Abnormality identification method and device of battery management system and nonvolatile storage medium

Abstract

The invention discloses an abnormity identification method and device of a battery management system and a nonvolatile storage medium. The method comprises the steps that current pulses corresponding to a target battery are acquired, and the current pulses comprise a charging pulse and a discharging pulse; based on the current pulse and an equivalent circuit model, battery model parameters are determined, the equivalent circuit model is used for simulating the operation state of the target battery, and the battery model parameters comprise ohmic internal resistance, polarization internal resistance and polarization capacitance; estimating the state of charge of the target battery based on the battery model parameters to obtain a first state of charge estimation value; acquiring a second charge state estimation value of the target battery recorded in the target battery management system; and comparing the first state-of-charge estimation value with the second state-of-charge estimation value, and determining an abnormal identification result of the target battery management system. The technical problem that the failure of the BMS battery management system cannot be found in time in the charging process at present is solved.

Description

Technical Field

[0001] This invention relates to the fields of new energy and energy-saving technology, and more specifically, to a method, apparatus, and non-volatile storage medium for identifying anomalies in a battery management system. Background Technology

[0002] With the widespread adoption of electric vehicles (EVs), charging safety has become a major concern, especially fires related to the charging process. These incidents not only threaten personal safety but also cause serious damage to vehicles and charging infrastructure. Statistical analysis shows that 70% of fires during EV charging occur under normal charging conditions, indicating that even in seemingly safe charging environments, significant risks remain. Another 25% of these incidents are caused by malfunctions in the charging equipment itself, while the remaining 5% are related to battery overcharging, where the battery management system (BMS) fails to detect overcharging in time, leading to battery damage and ultimately, a fire.

[0003] Battery Management System (BMS) is a critical component in electric vehicles responsible for monitoring battery status, managing the charging and discharging process, and protecting the battery from overcharging or over-discharging. The BMS ensures the battery operates in optimal condition by estimating parameters such as State of Charge (SOC), State of Health (SOH), and battery temperature, while preventing safety hazards caused by overcharging or over-discharging. However, BMS malfunctions can lead to inaccurate SOC estimations, affecting normal battery management and protection. One common BMS failure mode is that during battery cycling, the BMS stops updating its message data and instead retransmits data from the moment before failure. In this case, if the charging station or charging management system fails to recognize this abnormal state of the BMS in time, it may lead to incorrect execution of the charging strategy, increasing the risk of battery damage or fire.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a method, apparatus, and non-volatile storage medium for identifying anomalies in a battery management system, thereby at least solving the current technical problem of not being able to detect battery management system failures in a timely manner during charging.

[0006] According to one aspect of the present invention, an anomaly identification method for a battery management system is provided, comprising: acquiring a current pulse corresponding to a target battery, wherein the current pulse includes a charging pulse and a discharging pulse; determining battery model parameters based on the current pulse and an equivalent circuit model, wherein the equivalent circuit model is used to simulate the operating state of the target battery, and the battery model parameters include ohmic internal resistance, polarization internal resistance, and polarization capacitance; estimating the state of charge (SOC) of the target battery based on the battery model parameters to obtain a first SOC estimate; acquiring a second SOC estimate of the target battery recorded in the target battery management system; and comparing the first SOC estimate with the second SOC estimate to determine the anomaly identification result of the target battery management system.

[0007] Optionally, based on the current pulse and the equivalent circuit model, the battery model parameters are determined, including: applying the current pulse to the equivalent circuit model and recording the open-circuit voltage change data, current change data, and voltage change data across the polarization resistor in the equivalent circuit model; determining the ohmic internal resistance based on the open-circuit voltage change data and current change data; determining the polarization internal resistance and time constant through fitting calculation based on the current change data and the voltage change data across the polarization resistor, wherein the time constant characterizes the rate of voltage change across the polarization resistor; and determining the polarization capacitance based on the polarization internal resistance and the time constant.

[0008] Alternatively, the equivalent circuit model can be expressed as follows:

[0009]

[0010] Among them, U p R is the polarization voltage. p For polarization internal resistance, C p For polarization capacitors, R o Let I be the internal resistance in ohms, U be the pulse voltage applied to the equivalent circuit model, and I be the internal resistance in ohms. L U is the pulse current applied to the equivalent circuit model. OC This is the open-circuit voltage.

[0011] Optionally, based on battery model parameters, the state of charge (SOC) of the target battery is estimated to obtain a first SOC estimate, including: establishing an initial state-space equation for the target battery regarding its SOC based on the battery model parameters; determining a predicted terminal voltage of the target battery based on the initial state-space equation; obtaining the actual terminal voltage of the target battery; calculating the difference between the actual terminal voltage and the predicted terminal voltage as a first difference; correcting the initial state-space equation based on the first difference; repeating the above steps until the first difference is less than a preset threshold to obtain the target state-space equation; and determining the first SOC estimate based on the target state-space equation.

[0012] Optionally, the initial state-space equations are expressed as follows:

[0013]

[0014] Among them, U p,k For the kTth s The polarization voltage within each sampling period, U p,k+1 For the (k+1)Tth s The polarization voltage R within each sampling period p,k For the kTth s The polarization resistance within each sampling period, C p,k For the kTth s The polarization capacitance R during each sampling period o,k For the kTth s The ohmic internal resistance during each sampling period, U k For the target battery at kT s The terminal voltage within each sampling period, I L,k For the kTth s The current flowing through the target battery within each sampling period, U OC (SOC k ) for the kTth s Open-circuit voltage with respect to SOC within each sampling period k The function, SOC k For the target battery at kT s State of charge (SOC) estimate within each sampling period k+1 For the target battery at (k+1)T s The estimated state of charge over each sampling period, where Q is the maximum usable capacity of the target battery, and ω is the estimated state of charge over each sampling period. k and υ k For the parameter to be corrected, T s One sampling period.

[0015] Optionally, comparing the first state of charge estimate with the second state of charge estimate to determine the anomaly identification result of the target battery management system includes: calculating the difference between the first state of charge estimate and the second state of charge estimate as the second difference; determining whether the second difference is greater than a preset error threshold; and if the second difference is greater than the preset error threshold, determining that the anomaly identification result is that an anomaly exists.

[0016] According to another aspect of the present invention, an anomaly identification device for a battery management system is also provided, comprising: a first acquisition module, configured to acquire a current pulse corresponding to a target battery, wherein the current pulse includes a charging pulse and a discharging pulse; a first determination module, configured to determine battery model parameters based on the current pulse and an equivalent circuit model, wherein the equivalent circuit model is used to simulate the operating state of the target battery, and the battery model parameters include ohmic internal resistance, polarization internal resistance, and polarization capacitance; an estimation module, configured to estimate the state of charge (SOC) of the target battery based on the battery model parameters, thereby obtaining a first SOC estimate; a second acquisition module, configured to acquire a second SOC estimate of the target battery recorded in the target battery management system; and a second determination module, configured to compare the first SOC estimate with the second SOC estimate to determine the anomaly identification result of the target battery management system.

[0017] According to another aspect of the present invention, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored program, wherein, when the program is running, it controls the device where the non-volatile storage medium is located to execute any of the above-described battery management system anomaly identification methods.

[0018] According to another aspect of the present invention, a computer device is also provided, the computer device including a processor, the processor being configured to run a program, wherein the program executes any of the above-described methods for identifying anomalies in a battery management system.

[0019] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements any of the above-described methods for anomaly identification of a battery management system.

[0020] In this embodiment of the invention, an anomaly identification method for a battery management system is employed. This method involves acquiring the current pulse corresponding to the target battery, where the current pulse includes a charging pulse and a discharging pulse; determining battery model parameters based on the current pulse and an equivalent circuit model, where the equivalent circuit model simulates the operating state of the target battery, and the battery model parameters include ohmic internal resistance, polarization internal resistance, and polarization capacitance; estimating the state of charge (SOC) of the target battery based on the battery model parameters to obtain a first SOC estimate; acquiring a second SOC estimate of the target battery recorded in the target battery management system; and comparing the first SOC estimate with the second SOC estimate to determine the anomaly identification result of the target battery management system. This achieves the goal of effectively identifying abnormal situations in the battery management system, thereby improving the reliability of the battery management system and solving the current technical problem of not being able to detect BMS battery management system failures in a timely manner during charging. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0022] Figure 1 A hardware structure block diagram of a computer terminal for implementing an anomaly detection method for a battery management system is shown.

[0023] Figure 2 This is a flowchart illustrating an anomaly identification method for a battery management system provided according to an embodiment of the present invention;

[0024] Figure 3 This is a schematic diagram of an HPPC standard test current provided by an optional embodiment of the present invention;

[0025] Figure 4 This is a schematic diagram of a Thevenin equivalent circuit model provided by an optional embodiment of the present invention;

[0026] Figure 5 This is a schematic diagram of a process for joint estimation of model parameters and SOC provided by an optional embodiment of the present invention;

[0027] Figure 6 This is a schematic diagram of a pulse current provided according to an optional embodiment of the present invention;

[0028] Figure 7 This is a schematic diagram of a battery terminal voltage variation according to an optional embodiment of the present invention;

[0029] Figure 8 This is a schematic diagram of an EKF-based SOC estimation process provided by an optional embodiment of the present invention;

[0030] Figure 9 This is a schematic diagram of the SOC estimation principle based on EKF provided by an optional embodiment of the present invention;

[0031] Figure 10 This is a structural block diagram of an anomaly identification device for a battery management system provided according to an embodiment of the present invention. Detailed Implementation

[0032] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0034] According to an embodiment of the present invention, an embodiment of an anomaly identification method for a battery management system is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0035] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware block diagram of a computer terminal for implementing an anomaly detection method in a battery management system is shown. Figure 1 As shown, the computer terminal 10 may include one or more processors (shown as 102a, 102b, ..., 102n in the figure) (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0036] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be implemented wholly or partially as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be wholly or partially integrated into any other element in the computer terminal 10. As involved in the embodiments of this application, the data processing circuits serve as processor control (e.g., selection of a variable resistor termination path connected to an interface).

[0037] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the anomaly identification method of the battery management system in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby implementing the anomaly identification method of the battery management system described above. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0038] The display may be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10.

[0039] Figure 2 This is a flowchart illustrating an anomaly identification method for a battery management system provided according to an embodiment of the present invention, as shown below. Figure 2 As shown, the method includes the following steps:

[0040] Step S201: Obtain the current pulse corresponding to the target battery, wherein the current pulse includes a charging pulse and a discharging pulse.

[0041] In this step, during the electric vehicle charging process, based on the HPPC testing principle, a simplified HPPC test is performed on the battery pack at the beginning of charging by applying a current pulse consisting of a charging pulse and a discharging pulse combination. This allows for a rapid estimation of ohmic resistance and polarization impedance. The purpose of the standard HPPC (Hybrid Pulse Power Characterization) test is to determine the dynamic power capability of the battery pack under current pulse conditions, including the 10-second charging power and 10-second discharging power. Furthermore, by processing the current-voltage curves of the HPPC test data, the relationship between the ohmic resistance and polarization impedance of the cells and their state of charge (SOC) can be obtained. HPPC testing can also be used to evaluate the aging characteristics of the battery's internal resistance during battery aging tests. Figure 3 This is a schematic diagram of an HPPC standard test current provided by an optional embodiment of the present invention, such as... Figure 3 As shown, the standard HPPC test consists of multiple sets of current pulse tests, with each set of current pulse tests having a 10% difference in SOC.

[0042] Step S202: Based on the current pulse and the equivalent circuit model, determine the battery model parameters. The equivalent circuit model is used to simulate the operating state of the target battery. The battery model parameters include ohmic internal resistance, polarization internal resistance, and polarization capacitance.

[0043] In this step, specific current pulses (including charging and discharging pulses) emitted by the charging station, combined with an equivalent circuit model, can be used to estimate and determine key battery parameters, thereby enabling a more accurate understanding of the battery's operating state and performance. The equivalent circuit model can be the Thevenin equivalent model, a simplified model used to simulate the internal dynamic characteristics of a battery. Figure 4 This is a schematic diagram of a Thevenin equivalent circuit model provided by an optional embodiment of the present invention, such as... Figure 4 As shown, the behavior of a complex battery system can be simplified to an ideal voltage source, a series resistor, and a parallel RC circuit. By analyzing the battery's voltage response to a pulse current, the battery's ohmic internal resistance, polarization internal resistance, and polarization capacitance can be calculated. These battery model parameters are crucial for accurately estimating the battery's state of charge (SOC). At the charging station, the SOC can be estimated using the DEKF (Dual Extended Kalman Filter) algorithm combined with the real-time acquired battery model parameters. By comparing the SOC reported by the charging station, if the difference exceeds a preset threshold (e.g., 8%), it can be determined that the BMS has malfunctioned or the estimation error is too large.

[0044] Step S203: Based on the battery model parameters, estimate the state of charge of the target battery to obtain the first estimated state of charge value.

[0045] In this step, after issuing a low-frequency current measurement pulse at the beginning of charging, battery model parameters can be obtained through the battery voltage change curve. Next, the EKF (Extended Kalman Filter) algorithm can be used to continuously track and estimate the SOC of the target battery using these real-time battery model parameters, ultimately obtaining the first estimated state of charge (SOC). Combining online parameter identification and SOC estimation based on EKF to construct a joint online estimation method for model parameters and SOC allows battery parameters to better simulate the actual operating state of the battery, improving the accuracy of battery SOC estimation. This method can estimate the current model parameters in real time according to the operating conditions and use the parameters for SOC estimation.

[0046] Figure 5 This is a schematic diagram of a process for joint estimation of model parameters and SOC provided by an optional embodiment of the present invention, such as... Figure 5 As shown, excluding the state variable U p And SOC, input variable I L Along with the output variable U, the variables involved in SOC estimation include the ohmic internal resistance R. o Polarization internal resistance R p Polarization capacitor C p Open circuit voltage U OC And the maximum available capacity Q. Regarding the open-circuit voltage U... OC The open-circuit voltage value has been expressed as a polynomial in terms of SOC. At each time step, the current SOC is substituted into the polynomial to obtain the open-circuit voltage value. The remaining parameter is the ohmic internal resistance R. o Polarization internal resistance R p and polarization capacitor C p Because the model is highly susceptible to changes in operating conditions, temperature, and State of Charge (SOC), a real-time online parameter identification method based on Extended Kalman Filter (EKF) is used for identification. The SOC is then estimated using the parameters obtained from the online identification. This results in a joint online estimation method for model parameters and SOC. This method utilizes two EKF algorithms, which can be named the Dual Extended Kalman Filter (DEKF) algorithm.

[0047] Step S204: Obtain the second state-of-charge estimate of the target battery recorded in the target battery management system.

[0048] In this step, one of the main responsibilities of the Battery Management System (BMS) is to monitor and manage the battery's state of charge (SOC), which helps prevent overcharging or over-discharging and optimizes the energy efficiency of the electric vehicle. The second SOC estimate refers to the battery's SOC as estimated by the BMS during normal operation. This is typically based on a series of algorithms and models within the BMS that integrate various signals such as battery voltage, current, and temperature, as well as historical charge and discharge data.

[0049] Step S205: Compare the first state of charge estimate with the second state of charge estimate to determine the anomaly identification result of the target battery management system.

[0050] In this step, after obtaining the first state of charge (SOC) estimate, it is compared with the second SOC estimate obtained from the BMS. This comparison process aims to detect the difference between the two independent estimates to determine whether the BMS's SOC estimation function is functioning correctly. If the difference between the first and second SOC estimates is within a reasonable range (e.g., less than a certain percentage threshold, such as 8%), then the BMS's SOC estimation can be considered reliable. However, if the difference exceeds the predetermined threshold, this may indicate a malfunction or error in the BMS, leading to inaccurate SOC estimation.

[0051] Through the above steps, the goal of effectively identifying abnormal conditions in the battery management system is achieved, thereby improving the reliability of the battery management system and solving the current technical problem of not being able to detect battery management system failures in a timely manner during charging.

[0052] As an optional embodiment, the battery model parameters are determined based on current pulses and an equivalent circuit model, including: applying current pulses to the equivalent circuit model and recording open-circuit voltage change data, current change data, and voltage change data across the polarization resistor in the equivalent circuit model; determining the ohmic internal resistance based on the open-circuit voltage change data and current change data; determining the polarization internal resistance and time constant through fitting calculation based on the current change data and the voltage change data across the polarization resistor, wherein the time constant characterizes the rate of voltage change across the polarization resistor; and determining the polarization capacitance based on the polarization internal resistance and the time constant.

[0053] Optionally, Figure 6 This is a schematic diagram of a pulse current provided by an optional embodiment of the present invention. Figure 7This is a schematic diagram of the change in battery terminal voltage according to an optional embodiment of the present invention. As shown in the figure, according to the Thevenin equivalent circuit model and its circuit relationship, when a current pulse is applied to the battery, the parallel RC circuit containing the energy storage element will not change abruptly at the beginning. Only the ohmic internal resistance in the battery model will cause the terminal voltage to change abruptly, with a value of ΔU1. Since the voltage rise time is very short, this voltage change is difficult to measure. When the current pulse ends, the battery terminal voltage will drop rapidly due to the disappearance of the current.

[0054] The voltage drop is U2-U1, which is equal to ΔU1. The ohmic internal resistance can be obtained by calculating the ratio of the voltage drop to the amplitude of the current pulse, as shown in the following expression:

[0055]

[0056] For polarization resistance and polarization capacitance, a relationship is established in the time domain, where U p For R p Terminal voltage, τ a The time constant τ is the time constant of a first-order RC inertial element. This time constant τ can be obtained by fitting the charger's output voltage data. a The calculation formula is as follows:

[0057]

[0058] Where t equals t3-t2, U p (0) represents the terminal voltage value U1 at time t2. p This is the terminal voltage value at time t3.

[0059] Similarly, the polarization resistance of a first-order inertial element can be obtained through curve fitting, and the calculation formula is as follows:

[0060]

[0061] When τ is calculated a Then, by querying the terminal voltage value U2 at time t2 and substituting it with the pulse current value, R can be calculated. p The polarization capacitance is calculated using the time constant formula, as follows:

[0062] τ a =R p ·C p

[0063]

[0064] Based on the above principle, when a combined current pulse of a charging pulse and a discharging pulse is applied, the charging impedance and discharging impedance of the battery pack can be obtained respectively, thereby improving the identification accuracy of the battery pack model parameters and further improving the SOC estimation accuracy.

[0065] As an optional embodiment, the equivalent circuit model is expressed as follows:

[0066]

[0067] Among them, U p R is the polarization voltage. p For polarization internal resistance, C p For polarization capacitors, R o Let I be the internal resistance in ohms, U be the pulse voltage applied to the equivalent circuit model, and I be the internal resistance in ohms. L U is the pulse current applied to the equivalent circuit model. OC This is the open-circuit voltage.

[0068] Optionally, this expression illustrates how the battery terminal voltage is jointly determined by the open-circuit voltage, the instantaneous voltage drop due to the ohmic internal resistance, and the voltage drop caused by polarization. During battery charging or discharging, changes in current lead to instantaneous voltage drops and polarization voltage drops, both of which are reflected in the battery terminal voltage. In fault diagnosis, charging involves emitting pulsed current, and the battery's model parameters, particularly the polarization internal resistance R, are identified based on the response of the battery terminal voltage. p and polarization capacitor C p When a current pulse is applied to the battery, the battery's terminal voltage changes; this change includes the instantaneous voltage drop (caused by R). o Caused by) and hysteresis voltage drop (by R) p and C p (Caused by the RC circuit). By analyzing the battery terminal voltage change curve over time, the values ​​of these model parameters can be extracted.

[0069] As an optional embodiment, the state of charge (SOC) of the target battery is estimated based on battery model parameters to obtain a first SOC estimate, including: establishing an initial state-space equation for the target battery regarding its SOC based on the battery model parameters; determining a predicted terminal voltage of the target battery based on the initial state-space equation; obtaining the actual terminal voltage of the target battery; calculating the difference between the actual terminal voltage and the predicted terminal voltage as a first difference; correcting the initial state-space equation based on the first difference; repeating the above steps until the first difference is less than a preset threshold to obtain the target state-space equation; and determining the first SOC estimate based on the target state-space equation.

[0070] Optionally, Figure 8This is a schematic diagram of an EKF-based SOC estimation process provided by an optional embodiment of the present invention, as shown below. Figure 8 As shown, online estimation of the state of charge (SOC) using the extended Kalman filter algorithm requires constructing a state-space equation with SOC as the state variable to be estimated. The SOC calculation formula using the ampere-hour integral method is as follows:

[0071]

[0072] Where Q is the maximum usable capacity of the battery, and η is the charge-discharge coulombic efficiency. When the coulombic efficiency is ignored, η is taken as 1.

[0073] Assuming current I L (t) remains constant within a sampling period [kTs, (k+1)Ts) and is equal to the instantaneous value of the previous sampling time. When time t0 = kTs, t = (k+1)Ts, I L (t)=I L When (kTs) is equal to 1, we have:

[0074]

[0075] Taking Ts = 1s, we obtain the recursive formula for calculating SOC:

[0076]

[0077] By combining the equivalent circuit model formulas, we can construct a circuit with [U] p SOC T State-space equations for state variables:

[0078]

[0079] Among them, U OC (SOC k ) represents the open-circuit voltage U OC Functions related to SOC.

[0080] The state transition matrix after linearization using Taylor series is:

[0081]

[0082] The linearized observation matrix is ​​as follows:

[0083]

[0084] Perform an observability test on it, observability matrix It can be found that there is constancy. and Therefore, Q is full rank, and the state of the equation is observable.

[0085] Furthermore, the OCV-SOC curve, as a crucial parameter in the battery model, plays a decisive role in the state-of-charge (SOC) estimation results. Among several factors affecting the accuracy of SOC estimation, such as the OCV-SOC curve, internal resistance R, and capacity Q, the OCV-SOC curve has the greatest impact. Therefore, obtaining an accurate OCV-SOC curve is essential. To avoid errors introduced by linear interpolation and to facilitate derivative calculations... To find the function U OC (SOC k The OCV-SOC curves obtained offline were fitted using a polynomial.

[0086] Although online parameter identification can obtain U at each time step OC,k The value is given, but it cannot be directly substituted into the output equation for terminal voltage calculation. If the value is obtained directly from identification instead of using a function, the observability matrix of the system has rank Q = 1. According to the observability criterion, the observability matrix is ​​not full rank, and the state is not completely observable. Therefore, U OC Since real-time identification values ​​cannot be used directly, polynomial fitting of the OCV-SOC curve is a necessary condition for SOC estimation.

[0087] Figure 9 This is a schematic diagram of the SOC estimation principle based on EKF according to an optional embodiment of the present invention, such as... Figure 9 As shown, the extended Kalman filter algorithm is applied to estimate the state of charge (SOC) of a battery. Essentially, it uses the ampere-hour integral method to calculate the SOC, while simultaneously using the error between the actual measured value and the model prediction of the battery terminal voltage to correct the calculation result of the ampere-hour integral method. When this error is large, a large correction term is generated, accelerating the correction of the SOC estimate. This method overcomes the drawbacks of the ampere-hour integral method, such as requiring accurate initial values ​​and being prone to accumulating errors. It can converge quickly even with large initial errors and achieve good estimation results even under dynamic operating conditions with drastic current changes.

[0088] As an optional embodiment, the initial state-space equations are expressed as follows:

[0089]

[0090] Among them, U p,k For the kTth s The polarization voltage within each sampling period, U p,k+1 For the (k+1)Tth s The polarization voltage R within each sampling period p,k For the kTth s The polarization resistance within each sampling period, C p,k For the kTth s The polarization capacitance R during each sampling periodo,k For the kTth s The ohmic internal resistance during each sampling period, U k For the target battery at kT s The terminal voltage within each sampling period, I L,k For the kTth s The current flowing through the target battery within each sampling period, U OC (SOC k ) for the kTth s Open-circuit voltage with respect to SOC within each sampling period k The function, SOC k For the target battery at kT s State of charge (SOC) estimate within each sampling period k+1 For the target battery at (k+1)T s The estimated state of charge over each sampling period, where Q is the maximum usable capacity of the target battery, and ω is the estimated state of charge over each sampling period. k and υ k For the parameter to be corrected, T s One sampling period.

[0091] Optionally, state-space equations are a key tool in modern control theory and signal processing for describing the dynamic characteristics of systems. They characterize the system's behavior through a series of state variables and the mathematical relationships between them. Based on the aforementioned state-space model, the Extended Kalman Filter (EKF) can be used for online estimation of the battery's SOC and model parameters, such as R... o R p and C p EKF locally linearizes the nonlinear model at the current state point and then applies a Kalman filter algorithm to correct the state estimate. DEKF further combines parameter estimation and SOC estimation to form a more complex joint estimation process, thereby improving the accuracy and reliability of the estimation.

[0092] As an optional embodiment, comparing the first state of charge estimate with the second state of charge estimate to determine the anomaly identification result of the target battery management system includes: calculating the difference between the first state of charge estimate and the second state of charge estimate as a second difference; determining whether the second difference is greater than a preset error threshold; and if the second difference is greater than the preset error threshold, determining that the anomaly identification result is that an anomaly exists.

[0093] Optionally, when the second difference exceeds a preset error threshold, it indicates a significant discrepancy between the SOC estimate at the charging station and the SOC estimate at the BMS, which typically suggests a potential BMS malfunction. In this case, the charging station will record the anomaly identification result, indicating an anomaly exists. This could include issues such as inaccurate SOC algorithm in the BMS, faulty internal sensors in the BMS, or communication errors between the BMS and the charging station. Once an anomaly is confirmed, the charging station or vehicle control system will take appropriate action. For example, interrupting the charging process to prevent potential hazards, such as fires caused by battery overcharging; recording the anomaly for subsequent analysis and maintenance; activating a backup SOC estimation mechanism until the BMS problem is resolved; and sending a warning message to the driver or service center, recommending BMS inspection and repair.

[0094] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0095] Through the above description of the embodiments, those skilled in the art can clearly understand that the anomaly identification method of the battery management system according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0096] According to embodiments of the present invention, an anomaly identification device for a battery management system for implementing the above-described anomaly identification method for a battery management system is also provided. Figure 10 This is a structural block diagram of an anomaly detection device for a battery management system provided according to an embodiment of the present invention, such as... Figure 10 As shown, the device includes: a first acquisition module 1001, a first determination module 1002, an estimation module 1003, a second acquisition module 1004, and a second determination module 1005. The device will be described below.

[0097] The first acquisition module 1001 is used to acquire the current pulse corresponding to the target battery, wherein the current pulse includes a charging pulse and a discharging pulse.

[0098] The first determining module 1002, connected to the first acquiring module 1001, is used to determine the battery model parameters based on the current pulse and the equivalent circuit model. The equivalent circuit model is used to simulate the operating state of the target battery, and the battery model parameters include ohmic internal resistance, polarization internal resistance, and polarization capacitance.

[0099] The estimation module 1003, connected to the first determination module 1002, is used to estimate the state of charge of the target battery based on the battery model parameters, and obtain a first state of charge estimate.

[0100] The second acquisition module 1004, connected to the estimation module 1003, is used to acquire the second state-of-charge estimate of the target battery recorded in the target battery management system.

[0101] The second determining module 1005, connected to the second acquiring module 1004, is used to compare the first state of charge estimate with the second state of charge estimate to determine the anomaly identification result of the target battery management system.

[0102] It should be noted that the first acquisition module 1001, the first determination module 1002, the estimation module 1003, the second acquisition module 1004, and the second determination module 1005 mentioned above correspond to steps S201 to S205 in the embodiments. Multiple modules and their corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in the above embodiments. It should also be noted that the above modules, as part of the device, can run on the computer terminal 10 provided in the embodiments.

[0103] Embodiments of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one of a plurality of network devices in a computer network. The computer device includes a memory and a processor.

[0104] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the anomaly identification method and device of the battery management system in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby realizing the aforementioned anomaly identification method of the battery management system. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to a computer terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0105] The processor can access information and application programs stored in the memory via a transmission device to perform the following steps: acquiring the current pulse corresponding to the target battery, wherein the current pulse includes a charging pulse and a discharging pulse; determining the battery model parameters based on the current pulse and the equivalent circuit model, wherein the equivalent circuit model is used to simulate the operating state of the target battery, and the battery model parameters include ohmic internal resistance, polarization internal resistance, and polarization capacitance; estimating the state of charge (SOC) of the target battery based on the battery model parameters to obtain a first SOC estimate; acquiring the second SOC estimate of the target battery recorded in the target battery management system; and comparing the first SOC estimate with the second SOC estimate to determine the anomaly identification result of the target battery management system.

[0106] Optionally, the processor may also execute program code for the following steps: determining battery model parameters based on current pulses and an equivalent circuit model, including: applying current pulses to the equivalent circuit model and recording open-circuit voltage change data, current change data, and voltage change data across the polarization resistor in the equivalent circuit model; determining the ohmic internal resistance based on the open-circuit voltage change data and current change data; determining the polarization internal resistance and time constant through fitting calculation based on the current change data and the voltage change data across the polarization resistor, wherein the time constant characterizes the rate of voltage change across the polarization resistor; and determining the polarization capacitance based on the polarization internal resistance and the time constant.

[0107] Optionally, the processor described above can also execute program code with the following steps: The expression for the equivalent circuit model is as follows:

[0108]

[0109] Among them, U p R is the polarization voltage. p For polarization internal resistance, C p For polarization capacitors, R oLet I be the internal resistance in ohms, U be the pulse voltage applied to the equivalent circuit model, and I be the internal resistance in ohms. L U is the pulse current applied to the equivalent circuit model. OC This is the open-circuit voltage.

[0110] Optionally, the processor may also execute program code for the following steps: estimating the state of charge (SOC) of the target battery based on battery model parameters to obtain a first SOC estimate, including: establishing an initial state-space equation for the target battery regarding its SOC based on battery model parameters; determining a predicted terminal voltage value for the target battery based on the initial state-space equation; obtaining the actual terminal voltage value of the target battery; calculating the difference between the actual terminal voltage value and the predicted terminal voltage value as a first difference; correcting the initial state-space equation based on the first difference; repeating the above steps until the first difference is less than a preset threshold to obtain the target state-space equation; and determining the first SOC estimate based on the target state-space equation.

[0111] Optionally, the processor described above can also execute program code with the following steps: The expression for the initial state space equation is as follows:

[0112]

[0113] Among them, U p,k For the kTth s The polarization voltage within each sampling period, U p,k+1 For the (k+1)Tth s The polarization voltage R within each sampling period p,k For the kTth s The polarization resistance within each sampling period, C p,k For the kTth s The polarization capacitance R during each sampling period o,k For the kTth s The ohmic internal resistance during each sampling period, U k For the target battery at kT s The terminal voltage within each sampling period, I L,k For the kTth s The current flowing through the target battery within each sampling period, U OC (SOC k ) for the kTth s Open-circuit voltage with respect to SOC within each sampling period k The function, SOC k For the target battery at kT s State of charge (SOC) estimate within each sampling period k+1 For the target battery at (k+1)T s The estimated state of charge over each sampling period, where Q is the maximum usable capacity of the target battery, and ω is the estimated state of charge over each sampling period.k and υ k For the parameter to be corrected, T s One sampling period.

[0114] Optionally, the processor may also execute program code that performs the following steps: comparing the first state of charge estimate with the second state of charge estimate to determine the anomaly identification result of the target battery management system, including: calculating the difference between the first state of charge estimate and the second state of charge estimate as the second difference; determining whether the second difference is greater than a preset error threshold; and if the second difference is greater than the preset error threshold, determining that the anomaly identification result is that an anomaly exists.

[0115] This invention provides an anomaly identification method for a battery management system. It involves acquiring a current pulse corresponding to the target battery, where the current pulse includes a charging pulse and a discharging pulse; determining battery model parameters based on the current pulse and an equivalent circuit model, where the equivalent circuit model simulates the operating state of the target battery, and the battery model parameters include ohmic internal resistance, polarization internal resistance, and polarization capacitance; estimating the state of charge (SOC) of the target battery based on the battery model parameters to obtain a first SOC estimate; acquiring a second SOC estimate of the target battery recorded in the target battery management system; and comparing the first SOC estimate with the second SOC estimate to determine the anomaly identification result of the target battery management system. This effectively identifies abnormal situations in the battery management system, thereby improving the reliability of the battery management system and solving the current technical problem of not being able to detect battery management system failures in a timely manner during charging.

[0116] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a non-volatile storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc.

[0117] Embodiments of the present invention also provide a non-volatile storage medium. Optionally, in this embodiment, the aforementioned non-volatile storage medium can be used to store the program code executed by the anomaly identification method of the battery management system provided in the above embodiments.

[0118] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0119] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: acquiring a current pulse corresponding to the target battery, wherein the current pulse includes a charging pulse and a discharging pulse; determining battery model parameters based on the current pulse and an equivalent circuit model, wherein the equivalent circuit model is used to simulate the operating state of the target battery, and the battery model parameters include ohmic internal resistance, polarization internal resistance, and polarization capacitance; estimating the state of charge of the target battery based on the battery model parameters to obtain a first state of charge estimate; acquiring a second state of charge estimate of the target battery recorded in the target battery management system; and comparing the first state of charge estimate with the second state of charge estimate to determine the anomaly identification result of the target battery management system.

[0120] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining battery model parameters based on current pulses and an equivalent circuit model, including: applying current pulses to the equivalent circuit model and recording open-circuit voltage change data, current change data, and voltage change data across the polarization resistor in the equivalent circuit model; determining the ohmic internal resistance based on the open-circuit voltage change data and current change data; determining the polarization internal resistance and time constant through fitting calculation based on the current change data and the voltage change data across the polarization resistor, wherein the time constant characterizes the rate of voltage change across the polarization resistor; and determining the polarization capacitance based on the polarization internal resistance and the time constant.

[0121] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: The expression of the equivalent circuit model is as follows:

[0122]

[0123] Among them, U p R is the polarization voltage. p For polarization internal resistance, C p For polarization capacitors, R o Let I be the internal resistance in ohms, U be the pulse voltage applied to the equivalent circuit model, and I be the internal resistance in ohms. L U is the pulse current applied to the equivalent circuit model. OC This is the open-circuit voltage.

[0124] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: estimating the state of charge (SOC) of the target battery based on battery model parameters to obtain a first SOC estimate, including: establishing an initial state-space equation for the target battery regarding its SOC based on the battery model parameters; determining a predicted terminal voltage of the target battery based on the initial state-space equation; obtaining the actual terminal voltage of the target battery; calculating the difference between the actual terminal voltage and the predicted terminal voltage as a first difference; correcting the initial state-space equation based on the first difference; repeating the above steps until the first difference is less than a preset threshold to obtain a target state-space equation; and determining the first SOC estimate based on the target state-space equation.

[0125] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: The expression for the initial state-space equation is as follows:

[0126]

[0127] Among them, U p,k For the kTth s Polarization voltage within each sampling period, U p,k+1 For the (k+1)Tth s The polarization voltage R within each sampling period p,k For the kTth s The polarization resistance within each sampling period, C p,k For the kTth s The polarization capacitance R during each sampling period o,k For the kTth s The ohmic internal resistance during each sampling period, U k For the target battery at kT s The terminal voltage within each sampling period, I L,k For the kTth s The current flowing through the target battery within each sampling period, U OC (SOC k ) for the kTth s Open-circuit voltage with respect to SOC within each sampling period k The function, SOC k For the target battery at kT s State of charge (SOC) estimate within each sampling period k+1 For the target battery at (k+1)T s The estimated state of charge over each sampling period, where Q is the maximum usable capacity of the target battery, and ω is the estimated state of charge over each sampling period. k and υ k For the parameter to be corrected, T s One sampling period.

[0128] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: comparing a first state-of-charge estimate with a second state-of-charge estimate to determine the anomaly identification result of the target battery management system, including: calculating the difference between the first state-of-charge estimate and the second state-of-charge estimate as a second difference; determining whether the second difference is greater than a preset error threshold; and if the second difference is greater than the preset error threshold, determining that the anomaly identification result is that an anomaly exists.

[0129] Embodiments of the present invention also provide a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, it can: acquire a current pulse corresponding to a target battery, wherein the current pulse includes a charging pulse and a discharging pulse; determine battery model parameters based on the current pulse and an equivalent circuit model, wherein the equivalent circuit model is used to simulate the operating state of the target battery, and the battery model parameters include ohmic internal resistance, polarization internal resistance, and polarization capacitance; estimate the state of charge of the target battery based on the battery model parameters to obtain a first state of charge estimate; acquire a second state of charge estimate of the target battery recorded in the target battery management system; and compare the first state of charge estimate with the second state of charge estimate to determine the anomaly identification result of the target battery management system.

[0130] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0131] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0132] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0133] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0134] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0135] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0136] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for anomaly identification in a battery management system, characterized in that, include: Obtain the current pulse corresponding to the target battery, wherein the current pulse includes a charging pulse and a discharging pulse; Based on the current pulse and equivalent circuit model, the battery model parameters are determined, wherein the equivalent circuit model is used to simulate the operating state of the target battery, and the battery model parameters include ohmic internal resistance, polarization internal resistance, and polarization capacitance. Based on the battery model parameters, the state of charge of the target battery is estimated to obtain a first state of charge estimate. Obtain the second state-of-charge estimate of the target battery recorded in the target battery management system; The first state of charge estimate is compared with the second state of charge estimate to determine the anomaly identification result of the target battery management system.

2. The method according to claim 1, characterized in that, The process of determining battery model parameters based on the current pulse and equivalent circuit model includes: The current pulse is applied to the equivalent circuit model, and the open-circuit voltage change data, current change data, and voltage change data across the polarization resistor in the equivalent circuit model are recorded. The ohmic internal resistance is determined based on the open-circuit voltage change data and the current change data; Based on the current change data and the voltage change data across the polarization resistor, the polarization internal resistance and time constant are determined by fitting calculation, wherein the time constant characterizes the rate of voltage change across the polarization resistor; The polarization capacitance is determined based on the polarization internal resistance and the time constant.

3. The method according to claim 1, characterized in that, The equivalent circuit model is expressed as follows: Among them, U p R is the polarization voltage. p C is the polarization internal resistance. p For the polarization capacitor, R o Let I be the ohmic internal resistance, U be the pulse voltage applied to the equivalent circuit model, and I be the internal resistance. L U is the pulse current applied to the equivalent circuit model. OC This is the open-circuit voltage.

4. The method according to claim 1, characterized in that, The step of estimating the state of charge (SOC) of the target battery based on the battery model parameters to obtain a first SOC estimate includes: Based on the battery model parameters, the initial state-space equations of the target battery with respect to its state of charge are established. Based on the initial state-space equation, the predicted terminal voltage of the target battery is determined; Obtain the actual terminal voltage value of the target battery; The difference between the actual value of the terminal voltage and the predicted value of the terminal voltage is calculated as the first difference value; Based on the first difference, the initial state space equation is corrected; Repeat the above steps until the first difference is less than a preset threshold to obtain the target state space equation; Based on the target state-space equation, the estimated value of the first charged state is determined.

5. The method according to claim 4, characterized in that, The expression for the initial state space equation is as follows: Among them, U p,k For the kTth s The polarization voltage within each sampling period, U p,k+1 For the (k+1)Tth s The polarization voltage R within each sampling period p,k For the kTth s The polarization resistance within each sampling period, C p,k For the kTth s The polarization capacitance R during each sampling period o,k For the kTth s The ohmic internal resistance during each sampling period, U k For the target battery at the kT-th s The terminal voltage within each sampling period, I L,k For the kTth s The current flowing through the target battery within each sampling period, U OC (SOC k ) is the kTth s Open-circuit voltage with respect to SOC within each sampling period k The function, SOC k For the target battery at the kT-th s State of charge (SOC) estimate within each sampling period k+1 For the target battery at the (k+1)Tth s The estimated state of charge over each sampling period, Q is the maximum usable capacity of the target battery, ω k and υ k For the parameter to be corrected, T s One sampling period.

6. The method according to claim 1, characterized in that, The step of comparing the first state-of-charge estimate with the second state-of-charge estimate to determine the anomaly identification result of the target battery management system includes: The difference between the first state-of-charge estimate and the second state-of-charge estimate is calculated as the second difference. Determine whether the second difference is greater than a preset error threshold; If the second difference is greater than the preset error threshold, the anomaly identification result is determined to be abnormal.

7. An anomaly detection device for a battery management system, characterized in that, include: The first acquisition module is used to acquire the current pulse corresponding to the target battery, wherein the current pulse includes a charging pulse and a discharging pulse; The first determining module is used to determine battery model parameters based on the current pulse and the equivalent circuit model, wherein the equivalent circuit model is used to simulate the operating state of the target battery, and the battery model parameters include ohmic internal resistance, polarization internal resistance, and polarization capacitance. The estimation module is used to estimate the state of charge of the target battery based on the battery model parameters, and obtain a first state of charge estimate. The second acquisition module is used to acquire the second state-of-charge estimate of the target battery recorded in the target battery management system; The second determining module is used to compare the first state of charge estimate with the second state of charge estimate to determine the anomaly identification result of the target battery management system.

8. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the non-volatile storage medium to perform the anomaly identification method of the battery management system according to any one of claims 1 to 7.

9. A computer device, characterized in that, include: Memory and processor The memory stores computer programs; The processor is configured to execute a computer program stored in the memory, wherein when the computer program is executed, the processor performs the anomaly identification method of the battery management system according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the anomaly identification method of the battery management system according to any one of claims 1 to 6.

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