A battery management method, system, apparatus, device, and storage medium

Abstract

This application discloses a battery management method, system, device, equipment, and storage medium. The method includes: acquiring the current state of charge (SOC) of the battery; determining a target confidence factor corresponding to the current SOC based on the correlation between the SOC and a confidence factor, wherein the target confidence factor is used to characterize the reliability of the current model parameters of the battery state management model; using the battery state management model to simulate the voltage response characteristics of the battery; adjusting the current model parameters according to the target confidence factor to obtain target model parameters of the battery state management model; and updating the parameters of the battery state management model according to the target model parameters. The embodiments of this application effectively reduce the occurrence of parameter miscorrections, stably ensure the effectiveness of the battery state management model in simulating the battery voltage response characteristics, and effectively improve the estimation accuracy of various battery operating states, thereby further improving the precision of battery management.

Description

Technical Field

[0001] This application belongs to the field of electrical engineering technology, and in particular relates to a battery management method, system, device, equipment and storage medium. Background Technology

[0002] Battery Management Systems (BMS) typically rely on equivalent circuit models (such as Thevenin and DP models) to simulate the voltage response characteristics of batteries in real time, enabling high-precision estimations of state of charge, state of health, and state of power. Recursive least squares algorithms, with their advantages of low computational complexity and adaptability to online operating scenarios, are widely used in online parameter identification of battery equivalent circuit models. However, existing battery model parameter update methods often unconditionally accept the identified parameters or rely solely on a single threshold for update judgment. This fails to effectively assess the actual reliability of the identified model parameters, making it prone to erroneous parameter input into the model. This leads to incorrect parameter corrections, and the continuously accumulating estimation bias can affect the stability of overall battery state management.

[0003] Therefore, how to effectively suppress model miscorrection caused by parameter estimation errors, thereby improving the accuracy of battery management, is a technical problem that urgently needs to be solved. Summary of the Invention

[0004] This application provides a battery management method, system, device, equipment, and storage medium that can effectively reduce the occurrence of parameter miscorrection and significantly improve the accuracy of battery management.

[0005] In a first aspect, embodiments of this application provide a battery management method, including: Obtain the current state of charge of the battery; Based on the correlation between the battery's state of charge and the confidence factor, a target confidence factor corresponding to the current state of charge is determined. The target confidence factor is used to characterize the reliability of the current model parameters of the battery state management model, which is used to simulate the voltage response characteristics of the battery. Based on the target confidence factor, the current model parameters are adjusted to obtain the target model parameters of the battery state management model; The battery state management model is updated according to the target model parameters to obtain the updated battery state management model.

[0006] Secondly, embodiments of this application provide a battery management system, including: A controller is used to acquire the current state of charge (SOC) of the battery; determine a target confidence factor corresponding to the current SOC based on the correlation between the SOC and a confidence factor, wherein the target confidence factor is used to characterize the confidence level of the current model parameters of the battery state management model, and the battery state management model is used to simulate the voltage response characteristics of the battery; and adjust the current model parameters based on the target confidence factor to obtain the target model parameters of the battery state management model. The battery state management model is updated according to the target model parameters to obtain the updated battery state management model.

[0007] Thirdly, embodiments of this application provide a battery device, including a battery and a battery management system as described in the second aspect.

[0008] Fourthly, embodiments of this application provide a terminal device, the device including: a processor and a memory storing computer program instructions; The processor implements a battery management method as described in the first aspect when executing computer program instructions.

[0009] Fifthly, embodiments of this application provide a computer storage medium on which computer program instructions are stored, and when executed by a processor, the computer program instructions implement the battery management method of the first aspect.

[0010] In a sixth aspect, embodiments of this application provide a computer program product in which instructions, when executed by a processor of an electronic device, cause the electronic device to perform the battery management method as described in the first aspect.

[0011] The technical solutions provided by the embodiments of this application bring at least the following beneficial effects: This application provides a battery management method, comprising: acquiring the current state of charge (SOC) of a battery; determining a target confidence factor corresponding to the current SOC based on the correlation between the SOC and a confidence factor, wherein the target confidence factor characterizes the reliability of the current model parameters of a battery state management model, and the battery state management model simulates the voltage response characteristics of the battery; adjusting the current model parameters according to the target confidence factor to obtain target model parameters of the battery state management model; and updating the parameters of the battery state management model according to the target model parameters to obtain a parameter-updated battery state management model.

[0012] The technical solution provided in this application obtains the current state of charge of the battery, determines a target confidence factor that reflects the credibility of the current model parameters of the battery state management model based on the correlation between the state of charge and the confidence factor, and then makes reasonable adjustments to the current model parameters based on the target confidence factor to obtain the target model parameters and update the parameters of the battery state management model. This effectively reduces the occurrence of parameter miscorrection, stably ensures the effect of the battery state management model in simulating the battery voltage response characteristics, effectively improves the estimation accuracy of various battery operating states, and further improves the precision of battery management.

[0013] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 A schematic flowchart illustrating a battery management method according to one embodiment of this application; Figure 2 This is a schematic diagram of the overall process of a battery management method provided in one embodiment of this application; Figure 3 This is a schematic diagram of a battery management system provided in another embodiment of this application; Figure 4 This is a schematic diagram of the structure of a terminal device provided in another embodiment of this application. Detailed Implementation

[0016] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0017] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0018] It should be noted that the acquisition, storage, use, and processing of data in this application embodiment all comply with the relevant provisions of national laws and regulations.

[0019] Furthermore, it should be noted that in the embodiments of this application, certain software, components, models, and other existing solutions in the industry may be mentioned. These should be considered as exemplary, and their purpose is only to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0020] Battery management systems rely on equivalent circuit models (such as the Thevenin model and DP model) to estimate the battery's state of charge (SOC), state of health (SOH), and state of power (SOP) in real time. The accuracy of the model parameters is fundamental to the accuracy of state estimation. Recursive Least Squares (RLS) has become the mainstream method for parameter identification due to its low computational cost and suitability for online implementation.

[0021] However, RLS parameter estimation has inherent flaws in practical applications, leading to errors in the estimation results and the risk of incorrect corrections. Specifically, the BMS indiscriminately uses erroneous RLS estimates to update model parameters, causing model distortion. This manifests in several ways: 1. Insufficient data stimulation leading to parameter drift: When the battery is under constant current charging, constant current discharging, or idle for extended periods, the voltage and current signals lack sufficient effective information. The RLS algorithm cannot uniquely converge to the true parameters, and the estimated values ​​are prone to random drift or drastic jumps. 2. Low sensitivity in the voltage plateau region: Especially in lithium iron phosphate batteries, the OCV-SOC (Open Circuit Voltage - State of Charge) curve has an extremely long flat region (e.g., SOC 30%~90%), within which OCV changes by only a few millivolts. Small voltage noise or sampling errors are amplified by RLS, causing the estimated results for internal resistance, polarization parameters, etc., to deviate significantly from the true values. 3. Sensor noise and sampling asynchrony: Voltage and current measurement noise and sampling time deviations both introduce parameter estimation biases. 4. Traditional correction strategies have drawbacks: Existing BMS systems often unconditionally use all RLS estimates for model updates, or determine whether to update based solely on a simple current change rate threshold. This "blanket acceptance" or "hard decision" approach cannot quantitatively assess the reliability of the current estimates, and is highly susceptible to introducing incorrect parameters into the model, leading to erroneous corrections. In other words, parameters are incorrectly updated under inappropriate operating conditions, resulting in cumulative biases in subsequent SOC and SOH estimates, and in severe cases, even causing system divergence.

[0022] Based on the aforementioned technical problems, embodiments of this application provide a battery management method, system, device, equipment, and storage medium. The method includes: acquiring the current state of charge (SOC) of a battery; determining a target confidence factor corresponding to the current SOC based on the correlation between the SOC and a confidence factor, wherein the target confidence factor characterizes the reliability of the current model parameters of a battery state management model, and the battery state management model simulates the voltage response characteristics of the battery; adjusting the current model parameters according to the target confidence factor to obtain target model parameters of the battery state management model; and updating the parameters of the battery state management model according to the target model parameters to obtain a parameter-updated battery state management model.

[0023] The technical solution provided in this application obtains the current state of charge of the battery, determines a target confidence factor that reflects the credibility of the current model parameters of the battery state management model based on the correlation between the state of charge and the confidence factor, and then makes reasonable adjustments to the current model parameters based on the target confidence factor to obtain the target model parameters and update the parameters of the battery state management model. This effectively reduces the occurrence of parameter miscorrection, stably ensures the effect of the battery state management model in simulating the battery voltage response characteristics, effectively improves the estimation accuracy of various battery operating states, and further improves the precision of battery management.

[0024] Regarding the execution entity used in the embodiments of this application, it can specifically be a battery management system capable of monitoring the battery's terminal voltage and current, or other terminal devices capable of controlling the battery management system, such as desktop computers, laptops, etc., or servers, etc. In addition, the execution entity in the embodiments of this application can also be a software entity, such as a client or software program installed in the battery management system. The specific type of execution entity corresponding to the battery management method, system, device, equipment, and storage medium provided in the embodiments of this application is not strictly limited here; it can be flexibly selected and set according to the application scenario and actual needs.

[0025] It should be noted that the specific application scenarios of the battery management method, system, device, equipment and storage medium provided in the embodiments of this application are not limited. The technical solutions provided in the embodiments of this application can be flexibly applied to various actual scenarios that require battery management according to actual needs.

[0026] It should be noted that the application scenarios described in the above embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will understand that with the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0027] Figure 1 This is a schematic diagram of a battery management process provided in one embodiment of this application.

[0028] like Figure 1 As shown, the battery management method provided in this application includes S101 to S104.

[0029] S101: Obtain the current state of charge of the battery.

[0030] S102: Based on the correlation between the state of charge of the battery and the confidence factor, determine the target confidence factor corresponding to the current state of charge. The target confidence factor is used to characterize the credibility of the current model parameters of the battery state management model. The battery state management model is used to simulate the voltage response characteristics of the battery.

[0031] S103: Adjust the current model parameters according to the target confidence factor to obtain the target model parameters of the battery state management model.

[0032] S104: Update the parameters of the battery state management model according to the target model parameters to obtain the updated battery state management model.

[0033] In the aforementioned battery management method, this application obtains the current state of charge of the battery, determines a target confidence factor that reflects the credibility of the current model parameters of the battery state management model based on the correlation between the state of charge and the confidence factor, and then makes reasonable adjustments to the current model parameters based on the target confidence factor to obtain the target model parameters, and completes the parameter update of the battery state management model. This effectively reduces the occurrence of parameter miscorrection, stably ensures the effect of the battery state management model in simulating the battery voltage response characteristics, effectively improves the estimation accuracy of various battery operating states, and further improves the precision of battery management.

[0034] In step S101 above, the current state of charge (SOC) of the battery is the percentage of its remaining capacity relative to its rated capacity at the current moment. This SOC can be obtained through methods including, but not limited to, the following: real-time acquisition of the battery's terminal voltage and current; time integration of the terminal current to obtain the cumulative charge change; and calculation of the initial SOC value in conjunction with the initial SOC. Subsequently, the open-circuit voltage is determined based on the real-time acquired terminal voltage, and the initial SOC value is calibrated using this open-circuit voltage to obtain the accurate current SOC.

[0035] Alternatively, the battery terminal voltage can be collected in real time, and the corresponding state of charge value can be directly looked up as the current state of charge based on a pre-stored mapping table of state of charge and open circuit voltage.

[0036] In S102 above, the confidence factor is a numerical value used to quantify the reliability of the current model parameters in the battery state management model. The value range can be set to [0, 1]. The higher the value, the more reliable the estimation result of the current model parameters by RLS is, and the more it should be used for model update; the lower the value, the greater the risk of estimation error, and its update function should be suppressed. The battery state management model refers to the equivalent circuit model (such as the Thevenin model, DP model) used to simulate the battery voltage response characteristics, including but not limited to parameters such as ohmic resistance, polarization resistance, and polarization capacitance.

[0037] The current model parameters are the original, unfiltered values ​​obtained online through the Recursive Least Squares (RLS) algorithm. Battery response characteristics are the inherent electrical response laws governing the dynamic changes in the battery's terminal voltage when subjected to current excitation of different magnitudes and directions.

[0038] In some embodiments, the correlation between the battery's state of charge and the confidence factor is a data structure or functional logic used to reflect the correspondence between the battery's state of charge and the confidence factor. The correlation can be a static mapping table obtained through offline calibration, or a functional expression dynamically calculated based on the battery's real-time characteristics.

[0039] In some embodiments, during battery operation, the system acquires the relationship curve between the battery's open-circuit voltage (OCV) and state of charge (SOC) in real time. By calculating the local slope corresponding to the current state of charge on this curve, and using a preset proportional function or piecewise function (i.e., correlation), the slope value is dynamically converted into a target confidence factor. For example, when the local slope is large, it indicates that the battery is in a high-sensitivity region, and the output target confidence factor tends to be close to 1; when the local slope is small, it indicates that the battery is in a voltage plateau region, and the output target confidence factor tends to be close to 0. In addition to the above method, other methods can also be used to determine the target confidence factor, and this embodiment does not limit this.

[0040] In S103 above, the target confidence factor corresponding to the current state of charge is used as the basis for parameter adjustment. Combined with the actual operating conditions of the battery, the current model parameters with different confidence levels are corrected and optimized, and the adjusted current model parameters are used as the target model parameters of the battery state management model.

[0041] Specifically, adjustments can be made in ways including but not limited to: obtaining historical model parameters of the battery state management model, and weighting and fusing the current model parameters and historical model parameters based on the target confidence factor to obtain target model parameters that take into account the advantages of both new and old information.

[0042] In step S104 above, the target model parameters can be directly assigned to the corresponding parameters in the battery state management model to complete the real-time update of the model; or, to avoid sudden parameter changes causing a jump in the model output voltage, a moving average or low-pass filter is used to smooth the target model parameters, and the processed parameters are written into the battery state management model. The battery state management model with updated parameters can estimate the battery's state of charge (SOC), state of health (SOH), and state of power (SOP) in real time.

[0043] In some embodiments, S102 may specifically include the following steps: Based on the relationship curve between the open-circuit voltage and the state of charge of the battery, the local slope of the current state of charge on the relationship curve is determined. The target confidence factor is determined based on the preset functional relationship between the local slope and the confidence factor.

[0044] The aforementioned curve relating open-circuit voltage to state of charge refers to the battery's OCV-SOC curve, which characterizes the change in open-circuit voltage as the battery's state of charge changes. This curve can be obtained through offline experimental calibration and reflects the battery's electrochemical characteristics. The local slope refers to the slope of the tangent line at the data point corresponding to the current state of charge on the curve relating open-circuit voltage to state of charge. The larger the slope, the more sensitive the battery's voltage response at that point, and the higher the reliability of the parameter identification.

[0045] The aforementioned preset functional relationship is a pre-defined mathematical model or logical rule used to map local slopes to confidence factors.

[0046] In some embodiments, in the relationship curve between open-circuit voltage and state of charge, the data point corresponding to the current state of charge is located, and the tangent slope at that data point is calculated using a preset slope calculation method, thus obtaining the local slope corresponding to the current state of charge. Substituting the local slope into a preset function relationship, the corresponding confidence factor value is obtained through function calculation, and this value is the target confidence factor.

[0047] In this embodiment, the local slope of the data point corresponding to the current state of charge is accurately obtained by using the relationship curve between the battery open-circuit voltage and the state of charge. Then, the target confidence factor is solved based on the preset functional relationship between the local slope and the confidence factor, so that the target confidence factor can accurately characterize the credibility of the current parameters of the battery state management model, ensuring the accuracy and rationality of parameter adjustment.

[0048] In some embodiments, the aforementioned preset functional relationship is used to take the minimum value between the first preset value and the target value as the target confidence factor; the target value is the maximum value between the second preset value and the target ratio; the target ratio is the ratio between the first difference and the second difference; the first difference is the difference between the local slope and the first slope threshold, and the second difference is the difference between the first slope threshold and the second slope threshold; the first slope threshold is less than the second slope threshold; and the first preset value is greater than the second preset value.

[0049] The first preset value can be set to 1, and the second preset value can be set to 0; the first slope threshold Second slope threshold This can be a threshold pre-calibrated through experiments. It can be 0. A value of 5mv / 1% can be used, and the above threshold can also be adjusted according to needs. This embodiment does not impose any restrictions on this.

[0050] The specific formula for calculating the pre-defined functional relationship can be as follows: ;in, For the target confidence factor, For local slope, The first slope threshold, This is the second slope threshold.

[0051] In this embodiment, the above formula can efficiently generate a reliable target confidence factor, providing an accurate quantitative basis for subsequent model parameter adjustment and effectively suppressing the risk of parameter miscorrection under low confidence conditions.

[0052] In some embodiments, S102 may specifically include the following steps: Based on the relationship curve between the battery's open-circuit voltage and state of charge, the range of state of charge variation is divided into multiple state of charge intervals, and each state of charge interval corresponds to a preset confidence factor. The target confidence factor is determined based on the current state of charge interval.

[0053] The state of charge (OCC) interval refers to multiple intervals that divide the entire range of battery OCC (0%~100%) based on the voltage change characteristics of the open-circuit voltage versus OCC curve. Different intervals exhibit significantly different sensitivities to changes in OCC, such as voltage plateau regions, gradual transition regions, and dynamic steep change regions. The preset confidence factor is a quantitative value pre-configured for each OCC interval to characterize the reliability of the battery model parameter identification results within that interval. Its value range can be set to [0, 1], with a larger value indicating higher reliability of the parameter identification results within the corresponding interval.

[0054] In some embodiments, based on the relationship curve between the open-circuit voltage and the state of charge of the battery, the range of change of the state of charge is divided into N consecutive state of charge intervals. The division can be based on the local slope at each data point. Each state of charge interval corresponds to a specific SOC range (e.g., 0%-30%), and a preset confidence factor is configured for each state of charge interval.

[0055] Optionally, the preset confidence factor can be configured according to the following rules: For charging state intervals with a large slope (e.g., low-voltage polarization region, high-voltage polarization region), the parameter identification sensitivity is high, the RLS estimation result is relatively reliable, and the preset confidence factor is larger; for charging state intervals with a small slope (e.g., voltage plateau region), the parameter identification sensitivity is low, the RLS estimation result is easily affected by noise, and the error risk is high, so the preset confidence factor is smaller.

[0056] After obtaining the current state of charge of the battery, determine the state of charge interval to which the current state of charge belongs; obtain the preset confidence factor corresponding to the state of charge interval, and the preset confidence factor is the target confidence factor.

[0057] In this embodiment, by dividing the state of charge intervals based on the voltage change characteristics of the OCV-SOC curve and pre-configuring corresponding confidence factors for each interval, the pre-set confidence factor of the interval to which the current state of charge belongs can be directly matched as the target confidence factor after obtaining the current state of charge. This can effectively characterize the credibility of the model parameters under the current operating condition, provide a reliable basis for subsequent parameter adjustment, and suppress the risk of parameter miscorrection under low confidence operating conditions.

[0058] In some embodiments, the above-described relationship curve between the battery's open-circuit voltage and state of charge divides the range of state of charge variation into multiple state of charge intervals, including: Based on the relationship curve between the open-circuit voltage and the state of charge of the battery, the local slope corresponding to each data point on the relationship curve is determined. Based on the distribution characteristics of the local slope, the range of change in the state of charge is divided into multiple state of charge intervals; wherein, within the same state of charge interval, the amplitude of the change in the local slope is within a preset fluctuation range.

[0059] The aforementioned distribution characteristics of local slopes are the overall distribution patterns of the magnitude and fluctuations of local slopes at various points on the curve relating open-circuit voltage to state of charge. The preset fluctuation range refers to the maximum allowable variation of local slopes within the same state of charge interval, aiming to ensure that the voltage response characteristics remain consistent within the same interval, thereby guaranteeing the rationality of confidence factor allocation.

[0060] In some embodiments, the relationship curve between the battery's open-circuit voltage and state of charge is retrieved. For each set of data points corresponding to the open-circuit voltage on the curve, the local slope corresponding to each data point is calculated. This slope can clearly distinguish the battery polarization region with a large slope value and the voltage plateau region with a small slope value, thus quantifying the identification sensitivity characteristics of different charge positions.

[0061] The distribution characteristics of local slopes of all data points across the entire range are statistically analyzed. The range from 0% to 100% full charge of the battery is divided into segments. During the segmentation process, the variation of local slopes is controlled within a preset fluctuation range to ensure that the difficulty of parameter identification is basically the same in each segment.

[0062] For example, by setting one or more slope thresholds, the charging state intervals with local slopes greater than a first threshold are classified as high-sensitivity intervals, the charging state intervals with local slopes greater than a second threshold but less than or equal to the first threshold are classified as low-sensitivity intervals, and the charging state intervals with local slopes less than the second threshold are classified as high-sensitivity intervals. The variation range of the local slope within the three intervals obtained after the above classification is within a preset fluctuation range, and the first threshold is greater than the second threshold.

[0063] Optionally, the division of the state of charge (SOC) range and the allocation of confidence factors can be differentiated according to the cathode material of the battery. For example, the OCV-SOC curve of an LFP (Lithium Iron Phosphate) battery has an extremely long plateau with a local slope approaching zero. Its corresponding SOC ranges are: 0%-30%, 30%~90%, and 90%-100%. The preset confidence factors for the 0%-30% and 90%-100% ranges can be set to 0.9, and the preset confidence factor for the 30%~90% range can be set within the range of 0.1 to 0.2. NCM (Nickel Cobalt) batteries... The OCV-SOC curve of the manganese (nickel-cobalt-manganese ternary material) battery is monotonically increasing overall, with a relatively uniform slope in some areas. Its corresponding state-of-charge (SOC) ranges are: 0%-40%, 40%~60%, and 60%-100%. The preset confidence factors for the 0%-40% and 60%-100% ranges can be set within the range of 0.9 to 1, while the preset confidence factor for the 40%~60% range can be set within the range of 0.5 to 0.6. Through this targeted configuration, the LFP effectively suppresses miscorrection within a wide plateau region, while the NCM, due to its narrow plateau region and steeper slope, has a higher overall confidence factor and exhibits more aggressive correction.

[0064] In this embodiment, by using the local slope distribution characteristics of the open-circuit voltage and state of charge curve, the range of state of charge variation is divided into multiple intervals, and the fluctuation of the local slope within the same interval is ensured to be within a preset range. This achieves refined quantification and management of the battery parameter identification sensitivity, providing a reliable basis for subsequent stable matching of the corresponding confidence factors according to the intervals, and effectively reducing the credibility judgment deviation caused by differences in operating conditions.

[0065] In some embodiments, S102 may specifically include the following steps: The basic confidence factor is determined based on the correlation between the current state of charge and the confidence factor. If at least one of the voltage change value and the current change rate of the battery satisfies a preset abrupt change condition, an abnormal adjustment coefficient is determined based on at least one of the voltage change value and the current change rate. The target confidence factor is obtained by attenuating and correcting the basic confidence factor according to the anomaly adjustment coefficient.

[0066] In some embodiments, the preset mutation condition is a threshold standard used to determine whether the battery is in an abnormal transient condition. Specifically, the preset mutation condition includes at least one of the following: the voltage change value of the battery is greater than the change value threshold; the current change rate of the battery is greater than the change rate threshold.

[0067] The system continuously collects the battery's terminal voltage and current, and calculates the voltage change and current change rate. When any one or more operating parameters meet the preset sudden change conditions, it indicates a current spike or abnormal voltage fluctuation. A current spike refers to a large change in current within a very short time, usually caused by a sudden load change or sampling error. In this case, the current sensor output may show a spike or step, and parameters such as the internal resistance estimated by RLS will be momentarily distorted. Abnormal voltage fluctuations refer to a large change in the voltage measurement value of a single cell before and after sampling, usually caused by sensor noise, electromagnetic interference, poor contact, or communication abnormalities.

[0068] Specifically, the absolute value of the difference between the current terminal voltage and the terminal voltage collected at the previous moment can be used as the current voltage change value; the absolute value of the difference between the current terminal current and the terminal current collected at the previous moment divided by the sampling period can be used as the current current change rate.

[0069] In situations involving sudden current spikes or abnormal voltage fluctuations, the baseline confidence factor should be temporarily lowered immediately to avoid erroneous corrections caused by transient disturbances. In such cases, a corresponding abnormal adjustment coefficient should be determined based on the abnormal operating conditions. This abnormal adjustment coefficient is a pre-set coefficient used to correct the baseline confidence factor. Different voltage change ranges and different current change rate ranges correspond to different abnormal adjustment coefficients; the larger the voltage change / current change rate, the smaller the corresponding abnormal adjustment coefficient.

[0070] In some embodiments, a basic confidence factor is determined based on the correlation between the current state of charge (SOC) and the confidence factor. Specific determination methods include, but are not limited to: determining the local slope of the current SOC on the relationship curve between the battery's open-circuit voltage and SOC; determining the target confidence factor based on a preset functional relationship between the local slope and the confidence factor; or, dividing the range of SOC variation into multiple SOC intervals based on the relationship curve between the battery's open-circuit voltage and SOC, with each SOC interval corresponding to a preset confidence factor; and determining the target confidence factor based on the SOC interval in which the current SOC is located.

[0071] If at least one of the battery voltage change value and current change rate meets the preset abrupt change condition, an abnormal adjustment coefficient is determined based on at least one of the voltage change value and current change rate; the abnormal adjustment coefficient is applied to the basic confidence factor, and the basic confidence factor is numerically attenuated and reduced, thereby reducing the acceptance weight of the RLS parameter identification results under abrupt change conditions. After correction, the final usable target confidence factor is obtained.

[0072] In this embodiment, the basic confidence factor is determined based on the correlation between the state of charge and the confidence factor, and the reliability of parameters under stable operating conditions is determined. Then, the transient abnormal operating conditions of the battery are identified by monitoring the voltage change value and the current change rate. The basic confidence factor is attenuated and corrected by matching the corresponding abnormal adjustment coefficient. This greatly reduces the problem of incorrect model parameter updates caused by transient interference and significantly improves the accuracy of target confidence factor determination in complex operating scenarios.

[0073] In some embodiments, the above-mentioned attenuation correction of the base confidence factor according to the anomaly adjustment coefficient to obtain the target confidence factor includes: calculating the product of the anomaly adjustment coefficient and the base confidence factor; The product is processed based on a preset amplitude limiting function to obtain the target confidence factor.

[0074] The preset value range can be set to [0, 1]. The preset limiting function is used to extract the target confidence factor from the product based on the preset value range. Specifically, the formula for calculating the target confidence factor can be: ;in, For the target confidence factor, Basic confidence factor This is the abnormal adjustment coefficient; A preset limiting function is used to restrict the calculation result to the range of [0, 1].

[0075] Specifically, when one or more of the two transient abnormal operating conditions, namely current jump and voltage abnormal fluctuation, are detected, the basic confidence factor is immediately reduced to an extremely low level using the above formula to avoid erroneous corrections caused by transient interference. Once the abnormal operating condition disappears, the confidence factor is restored to normal update.

[0076] Specifically, under abnormal operating conditions, the configuration method of the abnormal adjustment coefficient can be shown in Table 1, where r is the current normalization rate; ΔU is the transformer change amount, the first change rate threshold is less than the second change rate threshold, the first change amount threshold is less than the second change amount threshold, and the values ​​of the abnormal adjustment coefficient in the table are only for illustrative purposes.

[0077] Table 1 In this embodiment, attenuation correction is achieved by multiplying the basic confidence factor with the anomaly adjustment coefficient, and then the calculation result is constrained within the effective value range of [0, 1] by using a limiting function. This enables the basic confidence factor under transient abnormal conditions to be quickly down-adjusted, accurately suppressing the impact of abnormal disturbances on the reliability of parameter identification, while ensuring the rationality of the target confidence factor value.

[0078] In some embodiments, when at least one of the voltage change value and the current change rate of the battery satisfies a preset abrupt change condition, determining the abnormal adjustment coefficient based on at least one of the voltage change value and the current change rate includes: When both the voltage change value and the current change rate of the battery meet the preset sudden change conditions, a first adjustment coefficient is determined based on the voltage change value, and a second adjustment coefficient is determined based on the current change rate. The minimum value between the first adjustment coefficient and the second adjustment coefficient is selected as the abnormal adjustment coefficient.

[0079] In some embodiments, when the voltage change value of the battery meets a preset abrupt change condition but the current change rate does not meet the preset abrupt change condition, an abnormal adjustment coefficient corresponding to the interval where the voltage change value is located is determined; when the voltage change value of the battery does not meet the preset abrupt change condition but the current change rate meets the preset abrupt change condition, an abnormal adjustment coefficient corresponding to the interval where the current change rate is located is determined.

[0080] When both the voltage change value and the current change rate of the battery meet the preset abrupt change conditions, the abnormal adjustment coefficient corresponding to the interval in which the voltage change value is located is determined and used as the first adjustment coefficient; the abnormal adjustment coefficient corresponding to the interval in which the current change rate is located is determined and used as the second adjustment coefficient; the minimum value between the first adjustment coefficient and the second adjustment coefficient is selected as the abnormal adjustment coefficient for subsequent adjustment of the basic confidence factor.

[0081] In this embodiment, when the voltage change value and current change rate of the battery simultaneously meet the preset sudden change conditions, the first and second adjustment coefficients corresponding to the voltage and current are determined respectively, and the minimum value of the two is selected as the abnormal adjustment coefficient. This effectively reduces the risk of model parameter erroneous update caused by multi-source interference and improves the stability and reliability of the battery management system under complex dynamic conditions.

[0082] In some embodiments, after determining the basic confidence factor based on the correlation between the current state of charge and the confidence factor, the method further includes: If neither the voltage change value nor the current change rate of the battery meets the preset abrupt change condition, a target change rate adjustment coefficient is determined according to the current change rate interval in which the current change rate is located; each current change rate interval corresponds to a preset change rate adjustment coefficient. The target residual adjustment coefficient is determined based on the residual interval in which the difference between the measured terminal voltage of the battery and the predicted terminal voltage output by the battery state management model lies; each residual interval corresponds to a preset residual adjustment coefficient. The base confidence factor is fine-tuned according to the target residual adjustment coefficient and the target rate of change adjustment coefficient to obtain the target confidence factor.

[0083] The current change rate range is a series of continuous intervals divided based on the reasonable fluctuation range of the current change rate under normal battery operating conditions. Each interval corresponds to a preset change rate adjustment coefficient, which is used to characterize the stability of parameter identification under different small current fluctuation intensities. The target change rate adjustment coefficient is a change rate adjustment coefficient matched according to the current current change rate range of the battery under steady-state conditions. It is used to fine-tune the basic confidence factor and reflect the slight impact of normal current fluctuations on the reliability of parameter identification. In this embodiment, the value range of the change rate adjustment coefficient can be set to [0.5, 1.2].

[0084] The adjustment rules corresponding to the target rate of change adjustment coefficient are as follows: RLS requires sufficient excitation to converge to the true parameters. Excitation that is too weak or too strong will lead to estimation distortion. When the current is almost constant (i.e., insufficient excitation), the system lacks dynamic information, and the RLS parameters are prone to drift. The confidence level should be significantly reduced to suppress error correction. When there is moderate dynamic change (i.e., optimal excitation), the moderate change in current provides sufficient excitation, the RLS estimation has high confidence, and the confidence level can be appropriately increased. When the current changes drastically (i.e., over-excitation), the current spike will cause the voltage transient response to lag and the measurement noise to be amplified. The instantaneous fluctuation of the RLS estimation is large, and the confidence level should be reduced.

[0085] To facilitate understanding, Table 2 provides an example of the rate of change adjustment coefficients corresponding to each current rate of change range. The values ​​are for illustrative purposes only, where r is the current rate of change.

[0086] Table 2 Specifically, at the boundary between adjacent current rate of change intervals, to avoid abrupt changes in the adjustment coefficient, linear interpolation is used to determine the rate of change adjustment coefficient within the gradual transition region. Specifically, when the current rate of change is between the under-excitation and optimal excitation range (e.g., 0.2 A / s to 0.5 A / s), linear interpolation is performed based on the relative position of the current rate of change within this range, using the lower limit of the adjustment coefficient at 0.2 A / s (e.g., 0.7) and the lower limit of the adjustment coefficient at 0.5 A / s (e.g., 1.0) as endpoints, to obtain a continuous adjustment coefficient. Similarly, when the current rate of change is between the optimal excitation and over-excitation range (e.g., 5 A / s to 10 A / s), linear interpolation is performed using the upper limit of the adjustment coefficient at 5 A / s (e.g., 1.2) and the upper limit of the adjustment coefficient at 10 A / s (e.g., 0.8) as endpoints. This gradual transition ensures that the adjustment coefficient remains continuous and smooth with changes in the current rate of change.

[0087] In some embodiments, the residual interval is a series of continuous intervals divided based on the reasonable fluctuation range of the voltage residual under normal battery operating conditions. Each interval corresponds to a preset residual adjustment coefficient, which is used to characterize the stability of parameter identification under different voltage residual small fluctuation intensities. The target residual adjustment coefficient is a residual adjustment coefficient matched according to the current voltage residual interval of the battery under steady-state conditions. It is used to fine-tune the basic confidence factor and reflect the slight impact of normal voltage residual fluctuation on the reliability of parameter identification. In this embodiment, the value range of the target residual adjustment coefficient can be set to [0.6, 1.1].

[0088] Among them, the voltage residual is the difference between the measured terminal voltage of the battery and the predicted terminal voltage output by the battery state management model, reflecting the degree of matching between the battery state management model and the actual battery behavior; the measured terminal voltage is the battery terminal voltage acquired in real time by a voltage sensor, and the predicted terminal voltage is the terminal voltage prediction value calculated by the battery state management model based on the current model parameters, input current and other data.

[0089] Specifically, the adjustment rules corresponding to the residual adjustment coefficient are as follows: When the residual is small (e.g., <10mV), the model has been accurately matched, the current RLS estimation result is reliable, and it can be accepted at the basic confidence level; when the residual is moderate (e.g., 10mV ≤ voltage residual ≤ 30mV), the parameters may have actually changed, and the model needs to be updated, so the confidence level should not be actively reduced. However, it is necessary to consider the voltage range: if it is in the plateau region, it should still be suppressed by the basic confidence factor; when the residual is large (e.g., 30mV < voltage residual), the abnormal amplification of the residual is usually caused by measurement noise, electromagnetic interference, or communication packet loss, which should not trigger parameter updates, and the confidence level should be reduced.

[0090] To facilitate understanding, Table 3 provides an example of the rate of change adjustment coefficient for each residual interval. The values ​​shown are for illustrative purposes only.

[0091] Table 3 In some embodiments, after determining the basic confidence factor based on the correlation between the current state of charge and the confidence factor, if the voltage change value and current change rate of the battery do not meet the preset mutation conditions, the basic confidence factor is fine-tuned according to the target residual adjustment coefficient and the target change rate adjustment coefficient to obtain the target confidence factor.

[0092] Specifically, the target confidence factor can be obtained by multiplying the basic confidence factor, the target rate of change adjustment coefficient, and the target residual adjustment coefficient. Besides the above method, other methods can also be used to adjust the basic confidence factor; this embodiment does not limit this approach.

[0093] In this embodiment, by introducing the target rate of change adjustment coefficient and the target residual adjustment coefficient under steady-state conditions to jointly fine-tune the basic confidence factor, the reliability of the model parameters is refined and dynamically managed. This effectively suppresses the risk of parameter drift and miscorrection caused by the lack of effective information or noise interference in the RLS algorithm, and significantly improves the robustness and estimation accuracy of the system under all operating conditions.

[0094] In some embodiments, the above-mentioned fine-tuning of the basic confidence factor according to the voltage residual adjustment coefficient and the target rate of change adjustment coefficient to obtain the target confidence factor includes: Calculate the product of the target residual adjustment coefficient, the target rate of change adjustment coefficient, and the basic confidence factor; The product is processed based on a preset amplitude limiting function to obtain the target confidence factor.

[0095] The preset value range can be set to [0, 1]. The preset limiting function is used to extract the target confidence factor from the product based on the preset value range. Specifically, the formula for calculating the target confidence factor can be: ;in, For the target confidence factor, Basic confidence factor The target rate of change adjustment coefficient. This is the voltage residual adjustment coefficient; A preset limiting function is used to restrict the calculation result to the range of [0, 1].

[0096] The above calculation formula enables dynamic quantitative fine-tuning of the reliability of parameter identification. A limiting function is introduced to constrain the calculation results, ensuring that the target confidence factor is always within a reasonable range. This achieves accuracy in reliability determination under steady-state conditions and improves the rationality and stability of model parameter updates.

[0097] In some embodiments, the above-described S103 can be processed in the following manner: If the target confidence factor is greater than or equal to a preset factor threshold, the historical model parameters of the battery state management model are obtained. The target model parameters of the battery state management model are obtained by weighted fusion of the current model parameters and the historical model parameters based on the target confidence factor.

[0098] The aforementioned preset factor threshold is a pre-defined critical value for the confidence factor, which can be set to 0.3 or dynamically adjusted as needed; this embodiment does not impose any restrictions on this. Historical model parameters are reliable model parameter values ​​that have been confirmed and stored at the previous moment, and can be taken from the most recent RLS estimate under the high confidence interval.

[0099] In the target confidence factor If the parameters are greater than or equal to a preset factor threshold, retrieve the historical model parameters of the battery state management model. ; Set the current model parameters and historical model parameters The target model parameters are obtained by weighted fusion according to the following formula. : .

[0100] In this embodiment, when the target confidence factor is greater than or equal to the preset factor threshold, the current model parameters and historical reliable model parameters are weighted and fused using the target confidence factor. This ensures the adaptability and stability of parameter updates, avoids parameter jumps under normal operating conditions, and effectively reduces the risk of incorrect corrections.

[0101] In some embodiments, the above-described S103 can be processed in the following manner: If the target confidence factor is less than a preset factor threshold, obtain the historical model parameters of the battery state management model; The current model parameters are adjusted to the historical model parameters to obtain the target model parameters of the battery state management model.

[0102] If the target confidence factor is less than the preset factor, the error of the current model parameters in the current RLS estimation is determined to be too large. Therefore, the current correction is rejected, and the current model parameters are adjusted to the historical model parameters, thus obtaining the target model parameters for the battery state management model. .

[0103] In this embodiment, when the target confidence factor is less than the preset factor threshold, the model parameters are set to historically reliable model parameters, and the correction of the current RLS estimation result is rejected. This can block the introduction of erroneous parameters from the source, avoid the continuous impact of erroneous correction on the battery state management model, ensure the stability of the model parameters and the accuracy of subsequent state estimation, and significantly improve the robustness and anti-interference ability of the battery management system under abnormal operating conditions.

[0104] In some embodiments, the above-mentioned S101 can be implemented in the following manner: Real-time acquisition of battery terminal voltage and terminal current; By integrating the terminal current over time, the change in cumulative charge is obtained. The initial state of charge is obtained based on the change in accumulated charge and the initial state of charge of the battery. The open-circuit voltage is determined based on the terminal voltage; The initial value of the state of charge is calibrated based on the open-circuit voltage to obtain the current state of charge of the battery.

[0105] In some embodiments, the output signals of the voltage sensor and the current sensor are periodically read at a preset sampling frequency and digitally filtered to obtain accurate battery terminal voltage and terminal current I(t); the terminal current is discretely summed (or integrated in a trapezoidal manner) at a fixed sampling period to obtain the cumulative charge change, which represents the total charge flowing into or out of the battery from the start of integration.

[0106] Based on the fundamental principle of ampere-hour integration, using the formula Calculate the initial values ​​of the charged state, where The initial state of charge, This represents the change in cumulative charge. The rated capacity of the battery. The sign is determined based on the direction of charging and discharging (minus sign for discharging, plus sign for charging). The initial state of charge (SOC) refers to the battery's SOC value at the start of this integration calculation; it can be the stored value before the last system power outage or the reset value after the battery is fully charged.

[0107] When the battery is in a steady-state condition where the current is zero or the rate of change of current is lower than a preset threshold (such as 0.1A / s), the battery terminal voltage is approximately equal to the open-circuit voltage. In this case, the terminal voltage is directly used as the open-circuit voltage; or the influence of polarization voltage can be eliminated by filtering, static compensation, or other processing of the terminal voltage, and the true open-circuit voltage can be extracted.

[0108] By using the OCV-SOC curve obtained through offline calibration, the standard SOC value corresponding to the current open-circuit voltage is found. This standard SOC value is used as a correction benchmark to correct the initial value of the state of charge obtained by ampere-hour integration. The correction method can be direct replacement (such as a long-term static steady-state condition) or weighted correction (such as a quasi-steady-state condition). Finally, the current state of charge after eliminating error accumulation is obtained, realizing high-precision and high-stability estimation of the state of charge.

[0109] In some embodiments, determining the current model parameters includes: Real-time acquisition of the battery's terminal voltage and terminal current; Based on the terminal voltage and the terminal current, a parameter identification function for the battery state management model is constructed. The parameter identification function is used to characterize the dynamic relationship between the voltage response characteristics and current excitation of the battery state management model. The parameter identification function is solved recursively until the solution meets the preset convergence condition, thus obtaining the current model parameters.

[0110] Specifically, based on the voltage response equation of a battery state management model (such as the Thevenin equivalent circuit model), the battery's terminal voltage U(t) and terminal current I(t) are transformed into a linear regression form suitable for the RLS algorithm, thereby constructing a parameter identification function. For example, the voltage response equation of the Thevenin model is: ;in, For ohmic internal resistance, This is the polarization voltage.

[0111] Considering that the battery model parameters change slowly over time, the above equation is discretized. Let the current time be k, and the polarization voltage... Expressed as the relationship between voltage and current at the previous moment, substituting this relationship and ignoring minor historical errors, the voltage response equation can be rearranged into a standard linear regression form: ,in: The target of observation at the current moment is the terminal voltage acquired in real time. The vector of model parameters to be identified includes, but is not limited to, ohmic internal resistance, polarization resistance, and polarization capacitance. The regression vector is composed of historical and current current data; T is the transpose. This parameter identification function transforms the battery's dynamic electrical characteristics into a linear regression problem solvable by the RLS algorithm, characterizing the correlation between voltage response characteristics and current excitation.

[0112] The RLS algorithm is used to recursively solve the parameter identification function, and the model parameter estimates are updated iteratively. Specifically, based on the parameter estimates from the previous time step... Calculate the estimation error at the current time. Then through Kalman gain Update parameter estimates Simultaneously update the covariance matrix. To control the confidence level of parameter updates, and through the forgetting factor The influence of historical data is mitigated to adapt to changes in battery characteristics. The recursive process continues until the change in the parameter estimate is below a preset threshold, the estimation error meets the accuracy requirements, or the number of iterations reaches the upper limit (i.e., the preset convergence condition is met). At this point, stable parameter estimates are obtained, which are the current model parameters of the battery state management model. The forgetting factor can be set to 0.98, or it can be adjusted according to actual needs; this embodiment does not impose any restrictions on this.

[0113] In some embodiments, in order to provide reliable model parameter support for subsequent battery voltage prediction, state estimation and safety control, the current model parameters include, but are not limited to, the battery's ohmic resistance, polarization resistance and polarization capacitance.

[0114] In this embodiment, by collecting battery terminal voltage and current in real time, a parameter identification function based on the battery state management model is constructed, and the model parameters are solved recursively using the RLS algorithm. There is no need to store all historical data; online parameter identification can be achieved simply by iterative updates. The final current model parameters can accurately reflect the dynamic electrical characteristics of the battery, thus improving the adaptability and accuracy of battery management.

[0115] In some embodiments, for ease of understanding, the following are used: Figure 2The overall process of the battery management method is described, specifically including the following steps: S21, Real-time acquisition of battery terminal voltage U(t) and terminal current I(t); S22, Based on the acquired terminal voltage and terminal current, the parameters of the battery state management model are identified online using the recursive least squares (RLS) algorithm to obtain the current model parameters; S23, Obtain the current state of charge of the battery; S24, Assign a basic confidence factor based on the current state of charge; S25, Detect whether the battery has abnormal current or voltage fluctuation conditions; If an abnormal current or voltage fluctuation is detected, proceed to S212; If no abnormal current or voltage fluctuation is detected, proceed to S26; S26, Fine-tune the basic confidence factor by detecting the rate of change of current and voltage residual to obtain the target confidence factor; S212, Temporarily reduce the basic confidence factor to obtain the target confidence factor; S27, Judge the adjusted target confidence factor. Is it greater than the preset factor threshold? If yes, proceed to S213; otherwise, proceed to S28. S28: Directly reject this parameter correction and use historical model parameters. S211: Perform weighted correction of the current model parameters and historical reliable model parameters based on the target confidence factor. S29: Determine the target model parameters based on S28 or S213. S210: Write the target model parameters into the battery state management model. S211: Predict SOC, SOH, and SOP states based on the updated model parameters. Finally, the process returns to step S21 to enter the next loop, continuously achieving online monitoring of battery state, adaptive updating of model parameters, and accurate prediction of battery state.

[0116] Based on the same inventive concept as the above-described battery management method, this application also provides a battery management system, which can be found in detail below. Figure 3 As shown.

[0117] Figure 3 This is a schematic diagram of a battery management system provided in another embodiment of this application.

[0118] like Figure 3 As shown in the illustration, this application also provides a battery management system 300, including: Controller 301 is used to acquire the current state of charge of the battery; determine a target confidence factor corresponding to the current state of charge based on the correlation between the battery's state of charge and a confidence factor, wherein the target confidence factor is used to characterize the confidence level of the current model parameters of the battery state management model, and the battery state management model is used to simulate the voltage response characteristics of the battery; and adjust the current model parameters based on the target confidence factor to obtain the target model parameters of the battery state management model. The battery state management model is updated according to the target model parameters to obtain the updated battery state management model.

[0119] In some embodiments, the controller 301 described above may include an acquisition module, a processing module, an adjustment module, and an update module.

[0120] The aforementioned acquisition module is used to acquire the current state of charge of the battery; The aforementioned processing module is used to determine a target confidence factor corresponding to the current state of charge based on the correlation between the state of charge of the battery and the confidence factor. The target confidence factor is used to characterize the reliability of the current model parameters of the battery state management model, and the battery state management model is used to simulate the voltage response characteristics of the battery. The aforementioned adjustment module is used to adjust the current model parameters according to the target confidence factor to obtain the target model parameters of the battery state management model; The aforementioned update module is used to update the parameters of the battery state management model according to the target model parameters, so as to obtain the updated battery state management model.

[0121] In some embodiments, the controller 301 described above is specifically used for: Based on the relationship curve between the open-circuit voltage and the state of charge of the battery, the local slope of the current state of charge on the relationship curve is determined. The target confidence factor is determined based on the preset functional relationship between the local slope and the confidence factor.

[0122] In some embodiments, the controller 301 described above is specifically used for: Based on the relationship curve between the battery's open-circuit voltage and state of charge, the range of state of charge variation is divided into multiple state of charge intervals, and each state of charge interval corresponds to a preset confidence factor. The target confidence factor is determined based on the current state of charge interval.

[0123] In some embodiments, the controller 301 described above is specifically used for: Based on the relationship curve between the open-circuit voltage and the state of charge of the battery, the local slope corresponding to each data point on the relationship curve is determined. Based on the distribution characteristics of the local slope, the range of change in the state of charge is divided into multiple state of charge intervals; wherein, within the same state of charge interval, the amplitude of the change in the local slope is within a preset fluctuation range.

[0124] In some embodiments, the controller 301 is further configured to: The basic confidence factor is determined based on the correlation between the current state of charge and the confidence factor. If at least one of the voltage change value and the current change rate of the battery satisfies a preset abrupt change condition, an abnormal adjustment coefficient is determined based on at least one of the voltage change value and the current change rate. The target confidence factor is obtained by attenuating and correcting the basic confidence factor according to the anomaly adjustment coefficient.

[0125] In some embodiments, the controller 301 is further configured to: Calculate the product of the anomaly adjustment coefficient and the baseline confidence factor; The product is processed based on a preset limiting function to obtain a target confidence factor. The preset limiting function is used to extract the target confidence factor from the product based on a preset value range.

[0126] In some embodiments, the controller 301 described above is specifically used for: When both the voltage change value and the current change rate of the battery meet the preset sudden change conditions, a first adjustment coefficient is determined based on the voltage change value, and a second adjustment coefficient is determined based on the current change rate. The minimum value between the first adjustment coefficient and the second adjustment coefficient is selected as the abnormal adjustment coefficient.

[0127] In some embodiments, the controller 301 is further configured to: If neither the voltage change value nor the current change rate of the battery meets the preset abrupt change condition, a target change rate adjustment coefficient is determined according to the current change rate interval in which the current change rate is located; each current change rate interval corresponds to a preset change rate adjustment coefficient. The target residual adjustment coefficient is determined based on the residual interval in which the difference between the measured terminal voltage of the battery and the predicted terminal voltage output by the battery state management model lies; each residual interval corresponds to a preset residual adjustment coefficient. The base confidence factor is fine-tuned according to the target residual adjustment coefficient and the target rate of change adjustment coefficient to obtain the target confidence factor.

[0128] In some embodiments, the controller 301 is further configured to: Calculate the product of the target residual adjustment coefficient, the target rate of change adjustment coefficient, and the basic confidence factor; The product is processed based on a preset limiting function to obtain a target confidence factor. The preset limiting function is used to extract the target confidence factor from the product based on a preset value range.

[0129] In some embodiments, the controller 301 is specifically used to: obtain historical model parameters of the battery state management model when the target confidence factor is greater than or equal to a preset factor threshold; The target model parameters of the battery state management model are obtained by weighted fusion of the current model parameters and the historical model parameters based on the target confidence factor.

[0130] In some embodiments, the controller 301 is specifically used to: obtain historical model parameters of the battery state management model when the target confidence factor is less than a preset factor threshold; The current model parameters are adjusted to the historical model parameters to obtain the target model parameters of the battery state management model.

[0131] In some embodiments, the controller 301 is specifically used to: acquire the battery's terminal voltage and terminal current in real time; By integrating the terminal current over time, the change in cumulative charge is obtained. The initial state of charge is obtained based on the change in accumulated charge and the initial state of charge of the battery. The open-circuit voltage is determined based on the terminal voltage; The initial value of the state of charge is calibrated based on the open-circuit voltage to obtain the current state of charge of the battery.

[0132] In some embodiments, the controller 301 is specifically used to: collect the terminal voltage and terminal current of the battery in real time; Based on the terminal voltage and the terminal current, a parameter identification function for the battery state management model is constructed. The parameter identification function is used to characterize the dynamic relationship between the voltage response characteristics and current excitation of the battery state management model. The parameter identification function is solved recursively until the solution meets the preset convergence condition, thus obtaining the current model parameters.

[0133] The Battery Management System (BMS) of this application is used to perform at least one of the following functions for individual battery cells: state monitoring, state analysis, charge / discharge control, safety protection, thermal management, high-voltage power distribution, and information management. In addition, the BMS of this application can also implement the functions of a controller in an electrical device, such as a vehicle control unit (VCU) or a motor control unit (MCU), etc., and this application does not impose any limitations on this.

[0134] It should be noted that the battery management system in this application can be integrated as a controller into the battery device, such as into the battery pack or energy storage box. The battery management system in this application can also be integrated as a controller into electrical devices, such as in a vehicle or vehicle chassis. The battery management system in this application can also be integrated into the charging device as a controller, such as into the charging device or the battery swapping device. The battery management system in this application can also be deployed as control software on a server. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms, such as vehicle networking cloud, APP backend, etc.

[0135] Based on the same inventive concept, embodiments of this application also provide a battery device, including a battery and a battery management system as described in the above embodiments.

[0136] Figure 4 This is a schematic diagram of the structure of a terminal device provided in another embodiment of this application.

[0137] The terminal device may include a processor 401 and a memory 402 storing computer program instructions.

[0138] Specifically, the processor 401 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0139] Memory 402 may include mass storage for data or instructions. For example, and not limitingly, memory 402 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 402 may include removable or non-removable (or fixed) media. Where appropriate, memory 402 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 402 is non-volatile solid-state memory.

[0140] In a particular embodiment, memory 402 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Thus, typically, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to any of the battery management methods disclosed in this application.

[0141] The processor 401 implements any of the battery management methods described in the above embodiments by reading and executing computer program instructions stored in the memory 402.

[0142] In one example, the terminal device may also include a communication interface 403 and a bus 410. Wherein, as... Figure 4 As shown, the processor 401, memory 402, and communication interface 403 are connected through bus 410 and complete communication with each other.

[0143] The communication interface 403 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0144] Bus 410 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 410 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0145] Furthermore, in conjunction with the battery management methods in the above embodiments, this application embodiment can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the battery management methods in the above embodiments.

[0146] This application also provides a computer program product, including a computer program that, when executed by a processor, implements any of the battery management methods described in the above embodiments.

[0147] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0148] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0149] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0150] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0151] The above are merely specific embodiments of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A battery management method, characterized in that, The method includes: Obtain the current state of charge of the battery; Based on the correlation between the battery's state of charge and the confidence factor, a target confidence factor corresponding to the current state of charge is determined. The target confidence factor is used to characterize the reliability of the current model parameters of the battery state management model, which is used to simulate the voltage response characteristics of the battery. Based on the target confidence factor, the current model parameters are adjusted to obtain the target model parameters of the battery state management model; The battery state management model is updated according to the target model parameters to obtain the updated battery state management model.

2. The method according to claim 1, characterized in that, The step of determining the target confidence factor based on the correlation between the current state of charge and the confidence factor includes: Based on the relationship curve between the open-circuit voltage and the state of charge of the battery, the local slope of the current state of charge on the relationship curve is determined. The target confidence factor is determined based on the preset functional relationship between the local slope and the confidence factor.

3. The method according to claim 2, characterized in that, The preset function relationship is used to take the minimum value between the first preset value and the target value as the target confidence factor; the target value is the maximum value between the second preset value and the target ratio; the target ratio is the ratio between the first difference and the second difference; The first difference is the difference between the local slope and the first slope threshold, and the second difference is the difference between the first slope threshold and the second slope threshold; the first slope threshold is less than the second slope threshold. The first preset value is greater than the second preset value.

4. The method according to claim 1, characterized in that, The step of determining the target confidence factor based on the correlation between the current state of charge and the confidence factor includes: Based on the relationship curve between the battery's open-circuit voltage and state of charge, the range of state of charge variation is divided into multiple state of charge intervals, and each state of charge interval corresponds to a preset confidence factor. The target confidence factor is determined based on the current state of charge interval.

5. The method according to claim 4, characterized in that, The relationship curve between the battery's open-circuit voltage and state of charge divides the range of state of charge variation into multiple state of charge intervals, including: Based on the relationship curve between the open-circuit voltage and the state of charge of the battery, the local slope corresponding to each data point on the relationship curve is determined. Based on the distribution characteristics of the local slope, the range of change in the state of charge is divided into multiple state of charge intervals; wherein, within the same state of charge interval, the amplitude of the change in the local slope is within a preset fluctuation range.

6. The method according to claim 1, characterized in that, The step of determining the target confidence factor based on the correlation between the current state of charge and the confidence factor includes: The basic confidence factor is determined based on the correlation between the current state of charge and the confidence factor. If at least one of the voltage change value and the current change rate of the battery satisfies a preset abrupt change condition, an abnormal adjustment coefficient is determined based on at least one of the voltage change value and the current change rate. The target confidence factor is obtained by attenuating and correcting the basic confidence factor according to the anomaly adjustment coefficient.

7. The method according to claim 6, characterized in that, The step of attenuating and correcting the base confidence factor according to the anomaly adjustment coefficient to obtain the target confidence factor includes: Calculate the product of the anomaly adjustment coefficient and the baseline confidence factor; The product is processed based on a preset limiting function to obtain a target confidence factor. The preset limiting function is used to extract the target confidence factor from the product based on a preset value range.

8. The method according to claim 6, characterized in that, The preset mutation condition includes at least one of the following: The voltage change of the battery is greater than the change threshold. The rate of change of the battery current is greater than the rate of change threshold.

9. The method according to claim 6, characterized in that, When at least one of the voltage change value and the current change rate of the battery satisfies a preset abrupt change condition, determining an abnormal adjustment coefficient based on at least one of the voltage change value and the current change rate includes: When both the voltage change value and the current change rate of the battery meet the preset sudden change conditions, a first adjustment coefficient is determined based on the voltage change value, and a second adjustment coefficient is determined based on the current change rate. The minimum value between the first adjustment coefficient and the second adjustment coefficient is selected as the abnormal adjustment coefficient.

10. The method according to claim 6, characterized in that, After determining the basic confidence factor based on the correlation between the current state of charge and the confidence factor, the method further includes: If neither the voltage change value nor the current change rate of the battery meets the preset abrupt change condition, a target change rate adjustment coefficient is determined according to the current change rate interval in which the current change rate is located; each current change rate interval corresponds to a preset change rate adjustment coefficient. The target residual adjustment coefficient is determined based on the residual interval in which the difference between the measured terminal voltage of the battery and the predicted terminal voltage output by the battery state management model lies; each residual interval corresponds to a preset residual adjustment coefficient. The base confidence factor is fine-tuned according to the target residual adjustment coefficient and the target rate of change adjustment coefficient to obtain the target confidence factor.

11. The method according to claim 10, characterized in that, The step of fine-tuning the basic confidence factor according to the target residual adjustment coefficient and the target rate of change adjustment coefficient to obtain the target confidence factor includes: Calculate the product of the target residual adjustment coefficient, the target rate of change adjustment coefficient, and the basic confidence factor; The product is processed based on a preset limiting function to obtain a target confidence factor. The preset limiting function is used to extract the target confidence factor from the product based on a preset value range.

12. The method according to claim 1, characterized in that, The step of adjusting the current model parameters according to the target confidence factor to obtain the target model parameters of the battery state management model includes: If the target confidence factor is greater than or equal to a preset factor threshold, the historical model parameters of the battery state management model are obtained. The target model parameters of the battery state management model are obtained by weighted fusion of the current model parameters and the historical model parameters based on the target confidence factor.

13. The method according to claim 1, characterized in that, The step of adjusting the current model parameters according to the target confidence factor to obtain the target model parameters of the battery state management model includes: If the target confidence factor is less than a preset factor threshold, obtain the historical model parameters of the battery state management model; The current model parameters are adjusted to the historical model parameters to obtain the target model parameters of the battery state management model.

14. The method according to claim 1, characterized in that, The process of obtaining the current state of charge of the battery includes: Real-time acquisition of battery terminal voltage and terminal current; By integrating the terminal current over time, the change in cumulative charge is obtained. The initial state of charge is obtained based on the change in accumulated charge and the initial state of charge of the battery. The open-circuit voltage is determined based on the terminal voltage; The initial value of the state of charge is calibrated based on the open-circuit voltage to obtain the current state of charge of the battery.

15. The method according to claim 1, characterized in that, The determination of the current model parameters includes: Real-time acquisition of the battery's terminal voltage and terminal current; Based on the terminal voltage and the terminal current, a parameter identification function for the battery state management model is constructed. The parameter identification function is used to characterize the dynamic relationship between the voltage response characteristics and current excitation of the battery state management model. The parameter identification function is solved recursively until the solution meets the preset convergence condition, thus obtaining the current model parameters.

16. The method according to any one of claims 1 to 15, characterized in that, The current model parameters include the battery's ohmic resistance, polarization resistance, and polarization capacitance.

17. A battery management system, characterized in that, The system includes: A controller is used to acquire the current state of charge (SOC) of the battery; determine a target confidence factor corresponding to the current SOC based on the correlation between the SOC and a confidence factor, wherein the target confidence factor is used to characterize the confidence level of the current model parameters of the battery state management model, and the battery state management model is used to simulate the voltage response characteristics of the battery; and adjust the current model parameters based on the target confidence factor to obtain the target model parameters of the battery state management model. The battery state management model is updated according to the target model parameters to obtain the updated battery state management model.

18. A battery device, characterized in that, The device includes a battery and a battery management system as described in claim 17.

19. An electronic device, characterized in that, The electronic device includes: a processor and a memory storing computer program instructions; The processor reads and executes the computer program instructions to implement the battery management method as described in any one of claims 1-16.

20. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that, when executed by a processor, implement the battery management method as described in any one of claims 1-16.

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