Electric vehicle battery remote monitoring system based on cloud computing

Abstract

The present invention is applicable to the field of electric vehicle intelligence, and provides an electric vehicle battery remote monitoring system based on cloud computing, the system comprising: a battery data processing module, receiving the operation data uploaded by the electric vehicle, analyzing the operation data to obtain the battery power data; a data coordination module, obtaining the travel plan information of the electric vehicle, the travel plan information includes multiple navigation paths, and searching for charging points along the way according to the navigation paths and calibrating them; a data analysis module, using cloud computing technology combined with the obtained power data to calculate and generate the remaining mileage information and send it to the electric vehicle; an information combination planning module, intercepting and processing multiple navigation paths based on the starting point according to the remaining mileage information to obtain the initial path, and selecting the optimal travel route with reference to the number of charging points calibrated in the initial path. The invention is an auxiliary expansion of battery monitoring technology, focusing on the aspect of electric vehicle travel.

Description

Technical Field

[0001] The present invention relates to the field of electric vehicle intelligence, and in particular to an electric vehicle battery remote monitoring system based on cloud computing. Background Art

[0002] The battery of an electric vehicle is a vital component. The performance of the battery plays a major role in the performance of the vehicle. Among them, battery capacity is generally the point that people pay most attention to, because this indicator directly determines the vehicle's endurance.

[0003] At present, electric vehicles have a complete intelligent and vehicle-centric control system, which can provide great convenience for car owners by monitoring various indicators and parameters of the vehicle. For example, the battery remote monitoring system can detect the battery power and health by collecting its operating data, and then feed back the relevant information to the car computer, so that the owner can know the vehicle's range.

[0004] At present, for car companies, a lot of vehicle information can be obtained through the battery monitoring system, and its role is obvious. For vehicle users, the battery power (i.e., cruising range) plays the most obvious role. During the use of the vehicle, the vehicle user is required to make his own judgment based on the power. The existing battery monitoring system is insufficient for users. Therefore, a cloud computing-based electric vehicle battery remote monitoring system is proposed to solve the above problems. Summary of the invention

[0005] In view of the deficiencies in the prior art, the purpose of the present invention is to provide an electric vehicle battery remote monitoring system based on cloud computing to solve the problems existing in the above-mentioned background technology.

[0006] The present invention is implemented in this way: an electric vehicle battery remote monitoring system based on cloud computing, the system comprising:

[0007] The battery data processing module receives the operation data uploaded by the electric vehicle and analyzes the operation data to obtain the battery power data when the authority is granted;

[0008] The data collaboration module obtains travel plan information of the electric vehicle, wherein the travel plan information includes multiple navigation routes, and searches for charging points along the route according to the navigation routes and calibrates them;

[0009] The data analysis module uses cloud computing technology to combine the acquired power data to calculate the remaining mileage information and send it to the electric vehicle;

[0010] The information is combined with the planning module, which intercepts and processes multiple navigation paths based on the starting point according to the remaining mileage information to obtain an initial path, and selects the optimal travel route based on the number of charging points calibrated in the initial path.

[0011] As a further solution of the present invention: the battery data processing module includes:

[0012] A data collection unit receives the operation data of the electric vehicle uploaded through the cloud platform and stores it in combination with the serial number information of the electric vehicle, wherein the serial number information is a unique identity of the electric vehicle;

[0013] A data splitting unit, which splits the operation data to obtain a plurality of data integration packages, wherein the data integration packages are obtained by different sensors in the electric vehicle;

[0014] The data identification unit extracts data about the battery from multiple data integration packages and identifies the data to obtain the battery power data.

[0015] As a further solution of the present invention: the data collaboration module includes:

[0016] A state detection unit is used to detect the owner's behavior of using the electric vehicle, and when the owner uses the electric vehicle's head unit for navigation, obtain multiple navigation paths generated during the navigation;

[0017] A route selection unit, which selects a navigation path with the longest distance from multiple navigation paths;

[0018] A comparison unit, for comparing the cruising range currently displayed by the electric vehicle with the selected navigation path distance obtained according to the operation data;

[0019] An integration unit, when the cruising range is less than or equal to the distance of the navigation path, integrates multiple navigation paths to obtain travel plan information and uploads the information;

[0020] The route processing unit, after obtaining the travel plan information, searches for and marks charging points along the way according to the navigation path in the travel plan information.

[0021] As a further solution of the present invention: the route processing unit includes:

[0022] The route expansion subunit searches and marks the charging points along the way according to the known navigation path;

[0023] The information supplement subunit searches for charging points in the opposite direction along the way according to the known navigation path and adds sub-markers;

[0024] The information screening unit screens the secondary marks according to the road condition information at the secondary marks on the navigation path, so as to verify whether the electric vehicle needs to take a detour.

[0025] As a further solution of the present invention: the data analysis module includes:

[0026] A data retrieval unit, which retrieves the travel records stored in the cloud platform, wherein the travel records are the data of the destination, distance, energy consumption, and endurance recorded during the travel;

[0027] A matching unit selects a plurality of travel records whose similarity exceeds a set value from among the travel records according to the destination location of the travel plan information;

[0028] The comprehensive calculation unit calculates the endurance according to the power data and in combination with the selected multiple travel records to obtain the remaining mileage information and sends it to the electric vehicle.

[0029] As a further solution of the present invention: the information combination planning module includes:

[0030] a route processing unit, selecting each navigation path in turn and intercepting the selected navigation path according to the remaining mileage information to obtain an initial path, wherein the initial path is a portion of the navigation path starting from the departure position and ending at a distance corresponding to the remaining mileage information;

[0031] A statistical unit, which performs a number statistics on the charging pile point marks contained in the initial path to obtain pile point quantity information, wherein the pile point quantity information corresponds to the initial path one by one;

[0032] The route selection unit selects the initial path containing the largest number of points according to the information on the number of points and determines the corresponding navigation path.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] The present invention utilizes the existing technology to remotely monitor the battery of an electric vehicle, and can easily obtain the battery power data. The characteristic is that the obtained power data is not only used to remind the owner about the vehicle's endurance, but also can make a more reasonable arrangement for the route of the electric vehicle in combination with the owner's travel plan. After obtaining the travel plan information of the electric vehicle, it can search for charging points along the way according to the navigation path contained therein, and select the most reasonable route. The starting section of the route can include the largest number of charging points, which can solve the range anxiety problem of electric vehicles when traveling, allowing owners to travel more at ease. The present invention is an auxiliary expansion of battery monitoring technology, focusing on the travel aspect of electric vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 Schematic diagram of the structure of the electric vehicle battery remote monitoring system based on cloud computing.

[0036] Figure 2 This is a schematic diagram of the structure of the battery data processing module in the electric vehicle battery remote monitoring system based on cloud computing.

[0037] Figure 3 This is a structural diagram of the data collaboration module in the electric vehicle battery remote monitoring system based on cloud computing.

[0038] Figure 4 This is a schematic diagram of the structure of the route processing unit in the electric vehicle battery remote monitoring system based on cloud computing.

[0039] Figure 5 This is a schematic diagram of the structure of the data analysis module in the electric vehicle battery remote monitoring system based on cloud computing.

[0040] Figure 6 This is a structural diagram of the information integration planning module in the cloud computing-based electric vehicle battery remote monitoring system. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0042] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0043] like Figure 1 As shown, an embodiment of the present invention provides an electric vehicle battery remote monitoring system based on cloud computing, the system comprising:

[0044] The battery data processing module 100 receives the operation data uploaded by the electric vehicle and analyzes the operation data to obtain the battery power data when the authority is granted;

[0045] The data collaboration module 200 obtains travel plan information of the electric vehicle, wherein the travel plan information includes a plurality of navigation routes, and searches for charging points along the route according to the navigation routes and calibrates them;

[0046] The data analysis module 300 calculates and generates the remaining mileage information by combining the acquired power data with the cloud computing technology and sends it to the electric vehicle;

[0047] The information is combined with the planning module 400, which intercepts and processes multiple navigation paths based on the starting point according to the remaining mileage information to obtain an initial path, and selects the optimal travel route by referring to the number of charging points calibrated in the initial path.

[0048] It should be noted that, for car companies, a lot of vehicle information can be obtained through the battery monitoring system, and its role is obvious. For vehicle users, the role of the battery power (i.e., cruising range) is the most obvious. During the use of the vehicle, the vehicle user is required to make his own judgment based on the power. The existing battery monitoring system provides insufficient functions for users. The present invention is an auxiliary expansion of the battery monitoring technology, focusing on the electric vehicle travel aspect, and utilizing the characteristics of cloud computing to make the data collection, processing, and calculation of electric vehicles more convenient and quick.

[0049] In the embodiment of the present invention, the present invention utilizes the existing technology to remotely monitor the battery of the electric vehicle, and can easily obtain the battery power data. The characteristic is that the obtained power data is not only used to remind the owner about the vehicle's endurance, but also can make a more reasonable arrangement for the route of the electric vehicle in combination with the owner's travel plan. After obtaining the travel plan information of the electric vehicle, it can search for charging points along the way according to the navigation path contained therein, and select the most reasonable route. The starting section of the route can include the largest number of charging points, which can solve the range anxiety problem of electric vehicles when traveling, allowing owners to travel with more peace of mind.

[0050] like Figure 2 As shown, as a preferred embodiment of the present invention, the battery data processing module 100 includes:

[0051] The data collection unit 101 receives the operation data of the electric vehicle uploaded through the cloud platform and stores it in combination with the serial number information of the electric vehicle, where the serial number information is the unique identity of the electric vehicle;

[0052] A data splitting unit 102 splits the operation data to obtain a plurality of data integration packages, wherein the data integration packages are obtained by different sensors in the electric vehicle;

[0053] The data identification unit 103 extracts data about the battery from the multiple data integration packages and identifies the data to obtain the battery power data.

[0054] In the embodiment of the present invention, not only the specific structure of the battery data processing module is explained, but also the process of battery data processing is explained. First, the data collection unit 101 can collect various data during the use of the electric vehicle, that is, operating data. These data can facilitate the car company to monitor the status of the electric vehicle, and these data are stored in units of electric vehicle numbers. These operating data include multiple data integration packages, which are obtained by various sensors in the electric vehicle. Although the data types are different, the battery-related data can be extracted, so that the required power data can be obtained.

[0055] like Figure 3 As shown, as a preferred embodiment of the present invention, the data collaboration module 200 includes:

[0056] The state detection unit 201 is used to detect the owner's behavior of using the electric vehicle, and when the owner uses the vehicle computer of the electric vehicle for navigation, obtain multiple navigation paths generated during the navigation;

[0057] A route selection unit 202 selects a navigation path with the longest distance from a plurality of navigation paths;

[0058] A comparison unit 203, comparing the remaining mileage currently displayed by the electric vehicle with the selected navigation path distance according to the operation data;

[0059] An integration unit 204, when the remaining mileage is less than or equal to the distance of the navigation path, integrates multiple navigation paths to obtain travel plan information and uploads the information;

[0060] After obtaining the travel plan information, the route processing unit 205 searches for and marks charging points along the way according to the navigation path in the travel plan information.

[0061] In an embodiment of the present invention, the background is first used to detect the owner's behavior of using the electric vehicle. When the owner uses the electric vehicle's computer for navigation, multiple navigation paths generated during the navigation are obtained. For example, the owner navigates through the computer voice assistant or manually inputs on the computer screen. These features are all detection indicators. Then, the navigation path with the longest distance is selected from the multiple navigation paths. The remaining mileage currently displayed by the electric vehicle is compared with the distance of the selected navigation path. The comparison unit 203 is mainly used to determine whether the electric vehicle needs to be charged midway during this trip. When the remaining mileage is less than or equal to the distance of the navigation path, it means that midway charging is required. Then, multiple navigation paths are integrated to obtain travel plan information and upload it. After obtaining the navigation path, the charging points along the existing route can be searched. This method can be implemented through existing navigation software.

[0062] like Figure 4As shown, as a preferred embodiment of the present invention, the route processing unit 205 includes:

[0063] The route expansion subunit 215 searches and marks charging points along the route according to the known navigation path;

[0064] The information supplement subunit 225 searches for charging points in the opposite direction along the route according to the known navigation path and adds a secondary mark;

[0065] The information screening unit 235 screens the secondary marks according to the road condition information at the secondary marks on the navigation path to verify whether the electric vehicle needs to take a detour.

[0066] In the embodiment of the present invention, first, the route expansion subunit 21 searches along the known navigation route to obtain the charging points along the route, but this search technology along the route generally searches for the charging points along the way. In order to increase the coverage of the charging points, the information supplement subunit 225 is also required to search for the charging points that are not along the way. In this embodiment, along the way and not along the way mean different sides of the road, and it is not necessary to deviate from the original route. The role of the information screening unit 235 is to screen out the charging points corresponding to some sub-markers. These charging points are considered to require detours and U-turns. These features can be judged according to the specific conditions of the road. For example, general national highways can make U-turns on the spot, while some expressways or highways cannot make U-turns. The above method can screen out the charging points with the largest number of coverage by the navigation route.

[0067] like Figure 5 As shown, as a preferred embodiment of the present invention, the data analysis module 300 includes:

[0068] The data retrieval unit 301 retrieves the travel records stored in the cloud platform, wherein the travel records are the data of the destination, distance, energy consumption, and endurance recorded during the travel;

[0069] A matching unit 302 selects a plurality of travel records whose similarity exceeds a set value from among the travel records according to the destination of the travel plan information;

[0070] The comprehensive calculation unit 303 performs a cruising range calculation based on the power data and in combination with the selected multiple travel records to obtain the remaining mileage information and sends it to the electric vehicle.

[0071] In the embodiment of the present invention, the electric vehicle is more advanced in terms of intelligence than the traditional vehicle. Due to the characteristics of the electric vehicle, the overall status of the electric vehicle can be reflected in the form of data. The cloud platform stores the travel records of the electric vehicle, that is, the travel records. The travel records can be screened according to the travel plan information, and the more similar travel situations can be screened out. Through these travel records, the power consumption used at that time and the power consumption data caused by the driver's driving habits can be obtained, so that the corresponding battery life of the current electric vehicle can be calculated more reasonably, and the battery life, that is, the remaining mileage information.

[0072] like Figure 6 As shown, as a preferred embodiment of the present invention, the information combination planning module 400 includes:

[0073] The route processing unit 401 selects each navigation path in turn and intercepts the selected navigation path according to the remaining mileage information to obtain an initial path, wherein the initial path is a portion of the navigation path starting from the departure position and ending at a distance corresponding to the remaining mileage information;

[0074] A statistical unit 402, which performs a number statistics on charging pile point marks contained in the initial path to obtain pile point quantity information, wherein the pile point quantity information corresponds to the initial path one by one;

[0075] The route selection unit 403 selects the initial route with the largest number of points according to the information on the number of points and determines the corresponding navigation route.

[0076] In an embodiment of the present invention, the initial path is intercepted from the navigation path, and the intercepted distance range is based on the remaining mileage information. The initial path corresponds to the farthest distance that can be reached during this trip without charging. By counting the number of marked points in the initial path, the number of marked points corresponding to each initial path can be obtained, and then the path containing the largest number of initial paths is selected. The initial path and the navigation path are in a one-to-one correspondence, so it can be determined that the navigation path is the route for this trip. This ensures that within the range of battery life, there are the most charging points on the electric vehicle route, making it convenient for the owner to charge at any time.

[0077] The above only describes in detail the preferred embodiments of the present invention, which is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0078] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0079] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0080] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the disclosure in the specification and examples. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

Claims

1. Electric vehicle battery remote monitoring system based on cloud computing, It is characterized in that The system comprises: The battery data processing module receives the operation data uploaded by the electric vehicle and analyzes the operation data to obtain the battery power data when the authority is granted; The data collaboration module obtains travel plan information of the electric vehicle, wherein the travel plan information includes multiple navigation routes, and searches for charging points along the route according to the navigation routes and calibrates them; The data analysis module uses cloud computing technology to combine the acquired power data to calculate the remaining mileage information and send it to the electric vehicle; The information is combined with the planning module to intercept and process multiple navigation paths based on the starting point according to the remaining mileage information to obtain the initial path, and select the optimal travel route based on the number of charging points marked in the initial path; The data collaboration module includes: A state detection unit is used to detect the owner's behavior of using the electric vehicle, and when the owner uses the electric vehicle's head unit for navigation, obtain multiple navigation paths generated during the navigation; A route selection unit, which selects a navigation path with the longest distance from multiple navigation paths; A comparison unit, for comparing the cruising range currently displayed by the electric vehicle with the selected navigation path distance obtained according to the operation data; an integration unit, when the cruising range is less than or equal to the distance of the selected navigation path, integrating multiple navigation paths to obtain travel plan information and uploading the information; A route processing unit, after obtaining the travel plan information, searches for and marks charging points along the way according to the navigation path in the travel plan information; The route processing unit comprises: The route expansion subunit searches and marks the charging points along the way according to the known navigation path; The information supplement subunit searches for charging points in the opposite direction along the way according to the known navigation path and adds sub-markers; The information screening unit screens the secondary marks according to the road condition information at the secondary marks on the navigation path, so as to verify whether the electric vehicle needs to take a detour.

2. The electric vehicle battery remote monitoring system based on cloud computing according to claim 1, It is characterized in that The battery data processing module includes: A data collection unit receives the operation data of the electric vehicle uploaded through the cloud platform and stores it in combination with the serial number information of the electric vehicle, wherein the serial number information is a unique identity of the electric vehicle; A data splitting unit, which splits the operation data to obtain a plurality of data integration packages, wherein the data integration packages are obtained by different sensors in the electric vehicle; The data identification unit extracts data about the battery from multiple data integration packages and identifies the data to obtain the battery power data.

3. The electric vehicle battery remote monitoring system based on cloud computing according to claim 1, It is characterized in that The data analysis module includes: A data retrieval unit retrieves the travel records stored in the cloud platform, wherein the travel records are data on the destination, distance, energy consumption, and endurance uploaded by the electric vehicle when traveling; A matching unit, selecting, according to the travel plan information, a plurality of travel records whose similarity exceeds a set value from the travel records, wherein the similarity is calculated by the overlap of the travel routes; The comprehensive calculation unit calculates the endurance according to the power data and in combination with the selected multiple travel records to obtain the remaining mileage information and sends it to the electric vehicle.

4. The electric vehicle battery remote monitoring system based on cloud computing according to claim 1, It is characterized in that The information combination planning module includes: a route processing unit, selecting each navigation path in turn and intercepting the selected navigation path according to the remaining mileage information to obtain an initial path, wherein the initial path is a portion of the navigation path starting from the departure position and ending at a distance corresponding to the remaining mileage information; A statistical unit, which performs quantitative statistics on charging pile points according to the marks of the charging pile points included in the initial path to obtain pile point quantity information, wherein the pile point quantity information corresponds to the initial path one by one; The route selection unit selects the initial path containing the largest number of points according to the information on the number of points and determines the corresponding navigation path.

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