Battery management device and method

Abstract

This invention relates to a battery management apparatus and method. The battery management apparatus includes a memory storing a battery model and an algorithm, and a processor obtaining battery state information using the battery model. The processor obtains a predicted voltage of the battery based on the battery model, determines the voltage deviation between the measured voltage and the predicted voltage, and updates the battery model by resetting the parameters of the battery model if the voltage deviation exceeds a first threshold.

Description

[0001] Cross-references to related applications

[0002] This application claims priority and benefit to Korean Patent Application No. 10-2024-0201082, filed on December 30, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This invention relates to battery management devices and methods, and more specifically, to techniques for more accurately determining battery state information. Background Technology

[0004] Batteries are increasingly used in a variety of electronic devices, and in recent years, their use has been increasing with the emergence of electric vehicles (e.g., electric vehicles or hybrid vehicles).

[0005] For efficiency and reliability reasons, it is recommended to replace batteries in electric vehicles before they become severely aged. Furthermore, the charging and discharging capacity of the battery can be adjusted based on the degree of battery aging.

[0006] Therefore, battery state information (including battery aging) can be used to properly manage the battery. The degree of battery aging can be identified through the battery's state of health (SOH).

[0007] Therefore, battery systems can determine the battery's state information to operate the battery effectively, and techniques have been proposed to notify users of the battery's state information.

[0008] However, battery state information can be inaccurate depending on the driving mode of the vehicle equipped with the battery. General-purpose battery models used to determine battery state information often fail to adequately reflect errors in battery state information caused by the vehicle's driving mode.

[0009] To more accurately determine the state of a battery, impedance analysis using electrochemical spectroscopy can be used. However, this method is difficult to apply to automotive batteries due to the need for separate experimental equipment and the long experimental time required.

[0010] Another approach is to use equivalent circuit models to understand the characteristics of car batteries. However, battery models based on equivalent circuit models have the drawback of lower accuracy because they do not reflect the internal physics of the battery.

[0011] The matters described in this background section are merely intended to enhance the understanding of the background of the invention. Therefore, this background section may contain information of prior art that is not known to those skilled in the art to which this invention pertains. Summary of the Invention

[0012] The present invention is made to solve the above-mentioned problems in the prior art, while fully retaining the advantages of the prior art.

[0013] Various aspects of the present invention provide battery management devices and methods for obtaining state information of batteries installed in vehicles in a simpler and more accurate manner.

[0014] Various aspects of the present invention provide battery management devices and methods that can more accurately acquire battery state information reflecting the conditions under which the battery operates.

[0015] The technical problems to be solved by this invention are not limited to those described above. Those skilled in the art will more clearly understand other technical problems not mentioned herein from the following description.

[0016] According to one aspect of the present invention, a battery management device is provided. The battery management device includes a memory configured to store a battery model and an algorithm, and a processor configured to obtain state information of a battery using the battery model. The processor is configured to obtain a predicted voltage of the battery based on the battery model, determine a voltage deviation between a measured voltage and a predicted voltage, and update the battery model by resetting its parameters based on determining that the voltage deviation meets a threshold condition.

[0017] In the implementation plan, parameters can be set based on electrochemistry to reflect the internal state information of the battery.

[0018] In one implementation, the processor can be configured to obtain a measured current of the battery and determine a predicted voltage of the battery by inputting the measured current into a battery model.

[0019] In one implementation, the processor may be configured to collect measured currents obtained during the operation of a battery-powered vehicle.

[0020] In one implementation, the processor can be configured to determine the voltage deviation based on a measured voltage acquired at the same time as the measured current.

[0021] In an implementation, the processor can be configured to determine the voltage deviation using the root mean square error obtained based on the voltage difference between “n” measured voltages and “n” predicted voltages, where n is a natural number greater than or equal to 2.

[0022] In one implementation, the processor can be configured to reset parameters to reduce the voltage deviation between the measured voltage and the predicted voltage.

[0023] In the implementation, the processor can be configured to generate new parameters, obtain a modified predicted voltage based on a battery model with the new parameters, and reset the parameters such that the voltage deviation between the measured voltage and the modified predicted voltage falls within a threshold range.

[0024] In the implementation, the processor may be configured to reset at least one of the parameters: internal resistance, diffusion coefficient, reaction rate constant, or porosity.

[0025] In the implementation, the processor may be configured to use a battery model to determine at least one of the battery's health status or the loss of active material.

[0026] According to another aspect of the present invention, a battery management method is provided. The battery management method includes: obtaining a predicted voltage of a battery based on a battery model, determining a voltage deviation between a measured voltage of the battery and the predicted voltage, and updating the battery model by resetting the parameters of the battery model based on determining that the voltage meets a threshold condition.

[0027] In the implementation plan, parameters can be set based on electrochemistry to reflect the internal state information of the battery.

[0028] In one implementation, determining the predicted voltage may include obtaining a measured current of the battery and inputting the measured current into a battery model.

[0029] In the implementation scheme, the measured current can be obtained during the operation of a battery-equipped vehicle.

[0030] In an implementation scheme, determining the voltage deviation may include using a measured voltage acquired at the same time as the measured current.

[0031] In an implementation scheme, determining the voltage deviation may include determining the root mean square error based on the voltage difference between “n” measured voltages and “n” predicted voltages, where n is a natural number greater than or equal to 2.

[0032] In an implementation, resetting parameters may include resetting parameters to reduce the voltage deviation between the measured voltage and the predicted voltage.

[0033] In the implementation, resetting parameters may include generating new parameters, obtaining a modified predicted voltage based on a battery model with the new parameters, and comparing the voltage deviation between the measured voltage and the modified predicted voltage with a threshold range.

[0034] According to another aspect of the present invention, a battery management server is provided. The battery management server includes a database storing battery models and algorithms, and a processor configured to obtain state information of a battery installed in a vehicle using the battery models. The processor is configured to collect measured voltage and measured current of the battery obtained during vehicle operation, obtain a predicted voltage of the battery based on the battery model, determine a voltage deviation between the measured voltage and the predicted voltage, and update the battery model by resetting the parameters of the battery model based on determining that the voltage deviation meets a threshold condition.

[0035] In the implementation scheme, the processor may be configured to generate battery state information using a battery model to include at least one of the battery's health status or loss of active material, and to transmit the battery state information to the vehicle via a communication device. Attached Figure Description

[0036] The above and other objects, features, and advantages of the present invention will become more apparent from the following detailed description taken in conjunction with the accompanying drawings, in which:

[0037] Figure 1 This is a block diagram illustrating the configuration of a battery management device according to an embodiment of the present invention;

[0038] Figure 2 This is a flowchart describing a battery management method according to an embodiment of the present invention;

[0039] Figure 3 A user interface according to an embodiment of the present invention is shown;

[0040] Figure 4 This is a schematic diagram illustrating a battery management system according to another embodiment of the present invention;

[0041] Figure 5 This is a flowchart describing a method for acquiring driving data according to an embodiment of the present invention;

[0042] Figure 6 This is a schematic diagram illustrating the process for determining the consistency of parameters according to an embodiment of the present invention;

[0043] Figure 7 This is a flowchart describing a process for resetting parameters according to an embodiment of the present invention;

[0044] Figure 8 This is a schematic diagram illustrating a computing system according to an embodiment of the present invention. Detailed Implementation

[0045] In the following, embodiments of the invention will be described in detail with reference to the accompanying drawings. When adding reference numerals to the components of the drawings, it should be noted that these components are represented by the same reference numerals even when the same or equivalent components are shown in different drawings. Furthermore, in describing embodiments of the invention, detailed descriptions of known features or functions are omitted where it is determined that such detailed descriptions would unnecessarily obscure the spirit of the invention.

[0046] In the following description, terms such as first, second, "A", "B", (a), (b), etc., may be used. These terms are intended only to distinguish one component from another. The terms do not limit the nature, order, or sequence of the components. Unless otherwise defined, all terms used herein (including technical or scientific terms) have the meaning commonly understood by one of ordinary skill in the art to which this invention pertains. Such terms as defined in a general dictionary should be interpreted as having the equivalent meaning in the context of the relevant technical field and should not be interpreted as having an ideal or overly formal meaning unless expressly defined as having such a meaning in this application.

[0047] In this invention, when the components, controllers, devices, elements, equipment, units, etc. of this invention are described as having a purpose or performing operations, functions, etc., the components, controllers, devices, elements, equipment, units, etc., shall be regarded herein as "configured to" satisfy the stated purpose or perform the stated operations or functions. Each component, controller, device, element, equipment, unit, etc. may individually embody or include a processor and memory (e.g., a non-volatile computer-readable medium) as part of the device.

[0048] In the following text, see references Figures 1 to 8 The embodiments of the present invention will be described in detail below.

[0049] Figure 1 This is a block diagram illustrating the configuration of a battery management device according to an embodiment of the present invention.

[0050] refer to Figure 1 According to an embodiment of the present invention, the battery management device 900 can obtain the state information of the battery 10 based on the sensing data of the battery 10 (e.g., current, voltage, temperature, etc.).

[0051] Battery 10 and battery management device 900 can be installed in vehicle 1.

[0052] Battery 10 can be used to supply voltage to a load mounted on vehicle 1. The load can be an electrical component of vehicle 1, a DC-DC converter, a motor driving the wheels, etc.

[0053] The battery management device 900 may include a sensor device 20, a battery model 30, a memory 40, and a processor 100.

[0054] Sensor device 20 may include sensors for measuring voltage, current, and temperature of battery 10. Sensor device 20 can acquire sensing data using the sensors. The sensing data can be used to acquire measured voltage, measured current, measured temperature, etc. of battery 10. Battery model 30 can be used to acquire state information of battery 10. State information of battery 10 may include state of health (SOH) of battery 10, active material loss information, etc.

[0055] The battery model 30 can be an electrochemical model. According to an embodiment of the present invention, the battery model 30 can be generated based on electrochemical aging modeling, which can reflect the internal state of the battery 10, thereby obtaining more accurate state information of the battery 10. In an embodiment, the battery model 30 can be stored in a memory 40.

[0056] The memory 40 can be used to store algorithms for the operation of the processor 100. The memory 40 can be implemented using a hard disk drive, flash memory, electrically erasable programmable read-only memory (EEPROM), static RAM (SRAM), ferroelectric RAM (FRAM), phase-change RAM (PRAM), magnetic RAM (MRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate SDRAM (DDR-SDRAM), or other similar types of memory.

[0057] The processor 100 can use the battery model 30 to obtain a predicted voltage of the battery 10, and can update the internal variables of the battery model 30 based on the predicted voltage and the measured voltage acquired by the sensor device 20. The processor 100 can use the updated internal variables of the battery model 30 to obtain the state information of the battery 10 more accurately. According to an embodiment of the present invention, the processor 100 can reset the parameters of the battery model 30 based on sensing data acquired during the time period in which the vehicle 1 is traveling.

[0058] In the implementation scheme, the processor 100 can acquire driving data, including sensing data, for each driving period.

[0059] Driving data may include the usage time and mileage of vehicle 1. The usage time of vehicle 1 may include the period from the time the battery of vehicle 1 is used to the time the driving data is acquired. The mileage of vehicle 1 may include the vehicle's accumulated mileage. Additionally, driving data may include parking data acquired when the vehicle is parked. Parking data may include the time vehicle 1 is parked, the temperature during the parking period, and / or the state of charge (SOC) during the parking period. For example, driving data may include charging data, which includes the charging output (charge / discharge rate, C-rate) and / or charging temperature for charging (or discharging) the battery.

[0060] The processor 100 can acquire driving data via sensors during a driving cycle, which may include the time when the vehicle 1 starts driving and the time when the vehicle 1 ends driving. Therefore, the driving data can correspond to a driving cycle.

[0061] The processor 100 can update the battery model 30 based on driving data obtained during driving to construct a battery model 30 that reflects various conditions of actual driving of the vehicle 1.

[0062] The following is for reference. Figure 3 The operation of updating the internal variables of the battery model 30 by the processor 100 according to the implementation scheme is described in more detail.

[0063] Figure 2 This is a flowchart describing a battery management method according to an embodiment of the present invention. Figure 2 Shown by Figure 1 The process executed by the processor 100 shown.

[0064] In operation S210, processor 100 can obtain the predicted voltage using battery model 30.

[0065] Battery model 30 can be used to obtain the state information of battery 10 using preset parameters.

[0066] The parameters of the battery model 30 can be set based on electrochemistry to reflect the internal state information of the battery 10. For example, the battery model 30 can be a pseudo-two-dimensional model (P2D model).

[0067] The processor 100 can input the measured current into the battery model 30 to obtain the predicted voltage. The measured current can refer to the current value of the battery 10 obtained via the sensor device 20.

[0068] The processor 100 can collect “n” measured currents (where n is a natural number greater than or equal to 2) from the sensor device 20 and obtain “n” predicted voltages based on the measured currents.

[0069] In order to obtain the predicted voltage, the processor 100 can utilize not only the measured current of the battery 10, but also the measured temperature.

[0070] In operation S220, processor 100 can determine the voltage deviation between the measured voltage and the predicted voltage of battery 10.

[0071] The measured voltage can refer to the voltage value of the battery 10 obtained through the sensor device 20.

[0072] The processor 100 can collect "n" measured voltages from the sensor device 20. Based on the "n" measured voltages and "n" predicted voltages, the processor 100 can determine the voltage deviation.

[0073] To determine the voltage deviation between the measured voltage and the predicted voltage of battery 10, processor 100 may utilize a first objective function. For example, the first objective function may utilize the root mean square error (RMSE) between “n” measured voltages and “n” predicted voltages.

[0074] In operation S230, based on the determination that the voltage deviation meets the threshold condition (e.g., exceeds the threshold), the parameters of the battery model can be reset.

[0075] Processor 100 can use a second objective function to reset the parameters of the battery model. The second objective function can be the same as the first objective function. For example, processor 100 can use the root mean square error between "n" measured voltages and "n" predicted voltages to reset the parameters.

[0076] The processor 100 can repeatedly execute the process of setting new parameters and comparing the result value of the second objective function with the threshold, based on the fact that the result value of the second objective function is less than the threshold. The new parameters can be randomly generated. When the result value of the second objective function obtained by applying the new parameters is less than the threshold, the processor 100 can terminate the parameter reset process. Then, the processor 100 can update the battery model 30 by applying parameters that make the result value of the second objective function lower than the threshold.

[0077] Alternatively, the processor 100 can use methods other than root mean square error to reset the parameters.

[0078] The processor 100 can obtain the state information of the battery 10 based on the updated battery model 30.

[0079] According to the implementation scheme, the processor 100 can use (e.g., an updated) battery model 30 to determine the state of health (SOH) of the battery 10.

[0080] Additionally, the processor 100 can use (e.g., a newer) battery model 30 to determine the loss of active material in the battery 10.

[0081] The processor 100 can output status information of the battery 10 through the user interface (UI) 201 of the vehicle 1. For example, the status information output through the UI 201 can warn the user that the battery 10 should be replaced if it has aged to a sufficient degree.

[0082] The processor 100 may additionally or alternatively manage the battery 10 based on the state information of the battery 10. For example, the processor 100 may adjust the charging and discharging capacity of the battery 10 based on the degree of battery aging.

[0083] Figure 3 A user interface according to an embodiment of the present invention is shown.

[0084] refer to Figure 3 The user interface 300 can be implemented as a display device that displays the first screen 301 to the seventh screen 307. The display device can receive touch input from the user.

[0085] The processor 100 can generate image data for configuring the first screen 301 to the seventh screen 307 based on the status information of the battery 10.

[0086] The first screen 301 can be a screen used to display the identification information of vehicle 1.

[0087] The second screen 302 can be a screen used to display a menu for changing the display type. When battery usage statistics are selected based on touch input from the user, the processor 100 can display the battery usage statistics via screens from the third screen 303 to the seventh screen 307. For example, the processor 100 can display the status information of the battery 10 via the sixth screen 306.

[0088] The third screen 303 can be a screen used to display information such as the previous page icon, home page icon, menu, energy information icon, charging information icon, settings icon, and currently playing content.

[0089] The fourth screen 304 can be a screen used to display the ratio of battery charging type, driving type, driving mode, etc.

[0090] The fifth screen 305 can be used to display battery status notifications and battery management guidelines.

[0091] The sixth screen 306 can be used to display the battery level when parked, the battery temperature when driving, and the battery aging status.

[0092] The seventh screen 307 can be a screen used to display search functions, time information, etc.

[0093] Figure 4 This is a schematic diagram illustrating a battery management system according to another embodiment of the present invention.

[0094] refer to Figure 4 According to an embodiment of the present invention, the battery management system may include a server 901 connected via a network to information collection vehicles VEH1 and VEH2 and a target vehicle VEH_tg.

[0095] Server 901 can transmit and receive wireless signals with at least one base station, external terminal, and central unit on a mobile communication network established according to technical standards or communication methods used for mobile communications. For example, the mobile communication device can be implemented based on 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), or LTE-A Advanced.

[0096] Information collection vehicles VEH1 and VEH2 can acquire driving data at predetermined time intervals. For example, VEH1 and VEH2 can acquire measured voltage, measured current, and measured temperature of battery 10 at regular time intervals and generate driving data. VEH1 and VEH2 can then send the driving data to server 901.

[0097] Server 901 may include a database (not shown) and a processor (not shown). The database may store a battery model, and the processor of server 901 may use the battery model to obtain the status information of battery 10. Server 901 may send the status information of battery 10 to the target vehicle VEH_tg.

[0098] The information gathering vehicles VEH1 and VEH2 and the target vehicle VEH_tg can be the same vehicle 1.

[0099] In the implementation scheme, server 901 can learn from the driving data collected by information collection vehicles VEH1 and VEH2, and identify vehicles with similar driving data as target vehicles VEH_tg. Accordingly, server 901 can update the battery model even for vehicles for which no driving data has been collected.

[0100] In addition, server 901 can receive driving data from information collection vehicles VEH1 and VEH2 and update the parameters of battery model 30 based on the driving data.

[0101] In the implementation scheme, server 901 can receive driving data at predetermined time intervals.

[0102] Once server 901 has collected a predefined number (n) of driving data, server 901 can obtain a predicted voltage by applying the collected "n" driving data to battery model 30. Server 901 can reset the parameters of battery model 30 based on determining that the difference between the predicted voltage obtained using battery model 30 and the voltage measured in the driving data meets a threshold condition (e.g., exceeds a threshold).

[0103] Server 901 can update battery model 30 based on reset parameters.

[0104] The detailed process of the battery management method according to an embodiment of the present invention is described below.

[0105] refer to Figures 5 to 7 The described battery management method can be executed at least in part by a processor installed in the vehicle and / or at least in part by a processor of a server.

[0106] Figure 5 This is a flowchart describing a method for acquiring driving data according to an embodiment of the present invention.

[0107] refer to Figure 5 During operations S510 and S520, processor 100 can collect sensing data while vehicle 1 is in motion. The sensing data may include measured voltage, measured current, and measured temperature of battery 10.

[0108] The processor 100 can acquire sensing data in units of vehicle 1's driving period. The driving period can be determined based on the vehicle's start signal. For example, the processor 100 can determine whether the vehicle has started driving based on the vehicle's start signal. The processor 100 can determine whether the vehicle has stopped driving based on the vehicle's stop signal.

[0109] The processor 100 can acquire vehicle data via sensors during a driving cycle, which includes the time when the vehicle starts driving and the time when the vehicle ends driving. The acquired vehicle data can correspond to one driving cycle.

[0110] During operations S530 and S540, after vehicle 1 has finished driving, processor 100 can perform preprocessing to remove abnormal driving data.

[0111] Preprocessing may involve removing sensing data that falls outside a predefined reference range. For example, minimum voltage, maximum voltage, minimum current, maximum current, minimum temperature, and maximum temperature may be predefined. Processor 100 may exclude sensing data where the measured voltage is less than the minimum voltage or greater than the maximum voltage, and then terminate the process. Similarly, processor 100 may exclude sensing data where the measured current is less than the minimum current or greater than the maximum current, and then terminate the process. Furthermore, processor 100 may exclude sensing data where the measured temperature is lower than the minimum temperature or higher than the maximum temperature, and then terminate the process.

[0112] In operation S550, when the sensed data falls within a reference range, the processor 100 can correlate time information with the sensed data to generate driving data. For example, the processor 100 can generate first driving data based on sensed data collected during a first time period, and generate second driving data based on sensed data collected during a second time period.

[0113] Driving data can be stored in a two-dimensional matrix format, such as "N" (number of layers) × "M" (number of sensing data segments). For example, the nth layer may include the most recently acquired nth driving data.

[0114] When operating the S560, the processor 100 can store driving data.

[0115] The processor 100 can store driving data in the memory 40 of the vehicle 1.

[0116] The processor 100 can store driving data in an external server 901.

[0117] Figure 6 This is a schematic diagram illustrating a process for determining the consistency of parameters according to an embodiment of the present invention. The process for determining the consistency of parameters can be a process for determining the reliability of parameters based on the predicted values ​​obtained from the battery model 30 and the sensing data from the battery 10.

[0118] refer to Figure 6 When operating the S610, the processor 100 can load driving data.

[0119] The processor 100 of vehicle 1 can read driving data stored in memory 40 or receive driving data from server 901. Alternatively, the processor of server 901 can read driving data stored in the server.

[0120] When operating the S620, the processor 100 can determine the predicted voltage.

[0121] The processor 100 can identify the measured current included in the driving data and input the measured current value into the battery model 30 to obtain the predicted voltage.

[0122] Battery model 30 can determine the predicted voltage of battery 10 using parameters that reflect the internal state of battery 10. Battery model 30 can determine the predicted voltage of battery 10 using the following [Equations 1] to [Equations 5].

[0123] [Equation 1]

[0124]

[0125] According to the implementation plan, Equation 1 is a transport equation that can be used to analyze the concentration distribution inside solid particles within the electrode.

[0126] [Equation 2]

[0127]

[0128] According to the implementation plan, Equation 2 is a transport equation that can be used to analyze the concentration distribution inside the electrolyte within the electrode.

[0129] [Equation 3]

[0130]

[0131] According to the implementation plan, Equation 3 is a conservation equation that can be used to analyze the potential distribution between solid particles within the electrode.

[0132] [Equation 4]

[0133]

[0134] According to the implementation plan, Equation 4 is a charge conservation equation that can be used to analyze the potential distribution inside the electrolyte within the electrode.

[0135] [Equation 5]

[0136]

[0137] Equation 5 is the Butler-Volmer equation.

[0138] The parameters in Equations 1 to 5 are defined as follows.

[0139] The subscripts 1 and 2 can be used to distinguish the state of a parameter. For example, X1 (where X is a parameter) can represent a physical quantity inside a solid particle, and X2 can represent a physical quantity in a liquid state.

[0140] X kIt can be used to distinguish domain regions. For example, when X = p, it can represent the anodic domain; when k = s, it can represent the membrane domain; and when k = n, it can represent the cathodic domain.

[0141] The superscript of t (e.g., X) t X t+1 It can indicate the time step.

[0142] "c" can represent the concentration of lithium ions, and "c*" can represent the concentration of lithium ions on the surface of the solid particles. avg " can represent the average lithium-ion concentration inside the solid particle. "Φ" can represent the electric potential. "j" can represent the amount of lithium ions per unit volume due to electrochemical reactions on the surface of the solid particle. "Rp" can represent the radius of the solid particle. "D" eff "σ" can represent the diffusion coefficient, and "ε" can represent porosity. eff "k" can represent the effective conductivity of a solid, and "k" eff "A" can represent the effective conductivity of the electrolytic cell. "A" can represent the ratio of the activated area of ​​the porous electrode, and "t" can represent the effective conductivity of the electrolytic cell. + " can represent yield. "F can represent Faraday's constant, and "R" can represent gas constant. "T" can represent temperature, "k" can represent reaction rate constant, and "R0" can represent internal resistance.

[0143] When operating the S630, the processor 100 can compute the first objective function.

[0144] The first objective function is used to evaluate the voltage deviation between the measured voltage of battery 10 and the predicted voltage obtained by battery model 30, and can be preset. For example, the first objective function (Of1) can utilize the measured voltage (V) meas_i (i is a natural number less than or equal to n) and the predicted voltage (V) sim_i The root mean square error between ) is shown in Equation 6 below.

[0145] [Equation 6]

[0146]

[0147] Predicted voltage (V) sim_i The predicted voltage is obtained based on the predicted current included in the i-th driving data, and the measured voltage (V) meas_i ) can represent the voltage measured in the i-th driving data.

[0148] In operation S640, processor 100 can compare the result value of the first objective function with the first threshold.

[0149] In operation S650, when the result value of the first objective function is less than or equal to the first threshold, the processor 100 can maintain the parameters of the battery model 30.

[0150] In operation S660, when the result value of the first objective function exceeds the first threshold, the processor 100 can proceed to the process of resetting parameters.

[0151] Figure 7 This is a flowchart describing a process for resetting parameters according to an embodiment of the present invention.

[0152] refer to Figure 7 When operating the S710, the processor 100 can generate new parameters.

[0153] In operation of S720, processor 100 can determine the predicted voltage using new parameters. Processor 100 can determine the predicted voltage by inputting the predicted current into battery model 30 with the new parameters applied.

[0154] When operating the S730, processor 100 can compute the second objective function.

[0155] The second objective function can be used to determine the reliability of the predicted voltage obtained based on the new parameters. Similar to the first objective function, the second objective function can utilize the root mean square error (RMSE) between the measured voltage and the predicted voltage.

[0156] In operation S740, processor 100 can compare the result of the second objective function with a second threshold. The second threshold may have the same size as the first threshold.

[0157] When the result of the second objective function exceeds the second threshold, the processor 100 can return to operation S710. In other words, the processor 100 can generate new parameters again.

[0158] In operation S750, when the result value of the second objective function is less than or equal to the second threshold, the processor 100 can update the parameters. Therefore, the battery model 30 can be updated to reflect parameters that can reduce the difference between the measured voltage and the predicted voltage to a certain level or lower.

[0159] like Figure 7 The process shown for resetting parameters can be executed using a genetic algorithm (GA).

[0160] According to an embodiment of the present invention, the processor 100 of the battery management device 900 can use the battery model 30 to obtain the state information of the battery 10.

[0161] The state information of battery 10 may include at least one of state of health (SOH), internal resistance, or loss of active material.

[0162] The processor 100 can determine the state of harmonics (SOH) of the battery 10 based on the ratio of the measured capacity to the initially defined nominal capacity, as shown in Equation 7.

[0163] [Equation 7]

[0164]

[0165] In equation 7, Q sim It can represent the capacity of the simulation, and Q nom It can represent the nominal capacity.

[0166] The simulated capacity can be determined based on a discharge simulation. For example, processor 100 can perform a discharge until the state of charge (SOC) of battery 10 reaches a specific SOC from 100 SOC, thereby determining the simulated capacity. Processor 100 can use the following [Equation 8] to determine the simulated capacity.

[0167] [Equation 8]

[0168]

[0169] In equation 8, I applied It can represent the applied current.

[0170] In the state information of battery 10, the loss of active material (LAM) can be obtained based on the following equation 9.

[0171] [Equation 9]

[0172]

[0173] In equation 9, C max It can represent the theoretical capacity of the active substance, and C max,0 It can represent the initial theoretical capacity.

[0174] Figure 8 A computing system according to an embodiment of the present invention is shown.

[0175] refer to Figure 8 The computing system 1000 may include at least one processor 1100, a memory 1300, a user interface input device 1400, a user interface output device 1500, a storage device 1600, and a network interface 1700 connected to each other via a bus 1200.

[0176] Processor 1100 may be a central processing unit (CPU) or 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) and random access memory (RAM).

[0177] Therefore, the operation of the methods or algorithms described in conjunction with the embodiments disclosed herein can be directly embodied in hardware or in software modules executed by processor 1100, or in a combination thereof. The software modules can reside on storage media (e.g., memory 1300 and / or storage device 1600), such as RAM, flash memory, ROM, EPROM, EEPROM, registers, hard disks, removable disks, and CD-ROMs.

[0178] The storage medium can be coupled to the processor 1100, and the processor 1100 can read information from the storage medium and record information in the storage medium. Alternatively, the storage medium can be integrated with the processor 1100. The processor 1100 and the storage medium can reside in an application-specific integrated circuit (ASIC). The ASIC can reside within the user terminal. In another case, the processor and the storage medium can reside as separate components in the user terminal.

[0179] The above description merely illustrates the technical concept of the present invention, and those skilled in the art can make various modifications and changes without departing from the essential characteristics of the present invention.

[0180] Accordingly, the embodiments described in this invention are not intended to limit the technical concept of the invention, but rather to describe the invention, and the scope of the technical concept of the invention is not limited by the embodiments. The scope of protection of this invention should be interpreted by the appended claims, and all technical concepts within the scope of the appended claims should be interpreted as being included within the scope of this invention.

[0181] According to an embodiment of the present invention, the parameters of the battery model can be updated in real time, thereby improving the accuracy of the battery model.

[0182] Furthermore, according to embodiments of the present invention, the parameters of the battery model can be updated based on the driving data of the vehicle equipped with the battery, thereby updating the battery model by more accurately reflecting the conditions under which the battery operates.

[0183] Furthermore, according to an embodiment of the present invention, the battery state information can be determined using a real-time updated battery model, thereby obtaining the battery state information more accurately.

[0184] In addition, various effects can be provided, either directly or indirectly, through this invention.

[0185] In the foregoing, although the invention has been described with reference to exemplary embodiments and the accompanying drawings, the invention is not limited thereto, but can be modified and altered by those skilled in the art without departing from the spirit and scope of the invention as claimed in the appended claims.

Claims

1. A battery management device, comprising: Memory, configured to store battery models and algorithms; as well as The processor is configured to obtain battery state information using a battery model. The processor is configured as follows: The predicted voltage of the battery is obtained based on the battery model. Determine the voltage deviation between the measured voltage and the predicted voltage of the battery. Based on the determination that the voltage deviation meets the threshold condition, the battery model is updated by resetting the parameters of the battery model.

2. The battery management device of claim 1, wherein, The parameters are set based on electrochemistry to reflect the internal state information of the battery.

3. The battery management device of claim 1, wherein, The processor is configured as follows: Obtain the measured current of the battery; The predicted voltage of the battery is determined by inputting the measured current into the battery model.

4. The battery management device of claim 3, wherein, The processor is configured to collect measured currents obtained during the operation of a battery-powered vehicle.

5. The battery management device according to claim 3, wherein, The processor is configured to determine the voltage deviation based on the voltage measured at the same time as the measured current.

6. The battery management device according to claim 5, wherein, The processor is configured to determine the voltage deviation using the root mean square error, which is obtained based on the voltage difference between n measured voltages and n predicted voltages, where n is a natural number greater than or equal to 2.

7. The battery management device according to claim 1, wherein, The processor is configured to reset parameters to reduce the voltage deviation between the measured voltage and the predicted voltage.

8. The battery management device according to claim 7, wherein, The processor is configured as follows: Generate new parameters; The modified predicted voltage is obtained by applying a battery model with new parameters. Reset the parameters so that the voltage deviation between the measured voltage and the modified predicted voltage falls within the threshold range.

9. The battery management device according to claim 8, wherein, The processor is configured to reset at least one of the parameters: internal resistance, diffusion coefficient, reaction rate constant, or porosity.

10. The battery management device according to claim 1, wherein, The processor is configured to determine at least one of the following: the health status of the battery or the loss of active material, using a battery model.

11. A battery management method, comprising: The predicted voltage of the battery is obtained based on the battery model; Determine the voltage deviation between the measured voltage and the predicted voltage of the battery; Based on the determination that the voltage deviation meets the threshold condition, the battery model is updated by resetting the parameters of the battery model.

12. The battery management method according to claim 11, wherein, The parameters are set based on electrochemistry to reflect the internal state information of the battery.

13. The battery management method according to claim 11, wherein, Determining the predicted voltage includes: Obtain the measured current of the battery; The measured current is input into the battery model.

14. The battery management method according to claim 13, wherein, The measured current was obtained during the operation of the battery-equipped vehicle.

15. The battery management method according to claim 13, wherein, Determining voltage deviation involves using the measured voltage acquired at the same time as the measured current.

16. The battery management method according to claim 15, wherein, Determining voltage deviation involves determining the root mean square error based on the voltage difference between n measured voltages and n predicted voltages, where n is a natural number greater than or equal to 2.

17. The battery management method according to claim 11, wherein, Resetting parameters includes: resetting parameters to reduce the voltage deviation between the measured voltage and the predicted voltage.

18. The battery management method according to claim 17, wherein, The reset parameters include: Generate new parameters; The modified predicted voltage is obtained by applying a battery model with new parameters. The voltage deviation between the measured voltage and the modified predicted voltage is compared with a threshold range.

19. A battery management server, comprising: A database configured to store battery models and algorithms; as well as The processor is configured to obtain the state information of the battery installed in the vehicle using a battery model. The processor is configured as follows: Collect measurements of battery voltage and current obtained during vehicle operation. The predicted voltage of the battery is obtained based on the battery model. Determine the voltage deviation between the measured voltage and the predicted voltage. Based on the determination that the voltage deviation meets the threshold condition, the battery model is updated by resetting the parameters of the battery model.

20. The battery management server according to claim 19, wherein, The processor is configured as follows: Battery state information is generated using a battery model, including at least one of the battery's health status or the loss of active materials. Battery status information is sent to the vehicle via a communication device.

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