Battery abnormity monitoring and predicting system, power battery system with battery abnormity monitoring and predicting system and vehicle with battery abnormity monitoring and predicting system

Abstract

The utility model provides a battery abnormity monitoring and predicting system, a power battery system with the same and a vehicle, and relates to the technical field of battery management. The battery abnormity monitoring and predicting system comprises a data acquisition module which is used for acquiring battery data, and the battery data at least comprises one of battery voltage, battery current and battery temperature; the state analysis module is electrically connected with the data acquisition module, the state analysis module is used for determining a battery state based on the battery data, the battery state at least comprises a battery current state and a battery future state, and the battery future state is the state of the battery after a preset duration. By using the battery abnormity monitoring and predicting system, the integrated data acquisition module and the state analysis module, after the voltage, temperature and current of the battery are analyzed, the current state of the battery can be monitored, the future state of the battery can be predicted, effective battery management is facilitated, and it is ensured that the battery works within a safe range.

Description

TECHNICAL FIELD

[0001] The utility model relates to battery management technical field, specifically, relate to a kind of battery abnormality monitoring and prediction system and the power battery system with its vehicle. BACKGROUND

[0002] Since the development and popularization of new energy vehicles, its safety and reliability have been placed in a crucial position. As one of the core parts of electric vehicles, the stability of the performance of the power battery plays a decisive role in the vehicle. Once a fault occurs, the system performance will be degraded, and in severe cases, it will lead to a major accident. Therefore, it is necessary to study the fault monitoring and early warning of the battery system.

[0003] The existing technology mainly based on experience or threshold value to judge the abnormal state of the battery, the accuracy is low, and the detection method based on threshold value can only send an alarm when the battery state is abnormal, and the abnormal state prediction ability of the battery is low.

[0004] For the above problems, there is no effective solution at present. UTILITY MODEL CONTENT

[0005] The main purpose of the utility model is to provide a kind of battery abnormality monitoring and prediction system and the power battery system with its vehicle, to solve the problem of low accuracy and low prediction ability of battery abnormal state in the prior art.

[0006] In order to achieve the above purpose, according to one aspect of the utility model, a kind of battery abnormality monitoring and prediction system is provided, comprising: data acquisition module, data acquisition module is used to collect battery data, battery data at least includes one of the following: battery voltage, battery current, battery temperature;State analysis module, state analysis module is electrically connected with data acquisition module, state analysis module is used to determine battery state based on battery data, battery state at least includes battery current state and battery future state, battery future state is the state of battery after a preset time.

[0007] Further, the battery abnormality monitoring and prediction system further comprises: alarm module, alarm module is electrically connected with state analysis module, alarm module is used to send battery alarm information in the case that at least one of battery current state and battery future state is abnormal state.

[0008] Further, the state analysis module comprises: data judgment module, data judgment module is electrically connected with data acquisition module and alarm module, data judgment module is used to determine battery current state according to battery data.

[0009] Further, the data judging module comprises: a threshold obtaining module, the threshold obtaining module is used for obtaining a parameter threshold of the battery, the parameter threshold at least comprises one of: a voltage threshold, a current threshold, a temperature threshold; a battery judging module, the battery judging module is electrically connected with the threshold obtaining module, the data collecting module and the alarm module, the battery judging module is used for determining the current state of the battery based on the parameter threshold and the battery data.

[0010] Further, the state analyzing module comprises: a data change analyzing module, the data change analyzing module is electrically connected with the data collecting module and the alarm module, the data change analyzing module is used for determining the future state of the battery based on the battery data.

[0011] Further, the data change analyzing module comprises: a data predicting module, the data predicting module is electrically connected with the data collecting module, the data predicting module at least has a data predicting model, the data predicting module is used for determining the predicting data of the battery based on the battery data and the data predicting model, the predicting data is the battery data after a preset time length; a state predicting module, the state predicting module is electrically connected with the data predicting module and the alarm module, the state predicting module is used for determining the future state of the battery based on the predicting data.

[0012] Further, the data change analyzing module further comprises: a data processing module, the data processing module is electrically connected with the data collecting module and the data predicting module, the data processing module is used for processing the battery data to obtain the input data of the data predicting model.

[0013] Further, the battery abnormality monitoring and predicting system further comprises: a data storage module, the data storage module is used for storing the battery information when the battery is in an abnormal state, the battery information at least comprises one of: the battery data, the abnormal occurrence time.

[0014] In order to achieve the above purpose, according to one aspect of the present application, a kind of power battery system, power battery system has battery abnormality monitoring and predicting system, battery abnormality monitoring and predicting system is described above battery abnormality monitoring and predicting system.

[0015] According to another aspect of the present application, a kind of vehicle is provided, vehicle has battery abnormality monitoring and predicting system, battery abnormality monitoring and predicting system is described above battery abnormality monitoring and predicting system.

[0016] The technical scheme of the utility model is applied, the data acquisition module detects the battery voltage, current and temperature in real time and acquires data, the state analysis module analyzes the collected data to quickly find the current abnormal state and potential abnormal state of the battery in the future, through use of the battery abnormality monitoring and prediction system, the data acquisition module and the state analysis module are integrated, the battery voltage, temperature and current are analyzed, the current state of the battery can be monitored, the future state of the battery can be predicted, effective battery management can be carried out based on the current state of the battery and the future state of the battery, and the battery can be ensured to work within a safe range. BRIEF DESCRIPTION OF DRAWINGS

[0017] The drawings accompanying the specification of the present application form a part thereof, serve to provide further understanding of the present application, and together with the description of the present application, serve to explain the present application. In the drawings:

[0018] Figure 1 The structure schematic view of the embodiment of the battery abnormality monitoring and prediction system according to the utility model is shown. DETAILED DESCRIPTION

[0019] It should be noted that the embodiments and features of the embodiments in the present application can be combined with each other without conflict. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0020] It should be noted that the terms used herein are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present application. As used herein, the singular form is intended to include the plural form, unless the context clearly indicates otherwise, and it should also be understood that the terms "comprise" and / or "include" as used in the specification indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0021] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the terms used herein can be interchanged as appropriate, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product or device.

[0022] Exemplary embodiments according to this application will now be described in more detail with reference to the accompanying drawings. However, these exemplary embodiments may be implemented in many different forms and should not be construed as being limited to the embodiments set forth herein. It should be understood that these embodiments are provided so that the disclosure of this application is thorough and complete, and that the concept of these exemplary embodiments is fully conveyed to those skilled in the art. In the drawings, for clarity, the thickness of layers and regions may be exaggerated, and the same reference numerals are used to denote the same devices, and therefore their description will be omitted.

[0023] Combination Figure 1 As shown, according to a specific embodiment of this application, a battery anomaly monitoring and prediction system is provided.

[0024] Specifically, such as Figure 1 As shown, the battery anomaly monitoring and prediction system includes a data acquisition module and a state analysis module. The data acquisition module is used to collect battery data, which includes at least one of the following: battery voltage, battery current, and battery temperature. The state analysis module is electrically connected to the data acquisition module and is used to determine the battery state based on the battery data. The battery state includes at least the current battery state and the future battery state, where the future battery state is the state of the battery after a preset time.

[0025] By applying the technical solution of this embodiment, the data acquisition module detects and collects data on battery voltage, current, and temperature in real time. The state analysis module analyzes the collected data to quickly identify the current abnormal state of the battery and potential future abnormal states. By using the battery anomaly monitoring and prediction system, which integrates the data acquisition module and the state analysis module, the battery voltage, temperature, and current can be analyzed to monitor the current state of the battery and predict its future state. This facilitates effective battery management based on the current and future states of the battery, ensuring that the battery operates within a safe range.

[0026] It should be noted that the future state of the battery refers to the state of the battery after a preset time. The preset time can be set differently depending on the battery type and characteristics, the working environment, and the data training model in the state analysis module. The preset time can be adjusted according to the battery state and the working environment to improve the accuracy and effectiveness of the warning.

[0027] Specifically, the battery abnormality monitoring and prediction system further comprises an alarm module electrically connected with the state analysis module, and the alarm module is configured to issue a battery alarm information when at least one of the current state of the battery and the future state of the battery is an abnormal state. The alarm module can timely alarm before the battery appears abnormal, issue a prompt, guide the user or the staff to take corresponding measures to cope with the battery abnormality, avoid current and potential safety risks, take measures in advance, and prevent overcharging, overdischarging or thermal runaway of the battery.

[0028] Further, the state analysis module comprises a data judgment module electrically connected with the data acquisition module and the alarm module, and the data judgment module is configured to determine the current state of the battery based on the battery data. The real-time analysis capability of the data judgment module can enable the system to timely feedback the working state of the battery, and can quickly respond to determine the current state of the battery, thereby ensuring the stability of the battery and significantly improving the use efficiency and safety of the battery.

[0029] Specifically, the data judgment module comprises a threshold acquisition module and a battery judgment module. The threshold acquisition module is configured to acquire a parameter threshold of the battery, and the parameter threshold comprises at least one of a voltage threshold, a current threshold and a temperature threshold. The battery judgment module is electrically connected with the threshold acquisition module, the data acquisition module and the alarm module, and is configured to determine the current state of the battery based on the parameter threshold and the battery data. By setting a reasonable threshold, the system can accurately distinguish between a normal working state and an abnormal state, i.e., when the real-time collected voltage, current and temperature of the battery exceed the parameter threshold, the battery is determined to be in an abnormal state. By using the threshold acquisition module in cooperation with the battery judgment module, the battery abnormality monitoring and prediction system can accurately identify an abnormal state during routine maintenance, intervene in an early stage when the performance of the battery begins to decline, reduce downtime caused by battery failure, and avoid more serious subsequent failures.

[0030] It should be noted that the parameter threshold can be set in combination with the type of the battery, working conditions, safety standards and historical data, so as to ensure that the parameter threshold is set within the safety limit of the battery, avoid overheat, overcharge or overdischarge of the battery, and at the same time, the parameter threshold should also reserve a fluctuation space of the battery to avoid triggering too many false alarms due to small fluctuations of the battery.

[0031] Further, the state analysis module comprises a data change analysis module electrically connected with the data acquisition module and the alarm module, and the data change analysis module is configured to determine the future state of the battery based on the battery data. The data change analysis module predicts the future health condition of the battery by analyzing the trend of the battery data, such as predicting the endurance of the battery, helping to make reasonable expectations and plans for the use of the equipment, i.e., providing strong support for long-term planning and management of the battery, and improving the cycle life and overall performance of the battery.

[0032] Specifically, the data change analysis module includes a data prediction module and a state prediction module, the data prediction module is electrically connected with the data acquisition module, the data prediction module at least has a data prediction model, the data prediction module is used for determining the predicted data of the battery based on the battery data and the data prediction model, the predicted data is the battery data after a preset time length; the state prediction module is electrically connected with the data prediction module and the alarm module, the state prediction module is used for determining the future state of the battery based on the predicted data. The data prediction module predicts the future trend of the battery state parameter, based on historical data and current data, using machine learning algorithms, time series analysis or other complex data analysis methods to predict the change of the battery state parameter within a preset time frame, in order to identify potential abnormal trends and issue warnings before the battery state parameter reaches a dangerous level; the state prediction module comprehensively analyzes and predicts the output of the data prediction module and the current battery state parameter to predict the overall state change trend of the battery. The combination of the data prediction module and the state prediction module greatly improves the forward-looking of the battery abnormal monitoring and prediction system, and through deep analysis of the battery data and model prediction, the state of the battery after a preset time length can be accurately predicted, realizing the prediction ability of the battery abnormal monitoring and warning system.

[0033] Further, the data change analysis module further includes a data processing module, the data processing module is electrically connected with the data acquisition module and the data prediction module, the data processing module is used for data processing of the battery data to obtain the input data of the data prediction model. The data processing module can clean and preprocess the original battery data collected by the data acquisition module, can eliminate outliers in the data, and the obtained input data can be used for training the model, calibrating the parameters, making real-time prediction, discovering potential abnormal situations in advance, ensuring the accuracy and reliability of the data prediction model, and continuously optimizing the model through continuous data collection and analysis to better adapt to the changes of battery performance. And the processed data can be analyzed through the cloud to optimize the battery management algorithm and improve the overall performance.

[0034] Further, the battery abnormal monitoring and prediction system further includes a data storage module, the data storage module is used for storing the battery information when the battery is in an abnormal state, the battery information at least includes one of the following: battery data, abnormal occurrence time. The data storage module not only records abnormal events, but also can be used for fault diagnosis and system optimization, and has important value for battery health monitoring and fault prediction model training. Through the record of the battery abnormal state information, when analyzing the battery fault, the root cause of the fault can be found out by tracing the abnormal data, and the prevention ability of the system is improved.

[0035] In the embodiment, the data storage module not only records and stores the battery data in the abnormal state, but also records and stores the data in the normal state of the battery, provides rich data resources for fault analysis and system optimization of the battery abnormal monitoring and prediction system, and provides the basis for subsequent fault prevention through the recording of the battery data and time, the system can perform backtracking analysis to find out the cause and time of the abnormality. At the same time, based on a large amount of data, rich learning samples can be provided for training of the fault prediction model, and the prediction accuracy of the model can be improved.

[0036] According to another specific embodiment of the present application, a power battery system is provided, and the power battery system has a battery abnormal monitoring and prediction system, which is the battery abnormal monitoring and prediction system in the above-mentioned embodiments. The battery abnormal monitoring and prediction system can comprehensively monitor the power battery system and manage the battery system to improve the safety and service life of the battery system.

[0037] According to another specific embodiment of the present application, a vehicle is also provided, and the vehicle has a battery abnormal monitoring and prediction system, which is the battery abnormal monitoring and prediction system in the above-mentioned embodiments. Preferably, the vehicle is a new energy electric vehicle, and the battery abnormal monitoring and prediction system can monitor the power battery in the new energy vehicle to improve the safety of the vehicle, optimize energy management, reduce energy consumption, and improve the endurance.

[0038] The present application also provides a preferred embodiment of a battery abnormal monitoring and prediction system, which realizes the functions of judging whether the battery is in or about to be in an abnormal state through the collection and analysis of battery-related data, and performs warning and data storage work.

[0039] Specifically, the battery abnormal monitoring and prediction system includes a data collection module, a state analysis module, a data judgment module, a data change analysis module, a warning module, and a data storage module. The data collection module is composed of a temperature sensor, a voltage sensor, and a current sensor. The temperature sensor is used to monitor the temperature of the power battery, the voltage sensor is used to monitor the voltage of the power battery, and the current sensor is used to monitor the current of the power battery. The collected temperature, voltage, and current data are sent to the data change analysis module.

[0040] The data judgment module is used to compare the temperature, voltage, and current with the pre-set temperature judgment threshold, voltage judgment threshold, and current judgment threshold to make a judgment on whether the battery system is in an abnormal state at this moment. The other end of the data judgment module is connected to the warning module. If the conclusion of the judgment is that an abnormality has occurred, the warning module will sound to prompt the user that the battery is in an abnormal state.

[0041] The data change analysis module is connected by the second branch of the data acquisition module sending end, and is used for predicting whether the battery system has the possibility of transferring from the normal state to the abnormal state within a certain working time.

[0042] The working principle of the data model is as follows: according to the historical data set of the battery state parameters (temperature, current, voltage), the data features are extracted, and the outlying factors of the working data are obtained by using a related algorithm, and are compared with the temperature early warning threshold value, the voltage early warning threshold value and the current early warning threshold value which can be calibrated in advance.

[0043] The alarm module receiving end receives the signals from the data judgment module and the data change analysis module, so that the dual functions of monitoring and predicting the abnormal state of the battery are realized.

[0044] The data storage module can record the occurrence of abnormal data, and can provide a reference for the training model introduced in the data change analysis module, which is beneficial to improve the training model precision and then improve the accuracy of the battery abnormality detection.

[0045] From the above description, it can be seen that the embodiments of the above-mentioned application achieve the following technical effects:

[0046] 1) When the battery has the possibility of transferring from the normal state to the abnormal state, the prediction alarm is realized.

[0047] 2) When the collected state parameters are analyzed, the related data training model is introduced, which improves the convenience and accuracy of the battery abnormality warning.

[0048] 3) The battery data storage module is provided, which can provide a reference for the training data model and improve the accuracy in addition to facilitating the subsequent data change analysis link.

[0049] For purposes of the description hereinafter, the terms "upper", "lower", "right", "left", "rear", "front", "vertical" and "horizontal" as can be perceived herein relative to the accompanying drawings refer to the orientation of the components being described. However, it is to be understood that the spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. For example, if the device described herein is turned over, elements described as "above" or "below" other elements would then be oriented "below" or "above" other elements, respectively. Thus, the exemplary term "above" can encompass both an orientation of above and below. The device can be otherwise oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly. The terms "first", "second", "third", etc. do not necessarily denote different or separate embodiments, or that any claim limitation can function or be used in only a single claimed embodiment. Such terms can be used interchangeably to distinguish or identify different embodiments.

[0050] In addition, it should be noted that throughout this specification discussions utilizing terms such as "about", "approximately", "substantially", or "essentially" and / or the like, can be understood as referring to "within 10% of the value stated in the specification", or "within 5% of the value stated in the specification", or "within 1% of the value stated in the specification", or "within 0.1% of the value stated in the specification", unless otherwise indicated or unless the context clearly indicates otherwise.

[0051] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0052] The preferred embodiments of the present application have been described above with the specific details. Obviously, the present application can be carried out in other forms without departing from the spirit and essential characteristics of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the scope of protection of the present application.

Claims

1. A battery abnormality monitoring prediction system characterized by, Comprising: a data acquisition module for acquiring battery data, the battery data including at least one of the following: battery voltage, battery current, battery temperature; a state analysis module electrically connected with the data acquisition module, the state analysis module being configured to determine a battery state based on the battery data, the battery state including at least a current battery state and a future battery state, the future battery state being a state of the battery after a preset time period.

2. The battery anomaly monitoring and prediction system of claim 1, wherein, The battery anomaly monitoring and prediction system further comprises: an alarm module electrically connected with the state analysis module, the alarm module being configured to issue a battery alarm information when at least one of the current battery state and the future battery state is an abnormal state.

3. The battery anomaly monitoring and prediction system of claim 2, wherein, The state analysis module comprises: a data judgment module electrically connected with the data acquisition module, the alarm module, the data judgment module being configured to determine the current battery state based on the battery data.

4. The battery anomaly monitoring and prediction system of claim 3, wherein, The data judgment module comprises: a threshold acquisition module configured to acquire a parameter threshold of the battery, the parameter threshold including at least one of the following: voltage threshold, current threshold, temperature threshold; a battery judgment module electrically connected with the threshold acquisition module, the data acquisition module, the alarm module, the battery judgment module being configured to determine the current battery state based on the parameter threshold and the battery data.

5. The battery anomaly monitoring and prediction system of claim 3, wherein, The state analysis module comprises: a data change analysis module electrically connected with the data acquisition module, the alarm module, the data change analysis module being configured to determine the future battery state based on the battery data.

6. The battery anomaly monitoring and prediction system of claim 5, wherein, The data change analysis module comprises: a data prediction module electrically connected with the data acquisition module, the data prediction module having at least a data prediction model, the data prediction module being configured to determine prediction data of the battery based on the battery data and the data prediction model, the prediction data being battery data after a preset time period; a state prediction module electrically connected with the data prediction module, the alarm module, the state prediction module being configured to determine the future battery state based on the prediction data.

7. The battery anomaly monitoring and prediction system of claim 6, wherein, The data change analysis module further comprises: a data processing module electrically connected with the data acquisition module, the data prediction module, the data processing module being configured to perform data processing on the battery data to obtain input data of the data prediction model.

8. The battery anomaly monitoring and prediction system of claim 2, wherein, The battery anomaly monitoring and prediction system further comprises: a data storage module configured to store battery information when the battery is in the abnormal state, the battery information including at least one of the following: the battery data, abnormal occurrence time.

9. A power battery system, characterized in that, The power battery system has a battery anomaly monitoring and prediction system, the battery anomaly monitoring and prediction system being any one of claims 1-8.

10. A vehicle characterized by comprising: The vehicle has a battery abnormality monitoring prediction system, and the battery abnormality monitoring prediction system is the battery abnormality monitoring prediction system according to any one of claims 1 to 8.

Images