A mobile heat source vehicle state of charge dynamic measurement method and system based on adaptive confidence weighting smoothing

Abstract

The application belongs to the technical field of thermochemical energy storage, and discloses a mobile heat source vehicle state of charge dynamic measurement method and system based on adaptive confidence weighted smoothing, wherein a confidence weight is calculated according to temperature fluctuation variance and deviation of an actual pressure interval relative to a standard pressure interval, an adaptive smoothing coefficient is determined according to the confidence weight, smoothing processing is performed on single measurement original state of charge and historical state of charge by using the adaptive smoothing coefficient, a material health degree coefficient is updated according to the smoothed state of charge and cumulative heat release, and the smoothed state of charge is corrected according to the material health degree coefficient, so that capacity attenuation of thermochemical energy storage materials caused by long-term cyclic use is compensated in state of charge calculation, the current final output state of charge is stored for next measurement iteration calculation, a smoothed output sequence with memory effect is formed, and long-term stability of state of charge measurement and self-adaptation to material aging are realized.

Description

Technical Field

[0001] This invention belongs to the field of thermochemical energy storage technology, specifically a method and system for dynamic measurement of the state of charge of a mobile heat source based on adaptive confidence weighted smoothing. Background Technology

[0002] Mobile heat source vehicles use calcium oxide / Ca(OH)2 hydration reaction as the energy storage medium, and realize the storage and release of thermal energy through reversible thermochemical reaction. Its state of charge directly determines the vehicle's heating capacity and dispatching scheme. Real-time and accurate measurement of the state of charge is a key link in the safe and efficient operation of the mobile heating system.

[0003] Existing methods for measuring state of charge are affected by factors such as sensor noise, steam flow fluctuations, and ambient temperature disturbances under dynamic operating conditions. The results of a single measurement are subject to large random fluctuations. At the same time, after long-term use, the energy storage material suffers capacity decay due to sintering and loss of active components. Existing methods lack compensation mechanisms for the above factors, resulting in insufficient accuracy and long-term reliability of state of charge measurement.

[0004] Current mobile heat source vehicle state of charge (SOC) measurement technology mainly employs an indirect measurement method based on the closed-loop pressure rise response time. Its basic principle is as follows: the steam emission assembly is closed to seal the tank, and steam is introduced into the tank at a constant mass flow rate. Part of the steam is consumed by the CaO hydration reaction, while the remainder accumulates in the gas phase space, causing the pressure to rise. The time required for the pressure inside the tank to rise from the initial value to the final value is recorded. Then, the current SOC is determined based on a pre-calibrated mapping relationship between SOC and response time under standard conditions. This method, which inversely calculates the SOC using a single response time parameter, is feasible to a certain extent under standard laboratory conditions.

[0005] The existing methods described above have the following inherent defects that cannot be overcome under the actual operating conditions of mobile heat source vehicles. First, the results of a single measurement are easily affected by random factors such as sensor noise, instantaneous fluctuations in steam flow, and ambient temperature disturbances, leading to non-repeatable fluctuations in response time. Existing methods treat each measurement as an independent event, lacking a mechanism for utilizing historical measurement information, resulting in large fluctuations in output results. Second, existing methods only establish a mapping relationship under calibration conditions, without quantitatively assessing the deviation between actual measurements and calibration conditions. When temperature fluctuations or deviations in pressure range occur, it is impossible to provide a quantitative characterization of the reliability of the current measurement results. Third, during long-term cyclic use, thermochemical energy storage materials undergo repeated high-temperature sintering, leading to densification of particle structure and decreased porosity, or particle breakage and loss of active components due to thermal stress, resulting in a gradual decline in actual effective energy storage capacity. Existing methods still use the calibration relationship of brand-new materials to calculate the state of charge, failing to detect material aging, leading to an overestimation of actual usable energy and affecting the accuracy of heating dispatching decisions. In summary, there are currently no solutions in the technology for confidence assessment, adaptive smoothing filtering, and online tracking of material health for measuring the state of charge of mobile heat sources. Summary of the Invention

[0006] This invention provides a method and system for dynamic measurement of the state of charge of a mobile heat source vehicle based on adaptive confidence weighted smoothing, which solves the problem that existing mobile heat source vehicle state of charge measurement methods lack confidence assessment and aging compensation methods, resulting in insufficient measurement accuracy and long-term reliability.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A dynamic measurement method for the state of charge of a mobile heat source vehicle based on adaptive confidence-weighted smoothing includes the following steps: Establish a benchmark mapping function between the state of charge and the closed-circuit boost response time; Steam is introduced into a sealed tank at a predetermined constant steam mass flow rate. The measured closed pressurization response time required for the tank pressure to rise from the initial pressure to the final pressure is obtained. The tank temperature signal during the pressurization process is collected, and the temperature fluctuation variance is calculated. The confidence weight is calculated based on the temperature fluctuation variance and the deviation of the actual pressure range from the standard pressure range. The measured closed-loop pressure boosting response time is corrected for temperature based on the tank temperature and for pressure based on the actual pressure range to obtain the corrected closed-loop pressure boosting response time. The corrected closed-loop pressure boosting response time is then substituted into the reference mapping function to obtain the original state of charge for a single measurement. An adaptive smoothing coefficient is determined based on the confidence weight, and the original state of charge and historical state of charge of the single measurement are smoothed using the adaptive smoothing coefficient to obtain the smoothed state of charge. The material health coefficient is updated based on the smoothed state of charge and the accumulated heat release, and the smoothed state of charge is corrected based on the material health coefficient to obtain the current final output state of charge.

[0008] Preferably, establishing a benchmark mapping function between the state of charge and the closed-loop boost response time includes: at a standard test temperature Standard pressure range and predetermined constant steam mass flow rate Under these conditions, closed-loop voltage boost tests were performed on multiple known state-of-charge calibration points, and the values ​​at each calibration point were recorded. The corresponding closed-loop pressurization response time t_k is used to construct a calibration dataset. ,in Furthermore, it uniformly covers the range of states of charge from 0% to 100%, and the reference mapping function is constructed using a piecewise linear interpolation method.

[0009] Preferably, the temperature fluctuation variance The calculation formula is

[0010] Where m is the number of sampling points For the first The temperature inside the tank at each sampling time. This represents the average temperature inside the tank. The average temperature inside the tank The calculation formula is: .

[0011] Preferably, confidence weight The calculation formula is:

[0012] in, For temperature fluctuation variance, The preset temperature fluctuation reference variance, This represents the actual pressure range. For the standard pressure range span, and The preset weighting coefficients, .

[0013] Preferably, the temperature correction includes: determining a temperature correction coefficient based on Arrhenius reaction kinetics theory.

[0014] in The activation energy for the CaO hydration reaction. This is the universal gas constant. The average temperature inside the tank. Standard test temperature; Pressure correction includes determining the pressure correction factor based on the integral relationship of an isothermal pressurization process of an ideal gas.

[0015] in and These are the standard starting pressure and standard ending pressure. and These are the actual starting pressure and the actual ending pressure.

[0016] Preferably, the method for calculating the closed-loop pressure boost response time is as follows:

[0017] in To measure the closed-loop pressure boost response time, This is a temperature correction factor. This is the pressure correction factor.

[0018] Preferably, the adaptive smoothing coefficient calculation method is as follows:

[0019] in, The minimum smoothness coefficient that satisfies , Confidence weights; The method for calculating the smoothed state of charge is as follows:

[0020] in For a single measurement of the initial state of charge, The state of charge of the last final output, the The range is from 0.10 to 0.40.

[0021] Preferably, the method for calculating the material health coefficient is as follows:

[0022] in For rated energy storage capacity, The current final output state of charge represents the cumulative heat dissipation since the last fully charged state. = .

[0023] Preferably, when the confidence weight Below the preset low confidence threshold At that time, the measurement results will not be included in the update of the health index, and will remain unchanged. The low confidence threshold remains unchanged. It is 0.3.

[0024] A dynamic measurement system for the state of charge of a mobile heat source vehicle based on adaptive confidence-weighted smoothing includes: A sealed container used to hold thermochemical energy storage materials; A steam generating assembly introduces steam into the sealed tank at a predetermined constant steam mass flow rate. A pressure sensor is installed inside the sealed tank to collect the pressure signal inside the tank; A temperature sensor is installed inside the sealed tank to collect the temperature signal inside the tank during the pressurization process; A calorimeter is installed in the heating pipeline to measure the heat released to the outside. The controller, connected to the steam generating assembly, the pressure sensor, the temperature sensor, and the calorimeter, is used to perform the above method.

[0025] Compared with existing technologies, the present invention has the following beneficial effects: The present invention provides a dynamic measurement method for the state of charge (SOC) of a mobile heat source vehicle based on adaptive confidence weighted smoothing. Confidence weights are calculated based on the temperature fluctuation variance and the deviation of the actual pressure range from the standard pressure range. When the measurement conditions deviate from the calibration conditions, the confidence weights automatically decrease, providing a calculable and reliable basis for subsequent adaptive smoothing. An adaptive smoothing coefficient is determined based on the confidence weights. This coefficient is used to smooth the original SOC of a single measurement from historical SOCs. The material health coefficient is updated based on the smoothed SOC and accumulated heat release. The smoothed SOC is then corrected based on the material health coefficient, compensating for the capacity decay of thermochemical energy storage materials caused by long-term cyclic use in the SOC calculation. The current final output SOC is stored for the next measurement iteration, allowing historical measurement information to be continuously transmitted and cumulatively influence subsequent outputs, forming a smooth output sequence with a memory effect. This achieves long-term stability of SOC measurement and adaptability to material aging. Attached Figure Description

[0026] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a flowchart of a method for dynamically measuring the electric state of a mobile heat source based on adaptive confidence weighted smoothing, according to the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0029] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0030] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0031] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0032] See Figure 1 In one embodiment of the present invention, a method and system for dynamic measurement of the state of charge (SOC) of a mobile heat source vehicle based on adaptive confidence-weighted smoothing is provided. This solves the technical problem that existing SOC measurement methods lack confidence assessment and aging compensation methods, resulting in insufficient measurement accuracy and long-term reliability. The system achieves stable measurement of SOC and adaptive compensation for material aging through closed-loop pressure boosting response time measurement, confidence assessment, temperature and pressure correction, adaptive smoothing filtering, and online tracking of material health.

[0033] See Figure 1 Based on the above system, one embodiment of the present invention also provides a method for dynamic measurement of the state of charge of a mobile heat source vehicle based on adaptive confidence weighted smoothing, including the following steps.

[0034] S1: Establish a baseline mapping function between the state of charge and the closed-circuit boost response time.

[0035] Specifically, at the standard test temperature Standard pressure range and predetermined constant steam mass flow rate Under these conditions, a closed-loop voltage boost test was performed on multiple known state-of-charge (SOC) calibration points. The number of these known SOC calibration points was no less than five, and they uniformly covered the SOC range from 0% to 100%. Record the values ​​at each SOC calibration point. Corresponding closed-loop pressure boost response time Construct a calibration dataset And thereby establish the state of charge. The benchmark mapping function between the closed pressurization response time t and the closed pressurization response time t The reference mapping function is constructed using piecewise linear interpolation. For any given response time t, if t lies at an adjacent calibration point... and Between (i.e.) Then, the corresponding state of charge is calculated by linear interpolation according to the following formula:

[0036] Interpretive, the reference mapping function serves as the fundamental reference for all subsequent state of charge measurements, and its accuracy directly affects the accuracy of the final measurement results. Piecewise linear interpolation maintains monotonicity and continuity between calibration points, is computationally simple, and has clear physical meaning, making it suitable for engineering applications.

[0037] S2: Real-time measurement and characteristic parameter acquisition.

[0038] Specifically, under the state of charge to be measured, the steam discharge assembly is shut off to keep the sealed tank in a sealed state, and the predetermined constant steam mass flow rate is maintained. Water vapor is introduced into the sealed container. After the water vapor is introduced, part of it is consumed by the hydration reaction of CaO inside the container, and the remainder accumulates in the gas phase space of the container, increasing the pressure inside. The pressure inside the container is obtained from the actual initial pressure. Increase to actual termination pressure Required measured closed-loop pressure boost response time Simultaneously, the tank temperature signal during the pressurization process is continuously acquired at a sampling frequency of not less than 5Hz. ,in The number of sampling points. The sampling time.

[0039] Based on the collected temperature signals, calculate the average temperature inside the tank during the pressurization process:

[0040] And calculate the temperature fluctuation variance during the pressurization process:

[0041] Explanatory temperature fluctuation variance This characterizes the stability of the tank temperature during the pressurization process. When the tank temperature stabilizes, the temperature at each sampling point is close to the average value. The temperature is relatively small; when the temperature inside the tank fluctuates drastically, the temperature at each sampling point is dispersed. The value is relatively large. This parameter is an important input for subsequent confidence assessment.

[0042] S3: Measurement confidence assessment.

[0043] Specifically, based on the measured temperature stability and pressure range deviation, the confidence weight w for this measurement is calculated. The confidence weight is a dimensionless number between 0 and 1, used to quantitatively characterize the reliability of the current measurement result.

[0044] in: The preset temperature fluctuation reference variance is determined by repeatedly measuring and statistically analyzing the variance of temperature fluctuations under standard calibration conditions. It represents the temperature fluctuation level under normal measurement conditions. Specifically, it is determined by: at the standard test temperature... and standard pressure range Under the given conditions, repeat the closed-loop pressurization test at least 10 times, calculate the temperature fluctuation variance for each test, and take the average or median of the obtained variances as the mean. This is the actual measurement range of the pressure interval, i.e., the difference between the actual termination pressure and the actual starting pressure. The standard calibration pressure range span, which is the difference between the standard upper pressure limit and the standard lower pressure limit; This represents the relative deviation of the actual pressure range from the standard pressure range. The smaller this value, the closer the actual measured pressure range is to the calibration conditions. and These are preset weighting coefficients, used to adjust the impact of temperature fluctuations and pressure range deviations on the confidence level assessment. , .

[0045] Explanatory confidence weights The physical meaning and characteristics are as follows: (1) When the temperature fluctuation variance When the index approaches zero and the actual pressure range perfectly matches the standard pressure range, both internal parameters of the index approach zero. A value close to 1 indicates that the measurement conditions are in complete agreement with the calibration conditions, and the measurement results are highly reliable; (2) When the temperature fluctuation increases, (3) When the actual pressure range deviates from the standard pressure range, w decreases, indicating that temperature instability leads to a decrease in the reliability of the measurement results; Increase A decrease indicates that the deviation of pressure conditions leads to a decrease in the reliability of the measurement results; (4) When the temperature fluctuation is extremely large or the pressure range deviates extremely large, A confidence level approaching 0 indicates that the measurement result is essentially unreliable. Using the exponentially decaying confidence assessment function described above, the two key factors affecting measurement reliability can be continuously and quantitatively mapped to confidence weights between 0 and 1.

[0046] S4: Determination of the initial state of charge in a single measurement.

[0047] Specifically, the measured closed-loop pressure rise response time is first corrected for temperature. Temperature has a significant impact on the CaO hydration reaction rate. According to Arrhenius reaction kinetics, temperature changes alter the rate of water vapor consumption and the rate of pressure rise within the tank. The temperature correction coefficient α(T) is determined according to the following formula:

[0048] in The activation energy for the CaO hydration reaction is expressed in J / mol and was determined beforehand using thermogravimetric analysis. R is the universal gas constant, R = 8.314 J / (mol·K). This represents the current measured average temperature inside the tank. For standard test temperature, and All units are .

[0049] When the actual temperature Temperatures above standard At this time, the reaction rate increases, water vapor consumption increases, pressure rise slows down, and response time becomes longer. ) is negative. The measured response time is corrected by subtracting the response time at the standard temperature; when the actual temperature is lower than the standard temperature, The response time is supplemented and corrected. Through temperature correction, the response time measured at non-standard temperatures can be converted to standard test conditions, reducing the impact of internal temperature changes on SOC determination.

[0050] Then, pressure correction is applied to the measured closed-loop pressure rise response time. This is done when the actual measured initial pressure... Or terminate pressure Compared with standard value , When deviations exist, the varying span of the pressure rise interval will directly affect the time required for pressure increase. Pressure correction factor. Determined according to the following formula:

[0051] This correction factor is derived based on the integral relationship of an isothermal pressurization process for an ideal gas. The essence of pressure correction is to eliminate the time scale differences introduced by the different pressure start and end points, making the corrected response time comparable to the response time of the standard pressure range. When the actual pressure range coincides with the standard pressure range, = 1, no correction needed; when the actual pressure range is larger than the standard range, , Reduce response time; conversely, The response time is supplemented.

[0052] After combining temperature and pressure corrections, the closed-loop pressurization response time is corrected. Determined according to the following formula:

[0053] The order in which temperature and pressure correction factors are applied does not affect the final product. Temperature correction converts the response time at non-standard temperatures to standard temperature conditions, while pressure correction converts the response time at non-standard pressure ranges to standard pressure range conditions. Both factors act independently on the measured response time, ultimately yielding a corrected response time comparable to the calibration conditions.

[0054] Correcting the closed pressurization response time Substituting the reference mapping function established in step S1, the original state of charge for the current single measurement is calculated using piecewise linear interpolation:

[0055] Explanatory, the original state of charge This indicates that the estimated state of charge is based solely on the current single measurement data, after temperature and pressure corrections, and has not yet been adjusted for historical smoothing and material health.

[0056] S5: Confidence-based adaptive exponentially weighted moving average smoothing.

[0057] Specifically, in actual measurements, due to factors such as sensor noise, slight fluctuations in steam flow, and temperature disturbances, the initial state of charge in a single measurement can vary. Random fluctuations are often present. However, the state of charge of thermochemical energy storage materials is a continuously changing physical quantity that does not change abruptly in a short period of time. Therefore, historical measurement information can be used to constrain and correct the current measurement results.

[0058] This invention employs an exponentially weighted moving average method to smooth the measurement results and adaptively adjusts the smoothing coefficient based on the confidence weight calculated in step S3. The core idea is to assign a larger weight to measurement results with high confidence, so that the output can quickly track changes in the true value; and to assign a smaller weight to measurement results with low confidence, so that the output mainly relies on reliable historical information, thereby suppressing the interference of outliers.

[0059] Adaptive smoothing coefficient Determined according to the following formula:

[0060] in: Let be the adaptive smoothing coefficient for the current k-th measurement, dimensionless, with a value range of . Smoothing coefficient, satisfying Preferably, The range is 0.10 to 0.40. The lowest retention weight of historical values ​​in the smoothing result, when At 25:00, even if the current measurement is completely unreliable ( The smoothed results retain 75% of historical information, preventing abrupt changes in the output. The confidence weights calculated in step S3 .

[0061] The adaptive smoothing coefficient The value characteristics are as follows: (1) When the confidence weight When it approaches 1, When the value approaches 1, the smoothed result is almost entirely determined by the current measurement. The decision is that the measurement response is fast and can reflect the real changes in the state of charge in a timely manner; (2) when the confidence weight When it approaches 0, Approaching The smoothed result is mainly composed of historical values. Decision (weight) Effectively suppresses interference from low-quality measurements; (3) When the confidence weight is in the middle, exist Linear changes between measurements enable a smooth transition between current measurements and historical information.

[0062] Determine the adaptive smoothing coefficient Then, the original state of charge of the current single measurement is... Compared with the previous final output state of charge Perform exponentially weighted moving average smoothing to obtain the smoothed state of charge:

[0063] in: The state of charge after smoothing is the k-th measurement; The original state of charge for a single measurement is calculated in step S4; For the first The final output state of charge is measured (the previous one), and this value is stored in the on-board storage unit for iterative calculation.

[0064] For the first measurement ( (No historical value) At this point, directly As The initial value, i.e. After obtaining two or more measurement results, smoothing calculation will begin.

[0065] S6: Online tracking and correction of material health.

[0066] Specifically, during long-term cyclic use, the actual effective energy storage capacity of thermochemical energy storage materials gradually decreases due to repeated high-temperature sintering, particle structure wear, and loss of active components. The material health coefficient H is defined as the ratio of the actual effective energy storage capacity of the current thermochemical energy storage material to the rated energy storage capacity of a brand-new material.

[0067] in This represents the actual effective energy storage capacity of current thermochemical energy storage materials, expressed in kWh or MJ. The rated energy storage capacity of the novel thermochemical energy storage material is expressed in kWh or MJ. .

[0068] Smoothed state of charge is obtained in each measurement. Subsequently, based on the current accumulated heat release, the material health coefficient is tracked and updated online. The basic principle is as follows: between two adjacent measurements, the heat released by the mobile heat source vehicle... (Obtained by integrating measurements from flow meters and thermometers in the heating pipeline) should be equal to the reduction in the energy stored in the energy storage material:

[0069] in This is the final state of charge as measured in the last measurement. This represents the health score from the last tracking session.

[0070] Therefore, the current estimated health coefficient is... It can be updated by the following formula:

[0071] in This represents the cumulative heat release since the last state of full charge (SOC = 100%), a value obtained through continuous integration using a calorimeter. The physical meaning of this formula is: the remaining effective usable energy in the current energy storage material is... The actual remaining effective usable energy is The ratio of the two is the ratio of the current actual effective energy storage capacity to the rated capacity.

[0072] It is important to note that when measuring confidence weights... Below the preset low confidence threshold At that time, the measurement corresponds to Its reliability is low, and using it for health updates may introduce significant errors. Therefore, a low confidence threshold should be set. (Preferably 0.3), only when Only then will the measurement results be included in the update of the health coefficient; when At that time, skip this update and keep constant.

[0073] Current health score By incorporating the final state of charge calculation, the final output state of charge for the k-th measurement is obtained:

[0074] This formula estimates the smoothed degree of reaction ( ) and actual available capacity discount factor ( Multiplying these values ​​yields the state of charge (SOC) value, which reflects the material's actual heating capacity. For example, when a material has aged to the point where its actual capacity is only 90% of its rated capacity (H = 0.9), even if the degree of reaction of CaO in the material reaches a certain level... = 80%, its actual heat supply is only equivalent to the heat corresponding to 72% of the state of charge of the new material. The above correction can avoid the overestimation of capacity when calculating the SOC based on the rated capacity of the new material.

[0075] Ultimately The output is converted into a percentage format for use in heating scheduling and operational decisions.

[0076] S7: Storage and iteration of historical smoothed values.

[0077] Specifically, the final output state of charge. Stored in the vehicle's onboard storage unit as historical values ​​for the next measurement: In the subsequent (k+1)th measurement, this value is used as Substituting these values ​​into the smoothing formula in step S5, iterative updates are achieved. Through this iterative mechanism, historical measurement information is continuously transmitted and accumulates, influencing subsequent outputs and forming a smooth output sequence with a "memory effect."

[0078] In one possible implementation, and The value of reflects the relative importance of temperature stability and pressure range accuracy in confidence evaluation. Preferably, and Both are set to 1.0, indicating that they have equal weight.

[0079] In one possible implementation, the smoothest coefficient The range is from 0.10 to 0.40, preferably. 25, at this point even if the current measurement is completely unreliable ( The smoothed results still retain 75% of the historical information.

[0080] In one possible implementation, the low confidence threshold It is 0.3, only when Only then will the measurement results be included in the update of the health coefficient; when At that time, skip this update and keep constant.

[0081] Validity verification In one possible implementation, the technical effects and feasibility of the present invention are illustrated using a mobile heat source vehicle calcium oxide hydration reaction energy storage system as an example.

[0082] The test subject was a mobile heat source vehicle using calcium oxide / Ca(OH)₂ hydration reaction as the energy storage medium. The rated capacity of the energy storage system... = 1000 kWh. Under dynamic operating conditions, the temperature, steam flow rate, and pressure conditions inside the tank fluctuate to varying degrees. The comparative test is divided into three groups: Group A uses the traditional single measurement method (no confidence assessment, no smoothing, no aging compensation); Group B uses the method of this invention (including confidence assessment and adaptive smoothing, but no health correction); Group C uses the complete method of this invention (including confidence assessment, adaptive smoothing, and online health tracking).

[0083] Calibration conditions: Standard test temperature T0 = 523K, standard pressure range = [0.2MPa, 0.6MPa], predetermined constant steam mass flow rate The number of calibration points, n = 11, uniformly cover the range from 0% to 100% state of charge. The temperature sensor sampling frequency is set to 10Hz, and the number of sampling points, m, for each voltage boost process is determined based on the voltage boost duration.

[0084] Confidence assessment parameter: Reference variance of temperature fluctuation (Obtained from statistics of 10 standard condition calibration tests), weighting coefficients Adaptive smoothing parameters: Health tracking parameters: Low confidence threshold = 0.3.

[0085] Temperature correction factor activation energy in Preliminary determination was made through thermogravimetric analysis experiments. = 42.7 kJ / mol (This value is the typical activation energy range for the CaO hydration reaction; the actual value should be determined experimentally).

[0086] Under standard test temperature T0=523K, standard pressure range [0.2MPa, 0.6MPa], and constant steam mass flow rate Q0=20g / s conditions, calibration was performed at 11 known states of charge (SOC0=0%, SOC1=10%, ..., SOC...). 10 =100%) respectively, perform closed pressure boosting tests, record the response time corresponding to each calibration point, construct a calibration dataset and establish a piecewise linear interpolation benchmark mapping function.

[0087] Under the test state of charge (actual SOC approximately 72%), the steam emission assembly was turned off, and steam was introduced into the tank at a rate of Q0 = 20 g / s. The measured closed-loop pressure rise response time was recorded. Temperature signals were collected at a sampling frequency of 10Hz, and the average temperature inside the tank was calculated. and temperature fluctuation variance .

[0088] Confidence weights are calculated based on the temperature fluctuation variance and the deviation of the actual pressure range. .

[0089] Calculate the temperature correction factor based on the temperature inside the tank. Calculate the pressure correction factor based on the actual pressure range. Obtain the corrected response time Substituting into the reference mapping function, the original state of charge for a single measurement is obtained. .

[0090] Based on confidence weights Calculate the adaptive smoothing coefficient ,right Compared with historical values Perform exponentially weighted moving average smoothing to obtain the smoothed state of charge. .

[0091] according to and the health coefficient of the material after the cumulative heat release Obtain the current state of charge of the final output. The data is stored for the next iteration.

[0092] In this embodiment, the traditional method in Group A did not suppress random fluctuations in the measurement, nor did it compensate for material aging. The output fluctuation was large, and after long-term use, the output remained consistently high due to capacity decay. The method of the present invention in Group B, through confidence assessment and adaptive smoothing, effectively suppressed random fluctuations while ensuring measurement response speed, significantly reducing the output fluctuation. However, because it did not introduce health tracking, the output still showed a high deviation due to material capacity decay after long-term operation. The complete method of the present invention in Group C, based on smoothing filtering, corrected the output through a health coefficient, making the state of charge output reflect the actual effective energy storage capacity of the material. After the material underwent 200 cycles of aging (the actual capacity was approximately 85% of the rated capacity), the system could incorporate capacity decay into the calculation through online health tracking. The output state of charge was closer to the actual usable energy value than the uncorrected scheme, verifying the effectiveness of the synergistic effect of confidence assessment, adaptive smoothing filtering, and online material health tracking.

[0093] In another embodiment of the present invention, a dynamic measurement system for the state of charge of a mobile heat source vehicle based on adaptive confidence weighted smoothing is provided, comprising: a sealed tank for containing thermochemical energy storage material; a steam generating assembly for introducing steam into the sealed tank at a predetermined constant steam mass flow rate; a pressure sensor disposed in the sealed tank for collecting the pressure signal inside the tank; a temperature sensor disposed in the sealed tank for collecting the temperature signal inside the tank during the pressurization process; a calorimeter disposed in the heating pipeline for measuring the heat released to the outside; and a controller connected to the steam generating assembly, the pressure sensor, the temperature sensor, and the calorimeter.

[0094] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the scope of the invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0095] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for dynamically measuring the state of charge of a mobile heat source vehicle based on adaptive confidence-weighted smoothing, characterized in that, Includes the following steps: Establish a benchmark mapping function between the state of charge and the closed-circuit boost response time; Steam is introduced into a sealed tank at a predetermined constant steam mass flow rate. The measured closed-loop pressure rise response time required for the tank pressure to rise from the initial pressure to the final pressure is obtained. The tank temperature signal during the pressure rise process is collected, and the temperature fluctuation variance is calculated. The confidence weights are calculated based on the temperature fluctuation variance and the deviation of the actual pressure range from the standard pressure range. The measured closed-loop pressure boosting response time is corrected for temperature based on the tank temperature and for pressure based on the actual pressure range to obtain the corrected closed-loop pressure boosting response time. The corrected closed-loop pressure boosting response time is then substituted into the reference mapping function to obtain the original state of charge for a single measurement. An adaptive smoothing coefficient is determined based on the confidence weight, and the original state of charge and historical state of charge of the single measurement are smoothed using the adaptive smoothing coefficient to obtain the smoothed state of charge. The material health coefficient is updated based on the smoothed state of charge and the accumulated heat release, and the smoothed state of charge is corrected based on the material health coefficient to obtain the current final output state of charge.

2. The method for dynamic measurement of the state of charge of a moving heat source vehicle based on adaptive confidence-weighted smoothing according to claim 1, characterized in that, Establishing a baseline mapping function between the state of charge and the closed-circuit boost response time includes: at the standard test temperature Standard pressure range and predetermined constant steam mass flow rate Under these conditions, closed-loop voltage boost tests were performed on multiple known state-of-charge calibration points, and the values ​​at each calibration point were recorded. The corresponding closed-loop pressurization response time t_k is used to construct a calibration dataset. ,in Furthermore, it uniformly covers the range of states of charge from 0% to 100%, and the reference mapping function is constructed using a piecewise linear interpolation method.

3. The method for dynamic measurement of the state of charge of a moving heat source vehicle based on adaptive confidence-weighted smoothing according to claim 1, characterized in that, Temperature fluctuation variance The calculation formula is Where m is the number of sampling points For the first The temperature inside the tank at each sampling time. This represents the average temperature inside the tank. The average temperature inside the tank The calculation formula is: 。 4. The method for dynamic measurement of the state of charge of a mobile heat source vehicle based on adaptive confidence-weighted smoothing according to claim 1, characterized in that, Confidence weight The calculation formula is: in, For temperature fluctuation variance, The preset temperature fluctuation reference variance, This represents the actual pressure range. For the standard pressure range span, and The preset weighting coefficients, .

5. The method for dynamic measurement of the state of charge of a mobile heat source vehicle based on adaptive confidence-weighted smoothing according to claim 1, characterized in that, The temperature correction includes: determining the temperature correction coefficient based on Arrhenius reaction kinetics theory. in The activation energy for the CaO hydration reaction. This is the universal gas constant. The average temperature inside the tank. Standard test temperature; Pressure correction includes determining the pressure correction factor based on the integral relationship of an isothermal pressurization process of an ideal gas. in and These are the standard starting pressure and standard ending pressure. and These are the actual starting pressure and the actual ending pressure.

6. The method for dynamic measurement of the state of charge of a mobile heat source vehicle based on adaptive confidence-weighted smoothing according to claim 5, characterized in that, The revised method for calculating the closed-loop pressure boost response time is as follows: in To measure the closed-loop pressure boost response time, This is a temperature correction factor. This is the pressure correction factor.

7. The method for dynamic measurement of the state of charge of a moving heat source vehicle based on adaptive confidence-weighted smoothing according to claim 1, characterized in that, The method for calculating the adaptive smoothing coefficient is as follows: in, The minimum smoothness coefficient that satisfies , Confidence weights; The method for calculating the smoothed state of charge is as follows: in For a single measurement of the initial state of charge, The state of charge of the last final output, the The range is from 0.10 to 0.

40.

8. The method for dynamic measurement of the state of charge of a mobile heat source vehicle based on adaptive confidence-weighted smoothing according to claim 1, characterized in that, The method for calculating the material health coefficient is as follows: in For rated energy storage capacity, The current final output state of charge represents the cumulative heat dissipation since the last fully charged state. = .

9. A method for dynamic measurement of the state of charge of a moving heat source vehicle based on adaptive confidence-weighted smoothing, as described in claim 8, is characterized in that... When the confidence weight Below the preset low confidence threshold At that time, the measurement results will not be included in the update of the health index, and will remain unchanged. The low confidence threshold remains unchanged. It is 0.

3.

10. A dynamic measurement system for the state of charge of a mobile heat source vehicle based on adaptive confidence-weighted smoothing, characterized in that, include: A sealed container used to hold thermochemical energy storage materials; A steam generating assembly introduces steam into the sealed tank at a predetermined constant steam mass flow rate. A pressure sensor is installed inside the sealed tank to collect the pressure signal inside the tank; A temperature sensor is installed inside the sealed tank to collect the temperature signal inside the tank during the pressurization process; A calorimeter is installed in the heating pipeline to measure the heat released to the outside. A controller, connected to the steam generating assembly, the pressure sensor, the temperature sensor, and the calorimeter, is used to perform the method according to any one of claims 1 to 9.

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