Battery management system, battery pack, electric vehicle, and battery management method

Abstract

A battery management system, a battery pack, an electric vehicle, and a battery management method are provided. The battery management system according to the present invention includes a sensing unit that measures the cell voltage, cell current, and negative electrode resistance of the battery cell, and a control unit configured to determine the state of the battery cell based on the measured values ​​of the cell voltage, cell current, and negative electrode resistance, respectively.

Description

Technical Field

[0001] The present invention relates to a technique for estimating the electrical state of a battery cell.

[0002] This application claims priority based on Korean Patent Application No. 10-2023-0134642 filed on October 10, 2023, and all the contents disclosed in the specification and drawings of the application are incorporated into this application.

Background Art

[0003] Recently, the demand for portable electronic products such as notebook computers, video cameras, and mobile phones has increased rapidly, and with the full-scale development of electric vehicles, energy storage batteries, robots, satellites, etc., research on high-performance batteries capable of repeated charging and discharging has been actively conducted.

[0004] Currently commercially available batteries include nickel-cadmium batteries, nickel-metal hydride batteries, nickel-zinc batteries, lithium batteries, etc. Among these, lithium batteries are in the spotlight because they have almost no memory effect compared to nickel-based batteries, so they can be freely charged and discharged, have a very low self-discharge rate, and have a high energy density.

[0005] Battery packs for applications that require a large capacity and high voltage, such as electric vehicles and energy storage systems, include dozens to hundreds of battery cells connected in series with each other. A battery management system is provided to acquire battery parameters (e.g., voltage, current, SOC, etc.) of each battery cell and execute various functions (e.g., balancing, cooling) to ensure the reliability and safety of each battery.

[0006] Currently, various types of rechargeable battery cells are widely used, and some types of battery cells, such as lithium iron phosphate (LFP) cells and lithium sulfur (LiS) cells, exhibit voltage flatness in a portion of the overall State of Charge (SOC) range (e.g., SOC 5% to 70%).

[0007] A voltage flat interval is a range in the SOC-OCV curve, which is a dataset representing the relationship between SOC and OCV, where the rate of change of OCV is below a threshold SOC range, and / or the rate of change of CCV (Closed Circuit Voltage) during constant current charging or discharging is below a threshold SOC range.

[0008] When a battery cell has a flat voltage profile, the SOC-OCV curve (sometimes called an "OCV map") and / or SOC-CCV curve (sometimes called a "CCV map") are useful for estimating the SOC outside the flat voltage range. However, within the flat voltage range, even small errors in the OCV or CCV can lead to a large difference between the actual SOC and its estimate.

[0009] Therefore, if the battery cell's SOC is within the voltage flat interval (SOC range with voltage flat characteristics), it is advantageous to determine the current SOC estimate of the battery cell based on the integrated current of the battery cell, rather than using an OCV map or CCV map.

[0010] However, when the State of Charge (SOC) of a battery cell is maintained within a voltage flat range for a long period while charging and discharging are repeatedly repeated, the error between the actual value of the battery cell current and the detected value continues to accumulate in the integrated current, causing the accuracy of the SOC estimation to gradually decrease. Furthermore, since SOC is considered very important when estimating the State of Health (SOH) of a battery cell, an inaccurate SOC estimate leads to a decrease in the accuracy of the SOH, which is another problem.

[0011] One solution to this problem is to intentionally charge or discharge the battery cell so that its State of Charge (SOC) falls outside the voltage flat interval, and then estimate the SOC of the battery cell using an OCV map or CCV map. However, this method has the problem that power is wasted by the intentional charging or discharging of the battery cell, which prolongs the time required to estimate the SOC. [Overview of the Initiative] [Problems that the invention aims to solve]

[0012] The inventors of this invention have discovered through numerous experiments that there is a strong correlation between the state of charge (SOC) of a battery cell and the negative electrode resistance.

[0013] The present invention was made to solve the above problems, and aims to provide a battery management system, battery pack, electric vehicle, and battery management method that suppress the decrease in SOC estimation accuracy due to voltage flatness characteristics by utilizing a SOC estimation logic (described later as the "second SOC estimation logic") in which the negative electrode resistance of the battery cell is used as an input variable.

[0014] Other objectives and advantages of the present invention can be understood from the following description and will be understood more clearly from the embodiments of the present invention. Furthermore, it will be readily understood that the objectives and advantages of the present invention can be achieved by the means and combinations thereof set forth in the claims. [Means for solving the problem]

[0015] A battery management system according to one aspect of the present invention includes a sensing unit that measures the cell voltage, cell current, and negative electrode resistance of a battery cell, and a control unit configured to determine the state of the battery cell based on the measured values ​​of the cell voltage, cell current, and negative electrode resistance.

[0016] The control unit may be configured to perform a first SOC estimation logic to determine a first SOC estimate based on the measured values ​​of the cell voltage and the cell current. The control unit may be configured to perform a second SOC estimation logic to determine a second SOC estimate based on the measured value of the negative electrode resistance. The control unit may be configured to determine the current SOC estimate of the battery cell based on at least one of the first SOC estimate and the second SOC estimate.

[0017] The control unit may be configured to determine the current SOC estimate for the battery cell in the same way as a weighted average of either the first SOC estimate or the second SOC estimate, based on the result of comparing the first SOC estimate with the voltage flattening interval.

[0018] The control unit may be configured to determine the current SOC estimate for the battery cell, similar to the first SOC estimate, if the first SOC estimate is outside the voltage flat interval.

[0019] The control unit may be configured to determine the current SOC estimate for the battery cell, similar to the second SOC estimate, if the first SOC estimate falls within the voltage flat interval.

[0020] The control unit may be configured to determine a first weight associated with the first SOC estimation logic and a second weight associated with the second SOC estimation logic, depending on the difference between the voltage flat interval and the first SOC estimate, if the first SOC estimate is outside the voltage flat interval. The control unit may be configured to determine the current SOC estimate for the battery cell, as well as a weighted average of the first and second SOC estimates based on the first and second weights.

[0021] The first weight may have a predetermined positive correlation with the difference between the voltage flattening interval and the first SOC estimate. The second weight may have a predetermined negative correlation with the difference between the voltage flattening interval and the first SOC estimate.

[0022] The control unit may be configured to determine the current SOC estimate for the battery cell, similar to the weighted average of the first and second SOC estimates based on a third and fourth weight, if the first SOC estimate falls within the voltage flattening interval.

[0023] The control unit may be configured to determine a first weight associated with the first SOC estimation logic and a second weight associated with the second SOC estimation logic, depending on the difference between the voltage flat interval and the first SOC estimate, if the first SOC estimate is outside the voltage flat interval. The control unit may be configured to determine the first and second weights depending on the residence time of the SOC within the voltage flat interval. The third weight may have a predetermined negative correlation with the residence time. The fourth weight may have a predetermined positive correlation with the residence time.

[0024] The control unit may be configured to determine the current SOH estimate of the battery cell based on the measured values ​​of the negative electrode resistance and the cell temperature of the battery cell, if the current SOC estimate indicates a fully discharged or fully charged state.

[0025] A battery pack according to another aspect of the present invention may include the battery management system.

[0026] An electric vehicle according to yet another aspect of the present invention may include the battery pack.

[0027] A battery management method according to yet another aspect of the present invention includes the steps of measuring the cell voltage, cell current, and negative electrode resistance of a battery cell, and determining the state of the battery cell based on the measured values ​​of the cell voltage, cell current, and negative electrode resistance.

[0028] The step of determining the electrical state of the battery cell may include: executing a first SOC estimation logic to determine a first SOC estimate based on measured values ​​of the cell voltage and the cell current; executing a second SOC estimation logic to determine a second SOC estimate based on measured values ​​of the negative electrode resistance; and determining the current SOC estimate of the battery cell based on at least one of the first SOC estimate and the second SOC estimate.

[0029] The step of determining the current SOC estimate for the battery cell may include determining the current SOC estimate for the battery cell in the same way as a weighted average of either the first SOC estimate or the second SOC estimate, depending on the result of comparing the first SOC estimate with the voltage flat interval. [Effects of the Invention]

[0030] According to at least one embodiment of the present invention, when estimating the SOC of a battery cell, by utilizing an additional SOC estimation logic (described later as "second SOC estimation logic") in which the negative electrode resistance of the battery cell is used as an input variable, along with the normal SOC estimation logic (described later as "first SOC estimation logic"), it is possible to suppress the decrease in the accuracy of SOC estimation due to voltage flatness characteristics.

[0031] Furthermore, according to at least one embodiment of the present invention, the accuracy of SOC estimation across the entire SOC range can be improved by determining the SOC of the battery cell as a weighted average of either the SOC estimate from the first SOC estimation logic or the SOC estimate from the second SOC estimation logic, depending on the difference between the SOC estimate from the first SOC estimation logic and the voltage flattening interval.

[0032] Furthermore, according to at least one embodiment of the present invention, the weights for the SOC estimate by the first SOC estimation logic and the SOC estimate by the second SOC estimation logic are determined according to the residence time during which the SOC estimate by the first SOC estimation logic remains inside or outside the voltage flat interval, and the SOC is determined as the SOC of the battery cell, similar to a weighted average of the two SOC estimates based on the two weights, thereby improving the accuracy of SOC estimation across the entire SOC range.

[0033] The effects of the present invention are not limited to those mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description of the claims.

[0034] The following drawings accompanying this specification illustrate preferred embodiments of the invention and, together with the detailed description of the invention, serve to further illustrate the technical idea of ​​the invention; therefore, the invention should not be construed as being limited solely to what is shown in the drawings. [Brief explanation of the drawing]

[0035] [Figure 1] This figure illustrates the configuration of an electric vehicle including a battery management system according to the present invention. [Figure 2a] This graph is used to illustrate the internal and external structure of a battery cell. [Figure 2b] This graph is used to illustrate the internal and external structure of a battery cell. [Figure 2c] This graph is used to illustrate the internal and external structure of a battery cell. [Figure 3] This graph illustrates the SOC-OCV curve of a battery cell. [Figure 4] This graph illustrates the SOC-negative electrode resistance curve of a battery cell. [Figure 5] This graph is referenced to explain the relationship between the State of Health (SOH) and State of Charge (SOC) of a battery cell and the negative electrode resistance curve. [Figure 6] Figure 1 is a flowchart illustrating the battery management method performed by the battery management system shown. [Figure 7] Figure 6 is a flowchart illustrating the subroutine for step S620. [Figure 8] Figure 7 is a flowchart that schematically shows an example of the subroutine in step S730. [Figure 9] Figure 7 is a flowchart illustrating another example of the subroutine in step S730. [Figure 10] This is an example of a weight map that can be used when executing the method shown in Figure 9. [Figure 11] Figure 7 is a flowchart illustrating yet another example of the subroutine in step S730. [Figure 12] This is an example of a first correction coefficient map used to correct the first weight. [Figure 13] This is an example of a second correction coefficient map used to correct for the fourth weight. [Figure 14] This is a flowchart illustrating a battery management method according to a second embodiment of the present invention. [Modes for carrying out the invention]

[0036] Preferred embodiments of the present invention will be described in detail below with reference to the attached drawings. Prior to this, terms and words used in this specification and in the claims shall not be interpreted in their usual and dictionary sense, but rather in a sense and concept appropriate to the technical idea of ​​the present invention, in accordance with the principle that the inventor himself may appropriately define the concept of terms in order to best describe the invention.

[0037] Therefore, it should be understood that the configurations shown in the embodiments described herein represent only one of the most preferred embodiments of the present invention and do not represent the entire technical concept of the present invention, and that there are various equivalents and modifications that can be substituted therein at the time of filing this application.

[0038] Terms that include ordinal numbers, such as "first," "second," etc., are used to distinguish one of the various components from the rest, and are not used to limit the components by such terms.

[0039] Throughout the specification, when a part "includes" a component, this means, unless otherwise stated, that it may include other components rather than excluding them. Furthermore, terms such as <control unit> in the specification mean a unit that processes at least one function or operation, which can be implemented by hardware, software, or a combination of hardware and software.

[0040] Furthermore, throughout the specification, when one part is described as being "connected" to another part, this includes not only cases where they are "directly connected," but also cases where they are "indirectly connected" to each other through other elements.

[0041] In this specification, SOC (State of Charge) is the ratio of the remaining capacity to the full charge capacity of an energy-storable unit (e.g., a battery cell or cell group), expressed as a percentage from 0% to 100%.

[0042] Figure 1 is a diagram illustrating the configuration of an electric vehicle including a battery management system according to the present invention.

[0043] Referring to Figure 1, the electric vehicle 1 includes a vehicle controller 2, a battery pack 10, an inverter 30, and a motor 40.

[0044] The charge and discharge terminals P+ and P- of the battery pack 10 can be electrically coupled to the inverter 30 and / or charger 3 via an electrical cable or the like. The charger 3 is either included in the electric vehicle 1 or provided at an external charging station outside the electric vehicle 1.

[0045] The vehicle controller 2 (e.g., ECU: Electronic Control Unit) is configured to transmit a key-on signal to the battery management system 100 in response to the user switching a start button (not shown) on the electric vehicle 1 to the ON position. The vehicle controller 2 is also configured to transmit a key-off signal to the battery management system 100 in response to the user switching the start button to the OFF position. The charger 3 can communicate with the vehicle controller 2 to supply charging power (e.g., constant current, constant voltage, constant power) through the charge / discharge terminals P+ and P- of the battery pack 10.

[0046] The battery pack 10 includes battery cells BC and relays 20, and may further include a battery management system 100.

[0047] The battery cell BC can be an energy storage element that is capable of repeated charging and discharging and has voltage flatness characteristics, such as a lithium iron phosphate (LFP) cell or a lithium sulfur (LiS) cell. The voltage flatness interval is a state of charge (SOC) range in which the rate of change of the battery cell BC is predetermined or confirmed to be below a threshold.

[0048] A more detailed explanation of the battery cell BC will be provided later, referring to Figures 2a to 2c.

[0049] The series circuit of the battery cell BC, current sensor 113, and relay 20 is electrically connected to the inverter 30 and / or charger 3 via the charge / discharge terminals P+ and P-.

[0050] The relay 20 is located in a power line that provides a current path for charging and discharging the battery pack 10. While the relay 20 is turned on, power can be transmitted from the battery pack 10 to the inverter 30 and / or from the charger 3 to the battery pack 10. The relay 20 can be implemented by one or more known switching devices, such as a mechanical contactor or a field-effect transistor (FET). The control unit 130 can control the relay 20 from one state to the other.

[0051] The inverter 30 is provided to convert the DC current from the battery cells BC contained in the battery pack 10 into AC current in response to commands from the battery management system 100 or the vehicle controller 2. The motor 40 can be driven using the AC power from the inverter 30. For example, a three-phase AC motor can be used as the motor 40.

[0052] The voltage across the terminals of a battery cell BC is sometimes called the "cell voltage." The state in which relay 20 is turned on and battery cell BC is being charged and discharged is sometimes called the load state (cycle state). The voltage of battery cell BC detected in the load state is sometimes called the closed circuit voltage (CCV).

[0053] When relay 20 switches from the ON state to the OFF state, battery cell BC enters an unloaded state (dormant state, calendar state). The voltage across both ends of battery cell BC in the unloaded state is sometimes called the no-load voltage. No-load voltage is a general term encompassing relaxation voltage and open-circuit voltage (OCV). Specifically, when battery cell BC switches from a loaded state to an unloaded state, the polarization generated in battery cell BC naturally begins to dissipate, and the no-load voltage of battery cell BC converges toward the OCV. OCV represents the no-load voltage at which battery cell BC is maintained in an unloaded state for a predetermined time (e.g., 2 hours) or more, and the rate of change of the voltage of battery cell BC is less than a certain value. In other words, OCV is the no-load voltage at which the polarization of battery cell BC has become negligibly small. Relaxation voltage refers to the no-load voltage before the polarization becomes sufficiently small.

[0054] The battery management system 100 is provided to monitor the status of the battery cells BC.

[0055] The battery management system 100 includes a sensing unit 110 and a control unit 130. The battery management system 100 may further include a communication unit 150.

[0056] The sensing unit 110 includes a voltage sensor 111, a current sensor 113, and a resistance sensor 115. The sensing unit 110 may further include a temperature sensor 117.

[0057] The voltage sensor 111 is provided so as to be electrically connectable to the positive lead and the first negative lead of the battery cell BC. The voltage sensor 111 detects the cell voltage of the battery cell BC using the potential difference between a pair of sensing lines connected to the positive lead PL and the first negative lead NL of the battery cell BC, respectively. The voltage sensor 111 can transmit a voltage signal representing the detected cell voltage of the battery cell BC to the control unit 130 by analog-to-digital conversion.

[0058] The current sensor 113 is connected in series with the battery cell BC via the charge / discharge current path of the battery cell BC. The current sensor 113 can be implemented by one or more known current sensors, such as a shunt resistor or a Hall effect element.

[0059] The current sensor 113 may also be configured to generate a current signal representing the cell current, which is the charge / discharge current flowing between the positive electrode lead PL and the first negative electrode lead NL of the battery cell BC, and output it to the control unit 130.

[0060] The resistance sensor 115 may be connected between the first negative electrode lead NL and the second negative electrode lead AL of the battery cell BC and provided to measure the negative electrode resistance of the battery cell BC. As the resistance sensor 115, for example, an EIS (Electrochemical Impedance Spectroscopy) device can be used.

[0061] EIS (Electron Intensification Spectroscopy) is a method of measuring the resistance of a battery cell by applying an AC voltage to the object being measured and analyzing the response signal to the applied AC voltage. It has the advantage of allowing for more precise analysis compared to constant current or constant voltage measurement methods. The amplitude range of the AC voltage is 0.01mV to 0.5mV, or 0.01mV to 0.2mV, or 0.2mV to 0.5mV. When the amplitude of the AC voltage is within the above range, the accuracy of the measured resistance is excellent, and the battery cell BC is not damaged.

[0062] In one embodiment of the present invention, the frequency of the AC voltage used when measuring the resistance is 100Hz to 1000Hz.

[0063] During charging and discharging of battery cell BC, the thickness of the negative electrode of battery cell BC changes as the negative electrode expands or contracts. Specifically, during charging, the negative electrode of battery cell BC expands, increasing its thickness, while during discharging, the negative electrode of battery cell BC contracts, decreasing its thickness. The resistance sensor 115 may be configured to output a resistance signal indicating the negative electrode resistance to the control unit 130 during charging and discharging of battery cell BC.

[0064] It is widely known that the resistance and thickness of any conductor are inversely proportional. Furthermore, the inventors of this invention recognized that the thickness of the negative electrode of a battery cell BC has a specific relationship with the state of charge (SOC) from the pattern of change in the thickness of the negative electrode of the battery cell BC during charging and discharging. From this, they also verified that the negative electrode resistance of the battery cell BC is an indicator that has a strong dependence on the SOC of the battery cell BC.

[0065] The temperature sensor 117 may be directly attached to the exterior of the battery cell BC, or it may be positioned at a certain distance away. It may be configured to output a temperature signal representing the temperature of the battery cell BC to the control unit 130. For example, a thermocouple can be used as the temperature sensor 117.

[0066] The control unit 130 is operably coupled to the relay 20, the sensing unit 110, the memory 140, and / or the communication unit 150. The operably coupled nature of the two components means that they are directly or indirectly connected to enable the transmission and reception of signals in one direction or bidirectionally.

[0067] The control unit 130 can be implemented in hardware using at least one of the following: DSPs (digital signal processors), DSPDs (digital signal processing devices), PLDs (programmable logic devices), FPGAs (field programmable gate arrays), microprocessors, or other electrical units for performing functions.

[0068] The memory 140 can pre-store programs and various data necessary for executing the battery management method according to the embodiment described later. The memory can include, for example, at least one type of computer-readable storage medium, such as flash memory type, hard disk type, SSD type (Solid State Disk type), SDD type (Silicon Disk Drive type), multimedia card micro type, RAM (random access memory), SRAM (static random access memory), ROM (read-only memory), EEPROM (electrically erasable programmable read-only memory), or PROM (programmable read-only memory). Although Figure 1 shows the memory 140 being provided independently of the control unit 130, the memory 140 can also be provided in a form integrated into the control unit 130.

[0069] The control unit 130 can determine voltage values, current values, temperature values, and negative electrode resistance values ​​based on the voltage signal, current signal, temperature signal, and resistance signal received from the sensing unit 110 at set time intervals (e.g., 0.01 seconds), and record these values ​​in the memory 140. Since the current signal contains information about the direction of the current, the control unit 130 can determine from the current signal whether the battery cell BC is charging, discharging, or in a dormant state. Dormancy (or dormant state) refers to a state in which both charging and discharging of the battery pack 10 are interrupted.

[0070] The control unit 130 can determine the current integration amount based on the current signal using the ampere count (current integration method). The current integration amount at any given time point represents the total current accumulated over the period from the time the current integration amount was last initialized prior to that time point to that time point.

[0071] The communication unit 150 can be communicatively coupled to the vehicle controller 2 of the electric vehicle 1. The communication unit 150 can transmit messages from the vehicle controller 2 to the control unit 130 and transmit messages from the control unit 130 to the vehicle controller 2. Messages from the control unit 130 may include information to notify the electrical status (e.g., SOC, SOH) of the battery cells BC. For communication between the communication unit 150 and the vehicle controller 2, wired networks such as LAN (local area network), CAN (controller area network), and daisy-chain, and / or short-range wireless networks such as Bluetooth, Zigbee, and Wi-Fi can be utilized. The battery management system 100 may further include an output device (e.g., display, speaker) that provides information received by the communication unit 150 from the control unit 130 and / or the vehicle controller 2 in a user-recognizable format. The vehicle controller 2 can control the inverter 30 and / or charger 3 based on the information collected via communication with the battery management system 100.

[0072] Figures 2a to 2c are graphs referenced to illustrate the internal and external structure of the battery cell. Specifically, Figure 2a shows a top view of the battery cell BC, Figure 2b shows a side cross-sectional view of the battery BC, and Figure 3b shows the structure of the negative electrode plate 12 of the battery BC.

[0073] Referring to Figures 2a to 2c, the battery cell BC includes the outer casing E and the electrode assembly EA.

[0074] The outer casing material E provides a space in which an electrode assembly EA can be housed. With the electrode assembly EA positioned in the outer casing material E, the manufacturing of the battery cell BC can be completed by performing a sealing process (e.g., heat sealing) on ​​the edge region of the outer casing material E. The symbol S refers to the edge portion of the outer casing material E where the sealing process has been performed.

[0075] The electrode assembly EA includes a positive electrode lead PL, a positive electrode plate 11, a separator membrane 13, a first negative electrode lead NL, a second negative electrode lead AL, and a negative electrode plate 12. The electrode assembly EA is formed by stacking or folding at least one positive electrode plate 11, a negative electrode plate 12, and a separator membrane 13 in a predetermined configuration. The positive electrode plate 11 and the negative electrode plate 12 are electrically insulated from each other by the separator membrane 13.

[0076] The positive electrode tab PT is a portion that protrudes from the positive electrode plate 11 toward the positive electrode lead PL. Similarly, the first negative electrode tab NT and the second negative electrode tab AT are portions that protrude from different regions of the negative electrode plate 12 toward the first negative electrode lead NL and the second negative electrode lead AL, respectively. For example, the first negative electrode tab NT protrudes from one corner of the negative electrode plate in the same direction as the positive electrode tab PT, while the second negative electrode tab AT protrudes from the opposite corner of the negative electrode plate from which the first negative electrode tab NT protrudes, in the opposite direction to the protrusion direction of the first negative electrode tab NT.

[0077] One end of the positive electrode tab PT, the first negative electrode tab NT, and the second negative electrode tab AT are individually coupled to one end of the positive electrode lead PL, the first negative electrode lead NL, and the second negative electrode lead AL inside the outer casing E. The other ends of the positive electrode lead PL, the first negative electrode lead NL, and the second negative electrode lead AL are exposed to the outside of the outer casing E.

[0078] The negative electrode plate 12 may include a lithium metal thin film connected to a first negative electrode tab NT and a second negative electrode tab AT. The negative electrode plate 12 may consist only of a lithium metal thin film without a separate current collector plate, in which case the lithium metal thin film may be a freestanding type that also performs the function of a normal current collector plate.

[0079] The other ends of the positive lead PL and the first negative lead NL serve as the positive and negative terminals for charging and discharging the battery cell BC, respectively. The voltage sensor 111 is connected to the positive and negative terminals of the battery cell BC and measures the cell voltage of the battery cell BC.

[0080] The other end of the second negative lead AL serves as an auxiliary terminal for the battery cell BC. The resistance sensor 115 is connected between the negative terminal NL of the battery cell BC and the auxiliary terminal AL to measure the negative resistance of the battery cell BC.

[0081] As described above, the first negative electrode tab NT and the second negative electrode tab AT are projected from two opposite corners of the negative electrode plate 12, and the first negative electrode lead NL and the second negative electrode lead AL are individually coupled to the first negative electrode tab NT and the second negative electrode tab AT.

[0082] Therefore, compared to a structure in which the first negative electrode tab NT and the second negative electrode tab AT protrude from the same corner of the negative electrode plate 12, the negative electrode resistance measured by the resistance sensor 115 can adequately reflect the actual state of the negative electrode plate 12.

[0083] The positive electrode plate 11 includes a positive electrode current collector and a positive electrode active material. The positive electrode active material may include a sulfur-carbon composite. The sulfur-carbon composite may include sulfur and at least one sulfur-based compound. Here, the sulfur-based compound may refer collectively to substances containing the element sulfur (S).

[0084] Sulfur compounds can include, for example, all sulfur-containing compounds that can be formed by the reduction reaction of inorganic sulfur (S8) or the oxidation reaction of lithium sulfide (Li2S), more specifically lithium sulfide (Li2S), lithium polysulfide (Li2Sx, integers 2 ≤ x ≤ 8), disulfide compounds, carbon-sulfur polymers ((C2S y ) n The formula may include y=2.5~50, n≧2, lithium sulfide (Li2S), or two or more of these.

[0085] The positive electrode active material can be coated on at least one surface of the positive electrode current collector. Examples of materials that can be used as the positive electrode current collector include copper, stainless steel, aluminum, nickel, titanium, palladium, calcined carbon, copper or stainless steel surface-treated with carbon, nickel, silver, etc., and aluminum-cadmium alloys.

[0086] The loading amount of the positive electrode active material is 1 to 2.5 times that of the negative electrode active material. If the loading amounts of the positive and negative electrode active materials are within this range, the battery cell BC can be evaluated as having sufficient energy density and lifespan.

[0087] The lithium metal thin film of the negative electrode plate 12 may include a lithium alloy. The lithium alloy may include elements that can be alloyed with lithium. Examples of elements that can be alloyed with lithium include Si, Sn, C, Pt, Ir, Ni, Cu, Ti, Na, K, Rb, Cs, Fr, Be, Mg, Ca, Sr, Sb, Pb, In, Zn, Ba, Ra, Ge, and Al.

[0088] As shown in Figure 2c, assuming that the negative electrode plate 12 is approximately rectangular, the negative electrode plate 12 can be divided into four regions A1, A2, B1, and B2 based on the center line in the width direction and the center line in the length direction of the negative electrode plate 12.

[0089] With respect to the longitudinal centerline, regions A1 and B1, and regions A2 and B2 are each symmetrical with respect to a line. Furthermore, regions A1 and B2, and regions A2 and B1, are each symmetrical with respect to a point.

[0090] The first negative electrode tab NT and the second negative electrode tab AT of the battery cell BC can be individually connected to two point-symmetrical regions A2 and B1 from regions A1, A2, B1, and B2 partitioned from the negative electrode plate 12, and can protrude in opposite directions from each other. This maximizes the distance between the first negative electrode tab NT and the second negative electrode tab AT, so that the negative electrode resistance measurement by the resistance sensor 115 accurately represents the actual negative electrode resistance.

[0091] Figure 3 is a graph illustrating the SOC-OCV curve of a battery cell, Figure 4 is a graph illustrating the SOC-negative electrode resistance curve of a battery cell, and Figure 5 is a graph referenced to explain the relationship between the SOH and SOC-negative electrode resistance curve of a battery cell.

[0092] Referring to Figure 3, memory 140 stores first relational data, including the SOC-OCV curve 300.

[0093] The SOC-OCV curve 300 may be mapped to a specific temperature interval and a specific SOH interval. The SOC-OCV curve 300 may have a voltage flat interval Z A ~Z B This is recorded. For example, the start of the voltage flat section SOC Z A and termination of SOC Z B These are 5% and 70%, respectively. For reference, the SOC-OCV curve is sometimes simply called the "OCV map." For reference, in Figure 3, the voltage flat section is Z. A ~ZB Although it is shown as a single entity, depending on the electrochemical specifications of the battery cell BC, there may be two or more voltage plateau regions.

[0094] When a plurality of temperature intervals are predefined, the memory 140 may record first relationship data individually created for each temperature interval. The control unit 130 can obtain, from the memory 140, the first relationship data associated with a single temperature interval to which the temperature value (measured value of the cell temperature) of the battery cell BC belongs among the plurality of temperature intervals, and utilize it for the estimation of the SOC.

[0095] Similarly, when a plurality of SOH intervals are predefined, the memory 140 may record first relationship data individually created for each SOH interval. The control unit 130 can obtain, from the memory 140, the first relationship data associated with a single SOH interval to which the SOH of the battery cell BC belongs among the plurality of SOH intervals, and utilize it for the estimation of the SOC. For reference, the SOH is sometimes also called the capacity retention.

[0096] For example, when there are 10 temperature intervals and 20 SOH intervals, a total of 200 first relationship data are stored in the memory 140 in advance, and the SOC-OCV curve 300 can be a data set included in any one of the total 200 first relationship data.

[0097] The battery cell BC has an OCV that is maintained substantially constant over the voltage plateau region Z A ~Z B That is, over the voltage plateau region Z A ~Z B the change rate (e.g., differential value) of the OCV with respect to the SOC is maintained below a predetermined reference value.

[0098] On the other hand, in the remaining range outside the voltage plateau region Z A ~Z B (0%~Z A %, Z BIn the range of % to 100%, the rate of change of OCV relative to SOC is greater than a predetermined baseline value. As a result, once the OCV is identified, the SOC corresponding to the identified OCV can be determined with high accuracy from the SOC-OCV curve 300.

[0099] Most rechargeable batteries, including battery cells BC, are known to degrade relatively quickly when continuously used (charged and discharged) outside the appropriate range, near 0% or 100% SOC. The safe voltage range V1 to V2 is predetermined considering the relationship between the SOC and degradation rate of battery cells BC. The SOC Z1 corresponding to the lower limit V1 of the safe voltage range V1 to V2 is the start SOC Z of the voltage flattening section. A It is smaller than the corresponding OCV. The SOC Z2 corresponding to the upper limit V2 of the safe voltage range V1~V2 is the end of the voltage flat section SOC Z B It is larger than that. The SOC range Z1 to Z2, which corresponds to the safe voltage range V1 to V2, is sometimes called the safe interval.

[0100] The control unit 130 periodically estimates the State of Charge (SOC) of the battery cell BC at set intervals, and the previous SOC estimate is used to determine the start SOC Z. A If it is smaller than or equal to the set value, or terminates SOC Z B If the value is greater than or equal to the set value (for example, if it falls outside the safety zone), the resistance sensor 115 can be deactivated. In other words, when calculating the SOC estimate, if the SOC-negative electrode resistance curve 400 described later is not necessary or is very low, deactivating the resistance sensor 115 saves the power required to drive the resistance sensor 115 and also reduces the computational load required to process the negative electrode resistance measurement.

[0101] On the other hand, battery cells BC manufactured to include a specific material (e.g., lithium metal) in their electrodes undergo volume changes during charging and discharging due to the insertion or removal of working ions (e.g., lithium ions) from the electrode grid. Therefore, the inventors of the present invention recognized that the change over time of the negative electrode resistance of battery cell BC, as detected by the resistance sensor 115, correlates well with the change over time of the state of charge (SOC) of battery cell BC.

[0102] Referring to Figure 4, memory 140 stores second relational data, including an SOC-negative resistance curve 400 representing the relationship between the state of charge (SOC) and negative electrode resistance of the battery cell BC. The SOC-negative resistance curve 400 may be mapped to specific temperature intervals and specific SOH intervals. The SOC-negative resistance curve is sometimes called a "negative resistance map."

[0103] If multiple temperature intervals are predefined, the memory 140 may record second relational data created individually for each temperature interval. The control unit 130 can retrieve the second relational data associated with the single temperature interval to which the temperature value of the battery cell BC belongs from the memory 140 and use it to estimate the State of Charge (SOC).

[0104] Similarly, if multiple SOH intervals are predefined, the memory 140 may record second relational data created individually for each SOH interval. The control unit 130 can retrieve from the memory 140 the second relational data associated with the single SOH interval to which the battery cell BC's SOH belongs, and use it to estimate the SOC. For example, if there are 10 temperature intervals and 20 SOH intervals, a total of 200 second relational data points are pre-stored in the memory 140, and the SOC-negative electrode resistance curve 400 may be a dataset included in any one of these 200 second relational data points.

[0105] According to the SOC-negative electrode resistance curve 400 shown in Figure 4, it can be confirmed that as the SOC of battery cell BC increases from 0% to 100%, the negative electrode resistance of battery cell BC gradually decreases. Unlike the SOC-OCV curve 300 shown in Figure 3, the voltage flat section Z A ~Z B Even in this case, the rate of change of the negative electrode resistance with respect to the State of Charge (SOC) of battery cell BC is sufficiently large. For reference, the SOC-negative electrode resistance curve 400 may be the result of applying curve fitting logic (e.g., a polynomial) to multiple data points obtained by repeatedly measuring the SOC and negative electrode resistance of multiple test cells having the same specifications as battery cell BC.

[0106] Therefore, the SOC of battery cell BC is in the voltage flat interval Z A ~Z B While it is estimated to be located within the range, the State of Charge (SOC) of battery cell BC can be estimated with high accuracy by using the SOC-negative resistance curve 400 alone, or by using a combination of the SOC-OCV curve 300 and the SOC-negative resistance curve 400, instead of the SOC-OCV curve 300.

[0107] Referring to Figure 5, the memory 140 stores third relational data, which includes at least one of the SOH-negative electrode resistance curve 510 and the SOH-negative electrode resistance curve 520 of the battery cell BC.

[0108] If multiple temperature intervals are predefined, the memory 140 can record third-order relational data created individually for each of the multiple temperature intervals. The control unit 130 can retrieve the third-order relational data mapped to the temperature value of the battery cell BC (measured cell temperature) from the memory 140 and use it to estimate the State of Health (SOH).

[0109] The SOH-negative electrode resistance curve 510 shown in Figure 5 illustrates the relationship between SOH and negative electrode resistance in a fully discharged state of battery cell BC (e.g., SOC 0%). The SOH-negative electrode resistance curve 520 also illustrates the relationship between SOH and negative electrode resistance in a fully charged state of battery cell BC (e.g., SOC 100%). As mentioned above, even if the SOH of battery cell BC is the same at a specific value, the negative electrode resistance of battery cell BC gradually decreases as the SOC of battery cell BC increases, and this can be confirmed from the two curves 510 and 520 in Figure 5.

[0110] The control unit 130 can determine the State of Health (SOH) of the battery cell BC by executing at least one of the first SOH estimation logic and the second SOH estimation logic.

[0111] The first SOH estimation logic may be one or more combinations of various known SOH estimation logics. For example, the control unit 130 can calculate the current maximum capacity of the battery cell BC by dividing the cumulative current amount over a specific or unspecified period by the change in SOC over the same period. The control unit 130 can then determine the SOH estimate by converting the value obtained by dividing the maximum capacity of the battery cell BC by the design capacity of the battery cell BC (maximum capacity when new) into a percentage. The first SOH estimate refers to the SOH estimated by the execution of the first SOH estimation logic.

[0112] The control unit 130 can execute a second SOH estimation logic when the battery cell BC is in a completely discharged or fully charged state to estimate the SOH of the battery cell BC from third relational data based on the measured values ​​of the cell temperature and negative electrode resistance of the battery cell BC. That is, the second SOH estimation logic may involve searching for SOH values ​​mapped to the measured values ​​of the cell temperature and negative electrode resistance of the battery cell BC from the third relational data. Curve 510 of the third relational data can be used when the battery cell BC is in a completely discharged state, and curve 520 of the third relational data can be used when the battery cell BC is in a fully charged state. The second SOH estimate refers to the SOH estimated by executing the second SOH estimation logic.

[0113] When the battery cell BC is under load, the control unit 130 can determine (estimate) the OCV of the battery cell BC based on the measured values ​​of cell voltage, cell current, and / or cell temperature collected from the sensing unit 110. For example, using Ohm's law, the estimated OCV of the battery cell BC can be calculated by subtracting the voltage value corresponding to the product of the current value and the internal resistance of the battery cell BC from the voltage value of the battery cell BC. The control unit 130 can also determine the internal resistance of the battery cell BC based on the ratio between the voltage change and current change of the battery cell BC at set time intervals, based on Ohm's law. Alternatively, the internal resistance of the battery cell BC can be determined from an internal resistance map that defines the relationship between the state of charge (SOC), temperature, and internal resistance of the battery cell BC.

[0114] Figure 6 is a flowchart illustrating a battery management method according to the first embodiment of the present invention. The method in Figure 6 can be performed periodically and repeatedly by the battery management system 100 at set intervals when the battery cell BC is charging or discharging, or when the rest time during which the battery cell BC is maintained in a rest state is less than a predetermined stabilization time. For reference, if the rest time is not sufficiently long, even during rest, the measured cell voltage of the battery cell BC detected by the voltage sensor 111 may have a difference that cannot be ignored from the actual OCV of the battery cell BC.

[0115] Referring to Figures 1 to 6, in step S610, the control unit 130 uses the sensing unit 110 to measure the cell voltage, cell current, and negative electrode resistance of the battery cell BC. That is, the control unit 130 can collect sensing signals from the sensing unit 110 that represent the measured values ​​of the cell voltage, cell current, and negative electrode resistance of the battery cell BC. At this time, the control unit 130 can also collect a measured value of the cell temperature of the battery cell BC.

[0116] In step S620, the control unit 130 determines the electrical state of the battery cell BC based on the measured values ​​of the cell voltage, cell current, and negative electrode resistance of the battery cell BC. The electrical state includes State of Control (SOC) and may further include State of Health (SOH).

[0117] Figure 7 is a flowchart illustrating the subroutine for step S620 in Figure 6.

[0118] Referring to Figures 1 to 7, in step S710, the control unit 130 executes a first SOC estimation logic to determine a first SOC estimate based on the measured values ​​of the cell voltage and cell current.

[0119] The first SOC estimation logic may be one or more combinations of known SOC estimation logics (e.g., OCV map, ampere count (current integration method), Kalman filter, etc.).

[0120] When executing the first SOC estimation logic, the control unit 130 determines the OCV estimate of the battery cell BC based on the measured values ​​of cell parameters (at least one of cell voltage, cell current, and cell temperature) obtained from the sensing unit 110, and then determines the SOC value associated with the OCV estimate from among the SOC values ​​recorded in the first relational data as the first SOC estimate.

[0121] The first SOC estimation logic calculates the SOC estimate of battery cell BC over the voltage flat interval Z. A ~Z B While outside, the OCV (October Voltage) estimate can be determined by subtracting the voltage value due to internal resistance from the voltage value representing the CCV (Cell Voltage) of the battery cell BC (measured cell voltage).

[0122] The first SOC estimation logic calculates the SOC estimate of battery cell BC over the voltage flat interval Z. A ~Z B During this time, the estimated SOC of battery cell BC is within the voltage flat interval Z A ~Z B The amount of change in SOC corresponding to the current integration amount calculated while the current remains in the zone is determined by the SOC estimate value in the voltage flat interval Z. A ~Z B The system can be designed to determine the first SOC estimate in the same way as the value obtained by summing it with past SOC estimates obtained just before entering the new SOC.

[0123] In step S720, the control unit 130 executes a second SOC estimation logic to determine a second SOC estimate based on the negative electrode resistance measurement. When executing the second SOC estimation logic, a series of steps can be performed to determine the second SOC estimate as the SOC value associated with the negative electrode resistance measurement collected in step S710 from among the negative electrode resistance values ​​recorded in the second relational data.

[0124] In step S730, the control unit 130 determines the current SOC estimate of the battery cell based on at least one of the first SOC estimate and the second SOC estimate.

[0125] Each time step S730 is performed, the control unit 130 records the estimated SOC value in the memory 140, thereby generating a history (time series) of changes in the estimated SOC value in the memory 140. The estimated SOC value determined in step S730 becomes the same as the previous estimated SOC value when the method shown in Figure 7 is performed again.

[0126] Figure 8 is a flowchart that schematically shows an example of the subroutine for step S730 in Figure 7.

[0127] Referring to Figures 1 to 8, in step S810, the control unit 130 determines that the first SOC estimate determined in step S710 is in the voltage flat interval Z A ~Z B Determine whether it is outside or outside. That is, the first SOC estimate is the starting SOC Z A Less than or terminated SOC Z B Step S810 determines whether or not it is an excess. If the value in step S810 is "yes", proceed to step S820. If the value in step S810 is "no", the first SOC estimate is for the voltage flat interval Z A ~Z B This means it is inside. If the value in step S810 is "no", proceed to step S830.

[0128] In step S820, the control unit 130 determines the current SOC estimate for battery cell BC, similar to the first SOC estimate.

[0129] In step S830, the control unit 130 determines the current SOC estimate for battery cell BC, similar to the second SOC estimate.

[0130] Figure 9 is a flowchart schematically showing another example of the subroutine in step S730 of Figure 7, and Figure 10 is an example of a weight map that can be used when executing the method in Figure 9.

[0131] Referring to Figures 1 to 7, 9 and 10, in step S910, the control unit 130 determines that the first SOC estimate determined in step S610 is in the voltage flat interval Z A ~Z B Determine whether it is outside or not. If the value of step S910 is "yes", proceed to step S920. If the value of step S910 is "no", proceed to step S940.

[0132] In step S920, the control unit 130 controls the voltage flat section Z A ~Z B Depending on the SOC difference between the first SOC estimate and the second SOC estimate, a first weight associated with the first SOC estimate and a second weight associated with the second SOC estimate are determined.

[0133] The first SOC estimate is the starting SOC Z A If less than, the voltage flat interval Z A ~Z B The SOC difference between the first SOC estimate and the starting SOC Z A It can be determined in the same way as the difference between [two conditions].

[0134] The first SOC estimate is the final SOC Z B If it exceeds the limit, the voltage flat section Z A ~Z B The SOC difference between the first SOC estimate and the final SOC Z B It can be determined in the same way as the difference between [two conditions].

[0135] The first weight is the voltage flat interval Z A ~Z B A predetermined positive correlation can be found between the SOC difference between the first SOC estimate and the second weight, which is the voltage flat interval Z. A ~Z BA predetermined negative correlation can exist between the SOC difference between the first SOC estimate and the second weight. The sum of the first and second weights can be a constant (e.g., 1), and therefore, once either the first or second weight is determined, the other can be automatically determined as well. The first weight can be greater than or equal to the second weight.

[0136] Figure 10 illustrates a weight map 1000 in which the relationship between the first weight and the SOC difference is recorded. The weight map 1000 can be pre-stored in memory 140.

[0137] The fact that the aforementioned SOC difference is close to 0 means that the first SOC estimate is within the voltage flat interval Z. A ~Z B This means that it is close to the lower limit Z. On the other hand, the SOC difference is lower limit Z A Or upper limit Z B Being close to means that the first SOC estimate is within the voltage flat interval Z A ~Z B It means far from.

[0138] The first SOC estimate is the lower bound Z A If it is less than, start SOC Z A While the maximum value on the horizontal axis of Figure 10 can be set to the upper limit Z, the first SOC estimate is set to the upper limit Z. B If it exceeds this limit, terminate SOC Z B This can be set to the maximum value on the horizontal axis of Figure 10.

[0139] In step S930, the control unit 130 determines the current SOC estimate for the battery cell BC, as well as the weighted average of the first and second SOC estimates based on the first and second weights.

[0140] For example, if the first weight is 0.8, the second weight is 0.2, the first SOC estimate is 80%, and the second SOC estimate is 81%, then the weighted average is (first SOC estimate × first weight) + (second SOC estimate × second weight) = (80% × 0.8) + (81% × 0.2) = 80.2%.

[0141] In step S940, the control unit 130 determines the current SOC estimate for battery cell BC, as well as a weighted average of the first and second SOC estimates based on a third weight associated with the first SOC estimate and a fourth weight associated with the second SOC estimate.

[0142] Unlike the first and second weights, the third and fourth weights may be constants pre-stored in memory 140. The fourth weight is greater than or equal to the third weight, and the sum of the third and fourth weights may be equal to the sum of the first and second weights. For example, if the third weight = 0.3, the fourth weight = 0.7, the first SOC estimate = 60%, and the second SOC estimate = 61%, then the weighted average = (first SOC estimate × third weight) + (second SOC estimate × fourth weight) = (60% × 0.3) + (61% × 0.7) = 18% + 42.7% = 60.7%. The third weight may be less than or equal to the minimum value that can be set as the first weight. The fourth weight may be greater than or equal to the maximum value that can be set as the second weight.

[0143] Figure 11 is a flowchart schematically showing yet another example of the subroutine in step S730 of Figure 7, Figure 12 is an example of a first correction coefficient map used to correct the first weight, and Figure 13 is an example of a second correction coefficient map used to correct the fourth weight.

[0144] Referring to Figures 1 to 7 and Figures 11 to 13, in step S1110, the control unit 130 determines that the first SOC estimate determined in step S610 is in the voltage flat interval Z A ~Z B Determine whether it is outside or not. If the value in step S1110 is "yes", proceed to step S1120. If the value in step S1110 is "no", proceed to step S1160.

[0145] In step S1120, the control unit 130 controls the voltage flat section Z A ~Z BBased on the SOC difference between the first SOC estimate and the second SOC estimate, a first weight associated with the first SOC estimate and a second weight associated with the second SOC estimate are determined.

[0146] In step S1130, the control unit 130 determines the voltage flat interval Z based on the time series of the SOC estimate. A ~Z B Determine the residence time of the SOC estimate outside the voltage flat interval Z. A ~Z B How long is the voltage flat interval Z from the most recent point after it deviates from the limit? A ~Z B It is determined whether or not to stay outside.

[0147] In step S1140, the control unit 130 corrects the first weight and the second weight according to the dwell time determined in step S1130. For example, after correcting one of the first weight and the second weight, the correction value of the other weight can be determined by subtracting the corrected weight from a predetermined constant (e.g., 1).

[0148] The first correction coefficient map 1200 shown in Figure 12 is pre-stored in memory 140 and can be used to correct the first weight.

[0149] According to the first correction coefficient map 1200, the voltage flat section Z A ~Z B The residence time of the SOC estimate outside is a predetermined reference time t R1 Until the residence time reaches t, the correction coefficient for the first weight can have a positive correlation with the residence time. R1 After reaching a certain point, the correction coefficient for the first weight can have a negative correlation with the residence time. The minimum value of the correction coefficient for the first weight may be 1. The reference value L is greater than 1 and may be a preset value to prevent the corrected first weight from becoming excessive.

[0150] Voltage flat section Z A ~ZB As the residence time outside increases, the current integration error also accumulates, so the accuracy of the first SOC estimate determined by the first SOC estimation logic may gradually decrease. Therefore, through prior experiments and simulations, the reference time t R1 If selected appropriately, the State of Computing (SOC) of the battery cell BC will be within the voltage flat interval Z. A ~Z B This prevents a decrease in SOC estimation accuracy caused by excessively long periods of time spent outdoors.

[0151] In relation to this, Figure 12 shows the reference time t. R1 The correction coefficient for the first weight is shown to change linearly before and after the process, but this should be understood as a simple example.

[0152] The corrected first weight may be the same as the product of the correction coefficient determined in the first correction coefficient map 1200 and the first weight. Once the corrected first weight is determined, the control unit 130 can calculate a corrected second weight such that the sum of the corrected first weight and the second weight equals a predetermined value (e.g., 1).

[0153] In step S1150, the control unit 130 determines the current SOC estimate for the battery cell BC, as well as the weighted average of the first and second SOC estimates based on the corrected first weight and the corrected second weight. For example, if the corrected first weight = 0.81, the corrected second weight = 0.19, the first SOC estimate = 80%, and the second SOC estimate = 81%, then the weighted average = (first SOC estimate × corrected first weight) + (second SOC estimate × corrected second weight) = (80% × 0.81) + (81% × 0.19) = 64.8% + 15.39% = 80.19%.

[0154] That is, even if the first SOC estimated value and the second SOC estimated value are 80% and 81% respectively, which are the same as the example described with reference to FIG. 9, the corrected first weight determined according to the length of the residence time increases compared to the original first weight, and the corrected second weight decreases compared to the original second weight, so that the SOC determined in step S1150 is confirmed to shift from 80.2% to 80.19% closer to the first SOC estimated value.

[0155] In step S1160, the control unit 130 determines the residence time of the SOC estimated value in the voltage flat section Z A ~Z B based on the time series of the SOC estimated value. That is, it is determined how long the SOC estimated value has remained in the voltage flat section Z A ~Z B since the most recent time point when the SOC estimated value entered the voltage flat section Z A ~Z B has entered.

[0156] In step S1170, the control unit 130 corrects the third weight and the fourth weight according to the residence time determined in step S1160.

[0157] FIG. 13 illustrates a second correction coefficient map 1300 in which the relationship between the correction coefficient used to correct the fourth weight and the residence time is recorded. The second correction coefficient map 1300 can be stored in the memory 140 in advance.

[0158] Referring to FIG. 13, until the residence time reaches a predetermined reference time t R2 , the correction coefficient can have a positive correlation with the residence time, and while the residence time is greater than or equal to the reference time t R2 , the correction coefficient can be maintained at the reference value U. The reference value U can be greater than 1. The reference value U can be a value set in advance to prevent the fourth corrected weight from being excessive.

[0159] In this regard, in FIG. 13, the reference time t R2The correction coefficient for the fourth weight is shown to change linearly over the following time range, but this should be understood as a simple example.

[0160] The corrected fourth weight may be the same as the product of the correction coefficient determined in the second correction coefficient map 1300 and the fourth weight. Once the corrected fourth weight is determined, the control unit 130 can calculate the corrected third weight such that the sum of the corrected fourth weight and the third weight is equal to a predetermined constant (e.g., 1).

[0161] In step S1180, the control unit 130 determines the current SOC estimate for battery cell BC, similar to the weighted average of the first and second SOC estimates based on the corrected third weight and the corrected fourth weight. For example, if the corrected third weight = 0.25, the corrected fourth weight = 0.75, the first SOC estimate = 60%, and the second SOC estimate = 61%, then the weighted average = (first SOC estimate × corrected third weight) + (second SOC estimate × corrected fourth weight) = 60.75%.

[0162] In other words, even if the first and second SOC estimates are 60% and 61%, respectively, as in the example in Figure 9, the corrected third weight determined according to the dwell time decreases compared to the original third weight, and the corrected fourth weight increases compared to the original fourth weight. This confirms that the SOC determined in step S1180 shifts from 60.7% to 60.75%, which is closer to the second SOC estimate.

[0163] Figure 14 is a flowchart illustrating a battery management method according to a second embodiment of the present invention. The method shown in Figure 14 can be executed after the estimated SOC value of the battery cell BC has been determined by the method shown in Figure 7.

[0164] Referring to Figure 14, in step S1410, the control unit 130 executes the first SOH estimation logic to determine the first SOH estimate.

[0165] In step S1420, the control unit 130 determines whether the battery cell BC is in a fully discharged state or a fully charged state. For example, if the current SOC estimate determined in step S730 is 0%, it indicates that the battery cell BC is in a fully discharged state. As another example, if the current SOC estimate determined in step S730 is 100%, it indicates that the battery cell BC is in a fully charged state.

[0166] If the value of step S1420 is "no", proceed to step S1430. If the value of step S1420 is "no", it means that battery cell BC is neither fully discharged nor fully charged. If the value of step S1420 is "yes", proceed to step S1440.

[0167] In step S1430, the SOH of battery cell BC is determined in the same way as the first SOH estimate.

[0168] In step S1440, the control unit 130 executes a second SOH estimation logic to determine a second SOH estimate. If it is determined in step S1420 that the device is in a fully discharged state, curve 510 is used to determine the second SOH estimate. On the other hand, if it is determined in step S1420 that the device is in a fully charged state, curve 520 is used to determine the second SOH estimate.

[0169] In step S1450, the control unit 130 determines the second SOH estimate, or the weighted average of the first SOH estimate and the second SOH estimate, and the current SOH estimate of the battery cell BC. The weighted average of the first SOH estimate and the second SOH estimate may be the same as the sum of the value obtained by multiplying the first SOH estimate by a predetermined weight (fifth weight) and the value obtained by multiplying the second SOH estimate by a predetermined weight (sixth weight). The sum of the fifth weight and the sixth weight may be a constant (for example, 1). The fifth weight and the sixth weight can each be determined in advance.

[0170] The embodiments of the present invention described above are not limited to apparatus and methods, but can also be realized by a program that implements functions corresponding to the configuration of the embodiments of the present invention, or by a recording medium on which such a program is recorded. Such implementation can be easily carried out by experts in the technical field to which the present invention belongs, based on the description of the embodiments described above.

[0171] Although the present invention has been described above with reference to limited embodiments and drawings, it goes without saying that the present invention is not limited thereto, and various modifications and variations are possible within the equivalent scope of the technical idea of ​​the present invention and the claims described below by persons with ordinary skill in the art to which the present invention pertains.

[0172] Furthermore, the present invention described above can be substituted, modified, and altered in various ways by a person with ordinary skill in the art to which the present invention belongs, without departing from the technical spirit of the invention. Therefore, it is not limited to the embodiments described above and the accompanying drawings, and all or part of each embodiment can be selectively combined to form various modifications.

Claims

1. A sensing unit that measures the cell voltage, cell current, and negative electrode resistance of a battery cell, A control unit configured to determine the state of the battery cell based on the measured values ​​of the cell voltage, the cell current, and the negative electrode resistance, A battery management system, including...

2. The control unit, The first SOC estimation logic is executed to determine the first SOC estimate based on the measured values ​​of the cell voltage and the cell current, respectively. The second SOC estimation logic is executed to determine the second SOC estimate based on the measured value of the negative electrode resistance. The battery management system according to claim 1, configured to determine the current SOC estimate of the battery cell based on at least one of the first SOC estimate and the second SOC estimate.

3. The control unit, The battery management system according to claim 2, configured to determine the current SOC estimate of the battery cell in the same manner as a weighted average of either the first SOC estimate or the second SOC estimate, based on the result of comparing the first SOC estimate with a voltage flat interval.

4. The control unit, The battery management system according to claim 3, wherein if the first SOC estimate is outside the voltage flat interval, the system is configured to determine the current SOC estimate of the battery cell in the same manner as the first SOC estimate.

5. The control unit, The battery management system according to claim 3, configured to determine the current SOC estimate of the battery cell in the same manner as the second SOC estimate when the first SOC estimate falls within the voltage flat interval.

6. The control unit, If the first SOC estimate is outside the voltage flat interval, a first weight associated with the first SOC estimation logic and a second weight associated with the second SOC estimation logic are determined according to the difference between the voltage flat interval and the first SOC estimate. The battery management system according to claim 3, configured to determine the current SOC estimate of the battery cell in the same manner as the weighted average of the first SOC estimate and the second SOC estimate based on the first weight and the second weight.

7. The first weight has a predetermined positive correlation with the difference between the voltage flat section and the first SOC estimate. The battery management system according to claim 6, wherein the second weight has a predetermined negative correlation with the difference between the voltage flat interval and the first SOC estimate.

8. The control unit, The battery management system according to claim 3, wherein, if the first SOC estimate falls within the voltage flat interval, the system is configured to determine the current SOC estimate for the battery cell in the same manner as the weighted average of the first SOC estimate and the second SOC estimate based on a third weight and a fourth weight.

9. The control unit, If the first SOC estimate is outside the voltage flat interval, a first weight associated with the first SOC estimation logic and a second weight associated with the second SOC estimation logic are determined according to the difference between the voltage flat interval and the first SOC estimate. The first weight and the second weight are corrected according to the residence time of the SOC within the voltage flat section. The battery management system according to claim 3, configured to determine the current SOC estimate of the battery cell in the same way as the weighted average of the first SOC estimate and the second SOC estimate based on the corrected first weight and the corrected second weight.

10. The control unit, The battery management system according to claim 2, configured to determine the current SOH estimate of the battery cell based on the measured values ​​of the negative electrode resistance and the cell temperature of the battery cell, respectively, when the current SOC estimate indicates a fully discharged or fully charged state.

11. A battery pack comprising a battery management system according to any one of claims 1 to 10.

12. An electric vehicle comprising the battery pack described in claim 11.

13. The steps include measuring the cell voltage, cell current, and negative electrode resistance of a battery cell, A step of determining the state of the battery cell based on the measured values ​​of the cell voltage, the cell current, and the negative electrode resistance, Battery management methods, including those mentioned above.

14. The step of determining the electrical state of the battery cell is: The steps include: executing a first SOC estimation logic to determine a first SOC estimate based on the measured values ​​of the cell voltage and the cell current, respectively; The steps include: executing a second SOC estimation logic to determine a second SOC estimate based on the measured value of the negative electrode resistance; The steps include determining the current SOC estimate of the battery cell based on at least one of the first SOC estimate and the second SOC estimate, The battery management method according to claim 13, including the method described in claim 13.

15. The step of determining the estimated SOC value of the aforementioned battery cell is: The step of determining the current SOC estimate for the battery cell, in accordance with the result of comparing the first SOC estimate with the voltage flat interval, similar to the weighted average of either the first SOC estimate or the second SOC estimate. The battery management method according to claim 14, including the method described in claim 14.

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