Battery power calibration method and device based on gate state, equipment and storage medium

Abstract

The present application relates to the technical field of battery management, and particularly relates to a battery power calibration method and device based on a gating state, equipment and a storage medium, the method obtains power prediction parameters and state parameters of a battery, calculates power estimation results and their credibility, dynamically adjusts the gating state in combination with static state, voltage stability and other determination conditions, and finally selects a corresponding correction algorithm to calibrate the power according to the gating state. By introducing the gating state mechanism, the power calibration process can flexibly switch the correction strategy according to the actual running state of the battery: in the static stable stage, the credibility weight is increased to ensure the calibration accuracy; in the fluctuation stage, the noise influence is reduced to avoid false correction; in the long-time stable stage, the result is frozen to reduce invalid calculation. The method determines the credibility by fusing multiple parameters, and adopts the appropriate correction strategy in different gating states, thereby improving the accuracy of power estimation.

Description

Battery power calibration method, apparatus, device, and storage medium based on gated state Technical Field

[0001] This invention relates to the field of battery management technology, and in particular to a battery power calibration method, apparatus, device, and storage medium based on gating status. Background Technology

[0002] With the rapid development of new energy technologies, batteries, as core components of energy storage and power supply, are receiving increasing attention for performance monitoring and management. Accurate estimation of battery capacity is fundamental to achieving efficient energy management and directly affects the overall system performance and user experience.

[0003] Currently, battery capacity estimation is typically based on measurements of parameters such as voltage, current, and temperature, combined with model predictions or algorithm corrections to output the final result. However, in practical applications, battery state parameters often change significantly due to factors such as load fluctuations, environmental changes, or aging. Traditional capacity estimation methods usually employ fixed-rule compensation strategies, which are difficult to adapt to dynamic changes under different operating conditions. Especially under high noise or transient parameter abrupt changes, they are prone to large deviations and may even lead to irreversible cumulative errors.

[0004] Therefore, how to achieve fast, stable, and low-complexity accurate SOC estimation under dynamic operating conditions has become a technical problem that urgently needs to be solved in this industry. Summary of the Invention

[0005] The main objective of this invention is to provide a battery power calibration method, apparatus, device, and storage medium based on gated states, aiming to solve the technical problem of how to achieve fast, stable, and low-complexity accurate SOC estimation under dynamic operating conditions in the prior art.

[0006] To achieve the above objectives, the present invention provides a battery power calibration method based on gated states, the method comprising the following steps:

[0007] Acquire real-time detection data and power prediction status parameters of the target battery, wherein the power prediction status parameters include integral drift rate;

[0008] Based on the real-time detection data and the predicted power status parameters, the real-time detection data is compensated using the integral drift rate, and the battery power integral estimate at the current moment is obtained using the Coulomb integral algorithm.

[0009] Based on the real-time detection data and the battery power integral estimate, the voltage-based power observation value at the current moment is obtained.

[0010] Based on the real-time detection data, the battery status parameters are obtained, and combined with the observed power level, the reliability of the prediction result and the gating status are obtained.

[0011] Based on the gating state and the reliability of the prediction results, the battery power integral prediction value and the power prediction state parameters are corrected using the voltage-based power observation value to obtain the actual battery power result.

[0012] Optionally, acquiring the real-time detection data and predicted charge status parameters of the target battery includes:

[0013] The static state determination result is obtained by recording the static time and current fluctuation amplitude of the target battery based on the real-time detection data.

[0014] Based on the open-circuit voltage change rate and voltage sampling accuracy calculated from the real-time detection data, a voltage stability determination result is obtained.

[0015] The battery state parameters are obtained based on the recorded resting time, the resting state determination result, and the voltage stability determination result.

[0016] Optionally, obtaining the voltage-based power observation value at the current moment based on the real-time detection data and the battery power integral estimate includes:

[0017] Based on the real-time detection data, the terminal voltage, charging / discharging current, and temperature parameters of the target battery are obtained.

[0018] Based on the temperature parameter and the estimated integral value of the battery capacity, the battery internal resistance at the current moment is obtained;

[0019] The calculated value of the compensated open-circuit voltage is obtained based on the terminal voltage, the charging and discharging current and the battery internal resistance;

[0020] Based on the compensated open-circuit voltage value, the voltage-based energy observation value is obtained by mapping the open-circuit voltage characteristic curve.

[0021] Optionally, obtaining the battery state parameters based on the real-time detection data and combining them with the observed power level to obtain the reliability of the prediction result and the gating state includes:

[0022] The reliability of the static state is obtained based on the static time recorded in the battery state parameters.

[0023] The reliability of the voltage slope is obtained based on the open-circuit voltage corresponding to the real-time detection data and the slope of the preset battery charge curve.

[0024] The confidence level of the static state and the confidence level of the voltage slope are weighted and fused to obtain the confidence level of the prediction result.

[0025] Based on the reliability of the predicted results and the system operating parameters, the gating conditions are determined to obtain the gating state.

[0026] Optionally, the step of determining the gating condition based on the reliability of the prediction result and the system operating parameters to obtain the gating state includes:

[0027] If the static duration reaches the static determination time and the system noise level is lower than the preset noise threshold, then the current gate control state is determined to be the preparatory state.

[0028] If the confidence level of the estimated result in the preparatory state is greater than the confidence level threshold, then the current gating state is determined to be active.

[0029] If the static duration reaches the static determination time and the open circuit voltage change rate is lower than the voltage slope freezing threshold, then the current gate state is determined to be frozen.

[0030] If the battery operating state changes from static to non-static or the system noise level exceeds the noise protection threshold, the current gate state is determined to be idle.

[0031] Optionally, the step of correcting the battery charge integral estimate and the charge prediction state parameters using the voltage-based charge observations based on the gating state and the confidence level of the prediction result to obtain the actual battery charge result includes:

[0032] When the gated state is idle, frozen, or ready, the estimated battery charge integral value is used as the actual battery charge value.

[0033] In the preparatory state, only the calculation and smoothing update of the observation correction gain are performed, and no correction operation is performed on the battery charge integral estimate;

[0034] When the gating state is active, the observation correction gain is calculated based on the confidence level of the prediction result, and the battery power integral prediction is corrected using the voltage-based power observation value to obtain the actual battery power result.

[0035] Optionally, when the gated state is active, calculating the observation correction gain based on the confidence level of the prediction result, and correcting the battery charge integral prediction using the voltage-based charge observation to obtain the actual battery charge result includes:

[0036] Calculate the current observation correction gain based on the reliability of the predicted results and the preset observation gain threshold;

[0037] When the gated state is in the preparatory state, the current observed correction gain is weighted and fused with the gain value of the previous cycle to obtain a smoothed gain correction value.

[0038] When the gated state is active, the current observation correction gain is adjusted to compensate for the change rate of the battery state of charge to obtain the compensated gain correction value.

[0039] Based on the processed gain correction value, the battery power integral estimate, and the voltage-based power observation value, the integral drift rate in the battery power integral estimate and the power prediction state parameter is corrected to obtain the actual battery power result.

[0040] Furthermore, to achieve the above objectives, the present invention also proposes a battery power calibration device based on gated states, the battery power calibration device based on gated states comprising:

[0041] The parameter acquisition module is used to acquire real-time detection data and power prediction status parameters of the target battery, including the integral drift rate.

[0042] The prediction module is used to compensate the real-time detection data with the integral drift rate based on the real-time detection data and the power prediction state parameters, and to obtain the battery power integral prediction value at the current moment through the Coulomb integral algorithm.

[0043] The estimation module is also used to obtain the voltage-based power observation value at the current moment based on the real-time detection data and the battery power integral estimation value.

[0044] The status determination module is used to obtain battery status parameters based on the real-time detection data, and combine them with the observed power level to obtain the reliability of the prediction result and the gate status.

[0045] The dynamic correction module is used to correct the battery power integral estimate and the power prediction state parameters based on the voltage-based power observation value according to the gating state and the reliability of the prediction result, so as to obtain the actual battery power result.

[0046] Furthermore, to achieve the above objectives, the present invention also proposes a gated state-based battery power calibration device, which includes: a memory, a processor, and a gated state-based battery power calibration program stored in the memory and executable on the processor. The gated state-based battery power calibration program is configured to implement the steps of the gated state-based battery power calibration method described above.

[0047] Furthermore, to achieve the above objectives, the present invention also proposes a storage medium storing a gated state-based battery power calibration program, wherein when the gated state-based battery power calibration program is executed by a processor, it implements the steps of the gated state-based battery power calibration method described above.

[0048] One or more technical solutions proposed in this application have at least the following technical effects: This invention obtains the battery's power prediction parameters and state parameters, calculates the power estimation result and its reliability, and dynamically adjusts the gating state based on judgment conditions such as static state and voltage stability. Finally, it selects the appropriate correction algorithm to calibrate the power based on the gating state. By introducing a gating state mechanism, the power calibration process can flexibly switch correction strategies according to the actual operating state of the battery: increasing the reliability weight during the static and stable stage to ensure calibration accuracy; reducing the impact of noise during the period of severe fluctuation to avoid incorrect correction; and freezing the results during the long-term stable stage to reduce invalid calculations. This method improves the accuracy of power estimation by using multi-parameter fusion to determine reliability and adopting appropriate correction strategies under different gating states. Attached Figure Description

[0049] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0050] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 is a flowchart illustrating the first embodiment of the battery power calibration method based on gated state of the present invention;

[0052] Figure 2 is a flowchart illustrating the second embodiment of the battery power calibration method based on gated state of the present invention;

[0053] Figure 3 is a schematic diagram of the entire process of the battery power calibration method based on gated state of the present invention;

[0054] Figure 4 is a schematic diagram of the gated state machine of the battery power calibration method based on gated state of the present invention;

[0055] Figure 5 is a structural block diagram of the first embodiment of the battery power calibration device based on gated state of the present invention;

[0056] Figure 6 is a schematic diagram of the structure of a gated battery power calibration device in the hardware operating environment of the embodiment of the present invention.

[0057] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0058] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0059] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods. The main solution of this application embodiment is: acquiring real-time detection data and power prediction state parameters of the target battery; obtaining the battery power prediction result at the current moment based on the battery power prediction parameters; obtaining the confidence level and gating state of the prediction result based on the battery state parameters; and correcting the battery power prediction result based on the gating state and the confidence level of the prediction result to obtain the actual battery power result.

[0060] Currently, in energy storage applications with frequent battery charging and discharging and short resting times, the traditional Coulomb integration method suffers from accumulated error, while conventional OCV correction methods, requiring long resting times, are prone to SOC correction fluctuations or failures, making it difficult to achieve fast, stable, and low-complexity accurate SOC estimation under dynamic operating conditions. Therefore, how to achieve fast, stable, and low-complexity accurate SOC estimation under dynamic operating conditions is a pressing technical problem that needs to be solved.

[0061] It should be noted that the executing entity of this invention can be a gated battery power calibration device, or a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or a thermal management device capable of implementing the above functions based on a gated battery power calibration device. This embodiment does not specifically limit it in this way. The following uses a gated battery power calibration device as the executing entity as an example to describe this embodiment and the following embodiments.

[0062] Based on this, this application provides a battery power calibration method based on gated state. Referring to Figure 1, Figure 1 is a flowchart of the first embodiment of the battery power calibration method based on gated state of this application.

[0063] In this embodiment, the battery power calibration method based on gated state includes steps S10~S40:

[0064] Step S10: Obtain real-time detection data and power prediction status parameters of the target battery.

[0065] It is important to note that the core purpose of this step is to simultaneously acquire two sets of key parameters from the target battery system, forming the basis for subsequent state estimation. The first set of parameters are the charge prediction state parameters, which refer to the dynamic core variables upon which the algorithm relies for recursive state of charge estimation. These mainly include the predicted state of charge calculated based on the current-time integral, and the equivalent integral drift rate value introduced to compensate for accumulated measurement errors. The former represents the estimated instantaneous remaining energy of the battery, while the latter is responsible for tracking and modeling systematic deviations in the measurement system. The charge prediction state parameters also include the integral drift rate. The second set of parameters is the real-time detection data of the target battery. This part provides the algorithm's observation input, including directly measurable physical quantities such as current, voltage, and temperature, as well as operating condition criteria calculated based on a certain time window. For example, the resting confidence level is used to assess whether the battery has entered a stable resting state, and the open-circuit voltage slope confidence level reflects the sensitivity of the current voltage point to changes in charge. For instance, the resting confidence level will increase after the battery has stopped working for a period of time.

[0066] Understandably, the synergistic effect of these two sets of parameters forms the foundation of the entire state estimation process. Predictive parameters constitute the system's internal state model, driven and evolving based on physical laws and historical data; while state parameters represent the system's real-time observations of the external environment, serving as the basis for calibrating the internal model to align with the physical world. For example, when the static reliability is high, it indicates that the obtained voltage observations are relatively reliable. The algorithm will use this voltage value to evaluate and correct any accumulated errors in the predictive parameters, while the drift rate value will be continuously fine-tuned based on historical deviation patterns.

[0067] In one embodiment, acquiring real-time detection data and power prediction state parameters of the target battery includes: calculating the resting time and current fluctuation amplitude of the target battery based on the real-time detection data to obtain a resting state determination result; calculating the open-circuit voltage change rate and voltage sampling accuracy based on the real-time detection data to obtain a voltage stability determination result; and obtaining the battery state parameters based on the resting time record, the resting state determination result, and the voltage stability determination result.

[0068] It's important to note that this process begins with an analysis from two independent physical dimensions. The first dimension assesses the battery's resting state, primarily based on a combination of resting time recordings and current fluctuation amplitude. Resting time recordings refer to the cumulative duration the battery system remains in a state without significant charging or discharging activity, while current fluctuation amplitude quantifies the stability of the current signal during this resting period. A sufficiently stable resting state is a crucial prerequisite for the voltage signal to approach the open-circuit voltage value, which characterizes the battery's internal chemical equilibrium. The second dimension evaluates the stability and sensitivity of the battery voltage signal, primarily based on the rate of change of the open-circuit voltage and the system's voltage sampling accuracy. The rate of change of the open-circuit voltage reflects the sensitivity of the battery terminal voltage to changes in charge within the current charge range. A slow rate of change indicates a voltage plateau, where the voltage's indication of charge is weak and unsuitable as a calibration benchmark.

[0069] Understandably, the results from these two dimensions—the resting state determination result and the voltage stability determination result—together constitute the core content of the battery state parameters. For example, even if the battery has been resting for a sufficiently long time, if it happens to be in the voltage plateau region, the system may still determine that the current conditions for accurate calibration are not met.

[0070] It should be understood that the battery state parameters in this step are not obtained by directly reading the raw voltage or current readings, but rather by calculating a set of high-order criteria with clear physical meaning based on these raw data. These criteria quantify the feasibility and reliability of using the battery terminal voltage to estimate its remaining internal charge at the current moment, providing key decision-making basis for whether to enable and how to perform voltage observation and correction in subsequent steps. This is an important step in achieving adaptive and robust state estimation.

[0071] Step S20: Based on the real-time detection data and the predicted power state parameters, the real-time detection data is compensated using the integral drift rate, and the battery power integral estimate at the current moment is obtained using the Coulomb integral algorithm.

[0072] It's important to note that the core of this step lies in preprocessing and dynamically integrating the real-time detection data to obtain a basic estimated battery capacity. The integral drift rate refers to the rate of systematic deviation between the estimated capacity and the true value caused by factors such as zero-point drift of the current sensor and accumulated sampling errors during long-term operation of the Coulomb integration method. The compensation process involves using an algorithm to predict and correct this deviation trend in real time. For example, each time the battery is detected to be in a completely quiescent state, the integrated capacity at this time is compared with the observed capacity obtained from a table based on the stable voltage. The difference is used to update the drift rate parameter, thereby applying a reverse correction to the real-time current data in subsequent integrations, aiming to suppress the accumulation of errors at the source.

[0073] Understandably, the compensated real-time current data is then processed using the Coulomb integral algorithm. This algorithm integrates the current over time, estimating the change in battery capacity by accumulating the amount of charge flowing into or out of the battery. Specifically, the compensated current value is multiplied by the sampling time interval to obtain the charge change within a small time period. This change is then added to the previous time-based estimated battery capacity to obtain the current time-based integrated estimated battery capacity. For example, if the average discharge current is measured to be a certain value within a sampling period, multiplying this current by time yields the amount of charge released during that period. Subtracting this amount from the initial capacity gives the updated estimated value.

[0074] It should be understood that this step is the cornerstone of the entire energy estimation, providing continuous, real-time dynamic tracking of the energy level. However, despite drift compensation, residual small errors may still be amplified after long-term integration. Therefore, this integral estimate still needs to be periodically calibrated based on voltage observations in subsequent steps to complement each other and jointly approximate the true state of battery charge.

[0075] Step S30: Based on the real-time detection data and the battery power integral estimate, obtain the voltage-based power observation value at the current moment.

[0076] It's important to note that the core objective of this step is to obtain a charge observation benchmark independent of the current integration path by utilizing the battery's open-circuit voltage characteristics. The real-time monitoring data here primarily refers to the battery's terminal voltage and load current, while the voltage-based charge observation refers to an independent estimate that maps the measured voltage to the battery's state of charge. The basic principle is that after most batteries have been left to recover, there is a definite correspondence between their open-circuit voltage and remaining charge. This relationship is usually preset in the system through prior experiments using tables or function curves.

[0077] Understandably, the key to obtaining this observation lies in accurately assessing the battery's actual open-circuit voltage. When the battery is in a no-load, quiescent state, the measured terminal voltage can be approximated as the open-circuit voltage, and the observed charge value can be obtained directly from a preset relationship curve. However, during dynamic operation with a load, the terminal voltage may be distorted due to internal resistance voltage drop. Therefore, the system needs to indirectly calculate the theoretical open-circuit voltage under the current load conditions using the battery charge integral estimate. For example, by combining the battery internal resistance model with the battery state reflected by the load current and the integral estimate, the effects of polarization voltage and internal resistance voltage drop are compensated, thereby estimating the virtual open-circuit voltage, and then obtaining the observed charge value accordingly.

[0078] It should be understood that the voltage-based charge observations generated in this step provide a relatively reliable and direct reference for subsequent calibration. This effectively compensates for the shortcomings of the Coulomb integration method in terms of the accumulation of errors over time, because the relationship between voltage and charge is essentially unaffected by historical accumulated errors. However, this method relies on the battery being in equilibrium or having an accurate internal resistance model, and its accuracy decreases under dynamic operating conditions. Therefore, this observation is not directly used as the final result, but rather as a benchmark to verify and correct deviations in the integral estimate. The two complement each other, jointly improving the overall estimation accuracy of the system.

[0079] In one embodiment, obtaining the voltage-based power observation value at the current moment based on the real-time detection data and the battery power integral estimate includes: obtaining the terminal voltage, charging / discharging current, and temperature parameters of the target battery based on the real-time detection data; obtaining the battery internal resistance at the current moment based on the temperature parameters and the battery power integral estimate; obtaining the compensated open-circuit voltage calculation value based on the terminal voltage, charging / discharging current, and battery internal resistance; and obtaining the voltage-based power observation value by mapping the compensated open-circuit voltage value through an open-circuit voltage characteristic curve.

[0080] It should be noted that this embodiment specifically illustrates the detailed calculation path for deriving voltage-based energy observation values ​​from real-time detection data. First, the system extracts three key parameters from the real-time detection data: the target battery terminal voltage refers to the actual voltage value measured between the positive and negative terminals of the battery; the charging / discharging current refers to the current value flowing into or out of the battery; and the temperature parameter refers to the battery body temperature obtained by the sensor.

[0081] Understandably, after obtaining the key parameters, the next step is to dynamically determine the battery's internal resistance at the current moment. Battery internal resistance refers to the resistance value inside the battery that changes significantly with battery temperature and its own health status. Here, the system uses temperature parameters and the battery's integral estimated value to determine the internal resistance. For example, it can obtain the internal resistance by looking up a preset three-dimensional mapping table that reflects the relationship between internal resistance changes at different temperatures and battery capacities. This integral estimated value serves as an indirect indicator of the battery's current health status or equivalent cycle life, making the estimation of internal resistance more accurate.

[0082] It should be understood that after obtaining the internal resistance, the terminal voltage can be compensated to estimate the open-circuit voltage. The core idea of ​​compensation is to use Ohm's law to eliminate the voltage drop generated by the load current across the battery's internal resistance. Specifically, during discharge, the measured terminal voltage is added to the product of the current and the internal resistance; during charging, this product is subtracted from the terminal voltage to obtain the compensated open-circuit voltage. Finally, the system compares and maps the calculated open-circuit voltage value with a pre-calibrated open-circuit voltage characteristic curve. This curve clearly defines the correspondence between open-circuit voltage and charge. By looking up a table or interpolating, the required voltage-based charge observation value can be finally output.

[0083] Step S40: Obtain battery status parameters based on the real-time detection data, and combine them with the observed power level to obtain the reliability of the prediction result and the gating status.

[0084] It should be noted that this step aims to assess the reliability of the battery charge observations obtained in the previous step and to make logical decisions accordingly. Battery state parameters refer to characteristic quantities extracted from real-time monitoring data that reflect the current operating status of the battery, such as the severity of terminal voltage fluctuations, the magnitude and stability of load current, and whether the battery temperature is within a suitable range. These parameters collectively depict the instantaneous environment and operating conditions of the battery and are important bases for judging the reliability of the battery charge observations.

[0085] Understandably, the reliability of the predicted result is a dynamically changing quantitative indicator, calculated by combining the reasonableness of the battery state parameters and the observed charge level itself. For example, if the system detects that the battery is in a high-current charging / discharging state with drastic voltage fluctuations and a temperature deviating from the normal range, the charge level calculated based on the voltage may be distorted due to severe polarization effects, and its reliability will be judged as low. Conversely, if the battery is in a static or low-current stable operating condition with a normal temperature, the reliability of the observed value will be high. This evaluation is usually achieved through a set of preset fuzzy logic rules or weighted scoring algorithms.

[0086] It should be understood that the gating state acts like a switch. For example, when the confidence level is above a certain threshold, the gating state is "on," allowing the observed energy level to be used in subsequent steps to calibrate and correct the integral estimate; when the confidence level is below the threshold, the gating state is "off," and the system will ignore the currently unreliable observation and temporarily rely solely on the integral estimate to avoid introducing larger errors. This mechanism ensures that the system only performs calibration when the observation is reliable, improving the robustness of the overall estimation.

[0087] In one embodiment, obtaining battery state parameters based on the real-time detection data and combining them with the observed power level to obtain the reliability of the prediction result and the gating state includes: obtaining the reliability of the resting state based on the resting time record in the battery state parameters; obtaining the reliability of the voltage slope based on the open-circuit voltage corresponding to the real-time detection data and the preset slope of the battery power curve; weighting and fusing the reliability of the resting state and the reliability of the voltage slope to obtain the reliability of the prediction result; and determining the gating condition based on the reliability of the prediction result and the system operating parameters to obtain the gating state.

[0088] It should be noted that the core of this embodiment lies in using battery state parameters to quantify and evaluate the reliability of voltage observations. The resting time record refers to the length of time the battery remains idle under no-load current. The reliability of the voltage slope depends on the slope characteristics of the currently estimated open-circuit voltage value on a preset battery charge curve; this slope reflects the sensitivity of the charge level to voltage changes. These two dimensions together form the basis of the reliability assessment.

[0089] Understandably, the reliability of the resting state is a sub-item calculated based on the resting time record. For example, if the battery has been resting for several hours, its internal polarization voltage has fully dissipated, and the terminal voltage is very close to the true open-circuit voltage; in this case, the reliability of the resting state is very high. Conversely, if the load has just been disconnected, the reliability of the resting state is lower. The voltage slope reliability is determined based on a physical phenomenon: in the flat region of the battery charge curve, a small voltage measurement error will lead to a large charge estimation error; therefore, the reliability in this region is set to low. In the steep region of the curve with a large slope, the voltage has a high resolution to the charge, and the corresponding reliability is rated as high. The system weights and fuses these two sub-reliability values ​​using preset weights to finally generate a comprehensive prediction result reliability.

[0090] It should be understood that the gating state is the final decision output. The system compares the reliability of the fused prediction results with the preset system operating parameters to determine the gating conditions.

[0091] In one embodiment, the process of determining the gating condition based on the reliability of the estimated result and the system operating parameters to obtain the gating state includes: if the static duration reaches the static determination time and the system noise level is lower than a preset noise threshold, then the current gating state is determined to be a preparatory state; if the reliability of the estimated result is greater than the reliability threshold in the preparatory state, then the current gating state is determined to be an active state; if the static duration reaches the static determination time and the open-circuit voltage change rate is lower than the voltage slope freezing threshold, then the current gating state is determined to be a frozen state; if the battery operating state is detected to change from static to non-static or the system noise level is greater than the noise protection threshold, then the current gating state is determined to be an idle state.

[0092] It should be noted that the preparatory state is the initial preparation stage for the system to enter the calibration process. This requires the resting period to reach the set resting threshold and the system noise level to be below a preset noise threshold, ensuring the battery is in a sufficiently stable physical environment and measurement conditions. The active state is the critical stage of calibration execution. It is entered when, based on the preparatory state, the reliability of the estimated results obtained through comprehensive evaluation further exceeds a set reliability threshold. This signifies that the system confirms the current data quality is sufficient for reliable calibration. The frozen state is the locking stage after calibration. It is triggered in the active state when the rate of change of the open-circuit voltage is detected to be below a very small voltage slope freezing threshold, indicating that the voltage reading is highly stable. At this point, the system locks and applies the calibration data. The idle state is the system's normal or protective exit state. Once the system detects that the battery has finished resting and started working, or that internal electrical noise interference exceeds the safety boundary, the system immediately switches to this state, suspending all calibration-related operations.

[0093] Understandably, this series of states constitutes a rigorous, conditionally progressive state machine. Its purpose is to ensure that the precise operation of voltage reference calibration is performed only within the safest and most reliable window of opportunity. From preparation to activation, and then to freezing, it's a process of progressively tightening conditions and gradually increasing confidence, effectively filtering out transient disturbances and uncertain operating conditions. Furthermore, if the operating environment deteriorates, a forced transition to an idle state protects the system from contamination by unreliable data. This design transforms calibration from a simple threshold judgment into a controlled, stateful process, greatly enhancing the algorithm's robustness.

[0094] It should be understood that the transition thresholds between different states, such as the resting time, noise threshold, confidence threshold, and voltage slope freeze threshold, all need to be carefully calibrated experimentally based on the specific battery chemistry, system hardware performance, and application scenario. For example, for batteries with slow polarization relaxation, the resting time may need to be set longer; while for systems with high noise in the measurement circuit itself, the noise protection threshold needs to be increased accordingly to avoid frequent false triggers. These parameters together constitute the tuning interface for this gating logic to adapt to different application requirements.

[0095] Step S50: Based on the gating state and the reliability of the prediction result, the battery power integral prediction value and the power prediction state parameter are corrected using the voltage-based power observation value to obtain the actual battery power result.

[0096] It should be noted that the purpose of this step is to dynamically correct the accumulated error of the continuous integration estimation using the highly reliable observation information generated in the preceding steps. The gating state determines whether the voltage-based energy observations are included in this correction; while the reliability of the prediction result determines the strength and weight of this correction. The correction targets not only the current battery energy integration prediction value, but also a set of internal variables—the energy prediction state parameters used to support future integration calculations—ultimately outputting a more accurate actual battery energy result.

[0097] Understandably, the correction process is an intelligent decision-making process involving conditional triggering and weighted fusion. The system only initiates the correction procedure when the gating state is open. The strength of the correction is directly related to the reliability of the prediction result. For example, if the reliability is extremely high, the system may tend to directly replace or significantly adjust the integral prediction value with the observed value; if the reliability is moderate, it may use a weighted average method to fuse the observed value and the integral prediction value, with reliability as the weighting coefficient of the observed value. Simultaneously, the correction of the power prediction state parameters is also carried out. This may include resetting the initial integral value or slowly updating parameters reflecting long-term trends such as battery capacity decay and internal resistance changes, thereby establishing subsequent integral estimations on a more accurate basis.

[0098] In one embodiment, the step of correcting the battery power integral estimate and the power prediction state parameters using the voltage-based power observation value based on the gating state and the confidence level of the prediction result to obtain the actual battery power result includes: when the gating state is idle, frozen, or ready, using the battery power integral estimate as the actual battery power result; in the ready state, only the calculation and smoothing update of the observation correction gain are performed, without performing the correction operation on the battery power integral estimate; when the gating state is active, the observation correction gain is calculated based on the confidence level of the prediction result, and the battery power integral estimate is corrected using the voltage-based power observation value to obtain the actual battery power result.

[0099] It should be noted that the gating states in this embodiment are divided into idle state, frozen state, ready state, and active state based on the real-time operating conditions and data reliability of the battery system. Different gating states correspond to different processing logics. In the idle or frozen state, the system determines that it is not suitable to make corrections at this time, possibly because the battery is in a transient process or the data reliability is too low. Therefore, the integral estimate is directly output as the actual result to maintain the continuity of the estimation. The ready state is a transitional state. At this time, the system begins to prepare for possible corrections, such as calculating the observation correction gain and updating it smoothly, but does not change the integral estimate. This is similar to a "warm-up" mechanism to ensure that the gain parameter is stable and reliable. Only when the gating state is active, indicating that the reliability of the voltage observation value is the highest and the system conditions are ideal, will the system calculate the gain based on the reliability and use the observation value to make a weighted correction to the integral estimate, thereby obtaining a more accurate actual result.

[0100] Understandably, the purpose of this fine division is to make more rational use of voltage observations for calibration while ensuring the stability of the estimation. For example, when the system is inactive or under poor conditions, blind correction may introduce noise or even cause the results to diverge; while a comprehensive correction operation is only performed when the data is highly reliable and the system is ready.

[0101] It should be understood that since battery capacity estimation is a long-term, continuous process, the integral method will accumulate errors, while voltage correction, although accurate, is limited by conditions. Therefore, in this embodiment, by setting multiple states, the system can avoid oscillations or drifts caused by hasty corrections when data is unreliable, and gradually introduce calibration when conditions permit. This design improves the robustness of the system, ensuring that the capacity estimation maintains high accuracy and stability under various complex operating conditions.

[0102] In this embodiment, by acquiring and analyzing the battery's prediction and state parameters in real time, a power estimation result and its reliability are generated, and a multi-condition triggered gating state decision mechanism is introduced. Based on the reliability level and system operating conditions, this mechanism intelligently determines whether the current state is within a reliable window suitable for voltage reference calibration, and then adaptively selects the appropriate correction strategy to dynamically adjust the estimation result.

[0103] In summary, this technical solution integrates a comprehensive evaluation method that combines the static state with the reliability of the voltage-charge curve slope. It also constructs a multi-level gating logic consisting of preparatory, activated, frozen, and idle states. This allows the system to accurately identify and utilize the brief stable period during which the battery reaches a quasi-equilibrium state. This not only ensures that voltage calibration is performed only when the data is highly reliable, effectively avoiding the risk of erroneous corrections under dynamic operating conditions or voltage plateaus, but also significantly improves the algorithm's robustness against complex environmental interference through the orderly progression and forced protection mechanism of the state machine. Therefore, this solution ultimately achieves simultaneous optimization of battery charge estimation accuracy and reliability under all operating conditions.

[0104] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to Figure 2. When the gating state is active, the observation correction gain is calculated according to the confidence level of the prediction result, and the battery power integral prediction value is corrected using the voltage-based power observation value to obtain the actual battery power result, including steps S401~S404:

[0105] Step S401: Calculate the current observation correction gain based on the reliability of the estimated result and the preset observation gain threshold.

[0106] It should be noted that the observation correction gain refers to the weighting coefficient assigned to the electrical baseline value obtained from open-circuit voltage observations in the data fusion algorithm. The preset observation gain threshold is used to divide different confidence intervals and corresponds to different gain calculation strategies. The core of this step is to dynamically determine a gain value between zero and one based on the quantification results of the confidence assessment and through a preset logical or functional relationship. This value directly determines the influence of the observation information in the subsequent correction process.

[0107] Understandably, the computational logic aims to achieve a smooth and reasonable change in gain with increasing confidence level. For example, when the confidence level of the predicted result is lower than a preset observation gain threshold, it indicates high uncertainty in the voltage observation. In this case, the system adopts a conservative strategy, calculating a very small or even near-zero gain. This means that unreliable voltage observation information is almost ignored during correction, relying primarily on the predicted value from the ampere-hour integral. Conversely, when the confidence level is significantly higher than this threshold, the system determines that the voltage reference is highly reliable and calculates a larger or even near-one gain, causing the correction result to strongly converge with the voltage observation, achieving rapid calibration. This design ensures that gain allocation is strictly positively correlated with data reliability, avoiding the risks of blindly correcting when information is unreliable.

[0108] It should be understood that the setting of the preset observation gain threshold needs to be calibrated in conjunction with battery characteristics and system requirements. Essentially, it represents a balance between the "trust integration" and "trust voltage" strategies. The entire calculation process embodies the idea of ​​adaptive filtering. Through dynamic gain, the system can fully utilize a high-precision voltage reference to correct accumulated errors when the battery is stationary and stable, while robustly relying on the integral method to maintain estimation continuity under dynamic operating conditions, thus providing optimal correction strength parameters for subsequent steps.

[0109] Step S402: When the gating state is in the preparatory state, the current observation correction gain is weighted and fused with the gain value of the previous cycle to obtain the smoothed gain correction value.

[0110] It should be noted that the preparatory state indicates that the system is initially approaching the calibration conditions, but environmental parameters may still fluctuate at this time. If the gain value calculated in the current cycle is used directly, occasional fluctuations in single-point data may cause unnecessary jitter in the subsequent correction process.

[0111] Understandably, the system doesn't directly apply the new gain; instead, it performs a weighted fusion with the gain value used or stored in the previous calculation cycle. This fusion operation is essentially a low-pass filter. By assigning weights to historical gains, it smooths out potential noise or rapidly changing components in the instantaneous gain, resulting in a more gradual and stable gain correction value. For example, a first-order inertial element can be designed to constrain the rate of change of the gain. Ultimately, using this smoothed gain correction value in subsequent data fusion corrections effectively avoids jumps in battery power estimates caused by abrupt gain changes. This ensures better continuity and stability of the actual battery power output during the transition from the uncalibrated state to the calibration preparation state, improving the user experience.

[0112] Step S403: When the gating state is active, the current observation correction gain is adjusted according to the rate of change of the battery state of charge to obtain the compensated gain correction value.

[0113] It should be noted that this step introduces a dynamic compensation mechanism when the system determines that it is in an active state (i.e., calibration is allowed). The active state means that the battery is in a relatively ideal static steady state, and the core task at this time is to accurately correct the power estimate using highly reliable voltage observations.

[0114] Understandably, even when at rest, the battery's state of charge (SOC) can change extremely slowly due to minute self-discharge or measurement drift. The purpose of this step is to detect this potential trend (i.e., the rate of SOC change) and adjust the observation correction gain accordingly. Specifically, if a slow decline in SOC is detected, the system will appropriately increase the gain value, making the correction algorithm more actively follow the voltage observation to more quickly "track" or "compensate" for this slow change; conversely, it may slightly decrease the gain to avoid overcorrection.

[0115] It should be understood that this compensation adjustment ensures that the gain value not only reflects the static reliability of the data, but also responds to the dynamic characteristics of the system, making the corrections made in the active state more timely and accurate in line with the true state of the battery, thereby further improving the final estimation accuracy within a long-term static calibration window.

[0116] Step S404: Based on the processed gain correction value, the battery power integral estimate, and the voltage-based power observation value, correct the integral drift rate in the battery power integral estimate and the power prediction state parameter to obtain the actual battery power result.

[0117] It should be noted that the gain correction value refers to the weighting coefficient after reliability assessment and smoothing filtering, which determines the weight of the observed information in the correction process. The battery capacity integral estimate refers to the preliminary result of the capacity calculated by the ampere-hour integration method, while the voltage-based capacity observation is a relatively independent capacity benchmark obtained by looking up the relationship between open-circuit voltage and state of charge. The integral drift rate describes the trend of the estimated capacity of the ampere-hour integration method shifting systematically over time due to factors such as cumulative errors.

[0118] Understandably, the correction operation is essentially a weighted fusion process. The system uses the processed gain correction value as the weight of the observation value, and performs a weighted average of the two to obtain the final actual battery capacity result. Simultaneously, the system uses the difference between the current observation and the prediction to slowly update the state parameter of the integral drift rate at a specific learning rate. For example, if the integral value is systematically higher than the voltage observation value for several consecutive observations, the estimate of the negative drift rate will be adjusted upwards accordingly, allowing future integral predictions to automatically compensate for this trend.

[0119] This embodiment dynamically calculates the observation correction gain by evaluating the reliability of the power estimation results, and adjusts the gain by smoothing or compensation according to a specific gating state decision mechanism. Finally, the processed gain is used to weight and fuse the voltage observation benchmark and the ampere-hour integral estimation value to output the actual battery power result.

[0120] In summary, this embodiment ensures the rationality of data fusion weights by dynamically calculating the gain based on confidence level; furthermore, it effectively suppresses gain jumps and enables the gain to respond to slow changes in battery state through a gating state mechanism, thereby improving the stability and adaptability of the gain; finally, the optimized gain-weighted fusion algorithm enables the power estimation results to both utilize the high precision of voltage observations to correct accumulated errors and maintain the continuity of the ampere-hour integration method, thus significantly improving the accuracy and reliability of the estimation under all operating conditions.

[0121] As shown in Figure 3, Figure 3 is a schematic diagram of the entire process of the battery power calibration method based on gating state of the present invention.

[0122] It should be noted that this flowchart fully illustrates an adaptive battery capacity estimation method. First, at the beginning of each estimation cycle, the system performs signal acquisition and basic state calculation steps. Specifically, it acquires the battery's current, voltage, and temperature signals, and calculates basic operating parameters such as resting time and voltage change rate based on these. These parameters are not directly used for capacity updates, but rather serve as core input criteria for evaluating whether the system enters the calibration window. Their purpose is to provide a reliable quantitative basis for subsequent state decisions.

[0123] Understandably, after the data preparation phase, the process enters the critical stage of operating condition reliability assessment and state decision-making. Based on the aforementioned basic parameters, the system dynamically assesses whether the current conditions meet the high-confidence criteria for allowing strong calibration using voltage signals, such as the battery being sufficiently quiescent and the voltage being in the high-sensitivity range. The assessment results are processed through threshold judgment and hysteresis handling, ultimately generating a correction gating signal and its associated correction strength coefficient. This stage essentially constitutes a nested state machine, managing the system's conditional switching between various modes such as "normal prediction," "preparation," "activated calibration," and "freeze protection." Its core function is to authorize the activation of the voltage calibration path only when the conditions are strictly met, thereby restricting the calibration operation to reliable static or quasi-static operating conditions and avoiding errors introduced by the dynamic process.

[0124] After the calibration process meets several preset conditions, the system performs observation compensation and data fusion steps. In this step, based on the currently measured terminal voltage, the polarization voltage is dynamically compensated using the internal resistance value obtained through real-time estimation or table lookup to obtain a more accurate open-circuit voltage estimate. Subsequently, a voltage reference charge value is obtained by mapping the open-circuit voltage-charge relationship curve. Finally, based on the aforementioned correction strength coefficient, this reference value and the predicted charge value obtained through ampere-hour integration are fused with adjustable weights to calculate the optimal charge estimate for the current cycle. This process completes the charge correction and adaptive update of model parameters in a controlled and gradual manner, ensuring a smooth transition of the estimated value.

[0125] The entire process constitutes a closed-loop iterative system. When conditions are not met, the system skips the strong calibration path and relies solely on ampere-hour integration for energy estimation and low-speed parameter adaptation. Through this structured process design, the system achieves a hybrid estimation strategy that prioritizes current integration prediction during dynamic operation and intelligently switches to voltage observation for calibration during reliable rest periods.

[0126] As shown in Figure 4, Figure 4 is a schematic diagram of the gated state machine of the battery power calibration method based on gated state of the present invention.

[0127] It's important to note that the gated state machine is a logic controller embedded on top of the main algorithm, operating according to a well-defined mode-switching rule. It primarily has several states: for example, the system is in idle mode during most dynamic operations; when the battery is detected to be quiescent and the voltage is stable, it enters prepared mode; and after further confirmation that conditions are met, it activates a high-precision voltage calibration mode. If, in any mode, the voltage change is detected to be too gradual (e.g., within the battery's voltage plateau region), the state machine enters a frozen mode, actively suspending potentially inaccurate strong calibration behavior. These state transitions are triggered by specific physical conditions (such as quiescent duration, voltage change rate threshold, etc.), ensuring the determinism and predictability of the system's behavior.

[0128] Understandably, the main triggering conditions for activating the calibration mode include: the system has been stably in the preparatory mode for a preset duration (e.g., >30 seconds); the voltage change rate is consistently below a preset threshold, indicating that the electrochemical polarization process has essentially relaxed; and the current estimated voltage value is not within the preset "voltage plateau region," ensuring that the voltage has sufficient sensitivity to changes in charge. The preparatory mode is an intermediate observation and confirmation state, preparing for a possible entry into the strong calibration mode. The main triggering conditions are that the battery is determined to have essentially disconnected external load or extremely low current, i.e., entering a "static" state, or the voltage initially shows signs of stabilization (its change rate begins to decrease, but has not yet fully met the requirements for strong calibration). The core purpose of the freeze protection mode is to actively suspend voltage calibration under specific low-sensitivity operating conditions to prevent the introduction of errors. The main triggering conditions are that the battery terminal voltage is detected to be within the preset "voltage plateau region" (e.g., the 3.2V-3.3V flat range for lithium iron phosphate batteries), and that the voltage change rate is detected to be too low (e.g., below a very small threshold), or that the charge estimation result shows a prolonged plateau state, indicating that the voltage's resolution to charge is extremely low, making forced calibration ineffective and dangerous. Idle mode is the default state in which the system most often runs. It means that the specific entry conditions for any of the above "ready", "activated" or "frozen" modes are not met at the current time. That is, the system is in a normal operating condition such as dynamic charging and discharging (any load or charging current), or the voltage is not stable in the initial stage of rest, or it is in a non-ideal calibration window in the non-platform area.

[0129] It should be understood that in this embodiment, the system continuously monitors the operating conditions in idle mode. When a static signal is detected, it enters a standby mode for observation. If the voltage remains stable and moves out of the plateau region during the standby period, it switches to active calibration mode to perform correction once the conditions are met. Regardless of whether it is in normal, standby, or active mode, once it detects entering the voltage plateau region, it immediately forces a jump to freeze protection mode, pausing the strong calibration. When exiting static mode or when the plateau region conditions are no longer met, the system switches back to normal prediction mode, thus forming a closed-loop, condition-driven state flow system with a protection mechanism.

[0130] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the battery power calibration method based on gating state of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0131] This application also provides a battery power calibration device based on gated state. Referring to Figure 5, the battery power calibration device based on gated state includes:

[0132] The parameter acquisition module 10 is used to acquire real-time detection data and power prediction status parameters of the target battery, wherein the power prediction status parameters include the integral drift rate.

[0133] The prediction calculation module 20 is used to compensate the real-time detection data using the integral drift rate based on the real-time detection data and the power prediction state parameters, and to obtain the battery power integral prediction value at the current moment using the Coulomb integral algorithm.

[0134] The prediction calculation module 20 is also used to obtain the voltage-based power observation value at the current moment based on the real-time detection data and the battery power integral prediction value.

[0135] The state determination module 30 is used to obtain battery state parameters based on the real-time detection data, and combine them with the power observation value to obtain the reliability of the prediction result and the gate state.

[0136] The dynamic correction module 40 is used to correct the battery power integral estimate and the power prediction state parameters based on the voltage-based power observation value according to the gating state and the reliability of the prediction result, so as to obtain the actual battery power result.

[0137] In one embodiment, the parameter acquisition module 10 is further configured to obtain a static state determination result based on the static time record and current fluctuation amplitude of the target battery calculated from the real-time detection data; obtain a voltage stability determination result based on the open-circuit voltage change rate and voltage sampling accuracy calculated from the real-time detection data; and obtain the battery state parameters based on the static time record, the static state determination result, and the voltage stability determination result.

[0138] In one embodiment, the estimation module 20 is further configured to obtain the terminal voltage, charging / discharging current, and temperature parameters of the target battery based on the real-time detection data; obtain the battery internal resistance at the current moment based on the temperature parameters and the battery capacity integral estimation value; obtain the compensated open-circuit voltage calculation value based on the terminal voltage, charging / discharging current, and battery internal resistance; and obtain the voltage-based capacity observation value by mapping the voltage-based open-circuit voltage characteristic curve based on the compensated open-circuit voltage value.

[0139] In one embodiment, the state determination module 30 is further configured to: obtain the reliability of the static state based on the static time record in the battery state parameters; obtain the reliability of the voltage slope based on the open circuit voltage corresponding to the real-time detection data and the preset battery charge curve slope; perform weighted fusion of the reliability of the static state and the reliability of the voltage slope to obtain the reliability of the estimated result; and determine the gating condition based on the reliability of the estimated result and the system operating parameters to obtain the gating state.

[0140] In one embodiment, the state determination module 30 is further configured to: determine the current gate state as a preparatory state if the static duration reaches the static determination time and the system noise level is lower than a preset noise threshold; determine the current gate state as an active state if the reliability of the estimated result is greater than the reliability threshold in the preparatory state; determine the current gate state as a frozen state if the static duration reaches the static determination time and the open circuit voltage change rate is lower than the voltage slope freezing threshold; and determine the current gate state as an idle state if the battery operating state changes from static to non-static or the system noise level is greater than the noise protection threshold.

[0141] In one embodiment, the dynamic correction module 40 is further configured to, when the gated state is idle, frozen, or ready, use the battery power integral estimate as the actual battery power result; in the ready state, only the calculation and smoothing update of the observation correction gain are performed, without performing the correction operation on the battery power integral estimate; when the gated state is active, the observation correction gain is calculated based on the confidence level of the estimate result, and the battery power integral estimate is corrected using the voltage-based power observation value to obtain the actual battery power result.

[0142] In one embodiment, the dynamic correction module 40 is further configured to calculate the current observation correction gain based on the reliability of the prediction result and a preset observation gain threshold; when the gating state is in the preparatory state, weightedly fuse the current observation correction gain with the gain value of the previous cycle to obtain a smoothed gain correction value; when the gating state is in the active state, compensate and adjust the current observation correction gain according to the rate of change of the battery state of charge to obtain a compensated gain correction value; and correct the battery state of charge integral prediction value and the integral drift rate in the power prediction state parameters based on the processed gain correction value, the battery power integral prediction value, and the voltage-based power observation value to obtain the actual battery power result.

[0143] The gated state-based battery power calibration device provided in this application, employing the gated state-based battery power calibration method described in the above embodiments, can solve the technical problem of how to achieve fast, stable, and low-complexity accurate SOC estimation under dynamic operating conditions. Compared with the prior art, the beneficial effects of the gated state-based battery power calibration device provided in this application are the same as those of the gated state-based battery power calibration method provided in the above embodiments, and other technical features in the gated state-based battery power calibration device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0144] This application provides a gated state-based battery power calibration device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the gated state-based battery power calibration method in the first embodiment described above.

[0145] Referring to Figure 6 below, a schematic diagram of a gated battery calibration device suitable for implementing embodiments of this application is shown. The gated battery calibration device in this application can include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. The gated battery calibration device shown in Figure 6 is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0146] As shown in Figure 6, the gated battery calibration device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.) that can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 1002 or a program loaded from storage device 1003 into random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the gated battery calibration device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the gated battery calibration device to communicate wirelessly or wiredly with other devices to exchange data. Although a gated battery calibration device with various systems is shown in the figure, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems may be implemented alternatively.

[0147] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0148] The gated state-based battery power calibration device provided in this application, employing the gated state-based battery power calibration method described in the above embodiments, solves the technical problem of how to achieve fast, stable, and low-complexity accurate SOC estimation under dynamic operating conditions. Compared with the prior art, the beneficial effects of the gated state-based battery power calibration device provided in this application are the same as those of the gated state-based battery power calibration method provided in the above embodiments, and other technical features of this gated state-based battery power calibration device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0149] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0150] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0151] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the battery power calibration method based on gating state in the above embodiments.

[0152] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0153] The aforementioned computer-readable storage medium may be included in a gated battery power calibration device; or it may exist independently and not assembled into a gated battery power calibration device.

[0154] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by a gated battery power calibration device, the gated battery power calibration device performs the following: acquires real-time detection data and power prediction state parameters of the target battery, the power prediction state parameters including an integral drift rate; compensates the real-time detection data using the integral drift rate based on the real-time detection data and the power prediction state parameters, and obtains the current battery power integral estimate using a Coulomb integral algorithm; obtains the current voltage-based power observation value based on the real-time detection data and the battery power integral estimate; obtains battery state parameters based on the real-time detection data, and combines them with the power observation value to obtain the prediction result confidence level and the gated state; and corrects the battery power integral estimate and the power prediction state parameters using the voltage-based power observation value based on the gated state and the prediction result confidence level to obtain the actual battery power result.

[0155] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0156] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0157] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0158] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described gated state-based battery power calibration method. This solves the technical problem of how to achieve fast, stable, and low-complexity accurate SOC estimation under dynamic operating conditions. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the gated state-based battery power calibration method provided in the above embodiments, and will not be repeated here.

[0159] The computer program product provided in this application can solve the technical problem of battery power calibration based on gated states. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the battery power calibration method based on gated states provided in the above embodiments, and will not be repeated here.

[0160] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A battery power calibration method based on gated states, characterized in that, The battery power calibration method based on gated state includes: acquiring real-time detection data and power prediction state parameters of the target battery, wherein the power prediction state parameters include an integral drift rate, which refers to the rate of systematic deviation of the estimated power value from the true value in long-term operation using the Coulomb integral method; compensating the real-time detection data using the integral drift rate based on the real-time detection data and the power prediction state parameters, and obtaining the current battery power integral estimate using the Coulomb integral algorithm on the compensated data; obtaining the current voltage-based power observation value based on the real-time detection data and the battery power integral estimate; obtaining the resting state reliability based on the resting time record in the battery state parameters; obtaining the voltage slope reliability based on the open-circuit voltage corresponding to the real-time detection data and the preset battery power curve slope, wherein the voltage slope reliability depends on the slope characteristics of the position of the currently estimated open-circuit voltage value on the preset battery power curve, and the slope reflects the sensitivity of the power to voltage changes; and performing a multiplication of the resting state reliability and the voltage slope reliability. Weighted fusion is performed to obtain the reliability of the predicted result. If the static duration reaches the static determination time and the system noise level is lower than the preset noise threshold, the current gated state is determined to be a preparatory state. If the reliability of the predicted result is greater than the reliability threshold in the preparatory state, the current gated state is determined to be an active state. If the static duration reaches the static determination time and the open-circuit voltage change rate is lower than the voltage slope freezing threshold, the current gated state is determined to be a frozen state. If the battery operating state is detected to change from static to non-static or the system noise level is greater than the noise protection threshold, the current gated state is determined to be an idle state. When the gated state is idle, frozen, or preparatory, the battery charge integral estimate is used as the actual battery charge result. In the preparatory state, only the calculation and smoothing update of the observation correction gain are performed, and no correction operation is performed on the battery charge integral estimate. When the gated state is active, the observation correction gain is calculated based on the reliability of the predicted result, and the battery charge integral estimate is corrected using the voltage-based charge observation value to obtain the actual battery charge result.

2. The battery power calibration method based on gated state according to claim 1, characterized in that, The process of acquiring real-time detection data and power prediction status parameters of the target battery includes: obtaining a static state determination result by calculating the static time record and current fluctuation amplitude of the target battery based on the real-time detection data; obtaining a voltage stability determination result by calculating the open-circuit voltage change rate and voltage sampling accuracy based on the real-time detection data; and obtaining the battery status parameters based on the static time record, the static state determination result, and the voltage stability determination result.

3. The battery power calibration method based on gated state according to claim 1, characterized in that, The step of obtaining the voltage-based power observation value at the current moment based on the real-time detection data and the battery power integral estimate includes: obtaining the terminal voltage, charging / discharging current, and temperature parameters of the target battery based on the real-time detection data; obtaining the battery internal resistance at the current moment based on the temperature parameters and the battery power integral estimate; obtaining the compensated open-circuit voltage calculation value based on the terminal voltage, charging / discharging current, and battery internal resistance; and obtaining the voltage-based power observation value by mapping the compensated open-circuit voltage value through the open-circuit voltage characteristic curve.

4. The battery power calibration method based on gated state according to claim 1, characterized in that, When the gated state is active, the process of calculating the observation correction gain based on the reliability of the prediction result and correcting the battery charge integral prediction value using the voltage-based charge observation value to obtain the actual battery charge result includes: calculating the current observation correction gain based on the reliability of the prediction result and a preset observation gain threshold; when the gated state is in a preparatory state, weighting and fusing the current observation correction gain with the gain value of the previous cycle to obtain a smoothed gain correction value; when the gated state is active, compensating and adjusting the current observation correction gain according to the rate of change of battery state of charge to obtain a compensated gain correction value; and correcting the battery charge integral prediction value and the integral drift rate in the charge prediction state parameters based on the processed gain correction value, the battery charge integral prediction value, and the voltage-based charge observation value to obtain the actual battery charge result.

5. A battery power calibration device based on gated states, characterized in that, The battery power calibration device based on gated state includes: a parameter acquisition module for acquiring real-time detection data and power prediction state parameters of the target battery, wherein the power prediction state parameters include an integral drift rate, which refers to the rate of systematic deviation of the estimated power value from the true value in long-term operation using the Coulomb integral method; a pre-calculation module for compensating the real-time detection data using the integral drift rate based on the real-time detection data and the power prediction state parameters, and obtaining the current battery power integral prediction value using the Coulomb integral algorithm on the compensated data; the pre-calculation module is also used to obtain the current voltage-based power observation value based on the real-time detection data and the battery power integral prediction value; a state determination module for obtaining the resting state reliability based on the resting time record in the battery state parameters; obtaining the voltage slope reliability based on the open-circuit voltage corresponding to the real-time detection data and the preset battery power curve slope, wherein the voltage slope reliability depends on the slope characteristics of the position of the currently estimated open-circuit voltage value on the preset battery power curve, and the slope reflects the sensitivity of the power to voltage changes; and determining the resting state... The reliability of the prediction result is obtained by weighted fusion of the reliability of the voltage slope. If the static duration reaches the static determination time and the system noise level is lower than the preset noise threshold, the current gate state is determined to be in the preparatory state. If the reliability of the prediction result is greater than the reliability threshold in the preparatory state, the current gate state is determined to be in the active state. If the static duration reaches the static determination time and the open circuit voltage change rate is lower than the voltage slope freeze threshold, the current gate state is determined to be in the frozen state. If the battery operating state changes from static to non-static or the system noise level is greater than the noise protection threshold, the current gate state is determined to be in the frozen state. If a threshold is reached, the current gating state is determined to be an idle state. The dynamic correction module is used to take the battery power integral estimate as the actual battery power result when the gating state is idle, frozen, or ready. In the ready state, only the calculation and smoothing update of the observation correction gain are performed, and no correction operation is performed on the battery power integral estimate. When the gating state is active, the observation correction gain is calculated based on the confidence level of the estimate, and the battery power integral estimate is corrected using the voltage-based power observation value to obtain the actual battery power result.

6. A battery power calibration device based on gated states, characterized in that, The gated state-based battery power calibration device includes: a memory, a processor, and a gated state-based battery power calibration program stored in the memory and executable on the processor, wherein the gated state-based battery power calibration program is configured to implement the gated state-based battery power calibration method as described in any one of claims 1 to 4.

7. A storage medium, characterized in that, The storage medium stores a gated state-based battery power calibration program, which, when executed by a processor, implements the gated state-based battery power calibration method as described in any one of claims 1 to 4.

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