A battery early warning management method and system for a new energy vehicle

Abstract

The present application relates to the field of new energy vehicle battery early warning, and more particularly to a battery early warning management method and system for new energy vehicles, comprising real-time collection of vehicle BMS data through a vehicle-mounted T-Box and dynamic adjustment of data transmission intervals through a cloud data receiving module, real-time data preprocessing using a Flink or Spark distributed stream processing engine; flexible scheduling and dependency management of early warning algorithm tasks are realized through a Dolphin Scheduler, and early warning and alarm signals are generated; real-time analysis results are classified and stored in MySQL and Redis databases to support historical queries and high-speed access respectively, and original BMS data is synchronized to a Hive data warehouse to support offline batch analysis; the global vehicle state, real-time alarm statistics, fault ranking and vehicle details are dynamically displayed through a visual monitoring module. The present application has high concurrent processing capability, realizes real-time, accurate and intelligent monitoring and early warning of the health state of new energy vehicle batteries, and significantly improves safety management and operation efficiency.

Description

Technical Field

[0001] This invention relates to the field of battery early warning for new energy vehicles, and in particular to a battery early warning and control method and system for new energy vehicles. Background Technology

[0002] With the widespread adoption of new energy vehicles, the safety of their core component, the power battery, has become a focal point of industry attention. Faults in the Battery Management System (BMS), such as overvoltage, undervoltage, abnormal temperature, and excessive voltage differential, can lead to decreased vehicle performance or even safety accidents. Currently, most common battery monitoring methods rely on post-event alarms, lacking early warning capabilities for potential risks and struggling to handle the high-concurrency processing and in-depth analysis of massive amounts of real-time vehicle data. Existing technological solutions typically have shortcomings in data processing real-time performance, algorithm model compatibility, and system scalability, failing to meet the needs of refined and intelligent battery health management for large-scale vehicles.

[0003] Chinese Patent Publication No. CN113173104A discloses a method and system for early warning of power batteries in new energy vehicles, comprising: acquiring power battery safety-related data of the vehicle connected to a big data platform; filtering the power battery safety-related data to obtain data related to power battery temperature, power battery voltage, and power battery insulation resistance; training a neural network on the filtered data to establish a vehicle power battery safety prediction model; acquiring the current power battery safety-related data of the vehicle connected to the big data platform after establishing the model, and inputting the data into the vehicle power battery safety prediction model to output a safety warning result.

[0004] Therefore, it is evident that the aforementioned early warning method and system for power batteries in new energy vehicles have the following problems:

[0005] The aforementioned early warning method and system for power batteries in new energy vehicles is based on neural network training using historical data. Essentially, it's a batch processing and offline prediction model that focuses more on "predicting" battery status than on "real-time detection" of ongoing anomalies. Furthermore, this early warning system only outputs a single warning signal, failing to provide technical guidance to maintenance personnel. It also lacks a modern, scalable data platform architecture that supports high concurrency, and lacks a complete engineering design for the access, processing, storage, and visualization of massive amounts of real-time data, making it difficult to support efficient operation and maintenance and business iteration for large-scale vehicles. Summary of the Invention

[0006] Therefore, the present invention provides a battery early warning and control method and system for new energy vehicles, in order to overcome the problem that the existing system architecture is outdated and cannot support large-scale vehicle access and business iteration.

[0007] To achieve the above objectives, a battery early warning and control method for new energy vehicles includes:

[0008] Based on the established network geographic information database, and combined with the network signal strength reported by the vehicle-mounted T-Box in the area, the area is classified into different levels, and the cloud data receiving module determines the basic data transmission interval of the T-Box in the area.

[0009] Based on the basic data transmission interval of the region determined by the network geographic information database, the data transmission interval of each vehicle T-Box is initially adjusted according to the network signal strength of the vehicle T-Box;

[0010] Based on the preset data detection cycle, the integrity of the vehicle BMS data sent by the T-Box obtained by the cloud data receiving module is detected, and the data transmission interval of each vehicle T-Box is adjusted accordingly.

[0011] Based on the data integrity within the preset data detection period and the network signal strength reported by each T-Box in the set area, the basic data transmission interval for the vehicle-mounted T-Box to send BMS data within the detection area is adjusted, and the cloud data receiving module sends the acquired BMS data to the stream computing analysis module for preprocessing.

[0012] Based on the preprocessed real-time data, the stream computing analysis module manages the scheduling of early warning algorithm tasks through the task scheduler, deploys the early warning algorithm code, analyzes the vehicle battery status in real time, and generates early warning or alarm signals.

[0013] Furthermore, based on the geographical area to which the control method is applied, the geographical area is divided according to a preset grid area to form multiple regular geographical detection grids;

[0014] Based on a preset signal strength threshold, statistical analysis is performed on the network signal strength data reported within the same geographical detection grid during the same time period, and the region type of the corresponding grid is determined based on the statistical results.

[0015] Based on the regional types determined by establishing a network geographic information database, the basic data transmission interval is determined by the location of vehicles in the corresponding regions.

[0016] Furthermore, the process of determining the basic data transmission interval includes,

[0017] Based on the fact that the network signal strength reported by the vehicle in the current time period is greater than the preset threshold of the preset network strength, it is determined that the vehicle is located in the first type of area, and the time interval for the vehicle T-Box to send BMS data in the first type of area is set as the first basic data transmission interval.

[0018] Based on the fact that the network signal strength reported by the vehicle in the current time period is less than the preset threshold of the preset network strength, it is determined that the vehicle is located in the second type of area, and the time interval for the vehicle T-Box to send BMS data in the second type of area is set as the second basic data transmission interval;

[0019] Among them, the first basic data transmission interval is greater than the second basic data transmission interval;

[0020] Based on the detection results of several consecutive data detection cycles, a change method is set for the corresponding area type.

[0021] Furthermore, the region type is configured with a change method.

[0022] Based on the set data detection period, if the threshold of the signal strength reported by the vehicle-mounted T-Box in the area exceeds or falls below the preset threshold of the network strength during the data detection period, the type of the current area will be changed accordingly and the basic data transmission interval will be re-determined based on the changed area type.

[0023] Further, based on the current vehicle T-Box network signal strength, the adjustment coefficient of the T-Box is determined, and the determination process includes:

[0024] Based on the current vehicle T-Box network signal strength data reported to the cloud data receiving module, when the network signal strength is greater than the first network signal strength threshold, the vehicle T-Box is determined to be a first-level vehicle T-Box, and the T-Box adjustment coefficient is set as the first-level adjustment coefficient.

[0025] Based on the current vehicle T-Box network signal strength data reported to the cloud data receiving module, when the network signal strength is less than the first network signal strength threshold but greater than the second network signal strength threshold, the vehicle T-Box is determined to be a second-level vehicle T-Box, and the T-Box adjustment coefficient is set as the second-level adjustment coefficient;

[0026] Based on the current vehicle T-Box network signal strength data reported to the cloud data receiving module, when the network signal strength is less than the second network signal strength threshold, the vehicle T-Box is determined to be a third-level vehicle T-Box, and the T-Box adjustment coefficient is set to the third-level adjustment coefficient.

[0027] in,

[0028] The first preset network strength threshold is greater than the second preset network strength threshold.

[0029] Furthermore, based on the classification results of the vehicle-mounted T-Box, a corresponding adjustment coefficient is determined. This adjustment coefficient is used to determine the actual data transmission interval of the corresponding T-Box in conjunction with the current basic data transmission interval.

[0030] Furthermore, for the vehicle-mounted T-Box level, an adjustment method is set to adjust the adjustment coefficient in different areas of the vehicle-mounted T-Box. If a vehicle-mounted T-Box fails to receive data for several consecutive data detection cycles, the level of the corresponding vehicle-mounted T-Box is dynamically adjusted, and the vehicle BMS data transmission interval is adjusted according to the corresponding vehicle-mounted T-Box level.

[0031] Furthermore, for multiple sets of vehicle BMS data acquired from the same in-vehicle T-Box within the data detection period, they are classified according to different T-Box levels. The cloud data receiving module sorts them separately based on the time order of the acquired vehicle BMS data to determine the temporal relationship of each valid and complete vehicle BMS data.

[0032] Based on the fact that the network signal strength of the current vehicle T-Box has not changed during the detection period, it is determined that the cloud data receiving module uses the earliest set of complete vehicle BMS data in the time sequence as the valid data for the current period.

[0033] Based on the changes in the network signal strength of the vehicle's T-Box during the detection period, the cloud data receiving module determines that the latest set of complete vehicle BMS data in the time sequence is used as the valid data for the current period.

[0034] Furthermore, based on the cloud data receiving module, the vehicle BMS data of the corresponding T-Box is obtained, and the data transmission interval of the corresponding vehicle T-Box is adjusted.

[0035] If the cloud data receiving module fails to acquire complete vehicle BMS data for the corresponding T-Box within the set data detection period, the level of the corresponding vehicle T-Box will be reduced for non-lowest level T-Boxes, while the level of the corresponding vehicle T-Box will be maintained for the lowest level T-Box. The data transmission interval of the corresponding vehicle T-Box will be adjusted in combination with the basic data transmission interval of the current area.

[0036] Based on the cloud data receiving module, complete vehicle BMS data of the corresponding T-Box is obtained in the set data detection period. For T-Boxes that are not at the highest level, the corresponding vehicle T-Box level is upgraded. For the highest level T-Box, the corresponding vehicle T-Box level is maintained. The data transmission interval of the corresponding vehicle T-Box is adjusted in combination with the basic data transmission interval of the current area.

[0037] If the cloud data receiving module fails to acquire complete vehicle BMS data for the corresponding T-Box after several consecutive data detection cycles, it will mark the corresponding T-Box and automatically adjust the T-Box data transmission interval to the preset minimum adjustment coefficient.

[0038] The present invention also provides a system for the above-mentioned battery early warning and control method for new energy vehicles, comprising,

[0039] The vehicle-mounted T-Box connects to the cloud data module to collect battery operation data from the vehicle's BMS data system in real time, perform preliminary encapsulation and protocol conversion, and upload the data to the cloud data receiving module via wireless network.

[0040] The cloud data receiving module is connected to the vehicle-mounted T-Box and the data storage module respectively. It is used to receive the RSRP signal strength and GPS location data sent by the vehicle-mounted T-Box, build a network geographic information database, formulate and distribute the T-Box data transmission mode, and dynamically adjust the T-Box data transmission interval and T-Box data transmission mode in combination with real-time vehicle driving information, and verify the integrity of the received data.

[0041] The data storage module is connected to the cloud data module, the visualization module, and the stream computing module respectively. It is used for hierarchical storage and management of multi-source data, classifies and stores data, sets the data storage period for different databases, and automatically processes expired data.

[0042] The stream computing and analysis module is connected to the data storage module and is used to process the data stream in real time, perform data parsing, cleaning, outlier removal, feature extraction and standardization, and output the results to the data storage module.

[0043] The visualization monitoring module is connected to the data storage module and is used for multi-dimensional data display and interaction. Based on the real-time and historical data in the data storage module, it dynamically generates visualization charts of vehicle statistics, alarm lists, vehicle model distribution, and alarm type ranking.

[0044] Compared with existing technologies, the beneficial effects of this invention are that it constructs a battery early warning system and method that integrates data acquisition, real-time processing, intelligent analysis and visual monitoring. This effectively overcomes the shortcomings of existing technologies in building a high-concurrency, scalable modern big data platform architecture, and fills the gaps in the complete engineering design of massive real-time data access, processing, storage and visualization. This provides strong support for efficient operation and maintenance and rapid business iteration under large-scale vehicle deployment.

[0045] Furthermore, the cloud-based data receiving module receives and processes data streams uploaded from multiple in-vehicle T-Boxes in real time. Based on the vehicle BMS data and network signal strength data provided by the T-Boxes in real time, the system dynamically adjusts the T-Box data transmission interval, improving the stability and accuracy of data transmission and significantly enhancing the overall efficiency and reliability of data collection. Through this optimized configuration and intelligent adjustment, the cloud system can better support remote vehicle monitoring and management, providing robust data support for the normal operation and maintenance of vehicles.

[0046] Furthermore, through the data storage module, the battery data and early warning results processed by the flow computing module are classified and stored according to data type, access frequency and application scenario, realizing multi-level data management, and providing optimized data support for real-time monitoring, historical query and offline analysis respectively. This significantly improves the access efficiency of high-frequency real-time data and the overall system response performance, and also effectively reduces storage costs and computing resource consumption. At the same time, it provides a complete and reliable data foundation for long-term tracking of battery health status and big data mining. Attached Figure Description

[0047] Figure 1 This is a flowchart of the cloud data receiving module of the battery early warning and control method for new energy vehicles according to an embodiment of the present invention;

[0048] Figure 2 This is a schematic diagram illustrating the verification of data integrity in a battery early warning and control method for new energy vehicles according to an embodiment of the present invention.

[0049] Figure 3 Flowchart of the data storage module of the battery early warning and control system for new energy vehicles according to an embodiment of the present invention;

[0050] Figure 4 This is a schematic diagram of a battery early warning and control system for new energy vehicles according to an embodiment of the present invention. Detailed Implementation

[0051] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0052] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0053] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0054] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0055] Please see Figure 1 This is a data processing flowchart of the cloud data receiving module in the battery early warning and control method for new energy vehicles according to an embodiment of the present invention, including:

[0056] Step S1: Based on the established network geographic information database and combined with the network signal strength reported by the vehicle-mounted T-Box in the area, the area level is divided, and the cloud data receiving module determines the basic data transmission interval of the T-Box in the area.

[0057] Step S2: Based on the network signal strength of the network geographic information database area, and according to the network signal strength of the vehicle-mounted T-Box, the data transmission interval of each vehicle-mounted T-Box is initially adjusted;

[0058] Step S3: Based on the preset data detection cycle, detect the integrity of the vehicle BMS data sent by the T-Box obtained by the cloud data receiving module, and adjust the data transmission interval of each vehicle T-Box.

[0059] Step S4: Based on the data integrity within the preset data detection period and the network signal strength reported by each T-Box in the set area, adjust the basic data transmission interval for the vehicle-mounted T-Box to send BMS data within the detection area. The cloud data receiving module sends the acquired BMS data to the stream computing analysis module for preprocessing.

[0060] Step S5: Based on the preprocessed real-time data, the stream computing analysis module schedules the early warning algorithm task management through the task scheduler, deploys the early warning algorithm code, analyzes the vehicle battery status in real time, and generates early warning or alarm signals.

[0061] By constructing a network geographic information database based on GPS and RSRP signal strength using a cloud-based data receiving module and predicting vehicle travel paths, the system achieves dynamic and intelligent control of T-Box data transmission strategies. This effectively overcomes the lag inherent in existing technologies that can only passively react based on the current network status. The system can pre-switch to cache mode before the vehicle enters a weak network area and promptly trigger data retransmission after entering a high-quality network area, thus significantly improving the integrity and timeliness of data transmission in complex and ever-changing network environments. Simultaneously, by executing data integrity verification and retransmission mechanisms in parallel, a solid and reliable data foundation is provided for the battery warning system, fundamentally ensuring the accuracy and reliability of vehicle safety warnings.

[0062] Specifically, the visualization module displays, based on real-time and historical data in the data storage module, the following core content, including but not limited to: comprehensive statistics and display of the total number of vehicles, the number of online vehicles, the number of alarm vehicles, and the number of warning vehicles; interval distribution and percentage statistics of total vehicle mileage; real-time alarm and warning vehicle information list, supporting display by VIN, region, time, alarm / warning type, and fault details; quantity distribution and proportion of different new energy vehicle models; categorized statistics and analysis based on alarm data for the day; and ranking and trend display of the number of alarm and warning types.

[0063] This invention constructs an end-to-end battery early warning and control system, encompassing vehicle data acquisition, real-time cloud processing, and multi-dimensional visualization analysis. Leveraging T-Box and Kafka, it achieves efficient and low-latency access to massive amounts of BMS data. Using a Flink / Spark streaming engine, it performs real-time cleaning, calculation, and feature extraction of key parameters such as battery state of charge, total voltage, total current, highest / lowest voltage cells and their codes, voltage difference, highest / lowest temperature probes and temperature values, battery temperature, and insulation resistance. A battery early warning algorithm model is used for real-time analysis and signal generation. Simultaneously, the system utilizes MySQL and Redis to construct a multi-layered data storage architecture integrating real-time data stream processing and batch data processing, combining caching and archiving. Finally, through a large-screen early warning monitoring and in-depth vehicle detail analysis dashboard, it achieves integrated macro-monitoring and micro-diagnosis of battery health status, providing maintenance personnel with real-time, accurate, and traceable decision support, and comprehensively improving the level of power battery safety management.

[0064] Specifically, the process of constructing the network geographic information database in this embodiment of the invention is as follows:

[0065] Based on the RSRP signal strength and GPS location data reported by all vehicle-mounted T-Boxes, the cloud data receiving module uses... Using grids as units, the data is classified according to the RSRP signal strength reported by vehicles in the current area and time period, and the basic data transmission interval is determined by the location of the vehicle in the corresponding area.

[0066] This invention constructs a network geographic information database and performs refined management and adaptive updates of network geographic information using grids as units. The system can intelligently match the optimal basic data transmission strategy for different geographic regions and time periods. This regionalized and time-based configuration effectively overcomes the limitations of a single fixed-frequency strategy. It avoids wasting communication resources in areas with good signal coverage and ensures the reliability of data reporting in areas with weak signal coverage through pre-configured enhanced transmission strategies. As a result, it significantly improves the overall system efficiency and resource utilization of vehicle network data communication.

[0067] Specifically, the determination of the basic data transmission interval in this embodiment of the invention is classified as follows:

[0068] In the first type of area scenario, based on the RSRP signal strength reported by all vehicle T-Boxes in the current time period, if more than 70% of the vehicle T-Boxes in the area report an RSRP signal strength greater than -90dBm, this embodiment of the invention sets the basic data transmission interval of the vehicle T-Boxes in the current area to 600 seconds.

[0069] In the second region scenario, based on the RSRP signal strength reported by all vehicle T-Boxes during the current time period, if less than 70% of the vehicle T-Boxes in the region report an RSRP signal strength of less than -90dBm, this embodiment of the invention sets the basic data transmission interval of the vehicle T-Boxes in the current region to 300 seconds.

[0070] However, the above values ​​are not limited to these, and those skilled in the art can adjust the values ​​according to actual needs. Subsequently, based on the RSRP signal strength reported by the vehicle-mounted T-Box, the T-Box data transmission interval is initially determined according to the set signal strength and the preset data transmission interval rules, and the data transmission interval is then dynamically adjusted.

[0071] Specifically, in this embodiment of the invention, the area type is configured with a change method. Based on a set data detection period, if the proportion of vehicles reporting a signal strength RSRP greater than -90dBm in the first type of area is less than 70% during the data detection period, the corresponding area type is changed to the second type of area, and the basic data transmission interval of the vehicle T-Box in the current area is changed to 300 seconds. If the proportion of vehicles reporting a signal strength RSRP greater than -90dBm in the second type of area is greater than 70% during the data detection period, the corresponding area type is changed to the second type of area, and the basic data transmission interval of the vehicle T-Box in the current area is changed to 600 seconds.

[0072] This embodiment achieves automatic and accurate identification and adjustment of area types and corresponding basic data transmission strategies based on the actual distribution ratio of vehicle signal strength within the data detection period by setting a dynamic change mechanism for area types. This adaptive adjustment mechanism upgrades network resource management from static configuration to dynamic optimization, avoiding the risk of data loss caused by using a lenient transmission strategy when the network quality of the first type of area deteriorates, and timely reducing the communication frequency after the overall network quality of the second type of area improves to save power consumption and network resources of the vehicle terminal. Thus, it achieves real-time matching between communication strategies and network environment at the system level, significantly improving the overall adaptive capability and resource utilization efficiency of the vehicle network system.

[0073] Specifically, the process of initially determining the T-Box data transmission interval is as follows:

[0074] Based on the construction of a network geographic information database, the basic data transmission interval of the T-Box in the current region and time period is determined. According to the RSRP signal strength actually reported by the vehicle-mounted T-Box, the cloud data receiving module... Determine the interval at which the vehicle-mounted T-Box sends BMS data after adjustment. The adjusted data transmission interval is represented by k, where k is the adjustment coefficient. Based on the base transmission interval for the current region and time period, the T-Box is classified into levels according to the RSRP signal strength:

[0075] The first-level vehicle-mounted T-Box reports the current vehicle's RSRP signal strength data through the T-Box communication module to the cloud data receiving module. When the network signal strength RSRP is greater than -90dBm, the T-Box adjustment coefficient k is set to 1.

[0076] The second-level vehicle-mounted T-Box reports vehicle BMS data and RSRP signal strength data to the cloud data receiving module through the T-Box communication module. When the signal strength is greater than -105dBm and less than -90dBm, the T-Box adjustment coefficient k is set to 0.8.

[0077] The third-level vehicle-mounted T-Box reports vehicle BMS data and RSRP signal strength data to the cloud data receiving module through the T-Box communication module. When the signal strength RSRP is less than -105dBm, the T-Box adjustment coefficient k is set to 0.5.

[0078] Based on the initial determination of the T-Box data transmission interval, and according to the integrity of the data within the preset data detection period, i.e. whether all fields of the vehicle BMS data are complete, the data transmission interval of the corresponding T-Box is adjusted according to the vehicle T-Box type and data integrity status.

[0079] This embodiment assumes that 90% of the vehicle-mounted T-Boxes in detection area I report a signal strength RSRP greater than -90dBm, and only 60% of the vehicle-mounted T-Boxes in area II report a signal strength RSRP greater than -90dBm. Based on the classification criteria, the basic data transmission interval for vehicles in area I is set to 600 seconds, and the basic data transmission interval for vehicles in area II is set to 300 seconds. Vehicle A, in area I, uploads a network signal strength RSRP greater than -90dBm, therefore the adjustment coefficient k is set to 1, and the adjusted data transmission interval is determined to be 600 seconds. Vehicle B, in area I, uploads a network signal strength greater than -105dBm and less than -90dBm, so the T-Box adjustment coefficient k is set to 0.8, and the adjusted data transmission interval is determined to be 480 seconds. When vehicle A travels to area II, if the uploaded network signal strength RSRP is still greater than -90dBm, the adjustment coefficient remains unchanged. However, the basic data transmission interval of area II changes to 300 seconds. Therefore, the data transmission interval of vehicle A is adjusted to 300 seconds at this time.

[0080] Specifically, in the process of adjusting the data transmission interval of the corresponding T-Box in this embodiment of the invention, the cloud data receiving module uses 600 seconds as a data detection cycle. For the vehicle BMS data sent by the vehicle T-Box that is not received within a data detection cycle, the cloud data receiving module sorts all the vehicle BMS data sent by the vehicle T-Box that is received within the cycle according to different T-Box levels and according to the time order, so as to adjust the T-Box level.

[0081] If the current T-Box is a first-level T-Box, then maintain its current T-Box level and keep the data transmission interval unchanged;

[0082] If the current T-Box is a Level 2 T-Box, it is upgraded to a Level 1 T-Box, and its adjustment coefficient k is adjusted to 1;

[0083] If the current T-Box is a Level 3 T-Box, it will be upgraded to a Level 2 T-Box, and its adjustment factor k will be adjusted to 0.8;

[0084] If the same vehicle fails to upload any complete set of data within the inspection cycle, the system will generate an instruction to downgrade the corresponding T-Box level to the third-level T-Box and adjust its adjustment coefficient k to 0.5.

[0085] In this embodiment, at 9:00, vehicle A, while in area I, uploads a network signal strength RSRP greater than -90dBm. According to the classification standard, vehicle A's T-Box belongs to the first level T-Box. Vehicle B, while in area I, uploads a network signal strength greater than -105dBm and less than -90dBm. According to the classification standard, vehicle B's T-Box belongs to the second level T-Box. Currently, vehicle A's data transmission interval is 600 seconds, and vehicle B's data transmission interval is 480 seconds. At 9:10, vehicle A and vehicle B are still in area I, but vehicle B's uploaded network signal strength is less than -105dBm. At this time, vehicle B's T-Box level is adjusted to the third level, and the data transmission interval is adjusted to 300 seconds. At 9:20, vehicle A arrives in area II, and its uploaded network signal strength RSRP is greater than -90dBm. At this time, vehicle A's T-Box level remains at the first level, but because the basic data transmission interval in area II has changed, vehicle A's current data transmission interval is 300 seconds.

[0086] Specifically, in this embodiment of the invention, when multiple sets of complete data are received from the same T-Box within a data detection period, differential extraction is performed based on the changes in the network signal strength of the current T-Box. If the network signal strength of the current vehicle T-Box does not change within the period, and the cloud data receiving module acquires multiple sets of complete vehicle BMS data, the cloud data receiving module uses the earliest time-series complete vehicle BMS data as the standard for adjusting the T-Box data transmission interval. If the network signal strength of the current vehicle T-Box changes within the period, and multiple sets of complete BMS data corresponding to different T-Box network signal strength types are uploaded, the cloud data receiving module uses the latest time-series complete vehicle BMS data as the standard for adjusting the T-Box level to determine its data transmission interval.

[0087] In this embodiment, at 9:30, vehicle A returns from region II to region I, and vehicle B arrives from region I to region II. The cloud data module receives two sets of T-Box data from vehicle A and two sets of T-Box data from vehicle B within a preset 600-second data detection period. The first set of data from vehicle A indicates that the T-Box uploaded by vehicle A in region II is at level one, and the second set indicates that the T-Box uploaded by vehicle A in region I is also at level one. Therefore, the earliest complete set of vehicle BMS data is used as the standard for adjusting the T-Box data transmission interval, i.e., the data transmission interval is determined to be 300 seconds based on region II. The first set of data from vehicle B indicates that the T-Box uploaded by vehicle B in region I is at level three, and the second set indicates that the T-Box uploaded by vehicle B in region II is at level two. Therefore, the latest complete set of vehicle BMS data is used as the standard for adjusting the T-Box data transmission interval, i.e., the data transmission interval is determined to be 480 seconds based on region I.

[0088] If the cloud data receiving module fails to acquire complete vehicle BMS data for the corresponding T-Box for five consecutive data detection cycles, the corresponding T-Box will be marked and the T-Box data transmission interval will be automatically adjusted to the shortest data transmission interval preset in this embodiment of the invention, i.e., the minimum adjustment coefficient is 0.2. The data will then be uploaded to the visualization module for early warning.

[0089] In this embodiment, if vehicle C fails to obtain complete vehicle BMS data from the T-Box for five consecutive data detection cycles from 9:00 to 9:50 within area I, the data transmission interval of vehicle C will be adjusted to 120 seconds based on the basic data transmission interval of area I.

[0090] Please see Figure 2 This is a schematic diagram illustrating the verification of data integrity in the battery early warning and control method for new energy vehicles according to an embodiment of the present invention.

[0091] Specifically, the data integrity verification process described in this embodiment of the invention is as follows:

[0092] The cloud-based data receiving module predefines a standard vehicle BMS data topic and its corresponding complete data architecture. This summary specifies the set of fields that the data should include, the field format, and the reasonable range of values. The data integrity verification process includes:

[0093] Topic matching verification: The received data topic is compared with a predefined standard topic to confirm whether the data is the expected valid BMS data topic;

[0094] Field completeness verification: Parse the data load and check whether the fields it actually contains are completely consistent with the set of required fields specified in the data architecture, and determine whether there are any missing or redundant fields;

[0095] Data rationality verification: Perform logical checks on the values ​​of key fields to determine whether they are within the preset reasonable value range, and check whether the logical relationships between fields are correct;

[0096] Integrity determination: Data that passes all the above checks is considered valid and complete and is used for subsequent processing; if any check fails, the data is considered incomplete or invalid.

[0097] The cloud-based data receiving module dynamically adjusts the T-Box's data transmission mode and data transmission interval based on a network geographic information database, real-time RSRP signal strength information reported by the in-vehicle T-Box, and the integrity of the transmitted vehicle BMS data. This dynamic adjustment process involves meticulous optimization configuration for different types and models of T-Boxes, aiming to significantly improve the efficiency of vehicle BMS data collection. Through this intelligent dynamic adjustment mechanism, the cloud system can more accurately and efficiently capture key data on the vehicle's battery status, including battery charge, health status, and temperature. This not only ensures the stability and accuracy of data during transmission but also greatly improves the overall efficiency and reliability of data collection. Through this optimized configuration and intelligent adjustment, the cloud system can better support remote vehicle monitoring and management, providing strong data support for the normal operation and maintenance of the vehicle.

[0098] Please see Figure 3 This is a data processing flowchart of the cloud data receiving module for the battery early warning and control method for new energy vehicles according to an embodiment of the present invention;

[0099] The data storage module is responsible for the unified, reliable, and efficient persistent storage of the raw vehicle BMS data uploaded by the in-vehicle T-Box and the real-time analysis results of the battery warning model analyzed by the stream computing module. This module adopts a hierarchical storage architecture, implementing classified storage strategies based on data type and access frequency, and has an automatic historical data cleanup mechanism that periodically removes expired data, thereby automatically maintaining efficient utilization of storage space while ensuring business needs are met.

[0100] Specifically, the data type classification process described in this embodiment of the invention is as follows:

[0101] Based on the T-Box, vehicle BMS data and early warning system analysis data are collected and stored in the battery early warning system's data storage module. The data is categorized into three types according to access frequency and query pattern:

[0102] The first type of data storage is based on the vehicle BMS data collected by the T-Box on the same day and the analysis data of the early warning system. The data is stored in the Redis database to facilitate high-frequency and large-volume data read and write access. At the same time, the data on the same day is synchronously stored in the MySQL database.

[0103] The second type of data storage is based on the vehicle BMS data collected by the T-Box within 30 days and the analysis data of the early warning system. The data is stored in a MySQL database to ensure long-term data storage and facilitate complex condition queries.

[0104] The third type of data storage involves synchronizing the raw BMS data sent by all T-Boxes to the Hive data warehouse to support offline batch analysis and long-term traceability.

[0105] Specifically, the automatic cleanup mechanism for expired data described in this embodiment of the invention is as follows:

[0106] The system sets storage periods for different databases based on data type, access frequency, and business rules. The storage period for Redis database is set to 1 day, the storage period for MySQL database is set to 30 days, and the storage period for raw BMS data in Hive data warehouse is set to 30 days. When the data in the database reaches the storage period, the system will automatically clean up the expired data to ensure efficient use of storage space.

[0107] However, the above values ​​are not limited to these, and those skilled in the art can adjust the values ​​according to actual needs.

[0108] By differentiating data read frequencies, separating structured storage from caching mechanisms, and introducing an automatic historical data cleanup mechanism, the system not only significantly improves the utilization efficiency of storage resources but also comprehensively optimizes overall performance and economic benefits. This architecture ensures efficient querying and analysis of massive amounts of historical data while achieving millisecond-level data response for real-time monitoring and early warning decisions. The automatic cleanup mechanism periodically removes expired data according to preset strategies, effectively controlling database and distributed storage capacity, avoiding storage redundancy, reducing expansion needs, and thus continuously maintaining high throughput, low latency response, and long-term scalability in high-concurrency scenarios.

[0109] Please see Figure 4 As shown, this is a schematic diagram of a battery early warning and control system for new energy vehicles. The present invention also provides a battery early warning and control system applied to the aforementioned battery early warning and control method for new energy vehicles, comprising:

[0110] The vehicle-mounted T-Box is used to collect battery operation data (including voltage, temperature, current, SOC, insulation resistance, etc.) from the vehicle's BMS data system in real time, and to perform preliminary encapsulation and protocol conversion, and then upload the data to the cloud Kafka message queue via wireless network.

[0111] The cloud data receiving module is used to receive RSRP signal strength and GPS location data sent by the vehicle T-Box, build a network geographic information database, formulate and distribute T-Box data transmission mode and combine it with real-time vehicle driving information to dynamically adjust the T-Box data transmission interval and T-Box data transmission mode, and verify the integrity of the received data.

[0112] The data storage module is used for hierarchical storage and management of multi-source data: it persistently stores structured data such as vehicle static information and early warning events in a MySQL database, caches real-time high-frequency access intermediate data in a Redis database, and synchronizes the original BMS data to the Hive data warehouse, using Hue for offline batch query and analysis.

[0113] The stream computing and analysis module is used to process data streams in real time: it subscribes to Kafka data through the Flink or Spark engine, performs data parsing, cleaning, outlier removal, feature extraction and standardization, and outputs the results to the downstream storage and algorithm module.

[0114] The visualization monitoring module is used for multi-dimensional data display and interaction. Based on real-time and historical data in the data storage module, it dynamically generates visual charts such as vehicle statistics, alarm lists, vehicle model distribution, and alarm type rankings. It supports fault analysis and provides intuitive basis for operation and maintenance decisions.

[0115] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0116] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A battery early warning and control method for new energy vehicles, characterized in that, include: Based on the established network geographic information database and combined with the network signal strength reported by vehicle-mounted T-Boxes within the region, the region is classified into different levels. The cloud data receiving module determines the basic data transmission interval for T-Boxes within the region. The process of determining the basic data transmission interval includes: based on the network signal strength reported by all vehicles in the region during the current time period, if the network signal strength reported by more than a preset proportion of vehicles in the region is greater than a preset threshold for network signal strength, then the vehicle is determined to be in the first type of region, and the time interval for vehicle-mounted T-Boxes in the first type of region to send BMS data is set as the first basic data transmission interval; based on the network signal strength reported by all vehicles in the region during the current time period, if the network signal strength reported by more than a preset proportion of vehicles in the region is less than or equal to a preset threshold for network signal strength, then the vehicle is determined to be in the second type of region, and the time interval for vehicle-mounted T-Boxes in the second type of region to send BMS data is set as the second basic data transmission interval; wherein, the first basic data transmission interval is greater than the second basic data transmission interval; based on the detection results of several consecutive data detection cycles, a change method is set for the corresponding region type. Based on the basic data transmission interval of the region determined by the network geographic information database, and according to the network signal strength of the vehicle's on-board T-Box, the data transmission interval of each on-board T-Box is initially adjusted; based on the network signal strength of the vehicle's on-board T-Box, the T-Boxes are classified into levels, and based on the classification results of the on-board T-Box levels, corresponding adjustment coefficients are determined. These adjustment coefficients are used to determine the actual data transmission interval of the corresponding T-Box in conjunction with the current basic data transmission interval; for each on-board T-Box level, an adjustment method is set to adjust the adjustment coefficients in different areas of the on-board T-Box. If an on-board T-Box fails to receive data for several consecutive data detection cycles, the level of the corresponding on-board T-Box is dynamically adjusted, and the vehicle BMS data transmission interval is adjusted according to the corresponding on-board T-Box level; Based on the preset data detection cycle, the integrity of the vehicle BMS data sent by the T-Box obtained by the cloud data receiving module is detected, and the data transmission interval of each vehicle T-Box is adjusted accordingly. Based on the data integrity within the preset data detection period and the network signal strength reported by each T-Box in the set area, the basic data transmission interval for the vehicle-mounted T-Box to send BMS data within the detection area is adjusted, and the cloud data receiving module sends the acquired BMS data to the stream computing analysis module for preprocessing. Based on the preprocessed real-time data, the stream computing analysis module manages the scheduling of early warning algorithm tasks through the task scheduler, deploys the early warning algorithm code, analyzes the vehicle battery status in real time, and generates early warning or alarm signals.

2. The battery early warning and control method for new energy vehicles according to claim 1, characterized in that, Based on the geographical area to which the control method is applied, the geographical area is divided according to a preset grid area to form multiple regular geographical detection grids. Based on a preset signal strength threshold, statistical analysis is performed on the network signal strength data reported within the same geographical detection grid during the same time period, and the region type of the corresponding grid is determined based on the statistical results. Based on the regional types determined by establishing a network geographic information database, the basic data transmission interval is determined by the location of vehicles in the corresponding regions.

3. The battery early warning and control method for new energy vehicles according to claim 2, characterized in that, The region type has a change method set. Based on the set data detection period, if the threshold of the signal strength reported by the vehicle-mounted T-Box in the area exceeds or falls below the preset threshold of the network strength during the data detection period, the type of the current area will be changed accordingly and the basic data transmission interval will be re-determined based on the changed area type.

4. The battery early warning and control method for new energy vehicles according to claim 3, characterized in that, Based on the current network signal strength of the vehicle's T-Box, the adjustment coefficient of the T-Box is determined, and the determination process includes: Based on the current vehicle T-Box network signal strength data reported to the cloud data receiving module, when the network signal strength is greater than the first network signal strength threshold, the vehicle T-Box is determined to be a first-level vehicle T-Box, and the T-Box adjustment coefficient is set as the first-level adjustment coefficient. Based on the current vehicle T-Box network signal strength data reported to the cloud data receiving module, when the network signal strength is less than the first network signal strength threshold but greater than the second network signal strength threshold, the vehicle T-Box is determined to be a second-level vehicle T-Box, and the T-Box adjustment coefficient is set as the second-level adjustment coefficient; Based on the current vehicle T-Box network signal strength data reported to the cloud data receiving module, when the network signal strength is less than the second network signal strength threshold, the vehicle T-Box is determined to be a third-level vehicle T-Box, and the T-Box adjustment coefficient is set to the third-level adjustment coefficient. in, The first preset network strength threshold is greater than the second preset network strength threshold.

5. The battery early warning and control method for new energy vehicles according to claim 1, characterized in that, For multiple sets of vehicle BMS data acquired from the same vehicle T-Box within the data detection period, they are classified according to different T-Box levels. The cloud data receiving module sorts them separately based on the time order of the acquired vehicle BMS data to determine the temporal relationship of each valid and complete vehicle BMS data. Based on the fact that the network signal strength of the current vehicle T-Box has not changed during the detection period, it is determined that the cloud data receiving module uses the earliest set of complete vehicle BMS data in the time sequence as the valid data for the current period. Based on the changes in the network signal strength of the vehicle's T-Box during the detection period, the cloud data receiving module determines that the latest set of complete vehicle BMS data in the time sequence is used as the valid data for the current period.

6. The battery early warning and control method for new energy vehicles according to claim 5, characterized in that, The vehicle BMS data for the corresponding T-Box is obtained based on the cloud data receiving module, and the data transmission interval of the corresponding vehicle T-Box is adjusted. If the cloud data receiving module fails to acquire complete vehicle BMS data for the corresponding T-Box within the set data detection period, the level of the corresponding vehicle T-Box will be reduced for non-lowest level T-Boxes, while the level of the corresponding vehicle T-Box will be maintained for the lowest level T-Box. The data transmission interval of the corresponding vehicle T-Box will be adjusted in combination with the basic data transmission interval of the current area. Based on the cloud data receiving module, complete vehicle BMS data of the corresponding T-Box is obtained in the set data detection period. For T-Boxes that are not at the highest level, the corresponding vehicle T-Box level is upgraded. For the highest level T-Box, the corresponding vehicle T-Box level is maintained. The data transmission interval of the corresponding vehicle T-Box is adjusted in combination with the basic data transmission interval of the current area. If the cloud data receiving module fails to acquire complete vehicle BMS data for the corresponding T-Box after several consecutive data detection cycles, it will mark the corresponding T-Box and automatically adjust the T-Box data transmission interval to the preset minimum adjustment coefficient.

7. A battery early warning and control system using the battery early warning and control method for new energy vehicles according to any one of claims 1-6, characterized in that, include, The vehicle-mounted T-Box connects to the cloud data module to collect battery operation data from the vehicle's BMS data system in real time, perform preliminary encapsulation and protocol conversion, and upload the data to the cloud data receiving module via wireless network. The cloud data receiving module is connected to the vehicle-mounted T-Box and the data storage module respectively. It is used to receive network signal strength and GPS location data sent by the vehicle-mounted T-Box, build a network geographic information database, formulate and distribute the T-Box data transmission mode, and dynamically adjust the T-Box data transmission interval and T-Box data transmission mode in combination with real-time vehicle driving information, and verify the integrity of the received data. The data storage module is connected to the cloud data module, the visualization module, and the stream computing module respectively. It is used for hierarchical storage and management of multi-source data, classifies and stores data, sets the data storage period for different databases, and automatically processes expired data. The stream computing and analysis module is connected to the data storage module and is used to process the data stream in real time, perform data parsing, cleaning, outlier removal, feature extraction and standardization, and output the results to the data storage module. The visualization monitoring module is connected to the data storage module and is used for multi-dimensional data display and interaction. Based on the real-time and historical data in the data storage module, it dynamically generates visualization charts of vehicle statistics, alarm lists, vehicle model distribution, and alarm type ranking.

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