Method for online monitoring and management of a battery pack

Abstract

The application relates to an online monitoring and management method for a battery pack, which comprises the following steps: collecting operation parameters of each single battery in the battery pack and loop connection resistance of the battery pack; dynamically adjusting a reference resistance value of a loop connection state based on a sample value of the loop connection resistance collected under a first preset condition; identifying a fault battery based on the operation parameters and activating a bypass module corresponding to the fault battery; and performing differentiated life cycle management on a new battery and a target battery in the battery pack in response to a replacement instruction that the target battery is replaced by the new battery. The application has the advantages that a new battery can be intelligently identified, and monitoring and diagnosis strategies thereof can be adaptively adjusted, so that false alarms can be eliminated and accurate judgment of real faults can be ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power supply guarantee, and in particular to an online monitoring and management method for a battery pack. BACKGROUND

[0002] In key infrastructures such as substations, data centers, communication base stations, etc., direct current power supply systems are particularly important. Among them, valve-regulated lead-acid battery packs must be able to provide instantaneous and reliable power supply for loads when the alternating current power supply is interrupted. Therefore, real-time online monitoring of the health status of the battery pack has become a necessary technical means to ensure the safe operation of the entire system.

[0003] The existing online monitoring system of the battery pack can usually collect the operating parameters such as voltage, internal resistance, temperature, etc. of each single battery in real time. By setting fixed alarm thresholds or performing simple intra-group data consistency comparison, these systems can to some extent find the batteries that have already deteriorated or failed. However, during the long-term operation and maintenance of the battery pack, individual failed or expired single batteries are frequently replaced. This leads to the situation that new and old batteries are mixed in the same battery pack. The inventors have found that the existing online monitoring method has serious technical defects when dealing with such new and old mixed working conditions.

[0004] Specifically, the electrical characteristics (such as internal resistance much lower than old batteries, floating current characteristics different) of new batteries are greatly different from those of other old batteries in the group that have been running for many years. The existing monitoring system usually adopts a one-size-fits-all management mode, i.e. the same alarm threshold and consistency analysis model are used for all batteries in the group. When a brand new, healthy battery is connected, its excellent parameters will be identified as statistical outliers by this rigid model, resulting in a large number of false alarms. In addition, the excellent parameters of this new battery will also lower the average internal resistance of the entire group and raise the statistical baseline, making it more difficult to detect some old batteries that are truly in the early stage of deterioration, resulting in missed reports of failures. SUMMARY

[0005] In order to intelligently identify new batteries and adaptively adjust their monitoring and diagnosis strategies to eliminate false alarms and ensure accurate judgment of real failures, the present application provides an online monitoring and management method for a battery pack.

[0006] The online monitoring and management method for a battery pack provided by the present application adopts the following technical solution:

[0007] An online monitoring and management method for a battery pack, comprising:

[0008] S1. Collecting operating parameters of each single battery in the battery pack and a loop connection resistance of the battery pack;

[0009] S2. dynamically adjusting a reference resistance value of a loop connection state based on a sample value of the loop connection resistance collected under a first preset condition;

[0010] S3. identifying a faulty battery based on the operating parameter, and activating a bypass module corresponding to the faulty battery;

[0011] S4. in response to a replacement instruction that a target battery in the battery pack is replaced by a new battery, performing differential life cycle management on the new battery and the target battery.

[0012] Optionally, the S4 comprises the following sub-steps:

[0013] S41. updating life cycle data associated with the new battery and the target battery;

[0014] S42. starting an observation period set for the new battery, and during the observation period, adopting an independent monitoring strategy for the new battery; wherein the independent monitoring strategy is different from a regular monitoring strategy;

[0015] S43. after the observation period ends, switching the monitoring of the new battery from the independent monitoring strategy to the regular monitoring strategy.

[0016] Optionally, the S43 comprises the following sub-steps:

[0017] S431. presetting a stable quantification index for characterizing an operating state of the new battery;

[0018] S432. during the observation period, periodically evaluating whether the operating parameter of the new battery meets the stable quantification index, and obtaining an evaluation result of the new battery;

[0019] S433. when the evaluation result of the new battery meets a second preset condition, terminating the observation period and switching the monitoring of the new battery from the independent monitoring strategy to the regular monitoring strategy, and if not, issuing an alarm.

[0020] Optionally, the S432 comprises the following sub-steps:

[0021] if the duration for which the new battery has been operated reaches a preset minimum observation duration, starting an evaluation operation;

[0022] wherein the evaluation operation comprises:

[0023] a. defining a data evaluation window;

[0024] b. based on a plurality of operating parameter samples collected within the data evaluation window, calculating actual values of the corresponding stable quantification indexes;

[0025] c. comparing the actual value of the stability quantification index with a preset evaluation criterion to obtain an evaluation result of the new battery corresponding to the data evaluation window.

[0026] Optionally, the S433 comprises:

[0027] S4331. continuously monitoring the evaluation result, and when the evaluation result meets the evaluation criterion in continuous, predetermined number of evaluation periods, determining that the observation period is successfully ended, and switching the monitoring of the new battery from the independent monitoring strategy to the regular monitoring strategy;

[0028] S4332. setting a preset maximum observation duration, and when the operation of the new battery has reached the preset maximum observation duration but fails to meet the evaluation criterion in continuous, predetermined number of evaluation periods, determining that the observation period is failed, and generating an alarm.

[0029] Optionally, the S42 comprises:

[0030] S421. setting an operation parameter alarm threshold of the new battery based on the initial parameters of the new battery registration;

[0031] S422. collecting the operation parameters of the new battery, and establishing an initial operation baseline of the new battery based on the operation parameters of the new battery; at the same time, suspending the application of the health state evaluation model based on the long-term aging trend to the new battery, and excluding the operation parameters of the new battery from the analysis data set for the health state evaluation model based on the long-term aging trend of the battery pack, and continuing the overall consistency analysis of the battery pack, wherein the analysis data set is used for the health state evaluation model based on the long-term aging trend of the battery pack.

[0032] Optionally, the first preset condition is that the battery pack is in a floating state, and a load current fluctuation of the battery pack is less than a preset current threshold continuously.

[0033] Optionally, the S43 further comprises:

[0034] S434. setting a group of hard alarm thresholds for the new battery in the observation period;

[0035] S435. when the operation parameters of the new battery touch the hard alarm thresholds before meeting the second preset condition, determining that the observation period is failed, and generating an alarm.

[0036] In summary, the present application includes at least one of the following beneficial technical effects:

[0037] 1. The monitoring and management method provided by the application can effectively solve the monitoring error caused by the mixed use of new and old batteries in the prior art by starting an observation period for the new battery after battery replacement and adopting a set of independent monitoring strategies based on the initial parameters of the individual, while excluding it from the consistency analysis of the whole group. This method not only avoids false alarms for healthy new batteries, but also ensures the accuracy of diagnosis of real faults of other old batteries in the group, significantly improving the overall reliability and usability of the online monitoring system.

[0038] 2. The application realizes intelligent diagnosis and active intervention of closed-loop management of the battery pack by taking the performance of the new battery as the basis for ending the observation period, and combining adaptive diagnosis of the loop connection state and automatic bypass function of the faulty battery. This method can actively avoid the risk of loss of direct current system power caused by single battery failure or line connection degradation without human intervention, and enhances the operation safety and autonomous operation and maintenance level of the backup power system. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 A program block diagram of the online monitoring and management method for the battery pack in an embodiment of the application is shown.

[0040] Figure 2 A program block diagram of the S4 sub-step in an embodiment of the application is shown.

[0041] Figure 3 A program block diagram of the S42 sub-step in an embodiment of the application is shown.

[0042] Figure 4 A program block diagram of the S43 sub-step in an embodiment of the application is shown.

[0043] Figure 5 A program block diagram of the step of the evaluation operation in an embodiment of the application is shown.

[0044] Figure 6 A program block diagram of the S433 sub-step in an embodiment of the application is shown. DETAILED DESCRIPTION

[0045] The application will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the application and not to limit the application.

[0046] In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the inventive concept. For clarity in understanding, the descriptions of specific implementations have not necessarily been described in detail all of the features that can be conceivable. Furthermore, the language used in the disclosure has been principally selected for readability and instructional purposes and can not have been selected to delineate or circumscribe the inventive subject matter, resort to the claims being necessary to determine such an inventive subject matter. Reference in the disclosure to "one implementation" or "an implementation" means that a particular feature, structure, or characteristic described in connection with the implementation is included in at least one implementation, and multiple references to "one implementation" or "an implementation" should not be understood as necessarily all referring to the same implementation.

[0047] The terms "a," "an," and "the" are not intended to refer to singular entities, but include the general class of which a specific example can be used for illustration. Thus, the use of the terms "a" or "an" can mean any number of including "one," "one or more," "at least one," and "one or more than one." The term "or" means any one of the alternatives, as well as any combination of the alternatives, including all of the alternatives, unless the alternatives are expressly indicated to be mutually exclusive.

[0048] The present application relates to an online monitoring and management method for battery packs. In critical infrastructures such as power grid substations, large data centers and communication hubs, DC power supply systems undertake the core task of providing uninterrupted power supply for control, protection and monitoring devices, and their stability and reliability of operation are the basis for ensuring the safety of the entire infrastructure. In these DC power supply systems, backup battery packs composed of multiple single batteries in series are the ultimate safety guarantee when AC mains are interrupted. Therefore, in order to ensure that the battery pack can be reliably discharged when needed, the industry generally uses online monitoring technology to collect and analyze the operating parameters of each single battery in the battery pack, such as single voltage, internal resistance and temperature, to evaluate its health status and predict potential failure risks.

[0049] In the whole life cycle operation and maintenance process of the battery pack, due to the slight differences in manufacturing processes between the single batteries and the different physical environments they are in, their aging rates are not completely consistent. Therefore, replacing individual single batteries that have failed due to faults or end of life is an inevitable routine maintenance operation. This operation leads to a common and challenging application scenario for monitoring systems: new batteries are mixed with old batteries that have been in operation for many years in the same battery pack.

[0050] However, the online monitoring method in the prior art usually adopts a non-discriminatory and unified management mode to monitor all single batteries in the battery pack. This mode is mainly implemented in two ways: one is to set a unified static alarm threshold for all batteries, for example, to generate an alarm when the internal resistance of all single batteries exceeds a certain fixed value; the second is to use intra-group consistency analysis to identify abnormal batteries by calculating the average value and standard deviation of the operating parameters of all batteries in the group.

[0051] This unified management mode has insurmountable technical defects when dealing with the above-mentioned mixed use of new and old batteries. On the one hand, the operating parameters of new batteries, especially the internal resistance, are significantly better than those of other aged old batteries in the group. For example, in a battery pack consisting of 108 old batteries, the average internal resistance may have generally risen to 1.0 mΩ due to long-term operation. At this time, if a brand new battery with an internal resistance of only 0.5 mΩ is replaced, the existing intra-group consistency analysis algorithm will incorrectly determine it as an abnormal outlier due to the large difference between the parameter of the new battery and the average value of the group, thus generating a false alarm for the healthy new battery. On the other hand, this excellent new battery parameter will also be included in the statistical calculation, thus lowering the average internal resistance benchmark of the entire group, which will indirectly raise the abnormal judgment threshold for old batteries, possibly causing some old batteries that are in the early stage of deterioration and whose internal resistance has already deteriorated significantly but have not yet reached the new, raised outlier standard to be missed, resulting in a missed report of real faults. Therefore, the existing method cannot differentiate and adaptively manage new and old batteries, and its monitoring accuracy and reliability will significantly decrease in the conventional replacement and maintenance scenarios.

[0052] Therefore, with reference to Figure 1 The embodiments of the present application disclose an online monitoring and management method for a battery pack, comprising S1-S4.

[0053] S1. Collecting the operating parameters of each single battery in the battery pack and the loop connection resistance of the battery pack.

[0054] In order to achieve comprehensive monitoring of the battery pack, data acquisition is required for each individual battery cell and the overall loop they form. In a typical backup battery pack application, for example in a 220V DC system of a 110kV substation, usually 108 individual battery cells with a rated voltage of 2V are connected in series to achieve a nominal system voltage of 216V. This series connection determines that all individual battery cells have the same current flowing through them during charging and discharging, and they work together to supply power to the load. However, this cooperation also means that the performance and reliability of the entire battery pack is limited by the worst-performing individual battery cell, i.e. there is a short board effect. If any one of the individual battery cells deteriorates or fails, it will affect the electrical performance of the entire series loop.

[0055] Therefore, collecting the operating parameters of each individual battery cell can be effectively used to diagnose the internal health status of the battery. The operating parameters specifically include the terminal voltage, internal resistance and case temperature of each individual battery cell. These parameters will influence each other. In a series battery pack, if the internal resistance of a certain individual battery cell increases significantly due to aging, then under the same load current, according to Ohm's law, the voltage drop across the cell will be greater than that of other healthy cells, and its terminal voltage will also be lower. At the same time, according to Joule's law, its internal power loss will also be greater, which will cause its temperature to rise abnormally, not only accelerating its further deterioration, but also possibly affecting the life of adjacent cells through heat conduction. Therefore, by comprehensively analyzing the three operating parameters of voltage, internal resistance and temperature, the short board battery in a deteriorated state can be effectively identified.

[0056] Unlike the operating parameters used to diagnose the internal health of the battery, measuring the loop connection resistance of the battery pack is aimed at diagnosing the connection health status of the external electrical loop of the battery pack. The loop connection resistance reflects the total resistance of the entire conductive path from the output terminal of the DC power supply screen, through the cable, switch, fuse, to the positive and negative pole terminals of the battery pack, and the connection strips between all individual battery cells. If there is any loose connection or corroded contact surface in the external loop of a battery pack with excellent operating parameters, the loop connection resistance will increase. In the state of small current floating charge, this defect may not be obvious, but when a large current is discharged, a huge voltage drop and heat will be generated at this place, which can cause the connection point to melt and cause the entire DC system to be interrupted. Therefore, the operating parameters and the loop connection resistance are key indicators representing two different but equally deadly failure modes, and must be collected and monitored simultaneously to achieve comprehensive state evaluation of the backup power supply system.

[0057] S2. dynamically adjusting the reference resistance value of the loop connection state based on the sample value of the loop connection resistance collected under the first preset condition.

[0058] In the present embodiment, the diagnosis of the connection state of the loop is achieved by comparing the real-time measurement value with a reference resistance value. The purpose of this step is to ensure that the reference resistance value can accurately reflect the health status of the loop under the current environment, so as to avoid misjudgment. For this purpose, it is necessary to collect sample values for learning in a normal electrical environment, which is defined by the first preset condition. For example, in an optional embodiment, the first preset condition is that the battery pack is in a floating state, and the load current fluctuation is less than a preset current threshold. The reason for collecting under this preset condition is that the floating state is the most common and stable static working mode of the battery pack, at this time the main loop current is small and stable. And limiting the load current fluctuation to be small can maximize the exclusion of the interference of the instantaneous voltage disturbance caused by the load mutation on the loop resistance calculation, and ensure that the sample values collected can truly reflect the physical state of the loop connection, rather than the transient behavior of the system operation.

[0059] The reason why the reference resistance value needs to be dynamically adjusted is that the static reference value set at the initial stage of system installation cannot adapt to the normal physical parameter drift caused by environmental changes. The loop connection resistance is mainly composed of copper connecting strips and cables, and its resistance value has a positive temperature coefficient characteristic, which will change slowly and predictably with the change of environmental temperature. For example, in a typical substation environment, the temperature difference of the battery room between summer and winter can reach more than 20 degrees Celsius, which is enough to cause a few percentage of normal fluctuation of the loop connection resistance. If a static reference value is used, then in summer it may trigger a false alarm of connection degradation due to the normal increase of resistance value with temperature; while in winter, it may reduce the detection sensitivity to real connection loosening faults due to the high reference value. Therefore, through the dynamic adjustment mechanism of the present step, the system can periodically learn and update the reference resistance value under the above-mentioned stable conditions by using the new sample values collected. This dynamic adjustment makes the reference value closely follow the normal drift caused by seasonal temperature changes and other factors, so as to accurately identify the real and abnormal resistance jump caused by screw loosening or terminal corrosion and other reasons while excluding environmental interference.

[0060] For example, assume that the method is applied to a 110 kV substation battery pack. In spring, when the ambient temperature is 20°C, the system completes the initial installation and commissioning, and at this time, through multiple measurements and calculations, the initial baseline resistance value is set to 5.0 mΩ. After entering summer, the average temperature in the battery room rises to 35°C, causing the overall resistance of the copper conductor in the loop to increase, and at this time, the loop connection resistance measured under the stable floating condition at night is generally around 5.2 mΩ. The method will identify that the current satisfies the preset stable condition, and start the adaptive learning process. It will collect multiple sample values around 5.2 mΩ, and through the built-in weighted average algorithm, the original 5.0 mΩ baseline value is smoothly corrected to a new value, for example, 5.18 mΩ. Thereafter, the system will use this new baseline value, which better reflects the current environmental state, to make alarm judgments.

[0061] S3. Identify a fault battery based on the operating parameters, and activate a bypass module corresponding to the fault battery.

[0062] The bypass module is an electronic switch unit configured one-to-one with each monomer battery, which functions to provide a parallel low-impedance current path when the corresponding monomer battery fails seriously, to maintain the integrity of the entire main loop of the series battery pack. In the normal working state, the power switch device inside the bypass module is in the off state and has no effect on the main loop, and the current of the battery pack normally flows through each monomer battery. When the master control module issues an activation instruction, the power switch device of the bypass module will quickly conduct, forming a short-circuit channel across the positive and negative electrodes of the fault battery. Since the resistance of the channel is much lower than the internal resistance of the fault battery, the main loop current will be immediately diverted to the bypass channel, thereby electrically isolating the fault battery from the main loop, avoiding the impact of its high internal resistance or open circuit state on the charging and discharging functions of the entire battery pack.

[0063] In order to accurately trigger the bypass module, the system first needs to identify the fault battery based on the collected operating parameters. This identification process is completed by a fault diagnosis model deployed in the master control module. Instead of simply relying on threshold value judgments of a single parameter, the model comprehensively analyzes and logically analyzes multiple operating parameters of the monomer battery. Those skilled in the art can understand that the fault diagnosis model can be implemented by a pre-set multiple threshold rule engine, a state machine, or a machine learning classifier trained based on historical data. For example, the fault diagnosis model can set a complex set of logical rules, when the internal resistance of a monomer battery exceeds a certain multiple (for example, 200%) of its initial baseline value, while its terminal voltage under stable floating state is continuously lower than a pre-set lower limit (for example, 2.15V), and its shell temperature is significantly higher than the average temperature of adjacent batteries, the system will comprehensively determine that the battery has occurred irreversible serious failure, and identify it as a fault battery.

[0064] Continuing with the example of the 110 kV substation battery bank described above. Suppose that after a period of operation, the 73rd cell in the battery bank starts to rapidly deteriorate due to severe sulfation of its internal plates. The monitoring system will capture the following changes in its operating parameters: its internal resistance rapidly climbs from the normal aging level of 1.2 mΩ to 3.0 mΩ within a few days; at the same time, its float voltage drops from the stable 2.23 V to 2.12 V; and its temperature consistently runs 6 °C higher than that of the adjacent 72nd and 74th cells. At this point, the fault diagnosis model in the master control module will simultaneously meet the severe alarm conditions for the internal resistance, voltage, and temperature difference, thus accurately identifying the 73rd cell as a faulty cell. Upon completion of the identification, the master control module immediately issues an activation command to the bypass module installed on the 73rd cell. Upon receiving the command, the power switching device inside the bypass module rapidly turns on within microseconds, and the main circuit current is immediately switched from flowing through the faulty cell to flowing through the bypass module. Although the total voltage of the entire battery bank is thus reduced by about 2 V, the main circuit remains unblocked, and the DC system can continue to supply power to the critical control and protection equipment.

[0065] S4. In response to a replacement instruction that a target cell in the battery bank is replaced by a new cell, performing differential life cycle management on the new cell and the target cell.

[0066] The replacement instruction is not an automatically generated signal, but a data update operation manually initiated by the operation and maintenance personnel after completing the physical replacement operation through the human-computer interaction interface of the monitoring system or the maintenance software. Specifically, the operation and maintenance personnel will select the position number of the replaced cell (i.e., the target cell) in the battery bank and enter the relevant information of the new cell, such as its unique serial number, model specification, and initial internal resistance value calibrated at the factory. After the system receives the operation and confirms the saving, it is considered to have received a replacement instruction.

[0067] The new cell and the old cell that has been in operation for many years have a large difference in the life cycle stage, so the monitoring targets should also be different, and differential life cycle management needs to be performed. For the old cell, the goal of monitoring is to continuously track the aging and decay trend of its performance and timely discover the failure characteristics at the end of its life. For a new cell that has just been put into use, the goal of monitoring is to verify whether its initial health status is good at the beginning of its operation and screen for early failure risks caused by manufacturing defects or transportation damage. Therefore, it is ineffective and unreasonable to adopt a uniform and indiscriminate regular monitoring strategy.

[0068] In an optional embodiment, the S4 includes the following sub-steps S41-S43.

[0069] S41. Update the lifecycle data associated with the new battery and the target battery.

[0070] S42. Start an observation period for the new battery, and during the observation period, apply an independent monitoring strategy to the new battery; wherein the independent monitoring strategy is different from a regular monitoring strategy.

[0071] S43. After the observation period ends, switch the monitoring of the new battery from the independent monitoring strategy to the regular monitoring strategy.

[0072] The regular monitoring strategy and the independent monitoring strategy will be described below by specific examples. Continuing with the example of the 110 kV substation battery pack, after the No. 73 battery is bypassed due to failure, the maintenance personnel replace it.

[0073] At this time, for the other 107 old batteries in the battery pack except the No. 73 battery, the system continues to perform the regular monitoring strategy on them. The core of this strategy is the consistency analysis within the group. The system will continuously calculate the average value and standard deviation of the internal resistance of the 107 batteries based on their operating parameters. If the internal resistance of a certain old battery deviates significantly from the statistical benchmark of the group, it is determined to be abnormal. At the same time, the system will continuously update the state of health (SOH) assessment based on its historical data to track its aging process.

[0074] For the newly replaced No. 73 battery, the system starts an observation period and performs a completely different independent monitoring strategy. In an optional embodiment, S42 includes S421-S422.

[0075] S421. Based on the initial parameters registered by the new battery, set the operating parameter alarm threshold of the new battery.

[0076] S422. Collect the operating parameters of the new battery, and establish the initial operating baseline of the new battery based on the operating parameters of the new battery; at the same time, suspend the application of the state of health assessment model based on the long-term aging trend to the new battery, and exclude the operating parameters of the new battery from the analysis data set for the long-term aging trend state of health assessment model of the battery pack, and continue to perform overall consistency analysis on the battery pack, wherein the analysis data set is used for the long-term aging trend state of health assessment model of the battery pack.

[0077] Specifically, when the operation and maintenance personnel enters the individual initial parameters of the new battery No. 73 with an initial resistance of 0.5 mΩ, the system executes the following independent monitoring strategy: first, the system no longer uses the statistical threshold of the old battery group, but sets a running parameter alarm threshold for the new battery, for example, 0.65 mΩ (i.e. 130% of the initial value). Second, when executing the above-mentioned intra-group consistency analysis for other old batteries in section 107, the system will automatically exclude the resistance data of 0.5 mΩ of the new battery from the analysis data set to avoid interference with the evaluation benchmark of the old battery group. Finally, the system will suspend the SOH evaluation algorithm based on the long-term aging trend for the new battery, and its SOH value will remain 100% during the observation period, while the system will use the densely collected running parameters of the new battery under the actual working conditions of the current site to establish a high-precision initial running baseline for the new battery, which will be used for health evaluation in the subsequent life cycle.

[0078] Further, in some embodiments, the S43 comprises the following sub-steps S431-S435.

[0079] S431. Pre-set a stability quantification index for characterizing the running state of the new battery.

[0080] S432. During the observation period, periodically evaluate whether the running parameters of the new battery meet the stability quantification index, and obtain the evaluation result of the new battery.

[0081] In order to quantify the running state of the new battery, the system will pre-set a stability quantification index for characterizing the running state of the new battery. These indicators are mathematical standards for evaluating whether the parameters of the battery under stable floating charging conditions have stabilized and no longer have large fluctuations. In a specific embodiment, these stability quantification indicators can include: floating voltage standard deviation, for measuring the dispersion of its voltage; resistance coefficient of variation, for measuring the consistency of its resistance measurement; and temperature difference with adjacent batteries, for measuring the balance of its own heat. The system will set corresponding evaluation standards for these indicators, for example, the floating voltage standard deviation within 24 hours must be less than 5 mV, the coefficient of variation of resistance measurement must be less than 3%, etc.

[0082] Further, optionally, the S432 comprises the following sub-steps:

[0083] When the length of time the new battery has been running reaches the pre-set minimum observation time, if yes, start the evaluation operation;

[0084] The evaluation operation comprises:

[0085] a. Draw a data evaluation window;

[0086] b. calculate actual values of the stability quantification indicators based on the plurality of operating parameter samples collected within the data evaluation window;

[0087] c. compare the actual values of the stability quantification indicators with the preset assessment criteria to obtain an evaluation result of the new battery corresponding to the data evaluation window.

[0088] After setting the above indicators, the operating parameters of the new battery are periodically evaluated to see if they meet the stability quantification indicators during the observation period. Taking the newly replaced battery No. 73 as an example, the system sets a minimum observation time of, for example, 7 days for it. During the 7 days, the system only collects data and performs the independent monitoring strategy described in S42, but does not perform any evaluation to end the observation period. Starting from the 8th day, the system initiates the first periodic evaluation. It will define a data evaluation window of the past 24 hours, and based on the plurality of operating parameter samples collected within the window, calculate the actual values of the stability quantification indicators set in S431. For example, the system calculates that the standard deviation of the float voltage of battery No. 73 in the past 24 hours is 3 mV, which meets the assessment criterion of less than 5 mV; the coefficient of variation of internal resistance is 1.5%, which meets the assessment criterion of less than 3%; and the average temperature difference with the adjacent battery is 0.8°C, which meets the assessment criterion of less than 2°C. At this time, the system obtains the evaluation result of this evaluation period as meeting. The evaluation operation will be repeated at every evaluation period (for example, every 24 hours) thereafter, and the system will continuously record the evaluation result of each time to provide a basis for the final determination of whether the observation period is ended.

[0089] S433. When the evaluation result of the new battery meets the second preset condition, terminate the observation period and switch the monitoring of the new battery from the independent monitoring strategy to the regular monitoring strategy, and if it does not meet, then perform an alarm.

[0090] The float state is also commonly known as float charging or trickle charging. The second preset condition here does not mean that a single evaluation result meets it, but rather a judgment logic that reflects continuous stability to ensure that the new battery has passed the initial unstable stage. It should be noted that in order to prevent some batteries that cannot reach a stable state for a long time from occupying system resources indefinitely, the present application also sets an exit mechanism.

[0091] In a specific embodiment, S433 includes S4331-S4332.

[0092] S4331. Continuously monitor the evaluation result, and when the evaluation result meets the assessment criteria in consecutive, predetermined number of evaluation periods, determine that the observation period is successfully ended, and switch the monitoring of the new battery from the independent monitoring strategy to the regular monitoring strategy;

[0093] S4332. Set a preset maximum observation duration. When the operation of the new battery has reached the preset maximum observation duration but fails to meet the evaluation criteria in consecutive, predetermined number of evaluation periods, the observation period is determined to be a failure, and an alarm is generated.

[0094] As an example, the two sub-steps are illustrated below in connection with two different scenarios.

[0095] Scenario 1: The new battery performs well and successfully ends the observation period. Continue with the example of the newly replaced battery No. 73, which meets the first evaluation result on the 8th day. Assume that the predetermined number of evaluation periods is 3 consecutive times. On the following 9th and 10th days, the battery’s operating parameters remain stable, so the periodic evaluation results are also both meets, that is, the condition of meeting the evaluation criteria in consecutive, predetermined number of evaluation periods is met. The system determines that the observation period of the battery No. 73 is successfully ended, and then switches its monitoring strategy from the independent monitoring strategy to the regular monitoring strategy. From now on, this new battery is considered as a healthy member of the battery pack, and its operating parameters will be included in the consistency analysis and long-term health state evaluation model of the whole pack.

[0096] Scenario 2: The new battery has potential defects and the observation period fails. Assume another scenario that the newly replaced battery No. 73 has a small, intermittent internal instability. It may meet the evaluation results on the 8th and 9th days, but on the 10th day, due to a slight fluctuation in the load, its terminal voltage produces a small jitter, which exceeds the evaluation criteria of the stability quantification index, resulting in a failure of the 10th day evaluation result. At this time, the count of consecutive meets is reset. This pattern of occasional meets and occasional failures continues to occur, causing the battery to always fail to meet the condition of meeting the evaluation result in 3 consecutive evaluation periods. At the same time, the system also monitors the total operation duration. Assume that the system sets the “preset maximum observation duration” to 90 days. When the operation duration of the battery reaches 90 days, the system checks that it still fails to meet the condition of S4331, at which time the logic of S4332 is triggered, determining that the observation period of the battery fails, and immediately generating a high-priority alarm information to inform the operation and maintenance personnel that the new battery has failed to meet the stable operation standard within the maximum observation period, and needs to be checked manually or replaced again.

[0097] S434. Set a set of hard alarm thresholds for the new battery during the observation period.

[0098] S435. When the operating parameters of the new battery touch the hard alarm thresholds before meeting the second preset condition, the observation period is determined to be a failure, and an alarm is generated.

[0099] The hard alarm thresholds are different in nature from the stability metrics. While the stability metrics are concerned with the volatility and consistency of the parameters over time, the hard alarm thresholds are absolute safety lines that are set for new batteries and should never be crossed under any circumstances. These thresholds usually correspond to explicit, irreversible signs of failure. For example, hard alarm thresholds can be set to include a float voltage instantaneous value below 2.10 V, which can indicate a micro-short inside the battery; an internal resistance instantaneous measurement value exceeding 200% of its initial value at factory, which can indicate a break in the internal connection; or a temperature rise rate exceeding 5 °C / hour, which can indicate a risk of thermal runaway.

[0100] S435 defines a fast-failure decision logic that is independent of and takes precedence over the periodic evaluation. The system continuously compares the real-time operating parameters of the new battery against the set of hard alarm thresholds in S434 at a high frequency throughout the observation period. This comparison operation is performed in parallel with the periodic stability evaluation. If any of the operating parameters of the new battery touches the corresponding hard alarm threshold at any time, the system immediately interrupts the observation period and decides that it has failed, without waiting for the result of the periodic evaluation or the end of the minimum or maximum observation duration.

[0101] It should be understood that the size of the serial number of each step in the above embodiments does not mean the order of execution, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0102] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is taken as an example, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above.

[0103] The above embodiments are only used to illustrate the technical solutions of the present application, but not limit it; although the foregoing embodiments of the present application have been described in detail, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. An online monitoring and management method for a battery pack, characterized in that, Comprising: S1. Collecting operation parameters of each single battery in the battery pack and loop connection resistance of the battery pack; S2. Dynamically adjusting the reference resistance value of the loop connection state based on the sample value of the loop connection resistance collected under the first preset condition; S3. Identifying a fault battery based on the operation parameters and activating a bypass module corresponding to the fault battery; S4. In response to a replacement instruction that a target battery in the battery pack is replaced by a new battery, performing differentiated life cycle management on the new battery and the target battery; The S4 comprises the following sub-steps: S41. Updating life cycle data associated with the new battery and the target battery; S42. Starting an observation period set for the new battery, and during the observation period, adopting an independent monitoring strategy for the new battery; wherein the independent monitoring strategy is different from a regular monitoring strategy; S43. After the observation period ends, switching the monitoring of the new battery from the independent monitoring strategy to the regular monitoring strategy.

2. The method for online monitoring and management of a battery pack according to claim 1, wherein, The S42 comprises: S421. Setting an operation parameter alarm threshold of the new battery based on initial parameters registered by the new battery; S422. Collecting operation parameters of the new battery and establishing an initial operation baseline thereof based on the operation parameters of the new battery; at the same time, suspending the application of a health state evaluation model based on long-term aging trend to the new battery, and excluding the operation parameters of the new battery from an analysis data set for the health state evaluation model based on long-term aging trend of the battery pack, and continuing to perform overall consistency analysis on the battery pack.

3. The method for online monitoring and management of a battery pack according to claim 1, wherein, The S43 comprises the following sub-steps: S431. Pre-setting a stability quantification index for characterizing the operation state of the new battery; S432. Periodically evaluating whether the operation parameters of the new battery meet the stability quantification index during the observation period, and obtaining an evaluation result of the new battery; S433. When the evaluation result of the new battery meets a second preset condition, terminating the observation period and switching the monitoring of the new battery from the independent monitoring strategy to the regular monitoring strategy, and otherwise, alarming.

4. The method for online monitoring and management of a battery pack according to claim 3, wherein, The S432 comprises the following sub-steps: When judging whether a running duration of the new battery reaches a preset minimum observation duration, if yes, starting an evaluation operation; The evaluation operation comprises: a. Defining a data evaluation window; b. Based on a plurality of operation parameter samples collected in the data evaluation window, calculating actual values of the corresponding stability quantification index; c. Comparing the actual values of the stability quantification index with a preset evaluation standard to obtain the evaluation result of the new battery corresponding to the data evaluation window.

5. The method for online monitoring and management of a battery pack according to claim 3, wherein, The S433 comprises: S4331. Continuously monitoring the evaluation result, and when the evaluation result meets the evaluation standard in continuous, predetermined number of evaluation periods, determining that the observation period is successfully ended, and switching the monitoring of the new battery from the independent monitoring strategy to the regular monitoring strategy; S4332. A preset maximum observation time length is set. When the operation of the new battery has reached the preset maximum observation time length but fails to meet the evaluation criteria in consecutive, predetermined number of evaluation periods, the observation period is determined to fail, and an alarm is generated.

6. The method for on-line monitoring and management of battery packs according to claim 1, wherein, The first preset condition is that the battery pack is in a floating state, and a load current fluctuation of the battery pack is less than a preset current threshold continuously.

7. The method for on-line monitoring and management of battery packs according to claim 3, wherein, The S43 further comprises: S434. A set of hard alarm threshold values is set for the new battery in the observation period. S435. When an operation parameter of the new battery touches the hard alarm threshold values before meeting the second preset condition, the observation period is determined to fail, and an alarm is generated.

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