Battery open-circuit voltage online acquisition method and battery management system

Abstract

The invention relates to the technical field of battery management, and discloses a battery open-circuit voltage online acquisition method and a battery management system. The method comprises the following steps: identifying a current disturbance event in a battery operation process; collecting time-varying data of battery voltage in a recovery stage after current disturbance; according to the time-varying data of the battery voltage, obtaining the open-circuit voltage of the battery through a fitting extrapolation method; wherein the recovery stage is a time period when the absolute value of the battery current is reduced to be below a set threshold value; according to the method, the open-circuit voltage of the battery is estimated through short-term recovery extrapolation after current disturbance, so that the technical bottleneck that the open-circuit voltage can be accurately obtained only after the battery stands for a long time in a traditional method is broken through, and real online self-calibration and dynamic updating of the open-circuit voltage of the battery are realized; the open-circuit voltage of the battery can be obtained in real time in the normal running process of the vehicle, and the battery does not need to be interrupted for long-time standing test.

Description

Technical Field

[0001] This invention relates to the field of battery management technology, and in particular to a method for online acquisition of battery open-circuit voltage and a battery management system. Background Technology

[0002] Open circuit voltage (OCV) is a key parameter in battery management. Accurately obtaining OCV is crucial for estimating the state of charge (SOC), diagnosing the state of health (SOH), and managing battery safety.

[0003] Current methods for obtaining battery open-circuit voltage primarily employ a static testing approach. This method involves allowing the battery to stand for 2-12 hours after disconnecting the load, enabling the internal electrochemical reactions to reach equilibrium before measuring the voltage across the battery terminals as the open-circuit voltage. However, this method of obtaining battery open-circuit voltage has the following problems:

[0004] 1. The static testing time is too long, which cannot meet the needs of online real-time applications and seriously restricts the battery;

[0005] 2. The curves calibrated offline have a fixed shape. However, the characteristics of the battery will drift and change due to factors such as aging and temperature changes in actual applications. The curves calibrated offline cannot adapt to the characteristic drift of the battery in actual operation.

[0006] In addition, although existing technologies also use offline model parameters to calculate the battery open-circuit voltage to avoid static testing, they use preset offline model parameters and lack real-time online identification capabilities. Furthermore, the battery open-circuit voltage is obtained through model calculation rather than extrapolation estimation based on the actual voltage recovery process.

[0007] Existing technologies also employ, for example Figure 1 The flowchart of the online algorithm shown only adjusts the R / C parameters and does not dynamically correct the shape of the OCV-SOC and internal resistance-SOC curves. This leads to long-term error accumulation, resulting in a lack of dynamic update capability for the obtained OCV-SOC and internal resistance-SOC curve shapes. It is difficult to meet the requirements for high-precision OCV-SOC and internal resistance-SOC curve shapes, which limits the self-calibration capability of the battery management system under dynamic operating conditions and affects the accuracy of SOC estimation and battery safety.

[0008] Therefore, there is an urgent need for online acquisition methods for battery open-circuit voltage and battery management systems to overcome the above-mentioned shortcomings. Summary of the Invention

[0009] The purpose of this invention is to provide a method for online acquisition of battery open-circuit voltage and a battery management system, which can acquire the battery open-circuit voltage in real time during battery operation through the voltage recovery process after current disturbance without the need for long-term static testing.

[0010] To achieve this objective, the present invention adopts the following technical solution:

[0011] In a first aspect, the present invention provides a method for online acquisition of battery open-circuit voltage, comprising:

[0012] S1. Identify current disturbance events during battery operation;

[0013] S2. Collect data on the change of battery voltage over time during the recovery phase after current disturbance;

[0014] S3. Based on the data of battery voltage change over time, obtain the open-circuit voltage of the battery by fitting extrapolation method;

[0015] The recovery phase is the time period during which the absolute value of the battery current drops below a set threshold.

[0016] Preferably, step S1 specifically includes:

[0017] S11. Monitor the change in the absolute value of the battery current from the first threshold to the second threshold;

[0018] Wherein, the first threshold is greater than the current value corresponding to one-tenth of the rated capacity of the battery, and the second threshold is less than the current value corresponding to one-fiftieth of the rated capacity of the battery.

[0019] Preferably, in step S3, obtaining the open-circuit voltage of the battery through fitting extrapolation specifically includes:

[0020] S31. The open-circuit voltage of the battery is calculated using the exponential recovery model V(t)=OCV+(V0-OCV)×exp(-t / τ), where V(t) is the voltage at time t in the recovery phase, V0 is the voltage at the beginning of the recovery phase, OCV is the open-circuit voltage, and τ is the voltage recovery time constant.

[0021] S32. By fitting the voltage data during the recovery phase, the open-circuit voltage and voltage recovery time constant τ are obtained.

[0022] Specifically, a stepwise linearization method is used to fit the voltage data during the recovery phase. This stepwise linearization method specifically includes:

[0023] The exponential recovery model V(t) is transformed into a linear form, V(t)', by performing a logarithmic transformation.

[0024] The time constant τ is obtained by performing linear regression on the linear form of the exponential recovery model V(t)'.

[0025] The open-circuit voltage is obtained by iteratively solving the linear exponential recovery model V(t)'.

[0026] Preferably, the online acquisition method for battery open-circuit voltage further constructs an open-circuit voltage-state-of-charge curve, wherein constructing the open-circuit voltage-state-of-charge curve specifically includes:

[0027] The current disturbance identification and open-circuit voltage acquisition steps are repeated under different states of charge of the battery.

[0028] Obtain multiple sets of state of charge and corresponding open-circuit voltage data, and store them as a state of charge-open-circuit voltage array;

[0029] Based on the state of charge-open circuit voltage array, construct the open circuit voltage-state of charge relationship curve.

[0030] Preferably, a polynomial is used to perform curve fitting on the state of charge-open circuit voltage array to obtain the open circuit voltage-state of charge relationship curve of the battery;

[0031] When new state-of-charge-open-circuit voltage data is obtained, update the coefficients of the polynomial;

[0032] The open-circuit voltage-state-of-charge curve is updated based on the updated polynomial coefficients.

[0033] Preferably, the online method for obtaining the battery open-circuit voltage also identifies the internal resistance of the battery under different states of charge. Specifically, identifying the internal resistance of the battery under different states of charge includes:

[0034] At the instant that the current disturbance occurs, record the instantaneous jump in battery voltage and the change in current;

[0035] Calculate the battery's internal resistance under its current state of charge;

[0036] Record multiple sets of states of charge and corresponding internal resistance values, and store the multiple sets of states of charge and corresponding internal resistance values ​​as a state of charge-internal resistance array.

[0037] An internal resistance-charge state relationship curve is constructed based on the aforementioned state of charge-internal resistance array.

[0038] Specifically, the set of parameters from the open-circuit voltage-state-of-charge curve and the set of parameters from the internal resistance-state-of-charge curve together constitute the local model. Updating the local model when a significant change in the parameters of the open-circuit voltage-state-of-charge curve and / or the internal resistance-state-of-charge curve is detected includes:

[0039] Upload locally collected data on state of charge, open-circuit voltage, internal resistance, and corresponding curve fitting parameters to a remote terminal.

[0040] The remote terminal performs high-precision fitting processing on the data;

[0041] Receive optimization parameters from the remote terminal and update the local model.

[0042] Furthermore, the step of receiving optimization parameters from the remote terminal and updating the local model specifically includes:

[0043] When the increase in internal resistance relative to the initial value of internal resistance exceeds a preset ratio, the shortening of the open-circuit voltage plateau range exceeds a preset threshold, or the rate of change of polynomial coefficients exceeds a set threshold, the remote terminal is triggered to send optimization parameters and update the local model.

[0044] Furthermore, when the remote terminal sends out optimization parameters, it uses an incremental update method to update the local model. The incremental update method includes parameter-level incremental update and firmware-level incremental update.

[0045] Preferably, the online method for obtaining the battery open-circuit voltage also adapts to the battery's chemical system, and the adaptation to the battery's chemical system specifically includes:

[0046] Pre-store the initial curve parameter sets of batteries with different chemical systems;

[0047] Calculate the fitting residuals between the measured data and each parameter set;

[0048] Select the parameter set that minimizes the sum of squared residuals as the working parameters.

[0049] Preferably, the online acquisition method for battery open-circuit voltage further includes anomaly detection and rollback processing, wherein the anomaly detection and rollback processing specifically includes:

[0050] Calculate the state of charge estimation error before and after the model update;

[0051] When the absolute value of the error exceeds the preset error threshold, the model parameters are restored to those before the update.

[0052] Record abnormal events and report them to the remote terminal.

[0053] In a second aspect, the present invention provides a battery management system for performing the online acquisition method for battery open-circuit voltage as described above, the battery management system comprising:

[0054] The data acquisition module is used to acquire battery terminal voltage and current at a preset frequency;

[0055] The disturbance detection module is used to monitor current changes in real time and identify disturbance events;

[0056] The parameter estimation module is used to perform open-circuit voltage extrapolation and curve fitting calculations;

[0057] The communication module is used for communication connections with remote terminals.

[0058] Thirdly, the present invention provides an energy storage device comprising the battery management system described above.

[0059] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the online battery open-circuit voltage acquisition method as described above.

[0060] Compared with the prior art, the present invention has the following beneficial effects:

[0061] By extrapolating the short-term recovery after current disturbance to estimate the battery's open-circuit voltage, this method overcomes the technical bottleneck of traditional methods that require the battery to be left to stand for a long time to accurately obtain the open-circuit voltage. It achieves true online self-calibration and dynamic updating of the battery's open-circuit voltage, enabling the battery's open-circuit voltage to be obtained in real time during normal vehicle operation without interrupting battery use for long-term stand-alone testing. This truly realizes the real-time nature of open-circuit voltage detection and greatly improves the efficiency of detection.

[0062] The present invention has other features and advantages, which will be apparent from or will be set forth in detail in the accompanying drawings and the following detailed description, which together serve to explain the particular principles of the invention. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1This is a flowchart of an existing technology for online calculation of battery open-circuit voltage by adjusting only the R / C parameter.

[0065] Figure 2 This is a flowchart of the online acquisition method for battery open-circuit voltage provided in an embodiment of the present invention.

[0066] Figure 3 This is a specific flowchart of a method for online acquisition of battery open-circuit voltage provided in an embodiment of the present invention.

[0067] Figure 4 This is a structural block diagram of the battery management system provided in an embodiment of the present invention. Detailed Implementation

[0068] To illustrate the possible application scenarios, technical principles, implementable specific solutions, and achievable objectives and effects of this application in detail, the following description, in conjunction with the listed specific embodiments and accompanying drawings, provides a detailed explanation. The embodiments described herein are merely illustrative of the technical solutions of this application and are therefore intended to limit the scope of protection of this application.

[0069] Example 1

[0070] Please see Figure 2 The online open-circuit voltage acquisition method for batteries in this embodiment is mainly for online open-circuit voltage detection of lithium-ion batteries. Of course, for other types of batteries, this method can be adapted and adjusted before being used to acquire the online open-circuit voltage. Here, the battery can be a battery pack or a single cell. When the battery is a battery pack, it is composed of multiple cells connected in series, parallel, or a combination of series and parallel.

[0071] The method for obtaining the open-circuit voltage of the battery online includes the following basic steps:

[0072] S1. Identify current disturbance events during battery operation;

[0073] S2. Collect data on the change of battery voltage over time during the recovery phase after current disturbance;

[0074] S3. Based on the data of battery voltage change over time, obtain the open-circuit voltage of the battery by fitting extrapolation method;

[0075] The recovery phase is the period during which the absolute value of the battery current drops below a set threshold.

[0076] Understandably, the system continuously monitors current changes during normal battery operation. When a current disturbance that meets the conditions is detected, the system automatically initiates the voltage data acquisition and open-circuit voltage calculation process. The system referred to here is the necessary software and hardware combination required to execute the online battery open-circuit voltage acquisition method of this embodiment.

[0077] Preferably, step S1 specifically includes:

[0078] S11. Monitor the change in the absolute value of the battery current from the first threshold to the second threshold;

[0079] The first threshold is the current value corresponding to a value greater than one-tenth of the battery's rated capacity, and the second threshold is the current value corresponding to a value less than one-fiftieth of the battery's rated capacity.

[0080] It is understood that the detection conditions in this embodiment are as follows:

[0081] Monitoring for sudden current changes: When |I| drops sharply from greater than 0.1C to equal to or less than 0.02C and remains so for 2 seconds, recovery is marked as starting, where I is the battery current. Specifically, 0.1C corresponds to the current value corresponding to one-tenth of the battery's rated capacity, and 0.02C corresponds to the current value corresponding to one-fiftieth of the battery's rated capacity. Here, C refers to the battery's rated capacity.

[0082] Generally, the above-mentioned current surge usually occurs when the vehicle brakes or the current suddenly decreases. In order to ensure that the current recovers cleanly after the accurate current disturbance event, this embodiment requires that the current at the beginning of the recovery window is less than 0.02C and |I(t)|≤0.02C throughout the entire window. The time during which |I(t)|≤0.02C throughout the entire window is regarded as the duration of the disturbance recovery interval.

[0083] Preferably, in step S3, obtaining the open-circuit voltage of the battery through fitting extrapolation specifically includes:

[0084] S31. The open-circuit voltage of the battery is calculated using the exponential recovery model V(t)=OCV+(V0-OCV)×exp(-t / τ), where V(t) is the voltage at time t in the recovery phase, V0 is the voltage at the beginning of the recovery phase, OCV is the open-circuit voltage, and τ is the voltage recovery time constant.

[0085] S32. By fitting the voltage data during the recovery phase, the open-circuit voltage and voltage recovery time constant τ are obtained.

[0086] Furthermore, a single exponential model is used to describe the recovery characteristics of the battery voltage over time after a current interruption:

[0087] V(t)=OCV+(V0-OCV)×exp(-t / τ),

[0088] Starting from recovery start time t0, the voltage and SOC are sampled every 100ms, and a total of N=300 data points are collected (corresponding to 30s), where t0 is the recovery start time and N is the total number of data points.

[0089] Select a short recovery interval after vehicle braking or sudden current reduction, typically T_obs = 30s, where T_obs is the observation time window.

[0090] During the data acquisition process, the system continuously monitors the current. If |I(t)|>0.02C is detected, the current acquisition process is interrupted and the system waits for the next disturbance event that meets the conditions.

[0091] If a complete dataset meeting the conditions cannot be obtained within the preset waiting time, the system will mark this identification as a failure and continue to monitor for new disturbance events.

[0092] When the temperature change rate exceeds 3℃ / minute, the system suspends data acquisition to avoid the impact of sudden temperature changes on the voltage recovery process.

[0093] Specifically, a stepwise linearization method is used to fit the voltage data during the recovery phase. The stepwise linearization method includes:

[0094] The exponential recovery model V(t) is transformed into a linear form of the exponential recovery model V(t)' by performing a logarithmic transformation.

[0095] Linear regression was performed on the linear form of the exponential recovery model V(t)' to obtain the time constant τ;

[0096] The open-circuit voltage is obtained by iteratively solving the linear exponential recovery model V(t)'.

[0097] Understandably, the stepwise linearization method is used in the vehicle MCU, and the specific implementation steps are as follows:

[0098] 1) Perform a logarithmic transformation on the exponential recovery model:

[0099] ln(V(t)-OCV^(k))=ln(V0-OCV^(k))-t / τ;

[0100] 2) Define a linear variable y: y = ln(V(t) - OCV^(k)), x = t;

[0101] 3) Obtain the linear relationship: y = α + βx, where α is the linear regression intercept and β is the linear regression slope;

[0102] 4) Estimate τ = -1 / β using linear regression;

[0103] 5) Iteratively update the open-circuit voltage estimate based on the new τ value, and repeat steps 1-4 above until convergence.

[0104] The termination condition for the above iteration convergence is: the difference in open-circuit voltage between two consecutive iterations is less than a preset threshold or the maximum number of iterations is reached. The preset threshold or the maximum number of iterations is set according to the battery type and is not limited here.

[0105] Preferably, the online acquisition method for battery open-circuit voltage in this embodiment further constructs an open-circuit voltage-state-of-charge curve. The construction of the open-circuit voltage-state-of-charge curve specifically includes:

[0106] The current disturbance identification and open-circuit voltage acquisition steps are repeated under different states of battery charge.

[0107] Obtain multiple sets of state of charge and corresponding open-circuit voltage data, and store them as a state of charge-open-circuit voltage array;

[0108] Construct an open-circuit voltage-open-circuit voltage relationship curve based on the state-of-charge-open-circuit voltage array.

[0109] Preferably, a polynomial is used to fit the state of charge-open circuit voltage array to obtain the open circuit voltage-state of charge relationship curve of the battery.

[0110] When new state-of-charge-open-circuit voltage data is obtained, update the coefficients of the polynomial;

[0111] The open-circuit voltage-state-of-charge curve is updated based on the updated polynomial coefficients.

[0112] Understandably, for the state-of-charge-open-circuit voltage data set, a third-order polynomial is used for fitting, constructing the following third-order polynomial OCV(SOC):

[0113] OCV(SOC)=a0+a1×SOC+a2×SOC+a3×SOC3, where a0 is the coefficient of the constant term, a1 is the coefficient of the linear term, a2 is the coefficient of the quadratic term, and a3 is the coefficient of the cubic term. The curve fitting calculates the polynomial coefficients {a0,a1,a2,a3} online using the least squares method.

[0114] Furthermore, the latest (SOC, OCV) data points are added to the multinomial fitting window, and the coefficients {a0, a1, a2, a3} are updated using recursive least squares (RLS). When the data points in the fitting window reach their upper limit, the oldest data points can be removed and new data points can be added using methods such as sliding windows. The sliding window method for removing the oldest data is a common method and will not be elaborated here.

[0115] Specifically, in order to obtain the initial polynomial coefficients {a0,a1,a2,a3} of the aforementioned third-order polynomial OCV(SOC), this embodiment performs the following steps when the battery is offline:

[0116] 1) Measure the complete recovery curve of the battery under different SOCs;

[0117] 2) The initial open-circuit voltage and τ value are obtained by fitting using the nonlinear least squares method as a reference template;

[0118] 3) Simultaneously, the polynomial coefficients {a0,a1,a2,a3} of the offline open-circuit voltage-state-of-charge curve are used as initial operating parameters.

[0119] Furthermore, after each recovery event is completed, the parameters are updated using the following steps:

[0120] 1) Obtain τ' and OCV' for this fitting, where τ' is the newly identified time constant and OCV' is the newly identified open-circuit voltage;

[0121] 2) Use weighted fusion to update storage parameters:

[0122] τ=λ×τ+(1-λ)×τ',

[0123] OCV=λ×OCV+(1-λ)×OCV', where λ is the fusion weight;

[0124] 3) Add the new (SOC,OCV) data points to the aforementioned polynomial fitting window and update the coefficients {a0,a1,a2,a3}.

[0125] Specifically, when t approaches infinity, the exponential term exp(-t / τ) in the single exponential model V(t)=OCV+(V0-OCV)×exp(-t / τ) will approach 0. At this time, V(t) approaches the open-circuit voltage value, that is, lim(t→∞)V(t)=OCV. The open-circuit voltage value corresponding to the SOC point is obtained by this extrapolation method.

[0126] Furthermore, the convergence criterion is: when the change in the fitted open-circuit voltage value of multiple consecutive recovery events is less than a preset threshold, the open-circuit voltage at the SOC point is considered to have converged and can be used to update the open-circuit voltage-state-of-charge relationship curve.

[0127] Preferably, the online acquisition method for battery open-circuit voltage in this embodiment also identifies the internal resistance of the battery under different states of charge. This identification of the internal resistance of the battery under different states of charge specifically includes:

[0128] At the instant that the current disturbance occurs, record the instantaneous jump in battery voltage and the change in current;

[0129] Calculate the battery's internal resistance under its current state of charge using Ohm's law;

[0130] Record multiple sets of states of charge and their corresponding internal resistance values, and store these multiple sets of states of charge and their corresponding internal resistance values ​​as a state of charge-internal resistance array;

[0131] Construct an internal resistance-charge state relationship curve based on the state-charge-internal resistance array.

[0132] It is understandable that at the instant the current disturbance occurs, the battery voltage will immediately jump. The system extracts the voltage difference ΔV and the current difference ΔI, and calculates the equivalent series resistance using R0 = ΔV / ΔI, where ΔV is the voltage jump of the battery at the instant the current disturbance occurs, ΔI is the current change of the battery at the instant the current disturbance occurs, and R0 is the equivalent series resistance of the battery.

[0133] To avoid errors from a single calculation, this embodiment performs multiple measurements during a disturbance event and takes the average value as the equivalent series resistance R0 at the current SOC point. The battery current sampling resistor is used for SOC calculation, overcurrent and short-circuit fault identification functions.

[0134] Similar to the method of constructing the polynomial OCV(SOC) for the state-of-charge-open-circuit voltage data point set, this embodiment also requires constructing the polynomial R(SOC) for the internal resistance-SOC relationship, specifically as follows:

[0135] R(SOC) = b0 + b1 × SOC + b2 × SOC + b3 × SOC³, where b0 is the coefficient of the constant term in the internal resistance polynomial, b1 is the coefficient of the first term, b2 is the coefficient of the second term, and b3 is the coefficient of the third term. The determination of the coefficients of the polynomial R(SOC) for constructing the internal resistance-SOC relationship is similar to the determination of the coefficients of the polynomial for constructing the state-of-charge-open-circuit voltage data point set, and will not be elaborated upon here.

[0136] In order to make better use of the open-circuit voltage-state-of-charge curve and the internal resistance-state-of-charge curve, the set of parameters of the open-circuit voltage-state-of-charge curve and the internal resistance-state-of-charge curve in this embodiment together constitute the local model.

[0137] The online acquisition method for battery open-circuit voltage also updates the local model when significant changes are detected in the parameters of the open-circuit voltage-state-of-charge curve and / or the parameters of the internal resistance-state-of-charge curve. Specifically, this includes:

[0138] Upload locally collected data on state of charge, open-circuit voltage, internal resistance, and corresponding curve fitting parameters to a remote terminal.

[0139] The remote terminal performs high-precision fitting processing on the data;

[0140] Receive optimization parameters from the remote terminal and update the local model.

[0141] Specifically, receiving optimization parameters from the remote terminal and updating the local model includes:

[0142] When the increase in internal resistance relative to the initial value of internal resistance exceeds a preset ratio, the open-circuit voltage plateau interval shortens beyond a preset threshold, or the rate of change of polynomial coefficients exceeds a set threshold, the remote terminal processing procedure is triggered.

[0143] It is understandable that the criteria for determining significant changes in the parameters of the open-circuit voltage-state-of-charge curve and / or the parameters of the internal resistance-state-of-charge curve include:

[0144] 1) The increase in internal resistance relative to the initial value exceeds the preset ratio;

[0145] 2) The open-circuit voltage plateau range shortens beyond a preset threshold;

[0146] 3) The rate of change of the polynomial coefficients exceeds the set threshold;

[0147] When any of the above conditions are met, the system determines that the battery has aged and degraded, thereby triggering remote terminal collaborative optimization to maximize the real-time performance of the parameters of the open-circuit voltage-state-of-charge curve and the internal resistance-state-of-charge curve.

[0148] Furthermore, the remote terminal collaboratively performs the following steps:

[0149] 1) Vehicle-side data preprocessing: Upload high-quality recovery event data;

[0150] 2) Remote terminal data fusion: Aggregate battery data of the same type from multiple vehicles and remove abnormal data points;

[0151] 3) High-precision modeling: The remote terminal uses high-order polynomials or machine learning algorithms for accurate fitting;

[0152] 4) Model delivery: The optimized curve parameters are pushed to the vehicle BMS via OTA.

[0153] Furthermore, when the remote terminal sends out optimization parameters, it uses an incremental update method to update the local model. This incremental update method includes parameter-level incremental update and firmware-level incremental update.

[0154] The parameter-level incremental update method is mainly suitable for small-scale updates to the local model. This method only sends the parameters that have changed; if there are no changes, no update needs to be transmitted. The MCU then matches and updates the parameters internally. In other words, the core idea of ​​the parameter-level incremental update method is to only transmit changes. It only pushes the changed parameter fragments to the MCU, which then maps and replaces the corresponding internal values, achieving real-time updates without redundant transmission.

[0155] Firmware-level incremental updates are primarily suitable for large-scale updates of local models, such as distributing a complete local model firmware update via a full update. This method compares the binary files of the old and new firmware versions using differential algorithms such as BSDiff, generating a "patch package" (incremental package) containing only the differences. The client downloads this incremental package and merges it with the local old version to generate the new firmware. In other words, the core idea of ​​firmware-level incremental updates is a patch-based upgrade, using differential algorithms (such as BSDiff) to compare the old and new firmware to generate a difference patch. After the terminal downloads the patch, it combines it with the local old firmware to create a complete new firmware, significantly reducing data transfer and saving storage.

[0156] Preferably, the online acquisition method for battery open-circuit voltage in this embodiment also adapts to the battery's chemical system. This adaptation specifically includes:

[0157] Pre-store the initial curve parameter sets of batteries with different chemical systems;

[0158] Calculate the fitting residuals between the measured data and each parameter set;

[0159] Select the parameter set that minimizes the sum of squared residuals as the working parameters.

[0160] Preferably, the online battery open-circuit voltage acquisition method of this embodiment also performs anomaly detection and rollback processing, which specifically includes:

[0161] Calculate the state of charge estimation error before and after the model update;

[0162] When the absolute value of the error exceeds the preset error threshold, the model parameters are restored to those before the update.

[0163] Record abnormal events and report them to the remote terminal.

[0164] Specifically, the anomaly detection process is as follows:

[0165] 1) Before updating the model, record a copy of the current parameters;

[0166] 2) After updating, verify the accuracy of the new model using the most recent SOC estimate;

[0167] 3) Calculate the absolute error: Error = |SOC' - SOC_ref|, where Error is the absolute error of the state of charge estimation, SOC' is the state of charge value estimated by the new model, and SOC_ref is the reference state of charge value;

[0168] 4) If the absolute error of the state of charge estimation exceeds the preset error threshold, it is judged as an abnormal update and automatically rolls back to the parameter copy;

[0169] 5) Record the details of abnormal events and report them to the remote terminal for analysis.

[0170] Please see Figure 3 , Figure 3 The flowchart illustrates a specific implementation process of the online acquisition method for battery open-circuit voltage in this embodiment, and the core idea of ​​this embodiment can be understood in conjunction with the flowchart.

[0171] Example 2

[0172] Please see Figure 4 The energy storage device in this embodiment includes a battery management system, which is used to execute the online battery open-circuit voltage acquisition method described above. The battery management system includes:

[0173] Data acquisition module 10 is used to acquire battery terminal voltage and current at a preset frequency;

[0174] Disturbance detection module 20 is used to monitor current changes in real time and identify disturbance events;

[0175] Parameter estimation module 30 is used to perform open-circuit voltage extrapolation and curve fitting calculations;

[0176] Communication module 40 is used for communication connection with a remote terminal.

[0177] It is understandable that the energy storage device here refers to an electric vehicle, but it can also be something like a Lev or other energy storage devices. When the energy storage device is an electric vehicle, the electric vehicle has a vehicle body and a battery. The battery management system manages the battery, and the battery provides electrical energy to the vehicle body so that the vehicle body can perform actions such as driving and cooling.

[0178] Example 3

[0179] This embodiment provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the online battery open-circuit voltage acquisition method described above.

[0180] It is understood that the computer program stored in the storage medium contains complete algorithms for implementing each step of the above method, including software implementations of functions such as current disturbance detection, data acquisition, exponential fitting, polynomial update, and remote collaboration.

[0181] Combination Figures 2-4 This invention estimates the battery's open-circuit voltage by extrapolating after short-term recovery following current disturbance, overcoming the technical bottleneck of traditional methods that require the battery to be left to stand for a long time to accurately obtain the open-circuit voltage. It achieves true online self-calibration and dynamic updating of the battery's open-circuit voltage, enabling the battery's open-circuit voltage to be obtained in real time during normal vehicle operation without interrupting battery use for long-term stand-alone testing. This truly realizes the real-time nature of open-circuit voltage detection and greatly improves detection efficiency.

[0182] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for online acquisition of battery open-circuit voltage, characterized in that, include: Identify current disturbance events during battery operation; Data on battery voltage changes over time were collected during the recovery phase following a current disturbance. Based on the data on the change of battery voltage over time, the open-circuit voltage of the battery is obtained by fitting extrapolation. The recovery phase is the time period during which the absolute value of the battery current drops below a set threshold.

2. The method for online acquisition of battery open-circuit voltage as described in claim 1, characterized in that, The identification of current disturbance events during battery operation specifically includes: Monitor the change in the absolute value of the battery current from the first threshold to the second threshold; Wherein, the first threshold is greater than the current value corresponding to one-tenth of the rated capacity of the battery, and the second threshold is less than the current value corresponding to one-fiftieth of the rated capacity of the battery.

3. The method for online acquisition of battery open-circuit voltage as described in claim 1, characterized in that, The method of obtaining the open-circuit voltage of the battery through fitting extrapolation specifically includes: The open-circuit voltage of the battery is calculated using the exponential recovery model V(t)=OCV+(V0-OCV)×exp(-t / τ), where V(t) is the voltage at time t during the recovery phase, V0 is the voltage at the start of the recovery phase, OCV is the open-circuit voltage, and τ is the voltage recovery time constant. By fitting the voltage data during the recovery phase, the open-circuit voltage and the voltage recovery time constant τ are obtained.

4. The method for online acquisition of battery open-circuit voltage as described in claim 3, characterized in that, A stepwise linearization method is used to fit the voltage data during the recovery phase. Specifically, the stepwise linearization method includes: The exponential recovery model V(t) is transformed into a linear form, V(t)', by performing a logarithmic transformation. The time constant τ is obtained by performing linear regression on the linear form of the exponential recovery model V(t)'. The open-circuit voltage is obtained by iteratively solving the linear exponential recovery model V(t)'.

5. The method for online acquisition of battery open-circuit voltage as described in any one of claims 1 to 4, characterized in that, Furthermore, an open-circuit voltage-state-of-charge curve is constructed, specifically including: The current disturbance identification and open-circuit voltage acquisition steps are repeated under different states of charge of the battery. Obtain multiple sets of state of charge and corresponding open-circuit voltage data, and store them as a state of charge-open-circuit voltage array; Based on the state of charge-open circuit voltage array, construct the open circuit voltage-state of charge relationship curve.

6. The method for online acquisition of battery open-circuit voltage as described in claim 5, characterized in that, The open-circuit voltage-open-circuit voltage array is fitted with a polynomial to obtain the open-circuit voltage-state-charge relationship curve of the battery. When new state-of-charge-open-circuit voltage data is obtained, update the coefficients of the polynomial; The open-circuit voltage-state-of-charge curve is updated based on the updated polynomial coefficients.

7. The method for online acquisition of battery open-circuit voltage as described in claim 5, characterized in that, The system also identifies the internal resistance of the battery under different states of charge. Specifically, this identification includes: At the instant that the current disturbance occurs, record the instantaneous jump in battery voltage and the change in current; Calculate the battery's internal resistance under its current state of charge; Record multiple sets of states of charge and corresponding internal resistance values, and store the multiple sets of states of charge and corresponding internal resistance values ​​as a state of charge-internal resistance array. An internal resistance-charge state relationship curve is constructed based on the aforementioned state of charge-internal resistance array.

8. The method for online acquisition of battery open-circuit voltage as described in claim 7, characterized in that, The set of parameters from the open-circuit voltage-state-of-charge curve and the internal resistance-state-of-charge curve together constitutes the local model. The online battery open-circuit voltage acquisition method also updates the local model when a significant change is detected in the parameters of the open-circuit voltage-state-of-charge curve and / or the parameters of the internal resistance-state-of-charge curve. Specifically, updating the local model when a significant change is detected in the parameters of the open-circuit voltage-state-of-charge curve and / or the internal resistance-state-of-charge curve includes: Upload locally collected data on state of charge, open-circuit voltage, internal resistance, and corresponding curve fitting parameters to a remote terminal. The remote terminal performs high-precision fitting processing on the data; Receive optimization parameters from the remote terminal and update the local model.

9. The method for online acquisition of battery open-circuit voltage as described in claim 8, characterized in that, The step of receiving optimization parameters from a remote terminal and updating the local model specifically includes: When the increase in internal resistance relative to the initial value of internal resistance exceeds a preset ratio, the shortening of the open-circuit voltage plateau range exceeds a preset threshold, or the rate of change of polynomial coefficients exceeds a set threshold, the remote terminal is triggered to send optimization parameters and update the local model.

10. The method for online acquisition of battery open-circuit voltage as described in claim 9, characterized in that, When the remote terminal sends out optimization parameters, it uses an incremental update method to update the local model. The incremental update method includes parameter-level incremental update and firmware-level incremental update.

11. The method for online acquisition of battery open-circuit voltage as described in claim 5, characterized in that, Furthermore, the battery's chemical system is adapted, specifically including: Pre-store the initial curve parameter sets of batteries with different chemical systems; Calculate the fitting residuals between the measured data and each parameter set; Select the parameter set that minimizes the sum of squared residuals as the working parameters.

12. The method for online acquisition of battery open-circuit voltage as described in claim 1, characterized in that, It also performs anomaly detection and rollback processing, which specifically includes: Calculate the state of charge estimation error before and after the model update; When the absolute value of the error exceeds the preset error threshold, the model parameters are restored to those before the update. Record abnormal events and report them to the remote terminal.

13. A battery management system, characterized in that, The battery management system is used to perform the online acquisition method for battery open-circuit voltage according to any one of claims 1 to 12, wherein the battery management system comprises: The data acquisition module collects battery terminal voltage and current at a preset frequency; The disturbance detection module monitors current changes in real time and identifies disturbance events; The parameter estimation module performs open-circuit voltage extrapolation and curve fitting calculations. The communication module is used for communication connections with remote terminals.

14. An energy storage device, characterized in that, Including the battery management system as described in claim 13.

15. A computer-readable storage medium, characterized in that, The device contains a computer program that, when executed by a processor, implements the steps of the method as described in any one of claims 1 to 12.

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