阅读说明：本技术 使用大数据的车辆动力控制系统 (Vehicle power control system using big data ) 是由 朴贤秀 于 2020-09-04 设计创作，主要内容包括：本发明提出一种使用大数据的车辆动力控制系统,该车辆动力控制系统可以包括：大数据服务器,配置为接收由车辆生成的车辆的行驶相关数据,以通过处理接收到的行驶相关数据来生成与车辆的加速模式有关的因素,并存储所生成的因素；以及控制器,配置为当车辆需要加速或推进时,参考预先存储的电池的可用功率和存储在大数据服务器中的因素来改变电池的输出功率。因此,可以实现驾驶员期望的加速或推进。(The present invention provides a vehicle power control system using big data, which may include: a big data server configured to receive travel-related data of the vehicle generated by the vehicle, to generate a factor related to an acceleration pattern of the vehicle by processing the received travel-related data, and to store the generated factor; and a controller configured to change an output power of the battery with reference to a pre-stored available power of the battery and a factor stored in the big data server when the vehicle needs to accelerate or propel. Thus, the acceleration or propulsion desired by the driver can be achieved.)

1. A vehicle dynamics control system using big data, the vehicle dynamics control system comprising:

a big data server configured to receive travel-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 travel-related data, and to store the generated factor; and

a controller installed in the vehicle and configured to change an output power of the battery with reference to a pre-stored available power of the battery and factors stored in the big data server when the vehicle needs to accelerate or propel.

2. The vehicle power control system of claim 1, wherein the big data server is configured to group acceleration patterns having a similarity according to the factors, and determine a high output tolerance corresponding to a corresponding acceleration pattern for each grouped group.

3. The vehicle dynamics control system of claim 1, wherein the big data server has a plurality of hierarchical structures and comprises:

a low-level cloud server lower than a predetermined-level cloud server, the low-level cloud server being configured to receive driving-related data of a vehicle directly from the vehicle and classify data for determining a factor related to the acceleration pattern; and

a high-level cloud server higher than the predetermined-level cloud server, the high-level cloud server configured to generate the factors by receiving and processing the data classified by the low-level cloud server, and group acceleration patterns having similarity according to the generated factors.

4. The vehicle power control system according to claim 1, wherein available power of a battery stored in advance is stored in the controller in the form of a data map based on a state of charge value of the battery and a temperature around the battery.

5. The vehicle power control system of claim 2, wherein the controller is configured to determine the output power of the battery by applying the high output tolerance to pre-stored available power of the battery while the vehicle is in an accelerating or propulsion state.

6. The vehicle power control system according to claim 2, wherein the high output tolerance is a weight that varies with time, reflecting a characteristic of an acceleration pattern belonging to each grouped group.

7. A method of controlling a vehicle dynamics control system using big data, the method comprising:

receiving, by a big data server, data related to travel of a vehicle at preset time intervals when the vehicle is powered on;

establishing, by the big data server, an acceleration pattern of a vehicle by processing data relating to travel of the vehicle received from a plurality of vehicles;

grouping acceleration patterns according to factors used to establish an acceleration pattern of the vehicle; and

changing, by a controller of the vehicle, an output power of the battery in the vehicle with reference to a pre-stored available power of the battery and a factor stored in the big data server.

8. The method of claim 7, wherein the first and second light sources are selected from the group consisting of,

wherein the acceleration modes include a propulsion acceleration mode and a passing acceleration mode,

wherein the propulsion acceleration mode is a mode in which the vehicle accelerates from a stationary state, and

wherein the overtaking acceleration mode is a mode in which the vehicle is accelerated at a speed higher than a predetermined speed when the vehicle is running at the predetermined speed or higher.

9. The method of claim 7, further comprising:

for each group of the groupings, a high output tolerance is determined corresponding to the acceleration pattern belonging to the respective group.

10. The method of claim 9, wherein the first and second light sources are selected from the group consisting of,

wherein the high output tolerance includes a high output propulsion tolerance and a high output cut-in tolerance, and

wherein the high output propel tolerance is applied to the propel acceleration mode and the high output cut-in tolerance is applied to the cut-in acceleration mode.

11. The method of claim 10, further comprising:

requesting, by a controller of a vehicle, to the big data server to obtain information about the group of acceleration patterns, and receiving the information by the controller.

12. The method of claim 11, further comprising:

when the vehicle requests acceleration, it is determined by the controller whether the corresponding acceleration is a propulsion acceleration or a passing acceleration.

13. The method of claim 12, wherein when the respective acceleration is a propulsive acceleration, the controller is configured to determine a final battery output power by applying a high output propulsive tolerance corresponding to the set of propulsive acceleration modes to an available power value for the battery.

14. The method of claim 12, wherein, when the respective acceleration is a cut-in acceleration, the controller is configured to determine a final battery output power by applying a high-output cut-in tolerance corresponding to the set of cut-in acceleration modes to an available power value of the battery.

15. The method of claim 7, wherein the big data server has a plurality of hierarchies, and comprises:

a low-level cloud server lower than a predetermined-level cloud server, the low-level cloud server being configured to receive driving-related data of a vehicle directly from the vehicle and classify data for determining a factor related to the acceleration pattern; and

a high-level cloud server higher than the predetermined-level cloud server, the high-level cloud server configured to generate the factors by receiving and processing the data classified by the low-level cloud server, and group acceleration patterns having similarity according to the generated factors.

16. The method of claim 7, wherein the pre-stored available power of the battery is stored in the controller in the form of a data map based on the state of charge value of the battery and the temperature surrounding the battery.

17. The method of claim 7, wherein the controller is configured to determine the output power of the battery by applying a high output tolerance to pre-stored available power of the battery when the vehicle is in an accelerating or propulsion state.

18. The method of claim 9, wherein the high output tolerance is a time-varying weight reflecting characteristics of acceleration patterns belonging to each grouped group.

Technical Field

The present invention relates to a vehicle power control system using big data, and more particularly, to a vehicle power control system using big data, which establishes an acceleration pattern of a vehicle using big data obtained through a big data server and controls power of the vehicle using the established acceleration pattern.

Background

In general, an available power value during charging and discharging of a high voltage battery for storing driving force of an eco-friendly vehicle is a value corresponding to a power value that can be continuously charged and discharged within a reference time, and the available power value may be predetermined and may be stored in a Battery Management System (BMS) of the vehicle in the form of a data map and may be applied to power control of the vehicle.

Therefore, when the driver wants higher vehicle acceleration or propulsion (propulsion), the vehicle is configured to output power only within a range of available power values stored in advance, and thus there is a problem in that the vehicle acceleration or propulsion required by the driver cannot be actually achieved.

The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and is not to be taken as an acknowledgement or any form of suggestion that this information forms the prior art known to a person skilled in the art.

Disclosure of Invention

Various aspects of the present invention are directed to provide a vehicle dynamics control system using big data for establishing an acceleration pattern of a vehicle using big data obtained through a big data server and controlling the dynamics of the vehicle using the established acceleration pattern to achieve acceleration or propulsion desired by a driver when propelling the vehicle.

In accordance with one aspect of the present invention, the above and other objects can be accomplished by the provision of a vehicle power control system using big data, comprising: a big data server configured to receive travel-related data of the vehicle generated by the vehicle, to generate a factor related to an acceleration pattern of the vehicle by processing the received travel-related data, and to store the generated factor; and a controller installed in the vehicle and configured to change an output power of the battery with reference to a pre-stored available power of the battery and factors stored in the big data server when the vehicle needs to accelerate or propel.

The big data server may be configured to group acceleration patterns having similarities according to the factors, and may determine a high output tolerance corresponding to a corresponding acceleration pattern for each grouped group.

The big data server may have multiple hierarchies and may include: a low-level cloud server lower than the predetermined-level cloud server, the low-level cloud server being configured to directly receive travel-related data of the vehicle from the vehicle and classify data for determining a factor related to an acceleration pattern; and a high-level cloud server higher than the predetermined-level cloud server, the high-level cloud server configured to generate factors by receiving and processing data classified by the low-level cloud server, and group acceleration patterns having similarity according to the generated factors.

The available power of the battery stored in advance may be stored in the controller in the form of a data map based on a state of charge (SOC) value of the battery and a temperature around the battery.

The controller may be configured to finalize the output power of the battery by applying a high output tolerance to the available power of the battery stored in advance when the vehicle is in an acceleration or propulsion state.

The high output tolerance may be a time-varying weight that reflects the characteristics of the acceleration pattern belonging to each grouped group.

The method and apparatus of the present invention have other features and advantages which will be apparent from or are set forth in more detail in the accompanying drawings, which are incorporated herein, and the following detailed description, which together serve to explain certain principles of the invention.

Drawings

Fig. 1 is a view showing the configuration of a vehicle power control system using big data according to various exemplary embodiments of the present invention;

FIG. 2 is a flowchart illustrating operation of a vehicle dynamics control system using big data according to various exemplary embodiments of the present invention; and

fig. 3, 4 and 5 are graphs for comparing battery output power at the time of vehicle propulsion with a conventional one in the case of a vehicle power control system using large data according to various exemplary embodiments of the present invention.

It should be understood that the drawings are not necessarily to scale, presenting a simplified representation of various features illustrative of the basic principles of the invention. The specific design features of the invention, including, for example, specific dimensions, orientations, locations, and shapes, as disclosed herein, will be determined in part by the particular intended application and use environment.

In the drawings, like or equivalent elements of the present invention are designated by reference numerals throughout the several views of the drawings.

Detailed Description

Reference will now be made in detail to various embodiments of the present invention, examples of which are illustrated in the accompanying drawings and described below. While the invention will be described in conjunction with the exemplary embodiments of the invention, it will be understood that they are not intended to limit the invention to these exemplary embodiments. On the contrary, the invention is intended to cover not only these exemplary embodiments of the invention, but also various alternatives, modifications, equivalents and other embodiments, which may be included within the spirit and scope of the invention as defined by the appended claims.

Hereinafter, a vehicle power control system using big data according to various embodiments of the present invention will be described with reference to the accompanying drawings.

Fig. 1 is a view showing the configuration of a vehicle power control system using big data according to various exemplary embodiments of the present invention.

Referring to fig. 1, a vehicle dynamics control system using big data according to various exemplary embodiments of the present invention may include a big data server 100 configured to receive travel-related data generated by a vehicle 10, generate factors related to an acceleration pattern 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 the battery 12 with reference to a pre-stored available power of the battery and factors stored in the big data server 100 when the vehicle 10 needs to accelerate or propel.

The big data server 100 may receive various data generated while the vehicle is running from the vehicle 10, may generate data by processing and analyzing the received data, and may store the generated data. The big data server 100 may generate a specific pattern related to acceleration of the vehicle based on data received from the vehicle or generated assistance data.

As shown in fig. 1, the big data server 100 may be implemented using a distributed cloud method having a hierarchical structure of cloud servers 110, 120, and 130 for respective layers.

For example, the first-tier cloud server 110 belonging to the lowermost tier of the plurality of hierarchies may communicate with the vehicle 10, may record data generated by the vehicle 10 in real time, and may provide the recorded data to the vehicle 10 if necessary, or may provide data to the cloud servers 120 and 130 belonging to the upper tiers of the lowermost tier 110.

The cloud servers 120 and 130 belonging to the higher layer may process and store data provided by the cloud servers of the lower layer, and may communicate with the vehicle 10 to transmit the processed data to the vehicle 10. Fig. 1 is a view for explaining an example of an exemplary embodiment in which a total of three layers are embodied, and the number of layers can be appropriately adjusted as necessary.

The exemplary embodiment of the present invention shown in fig. 1 may include a first-tier cloud server 110 configured to record vehicle data in real time while communicating with the vehicle 10; a second-tier cloud server 120 configured to generate a factor for generating an acceleration pattern of the vehicle 10 by processing data recorded by the first-tier cloud server 110; and a third tier cloud server 130 configured to generate acceleration patterns of the vehicle using the factors generated by the second tier cloud server 120 and group the similar acceleration patterns.

First tier cloud server 110 may record raw data generated by the vehicle in real time through communication with the vehicle. First tier cloud server 110 may record and store vehicle data at as low a sampling rate as possible but with the data lost. The first-tier cloud server 110 may set a limit to the amount of data to be recorded and stored for each vehicle as a communication target. Needless to say, if the resource permits, all data recorded from the vehicles may be stored, and the first-tier cloud server 110 communicates with the vehicles mainly in real time, so it is desirable to limit the amount of data that each vehicle can store to effectively utilize the resource.

The raw data recorded by first tier cloud server 110 may be data generated and transmitted 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 tier cloud server 110 may be data related to the power of the battery 12 installed in the vehicle, and may be, for example, a battery temperature of the vehicle 10, a battery voltage, a state of charge (SOC) value of the battery, a charge and discharge state of the battery, a current power of the battery, a vehicle speed, a rotation speed per minute (rpm) of a motor, a position or a gradient of the vehicle 10.

First tier cloud server 110 may receive various travel-related data directly from vehicle 10, and may also classify data used to determine factors related to the acceleration pattern of the vehicle.

As needed, the vehicle 10 may request the stored data from the first tier cloud server 110 and may also receive the data.

The second layer cloud server 120 may determine items such as an average value, a maximum value and a minimum value, a root mean square RMS, or a standard deviation by first processing raw data recorded by the first layer cloud server 110, and may store the determined items. The processed data may be stored and managed in the form of a preset data set. The data stored in the second layer cloud server 120 may be stored in a predetermined form of processing data instead of raw data, and may be stored together with weather, travel time, and the like of the corresponding data.

First tier cloud server 110 may immediately store the recorded raw data, but second tier cloud server 120 may process the recorded data and may not have to process and store the raw data in real-time, and may allow for some amount of time delay between the receipt of the data for data processing and storage.

In the battery power control according to various embodiments of the present invention, the data processed and determined by the second-tier cloud server 120 may correspond to a factor for generating an acceleration pattern of the vehicle 10. Factors for generating the acceleration pattern may include a maximum power of the battery 12, a time to maintain the maximum power, an average power, a temperature, a state of charge (SoC), a location or gradient on which the vehicle 10 travels, or a vehicle speed.

If necessary, the vehicle 10 may request the processed data from the second-tier cloud server 120 and may receive the processed data.

The third-tier cloud server 130 may perform secondary processing on the data processed through the second-tier cloud server 120 again. The third tier cloud server 130 may perform data processing that requires higher computing power than the computation used in the data processing of the second tier cloud server 120.

According to various exemplary embodiments of the present invention, the third-tier cloud server 130 may generate an acceleration pattern of the vehicle 10 providing data based on the maximum power generated by the second-tier cloud server 120, the time for maintaining the maximum power, the average power, the temperature, the state of charge (SoC), the position or gradient where the vehicle 10 travels, or the vehicle speed, and may group vehicles having similar acceleration patterns.

The controller 11 disposed in the vehicle 10 may check whether the vehicle is in an acceleration and/or propulsion condition, may derive a pre-stored available power value of the battery 12 in the acceleration and/or propulsion condition, and may adjust the power of the battery 12 based on the derived available power value stored in the big data server 100 and the acceleration pattern of the group to which the vehicle belongs.

Here, the acceleration and/or propulsion condition may be determined by receiving a detection value of a sensor for detecting a degree to which a driver depresses an accelerator pedal through another controller of the vehicle, and the controller 11 may receive information on the acceleration and/or propulsion condition from the other controller of the vehicle.

The controller 11 may monitor and manage the charging and discharging power of the battery 12, and thus the controller 11 may be a Battery Management System (BMS) for performing control related to the battery 12.

The battery 12 may be a high voltage battery for providing electrical power to propel an electric motor configured to provide electrical power to the propulsion wheels of the vehicle.

Detailed operations of the vehicle power control system using big data according to the various embodiments of the present invention configured as above will be described.

Fig. 2 is a flowchart illustrating an operation of a vehicle power control system using big data according to various exemplary embodiments of the present invention.

The operations shown in fig. 2 may be performed by the controller 11 and the big data server 100 of the vehicle 10.

Referring to fig. 2, when the vehicle 10 is powered on, the vehicle 10 may provide data related to vehicle driving to the big data server 100 at every preset time interval (S11). The big data server 100 may establish an acceleration pattern of the vehicle by processing data related to vehicle travel received from various vehicles, and may group similar patterns based on factors for establishing the acceleration pattern of the vehicle (S21). In operation S21, the acceleration patterns of the vehicle 10 providing data to the big data server 100 may be grouped with other acceleration patterns having similar characteristics.

The acceleration modes considered in the grouping may include a propulsion acceleration mode and a passing acceleration mode. The propulsion acceleration mode may refer to a mode in which the vehicle accelerates from a stationary state, and the passing acceleration mode refers to a mode in which the vehicle accelerates at a higher speed than a predetermined speed while traveling at the predetermined speed or higher.

The big data server 100 may determine high output tolerances γ and β corresponding to the acceleration patterns belonging to the respective groups for each group grouped in operation S21. The high output tolerances γ and β may be time dependent functions and may correspond to time varying weights that reflect the characteristics of the acceleration patterns belonging to the respective groups. The high output propulsion tolerance γ may be applied to the propulsion acceleration mode, and the high output overtaking tolerance β may be applied to the overtaking acceleration mode.

At every preset time interval or in a specific vehicle running state (for example, immediately after the vehicle is started), the controller 11 may request the big data server 100 to acquire information on the acceleration pattern group and may receive the information (S12).

Therefore, when the vehicle requests acceleration, the controller 11 may determine whether the corresponding acceleration is propulsion acceleration or overtaking acceleration (S13), and when the corresponding acceleration is propulsion acceleration, the controller 11 may apply the high-output propulsion tolerance γ corresponding to the propulsion acceleration pattern group to the available power value P of the battery 12 set in the prestored data map_{out_ref}To determine the final battery output power P_out(S141), and when the corresponding acceleration is the passing acceleration, the controller 11 may apply the high-output passing allowance β corresponding to the passing acceleration pattern group to the available power value P of the battery 12 set in the pre-stored data map_{out_ref}To determine the final battery output power P_out(S142)。

The map data stored by the controller 11 may be recorded as an available power value P preset for each reference of a state of charge (SOC) value of the battery 12 and a temperature around the battery 12_{out_ref}。

In operation S141, the controller 11 may apply the output power P of the battery 12 to the high output propulsion tolerance γ provided by the big data server 100_outTransmitted to various controllers of the vehicle and can be applied to various controls of the vehicle, in particular at the newly set output power P_outMotor control for propulsion and acceleration is within range.

Fig. 3, 4 and 5 are graphs for comparing battery output power at the time of vehicle propulsion with a conventional one in the case of a vehicle power control system using large data according to various exemplary embodiments of the present invention.

As shown in fig. 3, in the conventional vehicle power control scheme, it is not possible to achieve more than the available power value P stored in the data map_{out_map}Is output from the battery due toThis fails to achieve the output desired by the driver when the vehicle is accelerating or propelling.

However, as shown in fig. 4, the vehicle power control system according to various exemplary embodiments of the invention may obtain a sufficient output expected by the driver when propelling the vehicle by applying a high output propulsion tolerance γ, which is a weight that is set according to each propulsion acceleration mode of the driver and varies with time, when the vehicle requests propulsion and acceleration.

As shown in fig. 5, the vehicle power control system according to various exemplary embodiments of the present invention can obtain a sufficient output expected by a driver when a vehicle overtakes by applying a high-output overtaking allowance β, which is a weight set according to each overtaking acceleration pattern of the driver over time, when the vehicle requests both overtaking and acceleration.

The vehicle power control system using the big data can control the power of the vehicle based on the vehicle acceleration pattern established according to the big data without limiting the available power value mapped to the data stored in advance, and thus can achieve the acceleration and propulsion performance of the vehicle desired by the driver.

Further, the term "controller" refers to a hardware device that includes a memory and a processor configured to perform one or more steps that are interpreted as an algorithmic structure. The memory stores algorithm steps, and the processor executes the algorithm steps to perform one or more processes of the method according to various exemplary embodiments of the present invention. The controller according to an exemplary embodiment of the present invention may be implemented by a non-volatile memory configured to store an algorithm for controlling operations of various components of a vehicle or data regarding software commands for executing the algorithm, and a processor configured to perform the above-described operations using the data stored in the memory. The memory and the processor may be separate chips. Alternatively, the memory and the processor may be integrated in a single chip. The processor may be implemented as one or more processors.

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

The foregoing invention can also be embodied as computer readable codes on a computer readable recording medium. The computer readable recording medium is any data storage device that can store data which can be thereafter read by a computer system. Examples of the computer-readable recording medium include a Hard Disk Drive (HDD), a Solid State Disk (SSD), a Silicon Disk Drive (SDD), a Read Only Memory (ROM), a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like, and are implemented as a carrier wave (e.g., transmission through the internet).

For convenience in explanation and accurate definition in the appended claims, the terms "upper", "lower", "inner", "outer", "upper", "lower", "upward", "downward", "front", "rear", "inner", "outer", "inward", "outward", "inner", "outer", "forward" and "rearward" are used to describe features of the exemplary embodiments with reference to the positions of such features as displayed in the figures. It will also be understood that the term "connected," or derivatives thereof, refers to both direct and indirect connections.

The foregoing descriptions of specific exemplary embodiments of the present invention have been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise form disclosed, and obviously many modifications and variations are possible in light of the above teaching. The exemplary embodiments were chosen and described in order to explain certain principles of the invention and its practical application to enable others skilled in the art to make and use various exemplary embodiments of the invention and various alternatives and modifications thereof. It is intended that the scope of the invention be defined by the following claims and their equivalents.