Vehicle power control system using big data

Abstract

A vehicle power control system using big data is provided, which can include a big data server configured to receive driving-related data of a vehicle generated by the vehicle, to generate a factor related to an acceleration pattern of the vehicle by processing the received driving-related data, and to store the generated factor, and a controller configured to change output power of a battery with reference to available power of the battery stored in advance and the factor stored in the big data server when the vehicle needs to be accelerated or propelled. Accordingly, acceleration or propulsion desired by a driver can be achieved.

Description

Technical Field

[0001] This invention relates to a vehicle power control system using big data, and more specifically, to a vehicle power control system using big data obtained through a big data server to establish a vehicle acceleration mode, and using the established acceleration mode to control the vehicle's power. Background Technology

[0002] Typically, the available power value during the charging and discharging of a high-voltage battery used to store the driving force of an environmentally friendly vehicle is a value corresponding to the power value that can be continuously charged and discharged within a reference time. This available power value can be predetermined and stored in the vehicle's battery management system (BMS) in the form of a data map, and can be applied to the vehicle's power control.

[0003] Therefore, when the driver wants higher vehicle acceleration or propulsion, the vehicle is configured to output power only within the range of pre-stored available power values, thus creating a problem where the vehicle acceleration or propulsion required by the driver cannot actually be achieved.

[0004] The information disclosed in the background section of this invention is only intended to enhance the understanding of the overall background of this invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0005] Various aspects of the present invention aim to provide a vehicle power control system using big data, which uses big data obtained through a big data server to establish an acceleration pattern for the vehicle and uses the established acceleration pattern to control the vehicle's power to achieve the acceleration or propulsion desired by the driver when propelling the vehicle.

[0006] According to one aspect of the invention, the above and other objectives can be achieved by providing a vehicle powertrain control system using big data, the vehicle powertrain control system comprising: a big data server configured to receive vehicle driving-related data generated by the vehicle, to generate factors related to the vehicle's acceleration mode by processing the received driving-related data, and to store the generated factors; and a controller installed in the vehicle and configured to change the battery's output power by referencing pre-stored battery available power and factors stored in the big data server when the vehicle needs to accelerate or propel itself.

[0007] The big data server can be configured to group similar acceleration modes based on the factors mentioned above, and can determine a high output tolerance corresponding to the corresponding acceleration mode for each group.

[0008] The big data server can have multiple hierarchical structures and may include: a low-level cloud server below the predetermined cloud server, which is configured to directly receive vehicle driving-related data from the vehicle and classify the data used to determine factors related to acceleration patterns; and a high-level cloud server above the predetermined cloud server, which is configured to generate factors by receiving and processing the data classified by the low-level cloud server and grouping acceleration patterns with similarity based on the generated factors.

[0009] The pre-stored available power of the battery can be stored in the controller in the form of a data mapping based on the battery's state of charge (SOC) value and the temperature around the battery.

[0010] The controller can be configured to ultimately determine the battery's output power by applying a high output tolerance to the available power of a pre-stored battery when the vehicle is accelerating or propelled.

[0011] High output tolerance can be a weight that varies over time, reflecting the characteristics of the acceleration mode belonging to each group after each grouping.

[0012] The methods and apparatus of the present invention have other features and advantages that will be apparent from or set forth in more detail in conjunction with the accompanying drawings and the following detailed description, which together serve to explain the particular principles of the invention. Attached Figure Description

[0013] Figure 1 This is a view illustrating the configuration of a vehicle powertrain control system using big data according to various exemplary embodiments of the present invention;

[0014] Figure 2 This is a flowchart illustrating the operation of a vehicle powertrain control system using big data according to various exemplary embodiments of the present invention; and

[0015] Figure 3 , Figure 4 and Figure 5 It is a graph used to compare the battery output power during vehicle propulsion in the case of a vehicle power control system using big data and the battery output power during conventional vehicle propulsion, according to various exemplary embodiments of the present invention.

[0016] It should be understood that the accompanying drawings are not necessarily drawn to scale, but rather present a simplified representation of various features illustrating the basic principles of the invention. Specific design features of the invention, such as specific dimensions, orientations, positions, and shapes, as disclosed herein, will be determined in part by the particular intended application and environment of use.

[0017] In the accompanying drawings, reference numerals throughout the multiple figures indicate the same or equivalent parts of the invention. Detailed Implementation

[0018] Reference will now be made in detail to various embodiments of the invention, examples of which are shown in the accompanying drawings and described below. While the invention will be described in conjunction with exemplary embodiments thereof, it should be understood that this description is not intended to limit the invention to these exemplary embodiments. Rather, the invention is intended to cover not only these exemplary embodiments thereof, but also various alternatives, modifications, equivalents and other embodiments that may be included within the spirit and scope of the invention as defined by the appended claims.

[0019] In the following description, a vehicle powertrain control system using big data according to various embodiments of the present invention will be described with reference to the accompanying drawings.

[0020] Figure 1 This is a view illustrating the configuration of a vehicle powertrain control system using big data according to various exemplary embodiments of the present invention.

[0021] Reference Figure 1 According to various exemplary embodiments of the present invention, a vehicle powertrain control system using big data may include a big data server 100 configured to receive driving-related data generated by a vehicle 10, generate factors related to the acceleration mode of the vehicle 10 by processing the received data, and store the generated factors; and a controller 11 disposed in the vehicle 10 and configured to change the output power of a battery 12 by referring to the available power of a pre-stored battery and the factors stored in the big data server 100 when the vehicle 10 needs to accelerate or propel itself.

[0022] The big data server 100 can receive various data generated by the vehicle 10 while the vehicle is in motion, generate data by processing and analyzing the received data, and store the generated data. The big data server 100 can generate specific patterns related to the vehicle's acceleration based on the data received from the vehicle or the generated auxiliary data.

[0023] like Figure 1 As shown, the big data server 100 can be implemented using a distributed cloud approach with a hierarchical structure of cloud servers 110, 120, and 130 for each layer.

[0024] For example, the first-layer cloud server 110, which belongs to the lowest layer of a multi-layered structure, can communicate with the vehicle 10, record data generated by the vehicle 10 in real time, and provide the recorded data to the vehicle 10 if needed, or provide the data to the higher-layer cloud servers 120 and 130 that belong to the lowest layer 110.

[0025] The higher-level cloud servers 120 and 130 can process and store data provided by the lower-level cloud servers, and can communicate with vehicle 10 to send the processed data to vehicle 10. Figure 1 This is an example view used to illustrate an exemplary embodiment, which shows a total of three layers, and the number of layers can be adjusted appropriately as needed.

[0026] Figure 1 The exemplary embodiments of the present invention shown may include a first-layer cloud server 110 configured to record vehicle data in real time while communicating with the vehicle 10; a second-layer cloud server 120 configured to generate factors for generating an acceleration mode for the vehicle 10 by processing the data recorded by the first-layer cloud server 110; and a third-layer cloud server 130 configured to use the factors generated by the second-layer cloud server 120 to generate the vehicle's acceleration mode and group similar acceleration modes.

[0027] The first-layer cloud server 110 can record raw data generated by the vehicle in real time through communication with the vehicle. The first-layer cloud server 110 can record and store vehicle data at a sampling rate that minimizes data loss. The first-layer cloud server 110 can set limits on the amount of data to be recorded and stored for each vehicle as a communication target. Needless to say, if resources permit, all data recorded from the vehicle can be stored. Since the first-layer cloud server 110 primarily communicates with the vehicle in real time, it is desirable to limit the amount of data that can be stored for each vehicle to efficiently utilize resources.

[0028] The raw data recorded by the first-layer cloud server 110 can be data generated and sent by various controllers of the vehicle. In the battery power control according to various embodiments of the present invention, the real-time data provided from the vehicle 10 to the first-layer cloud server 110 can be data related to the power of the battery 12 installed in the vehicle, and can be, for example, the battery temperature, battery voltage, battery state of charge (SOC) value, battery charging and discharging state, current battery power, vehicle speed, motor speed (rpm), and vehicle position or slope.

[0029] The first-layer cloud server 110 can directly receive various driving-related data from the vehicle 10, and can also classify data used to determine factors related to the vehicle's acceleration mode.

[0030] As needed, vehicle 10 can request stored data from the first-layer cloud server 110, and can also receive data.

[0031] The second-layer cloud server 120 can determine items such as average, maximum and minimum values, root mean square (RMS) or standard deviation by first processing the raw data recorded by the first-layer cloud server 110, and can store the determined items. The processed data can be stored and managed in the form of a preset dataset. The data stored in the second-layer cloud server 120 can be stored in a predetermined form of processed data rather than the raw data storage, and can be stored together with the corresponding data such as weather and travel time.

[0032] The first-layer cloud server 110 can immediately store the recorded raw data, but the second-layer cloud server 120 can process the recorded data and does not have to process and store the raw data in real time, and can allow a certain amount of time delay between data reception, data processing and storage.

[0033] In battery power control according to various embodiments of the present invention, the data processed and determined by the second-layer cloud server 120 may correspond to factors used to generate an acceleration mode for the vehicle 10. Factors used to generate the acceleration mode may include the maximum power of the battery 12, the time to maintain maximum power, average power, temperature, state of charge (SoC), the location or gradient of the vehicle 10, or the vehicle speed.

[0034] When necessary, vehicle 10 can request processed data from the second-layer cloud server 120 and can also receive processed data.

[0035] The third-layer cloud server 130 can further process the data processed by the second-layer cloud server 120. The third-layer cloud server 130 can perform data processing that requires higher computing power than the data processing used by the second-layer cloud server 120.

[0036] According to various exemplary embodiments of the present invention, the third-layer cloud server 130 can generate an acceleration pattern of the vehicle 10 that provides data based on the maximum power generated by the second-layer cloud server 120, the time the maximum power is maintained, the average power, the temperature, the state of charge (SoC), the location or slope of the vehicle 10, or the vehicle speed, and can group vehicles with similar acceleration patterns.

[0037] The controller 11, located in the vehicle 10, can check whether the vehicle is under acceleration and / or propulsion conditions, can determine the pre-stored available power value of the battery 12 under acceleration and / or propulsion conditions, and can adjust the power of the battery 12 based on the exported available power value stored in the big data server 100 and the acceleration mode of the group to which the vehicle belongs.

[0038] Here, acceleration and / or propulsion conditions can be determined by receiving detection values ​​from sensors that detect the degree to which the driver depresses the accelerator pedal through another controller of the vehicle, and controller 11 can receive information about acceleration and / or propulsion conditions from other controllers of the vehicle.

[0039] The controller 11 can monitor and manage the charging and discharging power of the battery 12, and therefore the controller 11 can be a battery management system (BMS) for performing controls related to the battery 12.

[0040] Battery 12 may be a high-voltage battery used to provide power to propel an electric motor configured to provide power to the vehicle's drive wheels.

[0041] The detailed operation of a vehicle powertrain control system using big data according to various embodiments of the present invention configured as described above will be described.

[0042] Figure 2 This is a flowchart illustrating the operation of a vehicle powertrain control system using big data according to various exemplary embodiments of the present invention.

[0043] Figure 2 The operations shown can be performed by the controller 11 of vehicle 10 and the big data server 100.

[0044] Reference Figure 2 When vehicle 10 is powered on, it can provide data related to vehicle movement to big data server 100 at preset time intervals (S11). Big data server 100 can establish vehicle acceleration patterns by processing vehicle movement-related data received from various vehicles, and can group similar patterns based on factors used to establish vehicle acceleration patterns (S21). In operation S21, the acceleration patterns of vehicle 10 providing data to big data server 100 can be grouped with other acceleration patterns having similar characteristics.

[0045] The acceleration modes considered in the grouping can include propulsion acceleration mode and overtaking acceleration mode. Propulsion acceleration mode refers to the mode in which the vehicle accelerates from a standstill, while overtaking acceleration mode refers to the mode in which the vehicle accelerates at a speed higher than the predetermined speed when traveling at a predetermined speed or higher.

[0046] The big data server 100 can determine high output tolerances γ and β corresponding to the acceleration modes belonging to each group in operation S21. The high output tolerances γ and β can be time-dependent functions and can correspond to time-varying weights that reflect the characteristics of the acceleration modes belonging to each group. A high output propulsion tolerance γ can be applied to the propulsion acceleration mode, and a high output overtaking tolerance β can be applied to the overtaking acceleration mode.

[0047] At each preset time interval or in a specific vehicle driving state (e.g., immediately after the vehicle starts), the controller 11 may request information about the acceleration mode group from the big data server 100 and may receive such information (S12).

[0048] Therefore, when the vehicle requests acceleration, the controller 11 can determine whether the corresponding acceleration is propulsion acceleration or overtaking acceleration (S13). When the corresponding acceleration is propulsion acceleration, the controller 11 can apply the high output propulsion tolerance γ corresponding to the propulsion acceleration mode group to the available power value P of the battery 12 set in the pre-stored data mapping. out_ref Determine the final battery output power P out (S141), and when the corresponding acceleration is overtaking acceleration, the controller 11 can apply the high output overtaking tolerance β corresponding to the overtaking acceleration mode group to the available power value P of the battery 12 set in the pre-stored data map. out_ref Determine the final battery output power P out (S142).

[0049] The mapping data stored by controller 11 can be recorded as the available power value P for each reference preset based on the state of charge (SOC) value of battery 12 and the temperature around battery 12. out_ref .

[0050] In operation S141, the controller 11 can utilize the high output propulsion tolerance γ provided by the big data server 100 to apply the output power P of the battery 12. out Transmitted to various controllers in the vehicle, and can be applied to various vehicle controls, especially in newly set output power P out Electric motor control used for propulsion and acceleration within the specified range.

[0051] Figure 3 , Figure 4 and Figure 5 It is a graph used to compare the battery output power during vehicle propulsion in the case of a vehicle power control system using big data and the battery output power during conventional vehicle propulsion, according to various exemplary embodiments of the present invention.

[0052] like Figure 3 As shown, in traditional vehicle power control schemes, it is impossible to achieve a power value P greater than that stored in the data map. out_map The battery output is limited, so the output expected by the driver cannot be obtained when the vehicle accelerates or propels.

[0053] However, as Figure 4As shown, when the vehicle requests propulsion and acceleration, the vehicle power control system according to various exemplary embodiments of the present invention can obtain sufficient output for the driver to propel the vehicle by applying a high output propulsion tolerance γ, which is a time-varying weight set according to each propulsion acceleration mode of the driver.

[0054] like Figure 5 As shown, when a vehicle requests to overtake and accelerate, the vehicle power control system according to various exemplary embodiments of the present invention can obtain sufficient output as desired by the driver when the vehicle overtakes by applying a high output overtaking tolerance β, which is a time-varying weight set according to each overtaking acceleration mode of the driver.

[0055] Vehicle power control systems that utilize big data can control vehicle power based on vehicle acceleration patterns established using big data, without limiting the available power values ​​mapped from pre-stored data. Therefore, they can achieve the vehicle's acceleration and propulsion performance as desired by the driver.

[0056] Furthermore, the term "controller" refers to a hardware device including a memory and a processor configured to execute one or more steps interpreted as an algorithmic structure. The memory stores the algorithmic steps, and the processor executes the algorithmic steps to perform one or more processes of methods according to various exemplary embodiments of the invention. A controller according to exemplary embodiments of the invention may be implemented using a non-volatile memory configured to store algorithms for controlling the operation of various components of a vehicle or data regarding software commands for executing the algorithms, and a processor configured to perform the aforementioned operations using the data stored in the memory. The memory and processor may be separate chips. Alternatively, the memory and processor may be integrated into a single chip. The processor may be implemented as one or more processors.

[0057] The controller may be at least one microprocessor operated by a predetermined program, which may include a series of commands for performing methods according to various exemplary embodiments of the present invention.

[0058] The foregoing invention can also be embodied as computer-readable code on a computer-readable recording medium. The computer-readable recording medium is any data storage device capable of storing data that can subsequently be read by a computer system. Examples of computer-readable recording media include hard disk drives (HDDs), solid-state drives (SSDs), silicon disk drives (SDDs), read-only memory (ROM), random access memory (RAM), CD-ROMs, magnetic tapes, floppy disks, optical data storage devices, etc., and are implemented as carrier waves (e.g., transmitted over the Internet).

[0059] For ease of description and precise definition in the appended claims, the terms “upper,” “lower,” “internal,” “external,” “up,” “lower,” “upward,” “downward,” “front,” “rear,” “rearward,” “internal,” “external,” “inward,” “outward,” “internal,” “external,” “inner side,” “outer side,” “forward,” and “backward” are used to describe features in the positions shown in the accompanying drawings with reference to the features of the exemplary embodiments. It will also be understood that the term “connection” or its derivatives refer to both direct and indirect connections.

[0060] For purposes of illustration and description, the foregoing description of specific exemplary embodiments of the invention has been provided. This description is not intended to be exhaustive or to limit the invention to the exact forms disclosed, and various modifications and variations are apparent from the foregoing teachings. Exemplary embodiments were chosen and described to explain the specific principles of the invention and its practical application, thereby enabling others skilled in the art to implement and utilize various exemplary embodiments of the invention and their various alternatives and variations. The scope of the invention is intended to be defined by the appended claims and their equivalents.

Claims

1. A vehicle powertrain control system using big data, the vehicle powertrain control system comprising: A big data server is configured to receive vehicle driving-related data generated by the vehicle, to process the received driving-related data to generate factors related to the vehicle's acceleration mode, and to store the generated factors. as well as A controller, installed in the vehicle, is configured to change the battery's output power by referencing pre-stored battery power and factors stored in the big data server when the vehicle needs to accelerate or propel itself. The big data server is configured to group acceleration modes with similarity based on the factors, and for each group, determine a high output tolerance corresponding to the corresponding acceleration mode. The controller is configured to determine the output power of the battery by applying the high output tolerance to the available power of a pre-stored battery when the vehicle is in an acceleration or propulsion state.

2. The vehicle power control system of claim 1, wherein, The big data server has a multi-layered structure and includes: A lower-level cloud server, below the predetermined cloud server, configured to directly receive vehicle-related driving data from the vehicle and classify data used to determine factors related to the acceleration mode; and A higher-level cloud server above the predetermined cloud server, the higher-level cloud server is configured to generate the factors by receiving and processing data classified by the lower-level cloud server, and to group acceleration modes with similarity according to the generated factors.

3. The vehicle power control system of claim 1, wherein, The pre-stored available power of the battery is stored in the controller in the form of a data map based on the battery's state of charge value and the temperature around the battery.

4. The vehicle power control system of claim 1, wherein, The high output tolerance is a weight that varies over time and reflects the characteristics of the acceleration mode belonging to each group after grouping.

5. A method for controlling a vehicle powertrain control system that uses big data, the method comprising: When the vehicle is powered on, data related to the vehicle's operation is received at preset time intervals through a big data server; The big data server establishes the vehicle's acceleration mode by processing driving-related data received from multiple vehicles. Acceleration modes are grouped according to the factors used to establish the acceleration modes of the vehicle, and for each group, a high output tolerance corresponding to the acceleration mode belonging to each group is determined. as well as The vehicle's controller modifies the output power of the battery by referencing pre-stored battery power and factors stored in the big data server. The controller is configured to determine the output power of the battery by applying a high output tolerance to the available power of a pre-stored battery when the vehicle is in an acceleration or propulsion state.

6. The method according to claim 5, wherein The acceleration modes include propulsion acceleration mode and overtaking acceleration mode. The aforementioned acceleration mode is the mode in which the vehicle accelerates from a standstill. The overtaking acceleration mode is a mode in which the vehicle accelerates at a speed higher than the predetermined speed when it is traveling at a predetermined speed or higher.

7. The method according to claim 6, wherein The high output tolerance includes high output propulsion tolerance and high output overtaking tolerance, and The high output propulsion tolerance is applied to the propulsion acceleration mode, and the high output overtaking tolerance is applied to the overtaking acceleration mode.

8. The method according to claim 7, further comprising: The vehicle's controller requests information about the acceleration mode group from the big data server and receives the information through the controller.

9. The method according to claim 8, further comprising: When a vehicle requests acceleration, the controller determines whether the acceleration is propulsion acceleration or overtaking acceleration.

10. The method of claim 9, wherein, When the corresponding acceleration is propulsion acceleration, the controller is configured to determine the final battery output power by applying a high output propulsion tolerance corresponding to the group of propulsion acceleration modes to the available power value of the battery.

11. The method according to claim 9, wherein, When the corresponding acceleration is overtaking acceleration, the controller is configured to determine the final battery output power by applying a high output overtaking tolerance corresponding to the group of overtaking acceleration modes to the available power value of the battery.

12. The method according to claim 5, wherein, The big data server has a multi-layered structure and includes: A lower-level cloud server, below the predetermined cloud server, configured to directly receive vehicle-related driving data from the vehicle and classify data used to determine factors related to the acceleration mode; and A higher-level cloud server above the predetermined cloud server, the higher-level cloud server is configured to generate the factors by receiving and processing data classified by the lower-level cloud server, and to group acceleration modes with similarity according to the generated factors.

13. The method according to claim 5, wherein, The pre-stored available power of the battery is stored in the controller in the form of a data map based on the battery's state of charge value and the temperature around the battery.

14. The method according to claim 5, wherein, The high output tolerance is a weight that varies over time and reflects the characteristics of the acceleration mode belonging to each group after grouping.

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