Vehicle powertrain control systems using big data

Abstract

This invention provides a vehicle powertrain control system using big data. The system includes a big data server that receives vehicle driving-related data, processes and analyzes the received data to generate a vehicle drive power pattern, and stores the drive power pattern. The vehicle controller determines whether to limit the battery's charge / discharge power based on the battery's continuous charge / discharge time or the cumulative amount of continuous charge / discharge power, and when limiting the battery's charge / discharge power, calculates the limited battery charge / discharge power based on the drive power pattern received from the big data server.

Description

Technical Field

[0001] This disclosure relates to a system for controlling vehicle power using big data, and more specifically, to a vehicle power control system using big data, which constructs a power pattern of the vehicle using big data via a distributed cloud server and uses the constructed power pattern to limit vehicle power during vehicle charging / discharging. Background Technology

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

[0003] Therefore, if the high-voltage battery is continuously charged / discharged for a period exceeding the reference time, the stored data on available power cannot be applied to vehicle dynamics control. Consequently, the high-voltage battery is protected by rapidly limiting vehicle power to the actual voltage value of the high-voltage battery. This sudden change in available power negatively impacts vehicle driving stability and dynamic performance.

[0004] It should be understood that the above description in the prior art is only for the purpose of facilitating an understanding of the background of this disclosure and should not be considered as prior art known to those skilled in the art. Summary of the Invention

[0005] Therefore, this disclosure provides a vehicle power control system that uses big data to construct a power pattern for the vehicle by using big data built through a distributed cloud server, and uses the constructed power pattern to limit the vehicle power during the charging / discharging of the vehicle, which can prevent sudden changes in the available power of the vehicle.

[0006] According to one aspect of this disclosure, the above and other objectives can be achieved by providing a vehicle powertrain control system for big data, which may include: a big data server configured to receive vehicle driving-related data generated in the vehicle, process and analyze the received data to generate a drive power mode for the vehicle, and store the drive power mode; and a controller disposed in the vehicle and configured to determine whether to limit the battery's charge / discharge power based on the battery's continuous charge / discharge time or the cumulative amount of continuous charge / discharge power, and to calculate the limited battery charge / discharge power based on the drive power mode received from the big data server when limiting the battery's charge / discharge power.

[0007] In an exemplary embodiment of this disclosure, the big data server may include: a lower-layer cloud server configured to directly receive vehicle driving-related data from the vehicle; and an upper-layer cloud server configured to receive vehicle driving-related data from the lower-layer cloud server, process the vehicle driving-related data to generate a drive power mode, and store the drive power mode. In an exemplary embodiment of this disclosure, the controller may be configured to calculate the continuous charging / discharging time of the battery when the actual measured charging / discharging power of the battery is greater than a preset ratio of the preset maximum available charging / discharging power.

[0008] Additionally, the preset maximum available charging / discharging power can be stored in the controller in the form of a data graph based on the battery's state of charge and ambient temperature. When the continuous charging / discharging time of the battery exceeds a preset reference time, the controller can be configured to calculate a limited battery charging / discharging power by reflecting the drive power mode in the actually measured battery charging / discharging power.

[0009] When the continuous charging / discharging time of the battery exceeds a preset reference time, the controller can be configured to calculate the limited battery charging / discharging power by calculating the charging / discharging power limit ratio and multiplying the actual measured battery charging / discharging power by the charging / discharging power limit ratio, wherein the charging / discharging power limit ratio is a function of the actual measured battery charging / discharging power, the drive power mode, and the continuous charging / discharging time.

[0010] Additionally, the controller can be configured to compare the cumulative amount of continuous charging / discharging power of the battery with a preset reference cumulative amount, and when the cumulative amount of continuous charging / discharging power is greater than the reference cumulative amount, divide the cumulative amount of continuous charging / discharging power by the preset reference cumulative amount to calculate the excess rate.

[0011] In response to determining that the cumulative amount of continuous charging / discharging power is greater than a reference cumulative amount, the controller can be configured to calculate the limited battery charging / discharging power by calculating a charging / discharging power limit ratio and multiplying the actual measured battery charging / discharging power by the charging / discharging power limit ratio, wherein the charging / discharging power limit ratio is a function of the drive power mode and the cumulative amount exceeding the limit ratio.

[0012] In traditional battery power limiting methods based on data graphs, the vehicle may suddenly be limited by power due to reaching the upper or lower limit of the battery voltage, thus preventing the vehicle from moving. However, the system of the present invention that uses big data to control vehicle power can actively adjust the power limit by using the charging / discharging power behavior of the battery when the vehicle is in motion, thereby proactively protecting the battery in advance before the vehicle becomes inoperable.

[0013] In other words, according to a vehicle powertrain control system that uses big data, the available power value can be optimally set and reflected by the controller based on the battery charging / discharging mode when the vehicle is currently in motion. Furthermore, according to the vehicle powertrain control system that uses big data, optimal available battery power can be provided by reflecting the driver's driving habits and their regional variations, because the parameters for active battery power limiting change by learning from the vehicle's drive power mode in a big data server.

[0014] Those skilled in the art will recognize that the effects obtainable through this disclosure are not limited to those specifically described above, and that other unmentioned effects of this disclosure will become more clearly understood from the above detailed description. Attached Figure Description

[0015] The above and other objects, features and advantages of this disclosure will become clearer from the following detailed description taken in conjunction with the accompanying drawings, in which:

[0016] Figure 1 This is a configuration diagram illustrating a vehicle powertrain control system using big data according to an exemplary embodiment of the present disclosure;

[0017] Figure 2 This is a flowchart illustrating an operational example of limiting battery power based on continuous charge / discharge time in a vehicle powertrain control system using big data, according to exemplary embodiments of the present disclosure; and

[0018] Figure 3 This is a flowchart illustrating an operational example of limiting battery power based on the cumulative amount of continuous charging / discharging power in a vehicle powertrain control system using big data, according to an exemplary embodiment of the present disclosure. Detailed Implementation

[0019] It should be understood that the term “vehicle” or “of a vehicle” or other similar terms as used herein generally include motor vehicles such as passenger vehicles including sport utility vehicles (SUVs), buses, trucks, various commercial vehicles, vessels including boats and ships, aircraft, etc., and includes hybrid vehicles, electric vehicles, combustion plug-in hybrid vehicles, hydrogen-powered vehicles and other alternative fuel vehicles (e.g., fuels derived from resources other than petroleum).

[0020] Although exemplary embodiments are described as using multiple units to perform exemplary processes, it should be understood that exemplary processes can also be performed by one or more modules. Furthermore, it should be understood that the term controller / control unit refers to a hardware device including a memory and a processor, specifically programmed to perform the processes described herein. The memory is configured to store modules, and the processor is specifically configured to execute said modules to perform one or more processes, as will be further described below.

[0021] Furthermore, the control logic of this disclosure can be embodied in a non-transitory computer-readable medium containing executable program instructions that are executed by a processor, controller / control unit, etc. Examples of computer-readable media include, but are not limited to, ROM, RAM, optical disc (CD)-ROM, magnetic tape, floppy disk, flash drive, smart card, and optical data storage device. Computer-readable recording media can also be distributed across a network-connected computer system, thereby storing and executing the computer-readable media in a distributed manner, for example, via a telematics server or a controller area network (CAN).

[0022] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. As used herein, the singular forms “a,” “an,” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that when the terms “comprising” and / or “including” are used in this specification, they specify the presence of a defined feature, integer, step, operation, element, and / or component, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. As used herein, the term “and / or” includes any and all combinations of one or more of the associated listed items.

[0023] Unless specifically stated or obvious from the context, as used herein, the term “about” should be understood as being within the normal tolerance range in the field, such as within two standard deviations of the mean. “About” can be understood as being within 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, 0.5%, 0.1%, 0.05%, or 0.01% of the specified value. Unless the context clearly indicates otherwise, all numerical values ​​provided herein are modified by the term “about”.

[0024] In the following, a vehicle powertrain control system using big data according to various exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

[0025] Figure 1 This is a configuration diagram illustrating a vehicle powertrain control system using big data according to an exemplary embodiment of this disclosure. (Reference) Figure 1A vehicle powertrain control system using big data according to an exemplary embodiment of the present disclosure may include: a big data server 100 configured to receive data related to the driving of the vehicle 10 from the vehicle 10, process and analyze the received data to generate a drive power mode of the vehicle 10 and store the drive power mode; and a controller 11 disposed in the vehicle 10 and configured to determine whether to limit the charge / discharge power of the battery 12 based on the continuous charge / discharge time or the cumulative amount of continuous charge / discharge power of the battery 12, and when limiting the charge / discharge power of the battery 12, calculate the limited battery charge / discharge power based on the drive power mode received from the big data server 100.

[0026] The big data server 100 can be configured to receive various types of data generated during the driving of the vehicle 10, process and analyze the received data, and store the processed and analyzed data. Specifically, the big data server 100 can be configured to generate specific patterns related to the vehicle's driving based on inputs received from the vehicle or auxiliary data generated therefrom. Figure 1 As shown, the big data server 100 can be implemented using distributed cloud computing in a multi-layered structure, which has cloud servers 110, 120 and 130 provided to the corresponding layers.

[0027] For example, the lowest-level cloud server 110 in a multi-layered structure can be configured to communicate with the vehicle 10 to record data generated in the vehicle 10 in real time, and to provide the recorded data to the vehicle 10 or the upper-level cloud servers 120 and 130 as needed. The upper-level cloud servers 120 and 130 can be configured to process the data provided from the lower-level cloud servers, store the processed data, and send the processed data to the vehicle 10 by communicating with the vehicle 10. Figure 1 An exemplary embodiment is shown in which a total of three layers are provided, and the number of layers can be adjusted as needed.

[0028] according to Figure 1 The exemplary embodiment shown indicates that the system for controlling vehicle power may include: a first-layer cloud server 110 configured to communicate with the vehicle 10 and record data of the vehicle 10 in real time; and a second-layer cloud server 120 configured to process the data recorded by the first-layer cloud server 110 and provide the processed data to the vehicle 10.

[0029] The first-layer cloud server 110 can be configured to record raw data generated in vehicle 10 in real time via communication with the vehicle. The first-layer cloud server 110 can be configured to record and store vehicle data at the shortest possible sampling rate without data loss. Furthermore, the amount of data that can be recorded and stored for each vehicle communicating with the first-layer cloud server 110 can be limited. Although all data recorded from the vehicle can be stored if resources permit, the first-layer cloud server 110 can be configured for use with the vehicle through real-time communication; therefore, to efficiently utilize resources, it is desirable to limit the amount of data that can be stored for each vehicle.

[0030] The raw data recorded by the first-layer cloud server 110 is data generated and sent from various controllers in the vehicle, and may include, for example, vehicle temperature, state of charge (SoC) and battery voltage, revolutions per minute (rpm), motor voltage and temperature, vehicle speed, external temperature, engine rpm, etc. Specifically, in various exemplary embodiments of this disclosure for controlling battery power, the real-time data provided from vehicle 10 to the first-layer cloud server 110 is data related to the battery 12 included in the vehicle, and may include the charge / discharge state of battery 12, real-time current of battery 12, real-time voltage of battery 12, instantaneous power of battery 12 calculated by controller 11, current distance traveled, vehicle speed, etc. Vehicle 10 may be configured to request and receive stored data from the first-layer cloud server 110 as needed.

[0031] The second-tier cloud server 120 can be configured to primarily process the raw data recorded by the first-tier cloud server 110, calculating items such as average, maximum / minimum, RMS, and standard deviation, and storing the calculated results. The processed data can be stored and managed in the form of a preset dataset. Specifically, the data stored in the second-tier cloud server 120 can be stored in a processed data format rather than the raw data, and can be stored along with the corresponding data's date, travel time, etc.

[0032] Although the first-layer cloud server 110 immediately stores the recorded raw data, the second-layer cloud server 120, which processes the recorded data, does not need to process and store the raw data in real time, and a certain degree of delay is allowed from receiving the data to processing and storing it. Specifically, in various exemplary embodiments of this disclosure for controlling battery power, the data processed and calculated in the second-layer cloud server 120 may be the continuous discharge time and continuous charging time of the battery 12, the maximum / minimum / average power of the battery 12, the average / maximum current of the battery 12, vehicle speed, average mileage, etc. The vehicle 10 can be configured to request and receive processed data from the second-layer cloud server 120 as needed.

[0033] The third-layer cloud server 130 can be configured to reprocess the data processed in the second-layer cloud server 120. The third-layer cloud server 130 can be configured to perform data processing requiring more high-performance computing power than the data processing by the second-layer cloud server 120. Additionally, the third-layer cloud server 130 can be configured to generate and store data such as driving modes, power modes, acceleration modes, battery degradation levels, cooling performance, and to use data processed in the second-layer cloud server 120 for potential fault prediction. Furthermore, the third-layer cloud server 130 can be configured to group processed data values ​​into similar groups based on meaningful reference values. For example, the third-layer cloud server 130 can be configured to group the vehicle's driving modes and power modes into similar groups and store these similar groups.

[0034] The controller 11 included in the vehicle 10 can be configured to monitor the power of the battery 12 and determine whether to limit the battery's charging / discharging power based on the continuous charging / discharging time or the cumulative amount of continuous charging / discharging power. For example, the controller 11 can be configured to limit the power of the battery 12 when the continuous charging / discharging power of the battery 12 is equal to or greater than a predetermined ratio of a preset available power for a period of time exceeding a reference time. Furthermore, the controller 11 can be configured to limit the power of the battery 12 when the cumulative amount of continuous charging / discharging power exceeds a preset reference value.

[0035] Additionally, controller 11 can be configured to reset the charging / discharging power of battery 12 when the power of battery 12 is limited. More specifically, controller 11 can be configured to receive information about the vehicle's drive power mode from big data server 100, and to limit the battery charging / discharging power based on this information when determining to limit the power of battery 12.

[0036] The controller 11 can be configured to monitor and manage the charging / discharging power of the battery 12, and can be a battery management system (BMS) configured to perform controls related to the battery 12. The battery 12 can be a high-voltage battery used to provide power to drive an electric motor (not shown) that provides power to the drive wheels of the vehicle. The specific operation of a system using big data to control vehicle power according to various exemplary embodiments of the present disclosure, configured as described above, will now be described.

[0037] Figure 2 This is a flowchart illustrating an operational example of limiting battery power based on continuous charging / discharging time in a system that uses big data to control vehicle power, according to an exemplary embodiment of the present disclosure. Figure 2 The operations shown can be performed by controller 11.

[0038] refer to Figure 2 When charging or discharging the battery 12 is performed (S11), the controller 11 can be configured to determine whether the charging / discharging state of the battery 12 has been switched (S21 and S31). For example, when the battery 12 has switched from the previous charging state to the discharging state in step S11 (S21), the controller 11 can be configured to initialize monitoring variables used to determine whether the discharge continues (S22).

[0039] In step S22, the controller 11 can be configured to refer to a pre-stored data graph and use the variable "t" used to monitor the discharge duration. out Initialize it to "0", and set the available discharge power P of battery 12 to "0". out Initialized to "P" out_ref In particular, “P” out_ref "The maximum available discharge power, which corresponds to the state of charge (SoC) of battery 12 and the preset ambient temperature, is stored in the data graph stored in controller 11."

[0040] Similarly, in step S32, the controller 11 can be configured to refer to a pre-stored data graph and use the variable "t" used to monitor the charging duration. in "Initialize to 0, and set the available charging power P of battery 12 to 0." in Initialized to "P" in_ref In particular, “P” in_ref "The maximum available charging power, which corresponds to the state of charge (SoC) of battery 12 and the preset ambient temperature, is stored in the data graph stored in controller 11."

[0041] Subsequently, controller 11 can be configured to determine the actual battery discharge power P used. out_real And the actual battery charging power P in_real Are they respectively greater than the maximum available discharge power P? out_ref and maximum available charging power P in_ref The preset ratio α (0 < α < 1) (S23 and S33). Specifically, the actual battery discharge power P can be calculated by measuring the voltage and current of the battery 12 using the controller 11. out_real And the actual battery charging power P in_real .

[0042] Subsequently, controller 11 can be configured to respond to determining the actual battery discharge power P used. out_real And the actual battery charging power P in_real Each is greater than the maximum available discharge power P out_ref and maximum available charging power P in_refThe preset ratio α (0 < α < 1) is used to check the continuous charging / discharging time t of battery 12. out and t in (S24 and S34). Subsequently, in response to determining that the consecutive charging / discharging time checked in steps S24 and S34 is greater than a preset reference time A (S25 and S35), the controller 11 can be configured to limit and set the available charging / discharging capacity P based on the corresponding vehicle's drive power mode sent from the big data server 100. out and P in And then P out and P in The signals are sent to various controllers in the vehicle, which can then determine whether the battery's charging / discharging power is within the set limits to be set (S26 and S36).

[0043] It can be based on the actual measured charging / discharging power P out_real and P in_real The charging / discharging limit ratio β, determined by the driving power mode of the corresponding vehicle sent from the big data server 100, determines the limit of the charging / discharging capacity performed in steps S26 and S36.

[0044] In steps S26 and S36, the controller 11 can be configured to transmit the actual measured charging / discharging power P. out_real and P in_real The limited battery charging / discharging power is calculated by multiplying by the charging / discharging power limitation ratio β. In this way, in an exemplary embodiment of this disclosure, the limited battery charging / discharging power can be calculated based on the actual charging / discharging power P of battery 12. out_real and P in_real This limits the battery charging / discharging power, so the limitation of battery charging / discharging power can be implemented quickly and immediately after the limit is calculated and the battery charging / discharging power is limited.

[0045] Specifically, the charge / discharge power limiting ratio β can have a value in the range of 0 to 1, and can be expressed as the continuous charge / discharge time t. out and t in Actual measured battery charging / discharging power P out_real and P in_real And a function of the drive power mode. For example, the charge / discharge power limit ratio β can vary with the continuous charge / discharge time t of the battery. out and t in The power decreases as the continuous charging / discharging time t increases, because the power decreases as the continuous charging / discharging time t increases. out and t in When the increase in energy is essentially limited, the battery can be protected.

[0046] In addition, when the actual measured battery charging / discharging power P out_real and P in_real When the preset ratio α (0 < α < 1) of the maximum available charging / discharging power is high, the charging / discharging power limiting ratio β can be set to a smaller value to essentially limit power and protect the battery. Additionally, the drive power mode can be a type of learned value determined in the big data server 100.

[0047] For example, drive power mode refers to the severity from the battery's perspective, and modes in which the battery continuously charges / discharges at high power for extended periods and modes in which the battery continuously charges / discharges for short periods can be grouped according to the driver's driving habits and reflected in the charge / discharge power limit ratio β. Additionally, various driving habits that can affect battery performance, such as vehicle speed and the number of times the battery reaches its lower / upper voltage limits, as well as changes in road conditions such as uphill, downhill, and flat roads, can be reflected in the grouping of drive power modes.

[0048] When the consecutive charging / discharging time checked in steps S25 and S35 is shorter than the preset reference time A, the battery charging / discharging power limit can be implemented based on the maximum available charging / discharging power obtained from the preset data graph (S27 and S37).

[0049] Figure 3 This is a flowchart illustrating an operational example of limiting battery power based on the cumulative amount of continuous charging / discharging power in a system that uses big data to control vehicle power, according to an exemplary embodiment of the present disclosure. Figure 3 The steps S41 shown are Figure 2 Step S11 is basically the same. Figure 3 Steps S51 and S52 and Figure 2 Steps S21 and S22 are basically the same, and Figure 3 Steps S61 and S62 and Figure 2 Steps S31 and S32 are essentially the same, so repeated explanations are omitted.

[0050] refer to Figure 3 The controller 11 can be configured to accumulate the continuous charging / discharging power of the battery 12 after steps S52 and S62 (S53 and S63). Subsequently, the controller 11 can be configured to transfer the continuous charging / discharging power P of the battery... out_accu and P in_accu The cumulative amount and the preset reference cumulative amount P out_accu_ref and P in_accu_ref A comparison is made (S54 and S64). This is in response to determining the cumulative amount P of the battery's continuous charge / discharge power. out_accu and P in_accu Each greater than the predetermined reference cumulative amount Pout_accu_ref and P in_accu_ref The controller 11 can be configured to accumulate the continuous charging / discharging power P of the battery. out_accu and P in_accu Divide by the predetermined reference cumulative amount P out_accu_ref and P in_accu_ref To calculate the cumulative amount exceeding the ratio γ (steps S55 and S65).

[0051] Subsequently, controller 11 can be configured to re-limit and set the available charging / discharging power P of battery 12 based on the corresponding vehicle's drive power mode sent from big data server 100. out and P in And the available charging / discharging power P out and P in The signals are sent to the various controllers in the vehicle so that the battery charging / discharging power can be determined within the set values ​​to be limited (S56 and S66).

[0052] It can be based on the actual measured battery charging / discharging power P out_real and P in_real The charging / discharging power limit ratio δ, determined by the corresponding vehicle's drive power mode sent from the big data server 100, determines the charging / discharging power limit executed in steps S56 and S66.

[0053] exist Figure 3 In the example shown, the charge / discharge power limit ratio δ can be expressed as a function of the cumulative excess ratio γ calculated in steps S55 and S65 and the drive power mode. Specifically, the drive power mode can be a learned value determined based on the drive power mode in the big data server 100.

[0054] In steps S56 and S66, the controller 11 can be configured to transmit the actual measured charging / discharging power P. out_real and P in_real The limited battery charging / discharging power is calculated by multiplying by the charging / discharging power limitation ratio δ. In this way, in... Figure 3 In the operational example shown, the actual charging / discharging power P of battery 12 can also be used as a reference. out_real and P in_real This limits the charging / discharging power of the battery, so that the limitation on the battery charging / discharging power can be implemented immediately and quickly after the limited charging / discharging power is calculated and the limitation on the battery charging / discharging power is implemented according to the exemplary embodiments of this disclosure.

[0055] When the continuous charging / discharging time checked in steps S55 and S65 is equal to or less than the preset reference time A, the battery charging / discharging power limit can be implemented based on the maximum available charging / discharging power obtained from the preset data graph (S57 and S67).

[0056] It can be used with Figure 2 The method used in the exemplary embodiment to determine the charging / discharging power limit ratio β is similar to that used in determining the charging / discharging power limit ratio β. Figure 3 The charging / discharging power limiting ratio δ is described in [the text]. However, due to [the text]... Figure 3 In the exemplary embodiment, the limiting ratio is determined based on the accumulated charging / discharging power; therefore, the continuous charging / discharging time t can be disregarded when determining the charging / discharging power limiting ratio δ. out and t in .

[0057] When limiting the discharge power in steps S26 and S57, the vehicle's BMS 11 can be configured to limit the discharge power value P. out Provided to the vehicle controller included in the vehicle (e.g., the hybrid power control unit (HCU) in the case of a hybrid vehicle), and the vehicle controller can be configured to take into account a limited discharge power value P. out This is used to operate high-voltage components. For example, the vehicle controller can be configured to reduce the torque command provided to the inverter supplying three-phase power to the vehicle's drive motor, provide a shutdown command to the vehicle's air conditioning controller, provide a command to reduce the air conditioning power, or provide a shutdown command to the vehicle's converter, so that the discharge power does not exceed the limited discharge power value P. out If due to the limited discharge power value P out If the vehicle's motor fails to deliver the power required by the driver, the vehicle controller can be configured to increase the power of the hybrid vehicle's engine to compensate for the insufficient power from the motor, thereby providing the driver with satisfactory vehicle performance.

[0058] Although exemplary embodiments of the present disclosure have been disclosed for illustrative purposes, those skilled in the art will understand that various modifications, additions, and substitutions may be made without departing from the scope and spirit of the present disclosure as disclosed in the appended claims.

Claims

1. A vehicle powertrain control system using big data, comprising: A big data server is configured to receive vehicle driving-related data generated in the vehicle, process and analyze the received data to generate the driving power mode of the vehicle, and store the driving power mode. as well as A controller, located in the vehicle, is configured to: receive the vehicle's drive power mode from the big data server; determine whether to limit the battery's charge / discharge power based on the battery's continuous charge / discharge time or the cumulative amount of the battery's continuous charge / discharge power; and when the battery's charge / discharge power needs to be limited, calculate the limited battery charge / discharge power based on the vehicle's drive power mode received from the big data server. And limit the charging / discharging of the battery based on the calculated battery charging / discharging power. The driving power mode of the vehicle is a learned value determined based on the driving power mode in the big data server.

2. The system according to claim 1, wherein, The big data server includes: a lower-level cloud server configured to directly receive vehicle driving-related data from the vehicle; and an upper-level cloud server configured to receive the vehicle driving-related data from the lower-level cloud server, process the vehicle driving-related data to generate a drive power mode, and store the drive power mode.

3. The system according to claim 1, wherein, The controller is configured to calculate the continuous charging / discharging time of the battery in response to determining that the actual measured battery charging / discharging power is greater than a preset ratio of the preset maximum available charging / discharging power.

4. The system according to claim 3, wherein, The preset maximum available charging / discharging power is stored in the controller in the form of a data graph based on the battery's state of charge and ambient temperature.

5. The system according to claim 3, wherein, The controller is configured to calculate a limited battery charging / discharging power by reflecting the drive power mode to the actually measured battery charging / discharging power in response to determining that the continuous charging / discharging time of the battery is greater than a preset reference time.

6. The system according to claim 5, wherein, The controller is configured to, in response to determining that the continuous charging / discharging time of the battery is greater than the preset reference time, calculate the limited battery charging / discharging power by calculating a charging / discharging power limit ratio and multiplying the actually measured battery charging / discharging power by the charging / discharging power limit ratio. The charge / discharge power limit ratio is a function of the actual measured battery charge / discharge power, the drive power mode, and the continuous charge / discharge time.

7. The system according to claim 1, wherein, The controller is configured to compare the cumulative amount of continuous charging / discharging power of the battery with a preset reference cumulative amount, and in response to determining that the cumulative amount of continuous charging / discharging power is greater than the reference cumulative amount, to divide the cumulative amount of continuous charging / discharging power by the preset reference cumulative amount to calculate the cumulative amount excess ratio.

8. The system according to claim 7, wherein, In response to determining that the cumulative amount of continuous charging / discharging power is greater than the reference cumulative amount, the controller is configured to calculate the limited battery charging / discharging power by calculating a charging / discharging power limit ratio and multiplying the actually measured battery charging / discharging power by the charging / discharging power limit ratio. The charging / discharging power limit ratio is a function of the drive power mode and the cumulative excess ratio.

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