificial intelligence model. The fourth artificial intelligence model extracts and identifies characteristics of the historical battery status data and outputs second probability of occurrence assessment information. The second probability of occurrence assessment information predicts the probability of the vehicle battery experiencing an abnormal condition, given that the vehicle battery's state at the time of the first power-off event corresponds to the historical battery status data.

[0083] In step S202B, the second evaluation information of the degree of danger obtained in step S201B can be directly used as the degree of danger information obtained in step S2, and the second evaluation information of the probability of occurrence obtained in step S201B can be directly used as the probability of occurrence information obtained in step S2.

[0084] In steps S201A-S202A and S201B-S202B, the first artificial intelligence model, the second artificial intelligence model, the third artificial intelligence model, and the fourth artificial intelligence model used may be convolutional neural networks.

[0085] By executing steps S201A-S202A or S201B-S202B, the feature extraction and prediction capabilities of the artificial intelligence model can be utilized to determine the degree of danger information and occurrence probability information based on the battery status data.

[0086] In this embodiment, when executing step S2, that is, evaluating the battery status data to determine the danger level information and the occurrence probability information, the following steps may be specifically performed:

[0087] S201C is evaluated based on the historical battery status data to obtain the first assessment information on the degree of danger, based on the real-time battery status data to obtain the first assessment information on the probability of occurrence;

[0088] S202C is evaluated based on real-time battery status data to obtain a second assessment of the degree of danger, based on historical battery status data to obtain a second assessment of the probability of occurrence;

[0089] S203C. Determine the danger level information and the occurrence probability information based on the first danger level assessment information, the first occurrence probability assessment information, the second danger level assessment information, and the second occurrence probability assessment information.

[0090] Steps S201C-S202C are a third execution method of step S2.

[0091] Reference Figure 4 The principle of step S201C is the same as that of step S201A, and the principle of step S202C is the same as that of step S201B.

[0092] Reference Figure 4 When executing step S203C, that is, determining the danger level information and the occurrence probability information based on the first danger level assessment information, the first occurrence probability assessment information, the second danger level assessment information, and the second occurrence probability assessment information, the following steps may be specifically performed:

[0093] S203C01. Determine the first product based on the first assessment information of the degree of danger and the probability of occurrence of the first assessment information;

[0094] S203C02. Determine the second product based on the second assessment information of the degree of danger and the probability of occurrence of the second assessment information;

[0095] S203C03. When the deviation between the first product and the second product is less than a preset threshold, the first assessment information of the degree of danger or the second assessment information of the degree of danger is used as the degree of danger information, and the first assessment information of the probability of occurrence or the second assessment information of the probability of occurrence is used as the probability information;

[0096] S203C04. When the deviation between the first product and the second product is greater than or equal to a preset threshold, the larger of the first assessment information on the degree of danger and the second assessment information on the degree of danger is used as the degree of danger information, and the sum of the first assessment information on the probability of occurrence and the second assessment information on the probability of occurrence is used as the probability of occurrence information.

[0097] The first product calculated in step S203C01 is the expected value of the damage caused by the abnormal event occurring in the power battery obtained using the first artificial intelligence model and the second artificial intelligence model.

[0098] The second product calculated in step S203C02 is the expected value of the damage caused by the abnormal event occurring in the power battery obtained using the third artificial intelligence model and the fourth artificial intelligence model.

[0099] The absolute value of the difference between the first product and the second product can be calculated as the deviation between the first product and the second product. A preset threshold is set. If the deviation between the first product and the second product is less than the preset threshold, it can be determined that the first product and the second product are close and the first product is considered equal to the second product. If the deviation between the first product and the second product is greater than or equal to the preset threshold, it can be determined that the first product and the second product are far apart.

[0100] When the deviation between the first product and the second product is less than a preset threshold, that is, the first product is close to the second product, step S203C03 can be executed, and either the first assessment information of the degree of danger or the second assessment information of the degree of danger is used as the degree of danger information, and either the first assessment information of the probability of occurrence or the second assessment information of the probability of occurrence is used as the probability of occurrence information.

[0101] If the deviation between the first and second products is greater than or equal to a preset threshold, meaning the first and second products differ significantly, step S203C04 may be executed to select the greater of the first and second risk assessment information as the risk assessment information, and use the sum of the first and second probability assessment information as the probability assessment information. For example, if the first risk assessment information is greater than the second risk assessment information, the first risk assessment information is selected as the risk assessment information, and the sum of the first and second probability assessment information is calculated as the probability assessment information.

[0102] In this embodiment, the principle of executing steps S203C01-S203C04 is: if the expected values ​​of the damage caused by abnormal events in the power battery obtained by using different artificial intelligence models are close, then it is equivalent to cross-validation, and the results output by any group of artificial intelligence models can be selected as the danger level information and occurrence probability information; if the expected values ​​of the damage caused by abnormal events in the power battery obtained by using different artificial intelligence models are far different, then by using the larger of the first danger level assessment information and the second danger level assessment information as the danger level information, and the sum of the first probability of occurrence assessment information and the second probability of occurrence assessment information as the occurrence probability information, the danger level information and the occurrence probability information can be set to a higher level based on the results output by each group of artificial intelligence models, so that the battery management system BMS tends to execute delayed sleep with a longer delay time, thereby giving full play to the monitoring and protection role of the battery management system BMS on the power battery.

[0103] In this embodiment, when executing step S3, that is, the step of controlling the sleep mode of the battery management system according to the danger level information and the occurrence probability information, the following steps may be specifically performed:

[0104] S301. Determine the delay time based on the risk level information and the probability of occurrence information;

[0105] S302: Control the battery management system to delay sleep according to the delay time.

[0106] In this embodiment, the effects of steps S301-S302 are as follows: Figure 5 As shown. Figure 5 After executing step S301 to calculate the delay time T, execute step S302 to start timing from the detection of the first power-off event of the power battery. After the delay time T, the battery management system BMS starts to sleep.

[0107] Reference Figure 5 You can also set a preset duration, T0, which is used by the battery management system (BMS) to perform sleep operations. If T0 is set, the battery management system (BMS) will enter sleep mode after the first power-off event of the power battery is detected, and the BMS will enter sleep mode after the duration T+T0 has elapsed.

[0108] In this embodiment, when executing step S301, that is, determining the delay period according to the risk level information and the occurrence probability information, the following steps are specifically performed:

[0109] S30101. Set the risk level threshold and the probability threshold;

[0110] S30102. When the danger level information is greater than the danger level threshold and the occurrence probability information is greater than the occurrence probability threshold, a third product is determined based on the danger level information and the occurrence probability information, a fourth product is determined based on the danger level threshold and the occurrence probability threshold, and a delay duration is determined in a positive correlation based on the deviation between the third product and the fourth product;

[0111] S30103. When the danger level information is greater than the danger level threshold and the occurrence probability information is less than or equal to the occurrence probability threshold, the delay time is positively correlated with the deviation between the danger level information and the danger level threshold;

[0112] S30104. When the danger level information is less than or equal to the danger level threshold and the occurrence probability information is greater than the occurrence probability threshold, the delay time is positively correlated with the deviation between the occurrence probability information and the occurrence probability threshold;

[0113] S30105. When the danger level information is less than or equal to the danger level threshold and the occurrence probability information is less than or equal to the occurrence probability threshold, the delay time is set to zero.

[0114] In step S30101, the danger level threshold is used to measure the danger level information, and the occurrence probability threshold is used to measure the occurrence probability information. If the danger level information is greater than the danger level threshold, the danger level information can be determined to be high; if the danger level information is less than or equal to the danger level threshold, the danger level information can be determined to be low; if the occurrence probability information is greater than the occurrence probability threshold, the occurrence probability information can be determined to be high; if the occurrence probability information is less than or equal to the occurrence probability threshold, the occurrence probability information can be determined to be low.

[0115] In step S30102, if the danger level information is high and the occurrence probability information is high, then the product of the danger level information and the occurrence probability information is calculated to obtain a third product; the product of the danger level threshold and the occurrence probability threshold is calculated to obtain a fourth product; the absolute value of the difference between the third product and the fourth product is calculated to obtain the deviation between the third product and the fourth product, and the delay time is determined in a positive correlation, that is, the delay time set in step S30102 is positively correlated with the deviation between the third product and the fourth product.

[0116] In step S30103, if the danger level information is high and the occurrence probability information is low, then the absolute value of the difference between the danger level information and the danger level threshold is calculated to obtain the deviation between the danger level information and the danger level threshold, and the delay period is determined in a positive correlation, that is, the delay period set in step S30103 is positively correlated with the deviation between the danger level information and the danger level threshold.

[0117] In step S30104, if the danger level information is low and the occurrence probability information is high, then the absolute value of the difference between the occurrence probability information and the occurrence probability threshold is calculated to obtain the deviation between the occurrence probability information and the occurrence probability threshold, and the delay time is determined in a positive correlation, that is, the delay time set in step S30104 is positively correlated with the deviation between the occurrence probability information and the occurrence probability threshold.

[0118] In step S30105, if the danger level information is low and the occurrence probability information is low, the delay time is set to zero.

[0119] The situations of steps S30102-S30105 are shown in each row of Table 2 respectively.

[0120] Table 2

[0121]

[0122] In this embodiment, the principle of executing steps S30101-S30105 is that: by executing step S30102, the effect of "the higher the degree of danger and the higher the probability of occurrence, the longer the delay" can be achieved; by executing step S30103, the effect of "the higher the degree of danger, the longer the delay" can be achieved; by executing step S30104, the effect of "the higher the probability of occurrence, the longer the delay" can be achieved; by executing step S30105, the effect of not executing delayed sleep can be achieved; therefore, by executing steps S30101-S30105, delayed sleep can be implemented when the degree of danger or the probability of occurrence is high, so that the battery management system BMS can reduce the sleep time and fully ensure the working safety and stability of the power battery; when the degree of danger and the probability of occurrence are low, the sleep time of the battery management system BMS can be reduced, thereby achieving energy saving and other effects.

[0123] A computer program that executes the automotive battery management system sleep control method in this embodiment can be written and written into a computer device or storage medium. When the computer program is read out and run, the automotive battery management system sleep control method in this embodiment is executed, thereby achieving the same technical effect as the automotive battery management system sleep control method in the embodiment.

[0124] It should be noted that, unless otherwise specified, when a feature is referred to as being "fixed" or "connected" to another feature, it may be directly fixed or connected to the other feature, or it may be indirectly fixed or connected to the other feature. In addition, the descriptions of up, down, left, right, etc. used in this disclosure are only relative to the relative positional relationships of the components of the present disclosure in the accompanying drawings. The singular forms of "a", "" and "the" used in this disclosure are also intended to include the plural forms, unless the context clearly indicates otherwise. In addition, unless otherwise defined, all technical and scientific terms used in this embodiment have the same meaning as those generally understood by those skilled in the art. The terms used in the specification of this embodiment are only for describing specific embodiments and are not intended to limit the invention. The term "and / or" used in this embodiment includes any combination of one or more related listed items.

[0125] It should be understood that, although the terms first, second, third, etc. may be used to describe various elements in the present disclosure, these elements should not be limited to these terms. These terms are only used to distinguish elements of the same type from each other. For example, without departing from the scope of the present disclosure, the first element may also be referred to as the second element, and similarly, the second element may also be referred to as the first element. The use of any and all examples or exemplary language ("for example", "such as", etc.) provided in the present embodiment is only intended to better illustrate embodiments of the present invention, and unless otherwise required, will not impose limitations on the scope of the present invention.

[0126] It should be appreciated that embodiments of the present invention can be implemented or practiced by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer-readable memory. The methods can be implemented in a computer program using standard programming techniques - including a non-transitory computer-readable storage medium configured with a computer program, wherein the storage medium so configured causes the computer to operate in a specific and predefined manner - according to the methods and figures described in the specific embodiments. Each program can be implemented in a high-level procedural or object-oriented programming language to communicate with the computer system. However, if desired, the program can be implemented in assembly or machine language. In any case, the language can be a compiled or interpreted language. In addition, the program can be run on a programmed application-specific integrated circuit for this purpose.

[0127] Furthermore, the operations of the processes described in this embodiment may be performed in any suitable order, unless otherwise indicated in this embodiment or otherwise clearly contradicted by the context. The processes described in this embodiment (or variations and / or combinations thereof) may be executed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) that is executed collectively on one or more processors, by hardware, or a combination thereof. A computer program includes multiple instructions that can be executed by one or more processors.

[0128] Furthermore, the method can be implemented in any type of computing platform that is operably connected to a suitable computer, including but not limited to a personal computer, a minicomputer, a mainframe, a workstation, a network or distributed computing environment, a separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, etc. Various aspects of the present invention can be implemented as machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, an optical read and / or write storage medium, RAM, ROM, etc., so that it can be read by a programmable computer, and when the storage medium or device is read by the computer, it can be used to configure and operate the computer to perform the process described herein. In addition, the machine-readable code, or portions thereof, can be transmitted over a wired or wireless network. When such media includes instructions or programs that implement the above steps in conjunction with a microprocessor or other data processor, the invention of this embodiment includes these and other different types of non-transitory computer-readable storage media. When programmed according to the methods and techniques of the present invention, the present invention also includes the computer itself.

[0129] The computer program can be applied to input data to perform the functions of the present embodiment, thereby converting the input data to generate output data that is stored in a non-volatile memory. The output information can also be applied to one or more output devices such as a display. In a preferred embodiment of the present invention, the converted data represents a physical and tangible object, including a specific visual depiction of the physical and tangible object produced on the display.

[0130] The above are merely preferred embodiments of the present invention. The present invention is not limited to the aforementioned embodiments. As long as the technical effects of the present invention are achieved by the same means, any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention. Within the scope of protection of the present invention, various modifications and variations of the technical solutions and / or implementation methods may be made.

Claims

1. A dormancy control method for an automobile battery management system, characterized in that: The automotive battery management system sleep control method includes: Test the car battery and obtain battery status data; Evaluate the battery status data to determine danger level information and occurrence probability information; the danger level information indicates the danger level of the abnormal event faced by the vehicle battery, and the occurrence probability information indicates the probability of occurrence of the abnormal event faced by the vehicle battery; controlling a sleep mode of the battery management system according to the danger level information and the occurrence probability information; The controlling the sleep mode of the battery management system according to the danger level information and the occurrence probability information includes: Setting thresholds for degree of danger and probability of occurrence; When the danger level information is greater than the danger level threshold and the occurrence probability information is greater than the occurrence probability threshold, determining a third product based on the danger level information and the occurrence probability information, determining a fourth product based on the danger level threshold and the occurrence probability threshold, and determining a delay time in a positive correlation based on a deviation between the third product and the fourth product; When the danger level information is greater than the danger level threshold and the occurrence probability information is less than or equal to the occurrence probability threshold, the delay time is positively correlated with the deviation between the danger level information and the danger level threshold; When the danger level information is less than or equal to the danger level threshold and the occurrence probability information is greater than the occurrence probability threshold, the delay duration is positively correlated with the deviation between the occurrence probability information and the occurrence probability threshold; When the danger level information is less than or equal to the danger level threshold and the occurrence probability information is less than or equal to the occurrence probability threshold, setting the delay time to zero; According to the delay time, the battery management system is controlled to perform delayed sleep.

2. The automotive battery management system sleep control method according to claim 1, characterized in that: The method of testing the vehicle battery to obtain battery status data includes: Detecting the state of the vehicle battery before the first power-off event to obtain historical battery state data; Detect the state of the car battery when the first power-off event occurs and obtain real-time battery status data; The battery historical status data and the battery real-time status data are used as the battery status data.

3. The automotive battery management system sleep control method according to claim 2, characterized in that: The step of evaluating the battery status data to determine the degree of danger and probability of occurrence includes: Evaluate based on the battery historical status data to obtain first evaluation information of the degree of danger, and evaluate based on the battery real-time status data to obtain first evaluation information of the probability of occurrence; The first evaluation information of the risk level is used as the risk level information, and the first evaluation information of the occurrence probability is used as the occurrence probability information.

4. The automotive battery management system sleep control method according to claim 2, characterized in that: The step of evaluating the battery status data to determine the degree of danger and probability of occurrence includes: Evaluate based on the real-time battery status data to obtain second evaluation information on the degree of danger, and evaluate based on the historical battery status data to obtain second evaluation information on the probability of occurrence; The second evaluation information of the risk level is used as the risk level information, and the second evaluation information of the occurrence probability is used as the occurrence probability information.

5. The automotive battery management system sleep control method according to claim 2, characterized in that: The step of evaluating the battery status data to determine the degree of danger and probability of occurrence includes: Evaluate based on the battery historical status data to obtain first evaluation information of the degree of danger, and evaluate based on the battery real-time status data to obtain first evaluation information of the probability of occurrence; Evaluate based on the real-time battery status data to obtain second evaluation information on the degree of danger, and evaluate based on the historical battery status data to obtain second evaluation information on the probability of occurrence; The danger level information and the occurrence probability information are determined based on the first danger level assessment information, the first occurrence probability assessment information, the second danger level assessment information, and the second occurrence probability assessment information.

6. The automotive battery management system sleep control method according to claim 5, characterized in that: The determining the danger level information and the occurrence probability information according to the first danger level assessment information, the first occurrence probability assessment information, the second danger level assessment information, and the second occurrence probability assessment information includes: determining a first product according to the first assessment information of the degree of danger and the first assessment information of the probability of occurrence; determining a second product according to the second evaluation information of the degree of danger and the second evaluation information of the probability of occurrence; When the deviation between the first product and the second product is less than a preset threshold, the first risk level assessment information or the second risk level assessment information is used as the risk level information, and the first occurrence probability assessment information or the second occurrence probability assessment information is used as the occurrence probability information; When the deviation between the first product and the second product is greater than or equal to a preset threshold, the larger of the first assessment information on the degree of danger and the second assessment information on the degree of danger is used as the degree of danger information, and the sum of the first assessment information on the probability of occurrence and the second assessment information on the probability of occurrence is used as the probability of occurrence information.

7. A computer device, characterized in that: The system comprises a memory and a processor, wherein the memory is used to store at least one program, and the processor is used to load at least one program to execute the automotive battery management system sleep control method according to any one of claims 1 to 6.

8. A computer-readable storage medium storing a program executable by a processor, characterized in that: The program executable by the processor is used to execute the automotive battery management system sleep control method according to any one of claims 1 to 6 when executed by the processor.

Images