System for estimating the state of health of a battery using big data

Abstract

A system for estimating the state of health (SOH) of a battery using big data may include: a big data server that receives vehicle driving-related data generated from a vehicle and results of determining the state of health of the battery, processes the received vehicle driving-related data, and generates and stores factors related to the state of health of the battery installed in the vehicle; and a controller that is installed in the vehicle and determines the state of health of the battery with reference to the factors stored in the big data server.

Description

Technical Field

[0001] The present invention relates to a system for estimating the state of health (SOH) of a battery using big data. More specifically, the present invention relates to a system for estimating the SOH of a vehicle using big data, which can estimate the SOH of a battery in a vehicle using big data constructed through a big data server regardless of the specific state or condition of the vehicle. Background Art

[0002] Generally, a high-voltage battery that stores electric energy supplied to a drive motor of an eco vehicle deteriorates over time, and its state of health (SOH) also decreases. In particular, the degree of decrease in the SOH of the battery varies depending on the vehicle driving environment or the driving characteristics of the driver.

[0003] To estimate the SOH of a battery, traditionally, the SOH estimation calculation is only performed when the vehicle enters a specific mode. For example, the SOH estimation calculation is only executed when the vehicle is in a low-speed battery charging mode. Additionally, even if the vehicle's mode is satisfied, the SOH estimation calculation can only be performed when various conditions are met (e.g., conditions for entering the SOH calculation and conditions determined based on the battery voltage behavior).

[0004] Due to the limitations of traditional SOH estimation techniques, even after many such techniques have been developed, there are still some vehicles that do not perform the SOH calculation because they do not meet the above-mentioned mode or conditions.

[0005] The information included in the background art section of the present invention is only used to enhance the understanding of the general background of the present invention and should not be regarded as an admission or any form of indication that this information constitutes the prior art known to those skilled in the art. Summary of the Invention

[0006] Aspects of the present invention are directed to providing a system for estimating the state of health (SOH) of a battery using big data, which is configured to estimate the SOH of a battery in a vehicle using big data constructed through a big data server regardless of the specific state or condition of the vehicle.

[0007] According to various exemplary embodiments of the present invention, a system for estimating the state of health SOH of a battery using big data includes: a big data server that receives vehicle driving-related data generated from a vehicle and results for determining the SOH of the battery, processes the received vehicle driving-related data, and generates and stores factors related to the SOH of a battery installed in the vehicle; and a controller disposed in the vehicle and determining the SOH of the battery with reference to the factors stored in the big data server.

[0008] The big data server can group vehicles with similarities based on factors and generate information about the SOH of the battery for each group.

[0009] The big data server includes a cloud server in multiple layers. The cloud server includes: a lower-layer cloud server, which has a layer lower than a predetermined layer, directly receives vehicle driving-related data from the vehicle, and classifies the data for calculating factors related to the SOH of the battery; and an upper-layer cloud server, which has a layer higher than the predetermined layer, receives the classified data from the lower-layer cloud server, processes the classified data, generates factors related to the SOH of the battery, and groups vehicles with similarities to each other based on the generated factors related to the SOH of the battery.

[0010] When the vehicle is in a state that meets the preset conditions for determining the SOH of the battery, the controller can use a preset algorithm for calculating the SOH of the battery to calculate the SOH of the battery, and determine whether the calculated SOH of the battery is appropriate based on the information about the SOH of the battery for the group to which the vehicle belongs.

[0011] When the calculated SOH of the battery is a value between the maximum SOH value and the minimum SOH value of the batteries in the group to which the vehicle belongs, the controller can determine the calculated SOH of the battery as the final SOH of the battery.

[0012] When the vehicle is not in a state that meets the preset conditions for calculating the SOH of the battery, the controller can calculate the SOH of the battery by combining the previously determined SOH of the battery with the information about the SOH of the battery for the group to which the vehicle belongs.

[0013] When the vehicle is not in a state that meets the preset conditions for determining the SOH of the battery, the controller can, after assigning corresponding weight values, determine the SOH of the battery by combining the previously determined SOH of the battery with the representative SOH value of the batteries in the group to which the vehicle belongs.

[0014] The method and apparatus of the present invention have other features and advantages, which will become obvious or be more specifically described by incorporating the accompanying drawings herein and the following description that explains certain principles of the present invention. Figure 1 become obvious or be more specifically described by incorporating the accompanying drawings herein and the following description that explains certain principles of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a schematic diagram showing a system for estimating the state of health of a battery using big data according to various exemplary embodiments of the present invention.

[0016] Figure 2 is a flowchart showing a system for estimating the state of health of a battery using big data according to various exemplary embodiments of the present invention.

[0017] It should be understood that the drawings are not necessarily to scale, but rather present a somewhat simplified representation of various features that illustrate the basic principles of the present invention. Specific design features of the present invention included herein, including, for example, specific dimensions, orientations, positions, and shapes, will be determined in part by the particular design application and use environment.

[0018] In the drawings, reference numerals refer to the same or equivalent parts of the present invention throughout the several views of the drawings. Detailed Description of the Invention

[0019] Reference is now made in detail to various embodiments of the present invention, examples of which are illustrated in the accompanying drawings and described below. Although the present invention will be described in conjunction with the exemplary embodiments of the present invention, it should be understood that this description is not intended to limit the present invention to those exemplary embodiments. On the other hand, the present invention is intended to cover not only the exemplary embodiments of the present invention, but also various alternatives, modifications, equivalent forms, and other embodiments that may be included within the spirit and scope of the present invention as defined by the appended claims.

[0020] Hereinafter, a system for estimating the state of health (SOH) of a battery using big data according to various embodiments of the present invention will be described in detail with reference to the drawings.

[0021] Figure 1 is a schematic diagram showing a system for estimating the SOH of a battery using big data according to various exemplary embodiments of the present invention.

[0022] Referring to Figure 1 , a system for estimating the SOH of a battery using big data according to various exemplary embodiments of the present invention may include: a big data server 100 that receives vehicle driving-related data generated from a vehicle 10, processes the received vehicle driving-related data, and generates and stores factors related to the SOH of a battery installed in the vehicle 10; and a controller 11 that is provided in the vehicle 10 and calculates the SOH of the battery with reference to the factors stored in the big data server 100.

[0023] The big data server 100 may receive various types of data generated from the vehicle 10 while the vehicle is running, process the received data, and generate and store the analyzed data. The big data server 100 may generate and store factors related to the SOH of the battery based on the data received from the vehicle or based on secondary data generated using the data received from the vehicle.

[0024] As Figure 1 shown, the big data server 100 may be implemented in a distributed cloud type with a hierarchical structure having cloud servers 110, 120, and 130 in each layer.

[0025] For example, the first - layer cloud server 110, which belongs to the lowest layer in the multi - layer structure, can record in real time the data generated from the vehicle 10 when communicating with the vehicle 10, and if necessary, provide the recorded data to the vehicle 10 or the cloud servers 120 or 130 that belong to a layer higher than the lowest layer 110.

[0026] The first - layer cloud server 110 can record in real time the raw data generated from the vehicle by communicating with the vehicle. The first - layer cloud server 110 can record and store the vehicle data at the shortest possible sampling rate without losing data. In addition, the first - layer cloud server 110 can set a limit on the amount of data that can be recorded and stored for each communicating vehicle. Of course, if resources permit, all the data recorded from the vehicle can be stored. However, since the first - layer cloud server 110 mainly communicates with the vehicle in real time to control the vehicle, it is preferable to limit the amount of data that can be stored for each vehicle to effectively use resources.

[0027] The raw data recorded by the first - layer cloud server 110 can be data generated and sent from various controllers of the vehicle, such as the temperature, voltage, and SOC value of the battery in the vehicle 10, the rpm, voltage, and temperature of the electric motor, the vehicle speed, the outdoor temperature, and the rpm of the engine.

[0028] In various exemplary embodiments of the present invention for calculating the SOH of the battery, the first - layer cloud server 110 can directly receive various driving - related data from the vehicle 10 and classify the data for calculating factors related to the SOH of the battery 12. The real - time data provided from the vehicle 10 to the first - layer cloud server 110 can be data related to the battery 12 installed in the vehicle. For example, whether the battery 12 is in a charging state or a discharging state, the real - time current of the battery 12, the real - time voltage of the battery 12, the real - time state of charge (SOC) value of the battery 12, the current mileage, the vehicle speed, the instantaneous power of the battery 12 calculated by the controller 11, and the SOH of the battery 12 calculated by the controller 11.

[0029] The cloud servers 120 and 130 that belong to a layer higher than the first - layer cloud server 110 can process and store the data provided by the first - layer cloud server 110 and send the processed data to the vehicle 10 when communicating with the vehicle 10. Figure 1 An exemplary embodiment implemented by a total of three layers is described exemplarily, and the number of layers can be appropriately adjusted as needed.

[0030] For example, the second - layer cloud server 120 can mainly process the raw data recorded by the first - layer cloud server 110, and calculate and store items such as average values, maximum / minimum values, RMS, and standard deviation values. The processed data can be stored and managed in the form of a preset data set. The data stored in the second - layer cloud server 120 can be stored in the form of data processed through a predetermined format (rather than raw data), and stored together with the date or driving time of the data, vehicle identifier, etc.

[0031] The first - layer cloud server 110 immediately stores the collected raw data, while the second - layer cloud server 120 that processes the data collected from the first - layer cloud server 110 does not necessarily need to process and store the raw data in real - time. After obtaining the data, some delay time is allowed when processing and storing the data.

[0032] In various embodiments of the present invention for calculating the SOH of the battery, the data processed and calculated by the second - layer cloud server 120 can be factors related to the SOH of the battery. The factors related to the SOH of the battery can be the cumulative charge amount of the battery 12, the cumulative discharge amount and the number of high - speed / low - speed charging times, the average temperature of the battery 12, the highest temperature of the battery 12, the current amount used by the battery 12, the internal resistance of the battery 12, the charging time of the battery 12, the mileage of the vehicle, etc.

[0033] When needed, the vehicle 10 can request the processed data from the second - layer cloud server 120 and receive the processed data.

[0034] In addition, the third - layer cloud server 130 can perform secondary processing on the data processed by the second - layer cloud server 120 (i.e., factors related to the SOH of the battery). The third - layer cloud server 130 can execute data processing that requires a higher level of computing power than that required for the second - layer cloud processor 120 to process data.

[0035] The third - layer cloud server 130 can group each vehicle based on factors related to the SOH of the battery provided by the second - layer cloud server 120. That is, the third - layer cloud server 130 can group vehicles based on the similarity of factors related to the SOH of the battery. For example, the third - layer cloud server 130 can group vehicles with similarities in the following factors into the same group: the cumulative charge amount, cumulative discharge amount, high - speed / low - speed charging times of battery 12, the average temperature of battery 12, the maximum temperature of battery 12, the current used by battery 12, the internal resistance of battery 12, the charging time of battery 12, the mileage of the vehicle, etc., and generate and store information about the SOH calculated for multiple vehicles belonging to the group. The information about the SOH of the battery calculated for each group can be the maximum SOH value of the battery in the vehicles belonging to the group, the minimum SOH value of the battery, and the SOH value of a representative battery, etc. Here, the SOH value of the representative battery can be a value obtained by simply averaging the SOH of the batteries of the vehicles belonging to the group, or a value generated by using various known data - analysis techniques for generating representative values.

[0036] The controller 11 provided in the vehicle 10 can determine whether the conditions for calculating the SOH of the battery 12 are met, and use the data provided by the big - data server 100 in an appropriate manner to calculate the SOH of the battery 12 according to whether the conditions are met.

[0037] When implementing various exemplary embodiments of the present invention in an actual vehicle, the controller 11 that needs to perform various operations to calculate the SOH of the battery 12 can be a battery management system (BMS) that performs control related to the battery 12, and the battery 12 can be a high - voltage battery that provides power for driving an electric motor (providing power to the driving wheels of the vehicle).

[0038] The specific operations of a system for estimating the SOH of a battery using big data according to various embodiments of the present invention configured as described above will be described below.

[0039] Figure 2 is a flowchart showing a system for estimating the SOH of a battery using big data according to various exemplary embodiments of the present invention.

[0040] As Figure 2 shown, the operations can be performed by the controller 11 of the vehicle 10 and the big - data server 100.

[0041] Refer to Figure 2, when the vehicle 10 is in the powered-on state, it can provide vehicle driving-related data to the big data server 100 at a preset time interval (S11). The big data server 100 can process the vehicle driving-related data received from multiple vehicles and group each vehicle in the vehicle based on factors related to the SOH of the battery (S21). In step S21, it can be determined which group the vehicle 10 that provides data to the big data server 100 belongs to.

[0042] Subsequently, the big data server 100 can calculate information related to the SOH of the battery in the vehicles belonging to each group (the grouping of vehicles in step S21) (S22). In step S22, the big data server 100 can calculate the maximum SOH value of the battery, the minimum SOH value of the battery, and the representative SOH value in the vehicles belonging to each group.

[0043] Meanwhile, the controller 11 of the vehicle 10 can use the stored algorithm for calculating the SOH of the battery to check whether the conditions for calculating the SOH of the battery are met (S12). It can be checked whether the conditions for calculating the SOH of the battery are met at each preset time interval when the vehicle is powered on, or whenever the vehicle is in a specific driving state (for example, when the vehicle remains in the charging state or in the stationary state for a predetermined period or longer), using the stored algorithm for calculating the SOH of the battery (S12). Regarding the specific conditions for calculating the SOH of the battery, each vehicle manufacturer can apply its own unique technology.

[0044] In S12, when it is determined that the conditions for calculating the SOH of the battery are met, the controller 11 can use the pre-stored algorithm for calculating the SOH of the battery to calculate the state of health of the battery (SOH_bms). For the algorithm for calculating the SOH of the battery, one of various known techniques in the prior art can be selectively used, and each manufacturer can set the algorithm differently. For example, there is a known algorithm for calculating the SOH of the battery based on whether the battery capacity increases by charging the battery or decreases by discharging the battery during vehicle driving.

[0045] Subsequently, when the number of times of recalculating the state of health of the battery (SOH_bms) is less than a predetermined reference value N (S14), the controller 11 receives information related to the SOH of the battery from the big data server 100 and compares the received information with the calculated state of health of the battery (SOH_bms) (S15). Here, the information related to the SOH of the battery received from the big data server 100 can be the maximum SOH value (SOH_max) and the minimum SOH value (SOH_min) of the battery in the group to which the relevant vehicle belongs.

[0046] In step S15, when it is determined that the state of health of the battery (SOH_bms) calculated by the controller 11 is a value between the maximum SOH value (SOH_max) of the battery received from the big data server 100 and the minimum SOH value (SOH_min) of the battery, the controller 11 may determine the state of health of the battery (SOH_bms) calculated by itself as the final state of health of the battery 12 (SOH_final) (S16).

[0047] In step S15, when it is not determined that the state of health of the battery (SOH_bms) calculated by the controller 11 is a value between the maximum SOH value (SOH_max) of the battery received from the big data server 100 and the minimum SOH value (SOH_min) of the battery, the controller 11 may store the state of health of the battery (SOH_bms) calculated by itself and recalculate the SOH of the battery.

[0048] In step S14, when the SOH of the battery calculated based on step S15 is determined to be the same value in a preset number N or more times, the controller 11 may determine this value as the final state of health of the battery 12 (SOH_final) (S16) without going through step S15.

[0049] As described above, after determining whether the SOH of the battery calculated by the controller 11 is appropriate by comparing the calculated SOH of the battery with the information related to the SOH of the battery in the group to which the vehicle belongs, when the calculated SOH of the battery is not appropriate, the controller 11 postpones the decision on the final SOH of the battery and recalculates the SOH of the battery multiple times. If the same value is continuously obtained through multiple calculations, this value may be determined as the final SOH of the battery, and the determined final SOH of the battery may be provided to the big data server 100.

[0050] In an exemplary embodiment of the present invention, if the SOH of the battery is within a predetermined range, the SOH of the battery calculated by the controller 11 is appropriate.

[0051] Meanwhile, in step S12, when the conditions for calculating the SOH of the battery are not met, the controller 11 may maintain the previously calculated and determined SOH of the battery (SOH_prev) (S17), and determine the final state of health of the battery (SOH_final) by adding the previous SOH of the battery (SOH_prev) and the representative SOH value (SOH_est) of the battery provided by the big data server 100 after assigning the corresponding weight value α.

[0052] Here, the weight value α can be a value between 0 and 1 and is appropriately changed to reflect the data reliability of the big data server 100, which can be determined according to the quantity of data collected by the big data server 100, the number of vehicles belonging to a group, and the like. For example, when the quantity of data on the SOH of vehicles collected for each group is very small, that is, when there is no sufficient reliable collected data in the big data server 100, the weight value α can be determined to be 1. When the weight value α is determined to be 1, the SOH of the vehicle calculated in step S18 does not reflect the SOH value (SOH_est) of the representative battery provided by the big data server 100 with low reliability. By collecting sufficient data and increasing the quantity of data, the more reliable the big data server 100 is, the smaller the weight value α can be set.

[0053] As described above, the system for estimating the SOH of a battery using big data according to various exemplary embodiments of the present invention is configured to calculate the SOH of the battery in a vehicle at a desired time without considering the state of the vehicle (i.e., whether it enters a preset condition for calculating the SOH of the battery).

[0054] The system for estimating the SOH of a battery using big data according to various exemplary embodiments of the present invention is configured to utilize information related to the SOH of the battery derived from a large amount of data (obtained from vehicles belonging to a group with similar battery-related information) to improve the reliability of the battery SOH estimation.

[0055] In an exemplary embodiment of the present invention, the system for estimating the SOH of a battery using big data can display the estimated SOH of the battery on a display device, or provide information on the estimated SOH of the battery to the driver's mobile device or a scanner for the vehicle including the battery.

[0056] The effects that can be obtained from the present invention are not limited to the above effects, and those skilled in the art with ordinary knowledge in the technical field to which various exemplary embodiments of the present invention belong can clearly understand other unmentioned effects from the above description.

[0057] Additionally, the term "controller" refers to a hardware device including a memory and a processor configured to execute one or more steps interpreted as an algorithmic structure. The memory stores the algorithmic steps, and the processor executes the algorithmic steps to perform one or more processes of the methods according to various exemplary embodiments of the present invention. The controller according to the exemplary embodiments of the present invention can be implemented by a non-volatile memory and a processor, the non-volatile memory being configured to store algorithms for controlling operations of various components of a vehicle or data regarding software commands for executing the algorithms, and the processor being configured to perform the operations described above using the data stored in the memory. The memory and the processor can be separate chips. Alternatively, the memory and the processor can be integrated in a single chip. The processor can be implemented as one processor or multiple processors.

[0058] The controller can be at least one microprocessor operated by a predetermined program, which can include a series of commands for performing the methods according to various exemplary embodiments of the present invention.

[0059] The foregoing invention can also be embodied as computer-readable code on a computer-readable recording medium. A computer-readable recording medium is any data storage device that stores data that can subsequently be read by a computer system. Examples of computer-readable recording mediums include hard disk drives (HDDs), solid state drives (SSDs), silicon disk drives (SDDs), read-only memories (ROMs), random access memories (RAMs), CD-ROMs, magnetic tapes, floppy disks, optical data storage devices, etc., as well as implementations as carrier waves (e.g., transmitted via the Internet).

[0060] For the purpose of facilitating the interpretation and accurate definition of the appended claims, the terms "upper", "lower", "inner", "outer", "above", "below", "upward", "downward", "front", "rear", "back", "inner side", "outer side", "inward", "outward", "internal", "external", "inside", "outside", "forward", "backward" are used to describe the features of the exemplary embodiments with reference to the positions of the features shown in the figures. It should also be understood that the term "connected" or its derivatives refer to both direct connection and indirect connection.

[0061] For purposes of illustration and description, a description of specific exemplary embodiments of the present invention has been presented above. They are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed, and obviously several modifications and variations are possible in light of the above teachings. 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 utilize the various exemplary embodiments of the present invention, as well as various alternatives and modifications. The scope of the invention is intended to be defined by the appended claims and their equivalents.

Claims

1. A system for estimating the state of health of a battery using big data, the system comprising: A big data server that receives vehicle driving-related data generated from a vehicle and a determination result of the state of health of the battery, processes the received vehicle driving-related data, and generates and stores factors related to the state of health of the battery installed in the vehicle; And A controller that is provided in the vehicle and determines the state of health of the battery with reference to the factors stored in the big data server, Wherein, the big data server groups vehicles with similarity according to the factors, and generates information about the state of health of the battery for each group, Wherein, when the vehicle is in a state that meets a preset condition for determining the state of health of the battery, the controller is configured to: use a preset algorithm for determining the state of health of the battery to determine the state of health of the battery, and determine whether the determined state of health of the battery is appropriate based on the information about the state of health of the battery in the group to which the vehicle belongs, and Wherein, when the determined state of health of the battery is a value between the maximum state of health value and the minimum state of health value of the batteries in the group to which the vehicle belongs, the controller is configured to: determine the determined state of health of the battery as the final state of health of the battery.

2. The system according to claim 1, wherein, The big data server includes a cloud server in multiple layers, and the cloud server includes: A lower-layer cloud server having a layer lower than a predetermined layer, directly receives vehicle driving-related data from the vehicle, and classifies the vehicle driving-related data for determining the factors related to the state of health of the battery; and An upper-layer cloud server having a layer higher than the predetermined layer, receives the classified vehicle driving-related data from the lower-layer cloud server, processes the classified vehicle driving-related data, generates the factors related to the state of health of the battery, and groups vehicles having similarity to each other according to the generated factors related to the state of health of the battery.

3. The system according to claim 1, wherein When the vehicle is not in a state that meets a preset condition for determining the state of health of the battery, the controller is configured to: determine the state of health of the battery by combining the previously determined state of health of the battery and the information about the state of health of the battery in the group to which the vehicle belongs.

4. The system according to claim 1, wherein When the vehicle is not in a state that meets a preset condition for determining the state of health of the battery, the controller is configured to: determine the state of health of the battery by combining the previously determined state of health of the battery with the representative state of health value of the batteries in the group to which the vehicle belongs after assigning respective weight values.

5. A method for estimating the state of health of a battery using big data, the method comprising the following steps: When the vehicle is in a powered-on state, provide vehicle driving-related data to the big data server at a preset time interval; The big data server processes the vehicle driving-related data received from multiple vehicles, and groups each of the multiple vehicles based on factors related to the state of health of the battery; And Determine information related to the state of health of the battery in multiple vehicles belonging to each group, and the multiple vehicles in each group are assigned to Wherein, Among them, the big data server is configured to: determine the maximum value of the health state of the battery, the minimum value of the health state of the battery, and the representative health state value among multiple vehicles belonging to each group. Among them, when the conditions for determining the health state of the battery are determined to be met, the controller of the vehicle is configured to determine the health state of the battery. Among them, when the number of times of determining the health state of the battery is less than a predetermined reference value, the controller is configured to: receive information related to the health state of the battery from the big data server, and compare the received information with the determined health state of the battery. Among them, the information related to the health state of the battery received from the big data server is the maximum value of the health state of the battery and the minimum value of the health state of the battery in the group to which the relevant vehicle belongs, and Among them, when it is determined that the health state of the battery determined by the controller is a value between the maximum value of the health state of the battery received from the big data server and the minimum value of the health state of the battery, the controller is configured to: determine the health state of the battery determined by the controller as the final health state of the battery.

6. The method according to claim 5 further comprises the following steps: When the vehicle is powered on or when the vehicle is in a predetermined driving state, the controller checks whether the conditions for determining the health state of the battery are met at each preset time interval.

7. The method according to claim 6, wherein, The predetermined driving state is a state in which the vehicle remains charged or in a stationary state for a predetermined period of time or longer.

8. The method according to claim 5, wherein When it is determined that the health state of the battery determined by the controller is not a value between the maximum value of the health state of the battery received from the big data server and the minimum value of the health state of the battery, the controller is configured to: store the health state of the battery determined by the controller and re-determine the health state of the battery.

9. The method according to claim 5, wherein When the health state of the battery is determined to be the same value in a preset number of times or more, the controller is configured to: determine the health state of the battery as the final health state of the battery.

10. The method according to claim 6, wherein, When the conditions for determining the health state of the battery are not met, the controller is configured to: maintain the previously determined health state of the battery, and determine the final health state of the battery by adding the previously determined health state of the battery and the representative health state value of the battery provided by the big data server after assigning respective weight values.

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