Real-time monitoring and early warning system based on power battery parameters

Abstract

The application discloses a real-time monitoring and early warning system based on power battery parameters and relates to the technical field of battery monitoring.The application solves the technical problem that, in the prior art, the health state of a power battery is determined according to a single battery parameter in the process of monitoring the power battery, and there are accidental factors, which lead to inaccurate monitoring of the power battery and are prone to cause false alarms.The application collects and reports the charging data and battery parameters of the power battery through a control collection module, analyzes the operation state of the power battery according to the battery parameters, analyzes the charging state of the power battery according to the charging data, and accurately generates an early warning signal by combining the two, thereby improving the accuracy of monitoring of the power battery.The application analyzes the capacity and temperature between single batteries by a mathematical method to determine whether the capacity of the single batteries in the power battery is balanced and whether the temperature is uniform, and further determines whether the operation state of the power battery is normal, thereby improving the data processing efficiency and ensuring the accuracy of the analysis result.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of battery monitoring, and relates to a real-time monitoring technology based on power battery parameters, in particular to a real-time monitoring and early warning system based on power battery parameters. BACKGROUND

[0002] The power battery is the power source of the electric vehicle, and its working performance will have a great impact on the vehicle control, so it is necessary to monitor the power battery in real time.

[0003] The prior art (invention patent with publication number CN113787914A) discloses a power battery monitoring method, device, server and storage medium, determines the working state of the power battery based on the monitoring parameters such as temperature rise rate, and realizes the monitoring of the power battery. In the process of monitoring the power battery, the prior art determines the health status of the power battery according to a single battery parameter, which has accidental factors, resulting in inaccurate monitoring of the power battery and easy false alarms; therefore, a real-time monitoring and early warning system based on power battery parameters is urgently needed. SUMMARY

[0004] The present application aims to solve at least one of the technical problems existing in the prior art; for this purpose, the present application proposes a real-time monitoring and early warning system based on power battery parameters, which is used to solve the technical problems that the prior art determines the health status of the power battery according to a single battery parameter in the process of monitoring the power battery, which has accidental factors, resulting in inaccurate monitoring of the power battery and easy false alarms.

[0005] The present application collects and reports the charging data and battery parameters of the power battery through the control collection module, analyzes the running state of the power battery according to the battery parameters, analyzes the charging state of the power battery according to the charging data, and accurately generates an early warning signal by combining the two, which can improve the accuracy of monitoring the power battery.

[0006] To achieve the above-mentioned purpose, the first aspect of the present application provides a real-time monitoring and early warning system based on power battery parameters, which comprises an analysis and early warning module, and a control collection module connected thereto, and the control collection module is connected with the power battery;

[0007] The control collection module monitors the charging process of the power battery and collects and reports the charging data; wherein the charging data includes charging interval and corresponding consumption mileage; and

[0008] Real-time collection and reporting of battery parameters of the power battery; wherein the battery parameters include battery capacity and battery temperature;

[0009] The analysis and early warning module collects environmental data through the vehicle-mounted sensor, and the environmental data is associated with the charging data; and

[0010] jointly analyzing the battery parameters and the charging data, and generating a warning signal according to an analysis result; wherein the charging data is analyzed by an intelligent evaluation model.

[0011] Preferably, the analysis and warning module is in communication and / or electrical connection with the control and collection module and the vehicle-mounted sensor; wherein the vehicle-mounted sensor includes a temperature sensor and a humidity sensor.

[0012] The control and collection module is used to collect and report charging data and battery parameters of the power battery, and the control and collection module is connected with a plurality of balancing units, and the plurality of balancing units are used to adjust the capacity of each single battery in the power battery.

[0013] Preferably, the analysis and warning module collects environmental data in real time through the vehicle-mounted sensor connected thereto; wherein the environmental data includes temperature, humidity and air pressure.

[0014] The environmental data is associated with the corresponding charging data.

[0015] Preferably, the analysis and warning module jointly analyzes the battery parameters and the charging data, and generates a warning signal according to an analysis result, including:

[0016] obtaining a battery state label and a battery charging label;

[0017] identifying the battery state label to determine the running state of the power battery, and identifying the battery charging label to determine the charging state of the power battery;

[0018] only when the running state is abnormal, a battery abnormal signal is generated;

[0019] when the running state is normal and the charging state is abnormal, an environmental abnormal signal is generated.

[0020] Preferably, the analysis and warning module analyzes the battery parameters to obtain a battery state label corresponding to the power battery, including:

[0021] obtaining the battery capacity and the battery temperature of each single battery in the power battery;

[0022] analyzing the battery capacity to obtain a capacity state, and analyzing the battery temperature to obtain a temperature state; wherein the capacity state and the temperature state are analyzed in the same way;

[0023] when the capacity state and the temperature state corresponding to the power battery are both normal, the battery state label is set to 0; otherwise, the battery state label is set to 1.

[0024] Preferably, the analysis and early warning module analyzes the battery capacity to obtain the corresponding capacity state, including:

[0025] Obtaining the absolute value of the difference in battery capacity between each single battery and the mean square error;

[0026] Comparing the absolute value of the difference and the mean square error with the corresponding set threshold value respectively; wherein the set threshold value is set according to empirical data, including a difference threshold value and a mean square error threshold value;

[0027] When the absolute value of the difference and the mean square error are both less than or equal to the corresponding set threshold value, it is determined that the capacity state of the corresponding power battery is normal; otherwise, it is determined that the capacity state of the corresponding power battery is abnormal.

[0028] Preferably, the analysis and early warning module analyzes the charging data through the intelligent evaluation model to obtain the battery charging label of the corresponding power battery, including:

[0029] Data preprocessing is performed on the charging data to obtain a standard charging sequence; wherein the data preprocessing refers to screening and sorting the charging data;

[0030] The standard charging sequence is input into the intelligent evaluation model to obtain the output battery charging label; wherein the intelligent evaluation model is established based on an artificial intelligence model.

[0031] Preferably, the analysis and early warning module performs data preprocessing on the charging data, including:

[0032] According to the charging time, a plurality of charging data corresponding to the power battery are screened and obtained;

[0033] The corresponding charging interval, consumed mileage and environmental data are sorted and spliced to generate a standard sequence;

[0034] The plurality of standard sequences are integrated according to the charging time of the corresponding charging data to generate the standard charging sequence.

[0035] Compared with the prior art, the present application has the following advantages:

[0036] 1、The present application can accurately generate a warning signal by controlling the acquisition module to collect and report the charging data and battery parameters of the power battery, analyzing the running state of the power battery according to the battery parameters, and analyzing the charging state of the power battery according to the charging data, thereby improving the accuracy of monitoring the power battery.

[0037] 2、The application analyzes the capacity and temperature between single batteries by mathematical methods to determine whether the capacity of single batteries in the power battery is balanced and the temperature is uniform, and further determines whether the running state of the power battery is normal, which can improve the data processing efficiency and ensure the accuracy of the analysis result. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0039] Figure 1 The working steps of the present application are shown in the figure. DETAILED DESCRIPTION

[0040] The technical solutions of the present application will be described in detail below in conjunction with the embodiments. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0041] The prior art (invention patent with publication number CN113787914A) discloses a monitoring method, device, server and storage medium for power battery, which determines the working state of the power battery based on the temperature rise rate and other monitoring parameters, and realizes the monitoring of the power battery. In the process of monitoring the power battery, the prior art determines the health state of the power battery according to a single battery parameter, which has occasional factors, resulting in inaccurate monitoring of the power battery and easy false alarm.

[0042] The present application collects and reports the charging data and battery parameters of the power battery through the control collection module, analyzes the running state of the power battery according to the battery parameters, analyzes the charging state of the power battery according to the charging data, and accurately generates a warning signal by combining the two, which can improve the accuracy of monitoring the power battery.

[0043] Please refer to Figure 1 The first aspect embodiment of the present application provides a real-time monitoring and early warning system based on power battery parameters, which comprises an analysis and early warning module, and a control collection module connected thereto, and the control collection module is connected with the power battery;

[0044] The control collection module: monitors the charging process of the power battery, and collects and reports the charging data; and collects and reports the battery parameters of the power battery in real time;

[0045] The analysis and early warning module collects environmental data through the vehicle-mounted sensor, and associates the environmental data with the charging data; and jointly analyzes the battery parameters and the charging data, and generates a warning signal according to the analysis result.

[0046] The analysis and early warning module in the present application is equivalent to a data server, and is mainly used for data processing; the control collection module is equivalent to a data transfer station with data processing capability. The analysis and early warning module and the control collection module are both arranged inside the service subject of the power battery, such as a new energy vehicle.

[0047] The charging data in the present application includes charging intervals and corresponding consumed mileage, and can be used to evaluate the running environment of the power battery. The charging interval refers to the time difference between two charging times, and the consumed mileage refers to the mileage consumed by the power battery corresponding to the time difference. The battery parameters include the battery capacity and the battery temperature, and can evaluate the running state of the power battery itself. It can be understood that, in other preferred embodiments, the charging data and the battery parameters also include other data with the same attributes.

[0048] The charging data in the present application is analyzed by an intelligent evaluation model, which is obtained by training an artificial intelligence model. The artificial intelligence model includes a deep convolutional neural network model, an RBF neural network model and other models with strong nonlinear capability.

[0049] The analysis and early warning module in the present application is in communication and / or electrical connection with the control collection module and the vehicle-mounted sensor, respectively;

[0050] The control collection module is used to collect and report the charging data and the battery parameters of the power battery, and the control collection module is connected with a plurality of balancing units, and the plurality of balancing units are used to adjust the capacity of each single battery in the power battery.

[0051] The vehicle-mounted sensor includes a temperature sensor, a humidity sensor, an air pressure sensor and the like, and is used to collect data that may affect the charging data of the power battery. The control collection module can also be connected with the central control of the power battery service subject to read the environmental data.

[0052] The control collection module is also connected with a plurality of balancing units, and each balancing unit corresponds to a single battery, that is, the power battery corresponds to a plurality of balancing units. The balancing units balance the capacities of the plurality of single batteries during the working process of the power battery.

[0053] The analysis and early warning module in the present application collects environmental data in real time through the vehicle-mounted sensor connected thereto; the environmental data includes temperature, humidity and air pressure; and the environmental data is associated with the corresponding charging data.

[0054] The environmental data is combined with the charging data to analyze the charging state of the power battery, and therefore the time matching between the environmental data and the charging data.

[0055] The analysis and early warning module in the present application jointly analyzes the battery parameters and the charging data, generates an early warning signal according to the analysis result, and includes:

[0056] obtaining a battery state label and a battery charging label;

[0057] identifying the battery state label to determine the running state of the power battery, and identifying the battery charging label to determine the charging state of the power battery;

[0058] only when the running state is abnormal, a battery abnormal signal is generated;

[0059] when the running state is normal and the charging state is abnormal, an environmental abnormal signal is generated.

[0060] The power battery body is analyzed to identify and obtain the corresponding running state. If the running state is abnormal, the power battery itself has a fault, and a battery abnormal signal is generated. When the power battery itself is normal, the charging state of the power battery is identified and obtained. If the charging state is abnormal, the environment of the power battery is abnormal, and an environmental abnormal signal is generated. In other cases, it is determined that the power battery has no abnormality.

[0061] In a preferred embodiment, the analysis and early warning module analyzes the battery parameters to obtain a battery state label corresponding to the power battery, including:

[0062] obtaining the battery capacity and the battery temperature of each single battery in the power battery;

[0063] analyzing the battery capacity to obtain a capacity state, and analyzing the battery temperature to obtain a temperature state;

[0064] when the capacity state and the temperature state corresponding to the power battery are both normal, the battery state label is set to 0; otherwise, the battery state label is set to 1.

[0065] To evaluate the running state of the power battery, it is necessary to determine whether the capacity of the single battery is balanced and whether the temperature of the single battery is uniform. Both of these data are external manifestations of the running state of the power battery. When the capacity of the single batteries is balanced and the temperature is uniform, it is determined that the power battery as a whole is normal, i.e., the battery label is set to 0.

[0066] In an optional embodiment, the analysis and early warning module analyzes the battery capacity to obtain a corresponding capacity state, including:

[0067] obtaining the absolute value of the difference between the battery capacities of each single battery, and the mean square error;

[0068] The absolute value of the difference and the mean square error are compared with corresponding set thresholds respectively;

[0069] When the absolute value of the difference and the mean square error are both less than or equal to the corresponding set thresholds, it is determined that the capacity state of the corresponding power battery is normal; otherwise, it is determined that the capacity state of the corresponding power battery is abnormal.

[0070] The set thresholds are set according to empirical data, including a difference threshold and a mean square error threshold, that is, the absolute value of the difference is compared with the difference threshold, and the mean square error is compared with the mean square error threshold. The capacity difference between single batteries is used to evaluate whether the capacity is balanced, and the mean square error is used to evaluate whether all single battery capacities are concentrated. These two data are combined to determine whether the capacity state of the power battery is normal.

[0071] The temperature state is obtained in the same way as the capacity state, that is, the temperature state of the power battery is determined according to the temperature difference and the temperature mean square error. The imbalance of capacity and the unevenness of temperature both have an impact on the health of the power battery.

[0072] The analysis and early warning module in the present application analyzes the charging data through an intelligent evaluation model to obtain a battery charging label corresponding to the power battery, including:

[0073] The charging data is preprocessed to obtain a standard charging sequence;

[0074] The standard charging sequence is input into the intelligent evaluation model to obtain an output battery charging label.

[0075] The standard charging sequence includes both environmental data and charging data, so the standard charging sequence is analyzed through a nonlinear model. An intelligent evaluation model is established based on an artificial intelligence model, including:

[0076] Standard training data is obtained; wherein the standard training data includes input data obtained in a laboratory and corresponding output data, the input data is consistent with the content attributes of the standard charging sequence, and the output data is set by the staff analyzing the input data;

[0077] The artificial intelligence model is trained through the standard training data, the trained artificial intelligence model is marked as an intelligent evaluation model, and the intelligent evaluation model is updated at regular intervals and stored in the analysis and early warning module.

[0078] In a preferred embodiment, the analysis and early warning module pre-processes the charging data, including:

[0079] According to the charging time, a plurality of charging data corresponding to the power battery is obtained;

[0080] The corresponding charging interval, consumed mileage and environmental data are arranged and spliced to generate a standard sequence;

[0081] The standard sequences are integrated according to the charging time of the corresponding charging data to generate a standard charging sequence.

[0082] The corresponding charging interval, consumed mileage and environmental data in the charging data are integrated to generate a standard sequence, and the standard charging sequence can be obtained after the standard sequences are integrated in sequence.

[0083] The working principle of the present application is as follows:

[0084] The control acquisition module monitors the charging process of the power battery and collects and reports the charging data, and real-time collects and reports the battery parameters of the power battery.

[0085] The analysis and early warning module combines the environmental data to jointly analyze the battery parameters and the charging data, and generates a warning signal according to the analysis result.

[0086] The above examples are only used to illustrate the technical method of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical method of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical method of the present application.

Claims

1. A real-time monitoring and early warning system based on power battery parameters, comprising an analysis and early warning module and a control and acquisition module connected thereto, wherein the control and acquisition module is connected to the power battery, characterized in that: Control and acquisition module: monitors the charging process of the power battery and collects and reports charging data; wherein, the charging data includes charging intervals and corresponding mileage consumption; and The battery parameters of the power battery are collected and reported in real time; wherein, the battery parameters include battery capacity and battery temperature; Analysis and early warning module: collects environmental data through onboard sensors, and the environmental data is correlated with the charging data; and The battery parameters and charging data are jointly analyzed, and an early warning signal is generated based on the analysis results; wherein the charging data is analyzed through an intelligent evaluation model. The analysis and early warning module performs joint analysis on the battery parameters and the charging data, and generates an early warning signal based on the analysis results, including: Obtain the battery status tag and battery charging tag; The battery status label is identified to determine the operating status of the power battery, and the battery charging label is identified to determine the charging status of the power battery. A battery malfunction signal is generated only when the operating state is abnormal; When the operating state is normal and the charging state is abnormal, an environmental abnormality signal is generated. The analysis and early warning module analyzes the battery parameters and obtains the battery status tag corresponding to the power battery, including: Obtain the battery capacity and battery temperature of each individual cell in the power battery; The battery capacity is analyzed to obtain the capacity status, and the battery temperature is analyzed to obtain the temperature status; wherein the analysis and acquisition methods for the capacity status and the temperature status are the same. When the capacity status and temperature status of the power battery are both normal, the battery status label is set to 0; otherwise, the battery status label is set to 1. The analysis and early warning module analyzes the battery capacity and obtains the corresponding capacity status, including: Obtain the absolute value of the difference in battery capacity between each of the individual cells, as well as the mean square error; The absolute value of the difference and the mean square error are compared with the corresponding set thresholds; wherein the set thresholds are set according to empirical data and include a difference threshold and a mean square error threshold. When both the absolute value of the difference and the mean square error are less than or equal to the corresponding set threshold, the capacity state of the corresponding power battery is determined to be normal; otherwise, the capacity state of the corresponding power battery is determined to be abnormal.

2. The real-time monitoring and early warning system based on power battery parameters according to claim 1, characterized in that, The analysis and early warning module is communicatively and / or electrically connected to the control and acquisition module and the vehicle-mounted sensors, respectively; wherein, the vehicle-mounted sensors include a temperature sensor and a humidity sensor; The control acquisition module is used to collect and report the charging data and battery parameters of the power battery, and the control acquisition module is connected to several equalization units, which are used to adjust the capacity of each individual cell in the power battery.

3. The real-time monitoring and early warning system based on power battery parameters according to claim 2, characterized in that, The analysis and early warning module collects environmental data in real time through the vehicle-mounted sensors connected to it; wherein, the environmental data includes temperature, humidity and air pressure; The environmental data is associated with the corresponding charging data.

4. The real-time monitoring and early warning system based on power battery parameters according to claim 1, characterized in that, The analysis and early warning module analyzes the charging data through the intelligent evaluation model to obtain the battery charging tag corresponding to the power battery, including: The charging data is preprocessed to obtain a standard charging sequence; wherein, the data preprocessing refers to filtering and organizing the charging data. The standard charging sequence is input into the intelligent evaluation model to obtain the output battery charging tag; wherein, the intelligent evaluation model is established based on an artificial intelligence model.

5. The real-time monitoring and early warning system based on power battery parameters according to claim 4, characterized in that, The analysis and early warning module performs data preprocessing on the charging data, including: Based on the charging time, obtain several charging data entries corresponding to the power battery; The corresponding charging intervals, mileage consumption, and environmental data are organized and spliced ​​together to generate a standard sequence; The standard charging sequence is generated by integrating several standard sequences according to the charging time corresponding to the charging data.

Images