A battery online detection method and system

Abstract

The present invention discloses a method and system for online detection of batteries, and relates to the technical field of battery detection. Positional relationship data of battery identity identification are generated, and battery mapping nodes are constructed using the positional relationship data. Power output and battery call data are collected through a battery management system, and power output characteristics and battery call characteristics are analyzed to obtain power output characteristics and battery call characteristics. Based on these characteristics, weights of different branches in a relationship expansion tree are determined, and an inspection strategy for an online detection system is formulated. Multi-dimensional feature data of the battery is collected according to the inspection strategy, and transmitted to an online detection system for analysis. The state of the battery monomer is determined based on these feature data. The present invention realizes online state detection of the battery through the above technical solution. Compared with manual detection, timeliness is better and abnormality detection is more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of battery detection, and in particular to a battery online detection method and system. Background Art

[0002] In modern power systems, batteries, as important energy storage devices, are widely used in uninterruptible power supply systems (UPS), electric vehicles, solar power stations, wind power stations and other fields. The performance and status of batteries directly affect the reliability and operating efficiency of the entire system. However, during the use of batteries, due to their internal chemical reactions and physical changes, the performance will gradually decline, and even failures will occur, such as capacity decay, increased internal resistance, electrolyte leakage, etc. These problems not only affect the service life of the battery, but may also have a serious impact on the system that relies on the battery for power supply.

[0003] Traditional battery testing methods mostly use offline testing methods, which require the battery to be removed from the system and tested using special testing equipment. Although this method is accurate, it is cumbersome to operate, and the battery cannot provide power to the system during the testing process, which reduces the availability of the system. Therefore, developing a method and system that can perform accurate and real-time testing of batteries online is of great significance for improving battery maintenance efficiency, extending service life, and ensuring stable operation of the system. Summary of the invention

[0004] The purpose of the present invention is to provide a detection method and system capable of online inspection of battery status.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A battery online detection method, comprising:

[0007] A battery identity is configured for each battery cell, and based on the registration information of the battery cell, the location information corresponding to the battery identity and the battery cluster to which it belongs are determined, which are recorded as the location relationship data of the battery identity;

[0008] For the position relationship data of several battery identity identifiers, several battery mapping nodes are constructed, and the battery mapping nodes are connected in a relationship expansion tree manner;

[0009] Using the battery management system corresponding to each battery cluster, collect power output data at different time nodes and battery call data at different time nodes, analyze the power output data to obtain power output characteristics, analyze the battery call data, and determine the battery call characteristics for each battery cell;

[0010] Based on the power output characteristics, the weight parameters of different branches in the relationship expansion tree are determined, based on the battery call characteristics, the weight parameter adjustment coefficient of each battery mapping node is determined, and based on the weight parameters and the weight parameter adjustment coefficient, the inspection strategy of the online detection system for the relationship expansion tree is determined;

[0011] Based on the determined inspection strategy, the battery call characteristics, battery capacity change characteristics, battery temperature change characteristics and power output characteristics of the corresponding battery mapping node are collected and transmitted to the online detection system, and the combination of the battery call characteristics, battery capacity change characteristics, battery temperature change characteristics and power output characteristics is recorded as a battery detection feature group;

[0012] Based on the battery detection feature set, the status of different battery cells is determined.

[0013] In some embodiments disclosed in the present invention, a method for connecting battery mapping nodes in a relationship extension tree includes:

[0014] For each battery cluster monitored by the online detection system, a battery cluster mapping node is constructed, and based on the relationship between the battery clusters to which the battery cells belong, the corresponding battery mapping node is mapped to the subordinate range of the battery cluster mapping node;

[0015] If there are several other levels of battery sub-cluster mapping points between the battery cluster mapping node and the battery mapping node, the battery cluster mapping node, the several levels of battery sub-cluster mapping points and the battery mapping node are connected according to the upper and lower level affiliation.

[0016] In some embodiments disclosed in the present invention, a method for determining weight parameters of different branches in a relationship expansion tree based on power output characteristics includes:

[0017] The power output feature corresponding to each battery cluster mapping node is a power output power curve. Several levels of power output power threshold intervals are set for the power output power curve, and the power output power curve segments of each level of power output power threshold interval to which the power output power curve belongs are marked separately.

[0018] The ongoing power output power curve segment of the power output power curve within the preset time period is analyzed to determine the weight parameter of the branch corresponding to the battery cluster mapping node in the relationship expansion tree.

[0019] In some embodiments disclosed in the present invention, a method for determining a weight parameter of a branch corresponding to a battery cluster mapping node in a relationship expansion tree includes:

[0020] The power output power curve segments of different power output power threshold intervals are counted to determine the curve segment time length of the power output power curve segment of each power output power threshold interval, and the curve segment time ratio of the curve segment time length to the preset time period length is calculated;

[0021] A curve segment time ratio reference array ti[ti~1, ti~2, ti~3, ..., ti~n] is set for each level of power output power threshold interval, wherein ti~1 is the first preset curve segment time ratio of the i-th level power output power threshold interval, ti~2 is the second preset curve segment time ratio of the i-th level power output power threshold interval, ti~3 is the third preset curve segment time ratio of the i-th level power output power threshold interval, ti~n is the n-th preset curve segment time ratio of the i-th level power output power threshold interval, and ti~1<ti~2<ti~3<...<ti~n;

[0022] A sub-weight parameter array hi[hi~1, hi~2, hi~3, ..., hi~n] is set, wherein hi~1 is the first preset sub-weight parameter corresponding to the i-th level power output power threshold interval, hi~2 is the second preset sub-weight parameter corresponding to the i-th level power output power threshold interval, hi~3 is the third preset sub-weight parameter corresponding to the i-th level power output power threshold interval, hi~n is the n-th preset sub-weight parameter corresponding to the i-th level power output power threshold interval, and hi~1<hi~2<hi~3<...<hi~n;

[0023] Obtain the curve segment time proportion ti~0 of the i-th level power output power threshold interval;

[0024] If ti~0<ti~1, hi~1 is selected as the sub-weight parameter corresponding to the threshold interval of the i-th level power output power;

[0025] If ti~1≤ti~0<ti~2, hi~2 is selected as the sub-weight parameter corresponding to the threshold interval of the i-th level power output power;

[0026] If ti~2≤ti~0<ti~3, hi~3 is selected as the sub-weight parameter corresponding to the threshold interval of the i-th level power output power; ...;

[0028] If ti~n-1≤ti~0<ti~n, hi~n is selected as the sub-weight parameter corresponding to the threshold interval of the i-th level power output power;

[0029] The cumulative sum of the sub-weight parameters corresponding to the threshold intervals of the power output of all levels is calculated, and the cumulative sum is identified as the weight parameter of the branch corresponding to the battery cluster mapping node in the relationship expansion tree.

[0030] In some embodiments disclosed in the present invention, based on the battery call characteristics, the method for determining the weight parameter adjustment coefficient of each battery mapping node includes:

[0031] The battery call feature is the call time ratio of each battery cell in a preset time period, and the call time ratio is the ratio of the call time of the battery cell to the total call time of all battery cells in the preset time period;

[0032] A call time ratio reference array T[T1, T2, T3, ..., Tn] is set for the call time ratio, wherein T1 is the first preset call time ratio, T2 is the second preset call time ratio, T3 is the second preset call time ratio, Tn is the nth preset call time ratio, and T1<T2<T3<...<Tn;

[0033] A weight parameter adjustment coefficient reference array K[K1, K2, K3, ..., Kn] is set for the weight parameter adjustment coefficient, wherein K1 is a first preset weight parameter adjustment coefficient, K2 is a second preset weight parameter adjustment coefficient, K3 is a third preset weight parameter adjustment coefficient, Kn is an nth preset weight parameter adjustment coefficient, and K1<K2<K3<...<Kn;

[0034] Get the call time proportion T0 of the battery cell;

[0035] If T0<T1, K1 is selected as the weight parameter adjustment coefficient of the battery cell;

[0036] If T1<T0≤T2, K2 is selected as the weight parameter adjustment coefficient of the battery cell;

[0037] If T2<T0≤T3, K3 is selected as the weight parameter adjustment coefficient of the battery cell; ...;

[0039] If Tn-1<T0≤Tn, Kn is selected as the weight parameter adjustment coefficient of the battery cell.

[0040] In some embodiments disclosed in the present invention, a method for determining an inspection strategy of an online detection system for a relationship extension tree based on a weight parameter and a weight parameter adjustment coefficient includes:

[0041] Calculate the product of the weight parameter and the weight parameter adjustment coefficient to obtain the corrected weight parameter;

[0042] After associating the corrected weight parameters of each battery cell with the battery identity, the parameters are transmitted to the online detection system, and based on the proportional relationship between the corrected weight parameters and the total detection frequency preset by the online detection system, the corresponding detection frequencies are configured for different battery identity.

[0043] In some embodiments disclosed in the present invention, a method for determining the status of different battery cells based on a battery detection feature group includes:

[0044] Analyze the battery detection feature group, determine the number of battery cells called in the battery cluster and the calling time of each battery cell based on the battery calling feature, and determine the power output of the battery cluster based on the power output feature;

[0045] Based on the power output of the battery cluster, the number of battery cells in the battery cluster and the calling time of each battery cell, the theoretical battery capacity change characteristics of each battery cell are determined;

[0046] Compare the theoretical battery capacity change characteristics with the battery capacity change characteristics in the battery detection feature group to determine the difference in battery capacity change at different time nodes;

[0047] Determine the cumulative difference of the battery capacity change difference of each battery cell within a preset time period, and determine the initial abnormal parameter corresponding to the cumulative difference, and correct the initial abnormal parameter in combination with the battery temperature change characteristics to obtain a reference abnormal parameter.

[0048] In some embodiments disclosed in the present invention, the method for determining the initial abnormal parameter corresponding to the difference amount accumulation includes:

[0049] A difference accumulation amount reference array g[g1, g2, g3, ..., gn] is set for the difference accumulation amount, wherein g1 is a first preset difference accumulation amount, g2 is a second preset difference accumulation amount, g3 is a third preset difference accumulation amount, gn is an nth preset difference accumulation amount, and g1<g2<g3<...<gn;

[0050] An initial abnormal parameter reference array [y1, y2, y3, ..., yn] is set for the initial abnormal parameter, wherein y1 is a first preset initial abnormal parameter, y2 is a second preset initial abnormal parameter, y3 is a third preset initial abnormal parameter, yn is an nth preset initial abnormal parameter, y1<y2<y3<...<yn;

[0051] Get the accumulated difference g0 of the battery cell and substitute g0 into the accumulated difference reference array g for comparison:

[0052] If g0<g1, the first preset initial abnormal parameter y1 is selected as the initial abnormal parameter of the battery cell;

[0053] If g1<g0≤g2, the second preset initial abnormal parameter y2 is selected as the initial abnormal parameter of the battery cell;

[0054] If g2<g0≤g3, the third preset initial abnormal parameter y3 is selected as the initial abnormal parameter of the battery cell; ...;

[0056] If gn-1<g0≤gn, the nth preset initial abnormal parameter yn is selected as the initial abnormal parameter of the battery cell.

[0057] In some embodiments disclosed in the present invention, the method for correcting the initial abnormal parameter based on the battery temperature change characteristics includes:

[0058] Based on the battery temperature variation characteristics, the battery temperature of the battery cell at different time nodes is determined, and the temperature difference between the battery temperature at different time nodes and the standard battery temperature is calculated;

[0059] A number of preset temperature difference intervals are set for the temperature difference, and each temperature difference interval corresponds to a specific preset abnormal correction coefficient;

[0060] Determine the preset temperature difference interval to which the temperature difference belongs, select a corresponding preset abnormal correction coefficient and multiply it by the initial abnormal parameter to obtain a reference abnormal parameter.

[0061] In some embodiments disclosed in the present invention, there is also disclosed a battery online detection system, comprising:

[0062] The first module is used to configure a battery identity for each battery cell, and determine the location information corresponding to the battery identity and the battery cluster to which it belongs based on the registration information of the battery cell, which is recorded as the location relationship data of the battery identity;

[0063] The second module is used to construct a number of battery mapping nodes based on the position relationship data of a number of battery identification marks, and connect the battery mapping nodes in the form of a relationship expansion tree;

[0064] The third module is used to use the battery management system corresponding to each battery cluster to collect power output data at different time nodes and battery call data at different time nodes, analyze the power output data to obtain power output characteristics, analyze the battery call data, and determine the battery call characteristics for each battery cell;

[0065] The fourth module is used to determine the weight parameters of different branches in the relationship expansion tree based on the power output characteristics, determine the weight parameter adjustment coefficient of each battery mapping node based on the battery call characteristics, and determine the inspection strategy of the online detection system for the relationship expansion tree based on the weight parameter and the weight parameter adjustment coefficient;

[0066] The fifth module is used to collect and transmit the battery calling characteristics, battery capacity change characteristics, battery temperature change characteristics and power output characteristics of the corresponding battery mapping node to the online detection system based on the determined inspection strategy, record the combination of battery calling characteristics, battery capacity change characteristics, battery temperature change characteristics and power output characteristics as a battery detection feature group, and determine the status of different battery cells based on the battery detection feature group.

[0067] The present invention discloses a method and system for online detection of batteries, and relates to the technical field of battery detection. Positional relationship data of battery identity identification are generated, and battery mapping nodes are constructed using the positional relationship data. Power output and battery call data are collected through a battery management system, and power output characteristics and battery call characteristics are analyzed to obtain power output characteristics and battery call characteristics. Based on these characteristics, weights of different branches in a relationship expansion tree are determined, and an inspection strategy for an online detection system is formulated. Multi-dimensional feature data of the battery is collected according to the inspection strategy, and transmitted to an online detection system for analysis. The state of the battery monomer is determined based on these feature data. The present invention realizes online state detection of the battery through the above technical solution. Compared with manual detection, timeliness is better and abnormality detection is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 A method step diagram of a battery online detection method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0069] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0070] The purpose of the present invention is to provide a detection method and system capable of online inspection of battery status.

[0071] In order to achieve the above object, the present invention adopts the following technical solutions:

[0072] See also Figure 1 , a battery online detection method, comprising:

[0073] Step S100 , configuring a battery identity for each battery cell, and determining the location information corresponding to the battery identity and the battery cluster to which it belongs based on the registration information of the battery cell, which is recorded as the location relationship data of the battery identity.

[0074] In the battery online detection system, each battery cell must first be configured with a unique battery identity. For battery cells that have not yet been configured with an identity, the system determines their location information (such as installation location, equipment, etc.) and the battery cluster to which they belong based on their registration information. The principle of this step is to establish a mapping relationship between the battery identity and the battery cell and its location information, providing a basis for subsequent data collection, feature analysis, and status monitoring.

[0075] Step S200: construct a plurality of battery mapping nodes for the position relationship data of a plurality of battery identification marks, and connect the battery mapping nodes in a relationship expansion tree manner.

[0076] After determining the battery identity and location relationship, the system constructs several battery mapping nodes, each of which represents a battery cell. Then, the system connects these nodes in the form of a relationship extension tree to reflect the physical or logical connection relationship between battery cells. The construction principle of the relationship extension tree is to use the tree structure in graph theory to represent the complex relationship between battery cells, which is convenient for subsequent data processing and status monitoring.

[0077] In some embodiments disclosed in the present invention, a method for connecting battery mapping nodes in a relationship extension tree includes:

[0078] Step S201 : construct a battery cluster mapping node for each battery cluster monitored by the online detection system, and map the corresponding battery mapping node to a subordinate range of the battery cluster mapping node based on the relationship between the battery cells and the battery clusters to which they belong.

[0079] In the battery online detection system, in order to more effectively organize and represent the relationship between battery cells, the system first constructs a battery cluster mapping node for each monitored battery cluster. This node represents the battery cluster in the relationship expansion tree and serves as the parent node of all battery mapping nodes under the cluster. Then, based on the relationship between the battery clusters to which the battery cells belong, the system maps each battery mapping node to the subordinate range of the battery cluster mapping node to which it belongs. In this way, a mapping relationship between battery clusters and battery cells is established, providing a structured representation for subsequent data processing and status monitoring.

[0080] Step S202: if there are other levels of battery sub-cluster mapping points between the battery cluster mapping node and the battery mapping node, the battery cluster mapping node, the other levels of battery sub-cluster mapping points and the battery mapping node are connected according to the upper and lower level relationship.

[0081] In practical applications, there may be multi-level sub-cluster relationships between battery clusters and battery cells. In order to accurately represent this complex relationship structure, the system needs to connect the battery cluster mapping node, several levels of battery sub-cluster mapping points, and battery mapping nodes according to the superior-subordinate relationship. The principle of this step is to establish the connection relationship between the superior and subordinate nodes step by step through recursion or iteration until the bottom-level battery mapping node is reached. In this way, a complete relationship expansion tree is formed, which can clearly represent the hierarchical relationship and connection relationship between battery clusters, sub-clusters and battery cells. This structured representation method is helpful for subsequent data collection, feature analysis and status monitoring.

[0082] Step S300, using the battery management system corresponding to each battery cluster, collects power output data at different time nodes and battery call data at different time nodes, analyzes the power output data to obtain power output characteristics, analyzes the battery call data, and determines the battery call characteristics for each battery cell.

[0083] The system collects power output data and battery call data at different time nodes in real time through the battery management system corresponding to each battery cluster. Then, the system analyzes these data and extracts power output features and battery call features. Power output features reflect the discharge performance and power consumption mode of the battery cells, while battery call features reflect the frequency and mode of battery calls by the system. The principle of this step is to convert the original power consumption and call data into meaningful feature information through data analysis and feature extraction, providing a basis for subsequent status monitoring.

[0084] Step S400, based on the power output characteristics, determine the weight parameters of different branches in the relationship expansion tree, based on the battery call characteristics, determine the weight parameter adjustment coefficient of each battery mapping node, and based on the weight parameters and weight parameter adjustment coefficients, determine the inspection strategy of the online detection system for the relationship expansion tree.

[0085] Based on the power output characteristics and battery call characteristics, the system determines the weight parameters of different branches in the relationship expansion tree and the weight parameter adjustment coefficient of each battery mapping node. Then, the system combines these weight parameters and adjustment coefficients to determine the inspection strategy of the online detection system for the relationship expansion tree. The principle of formulating the inspection strategy is to optimize the detection path according to the importance and frequency of use of the battery, ensure more frequent monitoring of key batteries, and reduce unnecessary detection overhead.

[0086] In some embodiments disclosed in the present invention, a method for determining weight parameters of different branches in a relationship expansion tree based on power output characteristics includes:

[0087] Step S401: the power output feature corresponding to each battery cluster mapping node is a power output power curve. Several levels of power output power threshold intervals are set for the power output power curve, and the power output power curve segments of each level of power output power threshold interval are marked separately.

[0088] In the battery online detection system, the power output feature is an important indicator reflecting the battery performance and usage. In order to apply this feature to the relationship expansion tree and assign reasonable weight parameters to different branches, the system first defines a power output power curve for each battery cluster mapping node. This curve describes the changes in the power output of the battery cluster at different time nodes. In order to analyze the power output characteristics more finely, the system sets several levels of power output power threshold intervals and marks the curve segments in the power output power curve that belong to each level of threshold interval. In this way, the power output power curve is divided into multiple curve segments with different power levels, which is convenient for subsequent analysis and processing.

[0089] Step S402: Analyze the ongoing power output curve segment of the power output power curve within the preset time period to determine the weight parameter of the branch corresponding to the battery cluster mapping node in the relationship expansion tree.

[0090] In some embodiments disclosed in the present invention, a method for determining a weight parameter of a branch corresponding to a battery cluster mapping node in a relationship expansion tree includes:

[0091] Step S4021, statistics are collected on the power output power curve segments of different power output power threshold intervals, the curve segment time length of the power output power curve segment of each power output power threshold interval is determined, and the curve segment time ratio of the curve segment time length relative to the preset time period length is calculated.

[0092] In step S4022, a curve segment time ratio reference array ti[ti~1, ti~2, ti~3, ..., ti~n] is set for each level of power output power threshold interval, wherein ti~1 is the first preset curve segment time ratio of the i-th level power output power threshold interval, ti~2 is the second preset curve segment time ratio of the i-th level power output power threshold interval, ti~3 is the third preset curve segment time ratio of the i-th level power output power threshold interval, ti~n is the n-th preset curve segment time ratio of the i-th level power output power threshold interval, and ti~1<ti~2<ti~3<...<ti~n.

[0093] A sub-weight parameter array hi[hi~1, hi~2, hi~3, ..., hi~n] is set, wherein hi~1 is the first preset sub-weight parameter corresponding to the i-th level power output power threshold interval, hi~2 is the second preset sub-weight parameter corresponding to the i-th level power output power threshold interval, hi~3 is the third preset sub-weight parameter corresponding to the i-th level power output power threshold interval, hi~n is the nth preset sub-weight parameter corresponding to the i-th level power output power threshold interval, and hi~1<hi~2<hi~3<...<hi~n.

[0094] Obtain the curve segment time proportion ti~0 of the i-th level power consumption output power threshold interval.

[0095] If ti~0<ti~1, hi~1 is selected as the sub-weight parameter corresponding to the i-th level power output power threshold interval.

[0096] If ti~1≤ti~0<ti~2, hi~2 is selected as the sub-weight parameter corresponding to the i-th level power output power threshold interval.

[0097] If ti~2≤ti~0<ti~3, hi~3 is selected as the sub-weight parameter corresponding to the i-th level power output power threshold interval.

[0098] If ti~n-1≤ti~0<ti~n, hi~n is selected as the sub-weight parameter corresponding to the threshold interval of the i-th level power consumption output power.

[0099] Step S4023, calculating the cumulative sum of the sub-weight parameters corresponding to the threshold intervals of all levels of power output power, and identifying the cumulative sum as the weight parameter of the branch corresponding to the battery cluster mapping node in the relationship expansion tree.

[0100] In some embodiments disclosed in the present invention, based on the battery call characteristics, the method for determining the weight parameter adjustment coefficient of each battery mapping node includes:

[0101] Step S403, the battery call feature is the call time ratio of each battery cell in the preset time period, and the call time ratio is the ratio of the call time of the battery cell to the total call time of all battery cells in the preset time period.

[0102] Step S404, a call time proportion reference array T[T1, T2, T3, ..., Tn] is set for the call time proportion, wherein T1 is the first preset call time proportion, T2 is the second preset call time proportion, T3 is the second preset call time proportion, Tn is the nth preset call time proportion, and T1<T2<T3<...<Tn.

[0103] A weight parameter adjustment coefficient reference array K[K1, K2, K3, ..., Kn] is set for the weight parameter adjustment coefficient, wherein K1 is the first preset weight parameter adjustment coefficient, K2 is the second preset weight parameter adjustment coefficient, K3 is the third preset weight parameter adjustment coefficient, Kn is the nth preset weight parameter adjustment coefficient, and K1<K2<K3<...<Kn.

[0104] Get the call time ratio T0 of the battery cell.

[0105] If T0<T1, K1 is selected as the weight parameter adjustment coefficient of the battery cell.

[0106] If T1<T0≤T2, K2 is selected as the weight parameter adjustment coefficient of the battery cell.

[0107] If T2<T0≤T3, K3 is selected as the weight parameter adjustment coefficient of the battery cell.

[0108] If Tn-1<T0≤Tn, Kn is selected as the weight parameter adjustment coefficient of the battery cell.

[0109] In some embodiments disclosed in the present invention, a method for determining an inspection strategy of an online detection system for a relationship extension tree based on a weight parameter and a weight parameter adjustment coefficient includes:

[0110] Step S405, calculating the product of the weight parameter and the weight parameter adjustment coefficient to obtain the corrected weight parameter.

[0111] Step S406, after associating the corrected weight parameter of each battery cell with the battery identity, transmit it to the online detection system, and configure corresponding detection frequencies for different battery identity based on the proportional relationship between the corrected weight parameters and the total detection frequency preset by the online detection system.

[0112] Step S500, based on the determined inspection strategy, the battery call characteristics, battery capacity change characteristics, battery temperature change characteristics and power output characteristics of the corresponding battery mapping node are collected and transmitted to the online detection system, and the combination of the battery call characteristics, battery capacity change characteristics, battery temperature change characteristics and power output characteristics is recorded as a battery detection feature group.

[0113] According to the determined inspection strategy, the system collects various characteristic data of the corresponding battery mapping node, including battery call characteristics, battery capacity change characteristics, battery temperature change characteristics, and power output characteristics. Then, the system transmits these characteristic data to the online detection system for processing and analysis. The principle of this step is to ensure that the online detection system can obtain the most accurate and timely battery status information through targeted feature collection and transmission.

[0114] Step S600: determining the states of different battery cells based on the battery detection feature group.

[0115] The online detection system uses advanced algorithms and models to determine the status of the battery cells based on the received battery detection feature set. This includes evaluating key indicators such as the battery's health status and remaining capacity.

[0116] In some embodiments of the present invention, the method for determining the status of different battery cells based on the battery detection feature group includes:

[0117] Step S601 , analyzing the battery detection feature group, determining the number of battery cells called in the battery cluster and the calling time of each battery cell based on the battery calling feature, and determining the power output of the battery cluster based on the power output feature.

[0118] Step S602 : determining a theoretical battery capacity variation characteristic of each battery cell based on the power output of the battery cluster, the number of battery cells in the battery cluster, and the calling time of each battery cell.

[0119] Step S603 , comparing the theoretical battery capacity change characteristics with the battery capacity change characteristics in the battery detection characteristic group to determine the battery capacity change difference at different time nodes.

[0120] Step S604, determining the cumulative difference of the battery capacity change difference of each battery cell within a preset time period, and determining the initial abnormal parameter corresponding to the cumulative difference, and correcting the initial abnormal parameter in combination with the battery temperature change characteristics to obtain a reference abnormal parameter.

[0121] In some embodiments disclosed in the present invention, the method for determining the initial abnormal parameter corresponding to the difference amount accumulation includes:

[0122] Step S6041, a difference accumulation reference array g[g1, g2, g3, ..., gn] is set for the difference accumulation, wherein g1 is the first preset difference accumulation, g2 is the second preset difference accumulation, g3 is the third preset difference accumulation, gn is the nth preset difference accumulation, and g1<g2<g3<...<gn.

[0123] An initial abnormal parameter reference array [y1, y2, y3, ..., yn] is set for the initial abnormal parameter, wherein y1 is the first preset initial abnormal parameter, y2 is the second preset initial abnormal parameter, y3 is the third preset initial abnormal parameter, yn is the nth preset initial abnormal parameter, and y1<y2<y3<...<yn.

[0124] Get the accumulated difference g0 of the battery cell and substitute g0 into the accumulated difference reference array g for comparison:

[0125] If g0<g1, the first preset initial abnormal parameter y1 is selected as the initial abnormal parameter of the battery cell.

[0126] If g1<g0≤g2, the second preset initial abnormal parameter y2 is selected as the initial abnormal parameter of the battery cell.

[0127] If g2<g0≤g3, the third preset initial abnormal parameter y3 is selected as the initial abnormal parameter of the battery cell.

[0128] If gn-1<g0≤gn, the nth preset initial abnormal parameter yn is selected as the initial abnormal parameter of the battery cell.

[0129] In some embodiments disclosed in the present invention, the method for correcting the initial abnormal parameter based on the battery temperature change characteristics includes:

[0130] Step S6042, based on the battery temperature variation characteristics, determine the battery temperature of the battery cell at different time nodes, and calculate the temperature difference between the battery temperature at different time nodes and the standard battery temperature.

[0131] In step S6043, a plurality of preset temperature difference intervals are set for the temperature difference, and each temperature difference interval corresponds to a specific preset abnormal correction coefficient.

[0132] Step S6044, determining the preset temperature difference interval to which the temperature difference belongs, selecting a corresponding preset abnormal correction coefficient and multiplying the initial abnormal parameter to obtain a reference abnormal parameter.

[0133] In some embodiments disclosed in the present invention, there is also disclosed a battery online detection system, comprising:

[0134] The first module is used to configure a battery identity for each battery cell, and determine the location information corresponding to the battery identity and the battery cluster to which it belongs based on the registration information of the battery cell, which is recorded as the location relationship data of the battery identity.

[0135] The second module is used to construct a plurality of battery mapping nodes for the position relationship data of a plurality of battery identification marks, and connect the battery mapping nodes in the form of a relationship expansion tree.

[0136] The third module is used to use the battery management system corresponding to each battery cluster to collect power output data at different time nodes and battery call data at different time nodes, analyze the power output data to obtain power output characteristics, analyze the battery call data, and determine the battery call characteristics for each battery cell.

[0137] The fourth module is used to determine the weight parameters of different branches in the relationship expansion tree based on the power output characteristics, determine the weight parameter adjustment coefficient of each battery mapping node based on the battery call characteristics, and determine the inspection strategy of the online detection system for the relationship expansion tree based on the weight parameters and the weight parameter adjustment coefficient.

[0138] The fifth module is used to collect and transmit the battery calling characteristics, battery capacity change characteristics, battery temperature change characteristics and power output characteristics of the corresponding battery mapping node to the online detection system based on the determined inspection strategy, record the combination of battery calling characteristics, battery capacity change characteristics, battery temperature change characteristics and power output characteristics as a battery detection feature group, and determine the status of different battery cells based on the battery detection feature group.

[0139] The present invention discloses a method and system for online detection of batteries, and relates to the technical field of battery detection. Positional relationship data of battery identity identification are generated, and battery mapping nodes are constructed using the positional relationship data. Power output and battery call data are collected through a battery management system, and power output characteristics and battery call characteristics are analyzed to obtain power output characteristics and battery call characteristics. Based on these characteristics, weights of different branches in a relationship expansion tree are determined, and an inspection strategy for an online detection system is formulated. Multi-dimensional feature data of the battery is collected according to the inspection strategy, and transmitted to an online detection system for analysis. The state of the battery monomer is determined based on these feature data. The present invention realizes online state detection of the battery through the above technical solution. Compared with manual detection, timeliness is better and abnormality detection is more accurate.

[0140] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A battery online detection method, characterized in that: include: A battery identity is configured for each battery cell, and based on the registration information of the battery cell, the location information corresponding to the battery identity and the battery cluster to which it belongs are determined, which are recorded as the location relationship data of the battery identity; For the position relationship data of several battery identity identifiers, several battery mapping nodes are constructed, and the battery mapping nodes are connected in a relationship expansion tree manner; Using the battery management system corresponding to each battery cluster, collect power output data at different time nodes and battery call data at different time nodes, analyze the power output data to obtain power output characteristics, analyze the battery call data, and determine the battery call characteristics for each battery cell; Based on the power output characteristics, the weight parameters of different branches in the relationship expansion tree are determined, based on the battery call characteristics, the weight parameter adjustment coefficient of each battery mapping node is determined, and based on the weight parameters and the weight parameter adjustment coefficient, the inspection strategy of the online detection system for the relationship expansion tree is determined; Based on the determined inspection strategy, the battery call characteristics, battery capacity change characteristics, battery temperature change characteristics and power output characteristics of the corresponding battery mapping node are collected and transmitted to the online detection system, and the combination of the battery call characteristics, battery capacity change characteristics, battery temperature change characteristics and power output characteristics is recorded as a battery detection feature group; Based on the battery detection feature set, the status of different battery cells is determined.

2. A battery online detection method according to claim 1, characterized in that: Methods for connecting battery mapping nodes in a relationship expansion tree include: For each battery cluster monitored by the online detection system, a battery cluster mapping node is constructed, and based on the relationship between the battery clusters to which the battery cells belong, the corresponding battery mapping node is mapped to the subordinate range of the battery cluster mapping node; If there are several other levels of battery sub-cluster mapping points between the battery cluster mapping node and the battery mapping node, the battery cluster mapping node, the several levels of battery sub-cluster mapping points and the battery mapping node are connected according to the upper and lower level affiliation.

3. A battery online detection method according to claim 2, characterized in that: Based on the power output characteristics, the method for determining the weight parameters of different branches in the relationship expansion tree includes: The power output feature corresponding to each battery cluster mapping node is a power output power curve. Several levels of power output power threshold intervals are set for the power output power curve, and the power output power curve segments of each level of power output power threshold interval to which the power output power curve belongs are marked separately. The ongoing power output power curve segment of the power output power curve within the preset time period is analyzed to determine the weight parameter of the branch corresponding to the battery cluster mapping node in the relationship expansion tree.

4. A battery online detection method according to claim 3, characterized in that: The method for determining the weight parameter of the branch corresponding to the battery cluster mapping node in the relationship expansion tree includes: The power output power curve segments of different power output power threshold intervals are counted to determine the curve segment time length of the power output power curve segment of each power output power threshold interval, and the curve segment time ratio of the curve segment time length to the preset time period length is calculated; A curve segment time ratio reference array ti[ti~1, ti~2, ti~3, ..., ti~n] is set for each level of power output power threshold interval, wherein ti~1 is the first preset curve segment time ratio of the i-th level power output power threshold interval, ti~2 is the second preset curve segment time ratio of the i-th level power output power threshold interval, ti~3 is the third preset curve segment time ratio of the i-th level power output power threshold interval, ti~n is the n-th preset curve segment time ratio of the i-th level power output power threshold interval, and ti~1<ti~2<ti~3<...<ti~n; A sub-weight parameter array hi[hi~1, hi~2, hi~3, ..., hi~n] is set, wherein hi~1 is the first preset sub-weight parameter corresponding to the i-th level power output power threshold interval, hi~2 is the second preset sub-weight parameter corresponding to the i-th level power output power threshold interval, hi~3 is the third preset sub-weight parameter corresponding to the i-th level power output power threshold interval, hi~n is the n-th preset sub-weight parameter corresponding to the i-th level power output power threshold interval, and hi~1<hi~2<hi~3<...<hi~n; Obtain the curve segment time proportion ti~0 of the i-th level power output power threshold interval; If ti~0<ti~1, hi~1 is selected as the sub-weight parameter corresponding to the threshold interval of the i-th level power output power; If ti~1≤ti~0<ti~2, hi~2 is selected as the sub-weight parameter corresponding to the threshold interval of the i-th level power output power; If ti~2≤ti~0<ti~3, hi~3 is selected as the sub-weight parameter corresponding to the threshold interval of the i-th level power output power; ...； If ti~n-1≤ti~0<ti~n, hi~n is selected as the sub-weight parameter corresponding to the threshold interval of the i-th level power output power; The cumulative sum of the sub-weight parameters corresponding to the threshold intervals of the power output of all levels is calculated, and the cumulative sum is identified as the weight parameter of the branch corresponding to the battery cluster mapping node in the relationship expansion tree.

5. A battery online detection method according to claim 4, characterized in that: Based on the battery call characteristics, the method for determining the weight parameter adjustment coefficient of each battery mapping node includes: The battery call feature is the call time ratio of each battery cell in a preset time period, and the call time ratio is the ratio of the call time of the battery cell to the total call time of all battery cells in the preset time period; A call time ratio reference array T[T1, T2, T3, ..., Tn] is set for the call time ratio, wherein T1 is the first preset call time ratio, T2 is the second preset call time ratio, T3 is the second preset call time ratio, Tn is the nth preset call time ratio, and T1<T2<T3<...<Tn; A weight parameter adjustment coefficient reference array K[K1, K2, K3, ..., Kn] is set for the weight parameter adjustment coefficient, wherein K1 is a first preset weight parameter adjustment coefficient, K2 is a second preset weight parameter adjustment coefficient, K3 is a third preset weight parameter adjustment coefficient, Kn is an nth preset weight parameter adjustment coefficient, and K1<K2<K3<...<Kn; Get the call time proportion T0 of the battery cell; If T0<T1, K1 is selected as the weight parameter adjustment coefficient of the battery cell; If T1<T0≤T2, K2 is selected as the weight parameter adjustment coefficient of the battery cell; If T2<T0≤T3, K3 is selected as the weight parameter adjustment coefficient of the battery cell; ...； If Tn-1<T0≤Tn, Kn is selected as the weight parameter adjustment coefficient of the battery cell.

6. A battery online detection method according to claim 5, characterized in that: Based on the weight parameter and the weight parameter adjustment coefficient, the method for determining the inspection strategy of the online detection system for the relationship extension tree includes: Calculate the product of the weight parameter and the weight parameter adjustment coefficient to obtain the corrected weight parameter; After associating the corrected weight parameters of each battery cell with the battery identity, the parameters are transmitted to the online detection system, and based on the proportional relationship between the corrected weight parameters and the total detection frequency preset by the online detection system, the corresponding detection frequencies are configured for different battery identity.

7. A battery online detection method according to claim 1, characterized in that: Based on the battery detection feature group, the method for determining the status of different battery cells includes: Analyze the battery detection feature group, determine the number of battery cells called in the battery cluster and the calling time of each battery cell based on the battery calling feature, and determine the power output of the battery cluster based on the power output feature; Based on the power output of the battery cluster, the number of battery cells in the battery cluster and the calling time of each battery cell, the theoretical battery capacity change characteristics of each battery cell are determined; Compare the theoretical battery capacity change characteristics with the battery capacity change characteristics in the battery detection feature group to determine the difference in battery capacity change at different time nodes; The accumulated difference of the battery capacity change difference of each battery cell within a preset time period is determined, and the initial abnormal parameter corresponding to the accumulated difference is judged, and the initial abnormal parameter is corrected in combination with the battery temperature change characteristics to obtain a reference abnormal parameter.

8. A battery online detection method according to claim 7, characterized in that: The method for determining the initial abnormal parameter corresponding to the difference amount accumulation includes: A difference accumulation amount reference array g[g1, g2, g3, ..., gn] is set for the difference accumulation amount, wherein g1 is a first preset difference accumulation amount, g2 is a second preset difference accumulation amount, g3 is a third preset difference accumulation amount, gn is an nth preset difference accumulation amount, and g1<g2<g3<...<gn; An initial abnormal parameter reference array [y1, y2, y3, ..., yn] is set for the initial abnormal parameter, wherein y1 is a first preset initial abnormal parameter, y2 is a second preset initial abnormal parameter, y3 is a third preset initial abnormal parameter, yn is an nth preset initial abnormal parameter, y1<y2<y3<...<yn; Get the accumulated difference g0 of the battery cell and substitute g0 into the accumulated difference reference array g for comparison: If g0<g1, the first preset initial abnormal parameter y1 is selected as the initial abnormal parameter of the battery cell; If g1<g0≤g2, the second preset initial abnormal parameter y2 is selected as the initial abnormal parameter of the battery cell; If g2<g0≤g3, the third preset initial abnormal parameter y3 is selected as the initial abnormal parameter of the battery cell; ...； If gn-1<g0≤gn, the nth preset initial abnormal parameter yn is selected as the initial abnormal parameter of the battery cell.

9. A battery online detection method according to claim 8, characterized in that: Based on the battery temperature variation characteristics, the method for correcting the initial abnormal parameters includes: Based on the battery temperature variation characteristics, the battery temperature of the battery cell at different time nodes is determined, and the temperature difference between the battery temperature at different time nodes and the standard battery temperature is calculated; A number of preset temperature difference intervals are set for the temperature difference, and each temperature difference interval corresponds to a specific preset abnormal correction coefficient; Determine the preset temperature difference interval to which the temperature difference belongs, select a corresponding preset abnormal correction coefficient and multiply it by the initial abnormal parameter to obtain a reference abnormal parameter.

10. A battery online detection system, characterized in that: include: The first module is used to configure a battery identity for each battery cell, and determine the location information corresponding to the battery identity and the battery cluster to which it belongs based on the registration information of the battery cell, which is recorded as the location relationship data of the battery identity; The second module is used to construct a number of battery mapping nodes based on the position relationship data of a number of battery identification marks, and connect the battery mapping nodes in the form of a relationship expansion tree; The third module is used to use the battery management system corresponding to each battery cluster to collect power output data at different time nodes and battery call data at different time nodes, analyze the power output data to obtain power output characteristics, analyze the battery call data, and determine the battery call characteristics for each battery cell; The fourth module is used to determine the weight parameters of different branches in the relationship expansion tree based on the power output characteristics, determine the weight parameter adjustment coefficient of each battery mapping node based on the battery call characteristics, and determine the inspection strategy of the online detection system for the relationship expansion tree based on the weight parameter and the weight parameter adjustment coefficient; The fifth module is used to collect and transmit the battery calling characteristics, battery capacity change characteristics, battery temperature change characteristics and power output characteristics of the corresponding battery mapping node to the online detection system based on the determined inspection strategy, record the combination of battery calling characteristics, battery capacity change characteristics, battery temperature change characteristics and power output characteristics as a battery detection feature group, and determine the status of different battery cells based on the battery detection feature group.

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