Method and apparatus for estimating driving range

Abstract

Provided are a method and apparatus for estimating a driving range. The method for estimating a driving range according to the present invention comprises the steps of: obtaining battery information, ambient temperature information, and driving pattern information of an electric vehicle; determining available energy information corresponding to a reference SOC range of a battery of the electric vehicle on the basis of the battery information, the ambient temperature information, and the driving pattern information; and determining driving range information of the electric vehicle on the basis of the available energy information and the driving pattern information.

Description

Driving range estimation method and driving range estimation device

[0001] The present invention relates to a technology for estimating the driving range of an electric vehicle.

[0002] This application is a priority application for Korean Patent Application No. 10-2024-0169573 filed on November 25, 2024, and all contents disclosed in the specification and drawings of said application are incorporated into this application by reference.

[0003] Recently, as the demand for portable electronic products such as laptops, video cameras, and mobile phones has increased rapidly, and the development of electric vehicles, energy storage batteries, robots, and satellites has accelerated, research on high-performance batteries capable of repeated charging and discharging is actively underway.

[0004] Currently commercialized batteries include nickel-cadmium, nickel-hydrogen, nickel-zinc, and lithium batteries. Among these, lithium batteries are gaining attention for their advantages, such as the ability to freely charge and discharge with almost no memory effect compared to nickel-based batteries, a very low self-discharge rate, and high energy density.

[0005] The driving range of an electric vehicle can vary significantly depending on the driver's driving habits and external environment, as well as the performance of the battery equipped in the electric vehicle and the degree of aging of the electric vehicle.

[0006] However, conventional techniques used to estimate driving range focus on reflecting the driver's driving habits and external environment, and neither battery performance nor the degree of electric vehicle aging is sufficiently considered in the estimation of driving range.

[0007] In addition, generally only DTE (Distance To Empty) is provided as driving range information. However, since DTE corresponds to the completely discharged state of the electric vehicle battery, most drivers do not want their electric vehicle to reach a completely discharged state.

[0008] The present invention aims to provide a method and apparatus for providing a highly accurate estimation result of the driving range of an electric vehicle by additionally utilizing at least one of performance information (e.g., full charge capacity) of a battery equipped in an electric vehicle and aging information of the electric vehicle, in addition to the driver's driving habits and external environment.

[0009] Other objects and advantages of the present invention may be understood from the following description and will become more clearly apparent from the embodiments of the present invention. Furthermore, it will be readily apparent that the objects and advantages of the present invention can be realized by the means and combinations thereof set forth in the claims.

[0010] A method for estimating a driving range according to one aspect of the present invention comprises the steps of: obtaining battery information, ambient temperature information, and driving pattern information of an electric vehicle; determining available energy information corresponding to a reference SOC range of the battery of the electric vehicle based on the battery information, the ambient temperature information, and the driving pattern information; and determining driving range information of the electric vehicle based on the available energy information and the driving pattern information.

[0011] The battery information above may include at least one of capacity data, voltage data, SOC data, and temperature information of the battery.

[0012] The starting SOC of the above reference SOC range may be (i) a predetermined maximum allowable SOC or (ii) the current SOC of the battery. The ending SOC of the above reference SOC range may be (i) a predetermined minimum allowable SOC or (ii) a user-set SOC.

[0013] The driving pattern information may include personal driving pattern data representing an individual's driving tendencies. The available energy information may include personal available energy associated with the personal driving pattern data. The driving range information may include personal driving range associated with the personal available energy.

[0014] The step of determining driving range information of the electric vehicle may include the step of determining the latest personal energy efficiency based on the personal driving pattern data, and the step of determining the personal driving range by multiplying the latest personal energy efficiency by the personal available energy.

[0015] The driving pattern information may further include certified driving pattern data representing standardized driving tendencies. The available energy information may further include certified available energy associated with the certified driving pattern data. The driving range information may further include certified driving range associated with the certified available energy.

[0016] The above driving range estimation method may further include a step of determining the degree of energy efficiency degradation caused by the aging of the electric vehicle based on the above individual driving pattern data.

[0017] The step of determining the fuel efficiency degradation degree may include the step of extracting effective individual fuel efficiencies that meet reference driving conditions from the individual fuel efficiency history of the individual driving pattern data, and the step of determining the fuel efficiency degradation degree by applying linear regression analysis to the effective individual fuel efficiencies.

[0018] The above method for estimating the driving range may further include a step of determining the standard driving conditions based on the average of the individual driving pattern and the average of the ambient temperature during a recent predetermined period, wherein the step of determining the fuel efficiency degradation degree may further include a step of determining the standard driving conditions.

[0019] The step of determining the driving range information of the electric vehicle may include the step of determining the certified driving range by multiplying the certified available energy by the certified fuel efficiency of the certified driving pattern data and the fuel efficiency degradation degree.

[0020] A computer-readable medium according to another aspect of the present invention records a program for executing the driving distance estimation method on a computer.

[0021] A driving range estimation device according to another aspect of the present invention includes a memory and a processor configured to determine available energy information corresponding to a reference SOC range of the electric vehicle's battery based on battery information of the electric vehicle obtained from the memory, driving pattern information, and driving pattern information. The processor is configured to determine driving range information of the electric vehicle based on the available energy information and the driving pattern information.

[0022] An electric vehicle according to another aspect of the present invention includes the driving range estimation device.

[0023] A server according to another aspect of the present invention includes the driving distance estimation device.

[0024] According to at least one of the embodiments of the present invention, in addition to the driver's driving habits and external environment, at least one of performance information (e.g., full charge capacity) of the battery equipped in the electric vehicle and aging information of the electric vehicle is additionally utilized to provide a highly accurate estimation result for the driving range of the electric vehicle.

[0025] In addition, according to at least one embodiment of the present invention, an estimated result regarding the driving distance during the remaining time until the State of Charge (SOC) of a discharging battery reaches a SOC directly set by the driver can be obtained together with or instead of the Distance To Empty (DTE). Accordingly, by referring to the guidance information regarding the driving distance, the driver can be helped to take appropriate precautionary measures, such as visiting a charging station before the electric vehicle reaches a completely discharged state.

[0026] In addition, according to at least one of the embodiments of the present invention, the accuracy of estimating the certified driving range can be improved by updating correction information for the certified fuel efficiency (fuel efficiency degradation to be described later) according to the aging of the electric vehicle.

[0027] The effects of the present invention are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art from the description in the claims.

[0028] The following drawings attached to this specification illustrate preferred embodiments of the present invention and serve to further enhance understanding of the technical concept of the present invention together with the detailed description of the invention provided below; therefore, the present invention should not be interpreted as being limited only to the matters described in such drawings.

[0029] FIG. 1 is a diagram illustrating the configuration of a driving range estimation device according to the present invention.

[0030] FIG. 2 is a diagram illustrating an exemplary configuration of an electric vehicle including the driving range estimation device of FIG. 1.

[0031] Figure 3 is a drawing referenced to explain an example of the coupling relationship between the battery block and the sensing unit shown in Figure 2.

[0032] FIG. 4 is a flowchart schematically illustrating a method for estimating a driving distance according to an embodiment of the present invention.

[0033] FIGS. 5 to 7 are block diagrams referenced to explain the method of FIG. 4.

[0034] FIG. 8 is a flowchart schematically illustrating a method for estimating driving distance according to another embodiment of the present invention.

[0035] Figure 9 is a block diagram referenced to explain the method of Figure 8.

[0036] FIG. 10 is a flowchart showing an example of subroutines that may be included in step S822 of FIG. 8.

[0037] Figure 11 is a drawing referenced to explain the method of Figure 10.

[0038] FIG. 12 is a diagram illustrating an exemplary configuration of an electric vehicle including the driving range estimation device of FIG. 1.

[0039] Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the attached drawings. Prior to this, terms and words used in this specification and claims should not be interpreted as being limited to their ordinary or dictionary meanings, and should be interpreted in a meaning and concept consistent with the technical spirit of the present invention, based on the principle that the inventor can appropriately define the concept of the terms to best describe his invention.

[0040] Therefore, the embodiments described in this specification and the configurations illustrated in the drawings are merely the most preferred embodiments of the present invention and do not represent all of the technical ideas of the present invention; thus, it should be understood that various equivalents and modifications that can replace them may exist at the time of filing this application.

[0041] Terms including ordinal numbers, such as first, second, etc., are used for the purpose of distinguishing one of the various components from the rest, and are not used to limit the components by such terms.

[0042] Throughout the specification, when a part is described as "comprising" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components. Furthermore, terms such as "<unit>" as used in the specification refer to a unit that performs at least one function or operation and may be implemented in hardware, software, or a combination of hardware and software.

[0043] Additionally, throughout the specification, when it is said that a part is "connected" to another part, this includes not only cases where they are "directly connected," but also cases where they are "indirectly connected" with other components in between.

[0044] FIG. 1 is a diagram illustrating the configuration of a driving range estimation device according to the present invention, and FIG. 2 is a diagram illustrating the configuration of an electric vehicle including the driving range estimation device of FIG. 1.

[0045] Referring to FIGS. 1 and 2, an electric vehicle (1) may include a battery pack (10, corresponding to 'battery' in the claim), a battery management system (100), a relay (20), a vehicle controller (2), a power converter (30), and an electric load (40). The electric vehicle (1) may further include peripheral devices (50).

[0046] The battery pack (10) comprises a plurality of battery blocks (BB1~BB N , N is a natural number greater than or equal to 2), includes a first charge / discharge terminal (P1) and a second charge / discharge terminal (P2).

[0047] In this specification, a plurality of battery blocks (BB1~BBN In explaining the contents common to each, the symbol 'BB' or 'BB' for the battery block k It is decided to assign '. k is a natural number less than or equal to N.

[0048] Multiple battery blocks (BB1~BB N ) can be connected to each other in series, parallel, or a combination of series and parallel between the first charge / discharge terminal (P1) and the second charge / discharge terminal (P2).

[0049] A battery block (BB) may include a single battery module or two or more battery modules. If the battery block (BB) includes multiple battery modules, the multiple battery modules may be connected in series, parallel, or a combination of series and parallel.

[0050] Each battery module may include an assembly of two or more battery cells. If a battery module includes multiple battery cells, the multiple battery cells may be connected in series, parallel, or a combination of series and parallel. In this specification, a battery cell refers to a basic unit of a storage element capable of independent charging and discharging, and is not particularly limited as long as it is rechargeable, such as a lithium-ion cell, for example.

[0051] A relay (20) is installed in a power line connecting a battery pack (10) and a charging / discharging terminal (P1, P2). In FIG. 2, the relay (20) is illustrated as being connected between the positive terminal of the battery pack (10) and the charging / discharging terminal (P1), but an additional relay (20) connected between the negative terminal of the battery pack (10) and the charging / discharging terminal (P2) may be further included in the electric vehicle (1). The relay (20) is turned on / off in response to a switching signal from a battery management system (100) or a vehicle controller. According to one embodiment of the present invention, the relay (20) may be a mechanical contactor that is turned on / off by the magnetic force of a coil, or a semiconductor switch such as a MOSFET (Metal Oxide Semiconductor Field Effect Transistor).

[0052] The battery management system (100) includes a sensing unit (110) and a control unit (120). The battery management system (100) may further include a communication unit (130).

[0053] The sensing unit (110) is a plurality of battery blocks (BB1~BB) of the battery pack (10). N ) Generates battery monitoring information representing each state.

[0054] The sensing unit (110) comprises a plurality of battery blocks (BB1~BB N Each of at least one state parameter can be measured periodically or non-periodically, and battery monitoring information representing each measured state parameter can be provided to the control unit (120).

[0055] Battery Block (BB) k The state parameters of ) are the battery block (BB kIt may represent the temperature of the battery block (BB) (which may be referred to as 'block temperature'), the cell voltage of each battery cell (BC) included in the battery block (BB), or a secondary parameter (e.g., amount of change, rate of change) that can be derived through the application of a mathematical function therefrom. Of course, in addition to this, the battery block (BB k If it can directly or indirectly indicate the degree of thermal abnormality of ), the type of state parameter is not particularly limited.

[0056] A current sensor (A) is installed in a power line connecting the battery pack (10) and the charging / discharging terminals (P1, P2) to measure the current flowing through the battery pack (10). The current sensor (A) may be included in the sensing unit (110).

[0057] The ambient temperature sensor (111) is configured to measure the ambient temperature, which is the temperature at a predetermined location spaced apart from the battery pack (10), and to generate a temperature signal indicating the measured ambient temperature. The ambient temperature sensor (111) and the current sensor (A) may be included in the sensing unit (110).

[0058] The control unit (120) can be implemented in hardware using at least one of ASICs (application specific integrated circuits), DSPs (digital signal processors), DSPDs (digital signal processing devices), PLDs (programmable logic devices), FPGAs (field programmable gate arrays), microprocessors, and other electrical units for performing functions.

[0059] The control unit (120) is operably coupled to the sensing unit (110) and / or the communication unit (130). Being operably coupled to the two components means that the two components are connected so that signals can be transmitted and received in either a unidirectional or bidirectional manner.

[0060] The control unit (120) may have a memory device. The memory device may include at least one type of storage medium among flash memory type, hard disk type, SSD type (Solid State Disk type), SSD type (Silicon Disk Drive type), multimedia card micro type, RAM (random access memory; RAM), SRAM (static random access memory), ROM (read-only memory; ROM), EEPROM (electrically erasable programmable read-only memory), and PROM (programmable read-only memory). The memory device may store data and programs required for operation by the control unit (120). The memory device may store data representing the result of operation by the control unit (120).

[0061] The control unit (120), based on the status data received from the sensing unit (110), has a plurality of battery blocks (BB1~BB N ) determines whether each is abnormal. The control unit (120) determines whether there is an abnormality in a plurality of battery blocks (BB1~BB N If at least one of ) is diagnosed as abnormal, it is configured to perform at least one safety operation for the battery pack (10).

[0062] The safety operation is a plurality of battery blocks (BB1~BB N The safety operation may include electrically disconnecting at least one of the ) from the remaining battery blocks. The safety operation includes a plurality of battery blocks (BB1~BB NIt may include an operation of forcibly discharging at least one of the energy (which may also be referred to as an 'energy drain').

[0063] The power converter (30) may include at least one of a DC-AC inverter and a DC-DC converter. The power converter (30) may convert direct current power (discharge power) supplied from the battery pack (10) into alternating current power and supply it to an electric load (40) during the discharge of the battery pack (10). The electric load (40) may include a three-phase alternating current motor that generates kinetic energy for driving the electric vehicle (1).

[0064] The control unit (120), based on battery monitoring information, has a plurality of battery blocks (BB1~BB N Each state of charge (SOC) can be determined, and the state of health (SOH) can be further determined.

[0065] The SOC of a battery block (BB) is the ratio of the remaining capacity to the full charge capacity (FCC) of the battery block (BB), and is typically expressed in the range of 0 to 100% or 0 to 1. The remaining capacity represents the amount of charge currently stored in the battery block (BB).

[0066] The SOH of a battery block (BB) is the ratio of the maximum capacity to the design capacity of the battery block (BB), and is typically expressed in the range of 0 to 100% or 0 to 1. The design capacity represents the maximum amount of charge that can be stored in the battery block (BB) when the battery block (BB) is in a new condition. As the battery block (BB) deteriorates, the maximum capacity gradually decreases from the design capacity. Since the SOC and SOH can each be estimated from one or a combination of two or more of various known methods, a detailed explanation is omitted.

[0067] The SOC of the battery pack (10) is a plurality of battery blocks (BB1~BB N It can be determined based on at least one of the SOCs. For example, the SOC of the battery pack (10) is a plurality of battery blocks (BB1 to BB N It can be the same as the minimum SOC, maximum SOC, or average SOC of ).

[0068] The communication unit (130) includes at least one communication circuit that supports wired or wireless communication with the vehicle controller (2), peripheral device (50) and / or driving range estimation device (200). Wired communication may be, for example, CAN (controller area network) communication, and wireless communication may be, for example, Zeegbi or Bluetooth communication. Of course, as long as wired or wireless communication is supported, the type of communication protocol is not specifically limited to the examples listed above.

[0069] The peripheral device (50) may include vehicle sensor(s) that measure at least one parameter (e.g., vehicle speed, etc.) related to the state of the electric vehicle (1). The peripheral device (50) may include an output device (e.g., display, speaker) that provides information received from the control unit (120) and / or the vehicle controller (2) in a form recognizable by the user. The peripheral device (50) may be driven using direct current power or alternating current power supplied from the power converter (30).

[0070] The driving range estimation device (200) includes a storage unit (210) and a processor (220), and may further include a communication unit (230).

[0071] The storage unit (210) stores battery information, ambient temperature information, and driving pattern information of the electric vehicle. The storage unit (210) may include at least one type of storage medium among flash memory type, hard disk type, SSD type (Solid State Disk type), SSD type (Silicon Disk Drive type), multimedia card micro type, RAM (random access memory; RAM), SRAM (static random access memory), ROM (read-only memory; ROM), EEPROM (electrically erasable programmable read-only memory), and PROM (programmable read-only memory).

[0072] The processor (220) can obtain at least one of the battery information, ambient temperature information, and driving pattern information of the storage unit (210) repeatedly, either periodically or non-periodically.

[0073] The processor (220) can sequentially perform the procedures for determining the available energy information and driving range information of the electric vehicle, respectively, based on battery information, ambient temperature information and driving pattern information.

[0074] The communication unit (230) can collect at least one of battery information, ambient temperature information, and driving pattern information from the battery management system (100), the vehicle controller (2), and / or the peripheral device (50). Additionally, the communication unit (230) can transmit at least one of available energy information and driving range information, determined by the processor (220), to the vehicle controller (2) and / or the peripheral device (50). The peripheral device (50) can generate a visual signal and / or an auditory signal corresponding to the driving range information.

[0075] Battery information may include capacity data, voltage data, SOC data, and temperature data of the battery (10).

[0076] The capacity data may include the Full Charge Capacity (FCC) of the battery (10) and may further include at least one of the capacity change history of the current full capacity and the remaining capacity during the period of interest. The full charge capacity of the battery (10) may directly or indirectly indicate the degree of degradation of the battery (10). The period of interest may be a period having a predetermined length of time and whose last point in time is the current point in time.

[0077] The buffer capacity may be updated whenever a charging event of the electric vehicle (1) occurs. For example, the processor (220) may calculate the buffer capacity based on the SOC at the start and end of the charging period of the charging event, and the current integrated value based on amperage counting during the charging period. The capacity change history may be based on amperage counting applied to the current history of the battery (10) during the driving of the electric vehicle (1).

[0078] The voltage data may include the current voltage, and may further include at least one of the voltage change history of the battery (10) during the period of interest and the average voltage during the period of interest.

[0079] The SOC data may include the current SOC and reference SOC ranges. The SOC data may further include at least one of the SOC change history and average SOC during the period of interest.

[0080] As the starting SOC of the reference SOC range, at least one of the current SOC and the maximum allowable SOC of the battery (10) may be used. As the ending SOC of the reference SOC range, at least one of the user-defined SOC and the minimum allowable SOC may be used. The user-defined SOC may be a target SOC received from a user (individual driver) through a peripheral device (50) equipped in the electric vehicle (1). For example, if the user wants to check in advance how much longer the electric vehicle (1) can be driven during the remaining time until the SOC of the battery (10) drops to a specific value (e.g., 10%), the user-defined SOC may be entered into the peripheral device (50).

[0081] The temperature data may include the current battery temperature and may further include at least one of the battery temperature change history and average battery temperature during the period of interest.

[0082] The outdoor temperature information may include the current outdoor temperature and may further include at least one of the outdoor temperature change history and the average outdoor temperature during the period of interest.

[0083] The ambient temperature information may include an authorized ambient temperature. The authorized ambient temperature corresponds to the external environment used to determine the authorized fuel efficiency and may be predetermined to correspond to an authorized driving pattern. A set of authorized ambient temperatures and authorized driving patterns may be referred to as "authorized driving conditions" or "standard driving conditions."

[0084] Driving pattern information may include at least one of personal driving pattern data and certified driving pattern data.

[0085] Personal driving pattern data may be data indicating the driving tendency of the driver (user) of the electric vehicle (1).

[0086] The individual driving pattern data may include the current voltage of the battery (10), and may further include at least one of the voltage change history and average voltage of the battery (10) during the period of interest.

[0087] Individual driving pattern data may include the current individual fuel efficiency and may further include at least one of the history of changes in individual fuel efficiency during the period of interest and the average individual fuel efficiency. The fuel efficiency may represent the ratio of the increase in cumulative driving distance to a minute change in electrical energy. The average individual fuel efficiency may represent the ratio of the total driving distance (increase in cumulative driving distance) to the total electrical energy consumed during the period of interest.

[0088] Individual driving pattern data may include the current vehicle speed and may further include at least one of the vehicle speed change history and average vehicle speed during the period of interest.

[0089] Individual driving pattern data may include the current individual W / Q and may further include at least one of the individual W / Q change history and average W / Q during the period of interest. W / Q may represent the ratio of a minute change in electrical energy to a minute change in capacity.

[0090] Certified driving pattern data may be data representing a standardized driving tendency in advance for the determination of certified fuel efficiency.

[0091] The certified driving pattern data may include a predetermined certified fuel efficiency. The certified driving pattern data may further include at least one of a certified average voltage and a certified average vehicle speed. For reference, the certified fuel efficiency may be based on the results of a fuel efficiency measurement test under driving conditions having a certified driving pattern and a certified driving temperature, assuming that the electric vehicle (1) is in a new condition.

[0092] Although the battery pack (10) and the battery management system (100) are shown as physically independent in FIG. 2, the battery management system (100) may be included as a sub-component of the battery pack (10).

[0093] Although FIG. 2 shows the driving range estimation device (200) and the battery management system (100) as being physically independent, the driving range estimation device (200) may be included as a sub-component of the battery management system (100). In this case, the storage unit (210) and processor (220) of the driving range estimation device (200) may be replaced by the control unit (120) of the battery management system (100), and the communication unit (230) of the driving range estimation device (200) may be replaced by the communication unit (130) of the battery management system (100).

[0094] Alternatively, unlike as illustrated in FIG. 2, the driving range estimation device (200) may be included in a server (reference numeral 300 in FIG. 12) located remotely from the electric vehicle (1) instead of being included in the electric vehicle (1).

[0095] FIG. 3 is a drawing referenced to explain an example of the coupling relationship between the battery block and the sensing unit illustrated in FIG. 2. For convenience of explanation, FIG. 3 shows a plurality of battery blocks (BB1~BB2) included in a battery pack (10). N Among ), the battery block (BB k Only ) was depicted.

[0096] Referring to FIG. 3, the sensing unit (110) is a battery block (BB k The sensing circuit (SB) provided to ) k ...includes ). Accordingly, the sensing unit (110) includes a plurality of sensing circuits (SB1~SB N Those skilled in the art will easily understand that it may include ).

[0097] Sensing circuit (SB) k) includes a temperature sensor (TS) and may further include a voltage detection circuit (VS).

[0098] The temperature sensor (TS) is the battery block (BB k Attached to the outer surface of ) or battery block (BB k It is installed at a predetermined point spaced apart from ), and the battery block (BB k Measures the temperature of the battery block (i.e., block temperature). The temperature sensor (TS) measures the temperature of the battery block (BB k A temperature signal indicating the temperature of ) can be generated, and the control unit (120) can collect the temperature signal of the temperature sensor (TS).

[0099] The voltage detection circuit (VS) includes at least one voltage sensor. The voltage detection circuit (VS) includes a battery block (BB k The module voltage of ) can be measured. The module voltage is, battery block (BB k It is the voltage between the two ends of ). The voltage detection circuit (VS) also includes the battery block (BB k The cell voltage of each battery cell (BC) included in ) can be further measured. The cell voltage is the voltage across the terminals of the battery cell (BC). The voltage detection circuit (VS) is the battery block (BB k A voltage signal is generated representing the module voltage of ) and the cell voltage of each battery cell (BC), and the control unit (120) can collect the voltage signal of the voltage detection circuit (VS).

[0100] The temperature of the battery (10) indicated by the battery information is a plurality of battery blocks (BB1~BB N It can be the minimum temperature, maximum temperature, or average temperature.

[0101] FIG. 4 is a flowchart schematically illustrating a method for estimating a driving distance according to an embodiment of the present invention, and FIGS. 5 to 7 are block diagrams referenced to explain the method of FIG. 4.

[0102] Referring to FIG. 4, in step S410, the processor (220) obtains battery information, ambient temperature information, and driving pattern information of the electric vehicle (1). At least one type of parameter represented by the battery information, ambient temperature information, and / or driving pattern information may be retrieved from the storage unit (210), collected from the sensing unit (110), or calculated directly by the processor (220).

[0103] In step S420, the processor (220) can determine available energy information corresponding to the reference SOC range of the battery (10) of the electric vehicle (1) based on the battery information, ambient temperature information, and driving pattern information obtained in step S410.

[0104] The reference SOC range may represent at least one of the following first to fourth SOC ranges. The first SOC range may be from the current SOC of the battery (10) to a predetermined minimum allowable SOC (e.g., 0%). The second SOC range may be from the current SOC of the battery (10) to a user-set SOC (e.g., 30%). The third SOC range may be from a predetermined maximum allowable SOC (e.g., 100%) to a predetermined minimum allowable SOC (e.g., 0%). The fourth SOC range may be from a predetermined maximum allowable SOC (e.g., 100%) to a user-set SOC (e.g., 30%). The first SOC range and the third SOC range are related to DTE (Distance To Empty), whereas the second SOC range and the fourth SOC range may not be.

[0105] Available energy information may include at least one of private available energy and certified available energy.

[0106] Personal available energy may be an estimate of the total amount of electrical energy that can be obtained from the battery (10) when driving the electric vehicle (1) according to the personal driving pattern during the discharge of the battery (10) from the starting SOC to the ending SOC of the standard SOC range.

[0107] The authorized available energy may be an estimate of the total amount of electrical energy that can be obtained from the battery (10) when the electric vehicle (1) continues to drive according to a predetermined authorized driving pattern during the discharge of the battery (10) from the starting SOC to the ending SOC of the reference SOC range. Even if the authorized driving pattern itself is constant, the authorized available energy determined in step S420 may also decrease as the full charge capacity of the battery (10) decreases.

[0108] In step S430, the processor (220) determines the driving range information of the electric vehicle (1) based on available energy information and driving pattern information.

[0109] Driving range information may include at least one of a private driving range and an authorized driving range.

[0110] The individual driving range may be an estimate of the distance the electric vehicle (1) can travel while the individual available energy is consumed from the battery (10) when the electric vehicle (1) is driven continuously according to the individual's driving pattern. The processor (220) may determine the individual driving range by determining the latest individual energy consumption based on the individual driving pattern data, and then multiplying the latest individual energy consumption by the individual available energy. The latest individual energy consumption may be the average of the individual energy consumption over a recent predetermined period of time (e.g., 1 minute, 5 minutes, 1 hour, etc.).

[0111] The certified driving range may be an estimate of the distance the electric vehicle (1) can drive while the certified available energy from the battery (10) is consumed when the electric vehicle (1) is driven continuously according to the certified driving pattern. The processor (220) may determine the certified driving range by multiplying the certified fuel efficiency of the certified driving pattern data by the certified available energy. Alternatively, the certified driving range may be determined by multiplying not only the certified fuel efficiency but also the fuel efficiency degradation described later by the certified available energy.

[0112] The driving range estimation device (200) can actively set real-time driving conditions of the electric vehicle (1) based on driving range information.

[0113] For example, if the personal driving range is determined to be less than or equal to the threshold distance, the processor (220) may limit the maximum output power of the power converter (30). Here, the threshold distance may be a predetermined distance or the remaining distance to the destination of the electric vehicle (1) set by the driver, etc. The processor (220) may determine the limit amount of the maximum output power by applying a predetermined amount of correspondence to the difference between the personal driving range and the threshold distance.

[0114] As another example, when the individual driving range is determined to be below a threshold distance, the processor (220) may refer to the power conversion efficiency information of the power converter (30) and adjust the real-time output power of the power converter (30) toward an output power corresponding to the maximum conversion efficiency. The power conversion efficiency information may be pre-recorded in the storage unit (210) or battery management system (100) as a function or data table that may include a predetermined correspondence relationship between the output power of the power converter (30) and the power conversion efficiency.

[0115] FIG. 5 illustrates a block diagram that is referenced to exemplarily explain the input-output relationship between battery information, ambient temperature information, driving pattern information, available energy information, and driving range information according to the method of FIG. 4.

[0116] Referring to FIG. 5, an energy estimation model (510) and a distance estimation model (520) may be stored in the memory device.

[0117] The energy estimation model (510) may be an artificial intelligence model that has learned the relationship between battery information, ambient temperature information, driving pattern information, and available energy information, or may be a single mathematical function or a set of two or more mathematical functions. The energy estimation model (510) may output available energy information based on the battery information, ambient temperature information, and driving pattern information input thereto.

[0118] The distance estimation model (520) may be an artificial intelligence model that has learned the relationship between available energy information, driving pattern information, and driving distance information, or may be a single mathematical function or a set of two or more mathematical functions. The distance estimation model (520) may output available energy information based on the available energy information and driving pattern information input thereto.

[0119] The available energy and driving range of the battery (10) both have a high dependence on the driving pattern of the electric vehicle (1). That is, even if the state of the battery (10) and the ambient temperature are constant, the available energy changes according to the change in the driving pattern, and even if the available energy of the battery (10) is constant, the driving range changes according to the change in the driving pattern. Accordingly, as shown in FIG. 5, the driving pattern information can be used as input information in both the procedure for determining available energy information and the procedure for determining driving range information.

[0120] Figure 6 is a graph referenced to exemplarily explain the relationship characteristics between the voltage and capacity of a battery cell during discharge.

[0121] Referring to FIG. 6, the voltage curve (610) illustrates the voltage change according to the capacity of the battery cell (BC) while a small constant current (e.g., 1C) flows, and the voltage curve (620) illustrates the voltage change according to the capacity of the battery cell (BC) while a large constant current (e.g., 3C) flows. Let us assume that other discharge conditions besides the current are common to both situations.

[0122] When comparing the voltage curve (610) and the voltage curve (620), the voltage curve (610) is located above the voltage curve (620) when the change in capacity is the same. That is, at the same capacity value, the voltage of the voltage curve (610) is greater than the voltage of the voltage curve (620). This is because the greater the discharge current, the greater the voltage drop caused by the resistance component of the battery cell (BC).

[0123] Since the electrical energy obtained from the battery cell (BC) corresponds to the product of voltage and capacity, a person skilled in the art can easily understand that the available energy of the battery cell (BC) corresponding to the voltage curve (620) is smaller than the available energy of the battery cell (BC) corresponding to the voltage curve (610).

[0124] In this regard, since the battery pack (10) corresponds to a set of multiple battery cells (BC) connected in series, parallel, or a combination of series and parallel, the relationship characteristics between the voltage, capacity, and available energy of the battery cells (BC) can also be common to the battery pack (10).

[0125] FIG. 7 is a graph visualizing the results of a simulated driving test performed under specific driving conditions. From FIG. 7, an exemplary relationship between the cumulative driving distance, available energy, and reference SOC range of the electric vehicle (1) can be observed.

[0126] The starting SOC and ending SOC of the reference SOC range corresponding to the energy curve (710) are 95% and 50%, respectively. The starting SOC and ending SOC of the reference SOC range corresponding to the energy curve (720) are 95% and 60%, respectively. The starting SOC and ending SOC of the reference SOC range corresponding to the energy curve (720) are 95% and 70%, respectively.

[0127] An increase in accumulated driving distance leads to the aging of the electric vehicle (1) and the degradation of the battery (10), and thus, as the accumulated driving distance increases, the available energy decreases, which is common to the three energy curves (710, 720, 730).

[0128] Additionally, since the reference SOC range corresponding to the energy curve (710) is the widest, the available energy of the energy curve (710) is greater than that of the two energy curves (720, 730). Conversely, since the reference SOC range corresponding to the energy curve (730) is the narrowest, the available energy of the energy curve (730) is smaller than that of the two energy curves (710, 720).

[0129] The artificial intelligence model included in the energy estimation model (510) may be trained by a training data set based on the results of a number of simulated driving tests conducted under various driving conditions, including voltage curve data according to FIG. 6 and energy curve data according to FIG. 7.

[0130] FIG. 8 is a flowchart schematically illustrating a method for estimating driving distance according to another embodiment of the present invention.

[0131] Referring to FIG. 8, in step S810, the processor (220) obtains battery information, ambient temperature information, and driving pattern information of the electric vehicle (1). Step S810 may be substantially the same as step S410.

[0132] In step S820, the processor (220) can determine the certified available energy corresponding to the standard SOC range of the battery (10) of the electric vehicle (1) based on the battery information, certified ambient temperature, and certified driving pattern data obtained in step S810.

[0133] In step S822, the processor (220) determines the degree of energy deterioration caused by the aging of the electric vehicle (1) based on individual driving pattern data.

[0134] In step S830, the processor (220) determines the certified driving range based on the certified available energy, certified fuel efficiency, and fuel efficiency degradation.

[0135] FIG. 9 illustrates a block diagram that is referenced to exemplarily explain the input-output relationship between battery information, ambient temperature information, driving pattern information, available energy information, and driving range information according to the method of FIG. 8.

[0136] Referring to FIG. 9, the memory device may store a distance estimation model (920) instead of the distance estimation model (520) of FIG. 5, and may further store a full degradation estimation model (910).

[0137] The fuel efficiency degradation estimation model (910) may be an artificial intelligence model that has learned the relationship between driving pattern information and fuel efficiency degradation, or a single mathematical function or a set of two or more mathematical functions. The fuel efficiency degradation estimation model (910) may output a fuel efficiency degradation based on individual driving pattern data input thereto.

[0138] The distance estimation model (920) may be an artificial intelligence model that has learned the relationship between certified available energy, certified fuel efficiency, fuel efficiency degradation, and certified driving range, or it may be a single mathematical function or a set of two or more mathematical functions. The distance estimation model (920) may output a certified driving range based on the certified available energy, certified fuel efficiency, and fuel efficiency degradation input thereto. The certified driving range output from the distance estimation model (920) may be the same as the result of a product operation of the certified available energy, the certified fuel efficiency of the driving pattern information, and the fuel efficiency degradation.

[0139] FIG. 10 is a flowchart showing an example of subroutines that may be included in step S822 of FIG. 8, and FIG. 11 is a diagram referenced to explain the method of FIG. 10.

[0140] Referring to FIG. 10, in step S1010, the processor (220) may determine reference driving conditions based on the average of the personal driving pattern and the average of the ambient temperature over a recent predetermined period of time (e.g., one day, one week). The reference driving conditions may be a combination of a range with a median value for the average of each item of the personal driving pattern (e.g., -5% to +5% of the median value) and a range with a median value for the average of the ambient temperature. For example, if the average of the voltage over a predetermined period of time, which is one of the items of the personal driving pattern, is 300 [V], the voltage range of the reference driving conditions may be 285 to 315 [V]. For another example, if the average of the vehicle speed, which is another item of the personal driving pattern, is 60 [km / h], the vehicle speed range of the reference driving conditions may be 57 to 63 [km / h]. As another example, if the average ambient temperature is 30 degrees, the ambient temperature range under standard driving conditions may be 28.5 to 31.5 degrees.

[0141] Alternatively, the standard driving conditions may be predetermined, in which case step S1010 may be omitted from the method of FIG. 10.

[0142] In step S1020, the processor (220) extracts effective individual fuel consumptiones that meet the reference driving conditions from the individual fuel consumption history of the individual driving pattern data.

[0143] In step S1030, the processor (220) determines the degree of deterioration of the effective individual ratios by applying linear regression analysis to the ratios.

[0144] The degree of energy efficiency degradation can represent the degree of decrease in the certified energy efficiency caused by the aging of the electric vehicle (1) (e.g., wear and bending of the drive system). Specifically, the certified energy efficiency represents the energy efficiency value when driving according to the certified driving pattern, assuming that the electric vehicle (1) is in a new condition. However, as the electric vehicle (1) ages, the actual energy efficiency when driving according to the certified driving pattern may be lower than the certified energy efficiency. Therefore, the certified driving range calculated based not only on the certified energy efficiency but also on the degree of energy efficiency degradation may be useful to the user.

[0145] FIG. 11 shows that the accumulated driving distance of the electric vehicle (1) is D A D at (e.g., 180,000 km) B This is a graph illustrating the history of individual fuel consumption during the period (period of interest) until it reaches (e.g., 320,000 km). The data points shown in Fig. 11 represent individual fuel consumption at different timings.

[0146] The processor (220) can extract each data point mapped to a personal driving pattern and ambient temperature corresponding to a reference driving condition (see step S1010) from among all data points (personal fuel efficiencies) of the personal fuel efficiencies history as an effective personal fuel efficiency. For example, let’s assume that the vehicle speed range and ambient temperature range according to the reference driving condition are 60–67 km / h and 24–29 degrees, respectively. Then, in the personal fuel efficiencies history, the personal fuel efficiencies mapped to personal driving conditions of 65 km / h and 25 degrees are extracted as effective personal fuel efficiencies, whereas the personal fuel efficiencies mapped to personal driving conditions of 70 km / h and 25 degrees and the personal fuel efficiencies mapped to personal driving conditions of 65 km / h and 33 degrees are not extracted as effective personal fuel efficiencies.

[0147] The processor (220) can determine the degree of whole-ratio degeneration by applying linear regression analysis to the extracted effective whole-ratios. The curve (1100) of FIG. 11 is an example of a regression line derived as a result of linear regression analysis. The following Equation 1 may be used to calculate the degree of whole-ratio degeneration based on the regression line (1100).

[0148] <Formula 1>

[0149]

[0150] Equation 1 may be included in the total degradation estimation model (710). In Equation 1, E ea is the individual total ratio, E according to the regression line (1100) at the start of the interest period. eb is the individual mileage according to the regression line (1100) at the last point in time of the interest period, ΔD is the increase in cumulative mileage during the interest period (i.e., D A and D B The difference of), D total E is the total accumulated driving distance of the electric vehicle (1). d is the degree of pre-degeneration. In Equation 1, (E ea - E eb) / ΔD can represent the slope (absolute value) of the regression line (1100).

[0151] Since the degree of energy efficiency degradation is determined based on the average ambient temperature corresponding to individual driving patterns, seasonal characteristics can be sufficiently reflected in the degree of energy efficiency degradation.

[0152] Even if the electric vehicle (1) is driven according to certified driving conditions (i.e., certified driving pattern and certified ambient temperature), it is natural that the actual fuel efficiency decreases from the certified fuel efficiency, so E e2 Ga E e1 A value equal to or greater than that strongly suggests the possibility that a sensing error and / or a data operation error has occurred. In this case, the processor (220) may suspend the calculation of the energy efficiency degradation and re-execute the procedure for calculating the energy efficiency degradation on the condition that the cumulative driving distance of the electric vehicle (1) has increased by more than a predetermined standard distance.

[0153] Another embodiment of the present invention may provide a computer-readable medium having a program recorded thereon for executing the various embodiments described above on a computer.

[0154] A program may be implemented as hardware components, software components, and / or a combination of hardware and software components. A program may be executed by any system capable of executing computer-readable instructions.

[0155] Software may include computer programs, code, instructions, or a combination thereof, and may configure a processing unit to operate as desired or command the processing unit independently or collectively.

[0156] Software can be implemented as a computer program containing instructions stored on a computer-readable storage medium. Examples of computer-readable storage media include magnetic storage media (e.g., ROM (read-only memory), RAM (random-access memory), floppy disks, hard disks, etc.) and optical reading media (e.g., CD-ROMs, DVDs (Digital Versatile Discs)). Computer-readable storage media can be distributed across networked computer systems, allowing computer-readable code to be stored and executed in a distributed manner. The storage medium is readable by a computer, stored in memory, and can be executed by a processor.

[0157] Computer-readable media may be provided in the form of non-transitory recording media. Here, 'non-transitory storage media' simply means that it is a tangible device and does not contain a signal (e.g., electromagnetic waves), and the term does not distinguish between cases where data is stored semi-permanently and cases where it is stored temporarily. For example, 'non-transitory storage media' may include a buffer in which data is stored temporarily.

[0158] In addition, the program may be provided as part of a computer program product. Computer program products may be traded between a seller and a buyer as goods.

[0159] A computer program product may include a software program or a computer-readable recording medium on which the software program is stored. For example, a computer program product may include a product in the form of a software program that is distributed electronically through a manufacturer of an electronic device or an electronic market (e.g., a downloadable application). For electronic distribution, at least a portion of the software program may be stored on a recording medium or temporarily created. In this case, the recording medium may be a server of the manufacturer of the electronic device, a server of the electronic market, or a recording medium of a relay server that temporarily stores the software program.

[0160] The embodiments of the present invention described above are not limited to implementation through devices and methods, but may also be implemented through a program that realizes a function corresponding to the configuration of the embodiments of the present invention or a recording medium on which such a program is recorded. Such implementation can be easily achieved by a person skilled in the art to which the present invention pertains, based on the description of the embodiments described above.

[0161] Although the present invention has been described above by limited embodiments and drawings, the present invention is not limited thereto, and it is obvious that various modifications and variations are possible within the scope of the technical spirit of the present invention and the equivalent scope of the claims described below by those skilled in the art to which the present invention belongs.

[0162] Furthermore, since the present invention described above allows for various substitutions, modifications, and changes within the scope of the technical concept of the present invention to those skilled in the art without departing from the technical spirit of the present invention, it is not limited by the aforementioned embodiments and attached drawings, but rather all or part of each embodiment may be selectively combined to allow for various modifications.

Claims

1. A step of acquiring electric vehicle battery information, ambient temperature information, and driving pattern information; A step of determining available energy information corresponding to a reference SOC range of the electric vehicle's battery based on the battery information, ambient temperature information, and driving pattern information; and A step of determining driving range information of the electric vehicle based on the above available energy information and the above driving pattern information; A method for estimating driving range, including 2. In Paragraph 1, The above battery information is, A method for estimating driving range, comprising at least one of capacity data, voltage data, SOC data, and temperature data of the battery.

3. In Paragraph 1, The starting SOC of the above standard SOC range is (i) a predetermined maximum allowable SOC or (ii) the current SOC of the battery, and A method for estimating driving range, wherein the end SOC of the above-mentioned standard SOC range is (i) a predetermined minimum allowable SOC or (ii) a user-set SOC.

4. In Paragraph 1, The above driving pattern information includes personal driving pattern data representing an individual's driving tendencies, and The above available energy information includes the individual available energy associated with the individual driving pattern data, and A method for estimating driving range, wherein the driving range information includes a personal driving range associated with the personal available energy.

5. In Paragraph 4, The step of determining the driving range information of the above electric vehicle is, A step of determining the latest individual fuel efficiency based on the above individual driving pattern data; and A step of determining the personal driving range by multiplying the latest personal fuel efficiency by the personal available energy; A method for estimating driving range, including 6. In Paragraph 4, The above driving pattern information further includes certified driving pattern data representing standardized driving tendencies, and The above available energy information further includes the certified available energy associated with the above certified driving pattern data, and A method for estimating driving range, wherein the driving range information further includes a certified driving range associated with the certified available energy.

7. In Paragraph 6, A step of determining the degree of energy efficiency degradation caused by the aging of the electric vehicle based on the above individual driving pattern data; A method for estimating driving range, further including 8. In Paragraph 7, The step of determining the above-mentioned total degradation degree is, A step of extracting effective individual fuel efficiencies that meet standard driving conditions from the individual fuel efficiencies history of the above-mentioned individual driving pattern data; and A step of determining the degree of deterioration of the above-mentioned total ratios by applying linear regression analysis to the above-mentioned effective individual total ratios; A method for estimating driving range, including 9. In Paragraph 8, The step of determining the above-mentioned total degradation degree is, A step of determining the reference driving conditions based on the average value of the individual driving pattern and the average value of the ambient temperature over a recent predetermined period; A method for estimating driving range, further including 10. In Paragraph 7, The step of determining the driving range information of the above electric vehicle is, A step of determining the certified driving range by multiplying the certified available energy by the certified fuel efficiency of the certified driving pattern data and the fuel efficiency degradation rate; A method for estimating driving range, including 11. A computer-readable medium storing a program for executing a driving range estimation method according to any one of paragraphs 1 through 10 on a computer.

12. Memory; and The processor is configured to determine available energy information corresponding to a reference SOC range of the electric vehicle's battery based on battery information, driving pattern information, and driving pattern information of the electric vehicle obtained from the memory, and The above processor is, A driving range estimation device configured to determine driving range information of the electric vehicle based on the above available energy information and the above driving pattern information.

13. An electric vehicle including a driving range estimation device pursuant to Paragraph 12.

14. A server including a driving range estimation device according to paragraph 12.

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