Battery model parameter estimation device and method thereof

Abstract

A battery model parameter estimation apparatus and method thereof acquire first time-series data of state information of a battery provided in a vehicle. The apparatus and method correct state of charge (SOC) values and state of health (SOH) values in the first time-series data. The apparatus and method predict a model voltage corresponding to the corrected first time-series data using a battery model of the vehicle. The apparatus and method estimate parameters configured to minimize a difference between the model voltage and an actual voltage of the battery as parameters of the battery model. Accordingly, optimal parameters for the battery model are estimated.

Description

[0001] Cross-references to related applications

[0002] This application claims the benefit and priority of Korean Patent Application No. 10-2024-0173817, filed with the Korean Intellectual Property Office on November 28, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This disclosure relates to techniques for estimating parameters of a battery model installed in a vehicle with high accuracy. Background Technology

[0004] Typically, electric vehicles are vehicles powered by electricity and include a battery comprising multiple battery cells configured to store electrical energy. These battery cells either convert chemical energy into electrical energy to supply power (discharging) or convert externally supplied electrical energy into chemical energy to store chemical energy (charging).

[0005] Because such electric vehicles utilize electrical energy stored in batteries as their power source, the vehicle's performance depends on the battery's performance. Therefore, to improve the performance of electric vehicles, the battery should be managed to maximize its performance.

[0006] Recently, due to the use of high-performance battery cells to improve vehicle power and the growing trend of increasing battery cell numbers, the demand and requirements for battery management have increased. This battery management is typically performed by a battery management system (BMS).

[0007] This type of BMS measures the state information (such as voltage, current, or temperature) of the battery in an electric vehicle and uses the state information and option values ​​for controlling the battery to manage the charging and discharging of the battery.

[0008] In addition, the BMS can include state of charge (SOC) estimation algorithms and state of health (SOH) estimation algorithms as algorithms for estimating the battery state. Most SOC and SOH estimation algorithms are based on the battery equivalent circuit model to simulate voltage or directly use the parameters that make up the battery equivalent circuit model to simulate voltage.

[0009] The parameters of a battery equivalent circuit model fluctuate in a highly sensitive and nonlinear manner based on several operating environments, such as battery temperature or aging state. Due to these environments, it is difficult to accurately simulate terminal voltages. In other words, nonlinear parameters degrade the accuracy of state estimation algorithms. Therefore, nonlinear parameters lead to reduced energy efficiency, decreased output performance, and reduced stability. Consequently, techniques for real-time updating of the parameters of the battery equivalent circuit model are applied to accurately estimate the battery's state.

[0010] This technique (as discussed above for updating) uses only the battery's terminal voltage and terminal current to extract parameters. However, as mentioned above, because such parameters are nonlinear and their dynamic characteristics are difficult to identify, it is difficult to estimate accurate values ​​in real time. Therefore, various studies related to real-time accurate estimation are underway and are roughly classified into direct estimation schemes and model-based adaptive filter schemes based on the logic of updating parameters.

[0011] Direct estimation schemes are used to apply dynamic characteristics over time at each moment to update parameters. These schemes estimate parameters (e.g., internal resistance) that can be intuitively simulated by changes in current and voltage. However, parameters that can be simulated based on current and voltage data measured over a specific time period, similar to the internal capacitance used to simulate the polarization potential of a battery, have limitations when using direct estimation schemes.

[0012] On the other hand, because model-based adaptive filter schemes apply filter gains recursively to reflect past estimation results and increase the accuracy of the final estimate, they overcome the shortcomings of direct estimation schemes. Various representative types of error correction algorithms exist, such as the Extended Kalman Filter (EKF), which can be applied to both nonlinear and linear systems, providing efficient estimates of noise. Besides the Particle Filter (PF), error correction algorithms also include Recursive Least Squares (RLS), which has a simple estimation process based on the correction gain used to minimize the square of the error.

[0013] Because this parameter estimation technique requires reflecting past estimation results multiple times during parameter updates, estimating the optimal parameters can take a considerable amount of time. Furthermore, its accuracy is compromised because it only uses data acquired within a specific SOC interval of the battery (e.g., SOC 40% to SOC 100%).

[0014] The details described in the background art are intended to enhance understanding of the background of this disclosure and may include details beyond those of the prior art known to a person skilled in the art. Summary of the Invention

[0015] This disclosure aims to address the aforementioned problems in the prior art while maintaining the advantages achieved by the prior art.

[0016] One aspect of this disclosure provides a battery model parameter estimation apparatus and method. The apparatus and method acquire first time-series data of the state information of a battery disposed in a vehicle. The apparatus and method correct the state of charge (SOC) and state of health (SOH) values ​​in the first time-series data. The apparatus and method use a battery model of the vehicle to predict a model voltage corresponding to the corrected first time-series data. The apparatus and method estimate parameters configured to minimize the difference between the model voltage and the actual voltage of the battery, as parameters of the battery model. Thus, optimal parameters for the battery model can be estimated.

[0017] Another aspect of this disclosure provides a battery model parameter estimation apparatus and method. The apparatus and method acquire first time-series data of the state information of a battery disposed in a vehicle. The apparatus and method determine reference state of charge (SOC) values ​​and reference state of health (SOH) values ​​based on voltage, current, and temperature in the first time-series data. The apparatus and method correct the SOC and SOH values ​​in the first time-series data based on the reference SOC and reference SOH values, respectively. The apparatus and method use a battery model of the vehicle to predict a model voltage corresponding to the corrected first time-series data. The apparatus and method estimate parameters configured to minimize the difference between the model voltage and the actual voltage of the battery as parameters of the battery model. Thus, optimal parameters for the battery model can be estimated.

[0018] Another aspect of this disclosure provides a battery model parameter estimation apparatus and method. The apparatus and method acquire first time-series data of the state information of a battery disposed in a vehicle. The apparatus and method determine whether to update the parameters of the vehicle's battery model based on the state of health (SOH) values ​​in the first time-series data. The apparatus and method correct the state of charge (SOC) and SOH values ​​in the first time-series data. The apparatus and method use the battery model to predict a model voltage corresponding to the corrected first time-series data. The apparatus and method estimate parameters for minimizing the difference between the model voltage and the actual voltage of the battery, as parameters of the battery model. Thus, optimal parameters for the battery model can be estimated.

[0019] Another aspect of this disclosure provides a battery model parameter estimation apparatus and method. The apparatus and method acquire second time-series data of vehicle driving information and first time-series data of the state information of a battery installed in the vehicle. Based on the accumulated mileage displayed by the odometer (ODO) installed in the vehicle within the second time-series data of the driving information, the apparatus and method determine whether to update the parameters of the vehicle's battery model. The apparatus and method correct the state of charge (SOC) and state of health (SOH) values ​​in the first time-series data. The apparatus and method use the vehicle's battery model to predict a model voltage corresponding to the corrected first time-series data. The apparatus and method estimate parameters configured to minimize the difference between the model voltage and the actual battery voltage as parameters of the battery model. Thus, optimal parameters for the battery model can be estimated.

[0020] Another aspect of this disclosure provides a battery model parameter estimation apparatus and method. The apparatus and method acquire first time-series data of the state information of a battery installed in a vehicle. Based on an update request signal from a battery management system (BMS) installed in the vehicle, the apparatus and method determine whether to update the parameters of the vehicle's battery model. The apparatus and method correct the state of charge (SOC) and state of health (SOH) values ​​in the first time-series data. The apparatus and method use the battery model to predict a model voltage corresponding to the corrected first time-series data. The apparatus and method estimate parameters configured to minimize the difference between the model voltage and the actual voltage of the battery as parameters of the battery model. Thus, optimal parameters for the battery model can be estimated.

[0021] The purpose of this disclosure is not limited to the foregoing objectives. Any other objectives and advantages not mentioned herein should be clearly understood from the following description and may become more apparent from embodiments of this disclosure. Furthermore, it will be readily apparent that the objectives and advantages of this disclosure can be achieved by the devices and combinations thereof indicated in the claims.

[0022] According to one aspect of this disclosure, the battery model parameter estimation device may include a memory configured to store a battery model of a vehicle and may include a controller. The controller acquires first time-series data of the state information of a battery disposed in the vehicle. The controller corrects the state of charge (SOC) and state of health (SOH) values ​​in the first time-series data. The controller predicts a model voltage corresponding to the corrected first time-series data based on the battery model. The controller estimates parameters configured to minimize the difference between the model voltage and the actual voltage of the battery as optimal parameters for the battery model.

[0023] In embodiments of this disclosure, the controller can determine a reference SOC value and a reference SOH value based on first time-series data. The controller can use the reference SOC value to correct the SOC value. The controller can use the reference SOH value to correct the SOH value.

[0024] In embodiments of this disclosure, the controller can determine whether to update the parameters of the battery model based on the SOH value.

[0025] In embodiments of this disclosure, the controller can acquire second time-series data of the vehicle's driving information. Based on the accumulated mileage in the second time-series data of the driving information, the controller can determine whether to update the parameters of the battery model.

[0026] In embodiments of this disclosure, the controller may determine whether to update the parameters of the battery model based on an update request signal from the vehicle.

[0027] In embodiments of this disclosure, the battery state information may include at least one of SOC, SOH, current, voltage, and temperature.

[0028] In embodiments of this disclosure, the battery model may be a model configured to predict the battery voltage based on parameters, SOC, SOH, current, and temperature.

[0029] In embodiments of this disclosure, the controller can remove erroneous data from the first time-series data.

[0030] In embodiments of this disclosure, the controller can acquire first time-series data and second time-series data of vehicle driving information by loading driving information and battery status information. The driving information and status information are received from the vehicle each time. The controller can also acquire first time-series data and second time-series data of vehicle driving information by generating second time-series data of vehicle driving information and first time-series data of battery status information stored in the vehicle.

[0031] According to another aspect of this disclosure, a battery model parameter estimation method may include storing a battery model of a vehicle in a memory. The method may include acquiring first time-series data of the state information of a battery disposed in the vehicle by a controller. The method may include correcting the state of charge (SOC) and state of health (SOH) values ​​in the first time-series data by the controller. The method may include predicting a model voltage corresponding to the corrected first time-series data based on the battery model by the controller. The method may include estimating parameters configured by the controller to minimize the difference between the model voltage and the actual voltage of the battery as optimal parameters for the battery model.

[0032] In embodiments of this disclosure, correcting SOC and SOH values ​​may include determining reference SOC and reference SOH values ​​based on first time-series data. Correcting SOC and SOH values ​​may include correcting the SOC value using the reference SOC value. Correcting SOC and SOH values ​​may further include correcting the SOH value using the reference SOH value.

[0033] In embodiments of this disclosure, obtaining the first time series data may include determining whether to update the battery model based on the SOH value.

[0034] In embodiments of this disclosure, the method may further include acquiring second time-series data of vehicle driving information; and determining whether to update the parameters of the battery model based on the cumulative mileage in the second time-series data of driving information.

[0035] In embodiments of this disclosure, acquiring the first time-series data may include determining whether to update the parameters of the battery model based on an update request signal from the vehicle.

[0036] In embodiments of this disclosure, the battery state information may include at least one of SOC, SOH, current, voltage, and temperature.

[0037] In embodiments of this disclosure, the battery model may be a model configured to predict the battery voltage based on parameters, SOC, SOH, current, and temperature.

[0038] In embodiments of this disclosure, obtaining first time series data may include removing erroneous data from the first time series data.

[0039] In embodiments of this disclosure, acquiring the second time-series data of the first time-series data and vehicle driving information may include: loading driving information and battery status information. The driving information and status information are received from the vehicle each time. Acquiring the second time-series data of the first time-series data and vehicle driving information may include: generating second time-series data of vehicle driving information and first time-series data of battery status information disposed in the vehicle. Attached Figure Description

[0040] The above and other objects, features, and advantages of this disclosure will become more apparent from the following detailed description taken in conjunction with the accompanying drawings:

[0041] Figure 1 This is a configuration diagram of a battery model parameter estimation system according to an embodiment of the present disclosure;

[0042] Figure 2 This is a configuration diagram of a battery model parameter estimation device according to an embodiment of the present disclosure;

[0043] Figure 3 This is a diagram showing the state of charge (SOC) and reference SOC values ​​obtained by a controller provided in a battery model parameter estimation device according to an embodiment of the present disclosure;

[0044] Figure 4 This is a diagram showing the result of correcting the SOC value in a controller provided in a battery model parameter estimation device according to an embodiment of the present disclosure;

[0045] Figure 5 This is a diagram illustrating the process of determining the parameters of a battery model in a controller provided in a battery model parameter estimation device according to an embodiment of the present disclosure;

[0046] Figure 6 This is a diagram showing the result of adjusting the parameters of a battery model in a controller provided in a battery model parameter estimation device according to an embodiment of the present disclosure;

[0047] Figure 7 This is a flowchart of a battery model parameter estimation method according to embodiments of the present disclosure; and

[0048] Figure 8 This is a block diagram illustrating a computational system for performing a battery model parameter estimation method according to an embodiment of the present disclosure. Detailed Implementation

[0049] In the following, embodiments of the present invention will be described in detail with reference to the accompanying drawings. Furthermore, when adding reference numerals to components in each figure, it should be noted that identical or equivalent components are denoted by the same numbers, even if these components are shown in other figures. Additionally, detailed descriptions of well-known features or functions have been omitted in the description of embodiments of this disclosure to avoid unnecessarily obscuring the spirit of the disclosure.

[0050] In describing the components of embodiments of this disclosure, terms such as first, second, "A", "B", (a), (b), etc., may be used. These terms are used only to distinguish one component from another and do not limit the corresponding components regardless of their order or priority. Furthermore, unless otherwise defined, all terms used herein, including technical and scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning equivalent to that in the context of the relevant field. Unless explicitly defined herein as having an ideal or overly formal meaning, these terms should not be interpreted as having an ideal or overly formal meaning. When controllers, modules, components, devices, elements, parts, units, etc., of this disclosure are described as having a purpose or performing an operation, function, etc., controllers, modules, components, devices, elements, parts, units, etc., should be considered herein as being "configured" to satisfy that purpose or perform that operation or function. Each controller, module, component, device, element, part, unit, etc., may be embodied independently or included as part of a device together with a processor and memory (such as a non-transitory computer-readable medium).

[0051] Figure 1 This is a configuration diagram of a battery model parameter estimation system according to an embodiment of the present disclosure.

[0052] like Figure 1 As shown, the battery model parameter estimation system according to embodiments of this disclosure may include multiple vehicles 100, a data server 200, and a battery model parameter estimation device 300. The data server 200 can be implemented by incorporating it into the battery model parameter estimation device 300.

[0053] Vehicle 100 may include a battery management system (BMS) and may transmit driving information and battery status information to data server 200. Here, when vehicle 100 is in motion, it may transmit driving information and battery status information to data server 200 throughout the entire state of charge (SOC) range of the battery (e.g., 10% to 100%). Driving information may include accumulated mileage information, information indicating whether vehicle 100 is parked or driving, information indicating whether vehicle 100 is charging, information indicating whether vehicle 100 is slow charging or fast charging, charging status information, etc.

[0054] Data server 200 can be implemented as a cloud server to load driving information and battery status information received from vehicle 100 at each time unit or time value (i.e., each time instance or time interval) within a certain period. Data server 200 can generate time-series data (second time-series data) of the driving information of vehicle 100 within that period and time-series data (first time-series data) of the battery status information set in vehicle 100. This function of data server 200 can be implemented by battery model parameter estimation device 300.

[0055] The battery model parameter estimation device 300 can obtain time-series data of the state information of the battery set in the vehicle 100 from the data server 200. The battery model parameter estimation device 300 can correct the state of charge (SOC) and state of health (SOH) values ​​in the time-series data. The battery model parameter estimation device 300 can use the battery model of the vehicle 100 to predict the model voltage corresponding to the corrected time-series data. The battery model parameter estimation device 300 can estimate parameters to minimize the difference between the model voltage and the actual battery voltage, as the optimal parameters for the battery model.

[0056] The battery model parameter estimation device 300 can acquire time-series data of the state information of the battery installed in the vehicle 100. The battery model parameter estimation device 300 can determine reference SOC and reference SOH values ​​based on the voltage, current, and temperature in the time-series data. The battery model parameter estimation device 300 can correct the SOC and SOH values ​​in the time-series data based on the reference SOC and reference SOH values, respectively. In other words, the battery model parameter estimation device 300 can correct the SOC value in the time-series data to conform to the reference SOC value, and can correct the SOH value in the time-series data to conform to the reference SOH value.

[0057] The battery model parameter estimation device 300 can obtain time-series data of the state information of the battery set in the vehicle 100 from the data server 200. The battery model parameter estimation device 300 can determine whether to update the parameters of the battery model of the vehicle 100 based on the SOH value in the time-series data.

[0058] The battery model parameter estimation device 300 can obtain time-series data of the driving information of the vehicle 100 from the data server 200. Based on the cumulative mileage displayed by the odometer (ODO) set in the vehicle 100 in the time-series data of the driving information, the battery model parameter estimation device 300 determines whether to update the parameters of the battery model of the vehicle 100.

[0059] The battery model parameter estimation device 300 can determine whether to update the parameters of the battery model of the vehicle 100 based on the update request signal from the BMS set in the vehicle 100.

[0060] Figure 2 This is a configuration diagram of a battery model parameter estimation device according to an embodiment of the present disclosure.

[0061] like Figure 2 As shown, the battery model parameter estimation device 300 according to an embodiment of the present disclosure may include a memory 10 (i.e., a storage device), a communication device 20, an output device 30, and a controller 40. In this case, the various components may be coupled to each other to be implemented as a whole according to the scheme of executing the battery model parameter estimation device 300 according to an embodiment of the present disclosure, and some components may be omitted.

[0062] The memory 10 can store various logics, algorithms, and programs required in the process of acquiring time-series data of the state information of the battery set in the vehicle 100. These logics, algorithms, and programs are required in the process of correcting the SOC and SOH values ​​in the time-series data, the process of predicting the model voltage corresponding to the corrected time-series data using the battery model of the vehicle 100, and the process of estimating parameters for minimizing the difference between the model voltage and the actual voltage of the battery as parameters of the battery model.

[0063] The memory 10 can store various logics, algorithms, and programs required in the process of acquiring time-series data of the state information of the battery located in the vehicle 100. These logics, algorithms, and programs are required in the process of determining reference SOC and reference SOH values ​​based on voltage, current, and temperature in the time-series data. These logics, algorithms, and programs are also required in the process of correcting the SOC and SOH values ​​in the time-series data based on the reference SOC and reference SOH values ​​respectively; in the process of predicting the model voltage corresponding to the corrected time-series data using the battery model of the vehicle 100; and in the process of estimating parameters used to minimize the difference between the model voltage and the actual voltage of the battery as parameters for the battery model.

[0064] The memory 10 may store various logics, algorithms, and programs required in the process of acquiring time-series data of the state information of the battery located in the vehicle 100. These logics, algorithms, and programs are required in the processes of determining whether to update the parameters of the battery model of the vehicle 100 based on the SOH values ​​in the time-series data, correcting the SOH values ​​and SOH values ​​in the time-series data, predicting the model voltage corresponding to the corrected time-series data using the battery model, and estimating parameters used to minimize the difference between the model voltage and the actual voltage of the battery as parameters of the battery model.

[0065] The memory 10 can store various logics, algorithms, and programs required in the process of acquiring time-series data of driving information of the vehicle 100 and time-series data of state information of the battery set in the vehicle 100. These logics, algorithms, and programs are required in determining whether to update the parameters of the battery model of the vehicle 100 based on the accumulated mileage displayed by the ODO (On-Demand Detection) set in the vehicle 100 in the time-series data of driving information. These logics, algorithms, and programs are also required in the process of correcting the SOC and SOH values ​​in the time-series data, predicting the model voltage corresponding to the corrected time-series data using the battery model of the vehicle 100, and estimating parameters used to minimize the difference between the model voltage and the actual battery voltage as parameters of the battery model.

[0066] The memory 10 can store various logics, algorithms, and programs required for the following processes: acquiring time-series data of the state information of the battery set in the vehicle 100; determining whether to update the parameters of the battery model of the vehicle 100 based on an update request signal from the BMS set in the vehicle 100; correcting the SOC and SOH values ​​in the time-series data; using the battery model to predict the model voltage corresponding to the corrected time-series data; and estimating parameters for minimizing the difference between the model voltage and the actual voltage of the battery, as parameters of the battery model.

[0067] The memory 10 may include algorithms for calculating the State of Charge (SOC) value based on the battery's voltage, current, and temperature, and algorithms for calculating the State of Harm (SOH) value based on the battery's voltage, current, and temperature. Detailed descriptions of these algorithms are omitted here because they are well-known technologies.

[0068] The memory 10 can store a battery model for each vehicle 100 and parameters of that battery model, which are estimated by the controller 40. In this paper, the battery model can predict the battery voltage based on the battery's state information and parameters. The battery's state information may include SOC, SOH, temperature, current, voltage, etc.

[0069] The communication device 20 may be a module providing a communication interface with the vehicle 100 and a communication interface with the data server 200. The communication device 20 can receive parameter update signals from the vehicle 100's BMS. The communication device 20 can receive time-series data of the vehicle 100's driving information and time-series data of the battery status information stored in the vehicle 100 from the data server 200. The communication device 20 can transmit parameters of the battery model estimated by the controller 40 to the vehicle 100. This communication device 20 may include at least one of a mobile communication module, a wireless internet module, and a short-range communication module.

[0070] The mobile communication module can communicate with the vehicle 100 and the data server 200 through a mobile communication network established according to mobile communication technical standards or communication schemes (e.g., Global System for Mobile Communications (GSM), Code Division Multiple Access (CDMA), Code Division Multiple Access 2000 (CDMA2000), Enhanced Voice Data Optimized or Enhanced Voice Data Only (EV-DO), Wideband CDMA (WCDMA), High-Speed ​​Downlink Packet Access (HSDPA), High-Speed ​​Uplink Packet Access (HSUPA), Long Term Evolution (LTE), Advanced Long Term Evolution (LTE-A), etc.).

[0071] The wireless internet module can be a module for wireless internet access, which can communicate with vehicle 100 and data server 200 through wireless LAN (WLAN), Wi-Fi, Wi-Fi Direct, Digital Living Network Alliance (DLNA), WiBro, WiMAX, High-Speed ​​Downlink Packet Access (HSDPA), High-Speed ​​Uplink Packet Access (HSUPA), LTE, LTE-A, etc.

[0072] The short-range communication module can use Bluetooth. TM At least one of the following technologies supports short-range communication with vehicle 100 and data server 200: Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra Wideband (UWB), ZigBee, Near Field Communication (NFC), and Wireless Universal Serial Bus (USB).

[0073] The output device 30 can output the results of updating the parameters of the battery model (e.g., the rate of increase compared to the initial parameters).

[0074] The controller 40 can be electrically connected to the various components and can perform overall control, enabling each component to perform its own function normally. Such a controller 40 can be implemented in hardware, in software, or a combination of both. In one embodiment, the controller 40 can be implemented as, but is not limited to, a microprocessor.

[0075] The controller 40 can acquire time-series data of the state information of the battery set in the vehicle 100. The controller 40 can correct the SOC and SOH values ​​in the time-series data. The controller 40 can predict the model voltage corresponding to the corrected time-series data based on the battery model of the vehicle 100. The controller 40 can estimate parameters to minimize the difference between the model voltage and the actual battery voltage, as the optimal parameters for the battery model. The controller 40 can manage the battery model of the vehicle 100 based on the estimated optimal parameters. The controller 40 can control the vehicle 100 based on the battery model. The controller 40 can predict the model voltage V based on the following equation 1. model And the model voltage V can be determined based on the following equation 2. real The parameter θ that minimizes the difference between them.

[0076] [Formula 1]

[0077] V model =f(θ, SOC) corrected SOH corrected (temperature, current)

[0078] Here, V model The voltage represents the model voltage, θ represents the parameters of the battery model, and SOC represents the voltage. corrected This indicates the corrected SOC value, and SOH... corrected This indicates the corrected SOH value.

[0079] [Formula 2]

[0080] argmin θ (V model，θ -V real )

[0081] Here, θ represents the parameters of the battery model, V model Represents the model voltage, V real argmin represents the actual voltage of the battery. θ () represents the logic used to determine θ, which minimizes the result in parentheses.

[0082] The controller 40 can acquire time-series data of the state information of the battery installed in the vehicle 100. The controller 40 can determine a reference SOC value and a reference SOH value based on the voltage, current, and temperature in the time-series data. The controller 40 can correct the SOC value and SOH value in the time-series data based on the reference SOC value and the reference SOH value, respectively. In other words, the controller 40 can correct the SOC value in the time-series data to conform to the reference SOC value, and it can correct the SOH value in the time-series data to conform to the reference SOH value.

[0083] The controller 40 can acquire time-series data of the state of the battery set in the vehicle 100, and can determine whether to update the parameters of the battery model of the vehicle 100 (i.e., whether to estimate the optimal parameters) based on the SOH value in the time-series data. When the SOH value is not greater than a threshold, the controller 40 can start updating the parameters of the battery model of the vehicle 100. In other words, the controller 40 can start the process of estimating the optimal parameters of the battery model.

[0084] The controller 40 can acquire time-series data of the driving information of the vehicle 100, and can determine whether to update the parameters of the battery model of the vehicle 100 based on the accumulated mileage displayed by the odometer (ODO) set in the vehicle 100 in the time-series data of the driving information. When the accumulated mileage is greater than a threshold, the controller 40 can start updating the parameters of the battery model of the vehicle 100.

[0085] The controller 40 can determine whether to update the parameters of the battery model of the vehicle 100 based on the update request signal from the BMS set in the vehicle 100.

[0086] The controller 40 can acquire time-series data of the state information of the battery set in the vehicle 100, and can remove erroneous data from the time-series data.

[0087] The controller 40 can acquire time-series data of the driving information of the vehicle 100 and remove erroneous data from the time-series data.

[0088] When the data server 200 performs its functions, the controller 40 can load the driving information and battery status information received from the vehicle 100 at each time unit or time value, and can generate time-series data of the driving information of the vehicle 100 and time-series data of the battery status information set in the vehicle 100.

[0089] In the following text, see references Figures 3 to 6 Describe the operation of controller 40 in detail.

[0090] Figure 3This is a diagram illustrating the SOC and reference SOC values ​​obtained by a controller provided in a battery model parameter estimation device according to an embodiment of the present disclosure.

[0091] exist Figure 3 In the diagram, the vertical axis represents the SOC value, the horizontal axis represents time, reference numeral 310 represents the SOC value of the battery obtained by the controller 40 from the vehicle 100, and reference numeral 320 represents the reference SOC value determined by the controller 40 based on the battery's voltage, current, and temperature.

[0092] like Figure 3 As shown, a difference of 330 can be observed between the battery's SOC value of 310 and the reference SOC value of 320. This occurs due to errors in the parameters related to battery degradation, therefore the battery's SOC value of 310 needs to be corrected.

[0093] Figure 4 This is a diagram showing the result of correcting the SOC value in a controller provided in a battery model parameter estimation device according to an embodiment of the present disclosure.

[0094] The controller 40 can adjust the battery's SOC value 310 based on the above formula 1 to conform to the reference SOC value 320. For example... Figure 4 As shown, the battery's SOC value of 310 follows the reference SOC value of 320.

[0095] Figure 5 This is a diagram illustrating the process of determining the parameters of a battery model in a controller provided in a battery model parameter estimation device according to an embodiment of the present disclosure.

[0096] exist Figure 5 In the diagram, the vertical axis represents voltage, the horizontal axis represents time, and reference numeral 510 indicates the model voltage V. model And reference numeral 520 indicates the actual voltage V of the battery. real .

[0097] The controller 40 can determine the parameter θ, which minimizes the difference between the model voltage 510 and the actual voltage 520 of the battery, as the optimal parameter for the battery model based on the above formula 2.

[0098] Figure 6 This is a diagram showing the result of adjusting the parameters of a battery model in a controller provided in a battery model parameter estimation device according to an embodiment of the present disclosure.

[0099] like Figure 6As shown, vehicle A, which traveled 140,000 km, had an increase rate of 1.07 compared to the initial parameters; vehicle B, which traveled 300,000 km, had an increase rate of 1.25 compared to the initial parameters; and vehicle C, which traveled 330,000 km, had an increase rate of 1.31 compared to the initial parameters.

[0100] Figure 7 This is a flowchart of a battery model parameter estimation method according to an embodiment of the present disclosure.

[0101] First, in operation 701, memory 10 can store the battery model of vehicle 100.

[0102] Subsequently, in operation 702, the controller 40 can acquire time-series data of the state information of the battery set in the vehicle 100.

[0103] In operation 703, controller 40 can correct the SOC and SOH values ​​in the time series data.

[0104] In operation 704, controller 40 can predict the model voltage corresponding to the corrected time series data based on the battery model.

[0105] In operation 705, controller 40 can estimate parameters for minimizing the difference between the model voltage and the actual voltage of the battery, as the optimal parameters for the battery model.

[0106] Figure 8 This is a block diagram illustrating a computational system for performing a battery model parameter estimation method according to an embodiment of the present disclosure.

[0107] refer to Figure 8 The battery model parameter estimation method described above according to the embodiments of the present disclosure can be implemented via computing system 1000. Computing system 1000 may include at least one of processor 1100, memory 1300, user interface input device 1400, user interface output device 1500, storage device 1600 (i.e., storage device) and network interface 1700 connected to each other via system bus 1200.

[0108] Processor 1100 may be a central processing unit (CPU) or a semiconductor device that processes instructions stored in memory 1300 and / or storage device 1600. Memory 1300 and storage device 1600 may include various types of volatile or non-volatile storage media. For example, memory 1300 may include read-only memory (ROM) 1310 and random access memory (RAM) 1320.

[0109] Therefore, the operation of the methods or algorithms described in conjunction with the embodiments disclosed in this disclosure can be directly implemented using hardware modules, software modules, or a combination of hardware and software modules executed by processor 1100. Software modules may reside on storage media such as RAM, flash memory, ROM, EPROM, EEPROM, registers, hard disks, removable disks, and CD-ROMs (i.e., memory 1300 and / or storage device 1600). The storage media may be coupled to processor 1100. Processor 1100 can read information from the storage media and can write information to the storage media. Alternatively, the storage media may be integrated with processor 1100. Processor 1100 and storage media may reside in an application-specific integrated circuit (ASIC). The ASIC may reside within a user terminal. In another case, processor 1100 and storage media may reside as separate components in the user terminal.

[0110] The battery model parameter estimation system, device, and method according to embodiments of this disclosure can acquire time-series data of the state information of a battery installed in a vehicle. The system, device, and method can correct the state of charge (SOC) and state of health (SOH) values ​​in the time-series data. The system, device, and method can use a battery model of the vehicle to predict a model voltage corresponding to the corrected time-series data, and can estimate parameters for minimizing the difference between the model voltage and the actual battery voltage, as parameters of the battery model. Therefore, optimal parameters for the battery model can be estimated.

[0111] In the foregoing description of embodiments and accompanying drawings, this disclosure is not limited thereto. Instead, various modifications and alterations can be made by those skilled in the art to which this disclosure pertains without departing from the spirit and scope of this disclosure as claimed in the appended claims. Therefore, the embodiments of this disclosure are not intended to limit the technical spirit of this disclosure, but are provided for illustrative purposes only. The scope of this disclosure should be interpreted based on the appended claims, and all technical concepts within the scope of the claims should be included within the scope of this disclosure.

Claims

1. A battery model parameter estimation device, comprising: The memory is configured to store the vehicle's battery model; as well as The controller is configured as follows: First time-series data of the state information of the battery installed in the vehicle; Correct the state of charge and state of health values ​​in the first time series data; predict the model voltage corresponding to the corrected first time series data based on the battery model; and The parameters configured to minimize the difference between the model voltage and the actual voltage of the battery are estimated as the optimal parameters for the battery model.

2. The battery model parameter estimation device according to claim 1, wherein, The controller is configured to: Based on the first time series data, a reference state of charge value and a reference health value are determined; The state of charge value is corrected using the reference state of charge value; and The health status value is corrected using the reference health status value.

3. The battery model parameter estimation device according to claim 1, wherein, The controller is configured to: The battery model's parameters are determined based on the health status value.

4. The battery model parameter estimation device according to claim 1, wherein, The controller is configured to: Acquire second time-series data of the vehicle's driving information; and Based on the cumulative mileage in the second time-series data of the driving information, determine whether to update the parameters of the battery model.

5. The battery model parameter estimation device according to claim 1, wherein, The controller is configured to: Based on the update request signal from the vehicle, it is determined whether to update the parameters of the battery model.

6. The battery model parameter estimation device according to claim 1, wherein, The state information of the battery includes at least one of state of charge, state of health, current, voltage, and temperature.

7. The battery model parameter estimation device according to claim 1, wherein, The battery model is configured to predict the battery voltage based on parameters, state of charge, state of health, current, and temperature.

8. The battery model parameter estimation device according to claim 1, wherein, The controller is configured to: Remove erroneous data from the first time series data.

9. The battery model parameter estimation device according to claim 1, wherein, The controller is configured to acquire the first time-series data and the second time-series data of the vehicle's driving information in the following manner: Loading driving information and the battery status information, which are received from the vehicle over a period of time; and The second time-series data for generating the driving information of the vehicle and the first time-series data for generating the state information of the battery disposed in the vehicle.

10. A method for estimating battery model parameters, comprising: The vehicle's battery model is stored in a memory. The controller acquires first time-series data of the state information of the battery installed in the vehicle over a period of time. The controller corrects the state of charge and health values ​​in the first time series data; The controller predicts the model voltage corresponding to the corrected first time series data based on the battery model; and The parameters estimated by the controller, configured to minimize the difference between the model voltage and the actual voltage of the battery, are used as the optimal parameters for the battery model.

11. The battery model parameter estimation method according to claim 10, wherein, Correcting the state of charge value and the health state value includes: Based on the first time series data, a reference state of charge value and a reference health value are determined; The state of charge value is corrected using the reference state of charge value; and The health status value is corrected using the reference health status value.

12. The battery model parameter estimation method according to claim 10, wherein, Obtaining the first time series data includes: The battery model's parameters are determined based on the health status value.

13. The battery model parameter estimation method according to claim 10 further includes: Acquire second time-series data of the vehicle's driving information; and Based on the cumulative mileage in the second time-series data of the driving information, determine whether to update the parameters of the battery model.

14. The battery model parameter estimation method according to claim 10, wherein, Obtaining the first time series data includes: Based on the update request signal from the vehicle, it is determined whether to update the parameters of the battery model.

15. The battery model parameter estimation method according to claim 10, wherein, The state information of the battery includes at least one of state of charge, state of health, current, voltage, and temperature.

16. The battery model parameter estimation method according to claim 10, wherein, The battery model is configured to predict the battery voltage based on parameters, state of charge, state of health, current, and temperature.

17. The battery model parameter estimation method according to claim 10, wherein, Obtaining the first time series data includes: Remove erroneous data from the first time series data.

18. The battery model parameter estimation method according to claim 10, wherein, The second time-series data for obtaining the first time-series data and the vehicle's driving information includes: Loading driving information and the battery status information, the driving information and the status information being received from the vehicle at each time interval within the stated time period; and The second time-series data for generating the driving information of the vehicle and the first time-series data for generating the state information of the battery disposed in the vehicle.

Images