Systems and methods for estimating vehicle battery charging time using big data

Abstract

A system and method for estimating vehicle battery charging time using big data are provided. The system may include: a big data server configured to collect charging-related parameters of batteries in multiple vehicles to group the vehicles into groups based on the collected charging-related parameters, and to calculate and send a first estimated charging time for the group to which a vehicle belongs during vehicle charging; and a controller installed in each of the multiple vehicles and configured to calculate a second estimated charging time based on the state of the battery in the vehicle when the corresponding battery in the vehicle is being charged, and to calculate a final estimated charging time for the battery by combining the first and second estimated charging times.

Description

Technical Field

[0001] This disclosure relates to systems and methods for estimating vehicle battery charging time using big data, and more specifically to systems and methods for estimating vehicle battery charging time using big data obtained from a big data server. Background Technology

[0002] The statements in this section provide only background information in connection with this disclosure and may not constitute prior art.

[0003] Typically, environmentally friendly vehicles generate power by using electrical energy stored in batteries to drive electric motors. These vehicles require charging their batteries to store enough electrical energy to power the vehicle.

[0004] Methods for charging the battery include: a slow charging method that uses an on-board charger installed in the vehicle to convert externally supplied alternating current (AC) into direct current (DC) and apply the converted current to the battery; and a fast charging method that directly receives DC from the outside and applies the received DC to the battery without a separate conversion process.

[0005] Regardless of the battery charging method, when estimating and determining vehicle uptime, it is crucial to accurately estimate the time required to fully charge the battery to power the vehicle.

[0006] The traditional method of estimating vehicle battery charging time is achieved by estimating the battery charging time through simple calculations performed by the onboard controller (e.g., battery management system (BMS)) using parameters related to the battery itself, such as the battery temperature or state of charge (SoC) and the initial charging power provided by an external charging device.

[0007] In this traditional method, the onboard controller can only estimate the charging time based on the initial charging power of the charging equipment, making it infeasible to apply parameters that change during charging. Therefore, a problem with traditional charging time estimation methods is that a large error inevitably arises between the actual charging time and the estimated charging time.

[0008] It will be understood that the above description in the related 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

[0009] Therefore, this disclosure provides a system for estimating vehicle battery charging time using big data. The system uses a big data server to collect parameters that affect the determination of charging time and analyzes and statistically processes the collected parameters to statistically estimate the estimated charging time of the corresponding vehicle, and reduces the error between the estimated charging time and the actual charging time by comparing the estimated charging time and the actual charging time.

[0010] According to one aspect of this disclosure, the above and other objectives can be achieved by providing a system for estimating vehicle battery charging time using big data, the system comprising: a big data server configured to collect charging-related parameters of vehicle batteries from multiple vehicles to group the multiple vehicles into multiple groups based on the collected charging-related parameters, and to calculate and transmit a first estimated charging time for the group to which a vehicle is targeted for charging during vehicle charging; and a controller installed in each of the multiple vehicles and configured to calculate a second estimated charging time based on the state of the battery in the vehicle when a corresponding battery in the vehicle is being charged, and to calculate a final estimated charging time for the battery by combining the first estimated charging time and the second estimated charging time.

[0011] The big data server can group vehicles with similar charging environments or similar charging patterns together based on the collected charging-related parameters.

[0012] The big data server can store algorithms or tables that derive estimated charging times based on charging-related parameters for each of multiple groups; it can receive charging-related parameters from the vehicle being targeted for charging during vehicle charging; it can derive a first estimated charging time for the vehicle being targeted for charging by applying the received charging-related parameters to the algorithm or table of the group to which the vehicle being targeted for charging belongs; and it can send the derived first estimated charging time.

[0013] When the corresponding battery in the vehicle is being charged, the controller can send charging-related data to the big data server at the start of charging, and can calculate the final estimated charging time of the battery based on the sent charging-related data by combining the first estimated charging time exported by the big data server with the second estimated charging time.

[0014] After the battery charging is terminated, the controller can reset the weights used to combine the first estimated charging time and the second estimated charging time based on the result obtained by comparing the actual charging time consumed with the first estimated charging time and the second estimated charging time.

[0015] According to another aspect of this disclosure, a method for estimating vehicle battery charging time using big data is provided. The method includes: collecting charging-related parameters of batteries in multiple vehicles from multiple vehicles by a big data server; dividing the multiple vehicles into multiple groups by the big data server based on the collected charging-related parameters; when the battery in a vehicle is being charged, sending charging-related data to the big data server by a controller in the vehicle to which the charging target belongs at the start of charging; calculating and sending a first estimated charging time for the group to which the vehicle to which the charging target belongs based on the charging-related data at the start of charging by the big data server; calculating a second estimated charging time based on the state of the battery; and calculating a final estimated charging time for the battery by combining the first estimated charging time and the second estimated charging time received from the big data server.

[0016] Grouping may include grouping vehicles with similar charging environments or similar charging patterns based on collected charging-related parameters.

[0017] Grouping may include: storing algorithms or tables to derive estimated charging times based on charging-related parameters for each of the multiple groups.

[0018] The transmission may include: receiving charging-related parameters from the vehicle being targeted for charging; deriving a first estimated charging time for the vehicle being targeted for charging by applying the received charging-related parameters to an algorithm or table of the group to which the vehicle being targeted for charging belongs; and transmitting the derived first estimated charging time.

[0019] Calculating the second estimated charging time may include: after terminating battery charging, the controller resets the weights used to combine the first estimated charging time and the second estimated charging time based on the result obtained by comparing the actual charging time consumed with the first estimated charging time and the second estimated charging time. Attached Figure Description

[0020] 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, wherein:

[0021] Figure 1 This is a diagram illustrating the configuration of a system for estimating vehicle battery charging time using big data, as described in this disclosure; and

[0022] Figure 2 This is a flowchart illustrating one form of the method disclosed herein for estimating vehicle battery charging time using big data. Detailed Implementation

[0023] In the following description, systems and methods for estimating vehicle battery charging time using big data according to various embodiments of the present disclosure will be described with reference to the accompanying drawings.

[0024] Figure 1 This is a diagram illustrating the configuration of a system for estimating vehicle battery charging time using big data according to an embodiment of the present disclosure.

[0025] Reference Figure 1 A system for estimating vehicle battery charging time using big data according to embodiments of the present disclosure may include: a big data server 100 configured to collect charging-related parameters generated by the on-board battery 12 of a vehicle 10, to form multiple groups for determining estimated charging times based on the collected charging-related parameters, to determine the group to which a vehicle belongs when it is being charged, and to send a first estimated charging time according to the determined group; and a controller 11 installed in each of the plurality of vehicles 10 and configured to calculate a second estimated charging time based on the state of the battery when it is being charged, and to calculate a final estimated charging time for the battery 12 by combining the first estimated charging time provided by the big data server 100 with the calculated second estimated charging time.

[0026] The big data server 100 can receive various parameters related to the processor charging the on-board battery from the vehicle 10, and can generate and store data obtained by processing and analyzing the received parameters. Specifically, the big data server 100 can generate and store additional parameters related to battery charging time based on parameters received from the vehicle 10 when the battery 12 is being charged, or secondary data generated using parameters received from the vehicle 10, and can group multiple vehicles based on parameters received from the vehicle 10, automatically generated parameters, etc. Grouping is a process of forming multiple groups by associating vehicles with similar parameter values ​​based on key charging-related parameters within a group.

[0027] 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.

[0028] For example, the first-level 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 when necessary, or provide the data to the cloud servers 120 and 130 of the higher-level layer belonging to the lowest layer 110.

[0029] The first-layer cloud server 110 can record raw data generated by the vehicles in real time via communication with them. The first-layer cloud server 110 can record and store vehicle data at the lowest possible sampling rate without data loss. The first-layer cloud server 110 can set limits on the amount of data to be recorded and stored for each vehicle (i.e., the communication target). Needless to say, all data recorded from the vehicles can be stored if resources allow, but the first-layer cloud server 110 primarily communicates with the vehicles in real time, and therefore can limit the amount of data to be stored for each vehicle in order to utilize resources effectively.

[0030] The raw data recorded by the first-layer cloud server 110 can be data generated and sent by various controllers of the vehicle. Specifically, in the calculation of battery charging time according to embodiments of this disclosure, the first-layer cloud server 110 can directly receive various charging-related parameters from the vehicle 10 and can classify parameters to be used to calculate other factors related to battery charging. The real-time data provided from the vehicle 10 to the first-layer cloud server 110 can be charging-related data of the battery 12 installed in the vehicle, and the charging-related parameters of the battery 12 can be, for example, vehicle type, battery state of health (SoH), battery temperature at the start of charging, ambient temperature of the battery, maximum power of the external charging device 20 used to provide charging power, area where the external charging device 20 is installed, manufacturer of the external charging device 20, time range for starting charging, required charging amount, or actual charging time required to charge the battery.

[0031] The big data server 100 can collect the aforementioned charging-related parameters from multiple vehicles, group vehicles with similar charging environments or patterns based on the collected parameters, and then write and store the charging time estimation algorithm or charging time estimation table for each group. The grouping results or charging time estimation algorithms or charging time estimation tables for each group can be exported by higher-level cloud servers such as the second-level cloud server 120 and the third-level cloud server 130.

[0032] Then, the big data server 100 can search for the group to which the corresponding vehicle belongs while the vehicle is charging, and can estimate the charging time consumption by applying the charging-related parameters provided from the vehicle being charged to the charging time estimation algorithm or charging time estimation table of the found group. For example, when charging begins, the vehicle can send the charging-related parameters to the first-layer cloud server 110, which can provide the received parameters to the second-layer cloud server 120 or the third-layer cloud server 130; and the second-layer cloud server 120 or the third-layer cloud server 130 can calculate the estimated charging time by applying the received charging-related parameters to the stored algorithm or table to derive the estimated charging time (hereinafter, the estimated charging time calculated by the big data server is referred to as the "first estimated charging time"), and then send the calculated estimated charging time to the controller 11 of the vehicle 10.

[0033] Figure 1 This is a diagram illustrating an example of an implementation method that has a total of three layers and the number of layers can be adjusted as needed.

[0034] When the vehicle is electrically connected to the external charging device 20 and begins charging, the controller 11 installed in the vehicle 10 can identify data related to battery charging and provide this data to the big data server 100. The controller 11 can use a pre-embedded algorithm for calculating the estimated charging time to calculate the estimated charging time of the battery 12 (hereinafter, the estimated charging time calculated by the controller 11 of the vehicle 10 will be referred to as the "second estimated charging time"). Various algorithms known in the art to which this disclosure pertains can be applied as algorithms for calculating the estimated charging time of the battery.

[0035] The controller 11 can calculate the final estimated charging time by combining a first estimated charging time and a directly calculated second estimated charging time. The first estimated charging time is calculated by a big data server 100 that receives charging-related parameters sent during charging based on an algorithm or table stored in association with the group to which the vehicle belongs.

[0036] The controller 11 can calculate the final estimated charging time by applying corresponding preset weights to the first estimated charging time and the second estimated charging time.

[0037] When charging is terminated, the controller 11 can compare the actual charging time consumed when charging started with the final estimated charging time, and can adjust the weights used to calculate the final estimated charging time based on the comparison error.

[0038] Figure 2This is a flowchart illustrating a method for estimating vehicle battery charging time using big data according to an embodiment of the present disclosure. Through the description of the method for estimating vehicle battery charging time using big data according to an embodiment of the present disclosure, the operation and operational effects of a system having the above-described configuration for estimating vehicle battery charging time using big data will be more clearly understood.

[0039] Reference Figure 2 When the vehicle 10 is electrically connected to the external charging device 20 that provides charging power and then starts charging, the controller 11 can send the charging-related parameters of the current state to the big data server 100 (S10).

[0040] The big data server 100 can pre-collect charging-related parameters from multiple vehicles and group vehicles with similar charging environments or similar charging patterns based on these parameters (S110). The big data server 100 can cumulatively store charging-related data sent from vehicles during charging and can group multiple vehicles based on the stored charging-related data. For example, assuming grouping is based on the location of the charger and the lifespan of the vehicle's battery in the charging-related data, vehicles with similar battery lifespans that primarily use similar chargers can be grouped together.

[0041] In grouping (S110), the big data server 100 can export and store calculation algorithms or tables for calculating the estimated charging time for the corresponding group. In this case, the big data server 100 can use statistical methods. For example, when the big data server 100 has already performed grouping based on the location of the charger and the lifespan of the vehicle battery, the big data server 100 can derive a first estimated charging time based on the battery temperature of vehicles using the charger of the corresponding group and having the battery life of the corresponding group, by using the state of charge (SoC) at the start of charging and the average charging time. To calculate the estimated charging time, the big data server 100 can pre-store algorithms or tables for deriving algorithms that depend on the SoC and average charging time at the start of charging based on the battery temperature of vehicles using the charger of the corresponding group and having the battery life of the corresponding group.

[0042] In operation S10, when the controller 11 of the vehicle 10 at the start of charging receives the charging-related parameters at the start of charging, the big data server 100 can derive a first estimated charging time by applying the received charging-related data to an algorithm or table pre-stored for the corresponding vehicle group, and can send the first estimated charging time to the controller 11 of the vehicle 10 (S120).

[0043] The controller 11 of the vehicle 10 can calculate a second estimated charging time (S11) by applying a preset algorithm based on charging-related parameters at the start of charging. As an algorithm for calculating the estimated charging time of the battery by the controller 11 in the vehicle based on charging-related parameters of the battery 12 (e.g., battery temperature, battery SoC, or charging power provided from the charging device) when charging the battery, various algorithms known in the art to which this disclosure pertains can be applied.

[0044] Then, the controller 11 can calculate the final estimated charging time by combining the calculated second estimated charging time received from the big data server 100 with the first estimated charging time (S12).

[0045] For example, controller 11 can calculate the final estimated charging time by multiplying the first estimated charging time and the second estimated charging time by their respective weights and then summing the results using the following equation.

[0046] [Equation]

[0047] T EST_FINAL =α·T EST_1 +(1-α)·T EST_2

[0048] Here, T EST_FINAL This is the final estimated charging time, T EST_1 The first estimated charging time, T EST_2 It is the second estimated charging time, and α is a weight that is equal to or greater than 0 and less than or equal to 1.

[0049] Then, when charging of battery 12 terminates (S13, Yes), controller 11 can compare the actual charging time consumed with the first estimated charging time and the second estimated charging time received and calculated at the start of charging (S14), and may change the value of the weight (S15). For example, in the example of determining the final estimated charging time as shown in the above equation, when the actual charging time consumed is a value closer to the second estimated charging time than the first estimated charging time, the weight α can be decreased, and when the actual charging time consumed is a value closer to the first estimated charging time than the second estimated charging time, the weight α can be increased. In this way, when charging is performed next time, the changed weight can be applied to calculate the estimated charging time.

[0050] As described above, in the systems and methods for estimating vehicle battery charging time using big data according to various embodiments of the present disclosure, the big data server can receive charging-related parameters from the vehicle during actual vehicle charging, and can statistically estimate the estimated charging time of the corresponding vehicle by analyzing and statistically processing the accumulated charging-related parameters received from multiple vehicles. The on-board controller can compare the calculated actual charging time consumption with the estimated charging time estimated by the big data server, and can reduce the error between the two values.

[0051] Therefore, during charging, charging-related parameters provided by the vehicle for deriving estimated charging time are accumulated in the big data server, along with information about the error between the actual charging time and the calculated estimated charging time, thereby improving the accuracy of charging time estimation. The calculation of estimated charging time on the big data server reflects the aging of chargers and batteries in each region, and thus allows for the derivation of optimal estimated charging time based on charging patterns and the customer's residential area.

[0052] In a system that uses big data to estimate vehicle battery charging time, the big data server can receive charging-related parameters from the vehicle during actual vehicle charging. It can statistically estimate the estimated charging time of the corresponding vehicle by analyzing and statistically processing the accumulated charging-related parameters received from multiple vehicles. The on-board controller can compare the calculated actual charging time consumption with the charging time estimated by the big data server and reduce the error between the two values.

[0053] Therefore, in systems that use big data to estimate vehicle battery charging time, charging-related parameters provided by the vehicle during charging are accumulated in the big data server, and information about the error between the actual charging time and the calculated estimated charging time is also accumulated, thereby improving the accuracy of charging time estimation.

[0054] In addition, in systems that use big data to estimate vehicle battery charging time, the aging of chargers and batteries in each region can be reflected in the calculation of estimated charging time by the big data server. Therefore, the optimal estimated charging time can be derived based on the charging mode and the customer's residential area, thereby providing vehicle drivers with more accurate estimated charging time and improving the marketability of the vehicle.

[0055] 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.

[0056] Although preferred embodiments of the present disclosure have been disclosed for illustrative purposes, those skilled in the art will understand that various modifications, additions, and substitutions are possible without departing from the scope and spirit of the present disclosure as disclosed in the appended claims.

Claims

1. A system for estimating vehicle battery charging time using big data, the system comprising: The big data server is configured as follows: Collect charging-related parameters of the batteries in multiple vehicles to group the multiple vehicles into multiple groups based on the collected charging-related parameters; as well as Calculate and send the first estimated charging time for the group to which the vehicle to be targeted for charging belongs during vehicle charging; as well as A controller is installed in each of the plurality of vehicles, and the controller is configured to: When the corresponding battery in the vehicle is being charged, a second estimated charging time is calculated based on the state of the battery in the vehicle; and The final estimated charging time of the battery is calculated by combining the first estimated charging time and the second estimated charging time. In response to the termination of battery charging, the controller is configured to: Based on the result obtained by comparing the actual charging time consumption with the combination of the first estimated charging time and the second estimated charging time, the weights used to combine the first estimated charging time and the second estimated charging time are reset.

2. The system according to claim 1, wherein, The big data server is configured as follows: Based on the collected charging-related parameters, vehicles with similar charging environments or similar charging modes are grouped together.

3. The system according to claim 1, wherein, The big data server is configured as follows: Store an algorithm or table for deriving the first estimated charging time based on the charging-related parameters of each of the plurality of groups; During vehicle charging, the charging-related parameters are received from the vehicle that is the target of the charging process; The first estimated charging time for the vehicle being targeted for charging is derived by applying the received charging-related parameters to the algorithm or table of the group to which the vehicle being targeted for charging belongs. and Send the exported first estimated charging time.

4. The system according to claim 1, wherein, When the corresponding battery in the vehicle is charged, the controller is configured to: When charging begins, charging-related data is sent to the big data server; and The final estimated charging time of the battery is calculated by combining the second estimated charging time based on the transmitted charging-related data with the first estimated charging time derived from the big data server.

5. A method for estimating vehicle battery charging time using big data, the method comprising: The big data server collects charging-related parameters of the vehicle batteries from multiple vehicles; The big data server divides the multiple vehicles into multiple groups based on the collected charging-related parameters; When the battery in the vehicle is being charged, the controller in the vehicle sends charging-related data to the big data server at the start of charging. The big data server calculates and sends the first estimated charging time for the group to which the vehicle belongs, based on the charging-related data at the start of charging. Calculate the second estimated charging time based on the state of the battery; and The final estimated charging time of the battery is calculated by combining the second estimated charging time with the first estimated charging time received from the big data server. The calculation of the second estimated charging time includes: In response to the termination of battery charging, the controller resets the weights used to combine the first estimated charging time and the second estimated charging time based on a result obtained by comparing the actual charging time consumption with a combination of the first estimated charging time and the second estimated charging time.

6. The method according to claim 5, wherein, Grouping the multiple vehicles includes: Based on the collected charging-related parameters, vehicles with similar charging environments or similar charging modes are grouped together.

7. The method according to claim 5, wherein, Grouping the multiple vehicles includes: Store an algorithm or table for deriving the first estimated charging time based on the charging-related parameters of each of the plurality of groups.

8. The method according to claim 7, wherein, Calculating and sending the first estimated charging time for the group of vehicles includes: Receive the charging-related parameters from the vehicle; The first estimated charging time for the vehicle is derived by applying the received charging-related parameters to the algorithm or table of the vehicle's group; and Send the exported first estimated charging time.

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