Battery management device and server

Abstract

A battery management device according to one embodiment disclosed in this document may include a controller that extracts an operation pattern of a vehicle equipped with a battery based on operation information of the vehicle, extracts battery setting parameters corresponding to the operation pattern of the vehicle, and controls the battery based on the extracted battery setting parameters, and a communication unit that transmits at least one of information regarding the operation pattern of the vehicle and the extracted battery setting parameters to a server, and transmits the battery setting parameters applied to the battery to the server at regular intervals.

Description

[Technical field]

[0001] The embodiments disclosed in this document claim the benefit of priority based on Korean Patent Application No. 10-2022-0021123, filed on February 17, 2022, and all contents disclosed in the documents of the Korean patent application are incorporated by reference as part of this specification. SUMMARY OF THE DISCLOSURE The embodiments disclosed herein relate to a battery management device and a server. [Background technology]

[0002] Electric vehicles obtain power by charging batteries with electricity from an external source and then driving a motor with the voltage charged in the battery. The batteries of electric vehicles may generate heat due to chemical reactions that occur during the process of charging and discharging electricity, and this heat may damage the performance and lifespan of the battery. Therefore, a Battery Management System (BMS) that monitors the temperature, voltage, and current of the battery is operated to diagnose and control the battery status.

[0003] However, in the conventional vehicle development system, standard operation battery setting parameters developed through experiments are collectively applied to the batteries installed in all vehicles. Therefore, when the actual vehicle driving patterns differ from vehicle to vehicle, there is a problem that many errors occur in the battery system due to the generalized battery setting parameters. Summary of the Invention [Problem to be solved by the invention]

[0004] One objective of the embodiments disclosed herein is to provide a battery management device and server that precisely analyzes operational information of a vehicle equipped with a battery and applies battery setting parameters to the battery that are optimized for the vehicle's operational pattern.

[0005] The technical problems of the embodiments disclosed in this document are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the following description. [Means for solving the problem]

[0006] A battery management device according to one embodiment disclosed in this document may include a controller that extracts an operation pattern of a vehicle equipped with a battery based on operation information of the vehicle, extracts battery setting parameters corresponding to the operation pattern of the vehicle, and controls the battery based on the extracted battery setting parameters, and a communication unit that transmits at least one of information regarding the operation pattern of the vehicle and the extracted battery setting parameters to a server, and transmits the battery setting parameters applied to the battery to the server at regular intervals.

[0007] According to one embodiment, the vehicle control system further includes a memory for storing a plurality of battery setting parameters, and the controller is capable of repeatedly extracting a battery setting parameter from the plurality of battery setting parameters corresponding to an operation pattern of the vehicle at a constant period.

[0008] According to an embodiment, the communication unit can receive operation information of the vehicle from another control device of the vehicle. According to one embodiment, the communication unit receives from the server at least one optimal battery setting parameter generated based on an operating pattern of the vehicle and battery setting parameters already applied to the battery, and the memory can store the at least one optimal battery setting parameter by including it in the plurality of battery setting parameters.

[0009] According to one embodiment, the controller can repeatedly extract battery setting parameters corresponding to an operation pattern of the vehicle from the plurality of battery setting parameters including the at least one optimal battery setting parameter at a constant period, and control the battery based on the extracted battery setting parameters.

[0010] According to one embodiment, the controller may apply the extracted battery configuration parameters to the battery when the vehicle is operating in a Key-Off state, when the vehicle is operating in an idle state, or when the controller receives consent to change the battery configuration parameters of the vehicle from another electronic control unit of the vehicle.

[0011] In one embodiment, the battery setting parameters may include at least one of an internal resistance, a capacitance, a time constant, a capacity, and an open circuit voltage (OCV) of the battery. According to an embodiment, the controller may apply machine learning to the vehicle's operation information to extract the vehicle's operation pattern.

[0012] A server according to one embodiment disclosed in this document may include a communication unit that receives information regarding an operation pattern of a vehicle equipped with a battery and receives information regarding battery setting parameters that have already been applied to the battery at a constant interval, and a controller that extracts a similar vehicle operation pattern that is similar to the operation pattern of the vehicle equipped with the battery from a plurality of already stored vehicle operation patterns, extracts battery setting parameters for the similar vehicle operation pattern, and generates at least one optimal battery setting parameter based on the battery setting parameters of the similar vehicle operation pattern.

[0013] According to one embodiment, the system further includes a database for storing the plurality of vehicle operation patterns and a plurality of battery setting parameters, and the database may store battery setting parameters that have already been applied to the battery received by the communication unit.

[0014] According to one embodiment, the controller inputs battery setting parameters of the similar vehicle operation pattern and battery setting parameters already applied to the battery into a genetic algorithm to generate the at least one optimal battery setting parameter, and the communication unit can transmit the at least one optimal battery setting parameter to the vehicle.

[0015] According to one embodiment, the controller may iteratively perform the genetic algorithm based on a set number of times to iteratively generate the at least one optimal battery setting parameter, and may terminate the genetic algorithm when a number of the at least one optimal battery setting parameter generated exceeds a certain ratio to a number of battery setting parameters already applied to the battery.

[0016] According to one embodiment, the controller may additionally execute the genetic algorithm based on a previously set number of additional times when the number of the at least one optimal battery setting parameter generated relative to the number of battery setting parameters already applied to the battery is equal to or less than a certain percentage.

[0017] According to one embodiment, after additionally executing the genetic algorithm, if the number of the at least one optimal battery setting parameter generated relative to the number of battery setting parameters already applied to the battery is equal to or less than a certain percentage, the controller may include the battery setting parameters already applied to the battery in the at least one optimal battery setting parameter, and the communication unit may transmit the at least one optimal battery setting parameter including the battery setting parameters already applied to the battery to the vehicle.

[0018] According to one embodiment, when any one of the at least one optimal battery setting parameter is identical to a battery setting parameter already applied to the battery, the controller generates identification information of the optimal battery setting parameter that is identical to the battery setting parameter already applied to the battery, and the communication unit can transmit to the vehicle the at least one optimal battery setting parameter excluding the optimal battery setting parameter that is identical to the battery setting parameter already applied to the battery. Effect of the Invention

[0019] According to an embodiment of the battery management device and server disclosed in this document, it is possible to precisely analyze the operation information of a vehicle equipped with a battery and apply battery setting parameters optimized for the vehicle operation pattern to the battery. [Brief description of the drawings]

[0020] [Figure 1] FIG. 2 is a block diagram showing the relationship between a battery management device and a server according to an embodiment disclosed in this document. [Diagram 2] 1 is a block diagram showing a configuration of a battery management device according to an embodiment disclosed in this document. [Diagram 3] FIG. 2 is a block diagram showing a configuration of a server according to an embodiment disclosed in this document. [Figure 4] FIG. 2 illustrates an operation method of a battery management device and a server according to an embodiment disclosed herein. [Diagram 5] FIG. 2 is a block diagram showing a hardware configuration of a computing system for implementing a battery management device according to an embodiment disclosed herein. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0021] Some embodiments disclosed in this document will be described in detail below with reference to exemplary drawings. When referring to components in each drawing, it should be noted that the same components are referred to by the same reference numerals as much as possible when they are displayed in other drawings. In addition, when describing the embodiments disclosed in this document, if a detailed description of related publicly known configurations or functions is deemed to hinder understanding of the embodiments disclosed in this document, the detailed description will be omitted.

[0022] In describing the components of the embodiments disclosed in this document, terms such as first, second, A, B, (a), (b) and the like may be used. Such terms are merely intended to distinguish the components from other components, and do not limit the essence, order, or procedure of the components. In addition, unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the embodiments disclosed in this document belong. Terms as defined in commonly used dictionaries should be interpreted as having a meaning consistent with the contextual meaning of the relevant art, and should not be interpreted in an ideal or overly formal sense unless expressly defined in this document.

[0023] FIG. 1 is a diagram showing the relationship between a battery management device and a server according to an embodiment disclosed in this document. 1, a battery management device 120 can control a battery mounted on a vehicle by communicating with a server 200. Here, the vehicle can include an electric vehicle that rotates a motor using electricity stored in a battery of the vehicle.

[0024] The battery management device 120 can transmit battery data and vehicle data to the server 200. The battery management device 120 can transmit vehicle data acquired from another control device (not shown) of the vehicle and battery data generated based on the vehicle data to the server 200, and analyze and manage the battery data using the server 200 having abundant computing resources. In addition, the battery management device 120 can precisely analyze the battery data via the server 200 and acquire battery data optimized for battery operation.

[0025] The server 200 can generate battery data optimized for controlling the multiple batteries based on the vehicle data and battery data acquired from the battery management device 120. The server 200 can include, for example, cloud computing technology. The server 200 can generate battery data optimized for the multiple batteries for controlling the multiple batteries based on the cloud computing technology.

[0026] The server 200 may generate battery data optimized for a plurality of batteries and transmit the battery data to the battery management unit 120. When the battery management unit 120 receives the optimized battery data from the server 200, the battery management unit 120 may update the already stored battery data.

[0027] According to one embodiment, the batteries may be provided in an electric vehicle or an energy storage system (ESS) and implemented in the form of a battery pack 100 for storing electric energy. The battery pack 100 is a unit for storing electric energy in an electric vehicle. An electric vehicle may include one or more such battery packs 100.

[0028] The battery pack 100 may include a battery module 110, a battery management unit 120, and a relay 130. The battery module 110 may include a first battery cell 111, a second battery cell 112, a third battery cell 113, and a fourth battery cell 114. Although the number of battery cells is illustrated as four in FIG. 1, the number is not limited thereto, and the battery module 110 may include n (n is a natural number equal to or greater than 2) battery cells.

[0029] The battery module 110 may supply power to a target device (not shown). To this end, the battery module 110 may be electrically connected to the target device. Here, the target device may include an electrical, electronic, or mechanical device that operates by receiving power from a battery pack 100 including a plurality of battery cells 111, 112, 113, and 114, and may be, for example, but is not limited to, an electric vehicle (EV).

[0030] The plurality of battery cells 111, 112, 113, 114 may be, but are not limited to, lithium ion (Li-ion) batteries, lithium ion polymer (Li-ion polymer) batteries, nickel cadmium (Ni-Cd) batteries, nickel metal hydride (Ni-MH) batteries, etc. Meanwhile, although FIG. 1 shows one battery module 110, according to an embodiment, a plurality of battery modules 110 may be included.

[0031] A battery management system (BMS) 120 can manage and / or control the state and / or operation of the battery module 110. For example, the battery management system 120 can manage and / or control the state and / or operation of the multiple battery cells 111, 112, 113, 114 included in the battery module 110. The battery management system 120 can manage charging and / or discharging of the battery module 110.

[0032] Furthermore, the battery management device 120 can monitor the voltage, current, temperature, etc. of the battery module 110 and / or each of the multiple battery cells 111, 112, 113, 114 included in the battery module 110. For monitoring by the battery management device 120, sensors and various measurement modules (not shown) can be further provided in the battery module 110, a charge / discharge path, or any position on the battery module 110, etc. The battery management device 120 can calculate parameters indicating the state of the battery module 110, such as SOC (State of Charge) and SOH (State of Health), based on the measured values ​​of the monitored voltage, current, temperature, etc.

[0033] The battery management unit 120 can control the operation of the relay 130. For example, the battery management unit 120 can short-circuit the relay 130 to supply power to a target device. In addition, the battery management unit 120 can short-circuit the relay 130 when a charging device is connected to the battery pack 100.

[0034] The battery management unit 120 can calculate the cell balancing time for each of the multiple battery cells 111, 112, 113, and 114. Here, the cell balancing time can be defined as the time required for balancing the battery cells. For example, the battery management unit 120 can calculate the cell balancing time based on the SOC (State of Charge), battery capacity, and balancing efficiency of each of the multiple battery cells 111, 112, 113, and 114.

[0035] The battery management unit 120 and the server 200 will be described in detail below. First, a specific configuration and operation of the battery management device 120 will be described with reference to Fig. 2. Fig. 2 is a diagram showing the configuration of a battery management device according to an embodiment disclosed in this document. With reference to Fig. 2, the battery management device 120 can include a controller 121, a communication unit 122, and a memory 123.

[0036] The controller 121 can extract a vehicle operation pattern based on operation information of the vehicle equipped with the battery pack 100. Here, the vehicle operation information can be defined as a motion input by a user of the vehicle using the vehicle or an electronic device installed in the vehicle and a characteristic of the motion. For example, the vehicle operation information can include battery usage information due to the user's vehicle driving.

[0037] The controller 121 can extract vehicle operation patterns by applying machine learning to vehicle operation information acquired from other control devices of the vehicle. Here, machine learning is a technology that allows a computer to learn and predict certain results.

[0038] For example, the controller 121 can input vehicle operation information to machine learning of a time series analysis structure to extract a vehicle operation pattern. A time series can be defined as a sequence arranged at regular time intervals along the passage of time. The machine learning of a time series analysis structure can include, for example, LSTM (Long Short Term Memory). LSTM can be defined as an artificial neural network, which is an improved structure of a recurrent neural network (RNN) so that it can reflect long-term features. LSTM is mainly applied to problems of time series data that represent phenomena that change over time. LSTM can predict what the n+1th action is for user behavior time series data having a length of n.

[0039] The controller 121 can extract battery setting parameters of the battery pack 100 corresponding to the vehicle operation pattern based on the vehicle operation pattern extracted by applying the vehicle operation information to the machine learning model. Here, the battery setting parameters can include at least one of the voltage, internal resistance, capacitance, time constant, capacity, and open circuit voltage (OCV) of the multiple battery cells 111, 112, 113, 114, the battery module 110, or the battery pack 100. However, these parameters are merely exemplary and are not limited thereto.

[0040] The controller 121 can extract battery setting parameters that are optimal for the vehicle operation pattern from among a plurality of battery setting parameters already stored in the memory 123.

[0041] Specifically, the controller 121 can input a plurality of battery setting parameters to a recursive least squares (RLS) filter, a least mean squares (LMS) filter, or an extended Kalman filter (EKF) filter to extract battery setting parameters optimal for a vehicle operation pattern. Here, the RLS filter can be defined as a time series regression correction algorithm for minimizing errors. The LMS filter can be defined as an algorithm that extracts a time point that minimizes the average error of a crowd and estimates a probabilistic optimal value. The EKF filter can be defined as an algorithm that corrects the measurement error of a nonlinear equivalent model to estimate an actual value.

[0042] That is, the controller 121 can extract battery setting parameters that are optimal for a vehicle operation pattern from among multiple battery setting parameters based on a Recursive Least Squares (RLS) filter, a Least Mean Squares (LMS) filter, or an Extended Kalman Filter (EKF) filter that reflects the characteristics of the vehicle operation pattern.

[0043] The controller 121 can repeatedly extract the battery setting parameters corresponding to the vehicle operation pattern from among the plurality of battery setting parameters at a constant period. That is, the controller 121 can perform a periodic battery setting parameter extraction operation and dynamically extract the battery setting parameters corresponding to the vehicle operation pattern and the vehicle operation history.

[0044] The controller 121 can control the battery pack 100 based on the extracted battery setting parameters. That is, the controller 121 can control at least one of the voltage, internal resistance, capacitance, time constant, capacity, and open circuit voltage (OCV) of the multiple battery cells 111, 112, 113, 114, the battery module 110, or the battery pack 100 based on the extracted battery setting parameters.

[0045] The controller 121 can control the battery based on the optimal battery setting parameters received by the communication unit 122 from the server 200. When the communication unit 122 receives the optimal battery setting parameters from the server 200, the controller 121 can include the optimal battery setting parameters in a plurality of conventional battery setting parameters, and extract a battery setting parameter corresponding to a vehicle operation pattern from the plurality of battery setting parameters including at least one optimal battery setting parameter. The controller 121 can control the battery based on the extracted battery setting parameters.

[0046] The controller 121 may apply the extracted battery setting parameters to the battery when the vehicle is operating in a Key-Off state, when the vehicle is operating in an idle state, or when the controller 121 receives consent to change the vehicle's battery setting parameters from another control device of the vehicle.

[0047] The communication unit 122 can receive information related to vehicle operation from other control devices of the vehicle. Here, the other control devices of the vehicle may be electronic control devices (ECUs, Electronic Control Units) of the vehicle according to an embodiment. The electronic control devices of the vehicle can communicate with each other within the vehicle without a host computer via CAN communication.

[0048] The communication unit 122 can transmit to the server 200 a vehicle operation pattern generated by the controller 121 based on information regarding vehicle operation, or a battery setting parameter corresponding to a vehicle operation pattern extracted by the controller 121 from among a plurality of battery setting parameters.

[0049] The communication unit 122 can transmit battery setting parameters applied to the plurality of battery cells 111, 112, 113, 114, the battery module 110, or the battery pack 100 to the server 200 at regular intervals.

[0050] The communication unit 122 can receive from the server 200 at least one optimal battery setting parameter that the server 200 has generated based on the vehicle driving pattern and battery setting parameters already applied to the battery.

[0051] According to an embodiment, the communication unit 122 may verify the integrity of the at least one optimal battery setting parameter generated by the server 200 for loss or distortion. For example, the communication unit 122 may verify the integrity of the at least one optimal battery setting parameter generated by the server 200 by performing a checksum check, a cyclic redundancy check (CRC) check, or a secure hash algorithm (SHA) check.

[0052] Also, according to one embodiment, the communication unit 122 can encrypt and decrypt data using an AES or RSA authentication key when transmitting or receiving data to or from the server 200. Thus, the communication unit 122 can repeatedly perform self-integrity verification to ensure that the battery configuration parameters obtained from the server 200 are not modified, missing, or distorted by errors.

[0053] Memory 123 can store a plurality of battery setting parameters. Memory 123 can store at least one optimal battery setting parameter acquired from server 200 by including it in the plurality of battery setting parameters. In other words, memory 123 can accumulate the at least one optimal battery setting parameter acquired from server 200 in the plurality of battery setting parameters and store it.

[0054] Hereinafter, a specific configuration and operation of the server 200 will be described with reference to Fig. 3. Fig. 3 is a block diagram showing a configuration of a server according to an embodiment disclosed in this document. Referring to Fig. 3, the server 200 may include a communication unit 210, a controller 220, and a database 230.

[0055] The communication unit 210 can receive information about the driving pattern of the vehicle in which the battery is mounted from the battery management unit 120. Furthermore, the communication unit 210 can periodically receive information on battery setting parameters already applied to the battery from the battery management device 120. For example, the communication unit 210 can periodically receive battery setting parameters applied to the plurality of battery cells 111, 112, 113, 114, the battery module 110, or the battery pack 100 from the battery management device 120. The communication unit 210 can receive battery setting parameters of the operation patterns of each of the plurality of vehicles from the battery management devices of the plurality of vehicles other than the battery management device 120.

[0056] The communication unit 210 can transmit at least one optimal battery setting parameter generated by the controller 220 to the battery management device 120. That is, the communication unit 210 can transmit at least one optimal battery setting parameter generated by the server 200 based on the vehicle driving pattern and the battery setting parameters already applied to the battery to the battery management device 120.

[0057] The controller 220 can extract a similar vehicle driving pattern that is similar to the driving pattern of the vehicle in which the battery is installed from among the multiple vehicle driving patterns stored in the database 230. The controller 220 can extract battery setting parameters of the similar vehicle driving pattern. The controller 220 can generate at least one optimal battery setting parameter based on the battery setting parameters of the similar vehicle driving pattern.

[0058] Specifically, the controller 220 may input the battery setting parameters of similar vehicle driving patterns and the battery setting parameters applied to the battery into a genetic algorithm to generate at least one optimal battery setting parameter.

[0059] Here, the genetic algorithm is a search technique that finds an optimal solution by imitating the way living organisms adapt to the environment and evolve. The genetic algorithm can operate with a structure that selects the solution with the best fitness from an initially existing solution population, i.e., a parent population, and searches in the direction of the selected solution to find the optimal solution. In other words, the genetic algorithm can be defined as a technique that presents the most optimized solution to a problem, rather than an algorithm that solves a specific problem or generates an answer. Specifically, the genetic algorithm can generate a child chromosome, which is the solution of the next generation, by crossing parent chromosome populations. The genetic algorithm can usually generate a child chromosome by selecting two chromosomes from a parent chromosome population, and then performing crossover operation and mutation of each parent chromosome.

[0060] The controller 220 can extract at least one similar vehicle operation pattern similar to the operation pattern of the vehicle in which the battery is installed from among the multiple battery setting parameters stored in the database 230. The controller 220 can use the battery setting parameters of the at least one similar vehicle operation pattern and the battery setting parameters already applied to the battery periodically obtained from the battery management device 120 as a parent chromosome population.

[0061] The controller 220 can input the battery setting parameters of at least one similar vehicle driving pattern and the battery setting parameters already applied to the battery into the genetic algorithm to generate child battery setting parameters. That is, the controller 220 can input the battery setting parameters of at least one similar vehicle driving pattern and the battery setting parameters already applied to the battery into the genetic algorithm to generate optimal battery setting parameters optimized for the vehicle driving pattern.

[0062] The controller 220 may repeatedly perform the genetic algorithm based on a set number of times. The controller 220 may repeatedly generate at least one optimal battery setting parameter based on the set number of times, and may terminate the genetic algorithm when the number of the generated at least one optimal battery setting parameter exceeds a certain ratio to the number of battery setting parameters already applied to the battery.

[0063] The controller 220 may iteratively generate at least one optimal battery setting parameter based on a set number of times, and may additionally execute the genetic algorithm based on the already set additional number of times when the number of the generated at least one optimal battery setting parameter relative to the number of battery setting parameters already applied to the battery is equal to or less than a certain percentage.

[0064] The controller 220 may include the battery setting parameter already applied to the battery in the at least one optimal battery setting parameter if the number of the at least one optimal battery setting parameter generated is equal to or less than a certain ratio to the number of the battery setting parameters already applied to the battery after additionally executing the genetic algorithm. The controller 220 may generate identification information of the optimal battery setting parameter that is the same as the battery setting parameter already applied to the battery if any one of the at least one optimal battery setting parameter is the same as the battery setting parameter already applied to the battery. The controller 220 may record identification information of the optimal battery setting parameter that is the same as the battery setting parameter already applied to the battery.

[0065] The communication unit 210 may transmit at least one optimal battery setting parameter generated by the controller 220 to the battery management unit 120. For example, the communication unit 210 may transmit at least one optimal battery setting parameter to the battery management unit 120, excluding an optimal battery setting parameter that is the same as a battery setting parameter already applied to the battery.

[0066] The communication unit 210 can compress and encrypt the optimal battery setting parameters generated by the controller 220. For example, the controller 220 can encrypt the generated optimal battery setting parameters using an AES or RSA authentication key. Specifically, the communication unit 210 can repeatedly perform self-integrity verification so that the battery setting parameters and vehicle operation information collected, accumulated, managed, evaluated, and verified in the server 200 are not modified, missing, or distorted by errors. Therefore, the communication unit 210 can encrypt data using an AES or RSA authentication key when transmitting or receiving data to or from the battery management device 120.

[0067] According to one embodiment, the communication unit 210 may perform a checksum check, a cyclic redundancy check (CRC) check, or a secure hash algorithm (SHA) check of the at least one optimal battery setting parameter generated by the controller 220 to verify the integrity of the at least one optimal battery setting parameter for loss or distortion.

[0068] The database 230 may store the vehicle operation pattern received by the communication unit 210 from the battery management unit 120. In addition, the database 230 may store battery setting parameters applied to the battery that the communication unit 210 periodically receives from the battery management unit 120.

[0069] FIG. 4 is a diagram illustrating an operation method of the battery management device and server according to an embodiment disclosed herein. Referring to FIG. 4, in step S101, the battery management unit 120 can receive vehicle driving information from other devices of the vehicle.

[0070] In step S102, the battery management unit 120 can extract a vehicle driving pattern by applying machine learning to vehicle driving information acquired from other control devices of the vehicle.

[0071] In step S103, the battery management device 120 may transmit to the server 200 a vehicle driving pattern or a battery setting parameter that has already been applied to the battery among a plurality of battery setting parameters.

[0072] In step S103, the server 200 can receive information on battery setting parameters already applied to the battery at regular intervals from the battery management device 120. In step S103, the server 200 can receive battery setting parameters for the operation patterns of each of the multiple vehicles from the battery management devices of the multiple vehicles other than the battery management device 120. Although FIG. 4 illustrates that the server 200 receives battery setting parameters from one battery management device, this is not limited thereto, and the server 200 can receive battery setting parameters from n (n is a natural number equal to or greater than 2) battery management devices.

[0073] In step S104, the server 200 can extract a similar vehicle driving pattern that is similar to the driving pattern of the vehicle in which the battery is mounted, from among the plurality of stored vehicle driving patterns.

[0074] In step S105, the server 200 can input the battery setting parameters of the similar vehicle operation patterns and the battery setting parameters applied to the battery into a genetic algorithm.

[0075] In step S106, the server 200 can generate at least one optimal battery setting parameter based on the battery setting parameters of the similar vehicle driving patterns. In step S106, the server 200 may compress and encrypt the generated optimal battery setting parameters.

[0076] In step S107 , the server 200 may transmit the generated at least one optimal battery setting parameter to the battery management unit 120 . In step S108, the battery management unit 120 can accumulate and store at least one optimal battery setting parameter received from the server 200 in the plurality of battery setting parameters.

[0077] In step S109, the battery management unit 120 can extract battery setting parameters that are optimal for the vehicle operation pattern from among the multiple battery setting parameters. Specifically, in step S109, the battery management unit 120 can input the multiple battery setting parameters to a Recursive Least Squares (RLS) filter, a Least Mean Squares (LMS) filter, or an Extended Kalman Filter (EKF) filter, and extract the battery setting parameters that are optimal for the vehicle operation pattern.

[0078] In step S109, the battery management unit 120 can repeatedly extract battery setting parameters corresponding to the vehicle operation pattern from among the multiple battery setting parameters at a constant period. In step S109, the battery management unit 120 can perform a periodic battery setting parameter extraction operation to dynamically extract battery setting parameters corresponding to the vehicle operation pattern and the vehicle operation history.

[0079] In step S110, the battery management unit 120 can control the battery pack 100 based on the extracted battery setting parameters. In other words, in step S110, the battery management unit 120 can control at least one of the voltage, internal resistance, capacitance, time constant, capacity, and open circuit voltage (OCV) of the multiple battery cells 111, 112, 113, 114, the battery module 110, or the battery pack 100 based on the extracted battery setting parameters.

[0080] In step S110, the battery management unit 120 can apply the extracted battery setting parameters to the battery when the vehicle is operating in a key-off state, when the vehicle is operating in an idle state, or when consent to change the vehicle's battery setting parameters is received from another control unit of the vehicle.

[0081] As described above, the battery management device 120 and server 200 according to one embodiment disclosed in this document can precisely analyze driving information of a vehicle equipped with a battery and apply battery setting parameters to the battery that are optimized for the vehicle driving pattern.

[0082] In addition, the battery management device 120 and the server 200 first analyze driving information of a vehicle equipped with a battery using a vehicle pattern analysis model installed in the battery management device, and then transmit the vehicle driving pattern and battery setting parameters applied to the battery to a server with abundant computing resources for precise analysis, thereby reducing communication resources and alleviating the storage and computing capacity of the cloud computer included in the server.

[0083] Furthermore, the battery management device 120 and the server 200 transmit the minimum necessary battery data to the server without data loss, thereby reducing infrastructure investment costs and network costs for the server.

[0084] The battery management device 120 and server 200 can then periodically update the optimal battery setting parameters that reflect the driving pattern of the vehicle in which the battery is mounted, thereby constructing a battery system that is optimized for vehicle operation.

[0085] FIG. 5 is a block diagram showing a hardware configuration of a computing system that realizes a battery management device according to an embodiment disclosed in this document. Referring to FIG. 5, a computing system 300 according to one embodiment disclosed in this document may include an MCU 310, a memory 320, an input / output I / F 330, and a communication I / F 340.

[0086] The MCU 310 may be a processor that executes various programs (e.g., programs for controlling battery setting parameters of battery cells) stored in the memory 320, processes various data via such programs, and performs the functions of the battery management device 120 shown in FIG. 1 described above.

[0087] The memory 320 can store various programs related to operation, and can also store operation data. A plurality of such memories 320 may be provided as necessary. The memories 320 may be volatile memories or non-volatile memories. The memory 320 as a volatile memory may be a RAM, a DRAM, an SRAM, or the like. The memory 320 as a non-volatile memory may be a ROM, a PROM, an EAROM, an EPROM, an EEPROM, a flash memory, or the like. The examples of the memory 320 listed above are merely illustrative and are not limited to these examples.

[0088] The input / output I / F 330 may provide an interface that connects input devices (not shown) such as a keyboard, mouse, or touch panel, and output devices such as a display (not shown), to the MCU 310 to transmit and receive data.

[0089] The communication I / F 340 is configured to be able to transmit and receive various data to and from a server, and may be any of various devices capable of supporting wired or wireless communication. For example, programs for measuring resistance and diagnosing abnormalities, various data, and the like can be transmitted and received from a separately provided external server via the communication I / F 340.

[0090] In this manner, the computer program of one embodiment disclosed in this document may be recorded in memory 320 and processed by MCU 310 to be realized as, for example, a module that performs each function of battery management device 120 described with reference to Figures 1 and 2.

[0091] The above description is merely an illustrative example of the technical ideas of the present disclosure, and various modifications and variations can be made by a person having ordinary knowledge in the technical field to which the present disclosure pertains without departing from the essential characteristics of the present disclosure.

[0092] Therefore, the embodiments disclosed in this disclosure are intended to explain the technical idea of ​​the disclosure, not to limit it, and such embodiments do not limit the scope of the technical idea of ​​the disclosure. The scope of protection of the disclosure should be interpreted according to the claims below, and all technical ideas within the scope equivalent thereto should be interpreted as being included in the scope of rights of the disclosure.

Claims

1. a controller that extracts an operation pattern of a vehicle equipped with a battery based on operation information of the vehicle, extracts battery setting parameters corresponding to the operation pattern of the vehicle, and controls the battery based on the extracted battery setting parameters; a communication unit that transmits at least one of information regarding the vehicle operation pattern and the extracted battery setting parameters to a server, and transmits the battery setting parameters applied to the battery to the server at a constant interval; A battery management device comprising:

2. further comprising a memory for storing a plurality of battery setting parameters; The battery management device according to claim 1 , wherein the controller repeatedly extracts a battery setting parameter corresponding to an operation pattern of the vehicle from among the plurality of battery setting parameters at a constant cycle.

3. The battery management device according to claim 2 , wherein the communication unit receives operation information of the vehicle from another control device of the vehicle.

4. The communication unit receives from the server at least one optimal battery setting parameter generated based on a driving pattern of the vehicle and a battery setting parameter already applied to the battery; 4. The battery management device according to claim 3, wherein said memory stores said at least one optimum battery setting parameter as included in said plurality of battery setting parameters.

5. 5. The battery management device according to claim 4, wherein the controller repeatedly extracts battery setting parameters corresponding to an operation pattern of the vehicle from among the plurality of battery setting parameters including the at least one optimal battery setting parameter at a constant period, and controls the battery based on the extracted battery setting parameters.

6. 6. The battery management device of claim 5, wherein the controller applies the extracted battery setting parameters to the battery when the vehicle is operating in a key-off state, when the vehicle is operating in an idle state, or when the controller receives consent to change a battery setting parameter of the vehicle from another electronic control device of the vehicle.

7. The battery management device according to claim 1 , wherein the battery setting parameters include at least one of an internal resistance, a capacitance, a time constant, a capacity, and an open circuit voltage of the battery.

8. The battery management device according to claim 1 , wherein the controller applies machine learning to the vehicle operation information to extract an operation pattern of the vehicle.

9. a communication unit that receives information about a driving pattern of a vehicle equipped with a battery and receives information about battery setting parameters that have already been applied to the battery at a constant interval; a controller that extracts a similar vehicle operation pattern similar to the operation pattern of the vehicle equipped with the battery from among a plurality of vehicle operation patterns already stored, extracts battery setting parameters of the similar vehicle operation pattern, and generates at least one optimal battery setting parameter based on the battery setting parameters of the similar vehicle operation pattern; Including, the server.

10. The server according to claim 9 , further comprising a database for storing the plurality of vehicle operation patterns and a plurality of battery setting parameters, the database storing battery setting parameters already applied to the battery received by the communication unit.

11. The controller inputs battery setting parameters of the similar vehicle operation patterns and battery setting parameters already applied to the battery into a genetic algorithm to generate the at least one optimal battery setting parameter; The server of claim 9 , wherein the communication unit transmits the at least one optimal battery configuration parameter to the vehicle.

12. 12. The server of claim 11, wherein the controller repeatedly performs the genetic algorithm based on a set number of times to repeatedly generate the at least one optimal battery setting parameter, and terminates the genetic algorithm when a number of the at least one optimal battery setting parameter generated exceeds a certain ratio to a number of battery setting parameters already applied to the battery.

13. 13. The server of claim 12, wherein the controller additionally executes the genetic algorithm based on a previously set number of additional times when a number of the at least one optimal battery setting parameter generated relative to a number of battery setting parameters already applied to the battery is equal to or less than a certain percentage.

14. the controller, after additionally executing the genetic algorithm, includes the battery setting parameters already applied to the battery in the at least one optimal battery setting parameter when a number of the at least one optimal battery setting parameter generated is equal to or less than a certain ratio with respect to a number of the battery setting parameters already applied to the battery; The server of claim 13 , wherein the communication unit transmits the at least one optimal battery configuration parameter to the vehicle, the at least one optimal battery configuration parameter including a battery configuration parameter already applied to the battery.

15. the controller generates an identification of an optimal battery setting parameter that is the same as a battery setting parameter already applied to the battery if any one of the at least one optimal battery setting parameter is the same as a battery setting parameter already applied to the battery; 15. The server of claim 14, wherein the communication unit transmits to the vehicle the at least one optimal battery setting parameter excluding an optimal battery setting parameter that is the same as a battery setting parameter already applied to the battery.

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