Apparatus and method for detecting abnormality of battery pack

Abstract

This invention relates to an apparatus and method for detecting anomalies in a battery pack. Therefore, an apparatus and method for detecting anomalies in a battery pack based on a battery model are provided. The computing device includes a memory and a processor, the memory being configured to store instructions; the processor being configured to perform the following operations by executing the instructions: setting a battery model corresponding to the battery pack; estimating the battery voltage based on the set battery model; measuring the actual battery voltage; determining the battery pack voltage deviation based on the actual battery voltage; determining the model estimation error based on the battery voltage and the actual battery voltage; setting a threshold based on the model estimation error; and comparing the battery pack voltage deviation with the threshold to determine whether the battery pack is abnormal.

Description

[0001] Cross-references to related applications This application claims the benefit and priority of Korean Patent Application No. 10-2024-0202724, filed on December 31, 2024, with the Korean Intellectual Property Office, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This invention relates to a technique for detecting anomalies in battery packs. More specifically, this invention relates to a technique for detecting whether a battery pack is abnormal, either on an in-vehicle device or through an interconnection with a cloud server, based on a battery model. Background Technology

[0003] Today, due to issues such as de-oiling and environmental pollution, new renewable energy sources have become necessary rather than optional. Equipment for producing and storing renewable energy in various ways has also become essential. Recently, lithium-ion rechargeable batteries have received increased attention among various types of energy storage devices.

[0004] Recently, with the increase in electric vehicle (EV) users, and in conjunction with the certification of high-voltage battery status, certification of used EVs, and remanufacturing, the demand for EV battery state of health (SOH) and abnormal condition diagnosis and assessment has also increased. SOH indicates the battery's performance compared to its initial performance and is used as an indicator to provide information on remaining battery life and current performance status.

[0005] In addition, in various applications such as issuing battery certificates and remanufacturing, reusing and recycling batteries for EVs, it is necessary to diagnose abnormal conditions of high-voltage battery packs installed in vehicles or high-voltage batteries in a disassembled state.

[0006] With increasing focus on batteries, the importance of Battery Management Systems (BMS) is becoming increasingly apparent. BMS diagnoses and estimates the state, performance, and safety of batteries based on sensed information (e.g., voltage, current, and temperature) corresponding to battery cells / packets. In particular, voltage information is crucial for diagnosing battery state, aging, and abnormal behavior. Therefore, accurate analysis of voltage characteristics is essential for diagnosing battery anomalies.

[0007] Battery models can be applied to various fields, such as electrode design for battery packs, cell design, performance and life prediction, stability and anomaly diagnosis, and battery pack design.

[0008] Battery models can be broadly categorized into physics-based models and empirical models.

[0009] Physics-based models are those that formulate the main electro-electrochemical-thermal phenomena in a battery and use numerical analysis methods to calculate the battery's response. Empirical models, on the other hand, are based on experimental data and utilize data fitting, data trend analysis, equivalent circuit models, and other methods to represent the battery's response.

[0010] Currently, research is progressing towards developing ROM models with high accuracy, while compensating for the slow computation speed of physical models.

[0011] The background information described herein is intended to facilitate understanding of the background of this invention and may therefore include content not known to those skilled in the art. The statements in this section provide only background information related to this invention and do not constitute prior art. Summary of the Invention

[0012] This invention aims to solve the aforementioned problems in the prior art, while fully retaining the advantages achieved by the prior art.

[0013] One aspect of the present invention provides a method and apparatus for detecting anomalies in a battery pack based on a battery model.

[0014] Another aspect of the present invention provides a method and apparatus for detecting abnormalities in a battery pack, wherein the method detects whether the battery pack is abnormal on an in-vehicle device or by linking with a cloud server and based on a battery model.

[0015] Another aspect of the present invention provides a method and apparatus for detecting abnormalities in a battery pack. The method sets a threshold based on a battery voltage error estimated using a battery model and compares the actual measured voltage deviation of the battery pack with the threshold to more accurately detect abnormalities in the battery pack.

[0016] The technical problems solved by this invention are not limited to those described above. Those skilled in the art should be able to clearly understand any other technical problems not mentioned herein through the following description.

[0017] According to one aspect of the present invention, a computing device may include a memory and a processor, the memory being configured to store instructions, the processor being able to set a battery model corresponding to a battery pack by executing the instructions, the processor being able to estimate a battery voltage based on the set battery model, the processor being able to measure an actual battery voltage, the processor being able to determine a battery pack voltage deviation based on the actual battery voltage, the processor being able to determine a model estimation error based on the battery voltage and the actual battery voltage, the processor being able to set a threshold based on the model estimation error, and the processor being able to compare the battery pack voltage deviation with the threshold to determine whether the battery pack is abnormal.

[0018] As one implementation, the processor can determine that the battery pack is abnormal based on the battery pack voltage deviation being greater than the threshold.

[0019] As one implementation, the processor can determine the battery pack voltage deviation based on the state of charge (SOC) deviation and polarization deviation.

[0020] As one implementation, the processor can define the model estimation error as the maximum error value between the model-estimated voltage and the actual measured voltage.

[0021] As one implementation, the model estimation error may include SOC model error and polarization error. The SOC model error is defined by the difference between the model-estimated open-circuit voltage and the actual measured open-circuit voltage (OCV). The polarization error is defined by the difference between the estimated polarization voltage based on the battery model and the actual measured polarization voltage. The processor may determine the maximum error value as the sum of the maximum value of the SOC model error and the maximum value of the estimated polarization voltage based on the battery model.

[0022] As one implementation, the processor can apply specific weights to the maximum value of the SOC model error and the maximum value of the estimated polarization voltage based on the battery model to determine the threshold.

[0023] As one implementation, the processor can adaptively determine the maximum value of the estimated polarization voltage based on the battery model based on the SOC prediction; if the SOC prediction is included in the high SOC boundary region, the processor can determine the maximum value of the estimated polarization voltage based on the battery model under the condition that the SOC prediction is added to a predetermined SOC; if the SOC prediction is included in the low SOC boundary region, the processor can determine the maximum value of the estimated polarization voltage based on the battery model under the condition that the SOC prediction is subtracted from the predetermined SOC.

[0024] As one implementation scheme, the processor can receive and set relevant information about the battery model corresponding to the battery pack from a connected cloud server via a network.

[0025] As one implementation approach, the computing device can be implemented as a server in a cloud environment.

[0026] As one implementation scheme, the computing device can be implemented as an onboard unit in an electric vehicle (EV).

[0027] According to another aspect of the present invention, a method for diagnosing anomalies in a battery pack of an electric vehicle (EV) in a computing device may include: setting a battery model corresponding to the battery pack; the method may further include: estimating the battery voltage based on the set battery model; the method may further include: measuring the actual battery voltage; the method may further include: determining a battery pack voltage deviation based on the actual battery voltage; the method may further include: determining a model estimation error based on the battery voltage and the actual battery voltage; the method may further include: setting a threshold based on the model estimation error; the method may further include: comparing the battery pack voltage deviation with the threshold to determine whether the battery pack is abnormal.

[0028] As one implementation, the method may further include: determining a battery pack abnormality based on the battery pack voltage deviation being greater than the threshold.

[0029] As one implementation, the method may further include: determining the battery pack voltage deviation based on the state of charge (SOC) deviation and polarization deviation.

[0030] As one implementation, the method may further include: determining the model estimation error as the maximum error value between the model-estimated voltage and the actual measured voltage.

[0031] As one implementation, the model estimation error may include SOC model error and polarization error. The SOC model error is defined by the difference between the model-estimated open-circuit voltage (OCV) and the actual measured OCV. The polarization error is defined by the difference between the estimated polarization voltage based on the battery model and the actual measured polarization voltage. The method may further include: determining the maximum error value as the sum of the maximum value of the SOC model error and the maximum value of the estimated polarization voltage based on the battery model.

[0032] As one implementation, the method may further include: determining the threshold by applying specific weights to the maximum value of the SOC model error and the maximum value of the estimated polarization voltage based on the battery model, respectively.

[0033] As one implementation, the method may further include: adaptively determining the maximum value of the estimated polarization voltage based on the battery model according to the SOC prediction; the method may further include: determining the maximum value of the estimated polarization voltage based on the battery model based on the SOC prediction including a high SOC boundary region, under the condition of adding a predetermined SOC to the SOC prediction; the method may further include: determining the maximum value of the estimated polarization voltage based on the battery model based on the SOC prediction including a low SOC boundary region, under the condition of subtracting a predetermined SOC from the SOC prediction within the low SOC boundary region.

[0034] As one implementation scheme, the computing device can connect to a cloud server via a network, receive and set relevant information about the battery model corresponding to the battery pack from the cloud server.

[0035] As one implementation approach, the computing device can be implemented as a server in a cloud environment.

[0036] As one implementation scheme, the computing device can be implemented as an onboard device in an electric vehicle (EV). Attached Figure Description

[0037] The above and other objects, features, and advantages of the present invention will become more apparent from the following detailed description taken in conjunction with the accompanying drawings: Figure 1 A schematic diagram illustrating the configuration and driving principle of an electric vehicle according to the present invention; Figure 2 A schematic diagram illustrating the concepts of open-circuit voltage (OCV) and polarization for calculating battery voltage according to an embodiment of the present invention; Figure 3A Here is a flowchart describing a method for diagnosing battery pack anomalies based on a battery model in a battery pack anomaly diagnosis system according to an embodiment of the present invention; Figure 3B A schematic diagram illustrating a method for calculating an estimated polarization voltage based on a battery model according to an embodiment of the present invention based on state of charge (SOC) prediction; Figure 4 A flowchart illustrating a method for calculating a threshold for battery pack anomaly diagnosis according to an embodiment of the present invention; Figure 5 A block diagram illustrating the configuration of a system for performing battery pack anomaly diagnosis on a battery pack installed in an electric vehicle (EV) using diagnostic equipment, according to an embodiment of the present invention. Figure 6 Here is a block diagram illustrating a cloud-based system for performing battery pack anomaly diagnosis according to an embodiment of the present invention; Figure 7 A block diagram illustrating the configuration of a system for performing battery pack anomaly diagnosis based on an in-vehicle device according to an embodiment of the present invention; Figure 8 A computing system according to an embodiment of the present invention is shown. Detailed Implementation

[0038] In the following, some embodiments of the invention will be described in detail with reference to the accompanying drawings. When adding reference numerals to components in each drawing, it should be noted that even if the same component is shown in different drawings, it is denoted by the same reference numerals. Furthermore, in describing embodiments of the invention, detailed descriptions of well-known features or functions have been omitted so as not to unnecessarily obscure the spirit of the invention.

[0039] In describing components according to embodiments of the invention, terms such as first, second, "A", "B", (a), (b), etc., may be used. These terms are intended only to distinguish one component from another, and they do not limit the nature, order, or sequence of the respective components. Furthermore, unless otherwise defined, all terms used herein, including technical and scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Terms such as those defined in common dictionaries should be interpreted as having the same meaning as in the context of the relevant technical field, and should not be interpreted as having an idealized or overly formal meaning, unless expressly defined as such in this application. When a controller, module, component, device, element, etc., of the invention is described as having a purpose or performing an operation, function, etc., it should be considered herein as "configured" to satisfy that purpose or perform that operation or function. Each controller, module, component, device, element, etc., may be implemented individually or may include a processor and memory (e.g., a non-transitory computer-readable medium) as part of a device.

[0040] In the following text, see references Figures 1 to 8 The embodiments of the present invention will be described in detail below.

[0041] Figure 1 A schematic diagram illustrating the configuration and drive principle of the electric vehicle according to the present invention.

[0042] refer to Figure 1 The electric vehicle 1 can be configured to include an on-board charger (OBC) 10, a high-voltage DC-DC converter (HDC) 20, a low-voltage DC-DC converter (LDC) 30, a high-voltage battery 40, a battery management system (BMS) 41, a low-voltage battery 50, an inverter 60, and a motor 70.

[0043] OBC 10 can be a converter for receiving external AC power 80 and performing AC-DC conversion, and it can also be a component for slowly charging high-voltage battery 40.

[0044] HDC 20 can be a component used to perform DC-DC conversion on the power from the high-voltage battery 40 and supply power to the inverter 60.

[0045] LDC 30 can be a converter system for stepping down the DC power of the high-voltage battery 40 to convert the DC power into the 12V low-voltage power required by most components of the electric vehicle 1 (such as headlights, wipers, control units, etc.), and can supply 12V low-voltage power to the low-voltage battery 50.

[0046] Inverter 60 can be a power conversion device for driving motor 70. It can be a component for converting direct current into three-phase alternating current to control the speed and direction of motor 70 and perform regenerative braking.

[0047] In order to be charged quickly, the high-voltage battery 40 can be charged by directly receiving power from the fast charging device 90.

[0048] Figure 2 This is a schematic diagram illustrating the concepts of open-circuit voltage (OCV) and polarization for calculating battery voltage according to an embodiment of the present invention.

[0049] As shown by reference numeral 210 in the attached figure, OCV refers to the battery voltage when no load is connected. In other words, OCV refers to the voltage measured across the battery terminals when the circuit is open and no current flows within the battery. Batteries have a self-discharge characteristic. Due to this characteristic, the OCV value is initially small but gradually decreases. In particular, as shown by reference numeral 220 in the attached figure, self-discharge increases further when the battery is defective.

[0050] The OCV of a battery fluctuates with temperature. Therefore, maintaining a uniform temperature environment is crucial when measuring OCV. As indicated by reference numeral 230 in the attached figure, temperature correction refers to converting the OCV measurement value to the voltage at a reference temperature.

[0051] To analyze battery performance, attention should be paid to the voltage changes that occur during charging and discharging. In particular, the following phenomenon occurs: the voltage increases more than the initial voltage (OCV) during charging, while the voltage decreases more than the OCV during discharging. This phenomenon is caused by polarization.

[0052] Polarization refers to several phenomena that occur on the electrode surface during battery operation. Polarization can occur due to changes in reaction point concentration or factors such as skinning. Because polarization causes voltage loss, this phenomenon is called "overvoltage" or "polarization voltage." The greater the overvoltage, the higher the voltage required to obtain current. This immediately affects the battery's efficiency and performance.

[0053] The actual voltage of a battery can be described as the sum of its open-circuit voltage (OCV) and polarization voltage.

[0054] As shown by reference numeral 240 in the attached figure, the polarization voltage can be composed of the sum of the following three elements: 1. Activation polarization: The voltage required to overcome the energy barrier that initiates an electrochemical reaction; 2. Concentration (or mass transfer) polarization: Voltage loss that occurs during the movement of ions or electrons within a battery; 3. Ohmic polarization: Voltage loss that occurs due to the resistance of electrodes, electrolytes and other electrical components.

[0055] The battery pack anomaly diagnosis system or computing device for diagnosing battery pack anomalies according to the present invention (hereinafter collectively referred to as the "battery pack anomaly diagnosis system") can estimate the OCV using a SOC-based model. The system can estimate the polarization voltage based on an equivalent circuit model (ECM). Then, the system can determine whether the battery pack is abnormal based on the error between the voltage estimated using the battery model and the actual voltage.

[0056] Figure 3A This is a flowchart illustrating a method for diagnosing battery pack anomalies based on a battery model in a battery pack anomaly diagnosis system according to an embodiment of the present invention. Figure 3B This is a schematic diagram illustrating a method for calculating an estimated polarization voltage based on a battery model according to an embodiment of the present invention, based on state of charge (SOC) prediction.

[0057] refer to Figure 3A In S310, the battery pack anomaly diagnosis system can set a battery model. As an example, the battery model can include, but is not limited to, an equivalent circuit model (ECM) or an electrochemical model. The battery pack anomaly diagnosis system according to an embodiment of the present invention can also set a state of charge (SOC) model.

[0058] In the S320, the battery pack anomaly diagnosis system can estimate the battery voltage V based on the set battery model. model Here, V can be estimated based on at least one of the following parameters: battery SOC, current, and temperature. model .

[0059] In the S330, the battery pack fault diagnosis system can measure the actual battery voltage V. measured .

[0060] In the S340, the battery pack anomaly diagnosis system can be based on SOC deviation (V SOC differential ) and polarization deviation (V diff,max -V diff,min Calculate the battery pack voltage deviation V different .

[0061] V different =V measure,max - V measure,min = (OCV measure,max - OCVmeasure,min ) + (V diff,max - V diff,min ) = (V SOC differential ) + (V diff,max - V diff,min ) In the S350, the battery pack anomaly diagnosis system can be based on V model and V measured Calculate the estimation error of the model.

[0062] The model estimation error, based on the implementation plan, can be calculated as the maximum error between the model-estimated voltage and the actual measured voltage. The model estimation error may include the SOC model error (i.e., SOC...). model,error The SOC model error is the sum of the estimated open-circuit voltage and the polarization error. The SOC model error is the difference between the model-estimated open-circuit voltage and the actual measured open-circuit voltage. The polarization error is the estimated polarization voltage V based on the battery model. diff,model Compared with the actual measured polarization voltage V diff,real The difference between them. Therefore, the maximum value of the model estimation error can correspond to the case where the SOC model error and polarization error are each at their maximum values.

[0063] As one implementation scheme, the maximum value of the SOC model error can be determined as the maximum value of the SOC model errors for each cell (i=1,...,N). The maximum value of the polarization error can be calculated as the actual measured polarization voltage V. diff,real The estimated polarization voltage V is 0 and based on the battery model. diff,model The case where it is the maximum value.

[0064] Because the estimated polarization voltage based on the battery model can vary with the SOC prediction (e.g. Figure 3B As shown in the diagram, the calculation can be performed based on whether the SOC prediction falls within the high SOC boundary region (high SOC) or the low SOC boundary region (low SOC). In other words, when the SOC prediction is included in the high SOC boundary region, the estimated polarization voltage based on the battery model can have a maximum value when the SOC prediction is plus the predetermined SOC (SOC+α, or predetermined SOC). When the SOC prediction is included in the low SOC boundary region, the estimated polarization voltage based on the battery model can have a maximum value when the SOC prediction is minus the predetermined SOC (SOC-α, or predetermined SOC).

[0065] In the S360, the battery pack anomaly diagnosis system can calculate thresholds used to determine whether the battery pack is abnormal.

[0066] As an example, this threshold could be based on the maximum value of the SOC model error, SOC. model,error,max And the maximum value of the estimated polarization voltage V based on the battery modeldiff,model,max The calculation is performed. Specifically, the threshold can be based on the maximum value of the SOC model error by applying specific coefficients (or weights) β1 and β2, respectively. model,error,max And the maximum value of the estimated polarization voltage V based on the battery model diff,model,max The maximum value is obtained by adding the maximum value of the SOC model error applied with β1 and β2 to the maximum value of the estimated polarization voltage based on the battery model.

[0067] As another example, the threshold can be estimated based on a battery model, in response to the current SOC, current, and temperature, to determine the maximum voltage V. model,max With minimum voltage V model,min The difference between them is calculated.

[0068] In S370, the battery pack anomaly diagnosis system can detect the battery pack voltage deviation V calculated in S340. different The size is compared with the size of the threshold.

[0069] When V different When the voltage deviation exceeds the Threshold (S370 is "Yes"), in S380, the battery pack anomaly diagnosis system can diagnose a battery pack fault (or anomaly). A battery pack is constructed from multiple cells. Cells within a battery pack typically exhibit the same degradation trend. Therefore, when the voltage deviation of a specific cell in the battery pack exceeds the threshold, that cell can be identified as faulty. This allows for the diagnosis of a battery pack fault. In other words, when the actual voltage deviation exceeds the maximum voltage deviation set using a battery model, a battery pack or battery module fault can be diagnosed.

[0070] As one implementation scheme, for the cells of the battery pack, when the voltage deviation calculated based on SOC deviation and polarization deviation is greater than the maximum value of the SOC model error, SOC... model,error,max And the maximum value of the estimated polarization voltage V based on the battery model diff,model,max When the calculated threshold is reached, it can be described as the battery pack abnormality diagnosis system diagnosing an anomaly.

[0071] As another implementation, the battery pack anomaly diagnosis system can calculate the threshold and voltage deviation according to the following formula, and compare the threshold and voltage deviation to determine whether the battery pack is abnormal.

[0072] Threshold = argmax (V model,max (SOC, current, temperature) - V model,min (SOC, current, temperature) V different = (V measure,max - Vmeasure,min ) If threshold <V different If the battery malfunctions, then the battery is faulty. Figure 4 This is a flowchart illustrating a method for calculating a threshold for battery pack anomaly diagnosis according to an embodiment of the present invention.

[0073] refer to Figure 4 In S410, the battery pack anomaly diagnosis system can calculate the maximum value of the SOC model error, SOC. model,error,max .

[0074] In the S420, the battery pack anomaly diagnostic system can calculate the maximum value V of the estimated polarization voltage based on the battery model. diff,model,max .

[0075] In S430, the battery pack anomaly diagnosis system can apply a first coefficient to the maximum value of the SOC model error to correct the maximum value of the SOC model error (β1*SOC). model,error,max ).

[0076] In the S440, the battery pack anomaly diagnosis system can apply a second coefficient to the maximum value of the estimated polarization voltage based on the battery model to correct for the maximum value of the estimated polarization voltage based on the battery model (β2*V). diff,model,max ).

[0077] In the S450, the battery pack anomaly diagnosis system can add the maximum value of the corrected SOC model error to the maximum value of the corrected estimated polarization voltage based on the battery model to calculate the threshold (β1*SOC). model,error,max +β2*V diff,model,max ).

[0078] In the following text, reference will be made to Figure 5 , Figure 6 and Figure 7 A description of the configuration methods for battery pack anomaly diagnosis systems based on various implementation schemes is provided.

[0079] Figure 5 This is a block diagram illustrating the configuration of a system for performing battery pack anomaly diagnosis on a battery pack installed in an electric vehicle (EV) using diagnostic equipment, according to an embodiment of the present invention.

[0080] According to the implementation plan, the battery pack anomaly diagnosis system 500 can be generally configured to include an electric vehicle (EV) 510, a diagnostic device 560, a cloud server 570, and a charging or discharging device 580.

[0081] The electric vehicle 510 can be configured to include a high-voltage battery 520, a battery management system (BMS) 530, a charging gateway (CGW) 540, and a charging port 550.

[0082] The diagnostic device 560 can be configured to include a first communication device 561, an information acquisition device 562, a diagnostic device 563, an output device 564, and a second communication device 565.

[0083] The BMS 530 of the high-voltage battery 520 can communicate with the first communication device 561 of the diagnostic device 560 via the CGW 540.

[0084] The information acquisition device 562 can acquire sensing information from the high-voltage battery 520, such as voltage, current, and temperature.

[0085] The diagnostic device 563 can perform battery pack anomaly diagnosis based on the acquired sensing information and the set battery model. The battery pack anomaly diagnosis logic based on the battery model can be replaced by the description in the above figures.

[0086] Output device 564 can output the results of battery pack abnormality diagnosis.

[0087] The second communication device 565 can connect to the cloud server 570 via a network to obtain information about the battery model corresponding to the high-voltage battery 520 installed on the electric vehicle 510 from the cloud server 570, and provide the obtained information to the diagnostic device 563.

[0088] Figure 6 This is a block diagram illustrating a cloud-based system for performing battery pack anomaly diagnosis according to an embodiment of the present invention.

[0089] According to the implementation plan, the battery pack anomaly diagnosis system 600 can be generally configured to include an electric vehicle (EV) 610, a cloud server 670, and a charging or discharging device 680.

[0090] The electric vehicle 610 can be configured to include a high-voltage battery 620, a BMS 630, an audio-video navigation (AVN) 640, a vehicle charging management system (VCMS) 650, and a charging port 660.

[0091] The VCMS 650 can be an electric vehicle charging controller for controlling and managing the overall charging system of the EV 610. When power is supplied from or to an external source for the EV 610, the VCMS 650 can communicate with the charging or discharging device 680 and the charging port 660 to control charging / discharging. The VCMS 650 can be configured to include a charging management system (CMS) for controlling slow and fast charging, and a power line communication module (PCM) for controlling fast charging. The VCMS 650 can collaboratively control relevant controllers in the vehicle through the CMS and PCM, and can communicate bidirectionally with external charging or discharging devices to control the charging of the high-voltage battery 620 or the discharging of the power charged into the high-voltage battery 620.

[0092] The AVN 640 can be configured to include an output device 641 and a communication device 642.

[0093] The BMS 630 can be linked with the cloud server 670 through the communication device 642 of the AVN 640.

[0094] The cloud server 670 can perform battery pack anomaly diagnosis based on a battery model, using sensing information (e.g., current, voltage, temperature, etc.) received from the BMS 630 corresponding to the high-voltage battery 620. The cloud server 670 can obtain battery information from the BMS 630, including relevant information about the high-voltage battery 620's specifications, and set up a battery model based on this information. The cloud server 670 can then execute its built-in battery pack anomaly diagnosis logic based on the set battery model to diagnose battery pack anomalies.

[0095] The cloud server 670 can send the battery pack anomaly diagnosis results to the AVN 640. The AVN 640 can output the battery pack anomaly diagnosis results through the output device 641. According to the implementation scheme, the battery pack anomaly diagnosis results can also be output through the instrument cluster of the EV 610.

[0096] Figure 7 This is a block diagram illustrating the configuration of a system for performing battery pack anomaly diagnosis based on an embodiment of the present invention.

[0097] According to the implementation plan, the battery pack anomaly diagnosis system 700 can be roughly configured to include an electric vehicle 710, a cloud server 770, and a charging or discharging device 780.

[0098] The electric vehicle 710 can be configured to include a high-voltage battery 720, a BMS 730, an audio-video navigation (AVN) 740, a vehicle charging management system (VCMS) 750, and a charging port 760.

[0099] The VCMS 750 can be an electric vehicle charging controller for controlling and managing the overall charging system of the EV 710. When power is supplied from or to an external source for the EV 710, the VCMS 750 can communicate with the charging or discharging device 780 and the charging port 760 to control charging / discharging. The VCMS 750 can be configured to include a charging management system (CMS) for controlling slow and fast charging, and a power line communication module (PCM) for controlling fast charging. The VCMS 750 can collaboratively control relevant controllers in the vehicle through the CMS and PCM, and can communicate bidirectionally with external charging or discharging devices to control the charging of the high-voltage battery 720 or the discharging of the charged power into the high-voltage battery 720.

[0100] The AVN 740 can be configured to include an output device 741 and a communication device 742.

[0101] The BMS 730 can be linked with the cloud server 770 through the communication device 742 of the AVN 740.

[0102] BMS 730 can be configured to include a measurement device 731, a storage device 732, and a diagnostic device 733.

[0103] The measuring device 731 can use the voltage sensor, current sensor, temperature sensor and other installed sensors to measure the current, voltage and temperature of the high voltage battery 720 and record the measured sensing information in the storage device 732.

[0104] BMS 730 can obtain relevant information about the battery model corresponding to the high-voltage battery 720 from the cloud server 770, and can record the obtained information in the storage device 732. Here, the battery model may include the SOC model and ECM, and can be determined based on the specifications of the high-voltage battery 720.

[0105] The diagnostic device 733 can perform battery pack anomaly diagnosis based on the sensing information and battery model information recorded in the storage device 732.

[0106] The diagnostic device 733 can send the results of the battery pack anomaly diagnosis to the AVN 740. The AVN 740 can output the results of the battery pack anomaly diagnosis through the output device 741.

[0107] Figure 7 The illustrated implementation describes the battery pack anomaly diagnostic logic being implemented in the BMS 730, but this is only one implementation. According to another implementation, the battery pack anomaly diagnostic logic can also be implemented on a separate vehicle controller that is linked to the BMS 730 via in-vehicle communication.

[0108] Figure 8A computing system according to an embodiment of the present invention is shown.

[0109] refer to Figure 8 The computing system 800 may include at least one processor 820, a memory 830, a user interface input device 840, a user interface output device 850, a storage device 870, and a network interface 880 connected to each other via a bus 810.

[0110] According to the implementation scheme, network interface 880 can perform at least one of diagnostic communication, vehicle-to-vehicle communication, and / or communication with an external server. Network interface 880 may include a communication module (or communication modem) for at least one of wired communication with an electric vehicle via a diagnostic cable, vehicle-to-vehicle communication for a vehicle-to-vehicle communication network (e.g., CAN communication), or wireless communication via a mobile communication network.

[0111] Processor 820 may be a central processing unit (CPU) or semiconductor device for processing instructions stored in memory 830 and / or storage device 870. Memory 830 and storage device 870 may include various types of volatile or non-volatile storage media. For example, memory 830 may include read-only memory (ROM) 831 and random access memory (RAM) 832.

[0112] Therefore, the operation of the methods or algorithms described in conjunction with the embodiments disclosed herein can be directly implemented as hardware modules, software modules executed by processor 820, or a combination thereof. The software modules can reside on storage media (i.e., memory 830 and / or storage device 870), such as RAM, flash memory, ROM, EPROM, EEPROM, registers, hard disks, removable disks, and CD-ROMs.

[0113] The storage medium can be coupled to the processor 820. The processor 820 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 820. The processor and storage medium can reside in an application-specific integrated circuit (ASIC). The ASIC can reside within the user terminal. Alternatively, the processor and storage medium can reside as separate components in the EV, but this is only one implementation. The processor and storage medium can reside in a cloud server.

[0114] As one implementation scheme, the computing system 800 can be configured to perform the above-mentioned... Figures 1 to 7 The present invention discloses at least one function and method, and can be applied to at least one component of the above-mentioned battery pack abnormality diagnosis system.

[0115] This technology can provide a method and apparatus for detecting anomalies in battery packs based on battery models.

[0116] In addition, this technology can provide a method and apparatus for detecting abnormalities in battery packs, wherein the method detects whether the battery pack is abnormal on an in-vehicle device or by linking with a cloud server and based on a battery model.

[0117] Furthermore, this technology can provide a method and apparatus for detecting anomalies in battery packs. The method sets a threshold based on an estimated polarization voltage based on a battery model and compares the actual measured voltage deviation of the battery pack with the threshold to more accurately detect anomalies in the battery pack.

[0118] Furthermore, various effects can be provided directly or indirectly through the present invention.

[0119] Although the invention has been described above with reference to embodiments and accompanying drawings, it is not limited thereto, but can be modified and altered by those skilled in the art without departing from the spirit and scope of the invention as claimed in the appended claims.

[0120] Therefore, the embodiments of the present invention are intended to explain, rather than limit, the technical concept of the invention, and the scope and spirit of the invention are not limited by the above embodiments. The scope of the invention should be interpreted based on the accompanying drawings, and all technical concepts within the scope of the claims should be included within the scope of the invention.

Claims

1. A computing device for diagnosing abnormalities in a battery pack, the computing device comprising: Memory configured to store instructions; as well as A processor configured to perform the following operations by executing the instructions: Set the battery model corresponding to the battery pack; Estimate battery voltage based on the configured battery model; Measure the actual battery voltage; Determine the battery pack voltage deviation based on the actual battery voltage; The model estimation error is determined based on the stated battery voltage and the actual battery voltage. Set a threshold based on the model estimation error; The battery pack voltage deviation is compared with the threshold to determine whether the battery pack is abnormal.

2. The computing device for diagnosing an abnormality of a battery pack according to claim 1, wherein, The processor is configured as follows: The battery pack is identified as abnormal if the battery pack voltage deviation exceeds the threshold.

3. The computing device for diagnosing an abnormality of a battery pack according to claim 1, wherein, The processor is configured as follows: Battery pack voltage deviation is determined based on state of charge deviation and polarization deviation.

4. The computing device for diagnosing an abnormality of a battery pack according to claim 1, wherein, The processor is configured as follows: The model estimation error is defined as the maximum error between the model-estimated voltage and the actual measured voltage.

5. The computing device for diagnosing an anomaly of a battery pack according to claim 4, wherein, The model estimation error includes: The state of charge model error is defined as the difference between the model-estimated open-circuit voltage and the actual measured open-circuit voltage; and Polarization error is defined as the difference between the estimated polarization voltage based on the battery model and the actual measured polarization voltage. The processor is configured as follows: The maximum error value is determined as the sum of the maximum value of the state-of-charge model error and the maximum value of the estimated polarization voltage based on the battery model.

6. The computing device for diagnosing an anomaly of a battery pack according to claim 5, wherein, The processor is configured as follows: The threshold is determined by applying specific weights to the maximum value of the state-of-charge model error and the maximum value of the estimated polarization voltage based on the battery model.

7. The computing device for diagnosing an anomaly of a battery pack according to claim 6, wherein, The processor is configured as follows: The maximum value of the estimated polarization voltage based on the battery model is adaptively determined according to the state of charge prediction; The state of charge prediction includes determining the maximum value of the estimated polarization voltage based on the battery model within the high state of charge boundary region, under the condition of the state of charge prediction plus a predetermined state of charge. The state-of-charge prediction-based method involves determining the maximum value of the polarization voltage estimated based on the battery model within the low state-of-charge boundary region, under the condition that the state-of-charge prediction is subtracted from the predetermined state of charge.

8. The computing device for diagnosing an anomaly of a battery pack according to claim 1, wherein, The processor is configured as follows: The system receives and sets relevant information about the battery model corresponding to the battery pack from the connected cloud server via the network.

9. The computing device for diagnosing abnormalities in a battery pack according to claim 1, wherein, The computing device is implemented as a server in a cloud environment.

10. The computing device for diagnosing abnormalities in a battery pack according to claim 1, wherein, The computing device is implemented as an onboard unit in an electric vehicle.

11. A method for diagnosing anomalies in a battery pack of an electric vehicle in a computing device, the method comprising: Set the battery model corresponding to the battery pack; Estimate battery voltage based on the configured battery model; Measure the actual battery voltage; Determine the battery pack voltage deviation based on the actual battery voltage; The model estimation error is determined based on the stated battery voltage and the actual battery voltage. Set a threshold based on the model estimation error; The battery pack voltage deviation is compared with the threshold to determine whether the battery pack is abnormal.

12. The method of claim 11, further comprising: The battery pack is identified as abnormal if the battery pack voltage deviation exceeds the threshold.

13. The method of claim 11, further comprising: Battery pack voltage deviation is determined based on state of charge deviation and polarization deviation.

14. The method of claim 11, further comprising: The model estimation error is defined as the maximum error between the model-estimated voltage and the actual measured voltage.

15. The method according to claim 14, wherein, The model estimation error includes: The state of charge model error is defined as the difference between the model-estimated open-circuit voltage and the actual measured open-circuit voltage; and Polarization error is defined as the difference between the estimated polarization voltage based on the battery model and the actual measured polarization voltage. The method further includes: The maximum error value is determined as the sum of the maximum value of the state-of-charge model error and the maximum value of the estimated polarization voltage based on the battery model.

16. The method of claim 15, further comprising: The threshold is determined by applying specific weights to the maximum value of the state-of-charge model error and the maximum value of the estimated polarization voltage based on the battery model, respectively.

17. The method of claim 16, further comprising: The maximum value of the estimated polarization voltage based on the battery model is adaptively determined according to the state of charge prediction; The state of charge prediction includes determining the maximum value of the estimated polarization voltage based on the battery model within the high state of charge boundary region, under the condition of the state of charge prediction plus a predetermined state of charge. The state-of-charge prediction-based method involves determining the maximum value of the polarization voltage estimated based on the battery model within the low state-of-charge boundary region, under the condition that the state-of-charge prediction is subtracted from the predetermined state of charge.

18. The method of claim 11, wherein, The computing device is configured to connect with a cloud server via a network, and to receive and set relevant information about the battery model corresponding to the battery pack from the cloud server.

19. The method of claim 11, wherein, The computing device is implemented as a server in a cloud environment.

20. The method of claim 11, wherein, The computing device is implemented as an onboard unit in an electric vehicle.

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