Method for determining control parameters of range extender and electronic device

Abstract

The application provides a range extender control parameter determination method and an electronic device. The method obtains a plurality of reference value sets and a plurality of to-be-calibrated sample operating parameters of the range extender under different power generation powers in a to-be-calibrated driving interval. For a to-be-calibrated reference power generation power point, the average value difference of each control item is determined according to the measured average value of the to-be-calibrated parameter value of each control item in the to-be-calibrated sample operating parameter and the corresponding reference average value, the average value drift degradation degree is obtained, the standard deviation amplification multiple of part of the control items is determined according to the measured standard deviation of the to-be-calibrated parameter value of part of the control items in the to-be-calibrated sample operating parameter and the corresponding reference standard deviation, the fluctuation amplification degradation degree is obtained, the optimization strategy is determined, the corresponding range extender control parameter is determined according to the optimization strategy and the reference average value of at least one control item, and the NVH degradation degree is basically reduced, the fuel consumption increase is reduced, and the user experience is improved.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a method for determining control parameters of a range extender and an electronic device. Background Technology

[0002] As the core power source of range-extended electric vehicles, the range extender operates under steady-state power generation conditions for a long time. Its operating efficiency and control precision directly determine the energy consumption level and driving comfort of the entire vehicle.

[0003] In related technologies, range extender control and degradation management often only perform fault-preserving transient passive fine-tuning within fixed calibration boundaries. This approach cannot identify and compensate for gradual degradation. Throughout the entire lifecycle of the range extender, with the continuous accumulation of mileage, the system inevitably experiences various forms of gradual degradation. If these systemic degradations are not identified and compensated for in a timely manner, they will directly lead to increased fuel consumption throughout the entire lifecycle and cause severe deterioration in noise, vibration, and acoustic roughness performance, thus reducing the user's driving experience. Summary of the Invention

[0004] This application provides a method for determining range extender control parameters and an electronic device to solve the technical problem in related technologies where range extender control and degradation management cannot identify and compensate for progressive degradation, which may lead to increased fuel consumption throughout the entire life cycle and cause serious deterioration of noise, vibration and acoustic roughness performance, reducing the user's driving experience.

[0005] This application provides a method for determining control parameters of a range extender. The method includes: acquiring multiple sets of reference values ​​and multiple sample operating parameters of the range extender under different power generation at different power outputs within a calibrated driving range; wherein the sets of reference values ​​include a reference mean and a reference standard deviation of at least one control item, and the sets of reference values ​​are preset with corresponding reference power output points; the sample operating parameters to be calibrated include calibrated parameter values ​​of at least one control item; determining the mean difference of a control item based on the measured average value of the calibrated parameter value of a control item in the sample operating parameters to be calibrated corresponding to the reference power output point and the reference mean of the control item corresponding to the reference power output point; and then, based on all the mean values... The mean drift degradation of the reference power generation point to be calibrated is determined by the difference value; the standard deviation amplification factor of the control item is determined based on the measured standard deviation of the control parameter value of the control item in the operating parameters of the reference power generation point to be calibrated and the benchmark standard deviation of the control item corresponding to the reference power generation point to be calibrated, and then the fluctuation amplification degradation of the reference power generation point to be calibrated is determined based on all the standard deviation amplification factors; an optimization strategy is determined based on the mean drift degradation and the fluctuation amplification degradation, and the range extender control parameters of at least one control item corresponding to the reference power generation point to be calibrated are determined based on the optimization strategy and the benchmark mean of at least one control item in the driving range to be calibrated.

[0006] The above methods can identify degradation caused by accumulated mileage and optimize the parameter values ​​of the corresponding control items. By using a fixed initial sample set of operating parameters, the operating condition benchmark can be solidified, ensuring the consistency and uniqueness of the comparison benchmark throughout the entire life cycle. This largely avoids misjudgment of degradation caused by dynamic changes in operating conditions. Degradation is decomposed into two independent dimensions: "mean drift" and "fluctuation amplification," which correspond to systemic hardware degradation and combustion instability, respectively. The degree of fluctuation amplification degradation is introduced as a quantitative indicator of early fluctuations, which can identify early hidden NVH degradation. This essentially reduces NVH degradation caused by gradual degradation, reduces the increase in fuel consumption throughout the entire life cycle, and improves the user experience.

[0007] In one embodiment of this application, the method further includes: determining the mean drift degradation degree of the reference power generation point to be calibrated based on all mean differences, including: determining the sub-mean drift degradation degree of the control item based on the mean difference of a control item and the preset item weight of the control item; superimposing the sub-mean drift degradation degrees of all control items to obtain the mean drift degradation degree of the reference power generation point to be calibrated within the driving range to be calibrated, wherein all control items include at least one of the following: exhaust gas recirculation valve command opening, ignition angle, exhaust gas recirculation rate, or fuel injection quantity. 1. Determining the fluctuation amplification degradation degree of the reference power generation point to be calibrated based on all standard deviation amplification factors includes: determining the average value of all standard deviation amplification factors to obtain the average factor value, wherein the standard deviation amplification factor is the quotient of the measured standard deviation and the reference standard deviation, wherein the measured standard deviation includes at least one of the measured ignition angle standard deviation, the measured exhaust gas recirculation rate standard deviation, or the measured fuel injection quantity standard deviation; determining the fluctuation amplification degradation degree according to the average factor value and a preset fluctuation weight to obtain the fluctuation amplification degradation degree of the reference power generation point to be calibrated within the driving range to be calibrated.

[0008] Through the above methods, a dual evaluation mechanism of mean drift and fluctuation amplification using multi-parameter weighted fusion is adopted to achieve accurate, multi-dimensional, and quantifiable monitoring of engine performance degradation within the benchmark power generation point. This ensures both the robustness of the diagnosis and flexible engineering adaptability.

[0009] In one embodiment of this application, an optimization strategy is determined based on the mean drift degradation degree and the fluctuation amplification degradation degree, and the range extender control parameters corresponding to at least one control item of the reference power generation point to be calibrated in the calibrated driving range are determined based on the optimization strategy and the benchmark mean of at least one control item, including at least one of the following: when the mean drift degradation degree is greater than a preset mean drift threshold, the fluctuation amplification degradation degree is less than or equal to a preset fluctuation amplification threshold, and the total degradation degree is less than a preset total degradation threshold, the optimization strategy is determined as a hardware optimization strategy; the range extender is determined by the hardware optimization strategy and the benchmark mean of at least one control item in the calibrated driving range. The range extender control parameters corresponding to the reference power generation point to be calibrated within the calibrated driving range include at least one of the following: reference average value of at least one control item: reference opening degree of the exhaust gas recirculation valve, reference ignition angle, or reference fuel injection quantity; the range extender control parameters include at least one of the following: corrected opening degree of the exhaust gas recirculation valve, corrected ignition angle, or corrected fuel injection quantity; the total degradation degree is determined based on the mean drift degradation degree and the fluctuation amplification degradation degree; when the mean drift degradation degree is less than or equal to a preset mean drift threshold, the fluctuation amplification degradation degree is greater than a preset fluctuation amplification threshold, and the total degradation degree is less than a preset total degradation threshold, the optimization strategy is determined as a combustion optimization strategy; through the... The combustion optimization strategy and the baseline mean of at least one control item determine the range extender control parameters corresponding to the baseline power generation point to be calibrated in the calibrated driving range. The baseline mean of the at least one control item includes at least one of a baseline exhaust gas recirculation rate and a baseline ignition angle. The range extender control parameters include at least one of a real-time control maximum value of exhaust gas recirculation rate, a real-time control minimum value of exhaust gas recirculation rate, and a corrected ignition angle. When the mean drift degradation degree is greater than the preset mean drift threshold, the fluctuation amplification degradation degree is greater than the preset fluctuation amplification threshold, and the total degradation degree is less than the preset total degradation threshold, the optimization strategy is determined to be a combustion optimization strategy and hardware optimization. The strategy determines the range extender control parameters corresponding to the reference power generation point to be calibrated in the calibrated driving range based on the combustion optimization strategy, hardware optimization strategy, and the reference average of at least one control item. When the total degradation degree corresponding to all reference power generation points is greater than or equal to the preset total degradation threshold, the optimization strategy is determined as a protection strategy. The protection strategy includes at least one of the following: disabling the target power range, locking the ignition angle safety range, and reducing the real-time control maximum value of the exhaust gas recirculation rate. The target power range includes the preset power range and the power range where the reference power generation points corresponding to the top N total degradation degrees, sorted from low to high, are located.

[0010] The above approach automatically drives the correction strategy based on the degree of degradation, correcting only the specific degradation level without performing blind full-scale compensation. Basic MAP pre-compensation is performed for mean degradation, while bandwidth contraction and gain attenuation are applied to fluctuating degradation. The compensation magnitude is controlled by a saturation coefficient to avoid overcompensation, ensuring high engineering safety.

[0011] In one embodiment of this application, an optimization strategy is determined based on the mean drift degradation degree and the fluctuation amplification degradation degree, and the range extender control parameters corresponding to at least one control item of the reference power generation point to be calibrated in the calibrated driving range are determined based on the optimization strategy and the reference mean of at least one control item. This includes: determining the optimization strategy as a combustion optimization strategy when the mean drift degradation degree is less than or equal to a preset mean drift threshold, the fluctuation amplification degradation degree is less than or equal to a preset fluctuation amplification threshold, and the total degradation degree is less than a preset total degradation threshold; determining the corrected fuel injection quantity closed-loop gain corresponding to the reference power generation point to be calibrated in the calibrated driving range by the combustion optimization strategy and the zero-point reference gain of the fuel injection quantity closed-loop gain, wherein the reference value set also includes the zero-point reference gain of the fuel injection quantity closed-loop gain, and the range extender control parameters also include the corrected fuel injection quantity closed-loop gain.

[0012] By using the above methods, the fuel injection closed-loop response speed can be reduced in a timely manner to avoid amplifying fuel injection fluctuations.

[0013] In one embodiment of this application, determining the corrected fuel injection closed-loop gain corresponding to the reference power generation point to be calibrated in the calibrated driving range by means of the combustion optimization strategy and the zero-point reference gain of the fuel injection closed-loop gain includes: determining a fuel injection compensation coefficient based on the fluctuation amplification degradation degree, wherein the fuel injection compensation coefficient is inversely proportional to the fluctuation amplification degradation degree; and compensating the zero-point reference gain based on the fuel injection compensation coefficient to obtain the corrected fuel injection closed-loop gain, wherein the zero-point reference gain is the average value of the initial sample fuel injection closed-loop gain of the range extender in the initial driving range.

[0014] In one embodiment of this application, determining the range extender control parameters corresponding to the reference power generation point to be calibrated in the calibrated driving range using the hardware optimization strategy and the reference mean of at least one control item includes: determining an initial mean drift ratio based on the mean drift degradation degree and a preset maximum mean drift value; adjusting the initial mean drift ratio using a preset item compensation coefficient to obtain a mean drift compensation ratio; adjusting the mean difference of a control item using the mean drift compensation ratio to obtain a current compensation value; and compensating the reference mean of the control item based on the current compensation value to obtain the range extender control parameter of the control item.

[0015] By converting the degradation assessment results into real-time mean compensation corrections, and under the constraint of a preset maximum mean drift value, dynamic adaptive adjustment of the range extender control parameters is achieved. This strategy ensures power stability throughout the entire lifecycle, effectively avoids safety risks caused by overcompensation, and significantly improves the robustness and smoothness of the range extender system under degraded operating conditions.

[0016] In one embodiment of this application, determining the range extender control parameters corresponding to the reference power generation point to be calibrated in the calibrated driving range using the combustion optimization strategy and the reference mean of at least one control item includes: if the control item is exhaust gas recirculation rate, determining a degradation attenuation coefficient based on the fluctuation amplification degradation degree and the preset exhaust gas recirculation bandwidth degradation attenuation rate, shrinking the initial gain of the preset bandwidth using the degradation attenuation coefficient to obtain a current bandwidth shrinkage coefficient, adjusting the standard deviation of the reference exhaust gas recirculation rate based on the current bandwidth shrinkage coefficient to obtain a bandwidth shrinkage parameter value, and determining the exhaust gas recirculation rate based on the reference exhaust gas recirculation rate and the bandwidth shrinkage parameter value. The system controls the maximum value of the real-time recirculation rate and the minimum value of the real-time exhaust gas recirculation rate. The reference standard deviation of the at least one control item includes the reference exhaust gas recirculation rate standard deviation, and the reference mean of the at least one control item includes the reference exhaust gas recirculation rate. If the control item is the ignition angle, the fluctuation difference is determined based on the fluctuation amplification degradation degree and the preset fluctuation amplification threshold. The fluctuation difference is adjusted using a preset ignition fluctuation coefficient to obtain the ignition angle compensation amount. The reference ignition angle is compensated based on the ignition angle compensation amount to obtain the corrected ignition angle. The reference mean of the at least one control item includes the reference ignition angle, and the range extender control parameters include the corrected ignition angle.

[0017] The above methods can address situations where EGR is aging prematurely, valve plates are slightly stuck, and the average parameter value does not drift, thus bringing fluctuations back to a healthy level.

[0018] In one embodiment of this application, the method further includes: controlling a test vehicle to drive within the calibration range, and controlling the range extender to use corresponding range extender control parameters based on the real-time power generation during driving; collecting multiple test sample operating parameters of the range extender of the test vehicle under different power generation conditions within the calibration range; verifying the range extender control parameters of all control parameter items corresponding to each benchmark power generation point based on the multiple test sample operating parameters; if the verification is successful, determining all range extender control parameters corresponding to the benchmark power generation point within the calibration range as preset parameter values ​​for the actual vehicle; if the verification fails... After executing the adjustment strategy, and upon completion, new range extender control parameters are determined for at least one control item corresponding to at least one reference power generation point in the calibrated driving range. Parameter verification is then re-executed until verification is successful. The adjustment strategy includes at least one of the following: adjusting at least one of the preset item compensation coefficient and preset ignition fluctuation coefficient used in determining the range extender control parameters; adjusting the preset item weight used in determining the mean drift degradation; and adjusting the preset fluctuation weight used in determining the fluctuation amplification degradation. The parameter verification method includes: adjusting the control parameters based on the reference power generation point to be verified. The mean difference of a control item is determined by the verified average value of the parameter to be verified in the corresponding test sample operating parameters and the benchmark mean value of the control item corresponding to the benchmark power generation point to be verified. Then, based on all mean differences in the verification phase, the mean drift degradation degree of the benchmark power generation point to be verified is determined. Furthermore, the fuel consumption degradation rate is determined based on the fuel consumption ratio between the verified average value of real-time fuel consumption in the test sample operating parameters corresponding to the benchmark power generation point to be verified and the benchmark fuel consumption corresponding to the benchmark power generation point to be verified. The benchmark value set also includes benchmark fuel consumption. The verification standard deviation of the parameter value to be verified for a control item in the verification sample operating parameters and the benchmark standard deviation of the control item corresponding to the benchmark power generation point to be verified are used to determine the standard deviation amplification factor of the control item. Then, based on the standard deviation amplification factors of all the standard deviations in the verification stage, the fluctuation amplification degradation degree of the benchmark power generation point to be verified is determined. In addition, the NVH degradation rate is determined according to the verification average value of the real-time NVH jitter value in the test sample operating parameters corresponding to the benchmark power generation point to be verified and the jitter ratio between the benchmark NVH jitter value corresponding to the benchmark power generation point to be verified. The benchmark value set also includes the benchmark NVH jitter value.Verification passes if the verification conditions are met, and fails if the verification conditions are not met. The verification conditions include: the mean drift degradation degree during the testing phase being less than or equal to a preset mean drift threshold; the fuel consumption degradation rate being less than or equal to a preset fuel consumption threshold; the fluctuation amplification degradation degree during the testing phase being less than or equal to a preset fluctuation amplification threshold; and the NVH degradation rate being less than or equal to a preset NVH threshold.

[0019] The above methods enable the transmission of real vehicle operation data back to the cloud, continuously optimizing the degradation identification model, compensation strategy, and operating point selection logic. This achieves a closed-loop evolution of "identification model - compensation strategy - real vehicle data - model re-optimization," continuously improving the performance of the range extender throughout its entire lifecycle, rather than maintaining a static calibration.

[0020] In one embodiment of this application, the determination of the benchmark value set includes: obtaining multiple initial sample operating parameters of the range extender under different power generation at different initial calibrated driving ranges, wherein the initial sample operating parameters include the initial sample command opening of the exhaust gas recirculation valve, the initial sample ignition angle, the initial sample exhaust gas recirculation rate, the initial sample fuel injection quantity, the initial sample fuel consumption, the initial sample fuel injection quantity closed-loop gain, and the initial sample NVH vibration value; clustering all power generation into k clusters, where k is greater than 1; performing cluster optimization iteration on all power generation with the objective of minimizing the sum of squared power errors within each cluster; using the clusters after cluster optimization iteration as benchmark clusters, determining the benchmark power generation point corresponding to each benchmark cluster based on the average power generation within each benchmark cluster; and determining the power generation point corresponding to a benchmark cluster. The average values ​​of each parameter in the initial sample operating parameters are used to obtain the reference command opening degree of the exhaust gas recirculation valve, the reference ignition angle, the reference exhaust gas recirculation rate, the reference fuel injection quantity, the reference fuel consumption, the zero-position reference gain, and the reference NVH jitter value, thereby obtaining multiple reference averages. The standard deviations of each parameter in the initial sample operating parameters corresponding to the power generation within a reference cluster are determined to obtain the standard deviations of the reference ignition angle, the reference exhaust gas recirculation rate, or the reference fuel injection quantity, thereby obtaining multiple reference standard deviations. Based on the reference command opening degree of the exhaust gas recirculation valve, the reference ignition angle, the reference exhaust gas recirculation rate, the reference fuel injection quantity, the reference fuel consumption, the zero-position reference gain, the reference NVH jitter value, the standard deviation of the reference ignition angle, the standard deviation of the reference exhaust gas recirculation rate, or the standard deviation of the reference fuel injection quantity corresponding to a reference cluster, a set of reference values ​​for the reference power generation point corresponding to the reference cluster is generated.

[0021] By using the above method, a fixed power cluster benchmark is obtained through clustering, and no re-clustering is performed throughout the entire process. This ensures the consistency and uniqueness of the comparison benchmark throughout the entire life cycle, avoids misjudgment of degradation caused by dynamic changes in operating conditions, and improves the accuracy and stability of degradation identification.

[0022] This application embodiment also provides a range extender control parameter determination device, the device comprising: an acquisition module, configured to acquire multiple sets of reference values ​​and multiple sample operating parameters of the range extender under different power generation at different power outputs within a calibrated driving range, wherein the sets of reference values ​​include a reference mean and a reference standard deviation of at least one control item, the sets of reference values ​​are preset with corresponding reference power output points, and the sample operating parameters to be calibrated include calibrated parameter values ​​of at least one control item; and a mean drift degradation determination module, configured to determine the mean difference of a control item based on the measured average value of the calibrated parameter values ​​of a control item in the sample operating parameters to be calibrated corresponding to the reference power output point and the reference mean of the control item corresponding to the reference power output point, and then based on the mean difference of all control items... The system determines the mean drift degradation degree of the reference power generation point to be calibrated; the fluctuation amplification degradation degree determination module is used to determine the standard deviation amplification factor of the control item based on the measured standard deviation of the control item value of the control item in the operating parameters of the reference power generation point to be calibrated and the benchmark standard deviation of the control item corresponding to the reference power generation point to be calibrated, and then determines the fluctuation amplification degradation degree of the reference power generation point to be calibrated based on all the standard deviation amplification factors; the optimization module is used to determine the optimization strategy based on the mean drift degradation degree and the fluctuation amplification degradation degree, and optimize based on the optimization strategy and the benchmark mean of at least one control item to obtain the range extender control parameters of at least one control item corresponding to the reference power generation point to be calibrated in the driving range to be calibrated.

[0023] This application also provides an electronic device, including: a memory storing a computer program thereon; and a processor for executing the computer program in the memory to implement the steps of the method described in any of the above embodiments.

[0024] This application also provides a computer-readable storage medium having a computer program stored thereon, the computer program being used to cause a computer to perform the method provided in any of the above embodiments.

[0025] The beneficial effects of this application: The embodiments of this application propose a method and electronic device for determining control parameters of a range extender. This method acquires multiple sets of reference values ​​and multiple sample operating parameters of the range extender under different power generation conditions within a calibrated driving range. The set of reference values ​​includes the reference mean and the reference standard deviation of at least one control item. The set of reference values ​​is preset with corresponding reference power generation points. The sample operating parameters to be calibrated include the parameter values ​​to be calibrated for at least one control item. The method determines the mean difference of a control item based on the measured average value of the parameter values ​​to be calibrated for one control item in the sample operating parameters corresponding to the reference power generation point and the reference mean value of the same control item. Then, it determines the mean drift degradation degree of the reference power generation point to be calibrated based on all mean differences. Finally, it determines a control item based on the measured standard deviation of the parameter values ​​to be calibrated for one control item in the sample operating parameters corresponding to the reference power generation point and the reference standard deviation of the same control item. The standard deviation amplification factor is used to determine the fluctuation amplification degradation degree of the benchmark power generation point to be calibrated based on all standard deviation amplification factors. The optimization strategy is determined based on the mean drift degradation degree and the fluctuation amplification degradation degree. Based on the optimization strategy and the benchmark mean of at least one control item, the range extender control parameters of at least one control item corresponding to the benchmark power generation point to be calibrated in the driving range to be calibrated are determined. In this way, degradation caused by the accumulation of driving mileage can be identified and the parameter values ​​of the corresponding control items can be optimized. By using a fixed initial sample operating parameter set, the operating condition benchmark can be solidified, ensuring the consistency and uniqueness of the comparison benchmark throughout the entire life cycle. It basically avoids the misjudgment of degradation caused by dynamic changes in the operating condition point. The degradation is decomposed into two independent dimensions, "mean drift" and "fluctuation amplification", which correspond to the systematic degradation of hardware and combustion instability, respectively. The fluctuation amplification degradation degree is introduced as a quantitative indicator of early fluctuations, which can identify early hidden NVH degradation, basically reduce NVH degradation caused by gradual degradation, reduce the increase in fuel consumption throughout the entire life cycle, and improve the user experience. Attached Figure Description

[0026] 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. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0027] In the attached diagram: Figure 1 A schematic diagram of a system architecture provided in one embodiment of this application; Figure 2 A flowchart illustrating a method for determining control parameters of a range extender, as provided in an embodiment of this application; Figure 3 A schematic diagram of a range extender control parameter determination device provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0028] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0029] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. The drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the shape, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0030] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the present application. However, it will be apparent to those skilled in the art that embodiments of the present application may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the present application.

[0031] In this application, the collection and processing of data such as the operating parameters of the sample to be calibrated and the initial set of operating parameters of the sample must strictly comply with the requirements of relevant national laws and regulations, obtain the informed consent or separate consent of the personal information subject, and carry out subsequent data use and processing within the scope of laws and regulations and the authorization of the personal information subject.

[0032] The inventors discovered that as mileage accumulates, range extenders are prone to systemic degradation, such as carbon buildup and sticking in the EGR (Exhaust Gas Recirculation) valve, deviation in combustion operating conditions, and mismatch between ignition and fuel injection. This directly leads to increased fuel consumption and deterioration of NVH (Noise, Vibration, and Harshness) performance throughout their lifecycle. Related range extender combustion control and degradation management technologies suffer from at least the following problems: Because dynamic clustering is often used to update the operating condition center in real time for range extender combustion control and degradation management, although it can solve the problem of adjusting transient anomalies, the operating condition center is constantly changing and there is no fixed unified benchmark, which leads to chaotic comparison of degradation throughout the entire life cycle. Parameter drift and combustion fluctuations cannot be accurately quantified, resulting in prominent misjudgments and omissions in degradation.

[0033] Because aging is often determined by the average deviation of a single parameter and only focuses on severe hardware degradation, although degradation control can be achieved to some extent, the inventors found that the amplification of combustion cycle fluctuations can also lead to hidden NVH degradation. There is no corresponding solution in the relevant technologies, and it cannot provide full coverage identification of degradation in the early, middle and full mileage ranges.

[0034] The solutions in the relevant technologies can only make transient passive fine adjustments within the calibration range, and do not have the ability to perform full life cycle pre-MAP compensation. Moreover, they only correct the single EGR parameter separately, without linking the ignition angle and fuel injection quantity to perform collaborative optimization of the combustion system. At the same time, they lack compensation saturation constraints and severe degradation limit protection, which can easily lead to overcompensation causing knocking and excessive emissions.

[0035] Degradation monitoring in related technologies requires the installation of dedicated sensors, which is costly and has poor mass production compatibility.

[0036] To address the aforementioned issues, this application proposes a method for quantifying and adaptively compensating for the degradation of a range extender system throughout its entire lifecycle based on operating condition clustering (a method for determining range extender control parameters). This method establishes a lifelong fixed operating condition benchmark based on original vehicle operating data, identifies degradation through a dual-dimensional approach of mean drift and fluctuation amplification, implements graded pre-compensation by linking EGR opening, ignition angle, and fuel injection quantity, and sets a degradation threshold for extreme operating condition protection. This fills the technological gaps in benchmark fixing, dual-dimensional degradation perception, and collaborative compensation of the combustion system.

[0037] As an example, the operating parameters of the sample to be calibrated and the initial sample operating parameter set can be obtained through big data collection from the vehicle network cloud. By deeply mining the big data from the vehicle network cloud, a paradigm shift can be achieved in the degradation management of range extenders from "passive fault correction" to "active performance maintenance." Based on the operating condition clustering benchmark, accurate degradation perception and adaptive compensation throughout the entire life cycle can be achieved, accurately matching the combustion control requirements under aging conditions. At the same time, the deep coupling of big data clustering analysis and multi-parameter collaborative compensation can not only maintain the fuel consumption and NVH levels of the range extender at the level of a new vehicle in the long term, but also reduce the carbon emissions of the entire vehicle throughout its life cycle.

[0038] Please see Figure 1 , Figure 1 A schematic diagram of a system architecture provided in one embodiment of this application, such as... Figure 1As shown, by collecting sample data from multiple sample vehicles 110 of a certain model equipped with the same type of range extender and uploading it to the server 130, the operating parameters of the sample to be calibrated and the initial operating parameters of the sample can be obtained. The number of sample vehicles can be arbitrary. Figure 1This is merely an example and not a limitation on the number of sample vehicles. The server collects range extender control-related data from the sample vehicles as sample data. For example, taking a calibration range of 5000 kilometers followed by every 5000 kilometers of driving, and an initial calibration range of 0 to 5000 kilometers as an example, the server collects range extender operating data from 0 to 5000 kilometers (excluding 5000 kilometers) to obtain initial sample operating parameters. Collecting range extender operating data from 5000 to 10000 kilometers (excluding 10000 kilometers) yields one sample operating parameter to be calibrated. Collecting range extender operating data from 10000 to 15000 kilometers (excluding 15000 kilometers) yields another sample operating parameter to be calibrated, and so on, to obtain multiple sample operating parameters to be calibrated. It should be noted that the above division of the calibration range and the initial calibration range is merely an example, and those skilled in the art can adjust these values ​​as needed. For the range extender control parameters of each driving range to be calibrated, the mean difference of at least one control item can be determined by comparing the average value of the parameter values ​​in the calibration sample operating parameters corresponding to the same reference power point with the average value of the parameter values ​​in the initial sample parameter set. Then, the mean drift degradation of the reference power point can be determined based on the mean difference of all control items. The standard deviation amplification factor of at least one control item can be determined by comparing the standard deviation of the parameter values ​​in the calibration sample operating parameter set corresponding to the same reference power point with the standard deviation of the parameter values ​​in the initial sample operating parameter set. Then, the fluctuation amplification degradation of the reference power point can be determined based on the standard deviation amplification factor of all control items. If the mean drift degradation and fluctuation amplification degradation meet preset conditions, the reference mean of at least one control item of the range extender can be optimized based on the mean drift degradation and / or fluctuation amplification degradation. This yields the range extender control parameters for at least one control item corresponding to at least one reference power point in the driving range to be calibrated. Through this method, the range extender control parameters for different control items at different power outputs in multiple driving ranges to be calibrated can be obtained. The initial calibration driving range remains fixed. The driving range to be calibrated can be the same mileage interval or have different mileage intervals, which can be set by those skilled in the art as needed. Range extender control parameters include, but are not limited to, corrected fuel injection quantity closed-loop gain, corrected exhaust gas recirculation valve command opening, corrected ignition angle, corrected fuel injection quantity, real-time control maximum value of exhaust gas recirculation rate, and real-time control minimum value of exhaust gas recirculation rate. Control items include, but are not limited to, ignition angle, fuel injection quantity, EGR rate (exhaust gas recirculation rate), exhaust gas recirculation valve command opening, fuel consumption, fuel injection quantity closed-loop gain, and NVH vibration value.Initial sample operating parameters include the initial sample command opening degree of the exhaust gas recirculation valve, the initial sample ignition angle, the initial sample exhaust gas recirculation rate, the initial sample fuel injection quantity, the initial sample fuel consumption, the initial sample fuel injection quantity closed-loop gain, and the initial sample NVH vibration value. Among these, data such as EGR rate, ignition angle, fuel injection quantity, fuel consumption, and NVH vibration value are collected synchronously with the power generation. After obtaining the range extender control parameters for the control items, the test can be performed using 120 test vehicles (the number of which can be one or more). Figure 1 (This is merely an example and not a limit on the number of vehicles.) When driving to the calibration range, if the range extender's power output is at the aforementioned baseline power output point, the corresponding control items use the range extender control parameters obtained from the calibration. Then, the test sample operating parameter set of the test vehicle 120 is collected. Based on the test sample operating parameter set and the initial sample operating parameter set, the fluctuation amplification degradation degree and the mean drift degradation degree of the test phase are obtained again using the same method as described above for determining the mean drift degradation degree and fluctuation amplification degradation degree. If the verification conditions are met, the obtained range extender control parameters of the control items are determined as the vehicle's preset parameter values ​​and written into the vehicle's controller. When other vehicles drive to the calibration range, they use these vehicle preset parameter values ​​at the corresponding power output. This can compensate for NVH degradation caused by gradual degradation, reduce fuel consumption increases over the entire life cycle, and improve the user experience. If the verification conditions are not met, at least one of the preset item compensation coefficients and preset ignition fluctuation coefficients used in determining the range extender control parameters needs to be adjusted, as does the preset item weights used in determining the mean drift degradation degree, and the preset fluctuation weights used in determining the fluctuation amplification degradation degree. Then, all the above procedures must be re-executed until the verification passes. It should be noted that the vehicles mentioned in the above embodiments are of the same model and use the same model of range extender.

[0039] The above-described overall system architecture and related exemplary descriptions are merely one example provided by the embodiments of this application. This method can also be applied to other system frameworks according to the user's needs. The embodiments of this application do not limit the actual form of various devices, components, etc. included in this scenario. In the specific application of the solution, it can be set according to actual needs.

[0040] Please see Figure 2 , Figure 2 A flowchart illustrating a method for determining control parameters of a range extender provided in an embodiment of this application is shown below. Figure 2 As shown, the method includes the following steps: Step S210: Obtain multiple sets of benchmark values ​​and multiple operating parameters of the range extender under different power generation conditions within the calibration range.

[0041] The benchmark set includes the benchmark mean and the benchmark standard deviation of at least one control item. The benchmark set is preset with a corresponding benchmark power generation point. The operating parameters of the sample to be calibrated include the parameter values ​​to be calibrated for at least one control item.

[0042] As an example, this method can be executed through a cloud server or a cloud big data platform. By collecting the range extender operation data uploaded by the sample vehicles to the cloud server, the operating parameters of the sample to be calibrated and the initial set of sample operating parameters can be obtained.

[0043] During the operation of the sample vehicle, the operating parameters of the range extender are collected in real time, including power generation, actual exhaust gas recirculation (EGR) rate, actual ignition angle, actual fuel injection quantity, actual fuel consumption, and actual noise, vibration, and harshness (NVH) vibration values. This data is periodically uploaded to a cloud-based big data platform via the vehicle's wireless communication module. The cloud-based big data platform performs cleaning and other operations on the received data to obtain the operating parameters of the sample to be calibrated and the initial set of operating parameters for the sample.

[0044] The operating parameters of the sample to be calibrated and the operating parameters of the initial sample both include at least the real-time exhaust gas recirculation valve command opening, real-time EGR rate, real-time ignition angle and real-time fuel injection quantity corresponding to a certain power generation. The operating parameters of the initial sample also include at least the real-time fuel consumption, real-time NVH vibration value and real-time fuel injection quantity closed-loop gain.

[0045] In some embodiments, to facilitate the determination of subsequent range extender control parameters, the initial sample operating parameter set of the initial sample operating parameters can be preprocessed to obtain a benchmark value set. An exemplary method for determining the benchmark value set includes: obtaining multiple initial sample operating parameters of the range extender under different power generation at different power outputs in the initial calibration driving range. The initial sample operating parameters of the test sample operating parameter set include the initial sample command opening of the exhaust gas recirculation valve, the initial sample ignition angle, the initial sample exhaust gas recirculation rate, the initial sample fuel injection quantity, the initial sample fuel consumption, the initial sample fuel injection quantity closed-loop gain, and the initial sample NVH vibration value; clustering all power generation into k clusters, where k is greater than 1; performing cluster optimization iteration on all power generation with the objective of minimizing the sum of squared power errors within each cluster; using the clusters after cluster optimization iteration as benchmark clusters, and determining the test value based on the average power generation within each benchmark cluster. The sample operating parameter set is divided into several parts: the reference power generation point corresponding to the reference cluster; the average value of each parameter item of the initial sample operating parameter corresponding to the power generation within a reference cluster is determined to obtain the reference command opening degree of the exhaust gas recirculation valve, the reference ignition angle, the reference exhaust gas recirculation rate, the reference fuel injection quantity, the reference fuel consumption, the zero-position reference gain, and the reference NVH jitter value, and thus obtain multiple reference averages; the standard deviation of each parameter item of the initial sample operating parameter corresponding to the power generation within a reference cluster is determined to obtain the standard deviation of the reference ignition angle, the standard deviation of the reference exhaust gas recirculation rate, or the standard deviation of the reference fuel injection quantity, and thus obtain multiple reference standard deviations; based on the reference command opening degree of the exhaust gas recirculation valve, the reference ignition angle, the reference exhaust gas recirculation rate, the reference fuel injection quantity, the reference fuel consumption, the zero-position reference gain, the reference NVH jitter value, the standard deviation of the reference ignition angle, the standard deviation of the reference exhaust gas recirculation rate, or the standard deviation of the reference fuel injection quantity corresponding to a reference cluster, the test sample operating parameter set is generated, which is a set of reference values ​​for the reference power generation point corresponding to the reference cluster.

[0046] By clustering data during the initial calibrated driving period of a new vehicle to obtain a fixed power cluster benchmark, and without re-clustering throughout the entire lifecycle, the consistency and uniqueness of the comparison benchmark are guaranteed. This avoids misjudgments of degradation caused by dynamic changes in operating conditions, thus improving the accuracy and stability of degradation identification.

[0047] The termination condition for cluster optimization iteration is that the number of cluster optimization iterations reaches a preset threshold, or the mean (mean of power generation) of each cluster after the cluster optimization iteration no longer changes significantly (e.g., the difference between the mean of each cluster in the previous cluster optimization iteration and the mean of the corresponding cluster in the latest cluster optimization iteration is less than a preset stopping threshold).

[0048] The setting of k can be set by those skilled in the art as needed, and clustering can be implemented using methods known to those skilled in the art, such as K-Means clustering, which will not be elaborated here.

[0049] In the embodiments of this application, each reference cluster in the initial sample operating parameter set is fixed, and the reference value set is also fixed. Subsequently, each sample operating parameter to be calibrated in the corresponding fixed reference power point is compared with the same reference value set to determine the degradation status.

[0050] One example of how to determine the parameter partitioning for the initial sample runtime parameters is as follows: Collect cloud data on power generation during the new vehicle phase (0~M0, with M0=5000km as a reference; the specific value can be set as needed, this is just an example) to construct a feature sample set: The sample set (the set of initial sample running parameters) is X0={P 01 P 02 , ..., P 0N0}, where P 0i This is the i-th power generation sample (unit: kW) in the new vehicle stage (initial calibration driving range), where i can be 1 to N0, and N0 is the total number of samples in the new vehicle stage (the total number of samples of all vehicles in this mileage segment, and the number of initial sample operating parameters).

[0051] Perform one-dimensional K-Means clustering on the power generation samples to obtain k fixed benchmark cluster centers: P={P1, P2, ..., Pk}, where P is the set of benchmark cluster centers, P1 is the benchmark power generation of the first benchmark cluster (the average power generation within the cluster, i.e., the average of all power generation within the cluster), P2 is the benchmark power generation of the second benchmark cluster (the average power generation within the cluster), and Pk is the benchmark power generation of the kth benchmark cluster (the average power generation within the cluster).

[0052] The following is an example of a clustering method for power generation data: Using K-Means clustering, let the dataset (initial set of sample running parameters) be: X0={P 01 P 02 , ..., P 0N0 The dataset is divided into k clusters, where k is the number of high-frequency power generation points to be optimized, resulting in the new vehicle baseline cluster centers (baseline operating points): C0={μ P01 μ P02 , ..., μ P0k}, where: μ P0j The mean power generation (centroid) of the j-th (j is less than or equal to k, and takes values ​​from 1, 2 to k) reference operating point.

[0053] The sum of squares of the objective function is: Formula (1) in, Let X0 be the target loss function, k be the total number of clusters, j = 1, 2, ..., k, X0 be the initial set of sample running parameters, and μ be the target loss function. P0j Let x be the average power generation (centroid) of the j-th (j is less than or equal to k, and takes values ​​from 1, 2 to k) reference operating point, and let x be a reference power generation.

[0054] The clustering optimization iteration is illustrated below, where J is minimized through the following two iterative steps: Allocation steps: Assign each data point to the nearest centroid to obtain each power generation sample, and then assign it to the nearest candidate power point: Formula (2) Where Cj is the sample set of the j-th cluster, The centroid is the average power generation at the j-th (j ≤ k, taking values ​​1, 2 to k) reference operating point, and the initial cluster center of the j-th cluster. Let x be the initial cluster center of any j'th cluster other than the jth cluster, and let x be a reference power generation.

[0055] If sample x is located at the center of the j-th cluster The squared distance to x is less than or equal to the distance to all other remaining cluster centers. If the square of the distance is satisfied, x will be assigned to cluster Cj.

[0056] Update steps: In each iteration, the high-frequency power point of the cluster is corrected / optimized using the average value of all power generation within the cluster: that is, the average value of all data points in the cluster.

[0057] Formula (3) Where Ck is the number of samples in the k-th cluster. Let Cj be the mean power generation (centroid) of the j-th (j is less than or equal to k, and takes values ​​from 1, 2 to k) benchmark operating point, and let Pi be the benchmark power generation corresponding to the i-th operating point.

[0058] Optimization objective: Minimize the sum of squared power errors within the cluster, ensuring that the k power points are globally optimal. As an example, k can be taken as a reference value of 6. Formula (4) in, Let x be the objective loss function, k be the total number of clusters, j = 1, 2, ..., k, x be a baseline power generation, and Cj be the sample set of the j-th cluster. It represents the average power output of all generators within the j-th cluster.

[0059] Convergence condition: The power point no longer changes, and the final k optimal high-frequency power points are output: Iterate until the centroid no longer changes significantly or the maximum number of iterations is reached, and finally obtain the reference center C0.

[0060] If the reference power generation belongs to cluster j, then the corresponding EGR rate, ignition angle, fuel injection quantity and other parameters also belong to cluster j, and the reference power generation point corresponding to it is the average value of the power generation corresponding to cluster j.

[0061] The above method can be used to obtain the initial sample running parameter set for dividing multiple benchmark clusters.

[0062] As an example, in order to facilitate the calculation of mean drift degradation and fluctuation amplification degradation, since the initial sample running parameter set provided in this application embodiment is fixed, the various benchmark values ​​required for subsequent calculation of mean drift degradation and fluctuation amplification degradation can be pre-calculated to improve the efficiency of subsequent calculations. When using the corresponding benchmark values, real-time calculation is no longer required.

[0063] An exemplary method for constructing a new vehicle health benchmark library (benchmark value set) is as follows: Simultaneously collect operational data during the new vehicle phase (e.g., within 5000 kilometers of mileage), and simultaneously extract data from the center P of each fixed cluster. j The three corresponding observation variables—baseline centroid, baseline fuel consumption, and baseline NVH value—are used to construct a new vehicle health benchmark library. B j ={e 0j θ 0j q 0j , σe 0j , σθ 0j , σq 0j f 0j v 0j , , }Formula (5) Among them, B j For the new vehicle health benchmark library (benchmark value set) corresponding to the power generation power belonging to cluster j, e 0j θ is the baseline EGR rate (baseline exhaust gas recirculation rate). 0j As the reference ignition angle, q 0j As the baseline fuel injection quantity, f 0j Based on fuel consumption, v 0j σe is the baseline NVH jitter value. 0j σθ represents the standard deviation of the baseline EGR rate. 0j σq is the standard deviation of the reference ignition angle. 0jThe standard deviation of the baseline fuel injection quantity This is the reference command opening degree for the exhaust gas recirculation valve. This is the zero-position reference gain.

[0064] As an example, the actual opening degree of the EGR valve in real time can be obtained by monitoring with a sensor.

[0065] Among the above parameters, the centroid reference (mean dimension) is: op_eg 0j (Exhaust gas recirculation valve reference command opening), e 0j (Benchmark EGR rate), θ 0j (Reference ignition angle), q 0j (Baseline fuel injection quantity), which is the sample mean of the corresponding variable within the cluster; On the variance benchmark (volatility dimension): σe 0j (Standard deviation of benchmark EGR rate), σθ 0j (Standard deviation of reference ignition angle), σq 0j (Standard deviation of baseline fuel injection quantity), is the sample standard deviation of the corresponding variable within the cluster, representing the normal fluctuation level under healthy conditions. The formula for calculating the standard deviation is: Formula (6) Where x is the EGR rate, ignition angle, or fuel injection quantity, and N 0j Let j be the total number of samples in the j-th cluster during the new vehicle phase. The standard deviation of the reference EGR rate, the standard deviation of the reference ignition angle, or the standard deviation of the reference injection quantity. For the real-time EGR rate, real-time ignition angle, or real-time fuel injection quantity corresponding to the i-th reference power generation in the j-th cluster, This represents the reference EGR rate, reference ignition angle, or reference fuel injection quantity corresponding to the j-th cluster.

[0066] Performance benchmark: f 0j (Baseline fuel consumption), v 0j (Baseline NVH jitter value).

[0067] Step S220: Determine the mean difference of a control item based on the measured average value of the control parameter value of a control item in the operating parameters of the sample to be calibrated corresponding to the reference power point to be calibrated and the reference mean value of a control item corresponding to the reference power point to be calibrated. Then, determine the mean drift degradation degree of the reference power point to be calibrated based on all the mean differences.

[0068] As an example, determining which reference power point corresponds to the operating parameter of the sample to be calibrated can be achieved by separately determining the difference between each reference power point and the power output corresponding to the operating parameter of the sample to be calibrated, and then selecting the reference power point with the smallest difference as the reference power point corresponding to the operating parameter of the sample to be calibrated. Alternatively, the reference power point can be pre-determined based on the minimum power difference between the power values ​​of multiple reference power points and the power output corresponding to each operating parameter of the sample to be calibrated.

[0069] In some embodiments, determining the mean drift degradation of the reference power generation point to be calibrated based on all mean differences includes: determining the sub-mean drift degradation of a control item based on the mean difference of a control item and a preset item weight of a control item; superimposing the sub-mean drift degradation of all control items to obtain the mean drift degradation of the reference power generation point to be calibrated within the calibration driving range, wherein all control items include at least one of the following: exhaust gas recirculation valve command opening, ignition angle, exhaust gas recirculation rate, or fuel injection quantity.

[0070] In other embodiments, the determination of mean drift degradation includes: dividing the operating parameters of the samples to be calibrated into corresponding benchmark clusters according to their corresponding power generation, with each benchmark cluster having a different benchmark power generation point; determining the average value of the parameter values ​​to be calibrated for each control item of the operating parameters of multiple samples to be calibrated that are divided into the same benchmark cluster, and determining the average value of the parameter values ​​of the corresponding control items in the benchmark cluster, and determining the difference between the two average values ​​corresponding to the same control item as the mean difference of the control item; determining the mean drift degradation based on the mean difference of all control items and the preset item weight of each control item, and using it as the mean drift degradation of the benchmark power generation point corresponding to the benchmark cluster, thereby obtaining the mean drift degradation of the benchmark power generation point corresponding to each benchmark cluster.

[0071] The benchmark clusters can be obtained through clustering, and in step S220, the operating parameters of the sample to be calibrated need to be classified into various benchmark clusters. The classification criterion is based on which benchmark cluster the parameter belongs to, corresponding to the benchmark power point; thus, the operating parameter of the sample to be calibrated is classified into that benchmark cluster.

[0072] As an example, control items include, but are not limited to, EGR rate, ignition angle, and fuel injection quantity. Based on each control item, the average value A of the parameter value of the corresponding control item in the operating parameters of the sample to be calibrated is determined. Then, the absolute value of the difference between the average value A and the average value B of the parameter value of the control item in the reference cluster is taken as the mean difference of the control item.

[0073] For each control item, a preset weight is set, and the sum of all preset weights is less than or equal to 1 (or other rules set by those skilled in the art; this is just an example). The preset weights can be set by those skilled in the art according to the importance of the control item, or they can be calibrated through bench control variable tests, corresponding to the independent contribution of each parameter to fuel consumption degradation, which is strongly positively correlated with the fuel consumption degradation rate. By locking k fixed power generation points obtained from clustering, and simultaneously fixing all boundary conditions such as water temperature, oil temperature, intake pressure, ambient temperature, and generator load, completely consistent with the steady-state power generation conditions of the actual vehicle, it is ensured that only the variable to be measured changes during the entire test process, while all other parameters are locked at the new vehicle baseline value for calibration, thus obtaining the impact of single-parameter changes on fuel consumption and NVH.

[0074] As an example, mean drift degradation can be obtained by taking a weighted average of each control item and then summing the results, which is used to characterize the degree of systematic hardware degradation.

[0075] The above method can be used to obtain the mean drift degradation degree of each reference power point, which is convenient for judging the degree of systematic hardware degradation within the reference power point in the driving range to be calibrated, so as to facilitate subsequent optimization.

[0076] During the mileage segmentation sample collection and fixed cluster allocation phase, a preset mileage segmentation threshold ΔM (assumed to be 5000km) is used. When the vehicle's cumulative mileage reaches the next mileage segment extreme value Mm, all vehicle operation samples within the current mileage segment (a driving interval to be calibrated) are collected, including: Mm=M0+m Formula (7) for ΔM Where Mm is the extreme value of the next mileage segment, M0 is the extreme value of the initial calibrated driving interval, m is the segment number m=1,2,3..., and ΔM is the preset mileage segment threshold.

[0077] The whole vehicle operation sample, also known as the sample to be calibrated, includes the following operating parameters: real-time EGR rate e i Real-time ignition angle θ i Real-time fuel injection quantity q i And so on, as well as the corresponding power generation Pi.

[0078] All samples in the current mileage segment are assigned to the fixed reference power cluster (reference cluster) in step S210, with the following assignment rules: C j ={P i ||P i P j |≤|P i P j |, j '≠j} formula (8) Among them, C j Let P be the initial set of sample operating parameters corresponding to the j-th fixed power cluster (reference cluster). The cluster center Pi (the reference power generation corresponding to the i-th operating point) remains fixed, that is, the reference power generation point in the reference cluster remains unchanged. j This represents the power generation capacity of the current mileage segment.

[0079] In some embodiments, the centroid drift (mean dimension, corresponding to severe degradation in the later stages), i.e., the mean difference, of the driving range m to be calibrated is determined as follows: Formula (9) Formula (10) Formula (11) Formula (12) in, For reference cluster j (at the reference power point corresponding to reference cluster j), the mean difference in the commanded opening of the exhaust gas recirculation valve is given. This represents the average value of the initial sample command opening of the exhaust gas recirculation valve in the reference cluster j, which is also the reference command opening of the exhaust gas recirculation valve. This is the average value of the measured command opening of the exhaust gas recirculation valve for multiple test samples within the same reference cluster j (at the reference power point corresponding to reference cluster j), which is also the average reference command opening of the exhaust gas recirculation valve. The mean difference of the EGR rate corresponding to the benchmark cluster j. The average value of the parameter values ​​for the EGR rate in the reference cluster j, i.e., the reference EGR rate. This is the average value of the EGR rate parameters of multiple samples to be calibrated within the same reference cluster j, which is also the average measured EGR rate. Let be the mean difference of the ignition angles corresponding to the reference cluster j. The average value of the ignition angle parameter in the reference cluster j, i.e., the reference ignition angle. This is the average value of the ignition angle parameter of multiple samples to be calibrated within the same reference cluster j, which is also the average value of the measured ignition angle. The mean difference in fuel injection quantity corresponding to the reference cluster j. This is the average value of the parameter values ​​for the injection quantity in the reference cluster j, i.e., the reference injection quantity. It is the average value of the fuel injection quantity parameter of multiple samples to be calibrated in the same reference cluster j, that is, the average value of the measured fuel injection quantity.

[0080] As an example, the mean drift degradation degree in stage m (the driving range to be calibrated) is determined as follows: Formula (13) in, The mean drift degradation degree corresponding to the reference cluster j (the reference power point corresponding to the reference cluster j) , , , These are the corresponding preset project weights (preset weight coefficients). This represents the mean difference in the exhaust gas recirculation valve command opening corresponding to the baseline cluster j. The mean difference of the EGR rate corresponding to the benchmark cluster j. Let be the mean difference of the ignition angles corresponding to the reference cluster j. This represents the mean difference in fuel injection quantity corresponding to the baseline cluster j.

[0081] in, , , The independent contribution of each parameter to fuel consumption degradation can be determined through bench controlled variable testing, showing a strong positive correlation with the fuel consumption degradation rate. Six fixed power generation points obtained from clustering (specific values ​​can be set by those skilled in the art as needed; this is just an example) can be locked, along with fixed boundary conditions such as water temperature, oil temperature, intake pressure, ambient temperature, and generator load, completely consistent with the steady-state power generation conditions of a real vehicle. This ensures that only the variable to be measured changes during the entire test, while all other parameters are locked at the new vehicle's baseline values. Calibration can then be performed to obtain the impact of single-parameter changes on fuel consumption and NVH. For example, the test setup can refer to the following first group: only the actual opening of the EGR valve is changed by gradient, while other parameters remain at the baseline. The slope of the linear correlation between the absolute value of the opening deviation and the fuel consumption degradation rate is fitted to obtain the weights. The second group: Only the gradient changes the mean EGR rate, while keeping other parameters at the baseline. The slope of the linear correlation between the absolute value of the EGR rate deviation and the fuel consumption deterioration rate is fitted to obtain the weights. 2; Third group: Only the gradient offset of the mean ignition angle is considered, while other parameters remain at the baseline. The slope of the linear correlation between the absolute value of the ignition angle deviation and the fuel consumption deterioration rate is fitted to obtain the weights. Group 4: Only the average injection quantity is adjusted using gradient methods, while other parameters remain at the baseline. The slope of the linear correlation between the absolute value of the injection quantity deviation and the fuel consumption degradation rate is fitted to obtain the weights. .

[0082] By using the above method, the mean drift degradation can be obtained by referring to factors from multiple dimensions, and then the systemic hardware degradation can be comprehensively evaluated, making the evaluation more reliable.

[0083] Step S230: Determine the standard deviation amplification factor of a control item based on the measured standard deviation of the control parameter value of a control item in the operating parameters of the sample to be calibrated corresponding to the reference power point to be calibrated and the reference standard deviation of a control item corresponding to the reference power point to be calibrated. Then, determine the fluctuation amplification degradation degree of the reference power point to be calibrated based on all the standard deviation amplification factors.

[0084] In some embodiments, determining the fluctuation amplification degradation degree of the reference power generation point to be calibrated based on all standard deviation amplification factors includes: determining the average value of all standard deviation amplification factors to obtain the average factor value, wherein the standard deviation amplification factor is the quotient of the measured standard deviation and the reference standard deviation, and the measured standard deviation includes at least one of the measured ignition angle standard deviation, the measured exhaust gas recirculation rate standard deviation, or the measured fuel injection quantity standard deviation; determining the fluctuation amplification degradation degree according to the average factor value and the preset fluctuation weight to obtain the fluctuation amplification degradation degree of the reference power generation point to be calibrated within the calibrated driving range.

[0085] In other embodiments, the determination of fluctuation amplification degradation degree includes: dividing the operating parameters of the samples to be calibrated into corresponding benchmark clusters according to the driving range to be calibrated; determining the standard deviation of the parameter values ​​of at least one control item of the operating parameters of multiple samples to be calibrated into the same benchmark cluster, and determining the standard deviation of the initial sample operating parameter values ​​of the control items in the benchmark cluster, and determining the quotient of the two standard deviations corresponding to the same control item as the standard deviation amplification factor of the control item; determining the average multiple of the standard deviation amplification factors of all control items, and determining the fluctuation amplification degradation degree based on the average multiple and the preset fluctuation weight, as the fluctuation amplification degradation degree of the benchmark power point corresponding to the benchmark cluster, thereby obtaining the fluctuation amplification degradation degree of the benchmark power point corresponding to each benchmark cluster.

[0086] It should be noted that the execution order between steps S220 and S230 is not limited. After one step is completed, the classification result can be directly used for subsequent data processing without repeating the classification step.

[0087] As an example, the standard deviation for stage m (the driving range to be calibrated) is determined as follows: Formula (14) in, Let x be the standard deviation of the operating parameters to be calibrated for the control item x in the reference cluster j (power generation to be calibrated), where x is the EGR rate, ignition angle, or fuel injection quantity. Let x be the total number of parameter values ​​of the control item x to be calibrated sample in the j-th cluster of stage m (the driving section to be calibrated). Let x be the parameter values ​​of the control item x to be calibrated sample in the baseline cluster j. This is the average value of the operating parameters of all samples to be calibrated corresponding to control item x in the baseline cluster j.

[0088] As an example, the methods for determining the standard deviation amplification factor (fluctuation dimension, corresponding to early degradation, solving the problem of missed detection when the centroid has no shift) in stage m (the driving range to be calibrated) include: Formula (15) Formula (16) Formula (17) in, The standard deviation of the EGR rate in the reference cluster j is magnified by the factor. This represents the standard deviation of the EGR rate parameter values ​​in the operating parameters of the sample to be calibrated within the baseline cluster j, which is also the standard deviation of the measured exhaust gas recirculation rate. This represents the standard deviation of the EGR rate parameter values ​​in the initial sample operating parameters of the baseline cluster j, which is also the standard deviation of the baseline exhaust gas recirculation rate. The standard deviation magnification factor of the ignition angle in the reference cluster j. Let be the standard deviation of the ignition angle parameter values ​​in the operating parameters of the sample to be calibrated within the reference cluster j, which is also the measured standard deviation of the ignition angle. This represents the standard deviation of the ignition angle parameter values ​​in the initial sample operating parameters of the baseline cluster j, which is also the standard deviation of the baseline ignition angle. The standard deviation of the fuel injection quantity in the reference cluster j is magnified by the following factor. This represents the standard deviation of the fuel injection quantity parameter values ​​in the operating parameters of the sample to be calibrated within the reference cluster j, which is also the standard deviation of the measured fuel injection quantity. is the standard deviation of the fuel injection quantity parameter values ​​in the initial sample operating parameters of the reference cluster j, which is also the standard deviation of the reference fuel injection quantity.

[0089] As an example, the method for determining the degree of fluctuation amplification degradation is as follows: Formula (18) in, The fluctuation amplification degradation degree corresponding to the baseline cluster j, To preset the fluctuation weight, The standard deviation of the EGR rate in the reference cluster j is magnified by the factor. The standard deviation magnification factor of the ignition angle in the reference cluster j. The standard deviation of the fuel injection quantity in the reference cluster j is magnified by the following factor. is the average multiple of the baseline cluster j.

[0090] Among them, the preset volatility weight Bench tests have shown a strong positive correlation with NVH degradation rate.

[0091] By amplifying the degree of degradation through fluctuations, the extent of combustion instability degradation can be evaluated, which facilitates subsequent optimization.

[0092] In some embodiments, the total degradation degree can be obtained by superimposing the fluctuation amplification degradation degree with the mean drift degradation degree, thus achieving two-dimensional degradation quantification. As an example, one way to determine the total degradation degree in stage m (the driving range to be calibrated) is as follows: Formula (19) in, The total degradation degree corresponding to the baseline cluster j, , , , These are the corresponding preset project weights (preset weight coefficients). Let be the mean difference in power generation corresponding to the baseline cluster j. The mean difference of the EGR rate corresponding to the benchmark cluster j. Let be the mean difference of the ignition angles corresponding to the reference cluster j. The mean difference in fuel injection quantity corresponding to the reference cluster j. To preset the fluctuation weight, The standard deviation of the EGR rate in the reference cluster j is magnified by the factor. The standard deviation magnification factor of the ignition angle in the reference cluster j. is the magnification factor of the standard deviation of the fuel injection quantity in the reference cluster j.

[0093] The compensation strategy described above is automatically driven by the degree of degradation, correcting only the specific degradation level without performing blind full-scale compensation. Basic MAP pre-compensation is performed for mean degradation, while bandwidth contraction and gain attenuation are applied to fluctuating degradation. The compensation magnitude is controlled by a saturation coefficient to avoid overcompensation, ensuring high engineering safety.

[0094] Step S240: Determine the optimization strategy based on the mean drift degradation degree and the fluctuation amplification degradation degree, and determine the range extender control parameters of at least one control item corresponding to the reference power generation point to be calibrated in the calibrated driving range based on the optimization strategy and the benchmark mean of at least one control item.

[0095] In some embodiments, an optimization strategy is determined based on mean drift degradation and fluctuation amplification degradation, and range extender control parameters for at least one control item corresponding to the reference power generation point to be calibrated in the calibrated driving range are determined based on the optimization strategy and the reference mean of at least one control item. This includes: determining the optimization strategy as a hardware optimization strategy when the mean drift degradation is greater than a preset mean drift threshold, the fluctuation amplification degradation is less than or equal to a preset fluctuation amplification threshold, and the total degradation is less than a preset total degradation threshold; determining the range extender control parameters corresponding to the reference power generation point to be calibrated in the calibrated driving range by the hardware optimization strategy and the reference mean of at least one control item, wherein the reference mean of at least one control item includes at least one of the exhaust gas recirculation valve reference command opening, reference ignition angle, or reference fuel injection quantity, and the range extender control parameters include at least one of the exhaust gas recirculation valve correction command opening, correction ignition angle, or correction fuel injection quantity, and the total degradation is determined based on the mean drift degradation and fluctuation amplification degradation.

[0096] A preset mean drift threshold and a preset fluctuation amplification threshold can be pre-set or calibrated. A preset total degradation threshold is obtained by summing these two thresholds. If the mean drift degradation exceeds the preset mean drift threshold, systemic hardware degradation is identified. If the fluctuation amplification degradation exceeds the preset fluctuation amplification threshold, combustion instability degradation is identified. If the total degradation exceeds the preset total degradation threshold, limit protection needs to be triggered.

[0097] In some embodiments, determining the range extender control parameters corresponding to the reference power generation point to be calibrated in the calibrated driving range through a hardware optimization strategy and the reference mean of at least one control item includes: determining the initial mean drift ratio based on the mean drift degradation degree and a preset maximum mean drift value; adjusting the initial mean drift ratio using a preset item compensation coefficient to obtain the mean drift compensation ratio; adjusting the mean difference of a control item using the mean drift compensation ratio to obtain the current compensation value; and compensating the reference mean of a control item based on the current compensation value to obtain the range extender control parameter of a control item.

[0098] Scenario 1: When only mean degradation exists, specifically when the mean drift degradation is greater than a preset mean drift threshold, the fluctuation amplification degradation is less than or equal to a preset fluctuation amplification threshold, and the total degradation is less than or equal to a preset total degradation threshold: The underlying degradation is essentially EGR mid-term aging, with valve plate carbon buildup leading to insufficient systemic opening, parameter averages drifting, and increased fuel consumption, but with normal combustion fluctuations. In this scenario, the corrective strategy could be: Only the basic MAP system performs pre-compensation, pulling the average drift parameters back to the healthy baseline of a new car and matching the type of degradation. By accurately calculating the degradation gap through EGR opening deviation, the basic ECU MAP is directly rewritten to fill in systemic deviations that the ECU is unwilling to compensate for, and the formula is corrected as follows: If the controlled item is the exhaust gas recirculation valve commanded opening degree, the purpose of EGR rate baseline MAP pre-compensation is to compensate for insufficient opening caused by carbon buildup in the EGR valve, so that the actual EGR rate returns to the baseline value. An example is as follows: Formula (20) in, The EGR valve basic command opening (exhaust gas recirculation valve correction command opening) needs to be corrected for the current mileage segment m (driving range to be calibrated). This refers to the mean value of the EGR valve command opening in the initial sample operating parameters of the clustered operating point (baseline cluster j, the legal power range to be calibrated) under the healthy condition of a new vehicle, which is also the baseline command opening of the exhaust gas recirculation valve. The actual average opening degree of the EGR valve (measured commanded opening degree of the exhaust gas recirculation valve) in the operating parameters of the sample to be calibrated in the current mileage segment m and the operating point (baseline cluster j) obtained by clustering. The mean drift degradation degree at the current operating point. The upper limit threshold for mean degradation safety (the maximum value of mean drift is preset, which can be calibrated using bench and real vehicle big data as an example. The preferred value in engineering is 1.0, but it can also be set as needed to define the range of degradation that requires strong compensation). This is the EGR opening pre-compensation ratio coefficient (one of the preset project compensation coefficients). It can be calibrated using big data from multiple vehicles throughout their entire lifecycle and bench tests, with a value range of 0 to 1. This value is used to control the safety ratio of the compensation amplitude and can be adaptively adjusted according to the hardware characteristics of the EGR valve and emission requirements. For example, the value can be 0.8.

[0099] Real-time ignition angle baseline MAP pre-compensation. This method can match the repaired steady-state EGR rate, releasing knock margin; reverting long-delayed ignition references to restore combustion thermal efficiency and reduce aging fuel consumption; and working in conjunction with EGR opening pre-compensation to complete steady-state combustion parameter matching. It has no reverse impact on EGR, only adapting to the combustion state after EGR repair: it does not change EGR valve commands, actual opening, or steady-state EGR rate; it only optimizes in-cylinder combustion phase to adapt to the combustion characteristics after the EGR rate recovers, assisting EGR opening compensation to achieve fuel consumption reduction.

[0100] Formula (21) in, Corrected ignition angle for the current mileage segment m (driving range to be calibrated), The ignition angle is the mean value of the initial sample operating parameters in the cluster of the operating point (reference cluster j, the reference power generation point to be calibrated) obtained under the healthy condition of the new vehicle (initial calibration driving range), which is also the reference ignition angle. The measured average ignition angle for this operating condition point in the current mileage segment, obtained through clustering, is also known as the measured ignition angle. The mean drift degradation degree at the current operating point. The upper limit threshold for average degradation safety is set at 1.0 (calibrated using big data from bench tests and real vehicles, with an engineering preferred value of 1.0, used to define the degradation range that requires strong compensation). The ignition angle pre-compensation ratio coefficient (one of the preset project compensation coefficients) is calibrated through bench combustion stability tests and actual vehicle NVH / fuel consumption tests. The value ranges from 0 to 1 and is used to control the ignition angle compensation range. For example, the value can be 0.9.

[0101] The MAP pre-compensation formula for the base value of fuel injection quantity is as follows: Based on the deviation between the base value of fuel injection quantity and the measured fuel injection quantity under the current deterioration state, and combined with the improvement of combustion efficiency, the base value of fuel injection quantity is gradually corrected to reduce fuel consumption and avoid incomplete combustion caused by excessively low fuel injection quantity.

[0102] Formula (22) in, The base MAP value (corrected fuel injection quantity) of the fuel injection quantity that needs to be corrected for the current mileage segment m (driving range to be calibrated). The baseline fuel injection quantity (baseline fuel injection quantity) is obtained by clustering at this operating point under the healthy condition of a new vehicle. This refers to the average value of the fuel injection quantity among the parameter values ​​to be calibrated at this operating point in the current mileage segment obtained from clustering, which is also the measured fuel injection quantity. The mean drift degradation degree at the current operating point. The upper limit threshold for average degradation safety is set at 1.0 (calibrated using big data from bench tests and real vehicles, with an engineering preferred value of 1.0, used to define the degradation range that requires strong compensation). This is the fuel injection quantity pre-compensation ratio coefficient (one of the preset item compensation coefficients). It is verified through bench fuel consumption / emission calibration and real vehicle multi-mileage testing. The value range is 0~1. It is used to control the fuel injection quantity correction range. For example, the value can be 0.7.

[0103] In some embodiments, an optimization strategy is determined based on mean drift degradation and fluctuation amplification degradation, and range extender control parameters for at least one control item corresponding to the reference power generation point to be calibrated in the calibrated driving range are determined based on the optimization strategy and the reference mean of at least one control item. This includes: determining the optimization strategy as a combustion optimization strategy when the mean drift degradation is less than or equal to a preset mean drift threshold, the fluctuation amplification degradation is greater than a preset fluctuation amplification threshold, and the total degradation is less than a preset total degradation threshold; determining the range extender control parameters corresponding to the reference power generation point to be calibrated in the calibrated driving range by the combustion optimization strategy and the reference mean of at least one control item, wherein the reference mean of at least one control item includes at least one of a reference exhaust gas recirculation rate and a reference ignition angle, and the range extender control parameters include at least one of a real-time control maximum value of exhaust gas recirculation rate, a real-time control minimum value of exhaust gas recirculation rate, and a corrected ignition angle.

[0104] Following the above embodiments, the range extender control parameters corresponding to the benchmark power generation point to be calibrated in the calibrated driving range are determined by using a combustion optimization strategy and the benchmark mean of at least one control item. This includes: if one control item is the exhaust gas recirculation rate, determining a degradation attenuation coefficient based on the fluctuation amplification degradation degree and the preset exhaust gas recirculation bandwidth degradation rate; obtaining a current bandwidth contraction coefficient by contracting the initial gain of the preset bandwidth using the degradation attenuation coefficient; obtaining a bandwidth contraction parameter value by adjusting the standard deviation of the benchmark exhaust gas recirculation rate based on the current bandwidth contraction coefficient; and determining the real-time control maximum and minimum values ​​of the exhaust gas recirculation rate based on the benchmark exhaust gas recirculation rate and the bandwidth contraction parameter value. The range extender control parameters include the real-time control maximum and minimum values ​​of the exhaust gas recirculation rate, the benchmark standard deviation of at least one control item includes the benchmark exhaust gas recirculation rate standard deviation, and the benchmark mean of at least one control item includes the benchmark exhaust gas recirculation rate. Following the above embodiments, the range extender control parameters corresponding to the reference power generation point to be calibrated in the calibrated driving range are determined by the combustion optimization strategy and the reference average of at least one control item. This includes: if one control item is the ignition angle, determining the fluctuation difference based on the fluctuation amplification degradation degree and the preset fluctuation amplification threshold, adjusting the fluctuation difference through the preset ignition fluctuation coefficient to obtain the ignition angle compensation amount, and compensating the reference ignition angle based on the ignition angle compensation amount to obtain the corrected ignition angle. The reference average of at least one control item includes the reference ignition angle, and the range extender control parameters include the corrected ignition angle.

[0105] The above method can be used to determine the maximum and minimum values ​​of real-time control of exhaust gas recirculation rate, real-time ignition angle, and real-time fuel injection quantity suitable for a reference power point in the calibrated driving range. These parameters can be used when other vehicles with the same configuration are under the same conditions in the future.

[0106] In some embodiments, an optimization strategy is determined based on the mean drift degradation degree and the fluctuation amplification degradation degree, and the range extender control parameters of at least one control item corresponding to the reference power generation point to be calibrated in the calibrated driving range are determined based on the optimization strategy and the reference mean of at least one control item, including: determining the optimization strategy as a combustion optimization strategy when the mean drift degradation degree is less than or equal to a preset mean drift threshold, the fluctuation amplification degradation degree is greater than a preset fluctuation amplification threshold, and the total degradation degree is less than a preset total degradation threshold; determining the corrected fuel injection quantity closed-loop gain corresponding to the reference power generation point to be calibrated in the calibrated driving range through the combustion optimization strategy and the zero-point reference gain of the fuel injection quantity closed-loop gain, wherein the reference value set also includes the zero-point reference gain of the fuel injection quantity closed-loop gain, and the range extender control parameters also include the corrected fuel injection quantity closed-loop gain.

[0107] Following the above embodiments, the corrected fuel injection quantity closed-loop gain corresponding to the reference power generation point to be calibrated in the calibrated driving range is determined by using a combustion optimization strategy and the zero-point reference gain of the fuel injection quantity closed-loop gain. This includes: determining the fuel injection quantity compensation coefficient based on the fluctuation amplification degradation degree, wherein the fuel injection quantity compensation coefficient is inversely proportional to the fluctuation amplification degradation degree; and compensating the zero-point reference gain based on the fuel injection quantity compensation coefficient to obtain the corrected fuel injection quantity closed-loop gain, wherein the zero-point reference gain is the average value of the initial sample fuel injection quantity closed-loop gain of the range extender in the initial driving range.

[0108] Scenario 2: Fluctuation-only degradation, meaning the fluctuation amplification degradation degree is greater than the preset fluctuation amplification threshold, the mean drift degradation degree is less than or equal to the preset mean drift threshold, and the total degradation degree is less than or equal to the preset total degradation threshold. In this case, the degradation is essentially early EGR aging, with slight valve plate sticking, no parameter mean drift, but amplified combustion fluctuations and deteriorated NVH. The correction strategy is to only perform combustion fluctuation suppression correction without changing the baseline parameter mean, perfectly matching the degradation type.

[0109] If the controlled item is the exhaust gas recirculation rate, then a strategy of narrowing the EGR rate control bandwidth can be adopted to reduce the real-time control range of the EGR valve and limit circulation fluctuations. One implementation method is as follows: Formula (23) Formula (24) in, This refers to the maximum real-time control value of the exhaust gas recirculation rate at the reference power generation point j to be calibrated, within the current mileage segment m (the driving range to be calibrated). To achieve the minimum value for real-time control of the exhaust gas recirculation rate. The average ERG rate (baseline EGR rate) is the average value of the initial sample operating parameters of the base generation power point in the base cluster j under the initial calibration driving range. The standard deviation of the exhaust gas recirculation rate (standard deviation of the baseline exhaust gas recirculation rate) is the initial sample operating parameters corresponding to the same baseline power generation point in the initial calibration driving range for the baseline cluster j. This represents the current bandwidth contraction factor. To amplify the degree of degradation due to fluctuations, , This is the EGR bandwidth contraction factor. The preset initial bandwidth gain corresponds to a large bandwidth amplification when the degradation is very small. To preset the rate at which the exhaust gas recirculation bandwidth decreases with degradation, , This can be determined through multi-vehicle full lifecycle big data calibration. The greater the fluctuation degradation, the tighter the bandwidth is tightened, directly suppressing the fluctuation back to a healthy level.

[0110] As an example, if validation fails and weight adjustments are needed, the adjustment strategy also includes... , or Adjustments can be made, and the specific coefficients to be adjusted can be selected by those skilled in the art as needed. For example, only adjusting... Or adjust directly Or adjust synchronously , wait.

[0111] If the controlled item is the ignition angle, a slight advance of the ignition angle is performed to compensate for the instability of combustion speed caused by EGR fluctuations and reduce in-cylinder vibration. One way to achieve this is as follows: Formula (25) in, This is the corrected ignition angle at the reference generator power point j to be calibrated, under the current mileage segment m (the driving range to be calibrated). The reference ignition angle is the average real-time ignition angle corresponding to the initial calibration driving range in the reference power point to be calibrated in the initial sample operating parameter set, which is also the average ignition angle in the initial sample operating parameters of the initial calibration driving range in the reference cluster j. To amplify the degree of degradation due to fluctuations, To preset the fluctuation amplification threshold, The preset ignition fluctuation coefficient is a calibration coefficient, for example, it can be 0.5°CA (crankshaft angle). The ignition advance angle is greater when the fluctuation deterioration is greater.

[0112] If the controlled item is the fuel injection quantity, optimize the closed-loop gain of the fuel injection quantity by reducing the closed-loop response speed of the fuel injection to avoid amplifying the fuel injection fluctuation. Design an inverse proportional function: Formula (26) in, This refers to the closed-loop gain of the corrected fuel injection quantity at the reference power generation point j to be calibrated, within the current mileage segment m (the driving range to be calibrated). This is the zero-point reference gain at the reference power point j to be calibrated in the initial calibration driving range. To amplify the degradation degree due to fluctuations.

[0113] The above formula shows that, under the condition of no degradation Maintain zero reference As degradation fluctuations continue to increase This causes the gain to decrease linearly as the degree of fluctuation degradation increases.

[0114] In some embodiments, an optimization strategy is determined based on the mean drift degradation degree and the fluctuation amplification degradation degree, and the range extender control parameters corresponding to at least one control item of the reference power generation point to be calibrated in the calibrated driving range are determined based on the optimization strategy and the reference mean of at least one control item. This includes: when the mean drift degradation degree is greater than a preset mean drift threshold, the fluctuation amplification degradation degree is greater than a preset fluctuation amplification threshold, and the total degradation degree is less than a preset total degradation threshold, the optimization strategy is determined to be a combustion optimization strategy and a hardware optimization strategy, and the range extender control parameters corresponding to the reference power generation point to be calibrated in the calibrated driving range are determined based on the combustion optimization strategy, the hardware optimization strategy, and the reference mean of at least one control item, respectively. For example, the range extender control parameters corresponding to the reference power generation point to be calibrated in the calibrated driving range are first determined by the hardware optimization strategy and the reference mean of at least one control item, and then the range extender control parameters corresponding to the reference power generation point to be calibrated in the calibrated driving range are determined by the combustion optimization strategy and the reference mean of at least one control item.

[0115] For example, if the combustion optimization strategy and the hardware optimization strategy include the same control item, ignition angle, then the ignition angle compensation amount obtained from the combustion optimization strategy and the current compensation value obtained from the hardware optimization strategy are added together to obtain the cumulative compensation amount. This cumulative compensation amount is then added to the reference ignition angle to obtain the corrected ignition angle, which serves as the corresponding range extender control parameter. The specific method for determining the current compensation value can be found in the aforementioned embodiments and will not be elaborated upon here.

[0116] Scenario 3: Two-dimensional degradation (fluctuation amplification degradation degree D) std,mj Preset fluctuation amplification threshold D std,th And the mean drift degradation degree D mean,mj Preset mean drift threshold D mean,th ) The underlying cause of the degradation is severe EGR aging and valve plate congestion due to carbon buildup. This results in both systemic hardware degradation and significant combustion fluctuations, leading to simultaneous deterioration in fuel consumption and NVH (noise, vibration, and harshness). The corrective strategy is to first perform basic MAP pre-compensation (Scenario 1) to bring the parameter averages back to a healthy baseline, then apply combustion fluctuation suppression correction (Scenario 2) to address both fuel consumption and NVH degradation simultaneously.

[0117] In some embodiments, an optimization strategy is determined based on mean drift degradation and fluctuation amplification degradation, and the range extender control parameters of at least one control item corresponding to the reference power generation point to be calibrated in the calibrated driving range are determined based on the optimization strategy and the reference mean of at least one control item. This includes: when the total degradation corresponding to all reference power generation points is greater than or equal to a preset total degradation threshold, the optimization strategy is determined as a protection strategy. The protection strategy includes at least one of the following: disabling the target power range, locking the ignition angle safety range, and reducing the real-time control maximum value of the exhaust gas recirculation rate. The target power range includes a preset power range and the power range where the reference power generation points corresponding to the top N total degradation values ​​are located, sorted from low to high.

[0118] Supplementing severe degradation limit protection, when the comprehensive degradation index (total degradation degree) Dmj > Dmax (the preset total degradation threshold Dmax is recommended to be 3.0 based on experience), the limit protection is triggered: high power and high degradation operating conditions are forcibly disabled, only 2 to 3 healthy operating conditions with low power and low degradation are retained, the upper limit of EGR rate is lowered, the safe range of ignition angle is locked, and the instrument maintenance reminder is triggered at the same time, which basically avoids irreversible damage to the engine.

[0119] In some embodiments, if the mean drift degradation is less than or equal to a preset mean drift threshold and the fluctuation amplification degradation is less than or equal to a preset fluctuation amplification threshold, then the optimization strategy is to maintain the baseline mean of the current control item.

[0120] By employing the above method, the degradation of the range extender's EGR system is decomposed into two independent dimensions: mean drift degradation and fluctuation amplification degradation, corresponding to hardware degradation and combustion instability, respectively. Relative deviation and fluctuation multiples are introduced as quantitative indicators, directly linking the degradation index to fuel consumption and NVH performance. The model simultaneously covers early, mid, and late-stage degradation, with no missed detections or false positives, addressing the limitation of related technologies that can only identify severe degradation.

[0121] In some embodiments, the method further includes: controlling the test vehicle to drive within a calibrated driving range, and controlling the range extender to adopt corresponding range extender control parameters according to the real-time power generation during driving; collecting multiple test sample operating parameters of the test vehicle's range extender under different power generation conditions within the calibrated driving range; verifying the range extender control parameters of all control parameter items corresponding to each benchmark power generation point based on the multiple test sample operating parameters; if the verification is successful, determining all range extender control parameters corresponding to the benchmark power generation point within the calibrated driving range as preset parameter values ​​for the actual vehicle; if the verification fails, executing an adjustment strategy, and after execution, re-determining at least one benchmark power generation point for the range extender within the calibrated driving range. New range extender control parameters for at least one control item corresponding to the electric power point are determined, and parameter verification is re-executed until verification passes. The adjustment strategy includes at least one of the following: adjusting at least one of the preset item compensation coefficient and preset ignition fluctuation coefficient used when determining the range extender control parameters; adjusting the preset item weight used to determine the mean drift degradation degree; and adjusting the preset fluctuation weight used to determine the fluctuation amplification degradation degree. The parameter verification method includes: determining the mean difference of a control item based on the verified average value of the parameter to be verified for one control item in the test sample operating parameters corresponding to the benchmark generating power point and the benchmark mean value of the control item corresponding to the benchmark generating power point. Furthermore, based on all mean differences from the verification phase, the mean drift degradation degree of the benchmark power generation point to be verified is determined. Also, the fuel consumption degradation rate is determined based on the ratio between the verified average value of real-time fuel consumption in the operating parameters of the test sample corresponding to the benchmark power generation point and the benchmark fuel consumption corresponding to the benchmark power generation point. The benchmark value set also includes benchmark fuel consumption. The standard deviation amplification factor of a control item is determined based on the verification standard deviation of the parameter value of the control item in the operating parameters of the test sample corresponding to the benchmark power generation point and the benchmark standard deviation of the control item corresponding to the benchmark power generation point. Then, based on all standard deviation amplification factors from the verification phase, the benchmark power generation point to be verified is determined. The NVH degradation rate is determined by the ratio between the verified average value of the real-time NVH jitter value in the test sample operating parameters corresponding to the benchmark power generation point to be verified and the benchmark NVH jitter value corresponding to the benchmark power generation point to be verified. The benchmark value set also includes the benchmark NVH jitter value. If the verification conditions are met, the verification is successful; if the verification conditions are not met, the verification fails. The verification conditions include that the mean drift degradation rate during the test phase is less than or equal to the preset mean drift threshold, the fuel consumption degradation rate is less than or equal to the preset fuel consumption threshold, the fluctuation amplification degradation rate during the test phase is less than or equal to the preset fluctuation amplification threshold, and the NVH degradation rate is less than or equal to the preset NVH threshold.

[0122] The above methods enable the cloud-based transmission of real-vehicle operating data, continuously optimizing the degradation identification model (adjusting at least one of the preset compensation coefficients and preset ignition fluctuation coefficients used to determine range extender control parameters, adjusting the preset weights used to determine mean drift degradation, and adjusting the preset fluctuation weights used to determine fluctuation amplification degradation), compensation strategies (optimization strategies), and operating point (baseline generator power point) selection logic. This achieves a closed-loop evolution of "identification model—compensation strategy—real-vehicle data—model re-optimization," continuously improving the range extender's performance throughout its entire lifecycle, rather than maintaining static calibration. The closed-loop process includes: degradation quantification → matching correction → effect verification → iterative optimization → cloud feedback. This ensures the range extender maintains low fuel consumption, high NVH stability, and high combustion consistency throughout its entire lifecycle, addressing the core industry pain point of range extender performance degradation with mileage.

[0123] During the closed-loop verification phase, after each mileage segment (driving range to be calibrated) is completed, the control parameters of the range extender are calibrated, and the effect of the correction is verified. The pass / fail criteria are as follows: During the testing phase, the mean drift degradation degree is less than or equal to the preset mean drift threshold, the fuel consumption degradation rate is less than or equal to the preset fuel consumption threshold, the fluctuation amplification degradation degree is less than or equal to the preset fluctuation amplification threshold, and the NVH degradation rate is less than or equal to the preset NVH threshold. The preset fuel consumption threshold and preset NVH threshold can be adjusted according to the vehicle's requirements.

[0124] As an example, methods for determining fuel consumption degradation rate include: ηf=f mj / f 0j Formula (27) Where ηf is the fuel consumption degradation rate, f mj f is the verified average value of real-time fuel consumption in the test sample operating parameters corresponding to the benchmark power generation point to be verified. 0j This is the baseline fuel consumption.

[0125] As an example, methods for determining NVH degradation rate include: ηv=v mj / v 0j Formula (28) Where ηv is the NVH degradation rate, v mj v is the verified average value of the real-time NVH jitter value in the operating parameters of the test sample corresponding to the benchmark power point to be verified. 0j This represents the baseline NVH jitter value corresponding to the baseline power generation point to be verified.

[0126] If the corrected parameters meet the requirements for degradation, fuel consumption, and NVH performance, the current corrected parameters and weighting coefficients are retained. If they do not meet the requirements, the weighting coefficients and correction coefficients are recalibrated, and iterative optimization is performed until the requirements are met, thus forming a closed-loop control system for the entire lifecycle of "degradation quantification - matching correction - verification optimization".

[0127] Once the control strategy is developed, the correction coefficients corresponding to each degradation level (all range extender control parameters corresponding to the benchmark power generation point within the calibrated driving range) will be solidified into the vehicle controller, and the vehicle can then operate according to the optimized strategy. At the same time, based on the big data collected from the actual vehicle operation, the cloud platform can continuously provide feedback and iterative optimization of the operating point selection, further improving the working efficiency and performance stability of the range extender under common operating conditions.

[0128] The range extender control parameter determination method provided in the above embodiments obtains multiple sets of reference values ​​and multiple sample operating parameters of the range extender under different power generation at different power outputs within the calibration range. The set of reference values ​​includes the reference mean and the reference standard deviation of at least one control item, and the set of reference values ​​is preset with corresponding reference power output points. The sample operating parameters to be calibrated include the parameter values ​​to be calibrated for at least one control item. The method determines the mean difference of a control item based on the measured average value of the parameter values ​​to be calibrated for one control item in the sample operating parameters corresponding to the reference power output point and the reference mean value of the same control item. Then, it determines the mean drift degradation degree of the reference power output point to be calibrated based on all mean differences. Finally, it determines the standard deviation amplification factor of a control item based on the measured standard deviation of the parameter values ​​to be calibrated for one control item in the sample operating parameters corresponding to the reference power output point and the reference standard deviation of the same control item. The fluctuation amplification degradation degree of the benchmark power generation point to be calibrated is determined based on the amplification factor of all standard deviations. The optimization strategy is determined based on the mean drift degradation degree and the fluctuation amplification degradation degree. Based on the optimization strategy and the benchmark mean of at least one control item, the range extender control parameters of at least one control item corresponding to the benchmark power generation point to be calibrated in the driving range to be calibrated are determined. In this way, degradation caused by the accumulation of driving mileage can be identified and the parameter values ​​of the corresponding control items can be optimized. By using a fixed initial sample operating parameter set, the operating condition benchmark can be solidified, ensuring the consistency and uniqueness of the comparison benchmark throughout the entire life cycle. It basically avoids the misjudgment of degradation caused by the dynamic change of operating condition point. The degradation is decomposed into two independent dimensions, "mean drift" and "fluctuation amplification", which correspond to the systematic degradation of hardware and combustion instability, respectively. The fluctuation amplification degradation degree is introduced as a quantitative indicator of early fluctuations, which can identify early hidden NVH degradation, basically reduce NVH degradation caused by gradual degradation, reduce the increase in fuel consumption throughout the entire life cycle, and improve the user experience.

[0129] First, during the initial health phase of a new vehicle (initial calibration driving range), a lifelong, fixed steady-state power generation benchmark cluster (benchmark cluster) for the range extender is established through clustering. Parameters such as EGR command opening, actual opening, EGR rate, ignition angle, and fuel injection quantity are collected from the original vehicle ECU (Electronic Control Unit) built-in sensors to construct an unchanging health performance benchmark (benchmark value set). Then, real-vehicle lifecycle operation data is collected in segments according to mileage, resulting in multiple calibration sample operating parameters under different power generation levels within several calibration driving ranges. These parameters are uniformly merged into a fixed power cluster without re-clustering. A comprehensive degradation index with clear physical meaning is constructed from both mean drift and combustion fluctuation dimensions. This index quantifies the systemic hardware degradation caused by EGR valve carbon buildup and NVH degradation caused by amplified combustion cycle fluctuations. Unlike traditional ECUs that only perform fault-preserving transient passive fine-tuning within fixed calibration boundaries and cannot compensate for accumulated mileage systemic degradation, the method provided in the above embodiment addresses the limitations of traditional ECUs, which only perform fault-preserving transient passive fine-tuning within fixed calibration boundaries and cannot compensate for accumulated mileage systemic degradation. Using the degradation index as the sole driving factor, adaptive pre-compensation with a saturation upper limit is applied to the EGR opening, ignition angle, and fuel injection quantity. At the same time, control bandwidth contraction and fuel injection closed-loop gain attenuation are suppressed for combustion fluctuations, and a comprehensive degradation limit threshold is set to trigger condition degradation protection. Through degradation quantification, graded adaptive compensation, and performance verification iteration, a whole-vehicle life cycle control closed loop is formed. After development and finalization, the correction parameters corresponding to each degradation level are solidified into the controller. Then, relying on cloud-based real vehicle big data, continuous feedback is used to optimize the operating condition selection and control model. This fundamentally offsets the fuel consumption increase and NVH deterioration caused by hardware degradation throughout the range extender's life cycle. Without adding extra hardware and maintaining compatibility with the original vehicle ECU control architecture, the range extender's fuel consumption, NVH, and combustion stability are maintained at the optimal level for a new vehicle in the long term.

[0130] In one embodiment, a range extender control parameter determination apparatus is provided, which is used to execute the range extender control parameter determination method provided in any of the above embodiments. Please refer to [link to previous document]. Figure 3 , Figure 3 A schematic diagram of a range extender control parameter determination device provided in an embodiment of this application is shown below. Figure 3As shown, the range extender control parameter determination device 300 includes: an acquisition module 310, used to acquire multiple sets of reference values ​​and multiple sample operating parameters of the range extender under different power generation at different power outputs within the calibration range, wherein the set of reference values ​​includes the reference mean of at least one control item and the reference standard deviation of at least one control item, the set of reference values ​​is preset with a corresponding reference power output point, and the sample operating parameters of the range extender include the parameter values ​​of at least one control item to be calibrated; and a mean drift degradation determination module 320, used to determine the mean difference of a control item based on the measured average value of the parameter values ​​of a control item in the sample operating parameters of the sample operating parameters of the control item corresponding to the reference power output point to be calibrated and the reference mean of the control item corresponding to the reference power output point to be calibrated, and then determine the mean drift degradation degree based on all the mean differences. The mean drift degradation degree of the reference power generation point to be calibrated is determined; the fluctuation amplification degradation degree determination module 330 is used to determine the standard deviation amplification factor of the control item based on the measured standard deviation of the control item value of the control item in the operating parameters of the reference power generation point to be calibrated and the benchmark standard deviation of the control item corresponding to the reference power generation point to be calibrated, and then determine the fluctuation amplification degradation degree of the reference power generation point to be calibrated based on all the standard deviation amplification factors; the optimization module 340 is used to determine the optimization strategy based on the mean drift degradation degree and the fluctuation amplification degradation degree, and determine the range extender control parameters of at least one control item corresponding to the reference power generation point to be calibrated in the calibrated driving range based on the optimization strategy and the benchmark mean of at least one control item.

[0131] For specific limitations regarding the range extender control parameter determination device, please refer to the limitations on the range extender control parameter determination method above, which will not be repeated here. Each module in the aforementioned range extender control parameter determination device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the electronic device in hardware form or independently of it, or they can be stored in the memory of the electronic device in software form, so that the processor can call and execute the corresponding operations of each module.

[0132] In this embodiment, the range extender control parameter determination device is essentially equipped with multiple modules to execute the range extender control parameter determination method in any of the above embodiments. The specific functions and technical effects can be referred to in the above embodiments, and will not be repeated here.

[0133] See Figure 4 , Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown below. Figure 4As shown, this application embodiment also provides an electronic device 400, including a processor 401, a memory 402 and a communication bus 403; the communication bus 403 is used to connect the processor 401 and the memory 402; the processor 401 is used to execute a computer program stored in the memory 402 to implement the method mentioned in any of the above embodiments.

[0134] This application also provides a computer-readable storage medium having a computer program stored thereon, the computer program being used to cause a computer to perform the method provided in any of the above embodiments.

[0135] This application also provides a non-volatile readable storage medium storing one or more modules (programs) that, when applied to a device, enable the device to execute the instructions included in the steps provided in this application.

[0136] This application also provides a computer program product, including a computer program that, when executed by a processor, can implement the steps and corresponding content of the aforementioned method embodiments.

[0137] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, 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 device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0138] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0139] Computer program code for performing the operations of this disclosure 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 it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0140] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of this disclosure. 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, may 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.

[0141] It should be understood that the terms "first," "second," etc., used in this application are used to distinguish similar objects and do not necessarily indicate a specific order or sequence. The technical features to which these terms are used can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in a sequence other than that shown in the figures or text.

[0142] It should be understood that, in the various embodiments of this application, unless the context clearly indicates otherwise, "one or more" or "at least one" means one or more (including two).

[0143] It should be understood that although the flowcharts provided in the embodiments of this application indicate the various steps with arrows, the order indicated by the arrows does not necessarily limit the implementation order of these steps. Those skilled in the art can perform these steps in other orders according to different implementation scenarios and requirements.

[0144] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for determining control parameters of a range extender, characterized in that, The method includes: Multiple sets of benchmark values ​​and multiple operating parameters of the range extender under different power generation at different power outputs within the calibration range are obtained. The sets of benchmark values ​​include the benchmark mean and the benchmark standard deviation of at least one control item. The sets of benchmark values ​​are preset with corresponding benchmark power output points. The operating parameters of the samples to be calibrated include the calibration parameter values ​​of at least one control item. The mean difference of a control item is determined based on the measured average value of the control parameter value of a control item in the operating parameters of the control item corresponding to the benchmark power point to be calibrated and the benchmark mean value of the control item corresponding to the benchmark power point to be calibrated. Then, the mean drift degradation degree of the benchmark power point to be calibrated is determined based on all the mean differences. The standard deviation amplification factor of the control item is determined based on the measured standard deviation of the control item value of the control item in the operating parameters of the control item corresponding to the benchmark power point to be calibrated and the benchmark standard deviation of the control item corresponding to the benchmark power point to be calibrated. Then, the fluctuation amplification degradation degree of the benchmark power point to be calibrated is determined based on all the standard deviation amplification factors. An optimization strategy is determined based on the mean drift degradation and the fluctuation amplification degradation. Based on the optimization strategy and the benchmark mean of at least one control item, the range extender control parameters corresponding to the benchmark power generation point to be calibrated in the calibrated driving range are determined.

2. The method for determining control parameters of a range extender as described in claim 1, characterized in that, The method further includes: Determining the mean drift degradation of the reference power generation point to be calibrated based on all mean differences includes: determining the sub-mean drift degradation of a control item based on the mean difference of a control item and the preset item weight of the control item; superimposing the sub-mean drift degradation of all control items to obtain the mean drift degradation of the reference power generation point to be calibrated within the driving range to be calibrated, wherein all control items include at least one of exhaust gas recirculation valve command opening, ignition angle, exhaust gas recirculation rate, or fuel injection quantity; Determining the fluctuation amplification degradation degree of the reference power generation point to be calibrated based on all standard deviation amplification factors includes: determining the average value of all standard deviation amplification factors to obtain the average factor value, wherein the standard deviation amplification factor is the quotient of the measured standard deviation and the reference standard deviation, and the measured standard deviation includes at least one of the measured ignition angle standard deviation, the measured exhaust gas recirculation rate standard deviation, or the measured fuel injection quantity standard deviation; determining the fluctuation amplification degradation degree according to the average factor value and a preset fluctuation weight to obtain the fluctuation amplification degradation degree of the reference power generation point to be calibrated within the driving range to be calibrated.

3. The method for determining control parameters of a range extender as described in claim 1, characterized in that, An optimization strategy is determined based on the mean drift degradation and the fluctuation amplification degradation. Based on the optimization strategy and the benchmark mean of at least one control item, the range extender control parameters corresponding to the benchmark power generation point to be calibrated in the calibrated driving range are determined, including at least one of the following: When the mean drift degradation is greater than a preset mean drift threshold, the fluctuation amplification degradation is less than or equal to a preset fluctuation amplification threshold, and the total degradation is less than a preset total degradation threshold, the optimization strategy is determined as a hardware optimization strategy. The range extender control parameters corresponding to the reference power generation point to be calibrated in the calibrated driving range are determined by the hardware optimization strategy and the reference mean of at least one control item. The reference mean of the at least one control item includes at least one of the reference command opening of the exhaust gas recirculation valve, the reference ignition angle, or the reference fuel injection quantity. The range extender control parameters include at least one of the corrected command opening of the exhaust gas recirculation valve, the corrected ignition angle, or the corrected fuel injection quantity. The total degradation degree is determined based on the mean drift degradation degree and the fluctuation amplification degradation degree. If the mean drift degradation is less than or equal to a preset mean drift threshold, the fluctuation amplification degradation is greater than a preset fluctuation amplification threshold, and the total degradation is less than a preset total degradation threshold, the optimization strategy is determined as a combustion optimization strategy. The range extender control parameters corresponding to the reference power generation point to be calibrated in the driving range to be calibrated are determined by the combustion optimization strategy and the reference average of at least one control item. The reference average of the at least one control item includes at least one of the reference exhaust gas recirculation rate and the reference ignition angle. The range extender control parameters include at least one of the real-time control maximum value of exhaust gas recirculation rate, the real-time control minimum value of exhaust gas recirculation rate, and the corrected ignition angle. When the mean drift degradation is greater than the preset mean drift threshold, the fluctuation amplification degradation is greater than the preset fluctuation amplification threshold, and the total degradation is less than the preset total degradation threshold, the optimization strategy is determined to be a combustion optimization strategy and a hardware optimization strategy. The range extender control parameters corresponding to the reference power generation point to be calibrated in the driving range to be calibrated are determined according to the combustion optimization strategy, the hardware optimization strategy and the reference mean of at least one control item. When the total degradation degree corresponding to all reference power points is greater than or equal to the preset total degradation threshold, the optimization strategy is determined as a protection strategy. The protection strategy includes at least one of the following: disabling the target power range, locking the ignition angle safety range, and reducing the real-time control maximum value of the exhaust gas recirculation rate. The target power range includes the preset power range and the power range where the reference power points corresponding to the top N total degradation degrees, sorted from low to high, are located.

4. The method for determining control parameters of a range extender as described in claim 1, characterized in that, An optimization strategy is determined based on the mean drift degradation and the fluctuation amplification degradation. Based on the optimization strategy and the benchmark mean of at least one control item, range extender control parameters for at least one control item corresponding to the benchmark power generation point to be calibrated in the calibrated driving range are determined, including: When the mean drift degradation is less than or equal to a preset mean drift threshold, the fluctuation amplification degradation is greater than a preset fluctuation amplification threshold, and the total degradation is less than a preset total degradation threshold, the optimization strategy is determined as a combustion optimization strategy. The corrected fuel injection closed-loop gain corresponding to the reference power generation point to be calibrated in the calibrated driving range is determined by the combustion optimization strategy and the zero-point reference gain of the fuel injection closed-loop gain. The reference value set also includes the zero-point reference gain of the fuel injection closed-loop gain, and the range extender control parameters also include the corrected fuel injection closed-loop gain.

5. The method for determining control parameters of a range extender as described in claim 4, characterized in that, The corrected fuel injection quantity closed-loop gain corresponding to the reference power generation point to be calibrated in the calibrated driving range is determined by the combustion optimization strategy and the zero-point reference gain of the fuel injection quantity closed-loop gain, including: The fuel injection quantity compensation coefficient is determined based on the fluctuation amplification degradation degree, and the fuel injection quantity compensation coefficient is inversely proportional to the fluctuation amplification degradation degree; The zero-point reference gain is compensated according to the fuel injection quantity compensation coefficient to obtain the corrected fuel injection quantity closed-loop gain, wherein the zero-point reference gain is the average value of the initial sample fuel injection quantity closed-loop gain of the range extender in the initial driving range.

6. The method for determining control parameters of a range extender as described in claim 3, characterized in that, The range extender control parameters corresponding to the reference power generation point to be calibrated in the calibrated driving range are determined by the hardware optimization strategy and the reference average of at least one control item, including: The initial mean drift ratio is determined based on the mean drift degradation degree and the preset maximum mean drift value; The mean drift compensation ratio is obtained by adjusting the initial mean drift ratio using a preset project compensation coefficient. The current compensation value is obtained by adjusting the mean difference of a control item using the mean drift compensation ratio. The range extender control parameters of the control item are obtained by compensating the baseline mean of the control item based on the current compensation value.

7. The method for determining control parameters of a range extender as described in claim 3, characterized in that, The range extender control parameters corresponding to the reference power generation point to be calibrated in the calibrated driving range are determined by the combustion optimization strategy and the reference mean of at least one control item, including: If the control item is the exhaust gas recirculation rate, a degradation attenuation coefficient is determined based on the fluctuation amplification degradation degree and the preset exhaust gas recirculation bandwidth degradation rate. The initial gain of the preset bandwidth is contracted using the degradation attenuation coefficient to obtain the current bandwidth contraction coefficient. The standard deviation of the benchmark exhaust gas recirculation rate is adjusted based on the current bandwidth contraction coefficient to obtain the bandwidth contraction parameter value. The real-time control maximum and minimum values ​​of the exhaust gas recirculation rate are determined based on the benchmark exhaust gas recirculation rate and the bandwidth contraction parameter value. The benchmark standard deviation of the at least one control item includes the benchmark exhaust gas recirculation rate standard deviation, and the benchmark mean of the at least one control item includes the benchmark exhaust gas recirculation rate. If the control item is the ignition angle, the fluctuation difference is determined based on the fluctuation amplification degradation degree and the preset fluctuation amplification threshold. The fluctuation difference is adjusted by the preset ignition fluctuation coefficient to obtain the ignition angle compensation amount. The reference ignition angle is compensated based on the ignition angle compensation amount to obtain the corrected ignition angle. The reference mean value of the at least one control item includes the reference ignition angle, and the range extender control parameters include the corrected ignition angle.

8. The method for determining control parameters of a range extender as described in any one of claims 1 to 7, characterized in that, The method further includes: The test vehicle is controlled to drive within the specified driving range, and during the driving process, the range extender is controlled to use the corresponding range extender control parameters based on the real-time power generation. The operating parameters of the range extender of the test vehicle were collected from multiple test samples under different power generation conditions in the driving range to be calibrated. Based on the operating parameters of the multiple test samples, the range extender control parameters of all control parameter items corresponding to each benchmark power point are verified. If the verification is successful, all range extender control parameters corresponding to the reference power generation point within the driving range to be calibrated will be determined as the preset parameter values ​​for the actual vehicle. If the verification fails, an adjustment strategy is executed. After the execution is completed, new range extender control parameters are determined for at least one control item corresponding to at least one reference power generation point in the calibrated driving range, and parameter verification is re-executed until the verification is successful. The adjustment strategy includes at least one of the following: adjusting at least one of the preset item compensation coefficient and preset ignition fluctuation coefficient used when determining the range extender control parameters; adjusting the preset item weight used to determine the mean drift degradation degree; and adjusting the preset fluctuation weight used to determine the fluctuation amplification degradation degree. The parameter verification methods include: The mean difference of a control item is determined based on the verified average value of the parameter to be verified in the test sample operating parameters corresponding to the benchmark power generation point to be verified and the benchmark mean value of the control item corresponding to the benchmark power generation point to be verified. Then, the mean drift degradation degree of the benchmark power generation point to be verified is determined based on all mean differences in the verification stage. In addition, the fuel consumption degradation rate is determined based on the fuel consumption ratio between the verified average value of real-time fuel consumption in the test sample operating parameters corresponding to the benchmark power generation point to be verified and the benchmark fuel consumption corresponding to the benchmark power generation point to be verified. The benchmark value set also includes the benchmark fuel consumption. The standard deviation amplification factor of the control item is determined based on the verification standard deviation of the control item's parameter value in the operating parameters of the control item corresponding to the benchmark power point to be verified and the benchmark standard deviation of the control item corresponding to the benchmark power point to be verified. Then, the fluctuation amplification degradation degree of the benchmark power point to be verified is determined based on all the standard deviation amplification factors in the verification stage. In addition, the NVH degradation rate is determined based on the verification average value of the real-time NVH jitter value in the operating parameters of the test sample corresponding to the benchmark power point to be verified and the jitter ratio between the benchmark NVH jitter value corresponding to the benchmark power point to be verified. The benchmark value set also includes the benchmark NVH jitter value. If the verification conditions are met, the verification passes; if the verification conditions are not met, the verification fails. The verification conditions include that the mean drift degradation degree during the test phase is less than or equal to a preset mean drift threshold, the fuel consumption degradation rate is less than or equal to a preset fuel consumption threshold, the fluctuation amplification degradation degree during the test phase is less than or equal to a preset fluctuation amplification threshold, and the NVH degradation rate is less than or equal to a preset NVH threshold.

9. The method for determining control parameters of a range extender as described in any one of claims 1 to 7, characterized in that, The methods for determining the set of benchmark values ​​include: The range extender is equipped with multiple initial sample operating parameters under different power generation conditions in the initial calibrated driving range. The initial sample operating parameters include the initial sample command opening of the exhaust gas recirculation valve, the initial sample ignition angle, the initial sample exhaust gas recirculation rate, the initial sample fuel injection quantity, the initial sample fuel consumption, the initial sample fuel injection quantity closed-loop gain, and the initial sample NVH vibration value. Cluster the total power generation capacity into k clusters, where k is greater than 1; With the goal of minimizing the sum of squared power errors within a cluster, cluster optimization is performed iteratively on all generated power. The clusters optimized and iterated by clustering are used as the benchmark clusters, and the benchmark power generation point corresponding to the benchmark cluster is determined based on the average power generation within each benchmark cluster. The average value of each parameter item of the initial sample operating parameters corresponding to the power generation within a reference cluster is determined to obtain the reference command opening degree of the exhaust gas recirculation valve, the reference ignition angle, the reference exhaust gas recirculation rate, the reference fuel injection quantity, the reference fuel consumption, the zero-position reference gain, and the reference NVH jitter value, and then multiple reference average values ​​are obtained. Determine the standard deviation of each parameter item of the initial sample operating parameters corresponding to the power generation within a benchmark cluster, and obtain the standard deviation of the benchmark ignition angle, the standard deviation of the benchmark exhaust gas recirculation rate, or the standard deviation of the benchmark fuel injection quantity, and then obtain multiple benchmark standard deviations. The reference value set of the reference power point corresponding to the reference cluster is generated based on the reference command opening degree of the exhaust gas recirculation valve, the reference ignition angle, the reference exhaust gas recirculation rate, the reference fuel injection quantity, the reference fuel consumption, the zero-position reference gain, the reference NVH jitter value, the standard deviation of the reference ignition angle, the standard deviation of the reference exhaust gas recirculation rate, or the standard deviation of the reference fuel injection quantity.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 9.

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