A method and system for automatically analyzing the battery life of an electric vehicle

Abstract

The embodiment of the present invention discloses a method and system for automatic analysis of the battery life of an electric vehicle. The method includes: RMS receives first data transmitted in real time by an Internet of Things device; RMS filters and processes the first data to obtain second data; RMS uses a message middleware to forward the second data to the BMS, so that the BMS performs automatic analysis of the battery life according to the second data. In implementing the embodiment of the present invention, by using a message middleware (KAFKA), the battery data reported to the RMS in real time can be automatically connected to the BMS system, reducing repeated manual labor in the middle, saving manual time, and reducing the probability of errors, so that the data of the battery life analysis can be made more real-time and accurate. That is, the embodiment of the present invention realizes the automation of the entire process from automatic reporting of battery information by electric vehicles, automatic processing, storage and forwarding of battery information by RMS, and automatic analysis of battery life by BMS.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle battery management, and in particular to a method and system for automatically analyzing the life of an electric vehicle battery. Background Art

[0002] When driving, electric vehicles will automatically record various battery indicators according to different vehicle settings (usually every second), and transmit them to the new energy vehicle remote service and management platform RMS in real time through IoT devices. RMS will parse the received byte stream data, format the data according to platform requirements, and store it.

[0003] Please refer to Figure 1 When the battery life analysis is required, at this stage, it is necessary to manually query the battery data corresponding to each vehicle from the RMS, and transfer the data of each vehicle every day to EXCEL and then download it. Because the analysis of battery life requires a large amount of data support, a large amount of EXCEL needs to be downloaded to the local computer, and then the EXCEL data classified by vehicle needs to be uploaded to the BMS, and wait for the BMS to parse the EXCEL. Usually the data storage capacity of a vehicle can be as high as 1G, and uploading and downloading will consume a lot of time. After the BMS obtains the battery data, it analyzes the data and waits for 1 minute to 1 hour depending on the amount of data before finally obtaining the battery life analysis data.

[0004] The existing battery life analysis process has the following problems:

[0005] (1) When RMS and BMS exchange data, a lot of manual operations are required, and due to the large number of vehicles and the large amount of battery data reported by the vehicles, there is a high possibility of errors.

[0006] (2) Downloading a large amount of data from the RMS to a local computer or server and uploading it to the BMS again requires a lot of storage hardware costs and bandwidth transmission costs.

[0007] (3) Manually downloading data from the RMS, sorting it out, and uploading it to the BMS will result in unreliable time consumption, resulting in the final battery data not being real-time. Summary of the invention

[0008] In view of the problems existing in the prior art, an object of the embodiments of the present invention is to provide a method and system for automatically analyzing the battery life of an electric vehicle.

[0009] To achieve the above objectives, in a first aspect, an embodiment of the present invention provides a method for automatically analyzing the battery life of an electric vehicle, comprising:

[0010] The RMS receives first data transmitted in real time by the IoT device;

[0011] RMS performs screening processing on the first data to obtain second data;

[0012] The RMS uses message middleware to forward the second data to the BMS, so that the BMS can automatically analyze the battery life according to the second data.

[0013] In some preferred embodiments of the present application, before the RMS performs screening processing on the first data, the method further includes:

[0014] The RMS analyzes the first data, and formats and stores the analyzed first data according to platform requirements.

[0015] As a specific implementation of the present application, the RMS performs screening processing on the first data, specifically:

[0016] Filter the first data according to the filtering rule to obtain valid battery data of the vehicle;

[0017] The valid battery data is filtered according to the filtering rule to obtain the second data.

[0018] The screening rules include: (1) the vehicle mileage is too long, and there is a risk of battery damage; (2) the vehicle registration time is too long, and there is a battery aging problem; (3) the instantaneous battery cell temperature is too high;

[0019] The filtering rules include whether the important parameters are complete; the important parameters include the highest temperature subsystem number, the lowest temperature subsystem number, the highest voltage battery subsystem number, the lowest voltage battery subsystem number, the highest temperature value, the highest temperature probe serial number, the lowest temperature value, the lowest temperature probe serial number, the highest value of the battery cell voltage, the highest voltage battery cell code, the lowest value of the battery cell voltage and the lowest voltage battery code.

[0020] In a second aspect, an embodiment of the present invention provides another method for automatically analyzing the battery life of an electric vehicle, comprising:

[0021] The RMS receives first data transmitted in real time by the IoT device, filters and processes the first data to obtain second data, and forwards the second data to the message middleware;

[0022] The message middleware forwards the second data to the BMS;

[0023] The BMS uses multiple threads to simultaneously receive the second data, and automatically analyzes and calculates the battery life according to the second data to obtain a battery life analysis result.

[0024] In a third aspect, an embodiment of the present invention provides a system for automatically analyzing the battery life of an electric vehicle, comprising:

[0025] The RMS is used to receive first data transmitted in real time by the IoT device, filter and process the first data to obtain second data, and forward the second data;

[0026] A message middleware, used for forwarding the second data;

[0027] The BMS is used to use multiple threads to simultaneously receive the second data, and automatically analyze and calculate the battery life according to the second data to obtain a battery life analysis result.

[0028] In certain preferred embodiments of the present application, the RMS comprises:

[0029] A parsing unit, configured to receive first data transmitted in real time by an IoT device, parse the first data, and format and store the parsed first data according to platform requirements;

[0030] A screening unit, used to screen the first data according to a screening rule to obtain valid battery data of the vehicle;

[0031] The filtering unit is used to filter the valid battery data according to the filtering rule to obtain the second data.

[0032] By implementing the embodiment of the present invention, by using the message middleware (KAFKA), the battery data reported to the RMS in real time can be automatically connected to the BMS system, reducing the repeated manual labor in the middle, saving labor time, and reducing the probability of errors, so that the data of battery life analysis can be more real-time and accurate. That is, the embodiment of the present invention realizes the automation of the whole process from the automatic reporting of battery information by electric vehicles, the automatic processing, storage and forwarding of battery information by RMS, and the automatic analysis of battery life by BMS. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the specific implementation of the present invention or the technical solution in the prior art, the drawings required for use in the specific implementation or the description of the prior art will be briefly introduced below.

[0034] Figure 1 It is a data flow diagram between the existing electric vehicle, RMS and BMS;

[0035] Figure 2 is a schematic diagram of the data flow between the electric vehicle, RMS and BMS in an embodiment of the present invention;

[0036] Figure 3 This is a flow chart of a method for automatically analyzing the life of an electric vehicle battery provided by a first embodiment of the present invention;

[0037] Figure 4 This is a schematic diagram of the KAFKA multi-partition model;

[0038] Figure 5 is a flow chart of a method for automatic analysis of electric vehicle battery life provided by a second embodiment of the present invention;

[0039] Figure 6 This is a system structure diagram of an automatic analysis of the battery life of an electric vehicle provided by an embodiment of the present invention;

[0040] Figure 7 yes Figure 6 Structural diagram of RMS. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] In order to better understand the embodiments of the present invention, the relevant terms are explained as follows:

[0043] Message middleware: Message middleware is a supporting software system based on queue and message passing technology, which provides synchronous or asynchronous, reliable message transmission for application systems in a network environment.

[0044] KAFKA: A high-throughput distributed publish-subscribe messaging middleware that handles all action stream data from consumers on a website.

[0045] BMS: Battery Management System

[0046] RMS: New Energy Vehicle Remote Service and Management Platform

[0047] Thread: The smallest unit that the operating system can schedule operations on. Each thread can execute different tasks in parallel, which can improve performance.

[0048] Please refer to Figure 2 and Figure 3 The method for automatically analyzing the battery life of an electric vehicle provided by the first embodiment of the present invention includes:

[0049] S101, RMS receives first data transmitted in real time by an IoT device.

[0050] When driving, the electric vehicle will automatically record various indicator data of the battery according to the settings of different vehicles, that is, the first data, and transmit the first data to the RMS in real time through the Internet of Things device.

[0051] S102: RMS screens the first data to obtain second data.

[0052] After receiving the aforementioned first data, the RMS parses the data, formats the data and stores it according to the platform requirements.

[0053] Because electric vehicles need to analyze battery life, RMS needs to forward the parsed data to BMS. However, not all data will be sent to RMS, so it needs to be screened and filtered according to certain rules. Specifically, first filter according to the following screening rules to obtain the vehicle's valid battery data (that is, the data that needs to be reported to RMS): (1) The vehicle's mileage is too long, and there is a risk of battery damage; (2) The vehicle has been registered for too long, and there is a battery aging problem; (3) The instantaneous battery cell temperature is too high; (4) The battery life analysis is specified.

[0054] After completing the data screening, you also need to filter the valid battery data according to certain filtering principles:

[0055] (1) Whether the important parameters are complete, including the highest temperature subsystem number, the lowest temperature subsystem number, the highest voltage battery subsystem number, the lowest voltage battery subsystem number, the highest temperature value, the highest temperature probe number, the lowest temperature value, the lowest temperature probe number, the highest value of battery cell voltage, the highest voltage battery cell code, the lowest value of battery cell voltage, the lowest voltage battery cell code, etc.;

[0056] (2) Non-compensated data.

[0057] Filtering can reduce some data and unnecessary performance consumption of each system, so that the data that finally reaches the BMS are valid data.

[0058] S103, the RMS forwards the second data to the BMS using a message middleware, so that the BMS automatically analyzes the battery life according to the second data.

[0059] After RMS completes data screening, it needs to connect the data with the BMS system. After data volume evaluation, scalability evaluation, and performance evaluation, KAFKA message middleware was finally selected as the data forwarding tool.

[0060] Please refer to Figure 4The KAFKA multi-partition model shown uses the distributed and multi-partition features of KAFKA to simultaneously transmit a large amount of data in different shards, support data transmission under massive data, shorten transmission time, and make full use of server performance. The battery data transmitted through KAFKA will be received by multiple threads of BMS at the same time, and the battery life analysis and calculation will be performed at the same time, thus meeting the real-time requirements. Finally, the battery life analysis data will be obtained in BMS.

[0061] Please refer to Figure 2 and Figure 5 The method for automatically analyzing the battery life of an electric vehicle provided by the second embodiment of the present invention includes:

[0062] S201, RMS receives first data transmitted in real time by an IoT device.

[0063] S202: RMS performs screening processing on the first data to obtain second data.

[0064] S203, RMS forwards the second data to the message middleware.

[0065] S204: The message middleware forwards the second data to the BMS.

[0066] S205: The BMS uses multiple threads to simultaneously receive the second data.

[0067] S206, the BMS automatically analyzes and calculates the battery life according to the second data to obtain a battery life analysis result.

[0068] It should be noted that Figure 5 For the specific process of each step in the method embodiment shown, please refer to Figure 3 The description of the illustrated method embodiment will not be repeated here. Figure 3 and Figure 5 The illustrated embodiments differ only in that: Figure 3 The execution subject of the method steps is only RMS, and Figure 5 The execution entities of the method steps include RMS, message middleware and BMS.

[0069] From the above description, it can be known that the present invention uses the message middleware (KAFKA) so that the battery data reported to the RMS in real time can be automatically connected to the BMS system, reducing the repeated manual labor in the middle, saving manual time, and reducing the probability of errors, so that the data of battery life analysis can be more real-time and accurate. That is, the embodiment of the present invention realizes the automation of the whole process from the automatic reporting of battery information by electric vehicles, the automatic processing, storage and forwarding of battery information by RMS, and the automatic analysis of battery life by BMS.

[0070] Based on the same inventive concept, an embodiment of the present invention provides a system for automatically analyzing the battery life of an electric vehicle. Figure 6 As shown, the system includes:

[0071] The RMS is used to receive first data transmitted in real time by the IoT device, filter and process the first data to obtain second data, and forward the second data;

[0072] A message middleware, used for forwarding the second data;

[0073] The BMS is used to use multiple threads to simultaneously receive the second data, and automatically analyze and calculate the battery life according to the second data to obtain a battery life analysis result.

[0074] Furthermore, if Figure 7 As shown, RMS specifically includes:

[0075] A parsing unit, configured to receive first data transmitted in real time by an IoT device, parse the first data, and format and store the parsed first data according to platform requirements;

[0076] A screening unit, used to screen the first data according to a screening rule to obtain valid battery data of the vehicle;

[0077] The filtering unit is used to filter the valid battery data according to the filtering rule to obtain the second data.

[0078] The screening rules include: (1) the vehicle mileage is too long, and there is a risk of battery damage; (2) the vehicle registration time is too long, and there is a battery aging problem; (3) the instantaneous battery cell temperature is too high;

[0079] The filtering rules include whether the important parameters are complete; the important parameters include the highest temperature subsystem number, the lowest temperature subsystem number, the highest voltage battery subsystem number, the lowest voltage battery subsystem number, the highest temperature value, the highest temperature probe serial number, the lowest temperature value, the lowest temperature probe serial number, the highest value of the battery cell voltage, the highest voltage battery cell code, the lowest value of the battery cell voltage and the lowest voltage battery code.

[0080] It should be noted that, regarding the specific workflow of the system part, please refer to the aforementioned method embodiment part, which will not be repeated here.

[0081] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0082] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or it can be an electrical, mechanical or other form of connection.

[0083] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0084] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0085] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0086] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for automatically analyzing the battery life of an electric vehicle, characterized in that: include: The RMS receives first data transmitted in real time by the IoT device; RMS performs screening processing on the first data to obtain second data; The RMS uses the message middleware to forward the second data to the BMS, so that the BMS can automatically analyze the battery life according to the second data; the RMS screens and processes the first data, specifically: Filter the first data according to the filtering rule to obtain valid battery data of the vehicle; Filter the valid battery data according to the filtering rule to obtain second data; The screening rules include: (1) the vehicle mileage is too long, and there is a risk of battery damage; (2) the vehicle registration time is too long, and there is a battery aging problem; (3) the instantaneous battery cell temperature is too high; The filtering rules include whether the important parameters are complete; the important parameters include the highest temperature subsystem number, the lowest temperature subsystem number, the highest voltage battery subsystem number, the lowest voltage battery subsystem number, the highest temperature value, the highest temperature probe serial number, the lowest temperature value, the lowest temperature probe serial number, the highest value of the battery cell voltage, the highest voltage battery cell code, the lowest value of the battery cell voltage and the lowest voltage battery code.

2. The method for automatic analysis of electric vehicle battery life according to claim 1, characterized in that: Before the RMS performs screening processing on the first data, the method further includes: The RMS analyzes the first data, and formats and stores the analyzed first data according to platform requirements.

3. A method for automatically analyzing the battery life of an electric vehicle, characterized in that: include: The RMS receives first data transmitted in real time by the IoT device, filters and processes the first data to obtain second data, and forwards the second data to the message middleware; The message middleware forwards the second data to the BMS; The BMS uses multiple threads to simultaneously receive the second data, and automatically analyzes and calculates the battery life according to the second data to obtain a battery life analysis result; The RMS performs screening processing on the first data as follows: Filter the first data according to the filtering rule to obtain valid battery data of the vehicle; Filter the valid battery data according to the filtering rule to obtain second data; The screening rules include: (1) the vehicle mileage is too long, and there is a risk of battery damage; (2) the vehicle registration time is too long, and there is a battery aging problem; (3) the instantaneous battery cell temperature is too high; The filtering rules include whether the important parameters are complete; the important parameters include the highest temperature subsystem number, the lowest temperature subsystem number, the highest voltage battery subsystem number, the lowest voltage battery subsystem number, the highest temperature value, the highest temperature probe serial number, the lowest temperature value, the lowest temperature probe serial number, the highest value of the battery cell voltage, the highest voltage battery cell code, the lowest value of the battery cell voltage and the lowest voltage battery code.

4. A system for automatic analysis of electric vehicle battery life, characterized in that: include: The RMS is used to receive first data transmitted in real time by the IoT device, filter and process the first data to obtain second data, and forward the second data; A message middleware, used for forwarding the second data; The BMS is configured to use multiple threads to simultaneously receive the second data, and automatically analyze and calculate the battery life according to the second data to obtain a battery life analysis result; The RMS includes a screening unit for screening the first data according to a screening rule to obtain valid battery data of the vehicle; The screening rules include: (1) the vehicle mileage is too long, and there is a risk of battery damage; (2) the vehicle registration time is too long, and there is a battery aging problem; (3) the instantaneous battery cell temperature is too high; RMS also includes: A filtering unit, used for filtering the valid battery data according to the filtering rule to obtain second data; The filtering rules include whether the important parameters are complete; the important parameters include the highest temperature subsystem number, the lowest temperature subsystem number, the highest voltage battery subsystem number, the lowest voltage battery subsystem number, the highest temperature value, the highest temperature probe serial number, the lowest temperature value, the lowest temperature probe serial number, the highest value of the battery cell voltage, the highest voltage battery cell code, the lowest value of the battery cell voltage and the lowest voltage battery code.

5. The system for automatic analysis of electric vehicle battery life as claimed in claim 4, characterized in that: RMS includes: The parsing unit is used to receive first data transmitted in real time by the IoT device, parse the first data, and format and store the parsed first data according to platform requirements.

Images