A power battery status monitoring method and related equipment

Abstract

The present application discloses a power battery status monitoring method and related equipment, relating to the field of power batteries. The method includes: if a target vehicle is detected to enter a dormant state, performing at least one battery status monitoring operation on the target vehicle; wherein the battery status monitoring operation includes: obtaining the dormancy duration of the battery management system of the target vehicle, and waking up the battery management system when the dormancy duration is greater than the wake-up cycle corresponding to the current battery status monitoring operation; and obtaining the charge state of each cell of the power battery through the battery management system.

Description

Technical Field

[0001] This specification relates to the field of power batteries, and more specifically, to a power battery status monitoring method and related equipment. Background Art

[0002] In related technologies, abnormal monitoring of power batteries of electric vehicles is usually carried out through two main channels: one is monitoring through the big data background, and the other is monitoring through the Battery Management System (BMS).

[0003] However, when electric vehicles are parked for a long time and in hibernation, the vehicle's power battery information cannot be output to the big data backend. At the same time, because the BMS is dormant, the battery status cannot be effectively monitored. When the battery has an abnormality, it cannot be detected in time, causing damage to the vehicle.

[0004] Therefore, it is necessary to propose a power battery status monitoring method and related equipment to at least solve some of the above problems. Summary of the Invention

[0005] The Summary of the Invention introduces a series of simplified concepts that will be further described in the Detailed Description of the Invention. The Summary of the Invention of this application is not intended to limit the key features and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0006] In a first aspect, the present application proposes a method for monitoring the status of a power battery, comprising:

[0007] If it is detected that the target vehicle enters a dormant state, performing at least one battery status monitoring operation on the target vehicle;

[0008] Among them, the battery status monitoring operation includes:

[0009] Obtaining a sleep duration of the battery management system of the target vehicle, and waking up the battery management system if the sleep duration is greater than a wake-up cycle corresponding to a current battery status monitoring operation;

[0010] The state of charge of each cell of the power battery is obtained through the above-mentioned battery management system.

[0011] In a feasible implementation manner, the wake-up period corresponding to the first battery status monitoring operation is a preset period, and the above method further includes:

[0012] Determining a self-discharge rate of each battery cell based on the state of charge of each battery cell;

[0013] When the self-discharge rate of any of the battery cells is abnormal, the wake-up period is updated according to the abnormal self-discharge rate of the battery cell, and the updated wake-up period is used as the wake-up period corresponding to the next battery status monitoring operation.

[0014] In a feasible embodiment, the specific steps of obtaining the self-discharge rate of the battery cell include:

[0015] Obtaining a first cell voltage of a target battery cell when the target vehicle is dormant or the battery management system is dormant and a first time corresponding to the first cell voltage;

[0016] Obtaining a second cell voltage of the target battery cell after the battery management system wakes up and a second time corresponding to the second cell voltage;

[0017] Obtaining a first SOC value according to the first cell voltage and the corresponding relationship between the SOC value and the voltage;

[0018] Obtaining a second SOC value according to the second cell voltage and the corresponding relationship between the SOC value and the voltage;

[0019] The self-discharge rate of the target battery cell is determined based on the first SOC value, the second SOC value, the first time, and the second time.

[0020] In a feasible implementation, it further includes:

[0021] Obtain the number of abnormal cells with abnormal self-discharge rates;

[0022] When the number of the abnormal cells is less than or equal to a preset number, determining the modified wake-up period based on a larger self-discharge rate of the abnormal cells;

[0023] When the number of the abnormal battery cells is greater than the preset number, the modified wake-up period is determined based on the self-discharge rates of all the abnormal battery cells.

[0024] In a feasible implementation manner, the step of determining the modified wake-up period based on the larger value of the abnormal self-discharge rate of the battery cell includes:

[0025] According to the larger value of the self-discharge rate, the abnormality degree range is determined;

[0026] Determine the wake-up period corresponding to the abnormality level interval according to the abnormality level interval;

[0027] The wake-up period corresponding to the abnormality degree interval is determined as the modified wake-up period.

[0028] In a feasible implementation manner, determining the modified wake-up period based on the self-discharge rates of all abnormal battery cells includes:

[0029] Obtaining relative position information of each abnormal battery cell in the target power battery;

[0030] Determining position weight information of each abnormal battery cell according to the relative position information of each abnormal battery cell;

[0031] Determine an abnormality coefficient based on the self-discharge rates of all the abnormal cells and the corresponding position weight information of the abnormal cells;

[0032] The modified wake-up period is determined according to the abnormal coefficient.

[0033] In a feasible implementation, it further includes:

[0034] When the self-discharge rate of any of the above battery cells exceeds the warning threshold, the battery management system is continuously awakened to continuously monitor the above power battery;

[0035] The current status information of the power battery is sent to the management terminal and the customer mobile terminal.

[0036] In a second aspect, the present application proposes a power battery status monitoring device, comprising:

[0037] A battery status monitoring unit, configured to perform at least one battery status monitoring operation on the target vehicle if it is detected that the target vehicle has entered a dormant state;

[0038] Wherein, the battery status monitoring unit includes a first acquisition component and a second acquisition component;

[0039] The first acquisition component is used to obtain the sleep duration of the battery management system of the target vehicle, and wake up the battery management system when the sleep duration is greater than the wake-up cycle corresponding to the current battery status monitoring operation;

[0040] The second acquisition component is used to acquire the state of charge of each cell of the power battery through the battery management system.

[0041] In a third aspect, an electronic device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to implement the steps of the power battery status monitoring method of any one of the first aspects described above when executing the computer program stored in the memory.

[0042] In a fourth aspect, the present application further proposes a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the power battery status monitoring method of any one of the first aspects is implemented.

[0043] In summary, in the related art, during the vehicle's hibernation period, the big data background cannot obtain the vehicle status, and the BMS is also in low power mode, unable to collect and monitor battery data in real time. Power battery abnormalities (such as excessive self-discharge of battery cells, battery cell imbalance, etc.) may occur during hibernation and cannot be perceived in time, leading to safety hazards. This application monitors the duration of vehicle hibernation and dynamically determines whether the BMS needs to be woken up, thereby achieving intermittent monitoring of the battery status in the hibernation state. After waking up, the BMS collects SOC data for each battery cell in the battery pack one by one to ensure a comprehensive understanding of the battery status and avoid monitoring blind spots. In hibernation mode, the monitoring frequency can be dynamically adjusted to ensure that abnormal states of the power battery (such as abnormal SOC fluctuations) are captured in time. In the related art, if a fixed cycle or continuous wake-up monitoring method is adopted, although battery abnormalities can be detected in time, a large amount of power will be consumed, resulting in increased energy consumption of the power battery, especially when parked for a long time, which may cause over-discharge of the battery. This application designs a dynamic wake-up strategy based on the comparison of "sleep duration" and "wake-up cycle", which triggers the wake-up operation only when the sleep duration is longer than the preset wake-up cycle. When the sleep time is short or the battery status is normal, keep the BMS in sleep mode to avoid unnecessary energy consumption. The wake-up cycle can be dynamically adjusted according to the battery's self-discharge rate or other parameters to balance monitoring needs and energy-saving goals. It can significantly reduce the battery energy consumption during vehicle sleep, extend the battery's remaining power maintenance time, and improve battery safety in long-term parking scenarios. In related technologies, the BMS is unable to collect the status of individual cells in the battery pack after it goes into sleep mode, which may cause abnormalities in certain cells (such as SOC imbalance or voltage abnormality) to not be discovered in time. These abnormalities may accumulate and eventually cause serious damage to the entire power battery pack. After waking up the BMS, the method adopted in this application collects the SOC data of all cells in the power battery pack one by one to ensure that the status of each cell is fully monitored. Through SOC data analysis, it is possible to detect abnormalities in individual cells in a timely manner (such as excessive self-discharge or SOC significantly lower than other cells) and take corresponding measures to avoid the impact of individual cell abnormalities on the performance and safety of the entire battery pack, thereby achieving refined cell status monitoring. In related technologies, when parked for a long time, battery abnormalities (such as internal short circuit, excessive self-discharge, etc.) may cause serious accidents such as thermal runaway, capacity loss, and even fire. Existing monitoring solutions cannot achieve continuous monitoring in the dormant state, posing a potential threat to user and vehicle safety. This method can quickly respond to abnormal battery conditions (such as a significant drop in SOC or an abnormally high self-discharge rate) through intelligent wake-up and high-frequency data acquisition. Once an abnormality is detected, the battery status information can be sent to the big data background and user terminal through the real-time communication module to ensure that managers and car owners are aware of the risks in a timely manner and take countermeasures. Significantly improve the reliability of battery monitoring and vehicle safety, and reduce the risk of accidents caused by battery abnormalities. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present description. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0045] Figure 1 A schematic diagram of a process for monitoring the status of a power battery provided in an embodiment of the present application;

[0046] Figure 2 A schematic diagram of the structure of a power battery status monitoring device provided in an embodiment of the present application;

[0047] Figure 3 A schematic diagram of the structure of a power battery status monitoring electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0048] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or that are inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the embodiments described are only part of the embodiments of the present application, not all of the embodiments.

[0049] Figure 1 A schematic diagram of a process flow of a power battery status monitoring method provided in an embodiment of the present application, which may specifically include:

[0050] S110: If it is detected that the target vehicle enters a dormant state, perform at least one battery status monitoring operation on the target vehicle;

[0051] Among them, the battery status monitoring operation includes:

[0052] S1101: Obtaining a sleep duration of the battery management system of the target vehicle, and waking up the battery management system if the sleep duration is greater than a wake-up period corresponding to a current battery status monitoring operation;

[0053] S1102: Obtain the state of charge of each cell of the power battery through the battery management system.

[0054] For example, when the target vehicle is detected to be in a dormant state, the monitoring operation of the target vehicle's battery status is initiated. The dormant state generally refers to the vehicle being in a non-operating state, that is, the vehicle is not driving or charging, and the battery management system (BMS) is in a low-power dormant mode most of the time.

[0055] The sleep duration is the time interval from when the BMS entered sleep mode to the current moment. The wake-up period is a preset interval that determines the frequency of battery status monitoring operations. The monitoring system records the vehicle BMS sleep duration and compares it with the wake-up period of the current monitoring cycle. If the sleep duration is longer than the wake-up period, a wake-up operation is triggered; otherwise, the BMS remains in sleep mode to save energy.

[0056] The BMS wakeup process can be triggered by hardware control or a signal, switching the BMS from sleep mode to active mode for subsequent monitoring. After waking up, the BMS begins collecting the State of Charge (SOC) data for each cell in the power battery. SOC is an important parameter for measuring the remaining battery capacity and can reflect the battery's current charge level. Power batteries typically consist of multiple cells connected in series and parallel. Obtaining the SOC of each cell one by one ensures a comprehensive understanding of the battery status and prevents individual cell anomalies from being overlooked.

[0057] In summary, in the related art, during the vehicle's hibernation period, the big data background cannot obtain the vehicle status, and the BMS is also in low power mode, unable to collect and monitor battery data in real time. Power battery abnormalities (such as excessive self-discharge of battery cells, battery cell imbalance, etc.) may occur during hibernation and cannot be perceived in time, leading to safety hazards. This application monitors the duration of vehicle hibernation and dynamically determines whether the BMS needs to be woken up, thereby achieving intermittent monitoring of the battery status in the hibernation state. After waking up, the BMS collects SOC data for each battery cell in the battery pack one by one to ensure a comprehensive understanding of the battery status and avoid monitoring blind spots. In hibernation mode, the monitoring frequency can be dynamically adjusted to ensure that abnormal states of the power battery (such as abnormal SOC fluctuations) are captured in time. In the related art, if a fixed cycle or continuous wake-up monitoring method is adopted, although battery abnormalities can be detected in time, a large amount of power will be consumed, resulting in increased energy consumption of the power battery, especially when parked for a long time, which may cause over-discharge of the battery. This application designs a dynamic wake-up strategy based on the comparison of "sleep duration" and "wake-up cycle", which triggers the wake-up operation only when the sleep duration is longer than the preset wake-up cycle. When the sleep time is short or the battery status is normal, keep the BMS in sleep mode to avoid unnecessary energy consumption. The wake-up cycle can be dynamically adjusted according to the battery's self-discharge rate or other parameters to balance monitoring needs and energy-saving goals. It can significantly reduce the battery energy consumption during vehicle sleep, extend the battery's remaining power maintenance time, and improve battery safety in long-term parking scenarios. In related technologies, the BMS is unable to collect the status of individual cells in the battery pack after it goes into sleep mode, which may cause abnormalities in certain cells (such as SOC imbalance or voltage abnormality) to not be discovered in time. These abnormalities may accumulate and eventually cause serious damage to the entire power battery pack. After waking up the BMS, the method adopted in this application collects the SOC data of all cells in the power battery pack one by one to ensure that the status of each cell is fully monitored. Through SOC data analysis, it is possible to detect abnormalities in individual cells in a timely manner (such as excessive self-discharge or SOC significantly lower than other cells) and take corresponding measures to avoid the impact of individual cell abnormalities on the performance and safety of the entire battery pack, thereby achieving refined cell status monitoring. In related technologies, when parked for a long time, battery abnormalities (such as internal short circuit, excessive self-discharge, etc.) may cause serious accidents such as thermal runaway, capacity loss, and even fire. Existing monitoring solutions cannot achieve continuous monitoring in the dormant state, posing a potential threat to user and vehicle safety. This method can quickly respond to abnormal battery conditions (such as a significant drop in SOC or an abnormally high self-discharge rate) through intelligent wake-up and high-frequency data acquisition. Once an abnormality is detected, the battery status information can be sent to the big data background and user terminal through the real-time communication module to ensure that managers and car owners are aware of the risks in a timely manner and take countermeasures. Significantly improve the reliability of battery monitoring and vehicle safety, and reduce the risk of accidents caused by battery abnormalities.

[0058] In a feasible implementation manner, the wake-up period corresponding to the first battery status monitoring operation is a preset period, and the above method further includes:

[0059] S210, determining a self-discharge rate of each battery cell based on the state of charge of each battery cell;

[0060] S220 : When the self-discharge rate of any of the battery cells is abnormal, update the wake-up period according to the abnormal self-discharge rate of the battery cell, and use the updated wake-up period as the wake-up period corresponding to the next battery status monitoring operation.

[0061] Exemplarily, the first battery status monitoring operation uses a preset fixed wake-up period. For example, it can be set to 20 minutes or another suitable time length. This wake-up period is used to initiate the monitoring operation, collect battery cell SOC, and provide basic data for subsequent adjustments.

[0062] The self-discharge rate indicates the rate at which the SOC of a battery decreases when it is not in operation (such as in sleep mode).

[0063] The self-discharge rate can be calculated using the following formula:

[0064]

[0065] η is the self-discharge rate, ΔSOC is the change in SOC between two monitoring cycles, and Δt is the time interval between two monitoring cycles.

[0066] The BMS obtains the SOC data of all cells, calculates the self-discharge rate of each cell, and identifies potential abnormal cells. A reasonable threshold can be set (for example, a normal self-discharge rate should be less than 1% / hour). If the self-discharge rate of a cell exceeds the threshold, it is considered abnormal.

[0067] If an abnormal cell is detected, the wake-up cycle should be shortened to increase monitoring frequency and obtain battery status data more promptly. For example, if the threshold self-discharge rate is 0.5% / hour, the actual self-discharge rate is 1% / hour, and the current wake-up cycle is 2 hours, the new wake-up cycle should be adjusted to 1 hour. If the self-discharge rates of all cells are normal, the original wake-up cycle should be maintained or appropriately extended to reduce the number of wake-ups and save energy.

[0068] In a feasible embodiment, the specific steps of obtaining the self-discharge rate of the battery cell include:

[0069] S310, obtaining a first cell voltage of a target battery cell when the target vehicle is in sleep mode or after the battery management system is in sleep mode, and a first time corresponding to the first cell voltage;

[0070] S320, obtaining a second cell voltage of the target battery cell after the battery management system wakes up and a second time corresponding to the second cell voltage;

[0071] S330, obtaining a first SOC value according to the first cell voltage and the corresponding relationship between the SOC value and the voltage;

[0072] S340, obtaining a second SOC value according to the second cell voltage and the corresponding relationship between the SOC value and the voltage;

[0073] S350 : Determine the self-discharge rate of the target battery cell based on the first SOC value, the second SOC value, the first time, and the second time.

[0074] Exemplarily, the first cell voltage is a target cell voltage value recorded when the target vehicle or battery management system (BMS) enters a dormant state. The first cell voltage can be measured by a voltage acquisition module of the BMS. The first time is the time point corresponding to the recording of the first cell voltage, which can be marked as the first time (t1).

[0075] The second cell voltage is the cell voltage of the target cell measured after the battery management system wakes up from sleep mode. The second cell voltage reflects the voltage drop caused by self-discharge during the sleep period. The second time is the time at which the second cell voltage was recorded, which can be marked as the second time (t2).

[0076] The SOC-voltage correspondence includes a mapping curve or table of power battery cell voltage and SOC. The SOC mapping curve or table is derived by the battery manufacturer or through experimental testing and is typically stored in the battery characteristics database of the BMS.

[0077] According to the first cell voltage, by searching for a corresponding relationship between the SOC value and the voltage, a first SOC value at a first moment is obtained and marked as SOC1.

[0078] Similarly, based on the second cell voltage, by looking up the corresponding relationship between the SOC value and the voltage, the SOC value at the second moment is obtained and marked as SOC2.

[0079] By incorporating the nonlinear relationship between SOC and voltage, SOC values ​​can be calculated more accurately, improving the accuracy of self-discharge rate calculations. Real-time updates of the cell's self-discharge rate after each BMS wakeup help identify potential anomalies. Data from vehicle and BMS sleep periods can be leveraged to avoid continuous, high-frequency cell measurements, thereby reducing energy consumption.

[0080] In a feasible implementation, it further includes:

[0081] S410, obtaining the number of abnormal cells with abnormal self-discharge rates;

[0082] S420: When the number of the abnormal battery cells is less than or equal to a preset number, determining the modified wake-up period based on the larger self-discharge rate of the abnormal battery cells;

[0083] S430: When the number of the abnormal battery cells is greater than the preset number, determine the modified wake-up period based on the self-discharge rates of all the abnormal battery cells.

[0084] For example, an abnormal cell refers to a cell whose self-discharge rate exceeds a set threshold (e.g., 2% / hour). In each monitoring, the number of cells in the battery pack that meet the abnormal conditions is counted and marked as N. 异常

[0085] For example, suppose the battery pack contains 10 cells, of which the self-discharge rates of 3 cells are 2.5%, 3.0%, and 3.2%, respectively, exceeding the threshold. Then the number of abnormal cells N is 异常 =3.

[0086] The preset number is a threshold used to distinguish between a minor abnormality and a serious abnormality of the number of abnormal cells, marked as N 预设 .

[0087] If N 异常 ≤N 预设 , it is considered that the number of abnormal cells is small, and the wake-up cycle can be adjusted based on individual abnormal cells.

[0088] Such as N 异常 >N 预设 , it is considered that the number of abnormal cells is large, and the wake-up cycle can be adjusted by comprehensively considering all abnormal cells.

[0089] The method proposed in this embodiment distinguishes between minor and major anomalies based on the number of abnormal cells, employing different handling strategies to balance monitoring accuracy and energy consumption. For a small number of abnormal cells, the focus is on the single most serious cell; for a larger number of abnormal cells, the overall situation is considered to optimize monitoring efficiency. Dynamic adjustment of the wake-up cycle avoids overly frequent monitoring operations and reduces energy consumption.

[0090] In a feasible implementation manner, the above step S420 specifically includes:

[0091] S4201: Determine an abnormality level range based on the larger value of the self-discharge rate;

[0092] S4202: Determine a wake-up period corresponding to the abnormality level interval according to the abnormality level interval;

[0093] S4203: Determine the wake-up period corresponding to the abnormality level interval as the modified wake-up period.

[0094] For example, the abnormality degree interval is determined according to the larger value of the self-discharge rate, and the maximum value of the self-discharge rate of the abnormal battery cell (marked as η max ) is the core parameter for judging the degree of abnormality. This value reflects the discharge rate of the most seriously abnormal battery cell.

[0095] According to battery design and safety specifications, the self-discharge rate is divided into several abnormality ranges, such as:

[0096] η<η s0 It is within the normal range, with no obvious abnormalities. s0 ≤η<η s1 It is a mild abnormality. s1 ≤η<η s2 Moderate abnormality. s2 This is a severe abnormality and requires emergency treatment.

[0097] Assume that the threshold is defined as: η s0 =2%,η s1 =5%,η s2＝ 10%.

[0098] If the maximum self-discharge rate is 6% / hour, then η max =6% belongs to the moderate abnormal range.

[0099] Set the corresponding wake-up period T1, T2, T3 for each interval to ensure that the monitoring frequency matches the degree of abnormality:

[0100] In the normal range, the wake-up period is set to be longer (e.g., T1 = 4 hours). In the case of a mild abnormality, the wake-up period is shortened (e.g., T2 = 2 hours). In the case of a moderate abnormality, the wake-up period is further shortened (e.g., T3 = 1 hour). In the case of a severe abnormality, the BMS continues to work and does not enter the sleep state.

[0101] The corresponding wake-up period is directly used as the modified value according to the abnormality degree interval to ensure that the system adopts the period in the next monitoring operation.

[0102] If the system determines that it is a severe abnormality (ηmax≥η s2 ), the following emergency measures can be initiated: The mobile phone system remains awake and monitors the battery cell status in real time. Alarm information is sent to the cloud big data platform. Notification mechanism: After-sales service personnel and the vehicle owner are notified to perform emergency repairs or further testing.

[0103] The method proposed in the embodiment of the present application adjusts the monitoring frequency based on the degree of abnormality to avoid overly frequent or insufficient wake-up operations. In the case of severe abnormalities, the system quickly enters the continuous monitoring mode to prevent the expansion of potential risks. In the case of mild abnormalities and normal conditions, the wake-up cycle is extended to reduce unnecessary energy consumption. Through this solution, the abnormal conditions of the battery cells can be accurately identified and classified, ensuring the safe and stable operation of the battery system while improving operation and maintenance efficiency.

[0104] In a feasible implementation manner, the above step S430 specifically includes:

[0105] S4301. Obtaining relative position information of each abnormal battery cell in the target power battery;

[0106] S4302: Determine position weight information of each abnormal battery cell according to the relative position information of each abnormal battery cell;

[0107] S4303: Determine an abnormality coefficient based on the self-discharge rates of all the abnormal cells and the corresponding position weight information of the abnormal cells;

[0108] S4304: Determine the modified wake-up period according to the abnormal coefficient.

[0109] For example, a power battery is composed of multiple battery cells, and the relative position information of the battery cells refers to their arrangement positions in the battery pack, such as the module number, series / parallel sequence or physical position (such as center battery cell, edge battery cell) of the battery cells.

[0110] The relative position information can be determined by recording the location number of the abnormal cell in the BMS. The location information is usually provided by the cell monitoring unit of the hardware circuit, and the data includes the cell serial number or the physical location within the module.

[0111] Assume that the power battery pack consists of 10 cells: Abnormal cell 1: Position number 3. Abnormal cell 2: Position number 7. Abnormal cell 3: Position number 10.

[0112] Cells in different locations may have different impacts on the power battery pack. For example, edge cells may be more prone to abnormalities due to poor heat dissipation, and therefore have a higher weighting. Cells in locations with high current and heavy loads may be prioritized and given a higher weighting. Center cells generally have better heat dissipation or lower loads, and therefore may have a lower weighting.

[0113] Position weight w i This is a numerical value associated with the relative position of each cell. The specific weighting rule can be determined based on factors such as heat dissipation characteristics, module design, and current distribution. For example, suppose the weighting rule is: center-positioned cells have a weight of w = 1.0, while edge-positioned cells have a weight of w = 1.5.

[0114] The weights of abnormal cells are as follows:

[0115] Abnormal cell 1 (position 3, center cell): weight w3 = 1.0.

[0116] Abnormal cell 2 (position 7, middle position): weight w7 = 1.2.

[0117] Abnormal cell 3 (position 10, edge cell): weight w10 = 1.5.

[0118] Abnormal coefficient (C 异常 ) is the comprehensive self-discharge rate η of all abnormal cells i and its position weight w i The calculation results are used to quantify the overall degree of abnormality.

[0119]

[0120] N 异常：异常电芯数量。

[0121] η i is the self-discharge rate of the ith abnormal cell. i is the position weight of the i-th abnormal cell.

[0122] Assume that the self-discharge rates of abnormal cells are as follows: Abnormal cell 1: η3 = 2.5%. Abnormal cell 2: η7 = 3.0%. Abnormal cell 3: η 10 =3.5%.

[0123] The position weights are: w3=1.0, w7=1.2, w 10 =1.5.

[0124] Calculate the anomaly coefficient:

[0125] C 异常 =(2.5×1.0)+(3.0×1.2)+(3.5×1.5)

[0126] C 异常 =2.5+3.6+5.25=11.35

[0127] According to the design requirements of the power battery, set the corresponding wake-up cycle for different ranges of abnormal coefficients. For example:

[0128] C 异常 <5: Wake-up period T=4 hours (normal).

[0129] 5≤C 异常 <10: Wake-up period T = 2 hours (mild abnormality).

[0130] 10≤C异常 <15: Wake-up period T = 1 hour (moderate abnormality).

[0131] C 异常 ≥15: BMS continues to work (severe abnormality).

[0132] This embodiment of the application introduces location weighting to focus on cells that have a greater impact on battery performance, improving monitoring effectiveness. It also dynamically adjusts the wake-up period to avoid excessive or insufficient monitoring. It also extends the wake-up period in normal or mildly abnormal situations to reduce energy consumption.

[0133] In a feasible implementation, it further includes:

[0134] When the self-discharge rate of any of the above battery cells exceeds the warning threshold, the battery management system is continuously awakened to continuously monitor the above power battery;

[0135] The current status information of the power battery is sent to the management terminal and the customer mobile terminal.

[0136] Exemplarily, the warning threshold is a self-discharge rate critical value used to identify the severity of the abnormality. For example: η 预警 = 10%. When the self-discharge rate of any battery cell exceeds this threshold, it indicates that the battery cell may have serious problems, such as internal short circuit, excessive aging or extreme environmental influences.

[0137] Detect the self-discharge rate η of any battery cell i >η 预警 , the BMS switches from sleep mode to continuous working mode, collecting power battery status data at a high frequency, such as the voltage, SOC, and temperature of each battery cell. Continuous wake-up ensures real-time monitoring of the power battery under abnormal conditions, preventing potential risks from further escalating.

[0138] The current status information of the power battery includes but is not limited to the abnormal cell number, self-discharge rate, voltage, SOC, temperature, total voltage of the battery pack, total SOC, temperature distribution, and severity analysis of the abnormal cell (such as mild, moderate, or severe abnormality). It may also include recommendations for management and customers, such as "repair as soon as possible" or "stop use."

[0139] The management personnel are usually the after-sales service team or the battery operation management platform. The BMS collects and packages the current data, which is uploaded to the cloud via the vehicle communication module (such as 4G / 5G). The cloud platform pushes the status information to the management terminal.

[0140] The manager's terminal may be a monitoring system or a professional application that displays detailed battery status data and exception reports.

[0141] The customer is usually the owner or user of the vehicle. The data collected by the BMS is processed by the cloud platform and sent to the customer's mobile terminal (such as a mobile app). The message content can be presented in a simple and easy-to-understand manner, focusing on risks and necessary actions. The display format can include prompt notifications and status information. Prompt notifications include, for example, "Battery abnormality, please go to the service center for inspection as soon as possible." Status information includes, for example, "Battery cell 3 is abnormal, the discharge rate exceeds the standard, and the battery health risk is high."

[0142] The method proposed in the embodiments of this application enables the BMS to monitor battery status in real time after abnormal battery cells exceed the warning threshold, preventing the spread of risk. Status information is simultaneously sent to management personnel and customer terminals, ensuring that all parties are informed. Continuous monitoring and timely notifications minimize the risk of safety accidents caused by abnormal battery cells. Customers can quickly understand the vehicle battery status through mobile terminals and take necessary measures.

[0143] In summary, in the related art, during the vehicle's hibernation period, the big data background cannot obtain the vehicle status, and the BMS is also in low power mode, unable to collect and monitor battery data in real time. Power battery abnormalities (such as excessive self-discharge of battery cells, battery cell imbalance, etc.) may occur during hibernation and cannot be perceived in time, leading to safety hazards. This application monitors the duration of vehicle hibernation and dynamically determines whether the BMS needs to be woken up, thereby achieving intermittent monitoring of the battery status in the hibernation state. After waking up, the BMS collects SOC data for each battery cell in the battery pack one by one to ensure a comprehensive understanding of the battery status and avoid monitoring blind spots. In hibernation mode, the monitoring frequency can be dynamically adjusted to ensure that abnormal states of the power battery (such as abnormal SOC fluctuations) are captured in time. In the related art, if a fixed cycle or continuous wake-up monitoring method is adopted, although battery abnormalities can be detected in time, a large amount of power will be consumed, resulting in increased energy consumption of the power battery, especially when parked for a long time, which may cause over-discharge of the battery. This application designs a dynamic wake-up strategy based on the comparison of "sleep duration" and "wake-up cycle", which triggers the wake-up operation only when the sleep duration is longer than the preset wake-up cycle. When the sleep time is short or the battery status is normal, keep the BMS in sleep mode to avoid unnecessary energy consumption. The wake-up cycle can be dynamically adjusted according to the battery's self-discharge rate or other parameters to balance monitoring needs and energy-saving goals. It can significantly reduce the battery energy consumption during vehicle sleep, extend the battery's remaining power maintenance time, and improve battery safety in long-term parking scenarios. In related technologies, the BMS is unable to collect the status of individual cells in the battery pack after it goes into sleep mode, which may cause abnormalities in certain cells (such as SOC imbalance or voltage abnormality) to not be discovered in time. These abnormalities may accumulate and eventually cause serious damage to the entire power battery pack. After waking up the BMS, the method adopted in this application collects the SOC data of all cells in the power battery pack one by one to ensure that the status of each cell is fully monitored. Through SOC data analysis, it is possible to detect abnormalities in individual cells in a timely manner (such as excessive self-discharge or SOC significantly lower than other cells) and take corresponding measures to avoid the impact of individual cell abnormalities on the performance and safety of the entire battery pack, thereby achieving refined cell status monitoring. In related technologies, when parked for a long time, battery abnormalities (such as internal short circuit, excessive self-discharge, etc.) may cause serious accidents such as thermal runaway, capacity loss, and even fire. Existing monitoring solutions cannot achieve continuous monitoring in the dormant state, posing a potential threat to user and vehicle safety. This method can quickly respond to abnormal battery conditions (such as a significant drop in SOC or an abnormally high self-discharge rate) through intelligent wake-up and high-frequency data acquisition. Once an abnormality is detected, the battery status information can be sent to the big data background and user terminal through the real-time communication module to ensure that managers and car owners are aware of the risks in a timely manner and take countermeasures. Significantly improve the reliability of battery monitoring and vehicle safety, and reduce the risk of accidents caused by battery abnormalities.

[0144] like Figure 2 As shown, this application proposes a power battery status monitoring device, including:

[0145] A battery status monitoring unit 21 is configured to perform at least one battery status monitoring operation on the target vehicle if it is detected that the target vehicle has entered a dormant state;

[0146] The battery status monitoring unit 21 includes a first acquisition component 210 and a second acquisition component 220;

[0147] The first acquisition component 210 is used to obtain the sleep duration of the battery management system of the target vehicle, and wake up the battery management system when the sleep duration is greater than the wake-up cycle corresponding to the current battery status monitoring operation;

[0148] The second acquisition component 220 is used to obtain the state of charge of each cell of the power battery through the battery management system.

[0149] The above power battery status monitoring device can also perform the following steps:

[0150] In a feasible implementation manner, the wake-up period corresponding to the first battery status monitoring operation is a preset period, and further includes:

[0151] Determining a self-discharge rate of each battery cell based on the state of charge of each battery cell;

[0152] When the self-discharge rate of any of the battery cells is abnormal, the wake-up period is updated according to the abnormal self-discharge rate of the battery cell, and the updated wake-up period is used as the wake-up period corresponding to the next battery status monitoring operation.

[0153] In a feasible embodiment, the specific steps of obtaining the self-discharge rate of the battery cell include:

[0154] Obtaining a first cell voltage of a target battery cell when the target vehicle is dormant or the battery management system is dormant and a first time corresponding to the first cell voltage;

[0155] Obtaining a second cell voltage of the target battery cell after the battery management system wakes up and a second time corresponding to the second cell voltage;

[0156] Obtaining a first SOC value according to the first cell voltage and the corresponding relationship between the SOC value and the voltage;

[0157] Obtaining a second SOC value according to the second cell voltage and the corresponding relationship between the SOC value and the voltage;

[0158] The self-discharge rate of the target battery cell is determined based on the first SOC value, the second SOC value, the first time, and the second time.

[0159] In a feasible implementation, it further includes:

[0160] Obtain the number of abnormal cells with abnormal self-discharge rates;

[0161] When the number of the abnormal cells is less than or equal to a preset number, determining the modified wake-up period based on a larger self-discharge rate of the abnormal cells;

[0162] When the number of the abnormal battery cells is greater than the preset number, the modified wake-up period is determined based on the self-discharge rates of all the abnormal battery cells.

[0163] In a feasible implementation manner, the step of determining the modified wake-up period based on the larger value of the abnormal self-discharge rate of the battery cell includes:

[0164] According to the larger value of the self-discharge rate, the abnormality degree range is determined;

[0165] Determine the wake-up period corresponding to the abnormality level interval according to the abnormality level interval;

[0166] The wake-up period corresponding to the abnormality degree interval is determined as the modified wake-up period.

[0167] In a feasible implementation manner, determining the modified wake-up period based on the self-discharge rates of all abnormal battery cells includes:

[0168] Obtaining relative position information of each abnormal battery cell in the target power battery;

[0169] Determining position weight information of each abnormal battery cell according to the relative position information of each abnormal battery cell;

[0170] Determine an abnormality coefficient based on the self-discharge rates of all the abnormal cells and the corresponding position weight information of the abnormal cells;

[0171] The modified wake-up period is determined according to the abnormal coefficient.

[0172] In a feasible implementation, it further includes:

[0173] When the self-discharge rate of any of the above battery cells exceeds the warning threshold, the battery management system is continuously awakened to continuously monitor the above power battery;

[0174] The current status information of the power battery is sent to the management terminal and the customer mobile terminal.

[0175] In summary, in the related art, during the vehicle's hibernation period, the big data background cannot obtain the vehicle status, and the BMS is also in low power mode, unable to collect and monitor battery data in real time. Power battery abnormalities (such as excessive self-discharge of battery cells, battery cell imbalance, etc.) may occur during hibernation and cannot be perceived in time, leading to safety hazards. This application monitors the duration of vehicle hibernation and dynamically determines whether the BMS needs to be woken up, thereby achieving intermittent monitoring of the battery status in the hibernation state. After waking up, the BMS collects SOC data for each battery cell in the battery pack one by one to ensure a comprehensive understanding of the battery status and avoid monitoring blind spots. In hibernation mode, the monitoring frequency can be dynamically adjusted to ensure that abnormal states of the power battery (such as abnormal SOC fluctuations) are captured in time. In the related art, if a fixed cycle or continuous wake-up monitoring method is adopted, although battery abnormalities can be detected in time, a large amount of power will be consumed, resulting in increased energy consumption of the power battery, especially when parked for a long time, which may cause over-discharge of the battery. This application designs a dynamic wake-up strategy based on the comparison of "sleep duration" and "wake-up cycle", which triggers the wake-up operation only when the sleep duration is longer than the preset wake-up cycle. When the sleep time is short or the battery status is normal, keep the BMS in sleep mode to avoid unnecessary energy consumption. The wake-up cycle can be dynamically adjusted according to the battery's self-discharge rate or other parameters to balance monitoring needs and energy-saving goals. It can significantly reduce the battery energy consumption during vehicle sleep, extend the battery's remaining power maintenance time, and improve battery safety in long-term parking scenarios. In related technologies, the BMS is unable to collect the status of individual cells in the battery pack after it goes into sleep mode, which may cause abnormalities in certain cells (such as SOC imbalance or voltage abnormality) to not be discovered in time. These abnormalities may accumulate and eventually cause serious damage to the entire power battery pack. After the device used in this application wakes up the BMS, it collects the SOC data of all cells in the power battery pack one by one to ensure that the status of each cell is fully monitored. Through SOC data analysis, it is possible to detect abnormalities in individual cells in a timely manner (such as excessive self-discharge or SOC significantly lower than other cells) and take corresponding measures to avoid the impact of individual cell abnormalities on the performance and safety of the entire battery pack, thereby achieving refined cell status monitoring. In related technologies, when parked for a long time, battery abnormalities (such as internal short circuits, excessive self-discharge, etc.) may cause serious accidents such as thermal runaway, capacity loss, and even fire. Existing monitoring solutions cannot achieve continuous monitoring in the dormant state, posing a potential threat to user and vehicle safety. This device can quickly respond to abnormal battery conditions (such as a significant drop in SOC or an abnormally high self-discharge rate) through intelligent wake-up and high-frequency data acquisition. Once an abnormality is detected, the battery status information can be sent to the big data background and user terminal through the real-time communication module to ensure that managers and car owners are aware of the risks in a timely manner and take countermeasures. Significantly improve the reliability of battery monitoring and vehicle safety, and reduce the risk of accidents caused by battery abnormalities.

[0176] like Figure 3 As shown, an embodiment of the present application also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, the steps of any of the above-mentioned methods for monitoring the status of the power battery are implemented.

[0177] Since the electronic device introduced in this embodiment is a device used to implement a power battery status monitoring device in the embodiment of the present application, based on the method introduced in the embodiment of the present application, technical personnel in this field can understand the specific implementation of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present application is not introduced in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of the present application falls within the scope of protection to be protected by this application.

[0178] During the specific implementation process, when the computer program 311 is executed by the processor, any implementation method of the embodiments corresponding to the first aspect can be implemented.

[0179] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0180] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0181] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0182] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0183] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0184] An embodiment of the present application further provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device executes the process of power battery status monitoring in the corresponding embodiment.

[0185] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state drive (SSD)).

[0186] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0187] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0188] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0189] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0190] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0191] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for monitoring the state of a power battery, characterized in that: include: If it is detected that the target vehicle enters a dormant state, performing at least one battery status monitoring operation on the target vehicle; Among them, the battery status monitoring operation includes: Obtaining a sleep duration of a battery management system of the target vehicle, and waking up the battery management system if the sleep duration is greater than a wake-up cycle corresponding to a current battery status monitoring operation; Obtaining the state of charge of each cell of the power battery through the battery management system; Determining a self-discharge rate of each battery cell based on the state of charge of each battery cell; When the self-discharge rate of any of the battery cells is abnormal, updating the wake-up period according to the abnormal self-discharge rate of the battery cell, and using the modified wake-up period as the wake-up period corresponding to the next battery status monitoring operation; Obtain the number of abnormal cells with abnormal self-discharge rates; When the number of the abnormal battery cells is less than or equal to a preset number, determining the modified wake-up period based on a larger self-discharge rate value of the self-discharge rates of the abnormal battery cells; When the number of the abnormal battery cells is greater than the preset number, the modified wake-up period is determined based on the self-discharge rates of all the abnormal battery cells.

2. The power battery status monitoring method according to claim 1, characterized in that: The specific steps of obtaining the self-discharge rate of the battery cell include: Obtaining a first cell voltage of a target battery cell when the target vehicle is dormant or the battery management system is dormant and a first time corresponding to the first cell voltage; Acquire a second cell voltage of the target battery cell after the battery management system wakes up and a second time corresponding to the second cell voltage; Obtaining a first SOC value according to the first cell voltage and a correspondence between the SOC value and the voltage; Obtaining a second SOC value according to the second cell voltage and the corresponding relationship between the SOC value and the voltage; The self-discharge rate of the target battery cell is determined based on the first SOC value, the second SOC value, the first time, and the second time.

3. The power battery status monitoring method according to claim 1, characterized in that: The determining the modified wake-up period based on the larger value of the self-discharge rate of the abnormal battery cell includes: Determining an abnormality degree interval according to the larger value of the self-discharge rate; determining a wake-up period corresponding to the abnormality level interval according to the abnormality level interval; The wake-up period corresponding to the abnormality degree interval is determined as the modified wake-up period.

4. The power battery status monitoring method according to claim 1, characterized in that: The determining the modified wake-up period based on the self-discharge rates of all abnormal battery cells includes: Obtain the relative position information of each abnormal battery cell in the target power battery; Determining position weight information of each abnormal battery cell according to the relative position information of each abnormal battery cell; Determining an abnormality coefficient according to the self-discharge rates of all the abnormal cells and the position weight information of the corresponding abnormal cells; The modified wake-up period is determined according to the abnormal coefficient.

5. The power battery status monitoring method according to any one of claims 1 to 4, characterized in that: Also includes: When the self-discharge rate of any of the battery cells exceeds a warning threshold, the battery management system is continuously awakened to continuously monitor the power battery; The current status information of the power battery is sent to the administrator terminal and the customer mobile terminal.

6. A power battery status monitoring device, characterized in that: include: a battery status monitoring unit, configured to perform at least one battery status monitoring operation on the target vehicle if it is detected that the target vehicle has entered a dormant state; The battery status monitoring unit includes a first acquisition component and a second acquisition component: The first acquisition component is used to obtain the sleep duration of the battery management system of the target vehicle, and wake up the battery management system when the sleep duration is greater than the wake-up cycle corresponding to the current battery status monitoring operation; The second acquisition component is used to obtain the state of charge of each cell of the power battery through the battery management system; The second acquisition component is also used to determine the self-discharge rate of each battery cell based on the state of charge of each battery cell; when there is an abnormality in the self-discharge rate of any of the battery cells, update the wake-up cycle according to the self-discharge rate of the abnormal battery cell, and use the modified wake-up cycle as the wake-up cycle corresponding to the next battery status monitoring operation; obtain the number of abnormal battery cells with abnormal self-discharge rates; when the number of abnormal battery cells is less than or equal to a preset number, determine the modified wake-up cycle based on the larger self-discharge rate value of the abnormal battery cells; when the number of abnormal battery cells is greater than the preset number, determine the modified wake-up cycle based on the self-discharge rates of all abnormal battery cells.

7. An electronic device comprising: A memory and a processor, characterized in that the processor is used to implement the steps of the power battery status monitoring method according to any one of claims 1 to 5 when executing the computer program stored in the memory.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the power battery status monitoring method according to any one of claims 1 to 5 are implemented.

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