Motor home high-capacity battery safety management method and system

Abstract

The invention relates to the technical field of battery safety management, in particular to a motor home high-capacity battery safety management method and system, and provides the following scheme: firstly, obtaining operation data of a plurality of battery monomers, and constructing a measurement matrix; and identifying a cooperative change characteristic value through principal component analysis, judging an abnormal monomer candidate of a deviation characteristic, and calculating a disturbance coupling degree based on trend fitting and disturbance response analysis so as to determine a final abnormal monomer. And after the abnormal single body is identified, executing a hierarchical response management strategy which comprises a first scheduling scheme and a second scheduling scheme. According to the second scheduling scheme, the coupling strength between the power utilization action module and the abnormal single body is calculated, and the module structure is reconstructed based on the electrical characteristic fingerprint and the dependency relationship, so that the behavior unit is migrated to a battery pack power supply path with a higher adaptation degree, and behavior reservation and power supply optimization are realized. According to the method, the judgment precision and the scheduling flexibility of the battery system in a single cell abnormal scene are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery safety management, in particular to a motor home large-capacity battery safety management method and system. BACKGROUND

[0002] The existing motor home battery management system mainly relies on operating parameters such as voltage, temperature, and current to manage the battery pack as a whole. When performance anomalies occur in the battery monomer, the system often fails to identify the specific abnormal position in time, and the abnormal monomer cannot be accurately separated, resulting in an expanded fault handling range. Further, the existing technology generally adopts whole-pack isolation, average scheduling and other ways to respond to abnormal monomers, which cannot finely schedule or retain key behavior loads, causing a large range of power supply interruption and low system recovery efficiency, seriously affecting the continuity and stability of motor home multi-scene power consumption. To solve the above problems, the present application designs a motor home large-capacity battery safety management method and system. SUMMARY

[0003] The technical problem to be solved by the present application is to provide a motor home large-capacity battery safety management method and system to solve the problems of the prior art. First, the operating data of the plurality of battery monomers is obtained, and a measurement matrix is constructed. The cooperative change characteristic value is identified by principal component analysis to determine the abnormal monomer candidate deviating from the characteristic, and the disturbance coupling degree is calculated based on trend fitting and disturbance response analysis to determine the final abnormal monomer. After identifying the abnormal monomer, a hierarchical response management strategy is executed, including a first scheduling scheme and a second scheduling scheme. The second scheduling scheme calculates the coupling strength between the power consumption action module and the abnormal monomer, and reconstructs the module structure based on the electrical characteristic fingerprint and the dependency relationship, and then migrates the behavior unit to a higher adaptation degree power supply path of the battery pack, realizing behavior retention and power supply optimization.

[0004] To achieve the above purpose, the present application provides the following technical scheme:

[0005] A motor home large-capacity battery safety management method is applied to a BMS management system, the BMS management system includes a plurality of battery packs, the battery pack includes a plurality of battery monomers, a plurality of user power consumption behaviors constitute a power consumption action module, and the battery safety management method includes:

[0006] Obtaining the operating data of the battery monomer, and constructing a measurement matrix, wherein the operating data includes voltage signals, temperature signals, and transient current waves;

[0007] According to the measurement matrix, the cooperative change characteristic value is used to identify the battery monomer deviating from the characteristic within a set monitoring period to generate an abnormal monomer candidate set, wherein the cooperative change characteristic value is determined according to the statistical characteristics of the measurement matrix;

[0008] modeling time sequence abnormal behaviors of the abnormal monomer candidates based on evolutionary trend fitting, and determining abnormal monomers according to a disturbance coupling degree;

[0009] If there is an abnormal monomer, a hierarchical response management strategy is determined, and the hierarchical response management strategy is executed through the BMS system, wherein the hierarchical response management strategy includes a first scheduling scheme and a second scheduling scheme, and the second scheduling scheme includes judging the coupling strength between the multiple power action modules corresponding to the abnormal monomer and the abnormal monomer, and restructuring the power action modules according to the coupling strength.

[0010] The measurement matrix is constructed, including:

[0011] The voltage signal, temperature signal and transient current wave are respectively subjected to time alignment, normalization processing and denoising filtering to generate an electrical parameter feature sequence;

[0012] The electrical parameter feature sequence is sorted according to the battery monomer as the row and the sampling time sequence as the column to generate a measurement matrix, wherein each row represents the electrical parameter feature of a battery monomer in a monitoring period.

[0013] The battery monomer with a deviating feature in a set monitoring period is identified through a cooperative change feature value, including:

[0014] A covariance matrix of the measurement matrix is calculated, and principal component analysis is performed on the covariance matrix to obtain a principal component vector;

[0015] The electrical parameter feature of each battery monomer is projected in the principal component space constructed by the principal component vector, and a cooperative change feature value is calculated according to the projection value;

[0016] The cooperative change feature value is compared with an average cooperative change feature value of all battery monomers in the same monitoring period;

[0017] If the deviation value of the cooperative change feature value of a battery monomer from the average cooperative change feature value is greater than or equal to a preset deviation determination threshold, the corresponding battery monomer is determined as an abnormal monomer candidate.

[0018] Modeling time sequence abnormal behaviors of the abnormal monomer candidates based on evolutionary trend fitting, and determining abnormal monomers according to a disturbance coupling degree, including:

[0019] The electrical parameter feature of each abnormal monomer candidate is subjected to time window sliding modeling, and a change trend vector of the electrical parameter feature with time evolution is calculated;

[0020] The time sequence feature of the change trend vector is calculated, wherein the time sequence feature includes a fluctuation frequency, a mutation amplitude and an offset rate;

[0021] According to the time sequence characteristics, a linkage response change degree of the abnormal cell candidate to other battery cells in the same battery pack in a monitoring period is calculated to obtain a disturbance response matrix;

[0022] A normalized response offset value of the disturbance response matrix is calculated, and a disturbance coupling degree is obtained according to an average amplitude of the normalized response offset value;

[0023] If the disturbance coupling degree is greater than or equal to a preset response threshold, the abnormal cell candidate is determined as an abnormal cell.

[0024] The determination of the hierarchical response management strategy includes:

[0025] A user electricity consumption behavior corresponding to the supply of the abnormal cell is obtained, and a corresponding battery pack is determined according to one or more electricity action modules corresponding to the user electricity consumption behavior;

[0026] According to the running state of the battery cells of the corresponding battery pack, it is determined whether there is a first scheduling scheme, if there is, the first scheduling scheme is calculated, wherein the determination of whether there is a first scheduling scheme includes evaluating whether the total discharge capacity meets the operation power requirement of the electricity action module, if it meets, there is a first scheduling scheme, if it does not meet, there is no first scheduling scheme;

[0027] If there is no, a second scheduling scheme is calculated, wherein the second scheduling scheme includes judging the coupling strength between the corresponding multiple electricity action modules and the abnormal cell, reconstructing the electricity action module according to the coupling strength, and mapping the reconstructed electricity action module to the power supply path corresponding to the other battery pack, and the reconstruction includes splitting the electricity action module into sub-behavior units that can be independently executed.

[0028] The calculation of the second scheduling scheme includes:

[0029] The coupling strength between the multiple electricity action modules and the abnormal cell is calculated;

[0030] The electricity action module with the coupling strength greater than a preset coupling threshold is taken as a splitting module;

[0031] The electrical characteristic fingerprint of the user electricity consumption behavior of the splitting module is extracted, and the electrical characteristic fingerprint includes a transient current wave, a power change rate and a continuous discharge characteristic when the electricity consumption behavior is started;

[0032] According to the electrical characteristic fingerprint, the dependency relationship between the user electricity consumption behaviors is determined, and the splitting module is split into multiple sub-behavior units that can be independently executed;

[0033] A dynamic load image of the other battery pack is obtained, and the dynamic load image includes current load capacity, voltage stability and thermal stability parameters of the battery pack;

[0034] calculating a degree of adaptation of the electrical characteristic fingerprint of the sub-behavior unit to the dynamic load profile of other battery packs, and calculating a second scheduling scheme according to the degree of adaptation.

[0035] The calculation condition of the coupling strength includes one of the following:

[0036] The proportion of the current through the abnormal monomer power supply path during the running of the user electricity behavior corresponding to the electricity action module;

[0037] The temperature rise rate of the abnormal battery monomer during the running of the electricity action module;

[0038] The user electricity behavior in the electricity action module contains the abnormal monomer power supply;

[0039] The response correlation between the electricity action module and the abnormal monomer in the historical running process.

[0040] According to the electrical characteristic fingerprint, the dependency relationship between the user electricity behaviors is determined, including:

[0041] Obtain the electrical characteristic fingerprint vector of all user electricity behaviors in the same electricity action module;

[0042] By calculating the transient current wave spectrum similarity, power change rate similarity and discharge charge amount ratio between the user electricity behaviors, the coupling dependency between the user electricity behaviors is calculated;

[0043] According to the coupling dependency, a dependency relationship matrix between the user electricity behaviors is constructed.

[0044] The method for calculating the degree of adaptation of the electrical characteristic fingerprint of the sub-behavior unit to the dynamic load profile of the battery pack includes:

[0045] According to the transient current wave spectrum characteristics, power change rate and discharge charge amount of the sub-behavior unit, a load demand vector is generated;

[0046] After standardizing the load demand vector and the dynamic load profile vector of the battery pack, the Euclidean distance is calculated;

[0047] According to the calculated Euclidean distance, a degree of adaptation score is determined, and the battery pack with the highest degree of adaptation score is selected as the target power supply path of the sub-behavior unit.

[0048] A motor home large-capacity battery safety management system, the system comprises:

[0049] A plurality of battery packs, each battery pack comprising a plurality of battery monomers;

[0050] A BMS management system is configured to acquire operation data of the battery monomer and determine whether there is an abnormal battery monomer;

[0051] A behavior recognition module is configured to recognize a corresponding user power consumption behavior according to a power supply path of the abnormal battery monomer, and determine a power consumption action module to which the user power consumption behavior belongs and a corresponding battery pack;

[0052] A scheduling determination module is configured to determine whether there is a first scheduling scheme according to an operation state of other battery monomers in the battery pack;

[0053] A first scheduling execution module is configured to control the power consumption action module to be discharged by other battery monomers in the battery pack in a case where there is the first scheduling scheme;

[0054] A second scheduling module is configured to perform the following operations when the first scheduling scheme is not feasible:

[0055] Calculate a coupling strength between the power consumption action module and the abnormal battery monomer;

[0056] Reconstruct the power consumption action module with a coupling strength greater than a threshold value, extract an electrical characteristic fingerprint of the user power consumption behavior, and construct a behavior dependency relationship;

[0057] Obtain a dynamic load portrait of other battery packs, and map the reconstructed sub-behavior unit to a power supply path of the other battery packs for execution based on an adaptation degree of the characteristic fingerprint and the load portrait.

[0058] Compared with the prior art, the application has the following beneficial effects:

[0059] 1. The application can accurately determine the state of the battery monomer from multiple electrical parameter dimensions by constructing a measurement matrix and introducing a cooperative change eigenvalue for abnormal recognition, thereby improving the accuracy and stability of abnormal detection.

[0060] 2. The application adopts a hierarchical response management strategy to differentially process the power consumption action module under abnormal conditions, has higher scheduling flexibility and adaptability, and maps the independently executable power consumption behavior unit to the power supply path of other battery packs through structural reconstruction and behavior splitting, thereby realizing continuous operation of key behaviors under abnormal conditions, enhancing behavior retention capability and system power supply stability. BRIEF DESCRIPTION OF DRAWINGS

[0061] Other features, objects and advantages of the application will become more apparent from the following detailed description of non-limiting embodiments, made with reference to the accompanying drawings:

[0062] Figure 1 An exemplary application scenario of an embodiment of the application is shown in the following schematic diagram:

[0063] Figure 2 The embodiment of the application is a schematic diagram of a motor home large-capacity battery safety management method.

[0064] Figure 3 The embodiment of the application is a schematic diagram of a first scheduling scheme principle.

[0065] Figure 4 The embodiment of the application is a schematic diagram of a process for determining a schedulable battery pack.

[0066] Figure 5 The embodiment of the application is a schematic diagram of a module screening principle.

[0067] Figure 6 The embodiment of the application is a schematic diagram of a module splitting principle.

[0068] Figure 7 The embodiment of the application is a schematic diagram of a subgraph division principle. DETAILED DESCRIPTION

[0069] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments of the present application.

[0070] In this document, "embodiment" means that the specific features, structures or characteristics described in connection with the embodiment can be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily mean that the same embodiment is referred to, nor does it mean that the embodiments are mutually exclusive or alternative to each other. The skilled person in the art can explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0071] The present application is applicable to mobile energy terminal with compact structure, complex load and local constraint of power supply path. The application scenarios include but are not limited to:

[0072] Medium and large motor homes with multiple distributed battery packs;

[0073] Carrying multiple functional power consumption devices with high frequency start-stop and significant power change;

[0074] The power supply device has the control habit of continuity of power consumption scenarios and combination of behavior chains, that is, the user tends to continuously trigger a specific set of power consumption actions in some typical scenarios.

[0075] The selection of application scenarios is based on the common characteristics of the region. The selected application scenarios need to have the following characteristics:

[0076] Each battery pack is connected to several electrical loads through a power distribution unit, the power supply path can be switched, but in the scene has the default path priority or engineering wiring dependent characteristics;

[0077] The mounted electrical equipment has the characteristics of high frequency start and stop and transient power mutation, and part of the electrical behavior is easy to produce load scheduling conflict when the cell level is abnormal;

[0078] In the typical use scenario, the user often triggers multiple continuous actions in the form of behavior chain, and the continuous actions have time sequence dependence and behavior aggregation characteristics, which can be abstracted as a structural action module.

[0079] It should be noted that the present application does not divide the power supply according to the battery pack area, but faces the application scene with default power supply path dependent relationship under the influence of engineering wiring constraint or path switching complexity, and the power supply path information shared by the upper control system is compatible with the traditional BMS architecture and the behavior model, which indirectly judges the corresponding relationship between the abnormal battery and the current active load.

[0080] It can be understood that the present application does not require each electrical action module to be permanently bound to a certain battery pack, but according to the system wiring structure and the current path state of the current collected by the BMS, the main power supply battery pack path of the module at the moment is identified as the reference basis for scheduling control.

[0081] Please refer to Figure 1 , which is a schematic diagram of an exemplary application scenario provided by the present application.

[0082] As Figure 1 shown, the target system of the present application is applicable to the power supply system of a house car equipped with a multi-battery pack structure. The house car power supply system includes a plurality of battery packs (such as battery pack A and battery pack B shown in the figure, the specific number of which is not limited by the present application, Figure 1 only for reference example), each battery pack is connected in parallel through a main power supply bus, for providing distributed energy supply to various types of electrical loads inside the house car. It should be understood that Figure 1 only for a simplified example diagram for easy understanding, the specific house car power supply system can also include other battery packs or other battery monomers, can also include other devices or can also include other unit modules, the BMS management system and the application scenario described in the present embodiment are used to more clearly illustrate the technical solution of the present embodiment, and do not constitute a limitation on the technical solution provided by the present embodiment.

[0083] Further, those skilled in the art can understand that with the evolution of house car architecture and new power supply scenarios, the technical solution provided by the present embodiment is also applicable to similar technical problems.

[0084] Figure 1It is shown that each battery pack is composed of a plurality of battery monomers (such as battery monomer A, battery monomer B, battery monomer C, battery monomer D, battery monomer E and battery monomer F, the specific number and whether the number of battery monomers in each battery pack is consistent are not limited in the present application, Figure 1 For reference only).

[0085] Figure 1 It is shown that the battery monomers inside the battery pack can be connected in series or partially in parallel according to different design requirements. It should be noted that, in order to ensure the manageability, safety and voltage consistency of the system, the present application explicitly excludes the connection structure of all parallel connection of battery monomers in the battery pack, that is, each battery pack must contain at least a part of series-connected battery cell groups. In addition, the battery monomers in the embodiments of the present application can also be replaced by battery cells.

[0086] In one example, the motor home power supply system includes a plurality of power input paths including solar panels, power charging interfaces and vehicle-mounted engine power generation devices. In order to ensure power dynamic balance and electrical protection requirements, a plurality of battery packs are configured with independent power management submodules and access control logic in the system structure. Therefore, although the battery packs are in a bus parallel state, only part of the battery packs participate in the discharge task in some period or working condition, and the other battery packs are in standby, dormant or charging state. That is, the power supply scheduling faces the cross-pack restriction problem.

[0087] In another example, the motor home power supply system is configured with a user power consumption behavior recording and identification function for collecting the behavior chain mode of the user in a specific scene. The behavior chain mode is abstracted as a power consumption action module and stored and scheduled in a structured manner in the system. Based on the running relationship of these behavior modules and the battery path state, the present application can realize behavior reconstruction and power supply path redistribution.

[0088] In a further example, the motor home control system is composed of a master control unit and a motor home power supply system, wherein the motor home power supply system in the present application can be a battery management system (BMS). The BMS is only responsible for collecting the running state of the battery cell, executing the disconnect and protection command, while the behavior scheduling, path reconstruction and behavior module structure reconstruction strategies are executed by the master control unit, which sends control instructions to the BMS through standard CAN communication or UART bus.

[0089] Next, the motor home large-capacity battery safety management method provided by the embodiments of the present application will be introduced in combination with the drawings. Figure 2 The method shown can be applied to a BMS management system, the BMS management system comprising a plurality of battery packs, the battery packs comprising a plurality of battery monomers, a plurality of user power consumption behaviors constituting a power consumption action module, Figure 2 The method shown comprises the following S1-S4, and the specific steps are as follows:

[0090] S1: Obtain the operation data of the battery monomer, and construct a measurement matrix;

[0091] In this embodiment, the operation data includes but is not limited to the voltage signal, temperature signal and transient current wave of the battery monomer.

[0092] As can be appreciated by those skilled in the art, the operation data can be collected according to the actual situation, and the specific type can be deleted by the user, as long as it meets the minimum requirement of obtaining the operation state according to the operation data.

[0093] S2: According to the measurement matrix, the battery monomer deviating from the feature existing in the set monitoring period is identified through the cooperative change characteristic value, and the abnormal monomer candidate set is generated;

[0094] In this embodiment, based on the constructed measurement matrix, the principal component analysis (PCA) algorithm is used to extract the principal component and construct the feature space. The operation vector of each battery monomer is projected in the space to obtain its cooperative change characteristic value. Set the empirical deviation threshold, and according to the deviation of each monomer characteristic value from the mean value, the suspected deviating monomer is screened out to generate the abnormal monomer candidate set.

[0095] S3: Time sequence abnormal behavior modeling based on evolution trend fitting is performed on the abnormal monomer candidate set, and the abnormal monomer is determined according to the disturbance coupling degree;

[0096] In this embodiment, the time evolution trend vector of voltage, temperature and current of each monomer in the candidate set is further constructed. The sliding time window regression modeling technology is used to fit the trend, extract its fluctuation frequency, mutation amplitude and offset rate, and record its behavior consistency in multiple monitoring periods.

[0097] Further, taking the abnormal candidate monomer as the source, the linkage response caused by the fluctuation behavior of the monomer on other monomers in the same battery pack is compared. The normalized offset amplitude of other monomers is calculated, and the overall offset mean value is taken as the disturbance coupling degree index to judge whether the candidate monomer has a system-level impact.

[0098] S4: If there is an abnormal monomer, determine the hierarchical response management strategy, and execute the hierarchical response management strategy through the BMS system;

[0099] In this embodiment, if it is determined that there is an abnormal monomer, the response control process is entered. First, the user power consumption behavior supported by the monomer is extracted, and the power consumption action module to which it belongs and the corresponding default battery pack are queried. Based on the operation state of other monomers in the current battery pack, it is evaluated whether the operation power demand of the current module can be met, and if so, the first scheduling scheme, i.e. the in-pack shunt discharge, is executed to maintain the behavior structure unchanged.

[0100] If the evaluation is not feasible, a second scheduling scheme is triggered. This scheme includes: determining the coupling strength between the current abnormal monomer and the module, screening the high coupling module for structural reconstruction; through electrical characteristic fingerprint extraction and dependency calculation, the module is split into several independently executable sub-behavior units; combined with the dynamic load image of other battery packs, the adaptation degree is calculated, and the sub-unit is mapped into the battery pack path with the highest adaptation degree to complete the power supply migration.

[0101] In the actual motor home running scene, due to the certain closed nature of the energy system, the power supply configuration is often operated in an off-grid mode, and the user behavior pattern is also sudden and strongly structure-dependent. The motor home often has high load concentration in a unit of time. For example, after entering the vehicle, the user may start the overhead light, electric cabinet, refrigerator, air conditioner and other equipment in a very short time. Compared with the stable load fluctuation and unified dispatching center in the power grid system, the motor home scene is more event-driven in terms of scheduling structure.

[0102] The safety management method proposed in the present application takes the battery monomer abnormality identification as the starting point, reversely maps the user power consumption behavior supported thereby, and thereby locates the associated power consumption action module. Since the same user behavior can be reused by multiple modules, and the power supply paths of each module in different scenarios can be distributed in different battery packs, after the first scheduling fails, the execution structure of the action module is no longer taken as a static scheduling unit, but the independently executable behavior units are excavated from the internal behavior dependency structure, and the execution structure of the action module is reconstructed.

[0103] It should be noted that the present application includes two layers of scheduling allocation logic. The first allocation is performed at the granularity of user power consumption behavior to transfer the power supply path, and completes local scheduling without destroying the module structure. The second allocation targets the executable of the module, and after decoupling the structure, a new path mapping relationship is established, thereby balancing the running continuity and battery safety.

[0104] In one example, the constructing the measurement matrix comprises:

[0105] The voltage signal, temperature signal and transient current wave are respectively time-aligned, normalized and denoised, to generate an electrical parameter feature sequence;

[0106] The electrical parameter feature sequence is sorted according to the battery monomer as the row and the sampling time sequence as the column to generate a measurement matrix, wherein each row represents the electrical parameter features of a battery monomer in a monitoring period;

[0107] In one example, the specific steps of S2 are as follows:

[0108] S2.1: Calculate the covariance matrix of the measurement matrix, and perform principal component analysis on the covariance matrix to obtain a principal component vector;

[0109] Specifically, the calculation of the covariance matrix is performed to capture the joint change relationship between multiple battery monomers in multiple parameter dimensions, which essentially quantifies the consistency of the cell behavior. In the state where multiple cells are running simultaneously, even if the individual voltage, temperature or current fluctuation amplitude of the cells is different, if the fluctuation trend shows synchronization or approximate linkage characteristics, it will show a strong positive correlation structure in the covariance matrix.

[0110] In this embodiment, the selected measurement matrix contains three main data dimensions, namely the voltage signal, the temperature signal, and the transient current wave vector after feature compression processing. The principal component analysis operation is performed using the covariance matrix to extract the first three order principal component vectors as the feature space representing the main cooperative trend of the cell running state in the current measurement period.

[0111] S2.2: Project the electrical parameter features of each battery monomer in the principal component space constructed by the principal component vectors, and calculate the cooperative change feature value according to the projection value;

[0112] Specifically, the projection of the cell in the principal component space is performed to solve the problem of high redundancy and inconsistent direction between the original electrical parameters of the cell. The original data has non-orthogonality in physics, for example, when the temperature of a cell slowly rises, the voltage fluctuation may produce a reverse response due to load changes, which is difficult to reflect the actual cooperative relationship in the traditional Euclidean space. By projecting it into the feature space composed of principal components, the joint performance of each cell in high-dimensional space can be reduced to one-dimensional or two-dimensional numerical indicators, and the consistency degree with the main cooperative direction is ensured.

[0113] In this embodiment, the multi-dimensional feature vector of each cell is mapped into the orthogonal space composed of principal components in turn, the projection value on each principal axis is recorded, and the projection in the direction of the first principal component axis is taken as the reference expression of the cooperative change feature value, which can reflect whether the cell is consistent with the main cooperative trend in the current measurement period, and also can evaluate the contribution degree of the cell in the group behavior.

[0114] S2.3: Compare the cooperative change feature value with the average cooperative change feature value of all battery monomers in the same monitoring period;

[0115] In this embodiment, the principal component projection values of all cells are first counted to calculate the first statistical features, including the mean, standard deviation and maximum deviation. Then, the cooperative change feature value of each cell is subtracted from the mean to obtain the relative deviation value.

[0116] S2.4: If the deviation of the cooperative variation characteristic value of a battery monomer from the average cooperative variation characteristic value is greater than or equal to a preset deviation determination threshold, the corresponding battery monomer is determined as an abnormal monomer candidate;

[0117] In the embodiment, the deviation determination threshold can be modeled based on the standard distribution of the characteristic value deviation in the historical normal state, and set to a range of 2 times the standard deviation floating up and down from the mean value; or an adaptive boundary can be constructed in combination with the trend change amplitude of multiple periods to dynamically adjust the determination sensitivity.

[0118] In one example, the specific steps of S3 are as follows:

[0119] S3.1: Time window sliding modeling is performed on the electrical parameter characteristics of each abnormal monomer candidate, and a change trend vector of the electrical parameter characteristics with time evolution is calculated;

[0120] S3.2: The time sequence characteristics of the change trend vector are calculated, wherein the time sequence characteristics include fluctuation frequency, mutation amplitude and offset rate;

[0121] S3.3: The degree of change of the abnormal monomer candidate in the monitoring period in the linkage response to other battery monomers in the same battery pack is calculated according to the time sequence characteristics, and a disturbance response matrix is obtained;

[0122] S3.4: The normalized response deviation value of the disturbance response matrix is calculated, and the disturbance coupling degree is obtained according to the average amplitude of the normalized response deviation value;

[0123] S3.5: If the disturbance coupling degree is greater than or equal to a preset response threshold, the abnormal monomer candidate is determined as an abnormal monomer;

[0124] In an optional embodiment, the judgment condition for determining whether there is an abnormal monomer according to the operation data can further include the following two aspects:

[0125] The first aspect is to determine based on the absolute threshold of the voltage drop rate of the battery monomer;

[0126] In one case, when the voltage drop rate of the battery monomer is greater than or equal to a first threshold change rate, it can be determined that there is an abnormal monomer, wherein the first threshold change rate can be a voltage drop rate critical value preset according to the battery type, the ambient temperature and the load power;

[0127] For example, for a lithium iron phosphate battery, when discharging at 10C rate in a room temperature 25℃ environment, the first threshold change rate can be set to 0.15V / s. As can be understood by those skilled in the art, in the case of other types of battery or different temperature control strategies, the first threshold change rate can also be obtained through experimental data fitting or historical operation data statistics.

[0128] It can be understood that when the battery monomer is discharged too fast, the internal resistance increases sharply, or the micro-short circuit trend appears, the voltage drop rate will be higher than the smooth feature in the normal discharge process, at this time if not timely identification and intervention, it is easy to cause the battery monomer and the whole package voltage imbalance, thereby triggering the over-discharge protection of BMS or abnormal jump package operation, causing the whole package to stop supplying. Therefore, it can be determined that the corresponding battery monomer is in an abnormal state.

[0129] In another case, when the voltage drop rate of the battery monomer is less than the first threshold change rate, it means that the battery monomer does not have the risk condition of being judged as an abnormal monomer. At this time, the abnormal judgment process can skip this battery monomer and turn the scheduling judgment process to other battery monomers or maintain the current power supply path unchanged.

[0130] It can be understood that the discharge process under the current load working condition is in the normal range, and there is no phenomenon of rapid voltage decay. It can also be considered that the electrochemical reaction, conductive path and thermal stability are all within the controllable range, so the battery monomer is not an abnormal monomer in this judgment.

[0131] In a second aspect, the judgment is based on the absolute threshold of the temperature rise rate of the battery monomer itself.

[0132] In one case, when the temperature rise rate of the battery monomer is greater than or equal to the second threshold change rate, it can be determined that there is an abnormal monomer. The second threshold change rate can be preset according to factors such as the type of battery cell, the thermal management condition in the package, the load intensity, and the design of the heat dissipation structure.

[0133] For example, for a lithium iron phosphate battery cell without forced cooling structure, the safe temperature rise rate under the condition of typical discharge rate of 10C should not exceed 2.5℃ / s. As can be understood by those skilled in the art, in the scene with active thermal management system or using other types of positive electrode materials (such as ternary system), the temperature rise rate threshold should be calibrated according to the specific heat capacity, thermal conductivity and heat diffusion path through thermal simulation or historical statistics.

[0134] It can be understood that when the internal short-time overcurrent, reaction intensification or heat dissipation path is limited, the temperature change will be much faster than the thermal stability curve in the normal discharge process. If the temperature rise rate exceeds the second threshold change rate, it may mean a precursor of thermal runaway, increased interface impedance or uneven heating caused by local pressure difference. At this time, if the relevant behavior is not scheduled or isolated in time, local thermal spots may expand and the heat diffusion in the package may be unbalanced.

[0135] In another case, when the temperature rise rate of the battery monomer is less than the second threshold change rate, it means that the battery monomer does not have the risk condition of being judged as an abnormal monomer. At this time, the abnormal judgment process can skip this battery monomer and turn the scheduling judgment process to other battery monomers or maintain the current power supply path unchanged.

[0136] Further, the first aspect can be combined with the second aspect to jointly determine whether the battery monomer is abnormal.

[0137] When the first aspect is combined with the second aspect to determine whether the battery monomer is abnormal, if the result of the judgment based on the first aspect is inconsistent with the result of the judgment based on the second aspect, the result of the judgment based on the second aspect is given priority, that is, the judgment priority of the second aspect is higher than that of the first aspect. The reason for this setting is that the temperature rise rate as a thermal characteristic indicator usually has a more direct risk indication significance. When the battery cell is in an early failure state or has an internal reaction abnormality, the temperature rise usually appears a significant fluctuation before the voltage change, especially in the initial stage of implicit failure such as short-time overload, micro-short circuit or electrode debonding, the temperature change can better reflect the abnormal energy conversion efficiency and local heating phenomenon.

[0138] For example, in some high-rate discharge scenarios, if the voltage drop rate of the battery cell remains within the normal range, but due to the increase of the local impedance of the pole piece or the uneven heat diffusion, heat may be rapidly accumulated in the battery cell for a short time, triggering a high-temperature warning or temperature rise rate overrun. At this time, if only the voltage drop rate is used for judgment, the early identification of the abnormal battery cell may be missed, resulting in a risk reaction lag.

[0139] In addition, in addition to the above two aspects, the battery monomer can be determined to be abnormal by other methods or in combination with other methods.

[0140] For example, whether the battery monomer is abnormal can be determined according to the voltage difference between the voltage of the battery monomer and the voltage of other battery monomers in the same battery pack.

[0141] Optionally, if the difference between the static voltage value of a certain battery monomer and the average voltage value of other battery monomers in the same battery pack is greater than or equal to a third threshold voltage difference, the battery monomer can be determined to be abnormal. The third threshold voltage difference can be set based on historical statistical analysis, typical state of charge (SOC) distribution difference, and pack equalization efficiency.

[0142] For another example, whether the battery monomer is abnormal can be determined according to the temperature difference between the temperature of the battery monomer and the temperature of other battery monomers in the same battery pack. The specific determination method is similar to the above, which will not be repeated here.

[0143] In one example, the specific steps of S4 are as follows:

[0144] S4.1: Obtain the user power consumption behavior corresponding to the abnormal battery monomer, and determine the corresponding battery pack according to one or more power consumption action modules corresponding to the user power consumption behavior.

[0145] In the present embodiment, when an abnormal battery monomer is identified, the user's power consumption behavior that it is currently supporting is traced back. It is easy to understand that the user's power consumption behavior is usually composed of multiple specific devices, and the associated user's power consumption behavior can be attributed to a specific power consumption action module. Due to the solidification trend of the power supply path of the actual wiring of the motor home, the same battery monomer is only connected to a limited range of behavior paths in most scenarios.

[0146] As can be appreciated by those skilled in the art, the user's power consumption behavior in the present application can be understood as the device usage action triggered by the user in a specific time and in a specific scenario, which is usually manifested as an operation request for a certain power consumption component or function, including but not limited to turning on the cabin lighting, starting the water heater and starting the air conditioner. It is easy to understand that the user's power consumption behavior is manifested as a start and power adjustment control instruction for a single device in the control logic, and usually corresponds to a group of determined load nodes and power supply paths in the physical electrical path, and theoretically also includes the shutdown of the device, but this is not considered in the present application.

[0147] As can be appreciated by those skilled in the art, the power consumption action module in the present application can be understood as a composite control unit composed of two or more user power consumption behaviors with time correlation, spatial correlation or functional logical dependence, for example, when the user enters the motor home, the combined action of opening the electric door, turning on the indoor light, opening the roof window in the car and starting the air conditioner will be triggered in turn.

[0148] S4.2: determining whether there is a first scheduling scheme according to the operating state of the battery monomer of the corresponding battery pack, if there is, calculating the first scheduling scheme, wherein the determination whether there is a first scheduling scheme includes evaluating whether the total discharge capacity meets the operating power requirement of the power consumption action module, if it meets, there is a first scheduling scheme, if it does not meet, there is no first scheduling scheme;

[0149] In the present embodiment, the first scheduling scheme can be realized by overall evaluation of the operating state of the remaining monomers in the battery pack where the abnormal monomer is located. The evaluation content includes the number of available monomers, load balancing capability, current redundancy space and local temperature rise trend. If the evaluation result shows that other monomers still have load sharing capability, the power supply path can be adjusted within the pack, for example, by dynamically balancing the load or preferentially guiding the parallel branch discharge to maintain the operating integrity of the action module.

[0150] S4.3: if there is no, calculating a second scheduling scheme, wherein the second scheduling scheme includes judging the coupling strength between the corresponding multiple power consumption action modules and the abnormal monomer, reconstructing the power consumption action modules according to the coupling strength, mapping the reconstructed power consumption action modules to the power supply path corresponding to other battery packs, and the structure reconstruction includes splitting the power consumption action modules into independently executable sub-behavior units.

[0151] In one example, taking the presence of an abnormal monomer as an example, a first scheduling scheme of a certain battery pack is obtained by using a scheduling algorithm, which can be referred to as Figure 3 for understanding, Figure 3 It is shown that the battery pack includes four battery monomers, namely battery monomer A, battery monomer B, battery monomer C and abnormal monomer.

[0152] Among them, battery monomer A and abnormal monomer are combined in series, battery monomer B and battery monomer C are combined in series, and the two combinations are further combined in parallel. It is easy to understand that when there is an abnormal monomer in series, according to the determination mechanism of the embodiment, battery monomer A and abnormal monomer cannot normally supply power.

[0153] Figure 3 It is shown that for this abnormal state, the application does not directly interrupt the output function of the entire battery pack, but based on the state evaluation result of other parallel battery monomers in the battery pack, local load redistribution is performed in the pack, and the load originally borne by battery monomer A and abnormal monomer is distributed to battery monomer B and battery monomer C, so that the power supply participation of abnormal monomer is locally avoided without destroying the original power supply structure.

[0154] According to Figure 3 It can be known from the content shown that the first scheduling scheme obtained by the embodiment of the application includes:

[0155] In the corresponding battery pack, the user power consumption behavior corresponding to the power action module is discharged by other battery monomers, and the discharging includes parallel shunt.

[0156] In combination with Figure 3 It can be understood that in the case that there is an abnormal monomer in the loop of battery monomer A, the embodiment identifies the current running state of other non-abnormal monomers (such as battery monomers B and C) in the battery pack, and evaluates whether they have the ability to bear the remaining load. When the condition is met, the discharging task originally borne by the abnormal cell is internally transferred by controlling the internal balancing conduction loop or adjusting the current path weight, realizing the harmonious processing of local isolation and function reservation.

[0157] Further, how to determine whether there is a first scheduling scheme includes:

[0158] According to the running state of other battery monomers, it is evaluated whether the total discharging capacity meets the running power demand of the power action module; if it meets, there is a first scheduling scheme; if it does not meet, there is no first scheduling scheme.

[0159] Those skilled in the art can understand that whether the first scheduling scheme exists and how to determine the first scheduling scheme are prior art, which will not be repeated here.

[0160] It should be noted that only in the case of connecting the battery pack in series plus partial parallel, it can be determined whether the first scheduling scheme exists.

[0161] That is, when the battery pack is connected only in series, there is no need to determine whether the first scheduling scheme exists, because in the series structure, all battery monomers constitute the same current path, and the load current needs to flow through each cell continuously. If any cell is abnormal, it will directly limit the output performance of the entire series branch. In this structure, there is no physical sense of shunt capacity between monomers, and it is also impossible to realize the technical path of bypassing a certain monomer and independently completing power supply by other cells, so it does not have the basis to perform in-pack scheduling. In this application, the determination link of the first scheduling scheme is directly skipped, and the second scheduling scheme is entered.

[0162] Please refer to Figure 4 , which is a flowchart for determining a schedulable battery pack provided by an embodiment of the present application. Figure 4 The method shown can be applied to S4.1 of the foregoing method, and the specific steps are as follows:

[0163] S4.1.1: Obtain the working log of the abnormal monomer;

[0164] Specifically, after the abnormal monomer is located, the power supply task it undertakes may be in multiple concurrent power consumption behaviors, so it is necessary to combine the actual data of its running process to track the power supply path it accesses and the load behavior it participates in at different time periods.

[0165] In this embodiment, the way to obtain the working log includes extracting the voltage, current, temperature and on-off state data of the abnormal monomer from the running record cache built in the BMS, and combining the load distribution log recorded by the controller to establish the correspondence between the abnormal monomer and the power supply path in a specific time segment.

[0166] Preferably, the working log can be sliced according to the time window, and the time period in the low-power standby state or without load transfer is filtered out to ensure the pertinence and efficiency of behavior tracing.

[0167] S4.1.2: According to the power supply path record of the abnormal monomer one or more times, identify the user power consumption behavior corresponding thereto;

[0168] Specifically, in a multi-device, staggered power supply RV structure, one battery monomer can supply power to multiple devices at different times. In an easily understandable scenario, these devices can be continuously triggered by the user in a chain operation habit, so the supported device objects cannot be determined by current changes.

[0169] As can be easily understood, the BMS management system only allocates battery monomers for power supply, and does not select specific battery monomers for power supply according to specific user power consumption behaviors. That is, the BMS management system does not actively identify user intentions or behavior logic, nor does it bind specific battery cells to specific devices or control operations.

[0170] In the present embodiment, the power supply path record is taken as an intermediate quantity, and the controller or the behavior instruction log on the load side is combined to construct a time domain cross comparison between the cell power supply event and the user behavior event. As can be understood by those skilled in the art, by recording the time period during which a certain cell participates in power supply, the occurrence of a control signal instruction issued by the user, such as a light turning on, a cabinet door rising, or a multifunctional panel being enabled, can be determined through the load switch logic or the relay group on-off state connected to the cell. By matching the degree of coincidence between these signal triggers and the cell voltage fluctuation or discharge characteristics, it can be determined that the cell has assumed the responsibility of power supply during the corresponding user power consumption behavior period.

[0171] It should be noted that due to the inevitable concurrent interference of multiple behaviors in the same time window in actual scenarios, the present embodiment can improve the discrimination accuracy by adding control channels, such as adding redundant confirmation behavior logic in CAN instructions, relay states, and power module responses. As can be understood by those skilled in the art, the RV in the present application has default path priority or engineering wiring dependency characteristics, so that abnormal monomers participating in upper user behaviors can still be indirectly identified without explicit cell and device binding information.

[0172] S4.1.3: Match the identified user power consumption behavior with the power consumption action module;

[0173] Specifically, in actual application scenarios, user power consumption behaviors often have a multiplexing relationship in multiple power consumption action modules. The user power consumption behavior determined in the foregoing steps can occur in multiple power consumption action modules. Then, in the present application, whether other battery packs can support the same action module or part of the behavior unit can be deduced according to the running records of these power consumption action modules.

[0174] For example, the same device opening behavior, such as "lighting lamp opening", can be part of the "evening entertainment module" and can also appear in the "night ventilation module". It should be noted that the application does not limit the module name, but only combines according to the actual behavior of the user.

[0175] S4.1.4: In the matching obtained power consumption action module and the historical power supply mapping record of the battery pack, the path relationship of the current power consumption action module supplied by the battery pack is identified, and the battery pack is determined as the target power supply of the current power consumption action module;

[0176] It is easy to understand that in most cases, the power supply management system will not limit the power supply object of the battery pack. For example, during the initial configuration of the system, the management logic usually only dynamically selects the single and multiple battery packs most suitable for undertaking the discharging task according to the SOC state, voltage stability and power supply rationality of the current battery pack, and does not require a specific battery pack to serve only a certain module or a certain type of behavior. The method shown in the application only extracts alternative battery packs different from the current abnormal monomer as target battery packs for subsequent scheduling.

[0177] It should be noted that in the actual operation process of the battery management system, as the system continuously operates and records the power supply path corresponding to each behavior trigger, the historical mapping of a specific power consumption action module being supplied by one or more battery packs will be objectively formed at the log level. These mappings essentially reflect that under a given load structure and scheduling strategy, one or more battery packs have successfully supported the operation of a certain power consumption action module, thereby having the possibility of undertaking the power supply task of the module again.

[0178] As a person skilled in the art can understand, the scheduling process and the specific implementation process can be inconsistent or consistent, and the power supply process can be statically configured or dynamically configured according to the logic configured in the specific master unit. On this basis, the application realizes the optimized dynamic configuration through additional scheduling logic, which is the unique feature of the cross-path power supply mapping driven by the user power consumption behavior of the application.

[0179] For example, further filtering of the power consumption action module can be understood with reference to Figure 5 Figure 5 The module filtering principle diagram of the embodiment of the application is shown in Figure 5 It is shown that a certain user power consumption behavior appears in power consumption action module one, power consumption action module two and power consumption action module three, respectively. The coupling strength between power consumption action module one, power consumption action module two and power consumption action module three and the abnormal monomer is calculated in turn to obtain coupling strength one, coupling strength two and coupling strength three. Coupling strength one, coupling strength two and coupling strength three are compared with the preset coupling threshold value, and the power consumption action module greater than the coupling threshold value is taken as the to-be-split module, and​Figure 5 In some embodiments, the second electric action module is selected as the splitting module.

[0180] It can be understood that, Figure 5 The present application does not limit the number of splitting modules, but at least one splitting module is required, and the specific number can be limited by the coupling threshold. If there is no splitting module in the actual process, the corresponding user electricity behavior can be split separately, and the remaining user electricity behavior is not further split.

[0181] In one example, the steps of screening the electric action module are as follows:

[0182] Calculate the coupling strength between the plurality of electric action modules and the abnormal monomer;

[0183] The electric action module with a coupling strength greater than the preset coupling threshold is selected as the splitting module.

[0184] In one example, the calculation condition of the coupling strength includes one of the following:

[0185] The current proportion of the user electricity behavior corresponding to the electric action module through the abnormal monomer power supply path during operation;

[0186] The temperature rise rate of the abnormal battery monomer during operation of the electric action module;

[0187] The electric action module contains the user electricity behavior powered by the abnormal monomer;

[0188] The response correlation between the electric action module and the abnormal monomer in the historical operation process.

[0189] Specifically, the coupling strength reflects the functional dependence degree between an electric action module and a certain abnormal monomer in the electrical behavior, thermal influence and response linkage level, that is, when the coupling strength between a certain electric action module and an abnormal monomer is low, it can be understood that the module does not depend on the corresponding abnormal monomer, that is, the corresponding user electricity behavior in the module does not affect the work of the abnormal monomer.

[0190] It should be noted that when the coupling strength is high, it often means that the plurality of behavior nodes inside the module depend on the single body as the main or only power supply source during operation, and such dependence relationship will cause the original module structure to be unable to execute as a whole once the battery cell is unavailable, and it is easy to understand that if the entire use action module is directly migrated to other battery packs, due to the similarity of the battery cells, it may also cause abnormal conditions of other battery cells, which is a state that the person skilled in the art does not want to appear. Therefore, the module needs to be split and the behavior sequence and path matching relationship needs to be rebuilt.

[0191] For example, the module splitting can be understood with reference to Figure 6 Figure 6 The module splitting principle of the embodiment of the present application is shown in the figure, Figure 6 It is shown that a certain to-be-split module includes six user power consumption behaviors: user power consumption behavior one, user power consumption behavior two, user power consumption behavior three, user power consumption behavior four, user power consumption behavior five, and user power consumption behavior six. After electrical characteristic fingerprint extraction and dependence relationship analysis, the to-be-split module is divided into three to-be-executed subunits. There is no strong dependence relationship between user power consumption behavior one and other user power consumption behaviors, which is an independent execution subunit. There is a strong dependence relationship between user power consumption behavior two and user power consumption behavior six, and the two are jointly executed as a subunit. Similarly, user power consumption behavior three, user power consumption behavior four, and user power consumption behavior five are jointly executed as a subunit.

[0192] In one example, the steps of module splitting are as follows:

[0193] The electrical characteristic fingerprint of the user power consumption behavior of the to-be-split module is extracted, and the electrical characteristic fingerprint includes the transient current wave, power change rate, and continuous discharge characteristic when the power consumption behavior is started;

[0194] Specifically, before the module structure is reconfigured, the user power consumption behaviors in the module need to be quantitatively characterized to determine whether the behaviors can be decoupled and executed. Since the use action module is essentially composed of a plurality of user power consumption behaviors with correlation in logic and electrical path in the defined conditions of the present application, there is an electrical dependence, load following, or synchronous activation relationship between some behaviors. In the scheduling algorithm, such user power consumption behaviors with correlation are preferentially packaged and split together to meet the scheduling algorithm requirement of behavior execution continuity and power supply path structure consistency.

[0195] It should be noted that the aforementioned scheduling algorithm requirement is only a special requirement of the method of the present application, and does not mean that all energy management systems perform behavior division according to the scheduling algorithm requirement.

[0196] ​In the scheduling scheme proposed in the present application, the scheduling target not only includes the basic load power supply maintenance, but also includes how to retain the maximum proportion of behavioral functional integrity under the influence of abnormal monomer, and reduce the probability of behavioral failure at the user perception level as much as possible. Therefore, in the module structure reconstruction phase, the present application specially limits the splitting logic taking the behavioral electrical coupling relationship as the priority basis, that is, multiple user electrical behaviors with electrical linkage characteristics are grouped into one decoupling boundary to ensure that they can still be reused as a whole during migration, avoiding the situation that the behavior cannot be executed due to the disconnection of behaviors after splitting, the division of power supply path or the loss of synchronous execution capability.

[0197] In one example, the extracted electrical feature fingerprint mainly includes three features:

[0198] The first feature is the transient current wave, which is used to identify the starting load type and judge whether it has an impact feature, such as devices such as motor starting and compressor starting that can cause high-amplitude current spikes in a short time.

[0199] The second feature is the power change rate in the running phase, which is used to depict the load climbing characteristics of the behavior from activation to stable operation.

[0200] The third feature is the continuous discharge feature, including the average current value, discharge time window length and current fluctuation interval of the behavior in the stable phase, which is used to identify the pressure influence of the behavior on the continuous output capability of the battery.

[0201] As understood by those skilled in the art, the electrical feature fingerprint is similar to the user portrait, which can be extracted by existing technology, and the present application does not repeat it here.

[0202] According to the electrical feature fingerprint, the dependency relationship between user electrical behaviors is determined, and the to-be-split module is split into multiple independently executable sub-behavior units;

[0203] Specifically, after obtaining the electrical feature fingerprint of the behavior, the electrical coupling relationship between different user electrical behaviors in the actual running process needs to be analyzed, and the module is disassembled into several sub-behavior units that can be separated at the circuit level and execution logic.

[0204] In one example, according to the electrical feature fingerprint, the dependency relationship between user electrical behaviors is determined, including:

[0205] Obtain the electrical feature fingerprint vector of all user electrical behaviors in the same electrical action module;

[0206] By calculating the transient current wave spectrum similarity, power change rate similarity and discharge charge ratio between user electrical behaviors, the coupling dependency between user electrical behaviors is calculated;

[0207] According to the coupling degree, a dependency matrix between user electricity behaviors is constructed.

[0208] In one example, the coupling relationship in the dependency matrix is analyzed to determine which user electricity behaviors can be individually migrated to other power supply paths for execution, which user electricity behaviors need to be retained as a group due to the dependency relationship, or which user electricity behaviors are temporarily suspended for execution due to non-migration in the case that the current battery pack is unavailable.

[0209] In this embodiment, a weighted undirected graph is constructed according to the dependency matrix, with user electricity behaviors as nodes and coupling degrees between behaviors as edge weights.

[0210] Taking subgraph division as an example, reference can be made to Figure 7 for understanding, Figure 7 is a schematic diagram of subgraph division principle of an embodiment of the present application, Figure 7 shows a weighted undirected graph converted from a dependency matrix. Figure 7 shows four nodes including node one, node two, node three and node four.

[0211] Figure 7 shows that the edge weight between node one and node two is 0.1, the edge weight between node two and node three is 0.2, the edge weight between node one and node four is 0.8, the edge weight between node one and node three is 0.7, and the edge weight between node three and node four is 0.9.

[0212] Figure 7 Further shows that in the graph division process, the connections between node two and node one and between node three and node one are first cut according to the edge weight size. It is easy to understand that node two can exist as an independent execution node.

[0213] Figure 7 Further shows that in the graph division process, node one, node four and node three can belong to the same connected domain according to the connectivity algorithm, and the dependency relationship between node one and node three is smaller than the other two edges in the specific edge weight comparison, and can also be deleted. It is easy to understand that in the subgraph formed by node one, node three and node four, the specific dependency connection can be in the order of node three-node four-node one.

[0214] Those skilled in the art can understand that, Figure 7 Only as a simple example, the specific graph division algorithm can be screened by the connected domain algorithm, which is not described herein.

[0215] Further, according to the graph division algorithm, the weighted undirected graph is grouped into behaviors, and the maximum connected subgraph algorithm is used to obtain a plurality of subgraphs with maximum connectivity in the weighted undirected graph, wherein, in the graph division process, edges with smaller edge weights can be cut off, and the corresponding nodes are regarded as independent migration behavior units.

[0216] It should be noted that, due to the existence of multiple edges between nodes, in the specific maximum connectivity division process, the edge with the largest edge weight is used as the division standard.

[0217] In one example, the calculation step of the second scheduling scheme is as follows:

[0218] Obtaining a dynamic load portrait of the other battery pack, the dynamic load portrait including a current load capacity, a voltage stability parameter and a thermal stability parameter of the battery pack;

[0219] Specifically, the application solves the problem of power supply path allocation of the sub-behavior unit between multiple healthy battery packs after scheduling reconstruction through the dynamic load portrait.

[0220] In this embodiment, the dynamic load portrait is a multi-dimensional feature vector structure, mainly including three parameters:

[0221] The first dimension parameter is the current load capacity, which is calculated by the remaining SOC of each battery pack and the historical discharge efficiency to calculate the sustainable discharge capacity;

[0222] The second dimension parameter is a voltage stability parameter, which is derived from the statistical combination of the standard deviation of voltage fluctuation and the short-term fluctuation frequency in the last several operation cycles, and is used to measure the suppression ability of voltage response to load dynamic change;

[0223] The third dimension parameter is a thermal stability parameter, which is calculated based on the temperature change gradient of the battery pack and the local cell temperature rise uniformity index, and reflects the safety margin of the battery pack in the current thermal environment.

[0224] Calculating the adaptation degree of the electrical characteristic fingerprint of the sub-behavior unit and the dynamic load portrait of the other battery pack, and calculating the second scheduling scheme according to the adaptation degree;

[0225] The method of the application can be understood at this step, the adaptation problem between behaviors and battery packs is essentially a structural similarity matching process between two multi-dimensional vectors. Therefore, the electrical characteristic fingerprint needs to be expressed as a standardized demand vector structure, and the matching degree is calculated with the dynamic portrait of each battery pack to quantify the adaptability of the target battery pack.

[0226] The calculation method of the adaptation degree includes:

[0227] In this embodiment, first, according to the electrical characteristic fingerprint extracted by each sub-behavior unit, a corresponding load demand vector is generated. The vector includes the transient current spectrum distribution at the start time, the power change rate per unit time, and the discharge charge amount in the complete execution cycle.

[0228] Further, the load demand vector is standardized with the dynamic load portrait of each healthy battery pack at the current time, so that it is in the same numerical scale interval. Then the Euclidean distance value between the two is calculated, and the smaller the distance, the more suitable the battery pack is to carry the current sub-behavior unit, and has stronger discharge capacity matching and electrical response margin.

[0229] According to the calculated Euclidean distance, determine the adaptation score, and select the battery pack with the highest adaptation score as the target power supply path of the sub-behavior unit;

[0230] In actual scheduling, a priority list can be constructed according to the adaptation score of each candidate battery pack, and the target battery pack path is allocated to each behavior unit based on the priority recursive compensation method. If a behavior unit cannot meet the minimum adaptation threshold requirement in all candidate paths at the current time, the scheduling failure feedback process is entered, and the user confirmation, delay waiting or module structure secondary reconstruction is triggered by the control unit.

[0231] As understood by those skilled in the art, after obtaining the specific target power supply path and the corresponding sub-behavior unit, the remaining calculation steps of the second scheduling scheme are all belong to the conventional path configuration and control operation process, which can be naturally realized according to the existing load scheduling framework, and the present application does not repeat it here.

[0232] In one example, the embodiments of the present application provide a motor home large-capacity battery safety management system, which comprises:

[0233] A plurality of battery packs, each battery pack comprising a plurality of battery monomers;

[0234] A BMS management system for acquiring operation data of the battery monomers and determining whether there is an abnormal battery monomer;

[0235] A behavior recognition module for recognizing a corresponding user power consumption behavior according to a power supply path of the abnormal battery monomer, and determining a power consumption action module to which the user power consumption behavior belongs and a corresponding battery pack of the user power consumption behavior;

[0236] A scheduling judgment module for judging whether there is a first scheduling scheme according to the operation state of other battery monomers in the battery pack;

[0237] A first scheduling execution module for controlling the power consumption action module to be discharged by other battery monomers in the battery pack in the case that there is a first scheduling scheme;

[0238] The second scheduling module is configured to perform the following operation when the first scheduling scheme is not feasible:

[0239] Calculate the coupling strength between the power consumption action module and the abnormal battery monomer;

[0240] Reconstruct the structure of the power consumption action module with the coupling strength greater than the threshold, extract the electrical characteristic fingerprint of the user's power consumption behavior, and construct the behavior dependency relationship;

[0241] Obtain the dynamic load image of other battery packs, and based on the fitting degree of the characteristic fingerprint and the load image, map the reconstructed sub-behavior unit to the power supply path of other battery packs for execution.

[0242] Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A method for managing large-capacity batteries in recreational vehicles is applied to a BMS management system. The BMS management system includes multiple battery packs, each of which includes multiple battery cells. Multiple user electricity usage behaviors constitute an electricity usage action module, characterized in that: The battery safety management method includes: Acquiring operating data of the battery cell and constructing a measurement matrix, wherein the operating data includes a voltage signal, a temperature signal, and a transient current wave; According to the measurement matrix, identifying battery cells with deviating characteristics within a set monitoring period by using cooperative variation characteristic values, and generating an abnormal cell candidate set, wherein the cooperative variation characteristic values ​​are determined according to statistical characteristics of the measurement matrix; Performing time series abnormal behavior modeling based on evolution trend fitting on the abnormal monomer candidate set, and determining the abnormal monomer according to the disturbance coupling degree; If there is an abnormal cell, a hierarchical response management strategy is determined and executed through the BMS system, wherein the hierarchical response management strategy includes a first scheduling scheme and a second scheduling scheme, and the second scheduling scheme includes judging the coupling strength between multiple power action modules corresponding to the abnormal cell and the abnormal cell, and reconstructing the power action modules according to the coupling strength.

2. The method for safe management of large-capacity batteries for recreational vehicles according to claim 1, characterized in that: The constructing of the measurement matrix includes: performing time alignment, normalization processing, and denoising filtering on the voltage signal, temperature signal, and transient current wave, respectively, to generate an electrical parameter feature sequence; The electrical parameter characteristic sequence is sorted according to battery cells as rows and sampling time sequences as columns to generate a measurement matrix, wherein each row represents the electrical parameter characteristic of a battery cell within a monitoring period.

3. The method for safe management of large-capacity batteries for recreational vehicles according to claim 2, characterized in that: The method of identifying a battery cell having deviated characteristics within a set monitoring period by using a coordinated change characteristic value includes: Calculating a covariance matrix of the measurement matrix, performing principal component analysis on the covariance matrix, and obtaining a principal component vector; Projecting the electrical parameter characteristics of each battery cell into the principal component space constructed by the principal component vector, and calculating the coordinated variation characteristic value based on the projection value; Comparing the coordinated change characteristic value with the average coordinated change characteristic value of all battery cells in the same monitoring period; If the deviation between the coordinated variation characteristic value of a battery cell and the average coordinated variation characteristic value is greater than or equal to a preset deviation determination threshold, the corresponding battery cell is determined as an abnormal cell candidate.

4. The method for safe management of large-capacity batteries for recreational vehicles according to claim 3, characterized in that: The abnormal monomer candidate set is subjected to time series abnormal behavior modeling based on evolution trend fitting, and the abnormal monomer is determined according to the disturbance coupling degree, including: Perform time window sliding modeling on the electrical parameter characteristics of each abnormal monomer candidate and calculate the trend vector of the electrical parameter characteristics evolving over time; Calculating the time series characteristics of the change trend vector, wherein the time series characteristics include fluctuation frequency, mutation amplitude and offset rate; Calculating the degree of change in the linkage response of the abnormal cell candidate to other battery cells in the same battery pack during the monitoring period based on the time series characteristics to obtain a disturbance response matrix; Calculating a normalized response offset value of the disturbance response matrix, and obtaining a disturbance coupling degree according to an average amplitude of the normalized response offset value; If the disturbance coupling degree is greater than or equal to a preset response threshold, the abnormal monomer candidate is determined to be an abnormal monomer.

5. The method for safe management of large-capacity batteries for recreational vehicles according to claim 1, characterized in that: Determining the hierarchical response management strategy includes: Obtaining the power usage behavior of the user corresponding to the abnormal cell, and determining the corresponding battery pack according to one or more power usage action modules corresponding to the user power usage behavior; Determining whether a first scheduling solution exists based on the operating status of the battery cells of the corresponding battery pack, and if so, calculating the first scheduling solution, wherein determining whether the first scheduling solution exists includes evaluating whether the sum of the discharge capacities meets the operating power requirement of the power-consuming action module, and if so, the first scheduling solution exists; if not, the first scheduling solution does not exist; If it does not exist, calculate the second scheduling plan, wherein the second scheduling plan includes determining the coupling strength between the corresponding multiple power-consuming action modules and the abnormal cell, reconstructing the power-consuming action module according to the coupling strength, and mapping the reconstructed power-consuming action module to the power supply path corresponding to other battery packs, wherein the structural reconstruction includes splitting the power-consuming action module into independently executable sub-behavior units.

6. The method for safe management of large-capacity batteries for recreational vehicles according to claim 5, characterized in that: The calculating the second scheduling scheme includes: Calculate the coupling strength between multiple power-consuming action modules and abnormal monomers; The power-consuming action module with the coupling strength greater than the preset coupling threshold is used as a module to be separated; Extracting electrical characteristic fingerprints of the user's electricity usage behavior of the module to be separated, wherein the electrical characteristic fingerprints include transient current waves, power change rates, and continuous discharge characteristics when the electricity usage behavior is started; Determine the dependency relationship between the user's electricity usage behaviors based on the electrical feature fingerprint, and split the module to be split into multiple independently executable sub-behavior units; Obtaining dynamic load profiles of other battery packs, wherein the dynamic load profiles include current load capacity, voltage stability, and thermal stability parameters of the battery packs; The degree of compatibility between the electrical characteristic fingerprint of the sub-behavior unit and the dynamic load portraits of other battery packs is calculated, and a second scheduling scheme is calculated based on the degree of compatibility.

7. The method for safe management of large-capacity batteries for recreational vehicles according to claim 6, characterized in that: The calculation conditions of the coupling strength include one of the following: The proportion of current flowing through abnormal single-unit power supply paths during operation of the user's power consumption behavior corresponding to the power consumption action module; The temperature rise rate of abnormal battery cells during the operation of the power action module; The power usage action module includes the power usage behavior of users powered by abnormal cells; The response correlation between the power action module and the abnormal monomer during the historical operation process.

8. The method for safe management of large-capacity batteries for recreational vehicles according to claim 6, characterized in that: Determining the dependency relationship between the user's electricity usage behaviors based on the electrical feature fingerprint includes: Obtain the electrical feature fingerprint vector of all users' electricity usage behaviors in the same electricity usage action module; The coupling dependency between user electricity usage behaviors is calculated by calculating the transient current wave spectrum similarity, power change rate similarity, and discharge charge ratio between user electricity usage behaviors. A dependency matrix between users' electricity usage behaviors is constructed according to the coupling dependency.

9. The method for safe management of large-capacity batteries for recreational vehicles according to claim 6, characterized in that: The method for calculating the degree of compatibility between the electrical characteristic fingerprint of the sub-behavior unit and the dynamic load profile of the battery pack includes: Generate a load demand vector according to the transient current wave spectrum characteristics, power change rate and discharge charge amount of the sub-behavior unit; The load demand vector and the dynamic load portrait vector of the battery pack are normalized and then the Euclidean distance is calculated; The adaptation score is determined based on the calculated Euclidean distance, and the battery pack with the highest adaptation score is selected as the target power supply path of the sub-behavior unit.

10. A large-capacity battery safety management system for recreational vehicles, used to implement the large-capacity battery safety management method for recreational vehicles according to any one of claims 1 to 9, characterized in that: The system comprises: A plurality of battery packs, each battery pack including a plurality of battery cells; A BMS management system is used to obtain the operating data of the battery cells and determine whether there are abnormal battery cells; A behavior recognition module is used to identify the corresponding user power usage behavior according to the power supply path of the abnormal battery cell, and determine the power usage action module and its corresponding battery pack to which the user power usage behavior belongs; a scheduling judgment module, configured to judge whether a first scheduling scheme exists according to the operating status of other battery cells in the battery pack; A first scheduling execution module is configured to control the power-consuming action module to discharge other battery cells in the battery pack when a first scheduling scheme exists; The second scheduling module is configured to perform the following operations when the first scheduling solution is not feasible: Calculate the coupling strength between the power-using action module and the abnormal battery cell; Restructure the power-usage action modules with coupling strength greater than a threshold, extract the electrical characteristic fingerprint of the user's power-usage behavior, and build behavioral dependency relationships; The dynamic load profiles of other battery packs are obtained, and based on the degree of adaptation between the feature fingerprint and the load profile, the reconstructed sub-behavior units are mapped to the power supply paths of other battery packs for execution.

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