Battery management unit and method

Abstract

The invention discloses a battery management unit and method. The battery management unit includes a sensor, a first battery model, a second battery model, and a processor. The sensor measures a voltage of the battery. The first battery model and the second battery model estimate different characteristics of the battery based on the measured voltage. The processor obtains a first terminal voltage corresponding to the measured voltage based on the first battery model, obtains a second terminal voltage corresponding to the measured voltage based on the second battery model, and fuses the first terminal voltage and the second terminal voltage to determine a final terminal voltage of the battery.

Description

[0001] Cross-references to related applications

[0002] This application claims the benefit and priority of Korean Patent Application No. 10-2024-0118814, filed with the Korean Intellectual Property Office on September 2, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to battery management units and methods thereof, and more specifically, to techniques for determining the terminal voltage of a battery and for determining the performance of the battery using the terminal voltage. Background Technology

[0004] With the diversification of electronic devices, the applications of batteries are gradually expanding. Recently, with the emergence of electric vehicles or hybrid electric vehicles, battery usage is also increasing.

[0005] Battery performance and its state of charge (SOC) are crucial for the stability and driving performance of electric vehicles. Battery performance can be determined using parameters such as state of health (SOH). The SOH of a battery can be determined based on its terminal voltage.

[0006] Typically, the terminal voltage of a battery can be calculated using a battery model. While a battery model can reflect the specific characteristics of a battery well depending on its type, it may not accurately reflect certain characteristics. Therefore, traditional methods for measuring battery terminal voltage, or for measuring battery performance, have limitations in reflecting all battery characteristics caused by various factors. Summary of the Invention

[0007] This application is made to address the aforementioned problems in the prior art while maintaining the advantages achieved by the prior art.

[0008] Various aspects of this application provide battery management units and methods for reflecting various characteristics of a battery to determine its state.

[0009] Other aspects of this application provide battery management units and methods for reflecting both DC-based and AC-based battery characteristics.

[0010] The technical problem to be solved by this application is not limited to the problems mentioned above. Any other technical problems not mentioned herein should be more clearly understood by those skilled in the art to which this application pertains through the following description.

[0011] According to aspects of the present application, a battery management unit can include a sensor, a first battery model, a second battery model, and a processor. The sensor can measure a voltage of a battery. The first battery model and the second battery model can estimate different characteristics of the battery based on the measured voltage. The processor can obtain a first terminal voltage corresponding to the measured voltage based on the first battery model, can obtain a second terminal voltage corresponding to the measured voltage based on the second battery model, and can fuse the first terminal voltage and the second terminal voltage to determine a final terminal voltage of the battery.

[0012] According to embodiments, the first battery model and the second battery model can be pre-designed with different parameters based on a rate of change of voltage of the battery over time.

[0013] According to embodiments, the processor can determine a first weight of the first battery model based on a first voltage error between the measured voltage and the first terminal voltage. Also, the processor can determine a second weight of the second battery model based on a second voltage error between the measured voltage and the second terminal voltage. The processor can further determine the final terminal voltage based on the first weight and the second weight.

[0014] According to embodiments, the processor can determine a first covariance based on the first voltage error. The processor can further determine the first weight according to a probability that the first terminal voltage occurs based on the first covariance. Also, the processor can determine a second covariance based on the second voltage error. The processor can further determine the second weight according to a probability that the second terminal voltage occurs based on the second covariance.

[0015] According to embodiments, the processor can determine a third terminal voltage in a third battery model based on the measured voltage. The processor can further determine a third weight of the third battery model based on a third voltage error between the measured voltage and the third terminal voltage. Also, the processor can determine the final terminal voltage of the battery based on the first weight to the third weight.

[0016] According to embodiments, the first battery model, the second battery model, and the third battery model can be a direct current (DC) model, an alternating current (AC) model, and an open circuit voltage (OCV) model, respectively.

[0017] According to embodiments, the AC model can be designed based on AC impedance information obtained by transforming current and voltage data varying over time into a frequency domain by each frequency.

[0018] According to embodiments, the processor can determine a terminal voltage matching a maximum weight among the first weight to the third weight as the final terminal voltage.

[0019] According to an embodiment, the processor can correct the first terminal voltage using a first weight to determine a first corrected voltage. The processor can also correct the second terminal voltage using a second weight to determine a second corrected voltage. The processor can also correct the third terminal voltage using a third weight to determine a third corrected voltage. In addition, the processor can determine a final terminal voltage based on the first corrected voltage, the second corrected voltage, and the third corrected voltage.

[0020] According to an embodiment, the processor can determine a state of health (SOH) of the battery based on the final terminal voltage, and can determine an abnormal state of the battery based on the state of health.

[0021] According to another aspect of the present application, a battery management method can include obtaining, by a sensor, a measured voltage of a battery, obtaining, by a processor, a first terminal voltage corresponding to the measured voltage based on a first battery model, obtaining, by the processor, a second terminal voltage corresponding to the measured voltage based on a second battery model, and determining, by the processor, a final terminal voltage of the battery by fusing the first terminal voltage and the second terminal voltage.

[0022] According to an embodiment, the first battery model and the second battery model can be pre-designed based on a rate of change of voltage of the battery over time, with different parameters.

[0023] According to an embodiment, determining the final terminal voltage of the battery can include determining a first weight of the first battery model based on a first voltage error between the measured voltage and the first terminal voltage in the first battery model, determining a second weight of the second battery model based on a second voltage error between the measured voltage and the second terminal voltage in the second battery model, and determining the final terminal voltage based on the first weight and the second weight.

[0024] According to an embodiment, determining the first weight can include determining a first covariance based on the first voltage error, and determining the first weight according to a probability that the first terminal voltage occurs based on the first covariance. Determining the second weight can include determining a second covariance based on the second voltage error, and determining the second weight according to a probability that the second terminal voltage occurs based on the second covariance.

[0025] According to an embodiment, the battery management method can further include determining a third terminal voltage in a third battery model based on the measured voltage, and determining a third weight of the third battery model based on a third voltage error between the measured voltage and the third terminal voltage. Determining the final terminal voltage can include fusing the first weight to the third weight.

[0026] According to an embodiment, the first battery model, the second battery model, and the third battery model can be a direct current (DC) model, an alternating current (AC) model, and an open circuit voltage (OCV) model, respectively.

[0027] According to an embodiment, the alternating current model can be designed based on alternating current impedance information obtained by transforming time-varying current and voltage data into a frequency domain by each frequency.

[0028] According to an embodiment, determining the final terminal voltage can include determining a terminal voltage matching a maximum weight among the first to third weights as the final terminal voltage.

[0029] According to an embodiment, determining the final terminal voltage can include correcting the first terminal voltage using the first weight to determine a first corrected voltage, correcting the second terminal voltage using the second weight to determine a second corrected voltage, correcting the third terminal voltage using the third weight to determine a third corrected voltage, and determining the final terminal voltage based on the first, second, and third corrected voltages.

[0030] According to an embodiment, the battery management method can further include determining a state of health (SOH) of the battery based on the final terminal voltage, and determining an abnormal state of the battery based on the state of health. BRIEF DESCRIPTION OF DRAWINGS

[0031] The above and other objects, features and advantages of the present application will be more apparent from the following detailed description taken in conjunction with the accompanying drawings, in which:

[0032] Figure 1 A connection relationship of a battery management unit according to an embodiment of the present application is shown;

[0033] Figure 2 A configuration of a battery management unit according to an embodiment of the present application is shown;

[0034] Figure 3 A flowchart of a battery management method according to an embodiment of the present application is shown;

[0035] Figure 4 A battery management method according to an embodiment of the present application is shown;

[0036] Figure 5 Variations in current and voltage of a battery obtained by a sensor device are shown;

[0037] Figure 6 Variations in current and voltage based on a battery model according to an embodiment of the present application are shown schematically;

[0038] Figure 7 A direct current (DC) model is shown;

[0039] Figure 8 An open circuit voltage (OCV) model is shown;

[0040] Figure 9An impedance model is shown;

[0041] Figure 10 A method for setting parameters for a DC model and an OCV model is shown;

[0042] Figure 11 A frequency based on a battery model according to embodiments of the present application is shown;

[0043] Figure 12 A method of determining a final terminal voltage of a battery using a battery model according to embodiments of the present application is shown;

[0044] Figure 13 A voltage error is shown;

[0045] Figure 14 A covariance is shown;

[0046] Figure 15 A computing system according to embodiments of the present application is shown. DETAILED DESCRIPTION

[0047] Hereinafter, some embodiments of the present application will be described in detail with reference to the accompanying drawings. In adding reference numerals to components in each drawing, it should be noted that the same components are denoted by the same reference numerals even though they are shown on different drawings. Also, in order to avoid unnecessarily obscuring the main idea of the present application, detailed descriptions of related known features or functions will be omitted.

[0048] In describing components of embodiments of the present application, terms such as first, second, "A", "B", (a), (b), etc. can be used. These terms are used only to distinguish one element from another, but do not limit the corresponding elements regardless of the order or priority of the corresponding elements. Also, unless otherwise defined, all terms used herein, including technical terms and scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the present application pertains. Such terms defined in a commonly used dictionary are to be interpreted as having a meaning that is consistent with the meaning in the context of the relevant art. Unless explicitly defined otherwise, such terms are not to be interpreted as having an idealized or overly formal meaning.

[0049] In this application, various phrases such as "A or B", "at least one of A and B", "at least one of A or B", "A, B, or C", "at least one of A, B, and C", "at least one of A, B, or C", and "at least one of A, B, or C or a combination thereof" can include any or all possible combinations of the items listed together in the corresponding one of the phrases. When a component, processor, model, module, unit, device, element, apparatus, etc. (i.e., device) of the present application is described as having a purpose or performing an operation, function, etc., the component, processor, model, module, unit, device, element, apparatus, etc. should be construed as being "configured to" fulfill the purpose or perform the operation or function in this document. Each component, processor, model, module, unit, device, element, apparatus, etc. can be implemented individually or contain a processor and a memory (e.g., a non-transitory computer readable medium) as a part of the device.

[0050] Hereinafter, referring to Figures 1 to 15 Embodiments of the present application are described in detail.

[0051] Figure 1 is a diagram for describing a connection relationship of a battery management unit according to an embodiment of the present application. Figure 2 is a diagram showing a configuration of a battery management unit according to an embodiment of the present application.

[0052] Referring to Figure 1 and Figure 2 , the battery management unit BMU according to an embodiment of the present application can be loaded into the vehicle VEH to provide voltages to the control units 21, 22, and 23 in the vehicle VEH. For example, the first control unit 21 can supply a voltage to an external load 31. The second control unit 22 can supply a voltage to a heater 32, which can include a direct current / direct current (DC / DC) converter. The third control unit 23 can supply a voltage to a motor 33 for driving the vehicle VEH, which can include a direct current / alternating current (DC / AC) converter.

[0053] The battery management unit BMU according to an embodiment of the present application can include a battery device 60, a communication device 70, sensor devices CMU1 to CMUn, and a processor 100.

[0054] The battery device 60 can include n (where n is a natural number of 2 or more) battery modules BM1 to BMn. Each of the battery modules BM1 to BMn can include a plurality of batteries 10. Each of the plurality of batteries 10 can be referred to as a battery cell.

[0055] The sensor devices CMU1 to CMUn can be matched one-to-one with the battery modules BM1 to BMn. The first CMU CMU1 can sense a voltage of the first battery module BM1. Also, the sensor devices CMU1 to CMUn can obtain battery state information. The battery state information can be at least one of an internal resistance of the battery 10, a leakage current of the battery 10, or a state of health (SOH) of the battery 10.

[0056] The communication device 70 can be used for communication between the sensor devices CMU1 to CMUn and the processor 100, which can be achieved through wired or wireless communication.

[0057] For example, the communication device 70 can support short-range communication using at least one of Bluetooth, radio frequency identification (RFID), infrared data association (IrDA), ultra wideband (UWB), ZigBee, near field communication (NFC), wireless fidelity (Wi-Fi), Wi-Fi direct, and wireless universal serial bus (USB) technology.

[0058] The structure of the battery device 60 and the sensor devices CMU1 to CMUn can be implemented through various embodiments other than the structure shown. Figure 2

[0059] Also, when the processor 100 is located outside the vehicle VEH, the communication device 70 can perform wireless communication based on global system for mobile communication (GSM), code division multiple access (CDMA), code division multiple access 2000 (CDMA2000), enhanced voice data optimization or enhanced voice data only (EV-DO), wideband CDMA (WCDMA), high speed downlink packet access (HSDPA), high speed uplink packet access (HSUPA), long term evolution (LTE), long term evolution-advanced (LTE-A), or the like.

[0060] The processor 100 can diagnose an abnormal state of the plurality of batteries 10. The processor 100 can determine an end voltage of the battery 10 to diagnose the abnormal state of the battery 10. Hereinafter, the plurality of batteries 10 are collectively referred to as the battery 10.

[0061] The processor 100 can receive information about a measured voltage from the sensor devices CMU1 to CMUn to determine the end voltage of the battery 10.

[0062] The processor 100 can determine the end voltage in two or more battery models using the measured voltage, and can determine a final end voltage using the end voltage. According to an embodiment of the present application, since the processor 100 is capable of determining the final end voltage based on two or more battery models, the processor 100 can more accurately determine the final end voltage in response to various states of the battery 10.

[0063] ​The following is a detailed description of an implementation scheme for determining the final voltage of battery 10 according to the embodiments of this application.

[0064] Algorithms for the operation of processor 100 can be stored in memory 90. Memory 90 may include hard disk drives, flash memory, electrically erasable programmable read-only memory (EEPROM), static random access memory (SRAM), ferroelectric RAM (FRAM), phase-change RAM (PRAM), magnetic RAM (MRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR-SDRAM), etc.

[0065] Figure 3 This is a flowchart describing a battery management method according to an embodiment of this application. Figure 4 This is a schematic diagram illustrating a battery management method according to an embodiment of the present application. Figure 3 The program shown can be executed by processor 100.

[0066] Reference Figure 3 and Figure 4 Describes a battery management method according to an embodiment of this application.

[0067] In S310, the processor 100 can obtain a measured voltage based on the discharge of the battery 10, and can use the measured voltage to determine the first terminal voltage in the first battery model and the second terminal voltage in the second battery model.

[0068] Therefore, while the vehicle is running, the processor 100 can check the measured voltage of the battery 10 in real time.

[0069] The processor 100 can determine a first terminal voltage corresponding to the measured voltage of the battery 10 based on a first battery model, and can determine a second terminal voltage corresponding to the measured voltage of the battery 10 based on a second battery model.

[0070] The first and second battery models can be pre-designed using different parameters. They can be divided and designed based on the rate of voltage change of battery 10 over time. For example, the first battery model can be constructed to more accurately reflect the state of battery 10 when the voltage changes rapidly. The second battery model can be constructed to reflect the state of battery 10 when the voltage changes more slowly than in the first battery model.

[0071] Alternatively, the first battery model can be a DC-based battery model, and the second battery model can be an AC-based battery model.

[0072] In S320, the processor 100 can determine a first weight of the first battery model based on a first voltage error between the measured voltage and the first terminal voltage, and can determine a second weight of the second battery model based on a second voltage error between the measured voltage and the second terminal voltage.

[0073] The first voltage error can be determined based on a difference between the measured voltage and the first terminal voltage, and the second voltage error can be determined based on a difference between the measured voltage and the second terminal voltage.

[0074] The first weight can be set greater as the first voltage error is smaller. The first weight can be set smaller as the first voltage error is greater. The second weight can be set greater as the second voltage error is smaller. The second weight can be set smaller as the second voltage error is greater.

[0075] Alternatively, the first weight can be determined based on a probability of the first terminal voltage occurring among probabilities of the first voltage error occurring. The second weight can be determined based on a probability of the second terminal voltage occurring among probabilities of the second voltage error occurring. To this end, the processor 100 can utilize a predetermined conditional probability density function. The conditional probability density function can be used to determine the probability of the first terminal voltage occurring based on a covariance between the first terminal voltage and the first voltage error, and to determine the first weight. Further, the conditional probability density function can be used to determine the probability of the second terminal voltage occurring based on a covariance between the second terminal voltage and the second voltage error, and to determine the second weight.

[0076] In S330, the processor 100 can determine a final terminal voltage of the battery 10 based on the first weight and the second weight.

[0077] A description is given below of a method of determining a final terminal voltage based on a first terminal voltage V1 of a first battery model, a first weight W1, a second terminal voltage V2 of a second battery model, and a second weight W2.

[0078] The processor 100 can determine a terminal voltage corresponding to a maximum weight between the first weight and the second weight as the final terminal voltage. For example, when the first weight is greater than the second weight, the processor 100 can determine the first terminal voltage as the final terminal voltage of the battery 10.

[0079] Alternatively, the processor 100 can reflect the first weight on the first terminal voltage to determine a first corrected voltage, and can reflect the second weight on the second terminal voltage to determine a second corrected voltage. The processor 100 can determine the final terminal voltage based on the first corrected voltage and the second corrected voltage. For example, the processor 100 can determine the first corrected voltage based on V1 × W1, and can determine the second corrected voltage based on V2 × W2. The processor 100 can add the first corrected voltage and the second corrected voltage to determine the final terminal voltage.

[0080] The value obtained by adding the first weight and the second weight can be set to 1.

[0081] The first battery model and the second battery model in the battery management method according to the embodiments of the present application can be designed with different parameters. For example, the first battery model and the second battery model can be designed with parameters divided based on a voltage change rate of the battery over time.

[0082] Figure 5 and Figure 6 are schematic diagrams for describing a method of dividing current and voltage data and designing a battery model. Figure 5 is a schematic diagram showing a change in current and voltage of a battery obtained by a sensor device. Figure 6 is a schematic diagram schematically showing a current and voltage change rate based on a battery model according to the embodiments of the present application.

[0083] A description of a method of designing different battery models based on current and battery data is given below with reference to Figure 5 and Figure 6

[0084] As shown in Figure 5 and Figure 6 , during operation of a vehicle, a voltage of a battery 10 can vary depending on a current of the battery 10.

[0085] A current and a voltage of the battery 10 obtained by a sensor device CMU1 to CMUn can vary over time. The current and voltage data according to operation of the vehicle can be divided into a first interval A1 indicating periodicity and a second interval A2 maintaining an almost constant level.

[0086] The current and voltage data in the first interval A1 can be used to design a DC model.

[0087] In addition, the current and voltage data in the first interval A1 can be transformed into a frequency domain, and can be used to design an impedance model. The impedance model can be designed with data obtained by transforming time series data based on DC into a frequency domain through Fourier transform.

[0088] The second interval A2 can correspond to a no-load voltage interval, and can be used to design an open circuit voltage (OCV) model.

[0089] Detailed descriptions of each battery model are given below.

[0090] Figure 7 is a schematic diagram showing a DC model. Figure 8 is a schematic diagram showing an OCV model. Figure 9 is a schematic diagram showing an impedance model.​Figure 10 is a schematic diagram for describing a method of setting parameters for a DC model and an OCV model. Figure 11 is a schematic diagram for describing a frequency according to a battery model.

[0091] Hereinafter, a description of each battery model is given.

[0092] Referring to Figure 7 , the DC model can be modeled with parameters R i , R diff , C diff , and V ocv .

[0093] R i may refer to a resistance generated in a process in which lithium ions of the battery 10 are detached from an electrode, which can be an internal resistance. R diff may refer to a resistance generated in a process in which lithium ions of the battery 10 move within an electrolyte. C diff may refer to a double layer formed according to a redox reaction, and can refer to a capacitance between an electrode and an electrolyte. V ocv may refer to an end voltage in a state of chemical equilibrium in the battery 10.

[0094] The DC model can reflect characteristics of current and voltage data having a frequency component of 1 KHz or more.

[0095] Referring to Figure 8 , the OCV model can be modeled based on V ocv . Specifically, the OCV model can be modeled based on an open circuit voltage corresponding to a state of charge (SOC).

[0096] The OCV model can reflect characteristics of current and voltage data having a frequency component of 0.01 Hz or less.

[0097] Referring to Figure 9 , the impedance model can be modeled based on impedance information indicating an electrochemical internal state of the battery 10.

[0098] The impedance model can be modeled based on electrochemical impedance spectroscopy (EIS) using alternating current impedance information of the battery 10. The impedance model can be generated based on frequency characteristics within a range in which the impedance model has a lower frequency than the DC model and has a higher frequency than the OCV model.

[0099] The impedance model can be designed based on current and voltage data according to discharge of the battery 10 obtained during a vehicle operation as illustrated in Figure 5 .

[0100] The current and voltage data can be subjected to a discrete wavelet transform (DWT) and a short-time Fourier transform (STFT) to obtain an impedance model.

[0101] The DWT can be a procedure for noise cancellation. The data can be decomposed according to a frequency level, while the noise of the current and voltage data is cancelled through the DWT.

[0102] The STFT can be used to extract frequency characteristics. After the data of each frequency is extracted using the DWT, the time domain signal can be transformed into a frequency domain signal using the STFT. Since it is difficult to periodically extract each frequency from the current and voltage data obtained in real time while the vehicle is running over a long time interval, the current and voltage frequency characteristics can be obtained using the STFT.

[0103] The impedance model can be designed based on an alternating current impedance obtained according to Ohm's law for each frequency from the signal subjected to the DWT and the STFT.

[0104] The impedance model can reflect characteristics of the current and voltage data having a frequency component of about 1 Hz to 50 Hz.

[0105] The first battery model can be any one of a DC model, an OCV model, or an impedance model. The second battery model can be different from the first battery model, which can be any one of a DC model, an OCV model, or an impedance model. For example, when the first battery model is a DC model, the second battery model can be an OCV model and an impedance model.

[0106] The first weight of the first battery model can be determined based on a voltage error between the measured voltage and the first terminal voltage. The second weight of the second battery model can be determined based on a voltage error between the measured voltage and the second terminal voltage.

[0107] The processor 100 can determine a probability that the first voltage error occurs based on a first covariance as the first weight. The first covariance can be a covariance between the first terminal voltage and the first voltage error.

[0108] In addition, the processor 100 can determine a probability that the second voltage error occurs based on a second covariance as the second weight. The second covariance can be a covariance between the second terminal voltage and the second voltage error.

[0109] Embodiments of the present application can fuse the weights of two or more battery models to determine a final terminal voltage. For example, embodiments of the present application can determine a final terminal voltage using three battery models.

[0110] Figure 12 is a schematic diagram for describing a method of determining a final terminal voltage of a battery using first to third battery models according to embodiments of the present application.Figure 13 is a graph showing a voltage error. Figure 14 is a graph showing a covariance.

[0111] Referring to Figure 12 The final terminal voltage of the battery 10 according to the embodiment of the present application can be determined using a conditional probability density function.

[0112] To use the conditional probability density function, the processor 100 can determine a first voltage error Res1, a second voltage error Res2, and a third voltage error Res3, and can determine a first covariance Var1, a second covariance Var2, and a third covariance Var3.

[0113] The first voltage error Res1 can refer to a difference between the measured voltage and the first terminal voltage in the first battery model. The second voltage error Res2 can refer to a difference between the measured voltage and the second terminal voltage in the second battery model. The third voltage error Res3 can refer to a difference between the measured voltage and the third terminal voltage in the third battery model. The first battery model can be a DC model. The second battery model can be an impedance model. The third battery model can be an OCV model.

[0114] The first voltage error Res1, the second voltage error Res2, and the third voltage error Res3 obtained by the processor 100 can be expressed as Figure 13 .

[0115] The first covariance Var1 can be a value obtained by squaring and averaging the first voltage error. The second covariance Var2 can be a value obtained by squaring and averaging the second voltage error. The third covariance Var3 can be a value obtained by squaring and averaging the third voltage error.

[0116] The first covariance Var1, the second covariance Var2, and the third covariance Var3 obtained by the processor 100 can be expressed as Figure 14 .

[0117] The processor 100 can determine the first weight W1, the second weight W2, and the third weight W3 based on a conditional probability density function that can be expressed as Equation 1 below.

[0118] [Equation 1]

[0119]

[0120] In Equation 1 above, U t may be any one of the first terminal voltage U1, the second terminal voltage U2, or the third terminal voltage U3. Res nAny one of the first voltage error Res1, the second voltage error Res2, or the third voltage error Res3. Also, Var n Any one of the first covariance Var1, the second covariance Var2, or the third covariance Var3.

[0121] Referring to Equation 1 and Equation 2 above, Figure 12 The processor 100 can determine the first weight W1 based on a probability of the first terminal voltage U1 occurring, under a condition of the probability density function of the first battery model.

[0122] The processor 100 can determine the second weight W2 based on a probability of the second terminal voltage U2 occurring, under a condition of the probability density function of the second battery model.

[0123] The processor 100 can determine the third weight W3 based on a probability of the third terminal voltage U3 occurring, under a condition of the probability density function of the third battery model.

[0124] The processor 100 can fuse the first weight W1, the second weight W2, and the third weight W3 to determine the final terminal voltage. A sum of the first weight W1, the second weight W2, and the third weight W3 can be 1.

[0125] According to an embodiment, the processor 100 can determine a terminal voltage matching a weight having a maximum value among the first weight W1, the second weight W2, and the third weight W3 as the final terminal voltage. The first weight W1 can match the first terminal voltage U1. The second weight W2 can match the second terminal voltage U2. The third weight W3 can match the third terminal voltage U3. According to an embodiment, when the first weight W1 among the first weight W1, the second weight W2, and the third weight W3 is the maximum value, the processor 100 can determine the first terminal voltage as the final terminal voltage.

[0126] According to another embodiment, when a weight indicating a maximum value is greater than a sum of the other two weights, the processor 100 can determine a terminal voltage matching the maximum weight as the final terminal voltage. For example, when the first weight W1 indicates the maximum value and is greater than a sum of the second weight W2 and the third weight W3, the processor 100 can determine the first terminal voltage as the final terminal voltage.

[0127] According to another embodiment, the processor 100 can reflect weights to correct the terminal voltages, and can add the corrected terminal voltages to determine a final terminal voltage. For example, the processor 100 can multiply the first terminal voltage by a first weight W1 to determine a first corrected voltage. The processor 100 can multiply the second terminal voltage by a second weight W2 to determine a second corrected voltage. The processor 100 can multiply the third terminal voltage by a third weight W3 to determine a third corrected voltage. The processor 100 can add the first corrected voltage, the second corrected voltage, and the third corrected voltage to obtain the final terminal voltage.

[0128] Further, according to an embodiment of the present application, the processor 100 can determine the SOH of the battery 10 based on the final terminal voltage, and can determine an abnormal state of the battery 10 based on the SOH of the battery 10. The program of determining the abnormal state of the battery 10 can be a program of determining the performance of the battery 10 or determining whether an error occurs in the battery 10.

[0129] The step of determining the SOH of the battery 10 can include a program of verifying the SOH estimation performance. The step of verifying the SOH estimation performance of the battery 10 can utilize a method of comparing the measured capacity of the battery 10 with the estimated capacity of the battery 10. The measured capacity of the battery 10 can be a current measured from the battery 10, and the capacity of the battery 10 is determined using a current integration method. The estimated capacity of the battery 10 can be a capacity derived using a preset SOH estimation algorithm. When the error rate between the measured capacity and the estimated capacity is within a certain level, the processor 100 can determine that the estimation performance of the capacity of the battery 10 is excellent.

[0130] The program of determining the abnormal state of the battery 10 can be a program of generating error classification data based on impedance extraction. According to an embodiment of the present application, the processor 100 can reflect the chemical change characteristics in the battery 10 using an impedance model, and accurately predict the voltage change of the battery 10 based on a direct current model. Accordingly, according to an embodiment of the present application, the processor 100 can more accurately perform the abnormal state determination using the chemical change characteristics of the battery 10.

[0131] Figure 15 A computing system according to an embodiment of the present application is illustrated.

[0132] Reference Figure 15 The computing system 1000 can include at least one processor 1100, a memory 1300, a user interface input device 1400, a user interface output device 1500, a storage device 1600, and a network interface 1700 connected to each other by a bus 1200.

[0133] The processor 1100 can be a central processing unit (CPU) or a semiconductor device that processes instructions stored in the memory 1300 and / or the storage 1600. The memory 1300 and the storage 1600 can include various types of volatile or non-volatile storage media. For example, the memory 1300 can include read-only memory (ROM) 1310 and random access memory (RAM) 1320.

[0134] Accordingly, the operations of a method or algorithm described in connection with the embodiments disclosed in this specification can be embodied directly in hardware, in software, or in a combination of hardware and software executed by the processor 1100. A software module can reside in a storage medium (i.e., the memory 1300 and / or the storage 1600) such as RAM, flash memory, ROM, EPROM, EEPROM, registers, hard disk, a removable disk, and a CD-ROM.

[0135] The storage medium can be coupled to the processor 1100. The processor 1100 can read information from the storage medium and can write information to the storage medium. Alternatively, the storage medium can be integrated with the processor 1100. The processor and the storage medium can reside in an application-specific integrated circuit (ASIC). The ASIC can reside in the user terminal. In another case, the processor and the storage medium can reside in the user terminal as separate components.

[0136] According to an embodiment of the present application, the battery management unit can fuse characteristics of two or more battery models to obtain a battery terminal voltage, thereby reflecting various battery characteristics to obtain a more accurate battery terminal voltage.

[0137] Further, according to an embodiment of the present application, the battery management unit can fuse a battery model reflecting direct current characteristics of a battery and a battery model reflecting alternating current characteristics to obtain a terminal voltage of the battery, thereby obtaining a more accurate battery terminal voltage.

[0138] Further, various effects directly or indirectly determined by the present application can be provided.

[0139] In the foregoing, although the present application has been described with reference to embodiments and the attached drawings, the present application is not limited thereto, but various modifications and changes can be made by those skilled in the art to which the present application pertains without departing from the spirit and scope of the present application claimed in the appended claims.

[0140] Accordingly, the embodiments described in the present application are not intended to limit the technical idea of the present application. The scope of the technical idea of the present application is not limited to the disclosed embodiments. The scope of the present application should be interpreted based on the appended claims. All technical ideas within the scope equivalent to the claims should be interpreted as included in the claims of the present application.

Claims

1. A battery management unit comprising: a sensor configured to obtain a measured voltage of a battery; a first battery model and a second battery model, the first battery model and the second battery model configured to estimate different characteristics of the battery based on the measured voltage, respectively; and a processor configured to: obtain a first terminal voltage corresponding to the measured voltage based on the first battery model, obtain a second terminal voltage corresponding to the measured voltage based on the second battery model, fuse the first terminal voltage and the second terminal voltage to determine a final terminal voltage of the battery.

2. The battery management unit of claim 1, wherein, the first battery model and the second battery model are pre-designed with different parameters based on a rate of change of voltage of the battery over time.

3. The battery management unit of claim 1, wherein, the processor is further configured to: determine a first weight of the first battery model based on a first voltage error between the measured voltage and the first terminal voltage in the first battery model; determine a second weight of the second battery model based on a second voltage error between the measured voltage and the second terminal voltage in the second battery model; determine the final terminal voltage based on the first weight and the second weight.

4. The battery management unit of claim 3, wherein, the processor is further configured to: determine a first covariance based on the first voltage error; determine the first weight according to a probability of the first terminal voltage occurring based on the first covariance; determine a second covariance based on the second voltage error; determine the second weight according to a probability of the second terminal voltage occurring based on the second covariance.

5. The battery management unit of claim 3, wherein, the processor is further configured to: determine a third terminal voltage in a third battery model based on the measured voltage; determine a third weight of the third battery model based on a third voltage error between the measured voltage and the third terminal voltage; determine the final terminal voltage of the battery based on the first weight to the third weight.

6. The battery management unit of claim 5, wherein, the first battery model, the second battery model and the third battery model are direct current model, alternating current model and open circuit voltage model, respectively.

7. The battery management unit of claim 6, wherein, the alternating current model is designed based on alternating current impedance information obtained by transforming current and voltage data varying over time into frequency domain by each frequency.

8. The battery management unit of claim 5, wherein, the processor is further configured to: determine the terminal voltage matching a maximum weight among the first weight to the third weight as the final terminal voltage.

9. The battery management unit of claim 5, wherein, the processor is further configured to: correct the first terminal voltage with the first weight to determine a first corrected voltage; correct the second terminal voltage with the second weight to determine a second corrected voltage; correct the third terminal voltage with the third weight to determine a third corrected voltage; determine the final terminal voltage based on the first corrected voltage, the second corrected voltage and the third corrected voltage.

10. The battery management unit of claim 1, wherein, the processor is further configured to: determine a state of health of the battery based on the final terminal voltage; determine an abnormal state of the battery based on the state of health. 11.A battery management method comprising: obtaining a measured voltage of a battery by a sensor; obtaining a first terminal voltage corresponding to the measured voltage based on a first battery model by a processor; obtaining a second terminal voltage corresponding to the measured voltage based on a second battery model by the processor; determining a final terminal voltage of the battery by the processor by fusing the first terminal voltage and the second terminal voltage.

12. The battery management method of claim 11, wherein, the first battery model and the second battery model are pre-designed with different parameters based on a rate of change of voltage of the battery over time.

13. The battery management method of claim 11, wherein, determining the final terminal voltage includes: determining a first weight of the first battery model based on a first voltage error between the measured voltage and the first terminal voltage in the first battery model; determining a second weight of the second battery model based on a second voltage error between the measured voltage and the second terminal voltage in the second battery model; determining the final terminal voltage based on the first weight and the second weight.

14. The battery management method of claim 13, wherein determining the first weight includes: determining a first covariance based on the first voltage error; determining the first weight according to a probability of the first terminal voltage occurring based on the first covariance, determining the second weight includes: determining a second covariance based on the second voltage error; determining the second weight according to a probability of the second terminal voltage occurring based on the second covariance.

15. The battery management method of claim 13, further comprising: determining a third terminal voltage in a third battery model based on the measured voltage; determining a third weight of the third battery model based on a third voltage error between the measured voltage and the third terminal voltage, wherein determining the final terminal voltage includes: fusing the first weight to the third weight.

16. The battery management method of claim 15, wherein, the first battery model, the second battery model, and the third battery model are a direct current model, an alternating current model, and an open circuit voltage model, respectively.

17. The battery management method of claim 16, wherein, the alternating current model is designed based on alternating current impedance information obtained by transforming current and voltage data varying over time into a frequency domain by each frequency.

18. The battery management method of claim 15, wherein, determining the final terminal voltage includes: determining a terminal voltage matching a maximum weight among the first weight to the third weight as the final terminal voltage.

19. The battery management method of claim 15, wherein, determining the final terminal voltage includes: correcting the first terminal voltage using the first weight to determine a first corrected voltage; correcting the second terminal voltage using the second weight to determine a second corrected voltage; correcting the third terminal voltage using the third weight to determine a third corrected voltage; determining the final terminal voltage based on the first corrected voltage, the second corrected voltage, and the third corrected voltage.

20. The battery management method of claim 11, further comprising: determining a state of health of the battery based on the final terminal voltage; determining an abnormal state of the battery based on the state of health.

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