Lithium battery pack electrical state big data real-time monitoring and early warning system

Abstract

The invention discloses a lithium battery pack electrical state big data real-time monitoring and early warning system, and belongs to the technical field of lithium battery monitoring. The electrical state data corresponding to the lithium battery pack is acquired through the arranged acquisition unit group; performing classification processing on the electrical state data to obtain a corresponding state evaluation coefficient; acquiring a state type corresponding to the lithium battery pack according to the state evaluation coefficient; analyzing the state evaluation coefficient to obtain an abnormal state evaluation coefficient, and obtaining an abnormal state type of the state types; performing early warning on the abnormal state type to obtain an early warning signal, and obtaining abnormal electrical state data corresponding to the early warning signal; storing the abnormal electrical state data to generate a historical abnormal electrical state database; and the timeliness of electrical state monitoring of the lithium battery pack is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium battery monitoring, and in particular to a real-time monitoring and early warning system for big data of the electrical status of a lithium battery pack. Background Art

[0002] A lithium battery pack is a battery system composed of multiple lithium battery cells connected in series, parallel, or hybrid. It is widely used in electric vehicles, energy storage power stations, consumer electronics, and other fields. Since lithium batteries generate heat and gas during the charging and discharging process, if they are not monitored and controlled, they may cause safety problems such as overheating, short circuit and explosion; big data technology can help monitor the operating status and environmental parameters of lithium batteries in real time, such as temperature, humidity and pressure, to promptly detect abnormal situations and take appropriate measures to deal with them; by analyzing and mining a large amount of lithium battery safety event data, safety assessment models can also be extracted to help predict and prevent lithium battery safety issues; therefore, the present invention provides a real-time monitoring and early warning system for the electrical status of lithium battery packs based on big data. Summary of the Invention

[0003] In order to solve the above technical problems, the present invention provides a real-time monitoring and early warning system for the electrical status of a lithium battery pack based on big data; The object of the present invention can be achieved by the following technical solutions: A real-time monitoring and early warning system for the electrical status of a lithium battery pack based on big data, the system comprising a data acquisition module, a data processing module, a data analysis module, an early warning module, and a storage module; The data acquisition module is provided with an acquisition unit group, which is used to collect electrical status data corresponding to the lithium battery pack according to the acquisition unit group; The data processing module is used to classify the electrical state data to obtain the corresponding state evaluation coefficient; and obtain the state type corresponding to the lithium battery pack according to the state evaluation coefficient; The data analysis module is used to analyze the state evaluation coefficient to obtain the abnormal state evaluation coefficient and obtain the abnormal state type of the state type; The early warning module is used to obtain an early warning signal for the abnormal state type and obtain abnormal electrical state data corresponding to the early warning signal; The storage module is used to store abnormal electrical state data to generate a historical abnormal electrical state database.

[0004] Furthermore, the process of the data acquisition module setting the acquisition unit group includes: Generate a corresponding collection master node for each single cell of the lithium battery pack, set an electrical parameter collection node corresponding to each single cell, and then connect the corresponding collection master node to generate a first collection unit group corresponding to the single cell; the electrical parameter collection node includes a voltage collection node and a current collection node; set a corresponding temperature collection node on the surface of the corresponding single cell of the lithium battery pack, and connect the temperature collection nodes in sequence from left to right of the lithium battery pack to generate a second collection unit group corresponding to the lithium battery pack; set a single cell collection module for each single cell of the lithium battery pack, and set a charging collection node and a discharging collection node at the positive and negative ends of the single cell collection module respectively; connect the single cell collection modules in sequence to generate a third collection unit group; The first collection unit group, the second collection unit group, and the third collection unit group are combined to generate a collection unit group corresponding to the lithium battery group.

[0005] Furthermore, the process of collecting electrical status data corresponding to the lithium battery pack according to the collection unit group includes: Setting a collection frequency in the collection unit and wirelessly connecting the first collection unit group, the second collection unit group, and the third collection unit; setting corresponding collection devices at the collection nodes corresponding to the collection units, for collecting electrical status data corresponding to each collection node according to the collection frequency; The electrical state data collected by the voltage collection nodes and the current collection nodes corresponding to the first collection unit group are single cell voltage data and single cell current data; The electrical state data collected by the temperature collection node corresponding to the second collection unit group is temperature data; The electrical state data collected by the charging collection node and the discharging collection node corresponding to the third collection unit group are charging current data and discharging current data.

[0006] Furthermore, the process of the data processing module classifying the electrical status data to obtain the corresponding status assessment coefficient includes: Setting the electrical state of the electrical state data corresponding to the acquisition unit group; the electrical state includes normal state, warning state and fault state; Setting a data threshold range set corresponding to the electrical state of the electrical state data corresponding to the acquisition unit group; the data threshold range set is used to represent a set of data threshold ranges corresponding to the electrical state, and then comparing the real-time acquired electrical state data with the corresponding data threshold ranges. If a corresponding data threshold range set exists, the corresponding electrical state data is marked as the corresponding electrical state; If it is a fault state, a collection unit group for obtaining corresponding electrical state data; When the acquisition unit group is the first acquisition unit group, the electrical parameter acquisition node corresponding to the electrical status data is obtained, and the corresponding single battery is obtained according to the electrical parameter acquisition node, and then the first group of abnormal status types corresponding to the single battery is obtained; the first group of abnormal status types includes overvoltage abnormality, undervoltage abnormality and current abnormality; When the acquisition unit group is the second acquisition unit group, the temperature acquisition node corresponding to the electrical state data is obtained, and the corresponding single battery is obtained according to the temperature acquisition node, and then the temperature abnormal state type corresponding to the single battery is obtained; When the acquisition unit group is the third acquisition unit group, the monomer acquisition module corresponding to the electrical state data is obtained, and the corresponding positive and negative ends are obtained according to the monomer acquisition module, and then the abnormal charge and discharge state type corresponding to the monomer acquisition module is obtained.

[0007] Furthermore, if it is a warning state, the corresponding electrical state data is obtained and marked as warning electrical state data; the warning electrical state data is connected with the corresponding acquisition unit group to generate a warning acquisition unit group; and the warning electrical state data corresponding to each warning acquisition unit group is connected to generate a corresponding lithium battery warning group; Obtain the state assessment coefficient corresponding to the lithium battery early warning group according to the early warning electrical state data corresponding to the lithium battery early warning group; The specific formula is:

[0008] Wherein, E represents the state evaluation coefficient; i represents the number of the lithium battery warning group corresponding to the warning electrical state data, and i=1, 2, 3, ..., j, where j is a positive integer; w i It is represented as the dynamic weight coefficient corresponding to the i-th warning electrical status data; Y i and Y imax They represent the electrical state data corresponding to the i-th warning electrical state data and the warning threshold value corresponding to the electrical state data; a and b represent the control coefficients, and a+b=1; p SOH Expressed as the decay rate of the lithium battery pack in a healthy state; If it is in normal state, no action is taken.

[0009] Furthermore, the process of obtaining the abnormal status type of the status type includes: Setting an automatic trigger control group according to the acquisition unit group, wherein the automatic trigger control group includes current limiting, voltage balancing, temperature control, and user notification; and then setting a state assessment coefficient threshold range corresponding to the automatic trigger control group; If there is a corresponding automatic triggering control group for the state assessment coefficient, the corresponding lithium battery warning group automatically triggers the corresponding automatic triggering control group for control; otherwise, an abnormal state type is generated; the abnormal state type includes overvoltage abnormality, undervoltage abnormality, current abnormality, temperature abnormality state type and abnormal charging and discharging state type.

[0010] Furthermore, the process of the early warning module issuing an early warning for the abnormal state type to obtain an early warning signal and obtaining abnormal electrical state data corresponding to the early warning signal includes: Generate corresponding warning signals for overvoltage abnormality, undervoltage abnormality, current abnormality, temperature abnormality state type and abnormal charging and discharging state type, and send them to the management terminal; the management terminal obtains the corresponding electrical status data according to the warning signal and marks it as abnormal electrical status data.

[0011] Furthermore, the process of the storage module storing abnormal electrical state data to generate a historical abnormal electrical state database includes: Obtain the collection time corresponding to the abnormal electrical status data, connect the abnormal electrical status data with the corresponding abnormal status type, collection unit group and collection time to generate the abnormal electrical status data chain corresponding to the lithium battery pack, and store and obtain the historical abnormal electrical status database in real time.

[0012] Compared with the prior art, the beneficial effects of the present invention are: the present invention collects electrical status data corresponding to the lithium battery pack through the set acquisition unit group; classifies the electrical status data to obtain the corresponding status evaluation coefficient; obtains the status type corresponding to the lithium battery pack according to the status evaluation coefficient; analyzes the status evaluation coefficient to obtain the abnormal status evaluation coefficient, and obtains the abnormal status type of the status type; then warns the abnormal status type to obtain a warning signal, and obtains the abnormal electrical status data corresponding to the warning signal; then stores the abnormal electrical status data to generate a historical abnormal electrical status database; effectively improves the timeliness of the electrical status monitoring of the lithium battery pack. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0014] Figure 1 This is a schematic diagram of the present invention. DETAILED DESCRIPTION

[0015] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0016] like Figure 1 As shown, a real-time monitoring and early warning system for the electrical status of a lithium battery pack based on big data, the system includes a data acquisition module, a data processing module, a data analysis module, an early warning module, and a storage module; The data acquisition module is provided with an acquisition unit group, which is used to collect electrical status data corresponding to the lithium battery pack according to the acquisition unit group; The data processing module is used to classify the electrical state data to obtain the corresponding state evaluation coefficient; and obtain the state type corresponding to the lithium battery pack according to the state evaluation coefficient; The data analysis module is used to analyze the state evaluation coefficient to obtain the abnormal state evaluation coefficient and obtain the abnormal state type of the state type; The early warning module is used to obtain an early warning signal for the abnormal state type and obtain abnormal electrical state data corresponding to the early warning signal; The storage module is used to store abnormal electrical state data to generate a historical abnormal electrical state database.

[0017] It should be further explained that the process of the data acquisition module setting the acquisition unit group includes: Generate a corresponding collection master node for each single cell of the lithium battery pack, set an electrical parameter collection node corresponding to each single cell, and then connect the corresponding collection master node to generate a first collection unit group corresponding to the single cell; the electrical parameter collection node includes a voltage collection node and a current collection node; set a corresponding temperature collection node on the surface of the corresponding single cell of the lithium battery pack, and connect the temperature collection nodes in sequence from left to right of the lithium battery pack to generate a second collection unit group corresponding to the lithium battery pack; set a single cell collection module for each single cell of the lithium battery pack, and set a charging collection node and a discharging collection node at the positive and negative ends of the single cell collection module respectively; connect the single cell collection modules in sequence to generate a third collection unit group; Merging the first collection unit group, the second collection unit group, and the third collection unit group to generate a collection unit group corresponding to the lithium battery group; In the above embodiment, it should be further explained that by setting different collection unit groups for the lithium battery pack to separately collect different data of the lithium battery pack, the global monitoring system of the lithium battery pack from the cell level to the system level can be improved.

[0018] It should be further explained that the process of collecting electrical status data corresponding to the lithium battery pack according to the collection unit group includes: Setting a collection frequency in the collection unit and wirelessly connecting the first collection unit group, the second collection unit group, and the third collection unit; setting corresponding collection devices at the collection nodes corresponding to the collection units, for collecting electrical status data corresponding to each collection node according to the collection frequency; The electrical state data collected by the voltage collection nodes and the current collection nodes corresponding to the first collection unit group are single cell voltage data and single cell current data; The electrical state data collected by the temperature collection node corresponding to the second collection unit group is temperature data; The electrical state data collected by the charging collection node and the discharging collection node corresponding to the third collection unit group are charging current data and discharging current data; In the above embodiment, it should be further explained that the acquisition device includes but is not limited to a voltage sensor, a Hall effect current sensor, a temperature sensor, and the like.

[0019] It should be further explained that the process of the data processing module classifying the electrical status data to obtain the corresponding status assessment coefficient includes: Setting the electrical state of the electrical state data corresponding to the acquisition unit group; the electrical state includes normal state, warning state and fault state; Setting a data threshold range set corresponding to the electrical state of the electrical state data corresponding to the acquisition unit group; the data threshold range set is used to represent a set of data threshold ranges corresponding to the electrical state, and then comparing the real-time acquired electrical state data with the corresponding data threshold ranges. If a corresponding data threshold range set exists, the corresponding electrical state data is marked as the corresponding electrical state; If it is a fault state, a collection unit group for obtaining corresponding electrical state data; When the acquisition unit group is the first acquisition unit group, the electrical parameter acquisition node corresponding to the electrical status data is obtained, and the corresponding single battery is obtained according to the electrical parameter acquisition node, and then the first group of abnormal status types corresponding to the single battery is obtained; the first group of abnormal status types includes overvoltage abnormality, undervoltage abnormality and current abnormality; When the acquisition unit group is the second acquisition unit group, the temperature acquisition node corresponding to the electrical state data is obtained, and the corresponding single battery is obtained according to the temperature acquisition node, and then the temperature abnormal state type corresponding to the single battery is obtained; When the acquisition unit group is the third acquisition unit group, the monomer acquisition module corresponding to the electrical state data is obtained, and the corresponding positive and negative ends are obtained according to the monomer acquisition module, and then the abnormal charge and discharge state type corresponding to the monomer acquisition module is obtained; If it is a warning state, the corresponding electrical state data is obtained and marked as warning electrical state data; the warning electrical state data is connected with the corresponding acquisition unit group to generate a warning acquisition unit group; and the warning electrical state data corresponding to each warning acquisition unit group is connected to generate a corresponding lithium battery warning group; Obtain the state assessment coefficient corresponding to the lithium battery early warning group according to the early warning electrical state data corresponding to the lithium battery early warning group; The specific formula is:

[0020] Wherein, E represents the state evaluation coefficient; i represents the number of the lithium battery warning group corresponding to the warning electrical state data, and i=1, 2, 3, ..., j, where j is a positive integer; w i It is represented as the dynamic weight coefficient corresponding to the i-th warning electrical status data; Y i and Y imax They represent the electrical state data corresponding to the i-th warning electrical state data and the warning threshold value corresponding to the electrical state data; a and b represent the control coefficients, and a+b=1; p SOH Expressed as the decay rate of the lithium battery pack in a healthy state; If it is in normal state, no action will be taken; In the above embodiment, it needs to be further explained that the electrical status data is analyzed according to the electrical status to obtain the corresponding electrical status data, and then the electrical status data is processed to obtain the status assessment coefficient; this avoids the randomness and inaccuracy of the traditional method of directly issuing an early warning by obtaining the electrical status through the electrical status data.

[0021] It should be further explained that the process of obtaining the abnormal status type of the status type includes: Setting an automatic trigger control group according to the acquisition unit group, wherein the automatic trigger control group includes current limiting, voltage balancing, temperature control, and user notification; and then setting a state assessment coefficient threshold range corresponding to the automatic trigger control group; If there is a corresponding automatic triggering control group for the state assessment coefficient, the corresponding lithium battery warning group automatically triggers the corresponding automatic triggering control group for control; otherwise, an abnormal state type is generated; the abnormal state type includes overvoltage abnormality, undervoltage abnormality, current abnormality, temperature abnormality state type and abnormal charging and discharging state type.

[0022] In the above embodiment, it needs to be further explained that, in the early warning state, control measures such as current limiting, balancing, and temperature control are automatically triggered according to the state assessment coefficient to delay the deterioration of the state; in the fault state, the abnormality type (such as overvoltage, temperature abnormality) is accurately located through the acquisition unit group and associated with the specific single cell or module, which greatly shortens the fault investigation time.

[0023] It should be further explained that the process of the early warning module issuing an early warning for the abnormal state type, obtaining an early warning signal, and obtaining abnormal electrical state data corresponding to the early warning signal includes: Generate corresponding warning signals for overvoltage abnormality, undervoltage abnormality, current abnormality, temperature abnormality state type and abnormal charging and discharging state type, and send them to the management terminal; the management terminal obtains the corresponding electrical status data according to the warning signal and marks it as abnormal electrical status data.

[0024] It should be further explained that the process of the storage module storing abnormal electrical state data to generate a historical abnormal electrical state database includes: Obtain the collection time corresponding to the abnormal electrical status data, connect the abnormal electrical status data with the corresponding abnormal status type, collection unit group and collection time to generate the abnormal electrical status data chain corresponding to the lithium battery pack, and store and obtain the historical abnormal electrical status database in real time.

[0025] The features and exemplary embodiments of various aspects of the present application are described in detail above. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail in combination with the drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without some of these specific details. The above description of the embodiments is merely to provide a better understanding of the present application by showing examples of the present application.

[0026] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A real-time monitoring and early warning system for the electrical status of a lithium battery pack based on big data, characterized in that: The system includes a data acquisition module, a data processing module, a data analysis module, an early warning module and a storage module; The data acquisition module is provided with an acquisition unit group, which is used to collect electrical status data corresponding to the lithium battery pack according to the acquisition unit group; The data processing module is used to classify the electrical state data to obtain the corresponding state evaluation coefficient; and obtain the state type corresponding to the lithium battery pack according to the state evaluation coefficient; The data analysis module is used to analyze the state evaluation coefficient to obtain the abnormal state evaluation coefficient and obtain the abnormal state type of the state type; The early warning module is used to obtain an early warning signal for the abnormal state type and obtain abnormal electrical state data corresponding to the early warning signal; The storage module is used to store abnormal electrical state data to generate a historical abnormal electrical state database.

2. A lithium battery pack electrical status big data real-time monitoring and early warning system according to claim 1, characterized in that: The process of setting the acquisition unit group by the data acquisition module includes: Generate a corresponding collection master node for each single cell of the lithium battery pack, set an electrical parameter collection node corresponding to each single cell, and then connect the corresponding collection master node to generate a first collection unit group corresponding to the single cell; the electrical parameter collection node includes a voltage collection node and a current collection node; set a corresponding temperature collection node on the surface of the corresponding single cell of the lithium battery pack, and connect the temperature collection nodes in sequence from left to right of the lithium battery pack to generate a second collection unit group corresponding to the lithium battery pack; set a single cell collection module for each single cell of the lithium battery pack, and set a charging collection node and a discharging collection node at the positive and negative ends of the single cell collection module respectively; connect the single cell collection modules in sequence to generate a third collection unit group; The first collection unit group, the second collection unit group, and the third collection unit group are combined to generate a collection unit group corresponding to the lithium battery group.

3. The lithium battery pack electrical status big data real-time monitoring and early warning system according to claim 2, characterized in that: The process of collecting electrical status data corresponding to the lithium battery pack according to the collection unit group includes: Setting a collection frequency in the collection unit and wirelessly connecting the first collection unit group, the second collection unit group, and the third collection unit; setting corresponding collection devices at the collection nodes corresponding to the collection units, for collecting electrical status data corresponding to each collection node according to the collection frequency; The electrical state data collected by the voltage collection nodes and the current collection nodes corresponding to the first collection unit group are single cell voltage data and single cell current data; The electrical state data collected by the temperature collection node corresponding to the second collection unit group is temperature data; The electrical state data collected by the charging collection node and the discharging collection node corresponding to the third collection unit group are charging current data and discharging current data.

4. A lithium battery pack electrical status big data real-time monitoring and early warning system according to claim 3, characterized in that: The process of the data processing module classifying and processing the electrical status data to obtain the corresponding status assessment coefficient includes: Setting the electrical state of the electrical state data corresponding to the acquisition unit group; the electrical state includes normal state, warning state and fault state; Setting a data threshold range set corresponding to the electrical state of the electrical state data corresponding to the acquisition unit group; the data threshold range set is used to represent a set of data threshold ranges corresponding to the electrical state, and then comparing the real-time acquired electrical state data with the corresponding data threshold ranges. If a corresponding data threshold range set exists, the corresponding electrical state data is marked as the corresponding electrical state; If it is a fault state, a collection unit group for obtaining corresponding electrical state data; When the acquisition unit group is the first acquisition unit group, the electrical parameter acquisition node corresponding to the electrical status data is obtained, and the corresponding single battery is obtained according to the electrical parameter acquisition node, and then the first group of abnormal status types corresponding to the single battery is obtained; the first group of abnormal status types includes overvoltage abnormality, undervoltage abnormality and current abnormality; When the acquisition unit group is the second acquisition unit group, the temperature acquisition node corresponding to the electrical state data is obtained, and the corresponding single battery is obtained according to the temperature acquisition node, and then the temperature abnormal state type corresponding to the single battery is obtained; When the acquisition unit group is the third acquisition unit group, the monomer acquisition module corresponding to the electrical state data is obtained, and the corresponding positive and negative ends are obtained according to the monomer acquisition module, and then the abnormal charge and discharge state type corresponding to the monomer acquisition module is obtained.

5. The lithium battery pack electrical status big data real-time monitoring and early warning system according to claim 4, characterized in that: If it is a warning state, the corresponding electrical state data is obtained and marked as warning electrical state data; the warning electrical state data is connected with the corresponding acquisition unit group to generate a warning acquisition unit group; and the warning electrical state data corresponding to each warning acquisition unit group is connected to generate a corresponding lithium battery warning group; Obtain the state assessment coefficient corresponding to the lithium battery early warning group according to the early warning electrical state data corresponding to the lithium battery early warning group; If it is in normal state, no action is taken.

6. A lithium battery pack electrical status big data real-time monitoring and early warning system according to claim 5, characterized in that: The process of obtaining the abnormal status type of the status type includes: Setting an automatic trigger control group according to the acquisition unit group, wherein the automatic trigger control group includes current limiting, voltage balancing, temperature control, and user notification; and then setting a state assessment coefficient threshold range corresponding to the automatic trigger control group; If there is a corresponding automatic triggering control group for the state assessment coefficient, the corresponding lithium battery warning group automatically triggers the corresponding automatic triggering control group for control; otherwise, an abnormal state type is generated; the abnormal state type includes overvoltage abnormality, undervoltage abnormality, current abnormality, temperature abnormality state type and abnormal charging and discharging state type.

7. A lithium battery pack electrical status big data real-time monitoring and early warning system according to claim 6, characterized in that: The process of the early warning module issuing an early warning for the abnormal state type to obtain an early warning signal and obtaining abnormal electrical state data corresponding to the early warning signal includes: Generate corresponding warning signals for overvoltage abnormality, undervoltage abnormality, current abnormality, temperature abnormality state type and abnormal charging and discharging state type, and send them to the management terminal; the management terminal obtains the corresponding electrical status data according to the warning signal and marks it as abnormal electrical status data.

8. The lithium battery pack electrical status big data real-time monitoring and early warning system according to claim 7, characterized in that: The process of the storage module storing abnormal electrical state data to generate a historical abnormal electrical state database includes: Obtain the collection time corresponding to the abnormal electrical status data, connect the abnormal electrical status data with the corresponding abnormal status type, collection unit group and collection time to generate the abnormal electrical status data chain corresponding to the lithium battery pack, and store and obtain the historical abnormal electrical status database in real time.

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